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Clemente-Bautista S, Trocóniz IF, Segarra-Cantón Ó, Salvador-Marín S, Parramón-Teixidó CJ, Álvarez-Beltrán M, López-Fernández LA, Colom H, Cabañas-Poy MJ, Gorgas-Torner MQ, Miarons M. The Effect of Polymorphisms and Other Biomarkers on Infliximab Exposure in Paediatric Inflammatory Bowel Disease: Development of a Population Pharmacokinetic Model. Paediatr Drugs 2024; 26:331-346. [PMID: 38507036 DOI: 10.1007/s40272-024-00621-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Therapeutic drug monitoring (TDM) of infliximab has been shown to be a effective strategy for inflammatory bowel disease (IBD). Population pharmacokinetic (PopPK) modeling can predict trough concentrations for individualized dosing. OBJECTIVE The aim of this study was to develop a PopPK model of infliximab in a paediatric population with IBD, assessing the effect of single nucleotide polymorphisms (SNPs) and other biomarkers on infliximab clearance. METHODS This observational and ambispective single-centre study was conducted in paediatric patients with IBD treated with infliximab between July 2016 and July 2022 in the Paediatric Gastroenterology Service of the Hospital Universitari Vall d'Hebron (HUVH) (Spain). Demographic, clinical, and analytical variables were collected. Twenty SNPs potentially associated with variations in the response to infliximab plasma concentrations were analysed. infliximab serum concentrations and antibodies to infliximab (ATI) were determined by ELISA. PopPK modelling was performed using nonlinear mixed-effects analysis (NONMEM). RESULTS Thirty patients (21 males) were included. The median age (range) at the start of infliximab treatment was 13 years (16 months to 16 years). A total of 190 samples were obtained for model development (49 [25.8%] during the induction phase). The pharmacokinetics (PK) of infliximab were described using a two-compartment model. Weight, erythrocyte sedimentation rate (ESR), faecal calprotectin (FC), and the SNP rs1048610 (ADAM17) showed statistical significance for clearance (CL), and albumin for inter-compartmental clearance (Q). Estimates of CL1 (genotype 1-AA), CL2 (genotype 2-AG), CL3 (genotype 3-GG), Q, Vc, and Vp (central and peripheral distribution volumes) were 0.0066 L/h/46.4 kg, 0.0055 L/h/46.4 kg, 0.0081 L/h/46.4 kg, 0.0029 L/h/46.4 kg, 0.6750 L/46.4 kg, and 1.19 L/46.4 kg, respectively. The interindividual variability (IIV) estimates for clearance, Vc, and Vp were 19.33, 16.42, and 36.02%, respectively. CONCLUSIONS A popPK model utilising weight, albumin, FC, ESR, and the SNP rs1048610 accurately predicted infliximab trough concentrations in children with IBD.
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Affiliation(s)
- Susana Clemente-Bautista
- Pharmacy Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, 08035, Barcelona, Spain.
| | - Iñaki F Trocóniz
- Department of Pharmaceutical Technology and Chemistry, Faculty of Pharmacy and Nutrition, University of Navarra, 31009, Navarra, Spain
| | - Óscar Segarra-Cantón
- Paediatric Gastroenterology and Clinical Nutrition Unit, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, 08035, Barcelona, Spain
- Department of Paediatrics, Obstetrics and Gynaecology, Preventive Medicine and Public Health of the Universidad Autónoma de Barcelona, 08193, Bellaterra, Spain
| | - Sara Salvador-Marín
- Pharmacogenetics and Pharmacogenomics Laboratory, Pharmacy Department, Gregorio Marañón University Hospital, 28007, Madrid, Spain
| | - Carlos J Parramón-Teixidó
- Pharmacy Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, 08035, Barcelona, Spain
| | - Marina Álvarez-Beltrán
- Paediatric Gastroenterology and Clinical Nutrition Unit, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, 08035, Barcelona, Spain
| | - Luís A López-Fernández
- Pharmacogenetics and Pharmacogenomics Laboratory, Pharmacy Department, Gregorio Marañón University Hospital, 28007, Madrid, Spain
| | - Helena Colom
- Pharmacy and Pharmaceutical Technology and Physical Chemistry Department, University of Barcelona, 08028, Barcelona, Spain
| | - Maria J Cabañas-Poy
- Pharmacy Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, 08035, Barcelona, Spain
| | - Maria Q Gorgas-Torner
- Pharmacy Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, 08035, Barcelona, Spain
| | - Marta Miarons
- Pharmacy Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, 08035, Barcelona, Spain
- Pharmacy Department, Consorci Hospitalari de Vic, Vic, Barcelona, Spain
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Dias BB, Carreño F, Helfer VE, Olivo LB, Staudt KJ, Paese K, Barreto F, Meyer FS, Herrmann AP, Guterres SS, Rates SMK, de Araújo BV, Trocóniz IF, Dalla Costa T. Pharmacokinetic/pharmacodynamic modeling of cortical dopamine concentrations after quetiapine lipid core nanocapsules administration to schizophrenia phenotyped rats. CPT Pharmacometrics Syst Pharmacol 2024; 13:638-648. [PMID: 38282365 PMCID: PMC11015084 DOI: 10.1002/psp4.13107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/27/2023] [Accepted: 01/05/2024] [Indexed: 01/30/2024] Open
Abstract
Schizophrenia (SCZ) response to pharmacological treatment is highly variable. Quetiapine (QTP) administered as QTP lipid core nanocapsules (QLNC) has been shown to modulate drug delivery to the brain of SCZ phenotyped rats (SPR). In the present study, we describe the brain concentration-effect relationship after administrations of QTP as a solution or QLNC to SPR and naïve animals. A semimechanistic pharmacokinetic (PK) model describing free QTP concentrations in the brain was linked to a pharmacodynamic (PD) model to correlate the drug kinetics to changes in dopamine (DA) medial prefrontal cortex extracellular concentrations determined by intracerebral microdialysis. Different structural models were investigated to fit DA concentrations after QTP dosing, and the final model describes the synthesis, release, and elimination of DA using a pool compartment. The results show that nanoparticles increase QTP brain concentrations and DA peak after drug dosing to SPR. To the best of our knowledge, this is the first study that combines microdialysis and PK/PD modeling in a neurodevelopmental model of SCZ to investigate how a nanocarrier can modulate drug PK and PD, contributing to the development of new treatment strategies for SCZ.
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Affiliation(s)
- Bruna Bernar Dias
- Pharmacokinetics and PK/PD Modeling Laboratory, Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Fernando Carreño
- Pharmacokinetics and PK/PD Modeling Laboratory, Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Victória Etges Helfer
- Pharmacokinetics and PK/PD Modeling Laboratory, Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Laura Ben Olivo
- Pharmacokinetics and PK/PD Modeling Laboratory, Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Keli Jaqueline Staudt
- Pharmacokinetics and PK/PD Modeling Laboratory, Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Karina Paese
- Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Fabiano Barreto
- Federal Laboratory of Animal and Plant Health and Inspection – LFDA/RSPorto AlegreBrazil
| | - Fabíola Schons Meyer
- Laboratory Animal Reproduction and Experimentation CenterInstitute of Basic Health Sciences, Federal University of Rio Grande do SulPorto AlegreBrazil
| | - Ana Paula Herrmann
- Pharmacology and Therapeutics Graduate Program, Institute of Basic Health SciencesFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Sílvia Stanisçuaski Guterres
- Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Stela Maris Kuze Rates
- Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Bibiana Verlindo de Araújo
- Pharmacokinetics and PK/PD Modeling Laboratory, Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Iñaki F. Trocóniz
- Pharmacometrics & Systems Pharmacology Research UnitDepartment of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of NavarraPamplonaSpain
- IdiSNA, Navarra Institute for Health ResearchPamplonaSpain
| | - Teresa Dalla Costa
- Pharmacokinetics and PK/PD Modeling Laboratory, Pharmaceutical Sciences Graduate Program, Faculty of PharmacyFederal University of Rio Grande do SulPorto AlegreBrazil
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Sethi V, Qin L, Trocóniz IF, Van der Laan L, Cox E, Della Pasqua O. Model-Based Assessment of the Liver Safety Profile of Acetaminophen to Support its Combination Use with Topical Diclofenac in Mild-to-Moderate Osteoarthritis Pain. Pain Ther 2024; 13:127-143. [PMID: 38183572 PMCID: PMC10796898 DOI: 10.1007/s40122-023-00566-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 11/15/2023] [Indexed: 01/08/2024] Open
Abstract
INTRODUCTION The use of combination therapy of oral acetaminophen and topical diclofenac, having complementary mechanisms of action, is an attractive strategy to enhance the analgesic response in osteoarthritis (OA) pain. While topical diclofenac is considered as well tolerated due to its low systemic exposure, concerns of liver toxicity with acetaminophen at standard analgesic doses remain. Thus, this study aimed to assess the liver safety profile of acetaminophen, particularly in OA management, using a model-based meta-analysis (MBMA). METHODS A literature review was conducted using the MEDLINE database to identify randomized clinical trials (RCTs) reporting liver toxicity on acetaminophen use. An MBMA was implemented to assess the deviation from the upper limit of normal (ULN) of alanine aminotransferase or aspartate aminotransferase, namely > 0-1 × ULN, > 1.5-2 × ULN, and > 3 × ULN representing mild, moderate, and severe risk of liver abnormality, respectively. RESULTS A total of 15 RCTs were included in the MBMA, encompassing over 4800 subjects and exposure to acetaminophen ranging from 2 to 26 weeks. Of the 15 included studies, eight involved patients with OA pain, four involved healthy subjects and three were in patients with conditions such as asthma, glaucoma, chronic pain, and cardiovascular disease. Acetaminophen 1500-4000 mg/day was found to exhibit 23% (95% confidence interval (CI): 17.74-29.20), 1.35% (95% CI: 0.17-2.51) and 0.01% (95% CI: 0.00-0.32) increased risk for mild, moderate, and severe liver injury, respectively, versus placebo. Moreover, at therapeutic doses, no correlation was identified between acetaminophen intake and liver abnormality risk. CONCLUSIONS Overall, our analysis shows that short-term (~ 8-16 weeks) acetaminophen use at therapeutically recommended doses is associated with a low risk of clinically relevant changes in liver enzymes. Given the good tolerability of topical diclofenac, the findings support the safety of the combination of acetaminophen and topical diclofenac, at least over the short term, as treatment for mild-to-moderate OA pain.
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Affiliation(s)
- Vidhu Sethi
- Medical Affairs, Haleon (Formerly GSK Consumer Healthcare), GSK Asia House, Rochester Park, 139234, Singapore.
| | - Li Qin
- Quantitative Science, Certara, Princeton, USA
| | - Iñaki F Trocóniz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
| | | | - Eugène Cox
- Quantitative Science, Certara, Princeton, USA
| | - Oscar Della Pasqua
- Clinical Pharmacology and Therapeutics Group, University College London, London, UK
- Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, Brentford, UK
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Sethi V, Qin L, Cox E, Trocóniz IF, Della Pasqua O. Model-Based Meta-Analysis Supporting the Combination of Acetaminophen and Topical Diclofenac in Acute Pain: A Therapy for Mild-to-Moderate Osteoarthritis Pain? Pain Ther 2024; 13:145-159. [PMID: 38183573 PMCID: PMC10796861 DOI: 10.1007/s40122-023-00569-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/16/2023] [Indexed: 01/08/2024] Open
Abstract
INTRODUCTION Acetaminophen and topical diclofenac (AtopD) have complementary mechanisms of action and are therefore candidates for combination use in osteoarthritis (OA) pain. However, an evidence gap exists on their combination use in OA pain. This study aimed to assess the effects of this combination and compare its performance relative to monotherapies on pain score reduction and opioid-sparing effect by leveraging evidence from acute pain setting using a model-based meta-analysis (MBMA). METHODS A literature search was conducted using the MEDLINE database to identify randomized controlled trials (RCTs) studying the combination for acute pain. Subsequently, an MBMA of RCTs was implemented in conjunction with extrapolation principles to infer efficacy in the population of interest. Pain score reduction and opioid-sparing effect (OSE) were selected as the measures of efficacy. RESULTS A total of 11 RCTs encompassing 1396 patients were included. Exploratory evaluation revealed AtopD combination to show greater pain score reduction versus acetaminophen monotherapy. However, pain score reduction was more susceptible to confounding by opioid patient-controlled analgesia (PCA) than OSE. Therefore, a parsimonious MBMA evaluating OSE was developed from 5 of the 11 RCTs (n = 353 patients). The analysis revealed a statistically significant interaction coefficient, suggesting a reduction of 32% in opioid use with the combination versus acetaminophen monotherapy. Differences in the effect size of the combination were less conclusive versus diclofenac monotherapy. CONCLUSION Our results indicate greater pain reduction and opioid-sparing efficacy for the AtopD combination versus acetaminophen monotherapy. Given the similar pain pathways and mechanisms of action of the two drugs in acute and mild-to-moderate OA pain, comparable beneficial effects from the combination therapy may be anticipated following extrapolation to chronic OA pain. Prospective RCTs and real-world studies in OA pain are needed to confirm the differences in the efficacy of the combination treatment observed in our study.
