1
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Yin A, van Hasselt JGC, Guchelaar HJ, Friberg LE, Moes DJAR. Anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance. Sci Rep 2022; 12:4206. [PMID: 35273301 PMCID: PMC8913638 DOI: 10.1038/s41598-022-08012-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 02/17/2022] [Indexed: 12/18/2022] Open
Abstract
Quantitative characterization of evolving tumor resistance under targeted treatment could help identify novel treatment schedules, which may improve the outcome of anti-cancer treatment. In this study, a mathematical model which considers various clonal populations and evolving treatment resistance was developed. With parameter values fitted to the data or informed by literature data, the model could capture previously reported tumor burden dynamics and mutant KRAS levels in circulating tumor DNA (ctDNA) of patients with metastatic colorectal cancer treated with panitumumab. Treatment schedules, including a continuous schedule, intermittent schedules incorporating treatment holidays, and adaptive schedules guided by ctDNA measurements were evaluated using simulations. Compared with the continuous regimen, the simulated intermittent regimen which consisted of 8-week treatment and 4-week suspension prolonged median progression-free survival (PFS) of the simulated population from 36 to 44 weeks. The median time period in which the tumor size stayed below the baseline level (TTS<TS0) was prolonged from 52 to 60 weeks. Extending the treatment holiday resulted in inferior outcomes. The simulated adaptive regimens showed to further prolong median PFS to 56–64 weeks and TTS<TS0 to 114–132 weeks under different treatment designs. A prospective clinical study is required to validate the results and to confirm the added value of the suggested schedules.
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Affiliation(s)
- Anyue Yin
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan G C van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Lena E Friberg
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Dirk Jan A R Moes
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. .,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands.
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2
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Moradi Kashkooli F, Soltani M. Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach. Sci Rep 2021; 11:21475. [PMID: 34728726 PMCID: PMC8563754 DOI: 10.1038/s41598-021-00989-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 10/21/2021] [Indexed: 12/22/2022] Open
Abstract
The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches-maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)-as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy.
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Affiliation(s)
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
- Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
- Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran.
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3
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Tan Z, Yu Z, Chen K, Liu W, Zhao R. Effects of Pharmacist-Led Clinical Pathway/Order Sets on Cancer Patients: A Systematic Review. Front Pharmacol 2021; 12:617678. [PMID: 34093177 PMCID: PMC8176097 DOI: 10.3389/fphar.2021.617678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 05/05/2021] [Indexed: 01/04/2023] Open
Abstract
Background: Pharmacist-led clinical pathways/order sets (PLCOs) were first applied for designated diseases and surgical operations, such as cancer. They were not used in pharmacotherapy until recently. After screening a large number of publications, we found that PLCOs were rarely accessible. Objective: To evaluate the effects and the changes of relevant medical outcomes of PLCOs. Methods: Articles from PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang database, and China Biology Medicine disc (CBM) were systematically retrieved. Clinical research comparing cancer patients’ clinical effects with or without clinical pathway/order sets was performed. Two reviewers performed quality assessment, and the data were abstracted independently. A narrative synthesis of the extracted data was performed due to heterogeneity. Results: Nine studies were identified, including six uncontrolled before–after studies and three case-series studies. The scopes of PLCOs of included research can be divided into two types, one focusing on chemotherapy agents and the other on the managements of chemotherapy-induced complications. The PLCOs shortened hospital length of stay, decreased initial antibiotic time intervals in patients with febrile neutropenia, reduced medication error incidence, and increased physicians’ adherence rate to clinical pathway/order sets. Moreover, three articles included economic effects showing positive savings on medication costs through PLCOs. Conclusion: PLCOs can have beneficial effects on medication effectiveness, safety, and economic outcomes. Nevertheless, clinical pathway/order sets need to be further optimized and expanded to other clinical areas.
