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Prieto Garcia L, Vildhede A, Nordell P, Ahlström C, Montaser AB, Terasaki T, Lennernäs H, Sjögren E. Physiologically based pharmacokinetics modeling and transporter proteomics to predict systemic and local liver and muscle disposition of statins. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 38576225 DOI: 10.1002/psp4.13139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/22/2024] [Accepted: 03/25/2024] [Indexed: 04/06/2024] Open
Abstract
Statins are used to reduce liver cholesterol levels but also carry a dose-related risk of skeletal muscle toxicity. Concentrations of statins in plasma are often used to assess efficacy and safety, but because statins are substrates of membrane transporters that are present in diverse tissues, local differences in intracellular tissue concentrations cannot be ruled out. Thus, plasma concentration may not be an adequate indicator of efficacy and toxicity. To bridge this gap, we used physiologically based pharmacokinetic (PBPK) modeling to predict intracellular concentrations of statins. Quantitative data on transporter clearance were scaled from in vitro to in vivo conditions by integrating targeted proteomics and transporter kinetics data. The developed PBPK models, informed by proteomics, suggested that organic anion-transporting polypeptide 2B1 (OATP2B1) and multidrug resistance-associated protein 1 (MRP1) play a pivotal role in the distribution of statins in muscle. Using these PBPK models, we were able to predict the impact of alterations in transporter function due to genotype or drug-drug interactions on statin systemic concentrations and exposure in liver and muscle. These results underscore the potential of proteomics-guided PBPK modeling to scale transporter clearance from in vitro data to real-world implications. It is important to evaluate the role of drug transporters when predicting tissue exposure associated with on- and off-target effects.
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Affiliation(s)
- Luna Prieto Garcia
- Department of Pharmaceutical Bioscience, Translational Drug Discovery and Development, Uppsala University, Uppsala, Sweden
- DMPK, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Anna Vildhede
- DMPK, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Pär Nordell
- DMPK, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Christine Ahlström
- DMPK, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Ahmed B Montaser
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Tetsuya Terasaki
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Hans Lennernäs
- Department of Pharmaceutical Bioscience, Translational Drug Discovery and Development, Uppsala University, Uppsala, Sweden
| | - Erik Sjögren
- Department of Pharmaceutical Bioscience, Translational Drug Discovery and Development, Uppsala University, Uppsala, Sweden
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2
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Prieto Garcia L, Lundahl A, Ahlström C, Vildhede A, Lennernäs H, Sjögren E. Does the choice of applied physiologically‐based pharmacokinetics platform matter? A case study on simvastatin disposition and drug–drug interaction. CPT Pharmacometrics Syst Pharmacol 2022; 11:1194-1209. [PMID: 35722750 PMCID: PMC9469690 DOI: 10.1002/psp4.12837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 11/16/2022] Open
Abstract
Physiologically‐based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user‐friendly graphical interface, such as Simcyp and PK‐Sim. However, evaluations of platform differences and the potential implications for disposition‐related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK‐Sim and Simcyp as representatives of established whole‐body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK‐Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20–80 mg), BCRP and OATP1B1 drug–gene interactions (DGIs), and drug–drug interactions (DDIs) when co‐administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in‐depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK‐Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism‐ and transporter‐mediated DGIs and DDIs.
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Affiliation(s)
- Luna Prieto Garcia
- Department of Pharmaceutical Bioscience, Translational Drug Discovery and Development Uppsala University Uppsala Sweden
- DMPK, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Anna Lundahl
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Christine Ahlström
- DMPK, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Anna Vildhede
- DMPK, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Hans Lennernäs
- Department of Pharmaceutical Bioscience, Translational Drug Discovery and Development Uppsala University Uppsala Sweden
| | - Erik Sjögren
- Department of Pharmaceutical Bioscience, Translational Drug Discovery and Development Uppsala University Uppsala Sweden
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3
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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4
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Wojtyniak J, Selzer D, Schwab M, Lehr T. Physiologically Based Precision Dosing Approach for Drug‐Drug‐Gene Interactions: A Simvastatin Network Analysis. Clin Pharmacol Ther 2020; 109:201-211. [DOI: 10.1002/cpt.2111] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 11/07/2020] [Indexed: 12/25/2022]
Affiliation(s)
- Jan‐Georg Wojtyniak
- Clinical Pharmacy Saarland University Saarbrücken Germany
- Dr. Margarete Fischer‐Bosch‐Institute of Clinical Pharmacology Stuttgart Germany
| | - Dominik Selzer
- Clinical Pharmacy Saarland University Saarbrücken Germany
| | - Matthias Schwab
- Dr. Margarete Fischer‐Bosch‐Institute of Clinical Pharmacology Stuttgart Germany
- Departments of Clinical Pharmacology and Pharmacy and Biochemistry University of Tübingen Tübingen Germany
- Cluster of Excellence iFIT (EXC2180) "Image‐guided and Functionally Instructed Tumor Therapies" University of Tübingen Tübingen Germany
| | - Thorsten Lehr
- Clinical Pharmacy Saarland University Saarbrücken Germany
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Abstract
Physiology-based pharmacokinetic and toxicokinetic (PBPK/TK) models allow us to simulate the concentration of xenobiotica in the plasma and different tissues of an organism. PBPK/TK models are therefore routinely used in many fields of life sciences to simulate the physiological concentration of exogenous compounds in plasma and tissues. The application of PBTK models in ecotoxicology, however, is currently hampered by the limited availability of models for focal species. Here, we present a best practice workflow that describes how to build PBTK models for novel species. To this end, we extrapolated eight previously established rabbit models for several drugs to six additional mammalian species (human, beagle, rat, monkey, mouse, and minipig). We used established PBTK models for these species to account for the species-specific physiology. The parameter sensitivity in the resulting 56 PBTK models was systematically assessed to rank the relevance of the parameters on overall model performance. Interestingly, more than 80% of the 609 considered model parameters showed a negligible sensitivity throughout all models. Only approximately 5% of all parameters had a high sensitivity in at least one of the PBTK models. This approach allowed us to rank the relevance of the various parameters on overall model performance. We used this information to formulate a best practice guideline for the efficient development of PBTK models for novel animal species. We believe that the workflow proposed in this study will significantly support the development of PBTK models for new animal species in the future.