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Affiliation(s)
- Vidhu Sethi
- Medical Affairs, Haleon (Formerly GSK Consumer Healthcare), GSK Asia House, Rochester Park, Singapore, 139234, Singapore
| | - Li Qin
- Quantitative Science, Certara, Princeton, USA
| | - Eugène Cox
- Quantitative Science, Certara, Princeton, USA
| | - Iñaki F Trocóniz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
| | - Oscar Della Pasqua
- Clinical Pharmacology and Therapeutics Group, University College London, BMA House, Tavistock Square, London, WC1H 9JP, UK.
- Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, Brentford, UK.
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Riva N, Brstilo L, Sancho-Araiz A, Molina M, Savransky A, Roffé G, Sanz M, Tenembaum S, Katsicas MM, Trocóniz IF, Schaiquevich P. Population Pharmacodynamic Modelling of the CD19+ Suppression Effects of Rituximab in Paediatric Patients with Neurological and Autoimmune Diseases. Pharmaceutics 2023; 15:2534. [PMID: 38004515 PMCID: PMC10674351 DOI: 10.3390/pharmaceutics15112534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/09/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Limited pharmacotherapy and the failure of conventional treatments in complex pathologies in children lead to increased off-label use of rituximab. We aimed to characterize the time course of CD19+ B lymphocytes (CD19+) under treatment with intravenous rituximab in children with neurologic and autoimmune diseases and to evaluate the impact of covariates (i.e., demographics, diagnosis and substitution between innovator and biosimilar product) on rituximab pharmacodynamics and disease activity. METHODS Pre- and post-drug infusion CD19+ in peripheral blood were prospectively registered. A population pharmacodynamic model describing the time course of CD19+ was developed with NONMEM v7.4. Simulations of three different rituximab regimens were performed to assess the impact on CD19+. Logistic regression analysis was performed to identify predictors of clinical response recorded through disease activity scores. RESULTS 281 measurements of CD19+ lymphocyte counts obtained from 63 children with neurologic (n = 36) and autoimmune (n = 27) diseases were available. The time course of CD19+ was described with a turn-over model in which the balance between synthesis and degradation rates is disrupted by rituximab, increasing the latter process. The model predicts half-lives (percent coefficient of variation, CV(%)) of rituximab and CD19+ of 11.6 days (17%) and 173.3 days (22%), respectively. No statistically significant effect was found between any of the studied covariates and model parameters (p > 0.05). Simulations of different regimens showed no clinically significant differences in terms of CD19+ repopulation times. A trend towards a lack of clinical response was observed in patients with lower CD19+ repopulation times and higher areas under the CD19+ versus time curve. CONCLUSIONS Rituximab pharmacodynamics was described in a real-world setting in children suffering from autoimmune and neurologic diseases. Diagnosis, substitution between innovator rituximab and its biosimilars or type of regimen did not affect rituximab-induced depletion of CD19+ nor the clinical response in this cohort of patients. According to this study, rituximab frequency and dosage may be chosen based on clinical convenience or safety reasons without affecting CD19+ repopulation times. Further studies in larger populations are required to confirm these results.
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Affiliation(s)
- Natalia Riva
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmaceutical Sciences, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (I.F.T.)
- Unit of Innovative Treatments, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina; (L.B.); (M.M.); (P.S.)
- National Council of Scientific and Technical Research (CONICET), Buenos Aires C1425 FQB, Argentina
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain
| | - Lucas Brstilo
- Unit of Innovative Treatments, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina; (L.B.); (M.M.); (P.S.)
- National Council of Scientific and Technical Research (CONICET), Buenos Aires C1425 FQB, Argentina
| | - Aymara Sancho-Araiz
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmaceutical Sciences, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (I.F.T.)
- Unit of Innovative Treatments, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina; (L.B.); (M.M.); (P.S.)
| | - Manuel Molina
- Unit of Innovative Treatments, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina; (L.B.); (M.M.); (P.S.)
| | - Andrea Savransky
- Neurology Service, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina; (A.S.); (S.T.)
| | - Georgina Roffé
- Laboratory of Cellular Immunology, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina; (G.R.); (M.S.)
| | - Marianela Sanz
- Laboratory of Cellular Immunology, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina; (G.R.); (M.S.)
| | - Silvia Tenembaum
- Neurology Service, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina; (A.S.); (S.T.)
| | - Maria M. Katsicas
- Immunology and Rheumatology Service, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina;
| | - Iñaki F. Trocóniz
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmaceutical Sciences, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (I.F.T.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain
- Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, 31009 Pamplona, Spain
| | - Paula Schaiquevich
- Unit of Innovative Treatments, Hospital de Pediatría JP Garrahan, Buenos Aires C1245 CABA, Argentina; (L.B.); (M.M.); (P.S.)
- National Council of Scientific and Technical Research (CONICET), Buenos Aires C1425 FQB, Argentina
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Sancho-Araiz A, Parra-Guillen ZP, Bragard J, Ardanza S, Mangas-Sanjuan V, Trocóniz IF. Mechanistic characterization of oscillatory patterns in unperturbed tumor growth dynamics: The interplay between cancer cells and components of tumor microenvironment. PLoS Comput Biol 2023; 19:e1011507. [PMID: 37792732 PMCID: PMC10550146 DOI: 10.1371/journal.pcbi.1011507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/11/2023] [Indexed: 10/06/2023] Open
Abstract
Mathematical modeling of unperturbed and perturbed tumor growth dynamics (TGD) in preclinical experiments provides an opportunity to establish translational frameworks. The most commonly used unperturbed tumor growth models (i.e. linear, exponential, Gompertz and Simeoni) describe a monotonic increase and although they capture the mean trend of the data reasonably well, systematic model misspecifications can be identified. This represents an opportunity to investigate possible underlying mechanisms controlling tumor growth dynamics through a mathematical framework. The overall goal of this work is to develop a data-driven semi-mechanistic model describing non-monotonic tumor growth in untreated mice. For this purpose, longitudinal tumor volume profiles from different tumor types and cell lines were pooled together and analyzed using the population approach. After characterizing the oscillatory patterns (oscillator half-periods between 8-11 days) and confirming that they were systematically observed across the different preclinical experiments available (p<10-9), a tumor growth model was built including the interplay between resources (i.e. oxygen or nutrients), angiogenesis and cancer cells. The new structure, in addition to improving the model diagnostic compared to the previously used tumor growth models (i.e. AIC reduction of 71.48 and absence of autocorrelation in the residuals (p>0.05)), allows the evaluation of the different oncologic treatments in a mechanistic way. Drug effects can potentially, be included in relevant processes taking place during tumor growth. In brief, the new model, in addition to describing non-monotonic tumor growth and the interaction between biological factors of the tumor microenvironment, can be used to explore different drug scenarios in monotherapy or combination during preclinical drug development.
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Affiliation(s)
- Aymara Sancho-Araiz
- Pharmacometrics & Systems Pharmacology Group, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Zinnia P. Parra-Guillen
- Pharmacometrics & Systems Pharmacology Group, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Jean Bragard
- Department of Physics and Applied Math. University of Navarra, Pamplona, Spain
- Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, Spain
| | - Sergio Ardanza
- Department of Physics and Applied Math. University of Navarra, Pamplona, Spain
- Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, Spain
| | - Victor Mangas-Sanjuan
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, Faculty of Pharmacy, University of Valencia, Valencia, Spain
- Interuniversity Research Institute for Molecular Recognition and Technological Development, Valencia, Spain
| | - Iñaki F. Trocóniz
- Pharmacometrics & Systems Pharmacology Group, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, Spain
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Schiavo A, Maldonado C, Vázquez M, Fagiolino P, Trocóniz IF, Ibarra M. Quantitative systems pharmacology Model to characterize valproic acid-induced hyperammonemia and the effect of L-carnitine supplementation. Eur J Pharm Sci 2023; 183:106399. [PMID: 36740101 DOI: 10.1016/j.ejps.2023.106399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/29/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023]
Abstract
Valproic acid (VPA) is a short-chain fatty acid widely prescribed in the treatment of seizure disorders and epilepsy syndromes, although its therapeutic value may be undermined by its toxicity. VPA serious adverse effects are reported to have a significant and dose-dependent incidence, many associated with VPA-induced hyperammonemia. This effect has been linked with reduced levels of carnitine; an endogenous compound involved in fatty acid's mitochondrial β-oxidation by facilitation of its entrance via the carnitine shuttle. High exposure to VPA can lead to carnitine depletion causing a misbalance between the intra-mitochondrial β-oxidation and the microsomal ω-oxidation, a pathway that produces toxic metabolites such as 4-en-VPA which inhibits ammonia elimination. Moreover, a reduction in carnitine levels might be also related to VPA-induced obesity and lipids disorder. In turn, L-carnitine supplementation (CS) has been recommended and empirically used to reduce VPA's hepatotoxicity. The aim of this work was to develop a Quantitative Systems Pharmacology (QSP) model to characterize VPA-induced hyperammonemia and evaluate the benefits of CS in preventing hyperammonemia under both chronic treatment and after VPA overdosing. The QSP model included a VPA population pharmacokinetics model that allowed the prediction of total and unbound concentrations after single and multiple oral doses considering its saturable binding to plasma proteins. Predictions of time courses for 2-en-VPA, 4-en-DPA, VPA-glucuronide, carnitine, ammonia and urea levels, and for the relative change in fatty acids, Acetyl-CoA, and glutamate reflected the VPA induced changes and the efficacy of the treatment with L-carnitine. The QSP model was implemented to give a rational basis for the L-carnitine dose selection to optimize CS depending on VPA dosage regime and to assess the currently recommended L-carnitine rescue therapy after VPA overdosing. Results show that a L-carnitine dose equal to the double of the VPA dose using the same interdose interval would maintain the ammonia levels at baseline. The QSP model may be expanded in the future to describe other adverse events linked to VPA-induced changes in endogenous compounds.
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Affiliation(s)
- Alejandra Schiavo
- Department of Pharmaceutical Sciences, Faculty of Chemistry. Universidad de la República. Montevideo, Uruguay; Graduate Program in Chemistry, Faculty of Chemistry, Universidad de la República. Montevideo, Uruguay
| | - Cecilia Maldonado
- Department of Pharmaceutical Sciences, Faculty of Chemistry. Universidad de la República. Montevideo, Uruguay
| | - Marta Vázquez
- Department of Pharmaceutical Sciences, Faculty of Chemistry. Universidad de la República. Montevideo, Uruguay
| | - Pietro Fagiolino
- Department of Pharmaceutical Sciences, Faculty of Chemistry. Universidad de la República. Montevideo, Uruguay
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra. Pamplona, Spain; IdiSNA; Navarra Institute for Health Research, Pamplona, Spain
| | - Manuel Ibarra
- Department of Pharmaceutical Sciences, Faculty of Chemistry. Universidad de la República. Montevideo, Uruguay.
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Tamariz-Amador LE, Rodríguez-Otero P, Jiménez-Ubieto A, Rosiñol L, Oriol A, Ríos R, Sureda A, Blanchard MJ, Hernández MT, Cabañas Perianes V, Jarque I, Bargay J, Gironella M, De Arriba F, Palomera L, Gonzalez-Montes Y, Martí JM, Krsnik I, Arguiñano JM, González ME, Casado LF, González-Rodriguez AP, López-Anglada L, Puig N, Cedena MT, Paiva B, Mateos MV, San-Miguel J, Lahuerta JJ, Bladé J, Trocóniz IF. Prognostic Value of Serum Paraprotein Response Kinetics in Patients With Newly Diagnosed Multiple Myeloma. Clin Lymphoma Myeloma Leuk 2022; 22:e844-e852. [PMID: 35688793 DOI: 10.1016/j.clml.2022.04.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/26/2022] [Accepted: 04/29/2022] [Indexed: 06/15/2023]
Abstract
INTRODUCTION Response kinetics is a well-established prognostic marker in acute lymphoblastic leukemia. The situation is not clear in multiple myeloma (MM) despite having a biomarker for response monitoring (monoclonal component [MC]). MATERIALS AND METHODS We developed a mathematical model to assess the prognostic value of serum MC response kinetics during 6 induction cycles, in 373 NDMM transplanted patients treated in the GEM2012Menos65 clinical trial. The model calculated a "resistance" parameter that reflects the stagnation in the response after an initial descent. RESULTS Two patient subgroups were defined based on low and high resistance, that respectively captured sensitive and refractory kinetics, with progression-free survival (PFS) at 5 years of 72% and 59% (HR 0.64, 95% CI 0.44-0.93; P = .02). Resistance significantly correlated with depth of response measured after consolidation (80.9% CR and 68.4% minimal residual disease negativity in patients with sensitive vs. 31% and 20% in those with refractory kinetics). Furthermore, it modulated the impact of reaching CR after consolidation; thus, within CR patients those with refractory kinetics had significantly shorter PFS than those with sensitive kinetics (median 54 months vs. NR; P = .02). Minimal residual disease negativity abrogated this effect. Our study also questions the benefit of rapid responders compared to late responders (5-year PFS 59.7% vs. 76.5%, respectively [P < .002]). Of note, 85% of patients considered as late responders were classified as having sensitive kinetics. CONCLUSION This semi-mechanistic modeling of M-component kinetics could be of great value to identify patients at risk of early treatment failure, who may benefit from early rescue intervention strategies.