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Affiliation(s)
- Zhiyuan Tan
- Department of Pharmacy, Peking University Third Hospital, Beijing, China.,Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing, China
| | - Zhiheng Yu
- Department of Pharmacy, Peking University Third Hospital, Beijing, China.,Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing, China
| | - Ken Chen
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
| | - Wei Liu
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
| | - Rongsheng Zhao
- Department of Pharmacy, Peking University Third Hospital, Beijing, China.,Therapeutic Drug Monitoring and Clinical Toxicology Center of Peking University, Beijing, China
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4
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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] [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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5
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Centanni M, Moes DJAR, Trocóniz IF, Ciccolini J, van Hasselt JGC. Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors. Clin Pharmacokinet 2020; 58:835-857. [PMID: 30815848 PMCID: PMC6584248 DOI: 10.1007/s40262-019-00748-2] [Citation(s) in RCA: 199] [Impact Index Per Article: 49.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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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6
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Abstract
Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field.
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Affiliation(s)
- Matthew A Clarke
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Jasmin Fisher
- Department of Biochemistry, University of Cambridge, Cambridge, UK.
- UCL Cancer Institute, University College London, London, UK.
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7
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De Bastiani MA, Klamt F. Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates. Cancer Med 2019; 8:6717-6729. [PMID: 31503425 PMCID: PMC6825976 DOI: 10.1002/cam4.2493] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 07/18/2019] [Accepted: 07/31/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Lung adenocarcinoma is the major cause of cancer-related deaths in the world. Given this, the importance of research on its pathophysiology and therapy remains a key health issue. To assist in this endeavor, recent oncology studies are adopting Systems Biology approaches and bioinformatics to analyze and understand omics data, bringing new insights about this disease and its treatment. METHODS We used reverse engineering of transcriptomic data to reconstruct nontumorous lung reference networks, focusing on transcription factors (TFs) and their inferred target genes, referred as regulatory units or regulons. Afterwards, we used 13 case-control studies to identify TFs acting as master regulators of the disease and their regulatory units. Furthermore, the inferred activation patterns of regulons were used to evaluate patient survival and search drug candidates for repositioning. RESULTS The regulatory units under the influence of ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6, and UHRF1 transcription factors were consistently associated with the pathological phenotype, suggesting that they may be master regulators of lung adenocarcinoma. We also observed that the inferred activity of FOXA2, FOXM1, and UHRF1 was significantly associated with risk of death in patients. Finally, we obtained deptropine, promazine, valproic acid, azacyclonol, methotrexate, and ChemBridge ID compound 5109870 as potential candidates to revert the molecular profile leading to decreased survival. CONCLUSION Using an integrated transcriptomics approach, we identified master regulator candidates involved with the development and prognostic of lung adenocarcinoma, as well as potential drugs for repurposing.
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Affiliation(s)
- Marco Antônio De Bastiani
- Laboratory of Cellular Biochemistry, Department of Biochemistry, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.,National Institute of Science and Technology for Translational Medicine (INCT-TM), Porto Alegre, RS, Brazil
| | - Fábio Klamt
- Laboratory of Cellular Biochemistry, Department of Biochemistry, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.,National Institute of Science and Technology for Translational Medicine (INCT-TM), Porto Alegre, RS, Brazil
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8
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Yin A, Moes DJAR, van Hasselt JGC, Swen JJ, Guchelaar HJ. A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:720-737. [PMID: 31250989 PMCID: PMC6813171 DOI: 10.1002/psp4.12450] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 05/17/2019] [Indexed: 12/19/2022]
Abstract
Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.