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Baier V, Cordes H, Thiel C, Castell JV, Neumann UP, Blank LM, Kuepfer L. A Physiology-Based Model of Human Bile Acid Metabolism for Predicting Bile Acid Tissue Levels After Drug Administration in Healthy Subjects and BRIC Type 2 Patients. Front Physiol 2019; 10:1192. [PMID: 31611804 PMCID: PMC6777137 DOI: 10.3389/fphys.2019.01192] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 09/03/2019] [Indexed: 12/23/2022] Open
Abstract
Drug-induced liver injury (DILI) is a matter of concern in the course of drug development and patient safety, often leading to discontinuation of drug-development programs or early withdrawal of drugs from market. Hepatocellular toxicity or impairment of bile acid (BA) metabolism, known as cholestasis, are the two clinical forms of DILI. Whole-body physiology-based modelling allows a mechanistic investigation of the physiological processes leading to cholestasis in man. Objectives of the present study were: (1) the development of a physiology-based model of the human BA metabolism, (2) population-based model validation and characterisation, and (3) the prediction and quantification of altered BA levels in special genotype subgroups and after drug administration. The developed physiology-based bile acid (PBBA) model describes the systemic BA circulation in humans and includes mechanistically relevant active and passive processes such as the hepatic synthesis, gallbladder emptying, transition through the gastrointestinal tract, reabsorption into the liver, distribution within the whole body, and excretion via urine and faeces. The kinetics of active processes were determined for the exemplary BA glycochenodeoxycholic acid (GCDCA) based on blood plasma concentration-time profiles. The robustness of our PBBA model was verified with population simulations of healthy individuals. In addition to plasma levels, the possibility to estimate BA concentrations in relevant tissues like the intracellular space of the liver enhance the mechanistic understanding of cholestasis. We analysed BA levels in various tissues of Benign Recurrent Intrahepatic Cholestasis type 2 (BRIC2) patients and our simulations suggest a higher susceptibility of BRIC2 patients toward cholestatic DILI due to BA accumulation in the liver. The effect of drugs on systemic BA levels were simulated for cyclosporine A (CsA). Our results confirmed the higher risk of DILI after CsA administration in healthy and BRIC2 patients. The presented PBBA model enhances our mechanistic understanding underlying cholestasis and drug-induced alterations of BA levels in blood and organs. The developed PBBA model might be applied in the future to anticipate potential risk of cholestasis in patients.
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Affiliation(s)
- Vanessa Baier
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany.,Department of Surgery, University Hospital Aachen, Aachen, Germany
| | - Henrik Cordes
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany
| | - Christoph Thiel
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany
| | - José V Castell
- Unit of Experimental Hepatology, IIS Hospital La Fe, Faculty of Medicine, University of Valencia and CIBEREHD, Valencia, Spain
| | - Ulf P Neumann
- Department of Surgery, University Hospital Aachen, Aachen, Germany
| | - Lars M Blank
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany
| | - Lars Kuepfer
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany
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7
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Polak S, Tylutki Z, Holbrook M, Wiśniowska B. Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development. Drug Discov Today 2019; 24:1344-1354. [PMID: 31132414 DOI: 10.1016/j.drudis.2019.05.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 03/04/2019] [Accepted: 05/21/2019] [Indexed: 12/15/2022]
Abstract
Model-informed drug discovery and development (MID3) is an umbrella term under which sit several computational approaches: quantitative systems pharmacology (QSP), quantitative systems toxicology (QST) and physiologically based pharmacokinetics (PBPK). QSP models are built using mechanistic knowledge of the pharmacological pathway focusing on the putative mechanism of drug efficacy; whereas QST models focus on safety and toxicity issues and the molecular pathways and networks that drive these adverse effects. These can be mediated through exaggerated on-target or off-target pharmacology, immunogenicity or the physiochemical nature of the compound. PBPK models provide a mechanistic description of individual organs and tissues to allow the prediction of the intra- and extra-cellular concentration of the parent drug and metabolites under different conditions. Information on biophase concentration enables the prediction of a drug effect in different organs and assessment of the potential for drug-drug interactions. Together, these modelling approaches can inform the exposure-response relationship and hence support hypothesis generation and testing, compound selection, hazard identification and risk assessment through to clinical proof of concept (POC) and beyond to the market.
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Affiliation(s)
- Sebastian Polak
- Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland; Certara-Simcyp, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK.
| | - Zofia Tylutki
- Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland; Certara-Simcyp, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Mark Holbrook
- Certara-Simcyp, Level 2-Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK
| | - Barbara Wiśniowska
- Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Street, 30-688 Kraków, Poland
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8
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Ćetković Z, Cvijić S, Vasiljević D. In vitro/in silico approach in the development of simvastatin-loaded self-microemulsifying drug delivery systems. Drug Dev Ind Pharm 2017; 44:849-860. [PMID: 29228833 DOI: 10.1080/03639045.2017.1414835] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
OBJECTIVE The aims of this study were to formulate simvastatin (SV)-loaded self-microemulsifying drug delivery systems (SMEDDS), and explore the potential of these drug delivery systems to improve SV solubility, and also to identify the optimal place in the gastrointestinal (GI) tract for the release of SV using coupled in vitro/in silico approach. SIGNIFICANCE In comparison to other published results, this study considered the extensive pre-systemic clearance of SV, which could significantly decrease its systemic and hepatic bioavailability if SV is delivered into the small intestine. METHODS SV-loaded SMEDDS were formulated using various proportions of oils (PEG 300 oleic glycerides, propylene glycol monocaprylate, propylene glycol monolaurate), surfactant (PEG 400 caprylic/capric glycerides) and cosurfactant (polysorbate 80) and subjected to characterization, and physiologically-based pharmacokinetic (PBPK) modeling. RESULTS According to the in vitro results, the selected SMEDDS consisted of 10.0% PEG 300 oleic glycerides, 67.5% PEG 400 caprylic/capric glycerides, and 22.5% polysorbate 80. The use of acid-resistant capsules filled with SV-loaded SMEDDS was found helpful in protecting the drug against early degradation in proximal parts of the GI tract, however, in silico simulations indicated that pH-controlled drug release system that dissolve in the distal parts of the intestine might further improve SV bioavailability (up to 7.20%). CONCLUSION The obtained results suggested that combined strategy for the improvement of SV bioavailability should comprise solubility enhancement and delayed drug release. The developed SV-specific PBPK model could potentially be used to assess the influence of formulation factors on drug absorption and disposition when developing SV oral dosage forms.