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Affiliation(s)
- Luis-Esteban Tamariz-Amador
- Clínica Universidad de Navarra, CCUN, Centro de Investigación Médica Aplicada (CIMA), IDISNA, CIBERONC, Pamplona, Spain.
| | - Paula Rodríguez-Otero
- Clínica Universidad de Navarra, CCUN, Centro de Investigación Médica Aplicada (CIMA), IDISNA, CIBERONC, Pamplona, Spain.
| | | | - Laura Rosiñol
- Hospital Clínic de Barcelona, IDIBAPS, Barcelona, Spain
| | - Albert Oriol
- Institut Català d'Oncologia i Institut Josep Carreras, Badalona, Spain
| | - Rafael Ríos
- Hospital Universitario Puerta de Hierro, Majadahonda, Madrid, Spain
| | - Anna Sureda
- Institut Català d'Oncologia - Hospital Duran i Reynals, IDIBELL, Universitat de Barcelona, Barcelona, Spain
| | | | | | | | | | - Juan Bargay
- Hospital Son Llatzer, Palma de Mallorca, Spain
| | | | - Felipe De Arriba
- Hospital Universitario Morales Meseguer, IMIB-Arrixaca, Universidad de Murcia, Murcia, Spain
| | | | | | | | | | | | | | | | | | | | - Noemi Puig
- Hospital Universitario de Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain
| | | | - Bruno Paiva
- Clínica Universidad de Navarra, CCUN, Centro de Investigación Médica Aplicada (CIMA), IDISNA, CIBERONC, Pamplona, Spain
| | - Maria-Victoria Mateos
- Hospital Universitario de Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain
| | - Jesús San-Miguel
- Clínica Universidad de Navarra, CCUN, Centro de Investigación Médica Aplicada (CIMA), IDISNA, CIBERONC, Pamplona, Spain
| | | | - Joan Bladé
- Hospital Clínic de Barcelona, IDIBAPS, Barcelona, Spain
| | - Iñaki F Trocóniz
- Facultad de Farmacia y Nutrición, Universidad de Navarra, Pamplona, Spain
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9
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Vera-Yunca D, Córdoba KM, Parra-Guillen ZP, Jericó D, Fontanellas A, Trocóniz IF. Mechanistic modelling of enzyme-restoration effects for new recombinant liver-targeted proteins in acute intermittent porphyria. Br J Pharmacol 2022; 179:3815-3830. [PMID: 35170015 PMCID: PMC9310908 DOI: 10.1111/bph.15821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/18/2022] [Accepted: 02/08/2022] [Indexed: 11/28/2022] Open
Abstract
Background and Purpose Acute intermittent porphyria (AIP) is a rare disease caused by a genetic mutation in the hepatic activity of the porphobilinogen‐deaminase. We aimed to develop a mechanistic model of the enzymatic restoration effects of a novel therapy based on the administration of different formulations of recombinant human‐PBGD (rhPBGD) linked to the ApoAI lipoprotein. This fusion protein circulates in blood, incorporating into HDL and penetrating hepatocytes. Experimental Approach Single i.v. dose of different formulations of rhPBGD linked to ApoAI were administered to AIP mice in which a porphyric attack was triggered by i.p. phenobarbital. Data consist on 24 h urine excreted amounts of heme precursors, 5‐aminolevulinic acid (ALA), PBG and total porphyrins that were analysed using non‐linear mixed‐effects analysis. Key Results The mechanistic model successfully characterized over time the amounts excreted in urine of the three heme precursors for different formulations of rhPBGD and unravelled several mechanisms in the heme pathway, such as the regulation in ALA synthesis by heme. Treatment with rhPBGD formulations restored PBGD activity, increasing up to 51 times the value of the rate of tPOR formation estimated from baseline. Model‐based simulations showed that several formulation prototypes provided efficient protective effects when administered up to 1 week prior to the occurrence of the AIP attack. Conclusion and Implications The model developed had excellent performance over a range of doses and formulation type. This mechanistic model warrants use beyond ApoAI‐conjugates and represents a useful tool towards more efficient drug treatments of other enzymopenias as well as for acute intermittent porphyria.
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Affiliation(s)
- Diego Vera-Yunca
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Karol M Córdoba
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,Hepatology Program, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Zinnia P Parra-Guillen
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Daniel Jericó
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,Hepatology Program, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Antonio Fontanellas
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.,Hepatology Program, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
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10
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Ameijeiras Rodríguez C, Henriques SC, Sancho-Araiz A, Trocóniz IF, Almeida L, Silva NE. Untangling Absorption Mechanisms and Variability in Bioequivalence Studies Using Population Analysis. Pharm Res 2021; 38:2047-2063. [PMID: 34932170 DOI: 10.1007/s11095-021-03136-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 11/04/2021] [Indexed: 11/27/2022]
Abstract
PURPOSE Both inter-individual (IIV) and inter-occasion (IOV) variabilities are observed in bioequivalence studies. High IOV may be a cause of problems on the demonstration of bioequivalence, despite strict measures are taken to control it. The objective of this study is to investigate further means of controlling IIV by optimizing study design of crossover studies. METHODS Data from 18 bioequivalence studies were used to develop population pharmacokinetics (popPK) models to characterize the absorption and disposition processes of 14 drugs, to estimate IOV for each drug substance and to evaluate possible correlations with biopharmaceutical properties of drug substances, classified in accordance to the Biopharmaceutics Drug Disposition Classification System (BDDCS). RESULTS Plasma-pharmacokinetics profiles for the 14 drugs analyzed were successfully described using popPK. The pharmacokinetic parameters that showed greater variability were first-order rate constant of absorption, duration of the zero-order absorption process, relative bioavailability and time of latency. ISCV% estimated for Cmax seems to correlate with the log-Dose-Number for Class 1, 2 and 3, despite no direct correlation was observed between popPK model residual variability (RUV) and ISCV%. Nevertheless, higher RUV estimates were observed for Class 2 drugs in comparison to Class 1 and 3. CONCLUSION Pharmacokinetic parameters related to drug absorption showed greater variability. Ingestion of the IMP along with 240 mL of water showed to standardize gastric emptying. Given the dependency between Cmax variability and dose-solubility ratio, for classes 2 and 4, ad libitum water intake may increase Cmax and AUC ISCV%. A water ingestion standardization until the expected Tmax of the drug is suggested.
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Affiliation(s)
| | | | - Aymara Sancho-Araiz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Iñaki F Trocóniz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Luis Almeida
- MedInUP-Center for Drug Discovery and Innovative Medicines, University of Porto, Porto, Portugal.,BlueClinical, Porto, Portugal
| | - Nuno Elvas Silva
- BlueClinical, Porto, Portugal.,Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal
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11
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Sancho-Araiz A, Zalba S, Garrido MJ, Berraondo P, Topp B, de Alwis D, Parra-Guillen ZP, Mangas-Sanjuan V, Trocóniz IF. Semi-Mechanistic Model for the Antitumor Response of a Combination Cocktail of Immuno-Modulators in Non-Inflamed (Cold) Tumors. Cancers (Basel) 2021; 13:cancers13205049. [PMID: 34680196 PMCID: PMC8534053 DOI: 10.3390/cancers13205049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 10/05/2021] [Indexed: 11/30/2022] Open
Abstract
Simple Summary The clinical efficacy of immunotherapies when treating cold tumors is still low, and different treatment combinations are needed when dealing with this challenging scenario. In this work, a middle-out strategy was followed to develop a model describing the antitumor efficacy of different immune-modulator combinations, including an antigen, a toll-like receptor-3 agonist, and an immune checkpoint inhibitor in mice treated with non-inflamed tumor cells. Our results support that clinical response requires antigen-presenting cell activation and also relies on the amount of CD8 T cells and tumor resistance mechanisms present. This mathematical model is a very useful platform to evaluate different immuno-oncology combinations in both preclinical and clinical settings. Abstract Immune checkpoint inhibitors, administered as single agents, have demonstrated clinical efficacy. However, when treating cold tumors, different combination strategies are needed. This work aims to develop a semi-mechanistic model describing the antitumor efficacy of immunotherapy combinations in cold tumors. Tumor size of mice treated with TC-1/A9 non-inflamed tumors and the drug effects of an antigen, a toll-like receptor-3 agonist (PIC), and an immune checkpoint inhibitor (anti-programmed cell death 1 antibody) were modeled using Monolix and following a middle-out strategy. Tumor growth was best characterized by an exponential model with an estimated initial tumor size of 19.5 mm3 and a doubling time of 3.6 days. In the treatment groups, contrary to the lack of response observed in monotherapy, combinations including the antigen were able to induce an antitumor response. The final model successfully captured the 23% increase in the probability of cure from bi-therapy to triple-therapy. Moreover, our work supports that CD8+ T lymphocytes and resistance mechanisms are strongly related to the clinical outcome. The activation of antigen-presenting cells might be needed to achieve an antitumor response in reduced immunogenic tumors when combined with other immunotherapies. These models can be used as a platform to evaluate different immuno-oncology combinations in preclinical and clinical scenarios.
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Affiliation(s)
- Aymara Sancho-Araiz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
| | - Sara Zalba
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
| | - María J. Garrido
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
| | - Pedro Berraondo
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
- Program of Immunology and Immunotherapy, CIMA Universidad de Navarra, 31008 Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), 28029 Madrid, Spain
| | - Brian Topp
- Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Kenilworth, NJ 07033, USA; (B.T.); (D.d.A.)
| | - Dinesh de Alwis
- Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Kenilworth, NJ 07033, USA; (B.T.); (D.d.A.)
| | - Zinnia P. Parra-Guillen
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
| | - Víctor Mangas-Sanjuan
- Department of Pharmacy Technology and Parasitology, Faculty of Pharmacy, University of Valencia, 46100 Valencia, Spain;
- Interuniversity Institute of Recognition Research Molecular and Technological Development, Polytechnic University of Valencia-University of Valencia, 46100 Valencia, Spain
| | - Iñaki F. Trocóniz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
- Correspondence:
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12
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Sancho-Araiz A, Mangas-Sanjuan V, Trocóniz IF. The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives. Pharmaceutics 2021; 13:pharmaceutics13071016. [PMID: 34371708 PMCID: PMC8309057 DOI: 10.3390/pharmaceutics13071016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/24/2021] [Accepted: 06/29/2021] [Indexed: 12/12/2022] Open
Abstract
Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.
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Affiliation(s)
- Aymara Sancho-Araiz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31009 Pamplona, Spain; (A.S.-A.); (I.F.T.)
- Navarra Institute for Health Research (IdiSNA), 31009 Pamplona, Spain
| | - Victor Mangas-Sanjuan
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, 46100 Valencia, Spain
- Interuniversity Research Institute for Molecular Recognition and Technological Development, 46100 Valencia, Spain
- Correspondence: ; Tel.: +34-96354-3351
| | - Iñaki F. Trocóniz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31009 Pamplona, Spain; (A.S.-A.); (I.F.T.)
- Navarra Institute for Health Research (IdiSNA), 31009 Pamplona, Spain
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13
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Vera-Yunca D, Parra-Guillen ZP, Girard P, Trocóniz IF, Terranova N. Relevance of primary lesion location, tumour heterogeneity and genetic mutation demonstrated through tumour growth inhibition and overall survival modelling in metastatic colorectal cancer. Br J Clin Pharmacol 2021; 88:166-177. [PMID: 34087010 DOI: 10.1111/bcp.14937] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/21/2021] [Accepted: 05/30/2021] [Indexed: 12/20/2022] Open
Abstract
AIMS The aims of this work were to build a semi-mechanistic tumour growth inhibition (TGI) model for metastatic colorectal cancer (mCRC) patients receiving either cetuximab + chemotherapy or chemotherapy alone and to identify early predictors of overall survival (OS). METHODS A total of 1716 patients from 4 mCRC clinical studies were included in the analysis. The TGI model was built with 8973 tumour size measurements where the probability of drop-out was also included and modelled as a time-to-event variable using parametric survival models, as it was the case in the OS analysis. The effects of patient- and tumour-related covariates on model parameters were explored. RESULTS Chemotherapy and cetuximab effects were included in an additive form in the TGI model. Development of resistance was found to be faster for chemotherapy (drug effect halved at wk 8) compared to cetuximab (drug effect halved at wk 12). KRAS wild-type status and presenting a right-sided primary lesion were related to a 3.5-fold increase in cetuximab drug effect and a 4.7× larger cetuximab resistance, respectively. The early appearance of a new lesion (HR = 4.14), a large tumour size at baseline (HR = 1.62) and tumour heterogeneity (HR = 1.36) were the main predictors of OS. CONCLUSIONS Semi-mechanistic TGI and OS models have been developed in a large population of mCRC patients receiving chemotherapy in combination or not with cetuximab. Tumour-related predictors, including a machine learning derived-index of tumour heterogeneity, were linked to changes in drug effect, resistance to treatment or OS, contributing to the understanding of the variability in clinical response.
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Affiliation(s)
- Diego Vera-Yunca
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Zinnia P Parra-Guillen
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Pascal Girard
- Merck Serono S.A., Switzerland, an affiliate of Merck KGaA, Merck Institute for Pharmacometrics, Darmstadt, Germany
| | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Nadia Terranova
- Merck Serono S.A., Switzerland, an affiliate of Merck KGaA, Merck Institute for Pharmacometrics, Darmstadt, Germany
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Abstract
In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.