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Affiliation(s)
- Anyue Yin
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Dirk Jan A R Moes
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan G C van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
| | - Jesse J Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
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9
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Mathematical model of hemodynamic mechanisms and consequences of glomerular hypertension in diabetic mice. NPJ Syst Biol Appl 2018; 5:2. [PMID: 30564457 PMCID: PMC6288095 DOI: 10.1038/s41540-018-0077-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 06/29/2018] [Accepted: 10/22/2018] [Indexed: 12/12/2022] Open
Abstract
Many preclinically promising therapies for diabetic kidney disease fail to provide efficacy in humans, reflecting limited quantitative translational understanding between rodent models and human disease. To quantitatively bridge interspecies differences, we adapted a mathematical model of renal function from human to mice, and incorporated adaptive and pathological mechanisms of diabetes and nephrectomy to describe experimentally observed changes in glomerular filtration rate (GFR) and proteinuria in db/db and db/db UNX (uninephrectomy) mouse models. Changing a small number of parameters, the model reproduced interspecies differences in renal function. Accounting for glucose and Na+ reabsorption through sodium glucose cotransporter 2 (SGLT2), increasing blood glucose and Na+ intake from normal to db/db levels mathematically reproduced glomerular hyperfiltration observed experimentally in db/db mice. This resulted from increased proximal tubule sodium reabsorption, which elevated glomerular capillary hydrostatic pressure (Pgc) in order to restore sodium balance through increased GFR. Incorporating adaptive and injurious effects of elevated Pgc, we showed that preglomerular arteriole hypertrophy allowed more direct transmission of pressure to the glomerulus with a smaller mean arterial pressure rise; Glomerular hypertrophy allowed a higher GFR for a given Pgc; and Pgc-driven glomerulosclerosis and nephron loss reduced GFR over time, while further increasing Pgc and causing moderate proteinuria, in agreement with experimental data. UNX imposed on diabetes increased Pgc further, causing faster GFR decline and extensive proteinuria, also in agreement with experimental data. The model provides a mechanistic explanation for hyperfiltration and proteinuria progression that will facilitate translation of efficacy for novel therapies from mouse models to human. Many drugs for diabetic kidney disease appear to work in rodents, but fail in humans, reflecting incomplete understanding of disease processes. A team led by Melissa Hallow at the University of Georgia has developed a mathematical model that explains how elevated blood glucose in diabetes causes kidney injury in mice. They first showed that normal human, rat, or mouse kidney physiology could be reproduced with the same model by changing a small number of parameters. They then showed that diabetes-induced increases in sodium reabsorption cause unintuitive changes in kidney function that increase pressure on glomerular capillaries, causing protein leakage and nephron loss. The model reproduced faster disease progression observed in diabetic mice who have had one kidney removed. This mathematical understanding of diabetic kidney injury may improve translation of novel therapies from mice to human.
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10
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Fornari C, O'Connor LO, Yates JWT, Cheung SYA, Jodrell DI, Mettetal JT, Collins TA. Understanding Hematological Toxicities Using Mathematical Modeling. Clin Pharmacol Ther 2018; 104:644-654. [PMID: 29604045 DOI: 10.1002/cpt.1080] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 03/09/2018] [Accepted: 03/27/2018] [Indexed: 12/16/2022]
Abstract
Balancing antitumor efficacy with toxicity is a significant challenge, and drug-induced myelosuppression is a common dose-limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further.
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Affiliation(s)
- Chiara Fornari
- Safety and ADME Translational Sciences, Drug Safety and Metabolism, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | | | - James W T Yates
- DMPK, Oncology, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | - S Y Amy Cheung
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, Cambridge, UK
| | - Duncan I Jodrell
- CRUK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
| | - Jerome T Mettetal
- Safety and ADME Translational Sciences, Drug Safety and Metabolism, IMED Biotech Unit, AstraZeneca, Boston, Massachusetts, USA
| | - Teresa A Collins
- Safety and ADME Translational Sciences, Drug Safety and Metabolism, IMED Biotech Unit, AstraZeneca, Cambridge, UK
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11
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van Hasselt JGC, Iyengar R. Systems Pharmacology: Defining the Interactions of Drug Combinations. Annu Rev Pharmacol Toxicol 2018; 59:21-40. [PMID: 30260737 DOI: 10.1146/annurev-pharmtox-010818-021511] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.
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Affiliation(s)
- J G Coen van Hasselt
- Department of Pharmacological Sciences, Systems Biology Center, Mount Sinai Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; .,Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, 2333 Leiden, Netherlands;
| | - Ravi Iyengar
- Department of Pharmacological Sciences, Systems Biology Center, Mount Sinai Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
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12
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Carusi A, Davies MR, De Grandis G, Escher BI, Hodges G, Leung KMY, Whelan M, Willett C, Ankley GT. Harvesting the promise of AOPs: An assessment and recommendations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 628-629:1542-1556. [PMID: 30045572 PMCID: PMC5888775 DOI: 10.1016/j.scitotenv.2018.02.015] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 02/02/2018] [Accepted: 02/02/2018] [Indexed: 05/22/2023]
Abstract
The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment. In this paper we identify and describe those stakeholders who currently-or in the future-could benefit from the application of the AOP framework and knowledge to specific problems. We also summarize the challenges faced in implementing pathway-based approaches such as the AOP framework in biological sciences, and provide a series of recommendations to meet critical needs to ensure further progression of the framework as a useful, sustainable and dependable tool supporting assessments of both human health and the environment. Although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive long-term planning and strategies.