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Affiliation(s)
- Zora Ćetković
- a Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy , University of Belgrade , Belgrade , Serbia
| | - Sandra Cvijić
- a Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy , University of Belgrade , Belgrade , Serbia
| | - Dragana Vasiljević
- a Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy , University of Belgrade , Belgrade , Serbia
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9
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Translational learning from clinical studies predicts drug pharmacokinetics across patient populations. NPJ Syst Biol Appl 2017. [PMID: 28649438 PMCID: PMC5460240 DOI: 10.1038/s41540-017-0012-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Early indication of late-stage failure of novel candidate drugs could be facilitated by continuous integration, assessment, and transfer of knowledge acquired along pharmaceutical development programs. We here present a translational systems pharmacology workflow that combines drug cocktail probing in a specifically designed clinical study, physiologically based pharmacokinetic modeling, and Bayesian statistics to identify and transfer (patho-)physiological and drug-specific knowledge across distinct patient populations. Our work builds on two clinical investigations, one with 103 healthy volunteers and one with 79 diseased patients from which we systematically derived physiological information from pharmacokinetic data for a reference probe drug (midazolam) at the single-patient level. Taking into account the acquired knowledge describing (patho-)physiological alterations in the patient cohort allowed the successful prediction of the population pharmacokinetics of a second, candidate probe drug (torsemide) in the patient population. In addition, we identified significant relations of the acquired physiological processes to patient metadata from liver biopsies. The presented prototypical systems pharmacology approach is a proof of concept for model-based translation across different stages of pharmaceutical development programs. Applied consistently, it has the potential to systematically improve predictivity of pharmacokinetic simulations by incorporating the results of clinical trials and translating them to subsequent studies. Physiologically based modeling together with Bayesian statistics allows the prediction of drug pharmacokinetics in specific patient populations. An interdisciplinary group of clinicians and computational scientists led by Dr. Lars Kuepfer from Bayer developed a generic workflow consisting of several consecutive learning steps where knowledge about both individual physiology as well as drug physicochemistry can be efficiently derived from plasma concentration profiles. The acquired information is then be used for the prediction of the pharmacokinetic behavior of a new drug candidate in a diseased population. This allows to simulate the variability in drug exposure virtually before starting clinical investigation in real patients in order to evaluate drug safety or efficacy through the simulation of virtual populations. Further development of this workflow could improve the safety of clinical development programs to assess the risk-benefit ratio of novel drug candidates in silico.
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10
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Thiel C, Cordes H, Fabbri L, Aschmann HE, Baier V, Smit I, Atkinson F, Blank LM, Kuepfer L. A Comparative Analysis of Drug-Induced Hepatotoxicity in Clinically Relevant Situations. PLoS Comput Biol 2017; 13:e1005280. [PMID: 28151932 PMCID: PMC5289425 DOI: 10.1371/journal.pcbi.1005280] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 12/03/2016] [Indexed: 11/18/2022] Open
Abstract
Drug-induced toxicity is a significant problem in clinical care. A key problem here is a general understanding of the molecular mechanisms accompanying the transition from desired drug effects to adverse events following administration of either therapeutic or toxic doses, in particular within a patient context. Here, a comparative toxicity analysis was performed for fifteen hepatotoxic drugs by evaluating toxic changes reflecting the transition from therapeutic drug responses to toxic reactions at the cellular level. By use of physiologically-based pharmacokinetic modeling, in vitro toxicity data were first contextualized to quantitatively describe time-resolved drug responses within a patient context. Comparatively studying toxic changes across the considered hepatotoxicants allowed the identification of subsets of drugs sharing similar perturbations on key cellular processes, functional classes of genes, and individual genes. The identified subsets of drugs were next analyzed with regard to drug-related characteristics and their physicochemical properties. Toxic changes were finally evaluated to predict both molecular biomarkers and potential drug-drug interactions. The results may facilitate the early diagnosis of adverse drug events in clinical application. Liver toxicity may occur at drug levels above the therapeutic range and is thus a crucial problem in clinical care. However, the cellular changes induced by drug administration of therapeutic and toxic doses in humans are still not well understood. We here coupled patient-specific drug concentration-time profiles following oral administration of therapeutic and toxic doses with in vitro drug response data to predict toxic changes that quantitatively reflect the transition from desired drug effects to undesired toxic reactions. These toxic changes were comparatively evaluated for fifteen hepatotoxic drugs to identify subsets of drugs, which show similar drug effects on key cellular processes, functional classes of genes, and individual genes, respectively. In addition, analyzing toxic changes for individual genes allowed the prediction of molecular biomarkers and potential drug-drug interactions. Our results may hence support the early diagnosis of liver toxicity in clinical care in the future and may, moreover, help to assess potential risks of drug combination therapies.
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Affiliation(s)
- Christoph Thiel
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Henrik Cordes
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Lorenzo Fabbri
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Hélène Eloise Aschmann
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Vanessa Baier
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Ines Smit
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Francis Atkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Lars Mathias Blank
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
| | - Lars Kuepfer
- Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Worringerweg 1, Aachen, Germany
- * E-mail:
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11
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Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, Block M, Eissing T, Teutonico D. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model. CPT Pharmacometrics Syst Pharmacol 2016; 5:516-531. [PMID: 27653238 PMCID: PMC5080648 DOI: 10.1002/psp4.12134] [Citation(s) in RCA: 196] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 09/09/2016] [Indexed: 12/17/2022] Open
Abstract
The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.
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Affiliation(s)
- L Kuepfer
- Bayer Technology Services, Leverkusen, Germany
| | - C Niederalt
- Bayer Technology Services, Leverkusen, Germany
| | - T Wendl
- Bayer Technology Services, Leverkusen, Germany
| | | | | | - J Lippert
- Bayer HealthCare, Wuppertal, Germany
| | - M Block
- Bayer Technology Services, Leverkusen, Germany
| | - T Eissing
- Bayer Technology Services, Leverkusen, Germany
| | - D Teutonico
- Bayer Technology Services, Leverkusen, Germany.