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Affiliation(s)
- Belén P Solans
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, C/Irunlarrea s/n, 31008, Pamplona, Navarra, Spain. .,Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain.
| | - María Jesús Garrido
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, C/Irunlarrea s/n, 31008, Pamplona, Navarra, Spain.,Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, C/Irunlarrea s/n, 31008, Pamplona, Navarra, Spain. .,Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain.
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15
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Irurzun-Arana I, Rackauckas C, McDonald TO, Trocóniz IF. Beyond Deterministic Models in Drug Discovery and Development. Trends Pharmacol Sci 2020; 41:882-895. [PMID: 33032836 PMCID: PMC7534664 DOI: 10.1016/j.tips.2020.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 07/28/2020] [Accepted: 09/10/2020] [Indexed: 02/06/2023]
Abstract
The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug-disease models more versatile and realistic.
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Affiliation(s)
- Itziar Irurzun-Arana
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain.
| | - Christopher Rackauckas
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Thomas O McDonald
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain; Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, 31080, Spain.
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16
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Abstract
Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical impact in improving overall survival of several malignancies associated with poor outcomes; however, only 20–40% of patients will show long-lasting survival. Further clarification of factors related to treatment response can support improvements in clinical outcome and guide the development of novel immune checkpoint therapies. In this article, we have provided an overview of the pharmacokinetic (PK) aspects related to current ICIs, which include target-mediated drug disposition and time-varying drug clearance. In response to the variation in treatment exposure of ICIs and the significant healthcare costs associated with these agents, arguments for both dose individualization and generalization are provided. We address important issues related to the efficacy and safety, the pharmacodynamics (PD), of ICIs, including exposure–response relationships related to clinical outcome. The unique PK and PD aspects of ICIs give rise to issues of confounding and suboptimal surrogate endpoints that complicate interpretation of exposure–response analysis. Biomarkers to identify patients benefiting from treatment with ICIs have been brought forward. However, validated biomarkers to monitor treatment response are currently lacking.
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Affiliation(s)
- Maddalena Centanni
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Dirk Jan A R Moes
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
| | - Joseph Ciccolini
- SMARTc, CRCM Inserm U1068 Aix Marseille Univ and La Timone University Hospital of Marseille, Marseille, France
| | - J G Coen van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.
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Irurzun-Arana I, McDonald TO, Trocóniz IF, Michor F. Pharmacokinetic Profiles Determine Optimal Combination Treatment Schedules in Computational Models of Drug Resistance. Cancer Res 2020; 80:3372-3382. [PMID: 32561532 PMCID: PMC7442591 DOI: 10.1158/0008-5472.can-20-0056] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 04/01/2020] [Accepted: 06/16/2020] [Indexed: 12/21/2022]
Abstract
Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available in vitro data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non-small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment. SIGNIFICANCE: These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.
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Affiliation(s)
- Itziar Irurzun-Arana
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain
| | - Thomas O McDonald
- Department of Data Sciences, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts
- Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain
| | - Franziska Michor
- Department of Data Sciences, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts
- Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, Massachusetts
- The Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- The Ludwig Center at Harvard, Boston, Massachusetts
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18
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Fernandez-Teruel C, Gonzalez I, Trocóniz IF, Lubomirov R, Soto A, Fudio S. Population-Pharmacokinetic and Covariate Analysis of Lurbinectedin (PM01183), a New RNA Polymerase II Inhibitor, in Pooled Phase I/II Trials in Patients with Cancer. Clin Pharmacokinet 2020; 58:363-374. [PMID: 30090974 DOI: 10.1007/s40262-018-0701-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND OBJECTIVES Lurbinectedin is an inhibitor of RNA polymerase II currently under clinical development for intravenous administration as a single agent and in combination with other anti-tumor agents for the treatment of several tumor types. The objective of this work was to develop a population-pharmacokinetic model in this patient setting and to elucidate the main predictors to guide the late stages of development. METHODS Data from 443 patients with solid and hematologic malignancies treated in six phase I and three phase II trials with lurbinectedin as a single agent or combined with other agents were included in the analysis. The potential influence of demographic, co-treatment, and laboratory characteristics on lurbinectedin pharmacokinetics was evaluated. RESULTS The final population-pharmacokinetic model was an open three-compartment model with linear distribution and linear elimination from the central compartment. Population estimates for total plasma clearance, and apparent volume at steady state were 11.2 L/h and 438 L, respectively. Inter-individual variability was moderate for all parameters, ranging from 20.9 to 51.2%. High α-1-acid glycoprotein and C-reactive protein, and low albumin reduced clearance by 28, 20, and 20%, respectively. Co-administration of cytochrome P450 3A inhibitors reduced clearance by 30%. Combinations with other anti-tumor agents did not modify the pharmacokinetics of lurbinectedin significantly. CONCLUSION The population-pharmacokinetic model indicated neither a dose nor time dependency, and no clinically meaningful pharmacokinetic differences were found when co-administered with other anticancer agents. A chronic inflammation pattern characterized by decreased albumin and increased C-reactive protein and α-1-acid glycoprotein levels led to high lurbinectedin exposure. Co-administration of cytochrome P450 3A inhibitors increased lurbinectedin exposure.
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Affiliation(s)
- Carlos Fernandez-Teruel
- Department of Clinical Pharmacology, PharmaMar, S.A., Avda. de los Reyes, 1 Colmenar Viejo, 28770, Madrid, Spain.
| | - Ignacio Gonzalez
- Department of Clinical Pharmacology, PharmaMar, S.A., Avda. de los Reyes, 1 Colmenar Viejo, 28770, Madrid, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Rubin Lubomirov
- Department of Clinical Pharmacology, PharmaMar, S.A., Avda. de los Reyes, 1 Colmenar Viejo, 28770, Madrid, Spain
| | - Arturo Soto
- Department of Clinical Pharmacology, PharmaMar, S.A., Avda. de los Reyes, 1 Colmenar Viejo, 28770, Madrid, Spain
| | - Salvador Fudio
- Department of Clinical Pharmacology, PharmaMar, S.A., Avda. de los Reyes, 1 Colmenar Viejo, 28770, Madrid, Spain
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19
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Vera-Yunca D, Girard P, Parra-Guillen ZP, Munafo A, Trocóniz IF, Terranova N. Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival. AAPS J 2020; 22:58. [PMID: 32185612 PMCID: PMC7078147 DOI: 10.1208/s12248-020-0434-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 02/12/2020] [Indexed: 12/23/2022]
Abstract
Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to determine differences in TS dynamics by using the ClassIfication Clustering of Individual Lesions (CICIL) methodology. Results from subgroup analyses comparing genetic mutations and TS metrics were assessed and applied to survival analyses. Data from four mCRC clinical studies were analyzed (1781 patients, 6369 iTLs). CICIL was used to assess differences in lesion TS dynamics within a tissue (intra-class) or across different tissues (inter-class). First, lesions were automatically classified based on their location. Cross-correlation coefficients (CCs) determined if each pair of lesions followed similar or opposite dynamics. Finally, CCs were grouped by using the K-means clustering method. Heterogeneity in tumor dynamics was lower in the intra-class analysis than in the inter-class analysis for patients receiving cetuximab. More tumor heterogeneity was found in KRAS mutated patients compared to KRAS wild-type (KRASwt) patients and when using sum of longest diameters versus sum of products of diameters. Tumor heterogeneity quantified as the median patient's CC was found to be a predictor of overall survival (OS) (HR = 1.44, 95% CI 1.08-1.92), especially in KRASwt patients. Intra- and inter-tumor tissue heterogeneities were assessed with CICIL. Derived metrics of heterogeneity were found to be a predictor of OS time. Considering differences between lesions' TS dynamics could improve oncology models in favor of a better prediction of OS.
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Affiliation(s)
- Diego Vera-Yunca
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
| | - Pascal Girard
- Merck Institute for Pharmacometrics, Merck Serono S.A., Switzerland, a Subsidiary of Merck KGaA, Darmstadt, Germany
| | - Zinnia P Parra-Guillen
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Alain Munafo
- Merck Institute for Pharmacometrics, Merck Serono S.A., Switzerland, a Subsidiary of Merck KGaA, Darmstadt, Germany
| | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Nadia Terranova
- Merck Institute for Pharmacometrics, Merck Serono S.A., Switzerland, a Subsidiary of Merck KGaA, Darmstadt, Germany.
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20
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Solans BP, Chiesa R, Doncheva B, Prunty H, Veys P, Trocóniz IF, Standing JF. Modelling of neutrophil dynamics in children receiving busulfan or treosulfan for haematopoietic stem cell transplant conditioning. Br J Clin Pharmacol 2020; 86:1537-1549. [PMID: 32077123 DOI: 10.1111/bcp.14260] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 01/16/2020] [Accepted: 02/01/2020] [Indexed: 12/11/2022] Open
Abstract
AIMS Busulfan and treosulfan are cytotoxic agents used in the conditioning regime prior to paediatric haematopoietic stem cell transplantation (HSCT). These agents cause suppression of myeloid cells leaving patients severely immunocompromised in the early post-HSCT period. The main objectives were: (i) to establish a mechanistic pharmacokinetic-pharmacodynamic (PKPD) model for the treatment and engraftment effects on neutrophil counts comparing busulfan and treosulfan-based conditioning, and (ii) to explore current dosing schedules with respect to time to HSCT. METHODS Data on 126 patients, 72 receiving busulfan (7 months-18 years, 5.1-47.0 kg) and 54 treosulfan (4 months-17 years, 3.8-35.8 kg), were collected. In total, 8935 neutrophil count observations were recorded during the study period in addition to drug concentrations to develop a mechanistic PKPD model. Absolute neutrophil count profiles were modelled semimechanistically, accounting for transplant effects and differing set points pre- and post-transplant. RESULTS PK were best described by 2-compartment models for both drugs. The Friberg semimechanistic neutropenia model was applied with a linear model for busulfan and a maximum efficacy model for treosulfan describing drug effects at various stages of neutrophil maturation. System parameters were consistent across both drugs. The HSCT was represented by an amount of progenitor cells enhancing the neutrophils' proliferation and maturation compartments. Alemtuzumab was found to enhance the proliferative rate under which the absolute neutrophil count begin to grow after HSCT. CONCLUSION A semimechanistic PKPD model linking exposure to either busulfan or treosulfan to the neutrophil reconstitution dynamics was successfully built. Alemtuzumab coadministration enhanced the neutrophil proliferative rate after HSCT. Treosulfan administration was suggested to be delayed with respect to time to HSCT, leaving less time between the end of the administration and stem cell infusion.
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Affiliation(s)
- Belén P Solans
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Robert Chiesa
- Bone Marrow Transplantation Department, Great Ormond Street Hospital for Children, London, UK
| | - Bilyana Doncheva
- Department of Pharmacy, Great Ormond Street Hospital for Children, London, UK
| | - Helen Prunty
- Department of Chemical Pathology, Great Ormond Street Hospital for Children, London, UK
| | - Paul Veys
- Bone Marrow Transplantation Department, Great Ormond Street Hospital for Children, London, UK
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Joseph F Standing
- Department of Pharmacy, Great Ormond Street Hospital for Children, London, UK.,Infection, Immunity, Inflammation Programme, UCL Great Ormond Street Institute of Child Health, London, UK.,Paediatric Infectious Diseases Group, St George's, University of London, UK
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21
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Balbas-Martinez V, Michelet R, Edginton AN, Meesters K, Trocóniz IF, Vermeulen A. Corrigendum to "Physiologically-Based Pharmacokinetic model for Ciprofloxacin in children with complicated Urinary Tract Infection" [European Journal of Pharmaceutical Sciences 128 (2019) 171-179]. Eur J Pharm Sci 2020; 143:105182. [PMID: 31870584 DOI: 10.1016/j.ejps.2019.105182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Violeta Balbas-Martinez
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Ghent University, Faculty of Pharmaceutical Sciences, Laboratory of Medical Biochemistry and Clinical Analysis, Ghent, Belgium.
| | - Robin Michelet
- Ghent University, Faculty of Pharmaceutical Sciences, Laboratory of Medical Biochemistry and Clinical Analysis, Ghent, Belgium
| | - Andrea N Edginton
- School of Pharmacy, University of Waterloo, Waterloo, Ontario, Canada
| | - Kevin Meesters
- Ghent University Hospital, Department of Pediatric Nephrology, Ghent, Belgium; KidZ Health Castle, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - An Vermeulen
- Ghent University, Faculty of Pharmaceutical Sciences, Laboratory of Medical Biochemistry and Clinical Analysis, Ghent, Belgium.