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Affiliation(s)
- Annamaria Carusi
- Medical Humanities Sheffield, University of Sheffield, Medical School, Beech Hill Road, Sheffield S10 2RX, UK.
| | | | - Giovanni De Grandis
- Science, Technology, Engineering and Public Policy (STEaPP), Boston House, 36-37 Fitzroy Square, London W1T 6EY, UK.
| | - Beate I Escher
- UFZ - Helmholtz Centre for Environmental Research, 04318 Leipzig, Germany; Eberhard Karls University Tübingen, Environmental Toxicology, Centre for Applied Geosciences, 72074 Tübingen, Germany.
| | - Geoff Hodges
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK.
| | - Kenneth M Y Leung
- The Swire Institute of Marine Science and School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.
| | - Maurice Whelan
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
| | - Catherine Willett
- The Humane Society of the United States, 700 Professional Drive, Gaithersburg, MD, 20879, USA.
| | - Gerald T Ankley
- US Environmental Protection Agency, 6201 Congdon Blvd, Duluth, MN 55804, USA.
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13
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Yin A, Yamada A, Stam WB, van Hasselt JGC, van der Graaf PH. Quantitative systems pharmacology analysis of drug combination and scaling to humans: the interaction between noradrenaline and vasopressin in vasoconstriction. Br J Pharmacol 2018; 175:3394-3406. [PMID: 29859008 DOI: 10.1111/bph.14385] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 05/27/2018] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND AND PURPOSE Development of combination therapies has received significant interest in recent years. Previously, a two-receptor one-transducer (2R-1T) model was proposed to characterize drug interactions with two receptors that lead to the same phenotypic response through a common transducer pathway. We applied, for the first time, the 2R-1T model to characterize the interaction of noradrenaline and arginine-vasopressin on vasoconstriction and performed inter-species scaling to humans using this mechanism-based model. EXPERIMENTAL APPROACH Contractile data were obtained from in vitro rat small mesenteric arteries after exposure to single or combined challenges of noradrenaline and arginine-vasopressin with or without pretreatment with the irreversible α-adrenoceptor antagonist, phenoxybenzamine. Data were analysed using the 2R-1T model to characterize the observed exposure-response relationships and drug-drug interaction. The model was then scaled to humans by accounting for differences in receptor density. KEY RESULTS With receptor affinities set to published values, the 2R-1T model satisfactorily characterized the interaction between noradrenaline and arginine-vasopressin in rat small mesenteric arteries (relative standard error ≤20%), as well as the effect of phenoxybenzamine. Furthermore, after scaling the model to human vascular tissue, the model also adequately predicted the interaction between both agents on human renal arteries. CONCLUSIONS AND IMPLICATIONS The 2R-1T model can be of relevance to quantitatively characterize the interaction between two drugs that interact via different receptors and a common transducer pathway. Its mechanistic properties are valuable for scaling the model across species. This approach is therefore of significant value to rationally optimize novel combination treatments.
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Affiliation(s)
- Anyue Yin
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.,Department of Clinical Pharmacy and Toxicology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Akihiro Yamada
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.,Clinical Pharmacology PKMS Group, Astellas Pharma Inc., Tokyo, Japan
| | - Wiro B Stam
- Dutch Ministry of Health and Sports, Den Haag, The Netherlands
| | - Johan G C van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Piet H van der Graaf
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.,Certara QSP, Canterbury, UK
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14
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Snowden TJ, van der Graaf PH, Tindall MJ. Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models. J Pharmacokinet Pharmacodyn 2018; 45:537-555. [PMID: 29582349 PMCID: PMC6061126 DOI: 10.1007/s10928-018-9584-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 03/14/2018] [Indexed: 11/27/2022]
Abstract
In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.