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12
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A Physiologically Based Pharmacokinetic Model of Isoniazid and Its Application in Individualizing Tuberculosis Chemotherapy. Antimicrob Agents Chemother 2016; 60:6134-45. [PMID: 27480867 PMCID: PMC5038291 DOI: 10.1128/aac.00508-16] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Accepted: 07/26/2016] [Indexed: 01/05/2023] Open
Abstract
Due to its high early bactericidal activity, isoniazid (INH) plays an essential role in tuberculosis treatment. Genetic polymorphisms of N-acetyltransferase type 2 (NAT2) cause a trimodal distribution of INH pharmacokinetics in slow, intermediate, and fast acetylators. The success of INH-based chemotherapy is associated with acetylator and patient health status. Still, a standard dose recommended by the FDA is administered regardless of acetylator type or immune status, even though adverse effects occur in 5 to 33% of all patients. Slow acetylators have a higher risk of development of drug-induced toxicity, while fast acetylators and immune-deficient patients face lower treatment success rates. To mechanistically assess the trade-off between toxicity and efficacy, we developed a physiologically based pharmacokinetic (PBPK) model describing the NAT2-dependent pharmacokinetics of INH and its metabolites. We combined the PBPK model with a pharmacodynamic (PD) model of antimycobacterial drug effects in the lungs. The resulting PBPK/PD model allowed the simultaneous simulation of treatment efficacies at the site of infection and exposure to toxic metabolites in off-target organs. Subsequently, we evaluated various INH dosing regimens in NAT2-specific immunocompetent and immune-deficient virtual populations. Our results suggest the need for acetylator-specific dose adjustments for optimal treatment outcomes. A reduced dose for slow acetylators substantially lowers the exposure to toxic metabolites and thereby the risk of adverse events, while it maintains sufficient treatment efficacies. Vice versa, intermediate and fast acetylators benefit from increased INH doses and a switch to a twice-daily administration schedule. Our analysis outlines how PBPK/PD modeling may be used to design and individualize treatment regimens.
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Pichardo-Almarza C, Diaz-Zuccarini V. From PK/PD to QSP: Understanding the Dynamic Effect of Cholesterol-Lowering Drugs on Atherosclerosis Progression and Stratified Medicine. Curr Pharm Des 2016; 22:6903-6910. [PMID: 27592718 PMCID: PMC5403958 DOI: 10.2174/1381612822666160905095402] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 08/29/2016] [Indexed: 01/18/2023]
Abstract
Current computational and mathematical tools are demonstrating the high value of using systems modeling approaches (e.g. Quantitative Systems Pharmacology) to understand the effect of a given compound on the biological and physiological mechanisms related to a specific disease. This review provides a short survey of the evolution of the mathematical approaches used to understand the effect of particular cholesterol-lowering drugs, from pharmaco-kinetic (PK) / pharmaco-dynamic (PD) models, through physiologically based pharmacokinetic models (PBPK) to QSP. These mathematical models introduce more mechanistic information related to the effect of these drugs on atherosclerosis progression and demonstrate how QSP could open new ways for stratified medicine in this field.
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Affiliation(s)
- Cesar Pichardo-Almarza
- UCL Mechanical Engineering, University College London, Roberts Building, Torrington Place, WC1E 7JE, London, United Kingdom
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Campos-Martorell M, Cano-Sarabia M, Simats A, Hernández-Guillamon M, Rosell A, Maspoch D, Montaner J. Charge effect of a liposomal delivery system encapsulating simvastatin to treat experimental ischemic stroke in rats. Int J Nanomedicine 2016; 11:3035-48. [PMID: 27418824 PMCID: PMC4935044 DOI: 10.2147/ijn.s107292] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND AND AIMS Although the beneficial effects of statins on stroke have been widely demonstrated both in experimental studies and in clinical trials, the aim of this study is to prepare and characterize a new liposomal delivery system that encapsulates simvastatin to improve its delivery into the brain. MATERIALS AND METHODS In order to select the optimal liposome lipid composition with the highest capacity to reach the brain, male Wistar rats were submitted to sham or transitory middle cerebral arterial occlusion (MCAOt) surgery and treated (intravenous [IV]) with fluorescent-labeled liposomes with different net surface charges. Ninety minutes after the administration of liposomes, the brain, blood, liver, lungs, spleen, and kidneys were evaluated ex vivo using the Xenogen IVIS(®) Spectrum imaging system to detect the load of fluorescent liposomes. In a second substudy, simvastatin was assessed upon reaching the brain, comparing free and encapsulated simvastatin (IV) administration. For this purpose, simvastatin levels in brain homogenates from sham or MCAOt rats at 2 hours or 4 hours after receiving the treatment were detected through ultra-high-protein liquid chromatography. RESULTS Whereas positively charged liposomes were not detected in brain or plasma 90 minutes after their administration, neutral and negatively charged liposomes were able to reach the brain and accumulate specifically in the infarcted area. Moreover, neutral liposomes exhibited higher bioavailability in plasma 4 hours after being administered. The detection of simvastatin by ultra-high-protein liquid chromatography confirmed its ability to cross the blood-brain barrier, when administered either as a free drug or encapsulated into liposomes. CONCLUSION This study confirms that liposome charge is critical to promote its accumulation in the brain infarct after MCAOt. Furthermore, simvastatin can be delivered after being encapsulated. Thus, simvastatin encapsulation might be a promising strategy to ensure that the drug reaches the brain, while increasing its bioavailability and reducing possible side effects.