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22
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Vera-Yunca D, Serrano-Mendioroz I, Sampedro A, Jericó D, Trocóniz IF, Fontanellas A, Parra-Guillén ZP. Computational disease model of phenobarbital-induced acute attacks in an acute intermittent porphyria mouse model. Mol Genet Metab 2019; 128:367-375. [PMID: 30639045 DOI: 10.1016/j.ymgme.2018.12.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 11/27/2018] [Accepted: 12/19/2018] [Indexed: 11/23/2022]
Abstract
INTRODUCTION Acute intermittent porphyria (AIP) is characterized by hepatic over-production of the heme precursors when aminolevulinic acid (ALA)-synthase 1 is induced by endogenous or environmental factors. The aim of this study was to develop a semi-mechanistic computational model to characterize urine accumulation of heme precursors during acute attacks based on experimental pharmacodynamics data and support the development of new therapeutic strategies. METHODS Male AIP mice received recurrent phenobarbital challenge starting on days 1, 9, 16 and 30. 24-h urine excretion of ALA, porphobilinogen (PBG) and porphyrins from challenges D1, D9 and D30 constituted the training data set to build the mechanistic model using the population approach. In a second study, porphyrin and porphyrin precursor excretion from challenge D16 were used as a validation data set. RESULTS The computational model presented the following features: (i) urinary excretion of ALA, PBG and porphyrins was governed by unmeasured circulating heme precursor amounts, (ii) the circulating amounts of ALA and PBG were the precursors of circulating amounts of PBG and porphyrins, respectively, and (iii) the phenobarbital effect linearly increased the synthesis of circulating ALA and PBG levels. The model displayed good parameter precision (coefficient of variation below 32% in all parameters), and adequately described the experimental data. Finally, a theoretical hemin effect was implemented to illustrate the applicability of the model to dosage optimization in drug therapies. CONCLUSIONS A semi-mechanistic disease model was successfully developed to describe the temporal evolution of urinary heme precursor excretion during recurrent biochemical-induced acute attacks in AIP mice. This model represents the first computational approach to explore and optimize current and new therapies.
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Affiliation(s)
- Diego Vera-Yunca
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | | | - Ana Sampedro
- Hepatology Program, Centre for Applied Medical Research, University of Navarra, Spain
| | - Daniel Jericó
- Hepatology Program, Centre for Applied Medical Research, University of Navarra, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Antonio Fontanellas
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Hepatology Program, Centre for Applied Medical Research, University of Navarra, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III, Spain.
| | - Zinnia P Parra-Guillén
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
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23
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Cattrall JWS, Asín-Prieto E, Freeman J, Trocóniz IF, Kirby A. A pharmacokinetic-pharmacodynamic assessment of oral antibiotics for pyelonephritis. Eur J Clin Microbiol Infect Dis 2019; 38:2311-2321. [PMID: 31494827 PMCID: PMC6858297 DOI: 10.1007/s10096-019-03679-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 08/11/2019] [Indexed: 11/30/2022]
Abstract
Antibiotic resistance to oral antibiotics recommended for pyelonephritis is increasing. The objective was to determine if there is a pharmacological basis to consider alternative treatments/novel dosing regimens for the oral treatment of pyelonephritis. A systematic review identified pharmacokinetic models of suitable quality for a selection of antibiotics with activity against Escherichia coli. MIC data was obtained for a population of E. coli isolates derived from patients with pyelonephritis. Pharmacokinetic/pharmacodynamic (PK/PD) simulations determined probability of target attainment (PTA) and cumulative fraction response (CFR) values for sub-populations of the E. coli population at varying doses. There are limited high-quality models available for the agents investigated. Pharmacokinetic models of sufficient quality for simulation were identified for amoxicillin, amoxicillin-clavulanic acid, cephalexin, ciprofloxacin, and fosfomycin trometamol. These antibiotics were predicted to have PTAs ≥ 0.85 at or below standard doses for the tested E. coli population including cephalexin 1500 mg 8 hourly for 22% of the population (MIC ≤ 4 mg/L) and ciprofloxacin 100 mg 12 hourly for 71% of the population (MIC ≤ 0.06 mg/L). For EUCAST-susceptible E. coli isolates, doses achieving CFRs ≥ 0.9 included amoxicillin 2500 mg 8 hourly, cephalexin 4000 mg 6 hourly, ciprofloxacin 200 mg 12 hourly, and 3000 mg of fosfomycin 24 hourly. Limitations in the PK data support carrying out additional PK studies in populations of interest. Oral antibiotics including amoxicillin, amoxicillin-clavulanic acid, and cephalexin have potential to be effective for a proportion of patients with pyelonephritis. Ciprofloxacin may be effective at lower doses than currently prescribed.
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Affiliation(s)
| | - E Asín-Prieto
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - J Freeman
- University of Leeds, Leeds, LS2 9JT, UK.,Leeds Teaching Hospitals NHS Trust, Leeds, LS9 7TF, UK
| | - I F Trocóniz
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - A Kirby
- University of Leeds, Leeds, LS2 9JT, UK. .,Leeds Teaching Hospitals NHS Trust, Leeds, LS9 7TF, UK. .,Department of Microbiology, Old Medical School, Leeds General Infirmary, Leeds, LS1 3EX, UK.
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24
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Solans BP, López-Díaz de Cerio A, Elizalde A, Pina LJ, Inogés S, Espinós J, Salgado E, Mejías LD, Trocóniz IF, Santisteban M. Assessing the impact of the addition of dendritic cell vaccination to neoadjuvant chemotherapy in breast cancer patients: A model-based characterization approach. Br J Clin Pharmacol 2019; 85:1670-1683. [PMID: 30933365 DOI: 10.1111/bcp.13947] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 03/08/2019] [Accepted: 03/27/2019] [Indexed: 12/27/2022] Open
Affiliation(s)
- Belén P Solans
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain
| | - Ascensión López-Díaz de Cerio
- Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain.,Cell Therapy Area and Department of Immunology and Inmunotherapy, Clínica Universidad de Navarra, Pamplona, Navarra, Spain
| | - Arlette Elizalde
- Department of Radiology, Breast Cancer Unit, Clínica Universidad de Navarra, Pamplona, Navarra, Spain
| | - Luis Javier Pina
- Department of Radiology, Breast Cancer Unit, Clínica Universidad de Navarra, Pamplona, Navarra, Spain
| | - Susana Inogés
- Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain.,Cell Therapy Area and Department of Immunology and Inmunotherapy, Clínica Universidad de Navarra, Pamplona, Navarra, Spain
| | - Jaime Espinós
- Department of Medical Oncology, Breast Cancer Unit, Clínica, Universidad de Navarra, Pamplona, Navarra, Spain
| | - Esteban Salgado
- Department of Medical Oncology, Complejo Hospitalario de Navarra, Pamplona, Spain
| | - Luis Daniel Mejías
- Department of Pathology, Breast Cancer Unit, Clínica Universidad de Navarra, Pamplona, Navarra, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain
| | - Marta Santisteban
- Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain.,Department of Medical Oncology, Breast Cancer Unit, Clínica, Universidad de Navarra, Pamplona, Navarra, Spain
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25
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Ibarra M, Dalla Costa T, Schaiquevich P, Cristofoletti R, Hernández González I, Fajardo-Robledo NS, Aragón Novoa M, Pecchio M, Cortinez I, Trocóniz IF, Romero-Tejeda EM. Iberoamerican Pharmacometrics Network Congress 2018 Report: Fostering Modeling and Simulation Approaches for Drug Development and Regulatory and Clinical Applications in Latin America. CPT Pharmacometrics Syst Pharmacol 2019; 8:197-200. [PMID: 30681295 PMCID: PMC6482274 DOI: 10.1002/psp4.12387] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 01/14/2019] [Accepted: 01/15/2019] [Indexed: 11/11/2022]
Affiliation(s)
- Manuel Ibarra
- Pharmaceutical Sciences Department, Faculty of Chemistry, Bioavailability and Bioequivalence Centre for Medicine Evaluation, Universidad de la República, Montevideo, Uruguay
| | - Teresa Dalla Costa
- Pharmacokinetics and PK/PD Modeling Laboratory, Faculty of Pharmacy, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Paula Schaiquevich
- National Scientific and Technical Research Council, Buenos Aires, Argentina.,Unit of Clinical Pharmacokinetics, Hospital de Pediatria JP Garrahan, Buenos Aires, Argentina
| | - Rodrigo Cristofoletti
- Division of Therapeutic Equivalence, Brazilian Health Surveillance Agency, Brasilia, Brazil.,Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA
| | | | - Nicte S Fajardo-Robledo
- Pharmacobiology Department, University Center of Exact Sciences and Engineering, University of Guadalajara, Guadalajara, Mexico
| | | | - Marisín Pecchio
- Instituto de Investigaciones Científicas y Servicios de Alta Tecnología,, Panamá, República de Panamá
| | - Ignacio Cortinez
- Department of Anaesthesiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Elba M Romero-Tejeda
- Pharmacobiology Department, University Center of Exact Sciences and Engineering, University of Guadalajara, Guadalajara, Mexico
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26
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Balbas-Martinez V, Michelet R, Edginton AN, Meesters K, Trocóniz IF, Vermeulen A. Physiologically-Based Pharmacokinetic model for Ciprofloxacin in children with complicated Urinary Tract Infection. Eur J Pharm Sci 2018; 128:171-179. [PMID: 30503378 DOI: 10.1016/j.ejps.2018.11.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 11/13/2018] [Accepted: 11/28/2018] [Indexed: 01/05/2023]
Abstract
In a recent multicenter population pharmacokinetic study of ciprofloxacin administered to children suffering from complicated urinary tract infection (cUTI), the apparent volume of distribution (V) and total plasma clearance (CL) were decreased by 83.6% and 41.5% respectively, compared to healthy children. To understand these differences, a physiologically-based pharmacokinetic model (PBPK) for ciprofloxacin was developed for cUTI children. First, a PBPK model in adults was developed, modified incorporating age-dependent functions and evaluated with paediatric data generated from a published model in healthy children. Then, the model was then adapted to a cUTI paediatric population according to the degree of renal impairment (KF) affecting renal clearance (CLRenal,) and CYP1A2 clearance (CLCYP1A2). Serum and urine samples obtained from 22 cUTI children were used for model evaluation. Lastly, a parameter sensitivity analysis identified the most influential parameters on V and CL. The PBPK model predicted the ciprofloxacin exposure in adults and children, capturing age-related pharmacokinetic changes. Plasma concentrations and fraction excreted unchanged in urine (fe) predictions improved in paediatric cUTI patients once CLrenal and CLCYP1A2 were corrected by KF. The presented PBPK model for ciprofloxacin demonstrates its adequacy to simulate different dosing scenarios to obtain PK predictions in a healthy population from 3 months old onwards. Model adaptation of CLRenal and CLCYP1A2 according to KF explained partially the differences seen in the plasma drug concentrations and fe vs time profiles between healthy and cUTI children. Nevertheless, it is necessary to further investigate the disease-related changes in cUTI to improve model predictions.
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Affiliation(s)
- Violeta Balbas-Martinez
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Ghent University, Faculty of Pharmaceutical Sciences, Laboratory of Medical Biochemistry and Clinical Analysis, Ghent, Belgium.
| | - Robin Michelet
- Ghent University, Faculty of Pharmaceutical Sciences, Laboratory of Medical Biochemistry and Clinical Analysis, Ghent, Belgium.
| | - Andrea N Edginton
- School of Pharmacy, University of Waterloo, Waterloo, Ontario, Canada.
| | - Kevin Meesters
- Ghent University Hospital, Department of Pediatric Nephrology, Ghent, Belgium; KidZ Health Castlee, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium.
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.
| | - An Vermeulen
- Ghent University, Faculty of Pharmaceutical Sciences, Laboratory of Medical Biochemistry and Clinical Analysis, Ghent, Belgium.
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27
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Irurzun-Arana I, Janda A, Ardanza-Trevijano S, Trocóniz IF. Optimal dynamic control approach in a multi-objective therapeutic scenario: Application to drug delivery in the treatment of prostate cancer. PLoS Comput Biol 2018; 14:e1006087. [PMID: 29672523 PMCID: PMC5929575 DOI: 10.1371/journal.pcbi.1006087] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 05/01/2018] [Accepted: 03/12/2018] [Indexed: 11/19/2022] Open
Abstract
Numerous problems encountered in computational biology can be formulated as optimization problems. In this context, optimization of drug release characteristics or dosing schedules for anticancer agents has become a prominent area not only for the development of new drugs, but also for established drugs. However, in complex systems, optimization of drug exposure is not a trivial task and cannot be efficiently addressed through trial-error simulation exercises. Finding a solution to those problems is a challenging task which requires more advanced strategies like optimal control theory. In this work, we perform an optimal control analysis on a previously developed computational model for the testosterone effects of triptorelin in prostate cancer patients with the goal of finding optimal drug-release characteristics. We demonstrate how numerical control optimization of non-linear models can be used to find better therapeutic approaches in order to improve the final outcome of the patients.
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Affiliation(s)
- Itziar Irurzun-Arana
- Pharmacometrics & Systems Pharmacology group, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Navarra, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Navarra, Spain
| | - Alvaro Janda
- Department of Physics and Applied Mathematics, University of Navarra, Pamplona, Navarra, Spain
| | | | - Iñaki F. Trocóniz
- Pharmacometrics & Systems Pharmacology group, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Navarra, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Navarra, Spain
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28
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Ruiz-Cerdá L, Asín-Prieto E, Parra-Guillen ZP, Trocóniz IF. The Long Neglected Player: Modeling Tumor Uptake to Guide Optimal Dosing. Clin Cancer Res 2018; 24:3236-3238. [PMID: 29581133 DOI: 10.1158/1078-0432.ccr-18-0580] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 03/14/2018] [Accepted: 03/22/2018] [Indexed: 11/16/2022]
Abstract
Pharmacokinetic modeling, traditionally using drug exposure, is widely used to support decision-making in translational medicine and patient care. The development of mechanistic computational models that integrate drug concentrations at the site of action making use of existing knowledge opens a new paradigm in optimal dosing. Clin Cancer Res; 24(14); 3236-8. ©2018 AACRSee related article by Ribba et al., p. 3325.