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Affiliation(s)
- Thomas J. Snowden
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX UK
- Certara QSP, University of Kent Innovation Centre, Canterbury, CT2 7FG UK
| | - Piet H. van der Graaf
- Certara QSP, University of Kent Innovation Centre, Canterbury, CT2 7FG UK
- Leiden Academic Centre for Drug Research, Universiteit Leiden, 2333 CC Leiden, The Netherlands
| | - Marcus J. Tindall
- Department of Mathematics and Statistics, University of Reading, Reading, RG6 6AX UK
- The Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, RG6 6UR UK
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15
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van den Brink WJ, Hankemeier T, van der Graaf PH, de Lange ECM. Bundling arrows: improving translational CNS drug development by integrated PK/PD-metabolomics. Expert Opin Drug Discov 2018. [DOI: 10.1080/17460441.2018.1446935] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- W. J. van den Brink
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - T. Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - P. H. van der Graaf
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- Certara QSP, Canterbury Innovation House, Canterbury, United Kingdom
| | - E. C. M. de Lange
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
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16
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Systems Pharmacology Dissection of Cholesterol Regulation Reveals Determinants of Large Pharmacodynamic Variability between Cell Lines. Cell Syst 2017; 5:604-619.e7. [PMID: 29226804 PMCID: PMC5747350 DOI: 10.1016/j.cels.2017.11.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Revised: 08/17/2017] [Accepted: 11/02/2017] [Indexed: 01/06/2023]
Abstract
In individuals, heterogeneous drug-response phenotypes result from a complex interplay of dose, drug specificity, genetic background, and environmental factors, thus challenging our understanding of the underlying processes and optimal use of drugs in the clinical setting. Here, we use mass-spectrometry-based quantification of molecular response phenotypes and logic modeling to explain drug-response differences in a panel of cell lines. We apply this approach to cellular cholesterol regulation, a biological process with high clinical relevance. From the quantified molecular phenotypes elicited by various targeted pharmacologic or genetic treatments, we generated cell-line-specific models that quantified the processes beneath the idiotypic intracellular drug responses. The models revealed that, in addition to drug uptake and metabolism, further cellular processes displayed significant pharmacodynamic response variability between the cell lines, resulting in cell-line-specific drug-response phenotypes. This study demonstrates the importance of integrating different types of quantitative systems-level molecular measurements with modeling to understand the effect of pharmacological perturbations on complex biological processes.
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17
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Benson HE, Watterson S, Sharman JL, Mpamhanga CP, Parton A, Southan C, Harmar AJ, Ghazal P. Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway. Br J Pharmacol 2017; 174:4362-4382. [PMID: 28910500 PMCID: PMC5715582 DOI: 10.1111/bph.14037] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 08/10/2017] [Accepted: 08/30/2017] [Indexed: 12/22/2022] Open
Abstract
Background and Purpose An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway. Experimental Approach Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment. Key Results We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects. Conclusion and Implications We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.
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Affiliation(s)
- Helen E Benson
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, UK
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, University of Ulster, C-Tric, Derry, UK
| | - Joanna L Sharman
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, UK
| | - Chido P Mpamhanga
- Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh, UK
| | - Andrew Parton
- Northern Ireland Centre for Stratified Medicine, University of Ulster, C-Tric, Derry, UK
| | | | - Anthony J Harmar
- Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh, UK
| | - Peter Ghazal
- Division of Infection and Pathway Medicine, University of Edinburgh Medical School, Edinburgh, UK.,Centre for Synthetic and Systems Biology, CH Waddington Building, King's Buildings, Edinburgh, UK
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18
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Kohler I, Hankemeier T, van der Graaf PH, Knibbe CA, van Hasselt JC. Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine. Eur J Pharm Sci 2017; 109S:S15-S21. [DOI: 10.1016/j.ejps.2017.05.018] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 05/10/2017] [Indexed: 12/21/2022]
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19
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Systems pharmacology-based identification of pharmacogenomic determinants of adverse drug reactions using human iPSC-derived cell lines. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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20
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Traynard P, Tobalina L, Eduati F, Calzone L, Saez-Rodriguez J. Logic Modeling in Quantitative Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:499-511. [PMID: 28681552 PMCID: PMC5572374 DOI: 10.1002/psp4.12225] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/01/2017] [Accepted: 06/15/2017] [Indexed: 12/12/2022]
Abstract
Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).