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Affiliation(s)
- Mireia Campos-Martorell
- Neurovascular Research Laboratory, Institut de Recerca Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona
| | - Mary Cano-Sarabia
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and The Barcelona Institute of Science and Technology, Universitat Autònoma de Barcelona, Barcelona
| | - Alba Simats
- Neurovascular Research Laboratory, Institut de Recerca Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona
| | - Mar Hernández-Guillamon
- Neurovascular Research Laboratory, Institut de Recerca Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona
| | - Anna Rosell
- Neurovascular Research Laboratory, Institut de Recerca Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona
| | - Daniel Maspoch
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and The Barcelona Institute of Science and Technology, Universitat Autònoma de Barcelona, Barcelona; Institució Catalana de Recerca i Estudis Avançats (ICREA)
| | - Joan Montaner
- Neurovascular Research Laboratory, Institut de Recerca Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona; Neurovascular Unit, Department of Neurology, Universitat Autònoma de Barcelona, Hospital Vall d'Hebron, Barcelona, Spain
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Abstract
The concept of a pharmacokinetics-pharmacodynamics (PK/PD) assessment of drug development candidates is well established in pharmaceutical research and development, and PK/PD modeling is common practice in all pharmaceutical companies. A recent analysis (Morgan et al., Drug Discov Today 17(9-10):419-424, 2012) revealed however that insufficient certainty in the integrity of the causal chain of fundamental pharmacological steps from drug dosing through systemic exposure, target tissue exposure, and engagement of molecular target to pharmacological response is still the major driver of failure in phase II of clinical drug development. Despite the rise of molecular biomarkers, ethical, scientific, and practical constraints very often still prevent a direct assessment of each necessary step ultimately leading to an intended drug effect or an unintended adverse reaction. Yet, incomplete investigation of the causality of drug responses is a major risk for translational assessments and the prediction of drug responses in different species or other populations. Mechanism-based modeling and simulation (M&S) offers a means to investigate complex physiological and pharmacological processes and to complement experimental data for non-accessible steps in the pharmacological causal chain. With the help of two examples, it is illustrated, what level of physiological detail, state-of-the-art models can represent, how predictive these models are and how mechanism-based approaches can be combined with empirical correlation-based concepts.
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Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients. Arch Toxicol 2016; 91:865-883. [PMID: 27161439 PMCID: PMC5306109 DOI: 10.1007/s00204-016-1723-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Accepted: 04/20/2016] [Indexed: 12/13/2022]
Abstract
Understanding central mechanisms underlying drug-induced toxicity plays a crucial role in drug development and drug safety. However, a translation of cellular in vitro findings to an actual in vivo context remains challenging. Here, physiologically based pharmacokinetic (PBPK) modeling was used for in vivo contextualization of in vitro toxicity data (PICD) to quantitatively predict in vivo drug response over time by integrating multiple levels of biological organization. Explicitly, in vitro toxicity data at the cellular level were integrated into whole-body PBPK models at the organism level by coupling in vitro drug exposure with in vivo drug concentration–time profiles simulated in the extracellular environment within the organ. PICD was exemplarily applied on the hepatotoxicant azathioprine to quantitatively predict in vivo drug response of perturbed biological pathways and cellular processes in rats and humans. The predictive accuracy of PICD was assessed by comparing in vivo drug response predicted for rats with observed in vivo measurements. To demonstrate clinical applicability of PICD, in vivo drug responses of a critical toxicity-related pathway were predicted for eight patients following acute azathioprine overdoses. Moreover, acute liver failure after multiple dosing of azathioprine was investigated in a patient case study by use of own clinical data. Simulated pharmacokinetic profiles were therefore related to in vivo drug response predicted for genes associated with observed clinical symptoms and to clinical biomarkers measured in vivo. PICD provides a generic platform to investigate drug-induced toxicity at a patient level and thus may facilitate individualized risk assessment during drug development.
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Abstract
The development of new drug therapies requires substantial and ever increasing investments from the pharmaceutical company. Ten years ago, the average time from early target identification and optimization until initial market authorization of a new drug compound took more than 10 years and involved costs in the order of one billion US dollars. Recent studies indicate even a significant growth of costs in the meanwhile, mainly driven by the increasing complexity of diseases addressed by pharmaceutical research.Modeling and simulation are proven approaches to handle highly complex systems; hence, systems medicine is expected to control the spiral of complexity of diseases and increasing costs. Today, the main focus of systems medicine applications in industry is on mechanistic modeling. Biological mechanisms are represented by explicit equations enabling insight into the cooperation of all relevant mechanisms. Mechanistic modeling is widely accepted in pharmacokinetics, but prediction from cell behavior to patients is rarely possible due to lacks in our understanding of the controlling mechanisms. Data-driven modeling aims to compensate these lacks by the use of advanced statistical and machine learning methods. Future progress in pharmaceutical research and development will require integrated hybrid modeling technologies allowing realization of the benefits of both mechanistic and data-driven modeling. In this chapter, we sketch typical industrial application areas for both modeling techniques and derive the requirements for future technology development.
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Affiliation(s)
- Lars Kuepfer
- Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany
- Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany
| | - Andreas Schuppert
- Lehrstuhl für datenbasierte Modellierung in CES, Joint Research Center for Computational Biomedicine, AICES, RWTH Aachen University, Augustinerbach 2, Aachen, 52062, Germany.
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Krauss M, Schuppert A. Assessing interindividual variability by Bayesian-PBPK modeling. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.ddmod.2017.08.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Krauss M, Tappe K, Schuppert A, Kuepfer L, Goerlitz L. Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations. PLoS One 2015; 10:e0139423. [PMID: 26431198 PMCID: PMC4592188 DOI: 10.1371/journal.pone.0139423] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Accepted: 09/14/2015] [Indexed: 01/26/2023] Open
Abstract
Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.
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Affiliation(s)
- Markus Krauss
- Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany; Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Aachen, Germany
| | - Kai Tappe
- Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany
| | - Andreas Schuppert
- Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany; Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Aachen, Germany
| | - Lars Kuepfer
- Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany
| | - Linus Goerlitz
- Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany
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Schwen LO, Schenk A, Kreutz C, Timmer J, Bartolomé Rodríguez MM, Kuepfer L, Preusser T. Representative Sinusoids for Hepatic Four-Scale Pharmacokinetics Simulations. PLoS One 2015. [PMID: 26222615 PMCID: PMC4519332 DOI: 10.1371/journal.pone.0133653] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The mammalian liver plays a key role for metabolism and detoxification of xenobiotics in the body. The corresponding biochemical processes are typically subject to spatial variations at different length scales. Zonal enzyme expression along sinusoids leads to zonated metabolization already in the healthy state. Pathological states of the liver may involve liver cells affected in a zonated manner or heterogeneously across the whole organ. This spatial heterogeneity, however, cannot be described by most computational models which usually consider the liver as a homogeneous, well-stirred organ. The goal of this article is to present a methodology to extend whole-body pharmacokinetics models by a detailed liver model, combining different modeling approaches from the literature. This approach results in an integrated four-scale model, from single cells via sinusoids and the organ to the whole organism, capable of mechanistically representing metabolization inhomogeneity in livers at different spatial scales. Moreover, the model shows circulatory mixing effects due to a delayed recirculation through the surrounding organism. To show that this approach is generally applicable for different physiological processes, we show three applications as proofs of concept, covering a range of species, compounds, and diseased states: clearance of midazolam in steatotic human livers, clearance of caffeine in mouse livers regenerating from necrosis, and a parameter study on the impact of different cell entities on insulin uptake in mouse livers. The examples illustrate how variations only discernible at the local scale influence substance distribution in the plasma at the whole-body level. In particular, our results show that simultaneously considering variations at all relevant spatial scales may be necessary to understand their impact on observations at the organism scale.