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Affiliation(s)
- Leire Ruiz-Cerdá
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Eduardo Asín-Prieto
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Zinnia P Parra-Guillen
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain. .,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
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29
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Mangas-Sanjuan V, Navarro-Fontestad C, García-Arieta A, Trocóniz IF, Bermejo M. Computer simulations for bioequivalence trials: Selection of analyte in BCS class II and IV drugs with first-pass metabolism, two metabolic pathways and intestinal efflux transporter. Eur J Pharm Sci 2018; 117:193-203. [PMID: 29452210 DOI: 10.1016/j.ejps.2018.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 01/08/2018] [Accepted: 02/10/2018] [Indexed: 11/19/2022]
Abstract
A semi-physiological two compartment pharmacokinetic model with two active metabolites (primary (PM) and secondary metabolites (SM)) with saturable and non-saturable pre-systemic efflux transporter, intestinal and hepatic metabolism has been developed. The aim of this work is to explore in several scenarios which analyte (parent drug or any of the metabolites) is the most sensitive to changes in drug product performance (i.e. differences in in vivo dissolution) and to make recommendations based on the simulations outcome. A total of 128 scenarios (2 Biopharmaceutics Classification System (BCS) drug types, 2 levels of KM Pgp, in 4 metabolic scenarios at 2 dose levels in 4 quality levels of the drug product) were simulated for BCS class II and IV drugs. Monte Carlo simulations of all bioequivalence studies were performed in NONMEM 7.3. Results showed the parent drug (PD) was the most sensitive analyte for bioequivalence trials in all the studied scenarios. PM and SM revealed less or the same sensitivity to detect differences in pharmaceutical quality as the PD. Another relevant result is that mean point estimate of Cmax and AUC methodology from Monte Carlo simulations allows to select more accurately the most sensitive analyte compared to the criterion on the percentage of failed or successful BE studies, even for metabolites which frequently show greater variability than PD.
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Affiliation(s)
- Victor Mangas-Sanjuan
- Engineering: Pharmacy and Pharmaceutical Technology Area, Miguel Hernandez University, Spain; Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; Pharmacy and Pharmaceutical Technology Area, University of Valencia, Spain
| | | | - Alfredo García-Arieta
- División de Farmacología y Evaluación Clínica, Departamento de Medicamentos de Uso Humano, Agencia Española de Medicamentos y Productos Sanitarios, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
| | - Marival Bermejo
- Engineering: Pharmacy and Pharmaceutical Technology Area, Miguel Hernandez University, Spain.
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30
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Contreras-Sandoval AM, Merino M, Vasquez M, Trocóniz IF, Berraondo P, Garrido MJ. Correlation between anti-PD-L1 tumor concentrations and tumor-specific and nonspecific biomarkers in a melanoma mouse model. Oncotarget 2018; 7:76891-76901. [PMID: 27764774 PMCID: PMC5363557 DOI: 10.18632/oncotarget.12727] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 10/11/2016] [Indexed: 01/15/2023] Open
Abstract
Blockade of PD-L1 with specific monoclonal antibodies (anti-PD-L1) represents a therapeutic strategy to increase the capability of the immune system to modulate the tumor immune-resistance. The relationship between anti-PD-L1 tumor exposition and anti-tumor effect represents a challenge that has been addressed in this work through the identification of certain biomarkers implicated in the antibody's mechanism of action, using a syngeneic melanoma mouse model. The development of an in-vitro/in-vivo platform has allowed us to investigate the PD-L1 behavior after its blockage with anti-PD-L1 at cellular level and in animals. In-vitro studies showed that the complex PD-L1/anti-PD-L1 was retained mainly at the cell surface. The antibody concentration and time exposure affected directly the recycling or ligand turnover. In-vivo studies showed that anti-PD-L1 was therapeutically active at all stage of the disease, with a rapid onset, a low but durable efficacy and non-relevant toxic effect. This efficacy measured as tumor shrinkage correlated with tumor-specific infiltrating lymphocytes (TILs), which increased as antibody tumor concentrations increased. Both, TILS and antibody concentrations followed similar kinetic patterns, justifying the observed anti-PD-L1 rapid onset. Interestingly, peripheral lymphocytes (PBLs) behave as infiltrating lymphocytes, suggesting that these PBLs might be considered as a possible biomarker for antibody activity.
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Affiliation(s)
- Ana M Contreras-Sandoval
- School of Pharmacy, Department of Pharmacy and Pharmaceutical Technology, University of Navarra, 31008 Pamplona, Spain
| | - María Merino
- School of Pharmacy, Department of Pharmacy and Pharmaceutical Technology, University of Navarra, 31008 Pamplona, Spain
| | - Marcos Vasquez
- Program of Immunology and Immunotherapy, Center for Applied Medical Research (CIMA), Navarra Institute for Health Research (IdiSNA), Pamplona, Navarra, 31008, Spain
| | - Iñaki F Trocóniz
- School of Pharmacy, Department of Pharmacy and Pharmaceutical Technology, University of Navarra, 31008 Pamplona, Spain
| | - Pedro Berraondo
- Program of Immunology and Immunotherapy, Center for Applied Medical Research (CIMA), Navarra Institute for Health Research (IdiSNA), Pamplona, Navarra, 31008, Spain
| | - María J Garrido
- School of Pharmacy, Department of Pharmacy and Pharmaceutical Technology, University of Navarra, 31008 Pamplona, Spain
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31
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P. Solans B, Fleury A, Freiwald M, Fritsch H, Haug K, Trocóniz IF. Population Pharmacokinetics of Volasertib Administered in Patients with Acute Myeloid Leukaemia as a Single Agent or in Combination with Cytarabine. Clin Pharmacokinet 2017. [DOI: 10.1007/s40262-017-0566-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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32
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Irurzun-Arana I, Pastor JM, Trocóniz IF, Gómez-Mantilla JD. Advanced Boolean modeling of biological networks applied to systems pharmacology. Bioinformatics 2017; 33:1040-1048. [PMID: 28073755 DOI: 10.1093/bioinformatics/btw747] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 11/22/2016] [Indexed: 12/24/2022] Open
Abstract
Motivation Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets. Results In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets. Availability and Implementation The source code is available at https://github.com/SPIDDOR/SPIDDOR . Contact itzirurzun@alumni.unav.es , itroconiz@unav.es. Supplementary information Supplementary data are available at Bioinformatics online.
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33
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Garrido MJ, Berraondo P, Trocóniz IF. CORRIGENDUM: Commentary on Pharmacometrics for Immunotherapy. CPT Pharmacometrics Syst Pharmacol 2017; 6:277. [PMID: 28425210 PMCID: PMC5397560 DOI: 10.1002/psp4.12186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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34
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Garrido MJ, Berraondo P, Trocóniz IF. Commentary on Pharmacometrics for Immunotherapy. CPT Pharmacometrics Syst Pharmacol 2017; 6:8-10. [PMID: 27997736 PMCID: PMC5270298 DOI: 10.1002/psp4.12162] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 12/04/2016] [Indexed: 12/27/2022]
Abstract
This commentary provides an overview of recent examples of pharmacometrics applied during the clinical development of two antagonists of the programmed death‐1 (PD‐1) cell surface receptor, pembrolizumab and nivolumab. Despite the remarkable achievements obtained in predicting the correct dosing schedule from different quantitative approaches, data indicated a great degree of heterogeneity in tumor response. To achieve therapeutic goals the search for predictive biomarkers associated with a lack of response and mechanism‐based combination studies are warranted.
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Affiliation(s)
- M J Garrido
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain
| | - P Berraondo
- Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain.,Program of Immunology and Immunotherapy, Centre for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - I F Trocóniz
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdisNA), University of Navarra, Pamplona, Spain
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35
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Pérez-Castelló I, Mangas-Sanjuan V, González-García I, Gonzalez-Alvarez I, Bermejo M, Marco-Garbayo JL, Trocóniz IF. Population pharmacokinetic model of lithium and drug compliance assessment. Eur Neuropsychopharmacol 2016; 26:1868-1876. [PMID: 27865605 DOI: 10.1016/j.euroneuro.2016.11.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 10/19/2016] [Accepted: 11/08/2016] [Indexed: 12/22/2022]
Abstract
Population pharmacokinetic analysis of lithium during therapeutic drug monitoring and drug compliance assessment was performed in 54 patients and 246 plasma concentrations levels were included in this study. Patients received several treatment cycles (1-9) and one plasma concentration measurement for each patient was obtained always before starting next cycle (pre-dose) at steady state. Data were analysed using the population approach with NONMEM version 7.2. Lithium measurements were described using a two-compartment model (CL/F=0.41Lh-1, V1/F=15.3L, Q/F=0.61Lh-1, and V2/F = 15.8L) and the most significant covariate on lithium CL was found to be creatinine clearance (reference model). Lithium compliance was analysed using inter-occasion variability or Markovian features (previous lithium measurement as ordered categorical covariate) on bioavailability parameter. Markov-type model predicted the lithium compliance in the next cycle with higher success rate (79.8%) compared to IOV model (65.2%) and reference model (43.2%). This model becomes an efficient tool, not only being able to adequately describe the observed outcome, but also to predict the individual drug compliance in the next cycle. Therefore, Bipolar disorder patients can be classified regarding their probability to become extensive or poor compliers in the next cycle and then, individual probabilities lower than 0.5 highlight the need of intensive monitoring, as well as other pharmaceutical care measurements that might be applied to enhance drug compliance for a better and safer lithium treatment.
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Affiliation(s)
- Isabel Pérez-Castelló
- Program of Molecular and Cellular Biology, Department of Engineering, University Miguel Hernández de Elche, Carretera Alicante Valencia km 87, San Juan de Alicante, 03550 Alicante, Spain; Department of Clinical Pharmacy, Hospital of Francesc de Borja, Av/ de la Medicina 6, Gandia, 46702 Valencia, Spain
| | - Víctor Mangas-Sanjuan
- Program of Molecular and Cellular Biology, Department of Engineering, University Miguel Hernández de Elche, Carretera Alicante Valencia km 87, San Juan de Alicante, 03550 Alicante, Spain; Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Irunlarrea 1, 31008 Pamplona, Navarra, Spain
| | - Ignacio González-García
- Pharmacy and Pharmaceutical Technology Department, University of Valencia, Av/ Vicent Andres Estelles, s/n. 46100, Burjasot, 46100 Valencia, Spain
| | - Isabel Gonzalez-Alvarez
- Program of Molecular and Cellular Biology, Department of Engineering, University Miguel Hernández de Elche, Carretera Alicante Valencia km 87, San Juan de Alicante, 03550 Alicante, Spain
| | - Marival Bermejo
- Program of Molecular and Cellular Biology, Department of Engineering, University Miguel Hernández de Elche, Carretera Alicante Valencia km 87, San Juan de Alicante, 03550 Alicante, Spain
| | - Jose Luis Marco-Garbayo
- Department of Clinical Pharmacy, Hospital of Francesc de Borja, Av/ de la Medicina 6, Gandia, 46702 Valencia, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Irunlarrea 1, 31008 Pamplona, Navarra, Spain.
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36
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Ruiz-Cerdá ML, Irurzun-Arana I, González-Garcia I, Hu C, Zhou H, Vermeulen A, Trocóniz IF, Gómez-Mantilla JD. Towards patient stratification and treatment in the autoimmune disease lupus erythematosus using a systems pharmacology approach. Eur J Pharm Sci 2016; 94:46-58. [DOI: 10.1016/j.ejps.2016.04.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 04/07/2016] [Accepted: 04/07/2016] [Indexed: 01/28/2023]
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37
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Zalba S, Contreras AM, Merino M, Navarro I, de Ilarduya CT, Trocóniz IF, Koning G, Garrido MJ. EGF-liposomes promote efficient EGFR targeting in xenograft colocarcinoma model. Nanomedicine (Lond) 2016; 11:465-77. [DOI: 10.2217/nnm.15.208] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Aim: Development of EGF-liposomes (LP-EGF) for selective molecules delivery in tumors expressing EGFR. Material & methods: In vitro cellular interaction of EGF-LP and nontargeted liposomes (LP-N) was assayed at 37 and 4°C in cells expressing different EGFR levels. Receptor-mediated uptake was investigated by competition with a monoclonal antibody anti-EGFR. Selective intracellular drug delivery and efficacy was tested by oxaliplatin encapsulation. In vivo biodistribution of LP-N and LP-EGF was done in xenograft model. Results: LP-EGF was internalized by an active and selective mechanism through EGFR without receptor activation. Oxaliplatin LP-EGF decreased IC50 between 48 and 13% in cell EGFR+. LP-EGF was accumulated in tumor over 72 h postdosing, while LP-N in spleen. Conclusion: LP-EGF represents an attractive nanosystem for cancer therapy or diagnosis.