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Affiliation(s)
- Pauline Traynard
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Luis Tobalina
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany
| | - Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany.,European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
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21
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Jackson RC, Di Veroli GY, Koh SB, Goldlust I, Richards FM, Jodrell DI. Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy. PLoS Comput Biol 2017; 13:e1005529. [PMID: 28467408 PMCID: PMC5435348 DOI: 10.1371/journal.pcbi.1005529] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 05/17/2017] [Accepted: 04/19/2017] [Indexed: 12/11/2022] Open
Abstract
The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed "cyclotherapy". Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies. We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint (SAC), the EGF signalling pathway and apoptosis. We incorporated sites of action of four drugs (palbociclib, gemcitabine, paclitaxel and actinomycin D) to illustrate potential applications of this approach. We show how drug effects on multiple cell populations can be simulated, facilitating simultaneous prediction of effects on normal and transformed cells. The consequences of aberrant signalling pathways or of altered expression of pro- or anti-apoptotic proteins can thus be compared. We suggest that this approach, particularly if used in conjunction with pharmacokinetic modelling, could be used to predict effects of specific oncogene expression patterns on drug response. The strategy could be used to search for synthetic lethality and optimise combination protocol designs.
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Affiliation(s)
| | - Giovanni Y. Di Veroli
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- QCP, Early Clinical Development—Innovative Medicines, AstraZeneca, Cambridge, United Kingdom
| | - Siang-Boon Koh
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Ian Goldlust
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Frances M. Richards
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Duncan I. Jodrell
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
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22
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Yamamoto Y, Välitalo PA, van den Berg DJ, Hartman R, van den Brink W, Wong YC, Huntjens DR, Proost JH, Vermeulen A, Krauwinkel W, Bakshi S, Aranzana-Climent V, Marchand S, Dahyot-Fizelier C, Couet W, Danhof M, van Hasselt JGC, de Lange ECM. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations. Pharm Res 2016; 34:333-351. [PMID: 27864744 PMCID: PMC5236087 DOI: 10.1007/s11095-016-2065-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 11/07/2016] [Indexed: 12/19/2022]
Abstract
Purpose Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition. Methods A mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model. Results A common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error <37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error <90%). Conclusions A multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations. Electronic supplementary material The online version of this article (doi:10.1007/s11095-016-2065-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yumi Yamamoto
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Pyry A Välitalo
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Dirk-Jan van den Berg
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Robin Hartman
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Willem van den Brink
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Yin Cheong Wong
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Dymphy R Huntjens
- Quantitative Sciences, Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - Johannes H Proost
- Division of Pharmacokinetics, Toxicology and Targeting, University of Groningen, Groningen, The Netherlands
| | - An Vermeulen
- Quantitative Sciences, Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - Walter Krauwinkel
- Department of Clinical Pharmacology & Exploratory Development, Astellas Pharma BV, Leiden, The Netherlands
| | - Suruchi Bakshi
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | | | - Sandrine Marchand
- Department of Medicine and Pharmacy, University of Poitiers, Poitiers, France
| | - Claire Dahyot-Fizelier
- Department of Anaesthesiology and Intensive Care Medicine, University Hospital Center of Poitiers, Poitiers, France
| | - William Couet
- Department of Medicine and Pharmacy, University of Poitiers, Poitiers, France
| | - Meindert Danhof
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Johan G C van Hasselt
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Elizabeth C M de Lange
- Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
- Leiden University Gorlaeus Laboratories, Einsteinweg 55, 2333CC, Leiden, The Netherlands.
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23
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Systems pharmacology in drug development and therapeutic use - A forthcoming paradigm shift. Eur J Pharm Sci 2016; 94:1-3. [PMID: 27449395 DOI: 10.1016/j.ejps.2016.07.014] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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