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Affiliation(s)
| | - Arne Schenk
- Computational Systems Biology, Bayer Technology Services, Leverkusen, Germany
- Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen University, Aachen, Germany
| | - Clemens Kreutz
- Freiburg Center for Data Analysis and Modeling (FDM), Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Jens Timmer
- Freiburg Center for Data Analysis and Modeling (FDM), Institute of Physics, University of Freiburg, Freiburg, Germany
- BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | | | - Lars Kuepfer
- Computational Systems Biology, Bayer Technology Services, Leverkusen, Germany
- Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany
| | - Tobias Preusser
- Fraunhofer MEVIS, Bremen, Germany
- Jacobs University, Bremen, Germany
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Drasdo D, Bode J, Dahmen U, Dirsch O, Dooley S, Gebhardt R, Ghallab A, Godoy P, Häussinger D, Hammad S, Hoehme S, Holzhütter HG, Klingmüller U, Kuepfer L, Timmer J, Zerial M, Hengstler JG. The virtual liver: state of the art and future perspectives. Arch Toxicol 2015; 88:2071-5. [PMID: 25331938 DOI: 10.1007/s00204-014-1384-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Dirk Drasdo
- Institut National de Recherche en Informatique et en Automatique (INRIA), Domaine de Voluceau - Rocquencourt, Paris, France
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Physiologically based pharmacokinetic modeling of disposition and drug-drug interactions for atorvastatin and its metabolites. Eur J Pharm Sci 2015; 77:216-29. [PMID: 26116278 DOI: 10.1016/j.ejps.2015.06.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Revised: 05/07/2015] [Accepted: 06/22/2015] [Indexed: 01/06/2023]
Abstract
Atorvastatin is the most commonly used of all statins to lower cholesterol. Atorvastatin is extensively metabolized in both gut and liver to produce several active metabolites. The purpose of the present study is to develop a physiologically based pharmacokinetic (PBPK) model for atorvastatin and its two primary metabolites, 2-hydroxy-atorvastatin acid and atorvastatin lactone, using in vitro and in vivo data. The model was used to predict the pharmacokinetic profiles and drug-drug interaction (DDI) effect for atorvastatin and its metabolites in different DDI scenarios. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministration of atorvastatin with different medications such as itraconazole, clarithromycin, cimetidine, rifampin and phenytoin. This population based PBPK model was able to describe the concentration-time profiles of atorvastatin and its two metabolites reasonably well in the absence or presence of those drugs at different dose regimens. The predicted maximum concentration (Cmax), area under the concentration-time curve (AUC) values and between-phase ratios were in good agreement with clinically observed data. The model has also revealed the importance of different metabolic pathways on the disposition of atorvastatin metabolites. This PBPK model can be utilized to assess the safety and efficacy of atorvastatin in the clinic. This study demonstrated the feasibility of applying PBPK approach to predict the DDI potential of drugs undergoing complex metabolism.
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23
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Thiel C, Schneckener S, Krauss M, Ghallab A, Hofmann U, Kanacher T, Zellmer S, Gebhardt R, Hengstler JG, Kuepfer L. A Systematic Evaluation of the Use of Physiologically Based Pharmacokinetic Modeling for Cross-Species Extrapolation. J Pharm Sci 2015; 104:191-206. [DOI: 10.1002/jps.24214] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 09/22/2014] [Accepted: 09/22/2014] [Indexed: 01/06/2023]
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Tsamandouras N, Dickinson G, Guo Y, Hall S, Rostami-Hodjegan A, Galetin A, Aarons L. Development and Application of a Mechanistic Pharmacokinetic Model for Simvastatin and its Active Metabolite Simvastatin Acid Using an Integrated Population PBPK Approach. Pharm Res 2014; 32:1864-83. [PMID: 25446771 DOI: 10.1007/s11095-014-1581-2] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Accepted: 11/14/2014] [Indexed: 12/11/2022]
Abstract
PURPOSE To develop a population physiologically-based pharmacokinetic (PBPK) model for simvastatin (SV) and its active metabolite, simvastatin acid (SVA), that allows extrapolation and prediction of their concentration profiles in liver (efficacy) and muscle (toxicity). METHODS SV/SVA plasma concentrations (34 healthy volunteers) were simultaneously analysed with NONMEM 7.2. The implemented mechanistic model has a complex compartmental structure allowing inter-conversion between SV and SVA in different tissues. Prior information for model parameters was extracted from different sources to construct appropriate prior distributions that support parameter estimation. The model was employed to provide predictions regarding the effects of a range of clinically important conditions on the SV and SVA disposition. RESULTS The developed model offered a very good description of the available plasma SV/SVA data. It was also able to describe previously observed effects of an OATP1B1 polymorphism (c.521 T > C) and a range of drug-drug interactions (CYP inhibition) on SV/SVA plasma concentrations. The predicted SV/SVA liver and muscle tissue concentrations were in agreement with the clinically observed efficacy and toxicity outcomes of the investigated conditions. CONCLUSIONS A mechanistically sound SV/SVA population model with clinical applications (e.g., assessment of drug-drug interaction and myopathy risk) was developed, illustrating the advantages of an integrated population PBPK approach.