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Affiliation(s)
- Sara Zalba
- Department of Pharmacy & Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain
- Innovative Targeting, Laboratory Experimental Surgical Oncology, Department of Surgery, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ana Margarita Contreras
- Department of Pharmacy & Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain
| | - María Merino
- Department of Pharmacy & Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain
| | - Iñigo Navarro
- Department of Chemistry & Edaphology, University of Navarra, Pamplona, Spain
| | - Conchita Tros de Ilarduya
- Department of Pharmacy & Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain
| | - Iñaki F Trocóniz
- Department of Pharmacy & Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain
| | - Gerben Koning
- Innovative Targeting, Laboratory Experimental Surgical Oncology, Department of Surgery, Erasmus Medical Center, Rotterdam, The Netherlands
| | - María J Garrido
- Department of Pharmacy & Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain
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Buil-Bruna N, Dehez M, Manon A, Nguyen TXQ, Trocóniz IF. Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors. AAPS J 2016; 18:703-12. [PMID: 26908127 DOI: 10.1208/s12248-016-9884-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Accepted: 02/01/2016] [Indexed: 01/07/2023]
Abstract
The objective of this work was to establish the quantitative relationship between Lanreotide Autogel® (LAN) on serum chromogranin A (CgA) and progression-free survival (PFS) in patients with nonfunctioning gastroenteropancreatic neuroendocrine tumors (GEP-NETs) through an integrated pharmacokinetic/pharmacodynamic (PK/PD) model. In CLARINET, a phase III, randomized, double-blind, placebo-controlled study, 204 patients received deep subcutaneous injections of LAN 120 mg (n = 101) or placebo (n = 103) every 4 weeks for 96 weeks. Data for 810 LAN and 1298 CgA serum samples (n = 632 placebo and n = 666 LAN) were used to develop a parametric time-to-event model to relate CgA levels and PFS (76 patients experienced disease progression: n = 49 placebo and n = 27 LAN). LAN serum profiles were described by a one-compartment disposition model. Absorption was characterized by two parallel pathways following first- and zero-order kinetics. As PFS data were considered informative dropouts, CgA and PFS responses were modeled jointly. The LAN-induced decrease in CgA levels was described by an inhibitory E MAX model. Patient age and target lesions at baseline were associated with an increment in baseline CgA. Weibull model distribution showed that decreases in CgA from baseline reduced the hazard of disease progression significantly (P < 0.001). Covariates of tumor location in the pancreas and tumor hepatic tumor load were associated with worse prognosis (P < 0.001). We established a semimechanistic PK/PD model to better understand the effect of LAN on a surrogate endpoint (serum CgA) and ultimately the clinical endpoint (PFS) in treatment-naive patients with nonfunctioning GEP-NETs.
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Affiliation(s)
- Núria Buil-Bruna
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Irunlarrea 1, 31080, Pamplona, Spain.,IdiSNA Navarra Institute for Health Research, Pamplona, Spain
| | - Marion Dehez
- Clinical Pharmacokinetics, Pharmacokinetics and Drug Metabolism, Ipsen Innovation, Les Ulis, France
| | - Amandine Manon
- Clinical Pharmacokinetics, Pharmacokinetics and Drug Metabolism, Ipsen Innovation, Les Ulis, France
| | - Thi Xuan Quyen Nguyen
- Clinical Pharmacokinetics, Pharmacokinetics and Drug Metabolism, Ipsen Innovation, Les Ulis, France
| | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Irunlarrea 1, 31080, Pamplona, Spain. .,IdiSNA Navarra Institute for Health Research, Pamplona, Spain.
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39
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Gambús PL, Trocóniz IF. Pharmacokinetic-pharmacodynamic modelling in anaesthesia. Br J Clin Pharmacol 2015; 79:72-84. [PMID: 24251846 DOI: 10.1111/bcp.12286] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2013] [Accepted: 10/31/2013] [Indexed: 11/29/2022] Open
Abstract
Anaesthesiologists adjust drug dosing, administration system and kind of drug to the characteristics of the patient. They then observe the expected response and adjust dosing to the specific requirements according to the difference between observed response, expected response and the context of the surgery and the patient. The approach above can be achieved because on one hand quantification technology has made significant advances allowing the anaesthesiologist to measure almost any effect by using noninvasive, continuous measuring systems. On the other the knowledge on the relations between dosing, concentration, biophase dynamics and effect as well as detection of variability sources has been achieved as being the benchmark specialty for pharmacokinetic-pharmacodynamic (PKPD) modelling. The aim of the review is to revisit the most common PKPD models applied in the field of anaesthesia (i.e. effect compartmental, turnover, drug-receptor binding and drug interaction models) through representative examples. The effect compartmental model has been widely used in this field and there are multiple applications and examples. The use of turnover models has been limited mainly to describe respiratory effects. Similarly, cases in which the dissociation process of the drug-receptor complex is slow compared with other processes relevant to the time course of the anaesthetic effect are not frequent in anaesthesia, where in addition to a rapid onset, a fast offset of the response is required. With respect to the characterization of PD drug interactions different response surface models are discussed. Relevant applications that have changed the way modern anaesthesia is practiced are also provided.
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Affiliation(s)
- Pedro L Gambús
- Systems Pharmacology Effect Control & Modeling (SPEC-M) Research Group, Anesthesiology Department, Hospital CLINIC, Barcelona; Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS) Villarroel 170, Barcelona, 08036, Spain; Department of Anesthesia and Perioperative Care, University of California San Francisco (UCSF), San Francisco, CA, USA
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Buil-Bruna N, López-Picazo JM, Martín-Algarra S, Trocóniz IF. Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications. Oncologist 2015; 21:220-32. [PMID: 26668254 DOI: 10.1634/theoncologist.2015-0322] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 11/03/2015] [Indexed: 11/17/2022] Open
Abstract
UNLABELLED Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians. IMPLICATIONS FOR PRACTICE The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.
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Affiliation(s)
- Núria Buil-Bruna
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - José-María López-Picazo
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain Department of Medical Oncology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Salvador Martín-Algarra
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain Department of Medical Oncology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
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Allegaert K, Holford N, Anderson BJ, Holford S, Stuber F, Rochette A, Trocóniz IF, Beier H, de Hoon JN, Pedersen RS, Stamer U. Tramadol and o-desmethyl tramadol clearance maturation and disposition in humans: a pooled pharmacokinetic study. Clin Pharmacokinet 2015; 54:167-78. [PMID: 25258277 DOI: 10.1007/s40262-014-0191-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND AND OBJECTIVES We aimed to study the impact of size, maturation and cytochrome P450 2D6 (CYP2D6) genotype activity score as predictors of intravenous tramadol disposition. METHODS Tramadol and O-desmethyl tramadol (M1) observations in 295 human subjects (postmenstrual age 25 weeks to 84.8 years, weight 0.5-186 kg) were pooled. A population pharmacokinetic analysis was performed using a two-compartment model for tramadol and two additional M1 compartments. Covariate analysis included weight, age, sex, disease characteristics (healthy subject or patient) and CYP2D6 genotype activity. A sigmoid maturation model was used to describe age-related changes in tramadol clearance (CLPO), M1 formation clearance (CLPM) and M1 elimination clearance (CLMO). A phenotype-based mixture model was used to identify CLPM polymorphism. RESULTS Differences in clearances were largely accounted for by maturation and size. The time to reach 50 % of adult clearance (TM50) values was used to describe maturation. CLPM (TM50 39.8 weeks) and CLPO (TM50 39.1 weeks) displayed fast maturation, while CLMO matured slower, similar to glomerular filtration rate (TM50 47 weeks). The phenotype-based mixture model identified a slow and a faster metabolizer group. Slow metabolizers comprised 9.8 % of subjects with 19.4 % of faster metabolizer CLPM. Low CYP2D6 genotype activity was associated with lower (25 %) than faster metabolizer CLPM, but only 32 % of those with low genotype activity were in the slow metabolizer group. CONCLUSIONS Maturation and size are key predictors of variability. A two-group polymorphism was identified based on phenotypic M1 formation clearance. Maturation of tramadol elimination occurs early (50 % of adult value at term gestation).
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Affiliation(s)
- Karel Allegaert
- Neonatal Intensive Care Unit and Center for Clinical Pharmacology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium,
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Jacobo-Cabral CO, García-Roca P, Romero-Tejeda EM, Reyes H, Medeiros M, Castañeda-Hernández G, Trocóniz IF. Population pharmacokinetic analysis of tacrolimus in Mexican paediatric renal transplant patients: role of CYP3A5 genotype and formulation. Br J Clin Pharmacol 2015; 80:630-41. [PMID: 25846845 DOI: 10.1111/bcp.12649] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Revised: 03/10/2015] [Accepted: 03/27/2015] [Indexed: 12/22/2022] Open
Abstract
AIMS The aims of this study were (i) to develop a population pharmacokinetic (PK) model of tacrolimus in a Mexican renal transplant paediatric population (n = 53) and (ii) to test the influence of different covariates on its PK properties to facilitate dose individualization. METHODS Population PK and variability parameters were estimated from whole blood drug concentration profiles obtained at steady-state using the non-linear mixed effect modelling software NONMEM® Version 7.2. RESULTS Tacrolimus PK profiles exhibited high inter-patient variability (IPV). A two compartment model with first order input and elimination described the tacrolimus PK profiles in the studied population. The relationship between CYP3A5 genotype and tacrolimus CL/F was included in the final model. CL/F in CYP3A5*1/*1 and *1/*3 carriers was approximately 2- and 1.5-fold higher than in CYP3A5*3/*3 carriers (non-expressers), respectively, and explained almost the entire IPV in CL/F. Other covariates retained in the final model were the tacrolimus dose and formulation type. Limustin® showed markedly lower concentrations than the rest of the formulations. CONCLUSIONS Population PK modelling of tacrolimus in paediatric renal transplant recipients identified the tacrolimus formulation type as a significant covariate affecting the blood concentrations and confirmed the previously reported significant effect of CYP3A5 genotype on CL/F. It allowed the design of a proposed dosage based on the final model that is expected to help to improve tacrolimus dosing.
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Affiliation(s)
| | - Pilar García-Roca
- Nephrology Research Laboratory, Federico Gómez Children's Hospital of Mexico, Mexico City, Mexico
| | | | - Herlinda Reyes
- Nephrology Research Laboratory, Federico Gómez Children's Hospital of Mexico, Mexico City, Mexico
| | - Mara Medeiros
- Nephrology Research Laboratory, Federico Gómez Children's Hospital of Mexico, Mexico City, Mexico.,Department of Pharmacology, Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
| | | | - Iñaki F Trocóniz
- Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Navarra, Spain.,IdiSNA Navarra Institute for Health Research, Pamplona, Spain
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Cuesta-Gragera A, Navarro-Fontestad C, Mangas-Sanjuan V, González-Álvarez I, García-Arieta A, Trocóniz IF, Casabó VG, Bermejo M. Validation of a semi-physiological model for caffeine in healthy subjects and cirrhotic patients. Eur J Pharm Sci 2015; 73:57-63. [PMID: 25843043 DOI: 10.1016/j.ejps.2015.03.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2015] [Accepted: 03/23/2015] [Indexed: 10/23/2022]
Abstract
The objective of this paper was to validate a previously developed semi physiological model to simulate bioequivalence trials of drug products. The aim of the model was to ascertain whether the measurement of the metabolite concentration-time profiles would provide any additional information in bioequivalence studies (Fernandez-Teruel et al., 2009a,b; Navarro-Fontestad et al., 2010). The semi-physiological model implemented in NONMEM VI was used to simulate caffeine and its main metabolite plasma levels using caffeine parameters from bibliography. Data from 3 bioequivalence studies in healthy subjects at 3 different doses (100, 175 and 400mg of caffeine) and one study in cirrhotic patients (200 or 250mg) were used. The first aim was to adapt the previous semi-physiological model for caffeine, showing the hepatic metabolism with one main metabolite, paraxanthine. The second aim was to validate the model by comparison of the simulated plasma levels of parent drug and metabolite to the experimental data. The simulations have shown that the proposed semi-physiological model was able to reproduce adequately the pharmacokinetic behavior of caffeine and paraxanthine in both healthy subjects and cirrhotic patients at all the assayed doses. Therefore, the model could be used to simulate plasma concentrations vs. time of drugs with the same pharmacokinetic scheme as caffeine, as long as their population parameters are known, and it could be useful for bioequivalence trial simulation of drugs that undergo hepatic metabolism with a single main metabolite.