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Affiliation(s)
- Nikolaos Tsamandouras
- Centre for Applied Pharmacokinetic Research, Manchester Pharmacy School, The University of Manchester, Stopford Building, Room 3.32, Oxford Road, Manchester, M13 9PT, UK,
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Bosgra S, van de Steeg E, Vlaming ML, Verhoeckx KC, Huisman MT, Verwei M, Wortelboer HM. Predicting carrier-mediated hepatic disposition of rosuvastatin in man by scaling from individual transfected cell-lines in vitro using absolute transporter protein quantification and PBPK modeling. Eur J Pharm Sci 2014; 65:156-66. [DOI: 10.1016/j.ejps.2014.09.007] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Revised: 08/21/2014] [Accepted: 09/05/2014] [Indexed: 11/12/2022]
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Clinical translation in the virtual liver network. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2014; 3:e127. [PMID: 25076067 PMCID: PMC4120019 DOI: 10.1038/psp.2014.25] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Accepted: 04/22/2014] [Indexed: 02/04/2023]
Abstract
The liver is the central detoxifying organ, continuously removing xenobiotics from the vascular system. Given its role in drug metabolism, a functional understanding of liver physiology is crucial to optimizing drug efficacy and patient safety. The Virtual Liver Network (VLN), a German national flagship research program, focuses on producing validated computer models of human liver physiology. These models are used to analyze patient-derived data and thereby gain mechanistic insights in the processes underlying drug pharmacokinetics (PK).
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Dickschen K, Eissing T, Mürdter T, Schwab M, Willmann S, Hempel G. Concomitant use of tamoxifen and endoxifen in postmenopausal early breast cancer: prediction of plasma levels by physiologically-based pharmacokinetic modeling. SPRINGERPLUS 2014; 3:285. [PMID: 24936398 PMCID: PMC4058004 DOI: 10.1186/2193-1801-3-285] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Accepted: 05/28/2014] [Indexed: 11/10/2022]
Abstract
Purpose To overcome cytochrome P450 2D6 (CYP2D6) mediated tamoxifen resistance in postmenopausal early breast cancer, CYP2D6 phenotype-adjusted tamoxifen dosing in patients with impaired CYP2D6 metabolism and/or the application of endoxifen, the most potent tamoxifen metabolite, are alternative treatment options. To elucidate both strategies comprehensively we used a physiologically-based pharmacokinetic (PBPK) modeling approach. Methods Firstly simulation of increasing tamoxifen dosages was performed by a virtual clinical trial including populations of CYP2D6 poor (PM), intermediate (IM) and extensive metabolizers (EM) (N = 8,000). Secondly we performed PBPK-simulations under consideration of tamoxifen use plus concomitant increasing dosages of endoxifen (N = 7,000). Results Our virtual study demonstrates that dose escalation of tamoxifen in IMs resulted in endoxifen steady-state plasma concentrations similar to CYP2D6 EMs whereas PMs did not reach EM endoxifen levels. Steady-state plasma concentrations of tamoxifen, N-desmethyl-tamoxifen, 4-hydroxy-tamoxifen and endoxifen were similar in CYP2D6 IMs and PMs versus EMs using once daily dosing of 20 mg tamoxifen and concomitant CYP2D6 phenotype-adjusted endoxifen dosing in IMs and PMs (1 mg/d and 3 mg/d, respectively). Conclusion In conclusion, we suggest that co-administration of endoxifen in tamoxifen treated early breast cancer women with impaired CYP2D6 metabolism is a promising alternative to reach plasma concentrations comparable to CYP2D6 EM patients.
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Affiliation(s)
- Kristin Dickschen
- Institut für Pharmazeutische und Medizinische Chemie, Klinische Pharmazie, Westfälische Wilhelms-Universität Münster, Corrensstrasse 48, Münster, 48149 Germany ; Computational Systems Biology, Bayer Technology Services GmbH, Building 9115, Leverkusen, 51368 Germany
| | - Thomas Eissing
- Computational Systems Biology, Bayer Technology Services GmbH, Building 9115, Leverkusen, 51368 Germany
| | - Thomas Mürdter
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology and University Tübingen, Auerbachstrasse 112, Stuttgart, 70376 Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology and University Tübingen, Auerbachstrasse 112, Stuttgart, 70376 Germany ; Department of Clinical Pharmacology, University Hospital Tübingen, Auf der Morgenstelle 8, Tübingen, 72076 Germany
| | - Stefan Willmann
- Computational Systems Biology, Bayer Technology Services GmbH, Building 9115, Leverkusen, 51368 Germany ; Clinical Pharmacometrics, Bayer Pharma AG, Aprather Weg 18a, Wuppertal, 42113 Germany
| | - Georg Hempel
- Institut für Pharmazeutische und Medizinische Chemie, Klinische Pharmazie, Westfälische Wilhelms-Universität Münster, Corrensstrasse 48, Münster, 48149 Germany
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Schwen LO, Krauss M, Niederalt C, Gremse F, Kiessling F, Schenk A, Preusser T, Kuepfer L. Spatio-temporal simulation of first pass drug perfusion in the liver. PLoS Comput Biol 2014; 10:e1003499. [PMID: 24625393 PMCID: PMC3952820 DOI: 10.1371/journal.pcbi.1003499] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2013] [Accepted: 01/21/2014] [Indexed: 01/21/2023] Open
Abstract
The liver is the central organ for detoxification of xenobiotics in the body. In pharmacokinetic modeling, hepatic metabolization capacity is typically quantified as hepatic clearance computed as degradation in well-stirred compartments. This is an accurate mechanistic description once a quasi-equilibrium between blood and surrounding tissue is established. However, this model structure cannot be used to simulate spatio-temporal distribution during the first instants after drug injection. In this paper, we introduce a new spatially resolved model to simulate first pass perfusion of compounds within the naive liver. The model is based on vascular structures obtained from computed tomography as well as physiologically based mass transfer descriptions obtained from pharmacokinetic modeling. The physiological architecture of hepatic tissue in our model is governed by both vascular geometry and the composition of the connecting hepatic tissue. In particular, we here consider locally distributed mass flow in liver tissue instead of considering well-stirred compartments. Experimentally, the model structure corresponds to an isolated perfused liver and provides an ideal platform to address first pass effects and questions of hepatic heterogeneity. The model was evaluated for three exemplary compounds covering key aspects of perfusion, distribution and metabolization within the liver. As pathophysiological states we considered the influence of steatosis and carbon tetrachloride-induced liver necrosis on total hepatic distribution and metabolic capacity. Notably, we found that our computational predictions are in qualitative agreement with previously published experimental data. The simulation results provide an unprecedented level of detail in compound concentration profiles during first pass perfusion, both spatio-temporally in liver tissue itself and temporally in the outflowing blood. We expect our model to be the foundation of further spatially resolved models of the liver in the future.
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Affiliation(s)
| | - Markus Krauss
- Computational Systems Biology, Bayer Technology Services, Leverkusen, Germany
- Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen University, Aachen, Germany
| | - Christoph Niederalt
- Computational Systems Biology, Bayer Technology Services, Leverkusen, Germany
| | - Felix Gremse
- Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Fabian Kiessling
- Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | | | - Tobias Preusser
- Fraunhofer MEVIS, Bremen, Germany
- School of Engineering and Science, Jacobs University, Bremen, Germany
| | - Lars Kuepfer
- Computational Systems Biology, Bayer Technology Services, Leverkusen, Germany
- Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany
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Benson N, Matsuura T, Smirnov S, Demin O, Jones HM, Dua P, van der Graaf PH. Systems pharmacology of the nerve growth factor pathway: use of a systems biology model for the identification of key drug targets using sensitivity analysis and the integration of physiology and pharmacology. Interface Focus 2014; 3:20120071. [PMID: 24427523 DOI: 10.1098/rsfs.2012.0071] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.
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Affiliation(s)
- Neil Benson
- Xenologiq Ltd, Unit 7 , Denne Hill Business Park, Canterbury CT4 6HD , UK ; Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA
| | - Tomomi Matsuura
- Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA ; Astellas , 21 Miyukigaoka, Tsukuba, Ibaraki 305-8585 , Japan
| | - Sergey Smirnov
- Institute for Systems Biology SPb, Leninskie Gory 1/75G, Moscow 119992 , Russia
| | - Oleg Demin
- Institute for Systems Biology SPb, Leninskie Gory 1/75G, Moscow 119992 , Russia
| | - Hannah M Jones
- Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA
| | - Pinky Dua
- Pharmatherapeutics Clinical Pharmacology, Pfizer Neusentis , The Portway Building, Granta Park, Cambridge CB21 6GS , UK ; Neusentis, Pfizer Global Clinical Pharmacology , The Portway Building, Granta Park, Cambridge CB21 6GS , UK
| | - Piet H van der Graaf
- Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA ; Neusentis, Pfizer Global Clinical Pharmacology , The Portway Building, Granta Park, Cambridge CB21 6GS , UK ; Leiden Academic Centre for Drug Research (LACDR) , Leiden RA 2300 , The Netherlands
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30
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Systems pharmacology for drug discovery and development: paradigm shift or flash in the pan? Clin Pharmacol Ther 2013; 93:379-81. [PMID: 23598453 DOI: 10.1038/clpt.2013.40] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Systems pharmacology is an emerging approach that sets out to use quantitative concepts rooted in the synergy between modeling and simulation on one hand and large-scale data collection and analysis on the other to investigate biological systems and design therapeutic interventions. Interest in this field has recently increased substantially, but so have perceived challenges and misunderstandings, especially related to implementation. This Commentary explores the opportunities and challenges in the rapidly evolving area of systems pharmacology from a drug discovery and development perspective.
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Krauss M, Burghaus R, Lippert J, Niemi M, Neuvonen P, Schuppert A, Willmann S, Kuepfer L, Görlitz L. Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification. In Silico Pharmacol 2013; 1:6. [PMID: 25505651 PMCID: PMC4230716 DOI: 10.1186/2193-9616-1-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Accepted: 03/24/2013] [Indexed: 11/17/2022] Open
Abstract
Purpose Inter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations. Methods PBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks. Results Considering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1. Conclusions The presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs. Electronic supplementary material The online version of this article (doi:10.1186/2193-9616-1-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Markus Krauss
- Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany ; RWTH Aachen, Schinkelstr, Aachen Institute for Advanced Study in Computational Engineering Sciences, Aachen, 2, 52062 Germany
| | - Rolf Burghaus
- Clinical Pharmacometrics, Bayer Pharma AG, Wuppertal, 42117 Germany
| | - Jörg Lippert
- Clinical Pharmacometrics, Bayer Pharma AG, Wuppertal, 42117 Germany
| | - Mikko Niemi
- Department of Clinical Pharmacology, University of Helsinki, Helsinki, Finland ; HUSLAB, Helsinki University Central Hospital, Helsinki, Finland
| | - Pertti Neuvonen
- HUSLAB, Helsinki University Central Hospital, Helsinki, Finland
| | - Andreas Schuppert
- Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany ; RWTH Aachen, Schinkelstr, Aachen Institute for Advanced Study in Computational Engineering Sciences, Aachen, 2, 52062 Germany
| | - Stefan Willmann
- Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany
| | - Lars Kuepfer
- Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany
| | - Linus Görlitz
- Bayer Technology Services GmbH, Computational Systems Biology, Leverkusen, 51368 Germany
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Model-based drug development: a rational approach to efficiently accelerate drug development. Clin Pharmacol Ther 2013; 93:502-14. [PMID: 23588322 DOI: 10.1038/clpt.2013.54] [Citation(s) in RCA: 151] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The pharmaceutical industry continues to face significant challenges. Very few compounds that enter development reach the marketplace, and the investment required for each success can surpass $1.8 billion. Despite attempts to improve efficiency and increase productivity, total investment continues to rise whereas the output of new medicines declines. With costs increasing exponentially through each development phase, it is failure in phase II and phase III that is most wasteful. In today's development paradigm, late-stage failure is principally a result of insufficient efficacy. This is manifested as either a failure to differentiate sufficiently from placebo (shown for both novel and precedented mechanisms) or a failure to demonstrate sufficient differentiation from existing compounds. Set in this context, this article will discuss the role model-based drug development (MBDD) approaches can and do play in accelerating and optimizing compound development strategies through a series of illustrative examples.
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Abstract
Adverse drug events (ADEs) remain a universal problem in drug development, regulatory review, and clinical practice with a substantial financial burden on the global health-care system. Recent advances in molecular and "omics" technologies, along with online databases and bioinformatics, have enabled a more integrative approach to understanding drug-target (protein) interactions, both desirable and undesirable, within a biological system. This has led to the development of systems approaches to risk assessment in an attempt to complement and improve on contemporary observational and predictive strategies for assessing risk. Although still in an evolutionary phase, systems approaches have the potential to markedly advance our understanding of ADEs and ability to predict them. Systems approaches will also move personalized medicine forward by enabling better identification of individual and subgroup risk factors.
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