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Affiliation(s)
- Ana Cuesta-Gragera
- Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Valencia, Av. Vicente Andrés Estellés s/n, 46100 Burjassot, Valencia, Spain.
| | - Carmen Navarro-Fontestad
- Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Valencia, Av. Vicente Andrés Estellés s/n, 46100 Burjassot, Valencia, Spain.
| | - Victor Mangas-Sanjuan
- Department of Engineering, Pharmacy Section, Miguel Hernández University, Carretera Alicante Valencia, km 87, 03550 San Juan de Alicante, Alicante, Spain.
| | - Isabel González-Álvarez
- Department of Engineering, Pharmacy Section, Miguel Hernández University, Carretera Alicante Valencia, km 87, 03550 San Juan de Alicante, Alicante, Spain.
| | - Alfredo García-Arieta
- Pharmacokinetics Service, Division of Pharmacology and Clinical Evaluation, Department of Human Use Medicines, Spanish Agency for Medicines and Health Care Products (AEMPS), Campezo 1, 28022 Madrid, Madrid, Spain.
| | - Iñaki F Trocóniz
- Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Irunlarrea 1, 31008 Pamplona, Navarra, Spain.
| | - Vicente G Casabó
- Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Valencia, Av. Vicente Andrés Estellés s/n, 46100 Burjassot, Valencia, Spain.
| | - Marival Bermejo
- Department of Engineering, Pharmacy Section, Miguel Hernández University, Carretera Alicante Valencia, km 87, 03550 San Juan de Alicante, Alicante, Spain.
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Swat MJ, Moodie S, Wimalaratne SM, Kristensen NR, Lavielle M, Mari A, Magni P, Smith MK, Bizzotto R, Pasotti L, Mezzalana E, Comets E, Sarr C, Terranova N, Blaudez E, Chan P, Chard J, Chatel K, Chenel M, Edwards D, Franklin C, Giorgino T, Glont M, Girard P, Grenon P, Harling K, Hooker AC, Kaye R, Keizer R, Kloft C, Kok JN, Kokash N, Laibe C, Laveille C, Lestini G, Mentré F, Munafo A, Nordgren R, Nyberg HB, Parra-Guillen ZP, Plan E, Ribba B, Smith G, Trocóniz IF, Yvon F, Milligan PA, Harnisch L, Karlsson M, Hermjakob H, Le Novère N. Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development. CPT Pharmacometrics Syst Pharmacol 2015; 4:316-9. [PMID: 26225259 PMCID: PMC4505825 DOI: 10.1002/psp4.57] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 05/06/2015] [Indexed: 12/02/2022] Open
Abstract
The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.
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Affiliation(s)
- MJ Swat
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | | | - SM Wimalaratne
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | | | | | - A Mari
- National Research Council, Institute of Biomedical EngineeringPadova, Italy
| | - P Magni
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di PaviaPavia, Italy
| | - MK Smith
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - R Bizzotto
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - L Pasotti
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di PaviaPavia, Italy
| | - E Mezzalana
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di PaviaPavia, Italy
| | - E Comets
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - C Sarr
- Advanced Quantitative Sciences (AQS), NovartisBasel, Switzerland
| | - N Terranova
- Merck Institute for Pharmacometrics, Merck SeronoLausanne, Switzerland
| | | | - P Chan
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - J Chard
- Mango SolutionsChippenham, Wiltshire, UK
| | | | - M Chenel
- SGS Exprimo NV, Mechelen, Belgium, Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales ServierSuresnes, France
| | - D Edwards
- Simcyp (a Certara company)Sheffield, UK
| | - C Franklin
- CPMS Technology and DevelopmentSouthall, UK
| | - T Giorgino
- National Research Council, Institute of Biomedical EngineeringPadova, Italy
| | - M Glont
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - P Girard
- Merck Institute for Pharmacometrics, Merck SeronoLausanne, Switzerland
| | - P Grenon
- CHIME, University College LondonLondon, UK
| | - K Harling
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - AC Hooker
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - R Kaye
- Mango SolutionsChippenham, Wiltshire, UK
| | - R Keizer
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - C Kloft
- Freie Universtitaet Berlin, Germany, Institute of Pharmacy, Department of Clinical Pharmacy and BiochemistryBerlin, Germany
| | - JN Kok
- Leiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeiden, The Netherlands
| | - N Kokash
- Leiden Institute of Advanced Computer Science (LIACS), Leiden UniversityLeiden, The Netherlands
| | - C Laibe
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - C Laveille
- SGS Exprimo NV, Mechelen, Belgium, Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales ServierSuresnes, France
| | - G Lestini
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - F Mentré
- INSERM, IAME, UMR 1137, Paris, France, University Paris Diderot, IAME, UMR 1137Sorbonne Paris Cité, Paris, France
| | - A Munafo
- Merck Institute for Pharmacometrics, Merck SeronoLausanne, Switzerland
| | - R Nordgren
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - HB Nyberg
- Mango SolutionsChippenham, Wiltshire, UK
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - ZP Parra-Guillen
- Freie Universtitaet Berlin, Germany, Institute of Pharmacy, Department of Clinical Pharmacy and BiochemistryBerlin, Germany
| | - E Plan
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - B Ribba
- Inria Grenoble - Rhône-AlpesGrenoble, France
| | - G Smith
- Scientific Computing Group, Cyprotex Discovery LimitedMacclesfield, Crewe, UK
| | - IF Trocóniz
- Department of Pharmacy and Pharmaceutical Technology, University of NavarraPamplona, Spain
| | - F Yvon
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - PA Milligan
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - L Harnisch
- Global Clinical Pharmacology, PfizerSandwich, UK
| | - M Karlsson
- Department of Pharmaceutical Biosciences, Uppsala UniversityUppsala, Sweden
| | - H Hermjakob
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
| | - N Le Novère
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome CampusHinxton, Cambridgeshire, UK
- Babraham Institute, Babraham Research CampusCambridge, UK
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Mangas-Sanjuan V, Buil-Bruna N, Garrido MJ, Soto E, Trocóniz IF. Semimechanistic cell-cycle type-based pharmacokinetic/pharmacodynamic model of chemotherapy-induced neutropenic effects of diflomotecan under different dosing schedules. J Pharmacol Exp Ther 2015; 354:55-64. [PMID: 25948593 DOI: 10.1124/jpet.115.223776] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2014] [Accepted: 05/05/2015] [Indexed: 12/19/2022] Open
Abstract
The current work integrates cell-cycle dynamics occurring in the bone marrow compartment as a key element in the structure of a semimechanistic pharmacokinetic/pharmacodynamic model for neutropenic effects, aiming to describe, with the same set of system- and drug-related parameters, longitudinal data of neutropenia gathered after the administration of the anticancer drug diflomotecan (9,10-difluoro-homocamptothecin) under different dosing schedules to patients (n = 111) with advanced solid tumors. To achieve such an objective, the general framework of the neutropenia models was expanded, including one additional physiologic process resembling cell cycle dynamics. The main assumptions of the proposed model are as follows: within the stem cell compartment, proliferative and quiescent cells coexist, and only cells in the proliferative condition are sensitive to drug effects and capable of following the maturation chain. Cell cycle dynamics were characterized by two new parameters, FProl (the fraction of proliferative [Prol] cells that enters into the maturation chain) and kcycle (first-order rate constant governing cell cycle dynamics within the stem cell compartment). Both model parameters were identifiable as indicated by the results from a bootstrap analysis, and their estimates were supported by date from the literature. The estimates of FProl and kcycle were 0.58 and 1.94 day(-1), respectively. The new model could properly describe the neutropenic effects of diflomotecan after very different dosing scenarios, and can be used to explore the potential impact of dosing schedule dependencies on neutropenia prediction.
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Affiliation(s)
- Víctor Mangas-Sanjuan
- Department of Engineering, Department of Pharmacy and Pharmaceutical Technology Area, University of Miguel Hernández de Elche, San Juan de Alicante, Alicante, Spain (V.M.-S.); Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Navarra, Spain (N.B.-B., M.J.G., I.F.T.); and Pharmacometrics, Pfizer, Sandwich, United Kingdom (E.S.)
| | - Núria Buil-Bruna
- Department of Engineering, Department of Pharmacy and Pharmaceutical Technology Area, University of Miguel Hernández de Elche, San Juan de Alicante, Alicante, Spain (V.M.-S.); Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Navarra, Spain (N.B.-B., M.J.G., I.F.T.); and Pharmacometrics, Pfizer, Sandwich, United Kingdom (E.S.)
| | - María J Garrido
- Department of Engineering, Department of Pharmacy and Pharmaceutical Technology Area, University of Miguel Hernández de Elche, San Juan de Alicante, Alicante, Spain (V.M.-S.); Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Navarra, Spain (N.B.-B., M.J.G., I.F.T.); and Pharmacometrics, Pfizer, Sandwich, United Kingdom (E.S.)
| | - Elena Soto
- Department of Engineering, Department of Pharmacy and Pharmaceutical Technology Area, University of Miguel Hernández de Elche, San Juan de Alicante, Alicante, Spain (V.M.-S.); Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Navarra, Spain (N.B.-B., M.J.G., I.F.T.); and Pharmacometrics, Pfizer, Sandwich, United Kingdom (E.S.)
| | - Iñaki F Trocóniz
- Department of Engineering, Department of Pharmacy and Pharmaceutical Technology Area, University of Miguel Hernández de Elche, San Juan de Alicante, Alicante, Spain (V.M.-S.); Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Navarra, Spain (N.B.-B., M.J.G., I.F.T.); and Pharmacometrics, Pfizer, Sandwich, United Kingdom (E.S.)
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Buil-Bruna N, Sahota T, López-Picazo JM, Moreno-Jiménez M, Martín-Algarra S, Ribba B, Trocóniz IF. Early Prediction of Disease Progression in Small Cell Lung Cancer: Toward Model-Based Personalized Medicine in Oncology. Cancer Res 2015; 75:2416-25. [PMID: 25939602 DOI: 10.1158/0008-5472.can-14-2584] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2014] [Accepted: 03/29/2015] [Indexed: 11/16/2022]
Abstract
Predictive biomarkers can play a key role in individualized disease monitoring. Unfortunately, the use of biomarkers in clinical settings has thus far been limited. We have previously shown that mechanism-based pharmacokinetic/pharmacodynamic modeling enables integration of nonvalidated biomarker data to provide predictive model-based biomarkers for response classification. The biomarker model we developed incorporates an underlying latent variable (disease) representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment. Here, we show that by integrating CT scan data, the population model can be expanded to include patient outcome. Moreover, we show that in conjunction with routine medical monitoring data, the population model can support accurate individual predictions of outcome. Our combined model predicts that a change in disease of 29.2% (relative standard error 20%) between two consecutive CT scans (i.e., 6-8 weeks) gives a probability of disease progression of 50%. We apply this framework to an external dataset containing biomarker data from 22 small cell lung cancer patients (four patients progressing during follow-up). Using only data up until the end of treatment (a total of 137 lactate dehydrogenase and 77 neuron-specific enolase observations), the statistical framework prospectively identified 75% of the individuals as having a predictable outcome in follow-up visits. This included two of the four patients who eventually progressed. In all identified individuals, the model-predicted outcomes matched the observed outcomes. This framework allows at risk patients to be identified early and therapeutic intervention/monitoring to be adjusted individually, which may improve overall patient survival.
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Affiliation(s)
- Núria Buil-Bruna
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain
| | - Tarjinder Sahota
- Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, London, United Kingdom
| | - José-María López-Picazo
- Department of Medical Oncology, University Clinic of Navarra, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain
| | - Marta Moreno-Jiménez
- Department of Radiation Oncology, University Clinic of Navarra, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain
| | - Salvador Martín-Algarra
- Department of Medical Oncology, University Clinic of Navarra, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain
| | | | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, IdiSNA Navarra Institute for Health Research, Pamplona, Spain.
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Asín-Prieto E, Soraluce A, Trocóniz IF, Campo Cimarras E, Sáenz de Ugarte Sobrón J, Rodríguez-Gascón A, Isla A. Population pharmacokinetic models for cefuroxime and metronidazole used in combination as prophylactic agents in colorectal surgery: Model-based evaluation of standard dosing regimens. Int J Antimicrob Agents 2015; 45:504-11. [DOI: 10.1016/j.ijantimicag.2015.01.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2014] [Revised: 01/08/2015] [Accepted: 01/10/2015] [Indexed: 01/22/2023]
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Yuen ES, Trocóniz IF. Can pentylenetetrazole and maximal electroshock rodent seizure models quantitatively predict antiepileptic efficacy in humans? Seizure 2015; 24:21-7. [DOI: 10.1016/j.seizure.2014.11.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Revised: 11/12/2014] [Accepted: 11/17/2014] [Indexed: 10/24/2022] Open
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Venkatakrishnan K, Friberg LE, Ouellet D, Mettetal JT, Stein A, Trocóniz IF, Bruno R, Mehrotra N, Gobburu J, Mould DR. Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities. Clin Pharmacol Ther 2014; 97:37-54. [PMID: 25670382 DOI: 10.1002/cpt.7] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Accepted: 10/15/2014] [Indexed: 01/01/2023]
Abstract
Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.
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Affiliation(s)
- K Venkatakrishnan
- Clinical Pharmacology, Takeda Pharmaceuticals International Co., Cambridge, Massachusetts, USA
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Parra-Guillen ZP, Cendrós Carreras JM, Peraire C, Obach R, Prunynosa J, Chetaille E, Trocóniz IF. Population Pharmacokinetic Modelling of Irosustat in Postmenopausal Women with Oestrogen-Receptor Positive Breast Cancer Incorporating Non-Linear Red Blood Cell Uptake. Pharm Res 2014; 32:1493-504. [DOI: 10.1007/s11095-014-1555-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Accepted: 10/17/2014] [Indexed: 10/24/2022]
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