Physiologically Based Pharmacokinetics Joined With In Vitro–In Vivo Extrapolation of ADME: A Marriage Under the Arch of Systems Pharmacology

Classic pharmacokinetics (PK) rarely takes into account the full knowledge of physiology and biology of the human body. However, physiologically based PK (PBPK) is built mainly from drug‐independent “system” information. PBPK is not a new concept, but it has shown a very rapid rise in recent years. This has been attributed to a greater connectivity to in vitro–in vivo extrapolation (IVIVE) techniques for predicting drug absorption, distribution, metabolism, and excretion (ADME) and their variability in humans. The marriage between PBPK and IVIVE under the overarching umbrella of “systems biology” has removed many constraints related to cutoff approaches on prediction of ADME. PBPK–IVIVE linked models have repeatedly shown their value in guiding decisions when predicting the effects of intrinsic and extrinsic factors on PK of drugs. A review of the achievements and shortcomings of the models might suggest better strategies in extending the success of PBPK–IVIVE to pharmacodynamics (PD) and drug safety.

[1]  Yuan Chen,et al.  Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies , 2012, Biopharmaceutics & drug disposition.

[2]  Shiew-Mei Huang,et al.  Utility of a physiologically–based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug–drug–disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice , 2012, Biopharmaceutics & drug disposition.

[3]  Amin Rostami-Hodjegan,et al.  Prediction of the Clearance of Eleven Drugs and Associated Variability in Neonates, Infants and Children , 2006, Clinical pharmacokinetics.

[4]  R. Obach,et al.  Using Simcyp to project human oral pharmacokinetic variability in early drug research to mitigate mechanism‐based adverse events , 2012, Biopharmaceutics & drug disposition.

[5]  M. Jamei,et al.  Application of a Systems Approach to the Bottom-Up Assessment of Pharmacokinetics in Obese Patients , 2011, Clinical pharmacokinetics.

[6]  L Zhang,et al.  Applications of Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation During Regulatory Review , 2011, Clinical pharmacology and therapeutics.

[7]  K. Rowland Yeo,et al.  Prediction of drug clearance in a smoking population: modeling the impact of variable cigarette consumption on the induction of CYP1A2 , 2012, European Journal of Clinical Pharmacology.

[8]  R. Tukey,et al.  Human UDP-glucuronosyltransferases: metabolism, expression, and disease. , 2000, Annual review of pharmacology and toxicology.

[9]  G. Tucker,et al.  Measurement of the renal clearance of drugs. , 1981, British journal of clinical pharmacology.

[10]  Erik Mosekilde,et al.  Biosimulation in Biomedical Research, Health Care and Drug Development , 2012, Springer Vienna.

[11]  W. Heizer,et al.  In Vitro–In Vivo Correlation of Hepatobiliary Drug Clearance in Humans , 2007, Clinical pharmacology and therapeutics.

[12]  M Jamei,et al.  Towards a quantitative framework for the prediction of DDIs arising from cytochrome P450 induction. , 2009, Current drug metabolism.

[13]  Na Li,et al.  LC-MS/MS mediated absolute quantification and comparison of bile salt export pump and breast cancer resistance protein in livers and hepatocytes across species. , 2009, Analytical chemistry.

[14]  A. Rostami-Hodjegan,et al.  Modelling and simulation in prediction of human xenobiotic absorption, distribution, metabolism and excretion (ADME): in vitro–in vivo extrapolations (IVIVE) , 2007, Xenobiotica; the fate of foreign compounds in biological systems.

[15]  Jan Snoeys,et al.  From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example , 2012, Biopharmaceutics & drug disposition.

[16]  J. Woodcock.  Precompetitive Research: A New Prescription for Drug Development? , 2010, Clinical pharmacology and therapeutics.

[17]  K. Stormark,et al.  Significant Increase in Systemic Exposure of Atorvastatin After Biliopancreatic Diversion With Duodenal Switch , 2010, Clinical pharmacology and therapeutics.

[18]  T. Teorell.  STUDIES ON THE DIFFUSION EFFECT UPON IONIC DISTRIBUTION , 1937, The Journal of general physiology.

[19]  M C Willingham,et al.  Cellular localization of the multidrug-resistance gene product P-glycoprotein in normal human tissues. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[20]  J. Lipscomb,et al.  Covariation of Human Microsomal Protein Per Gram of Liver with Age: Absence of Influence of Operator and Sample Storage May Justify Interlaboratory Data Pooling , 2008, Drug Metabolism and Disposition.

[21]  M. Makuuchi,et al.  Bottom‐up modeling and simulation of tacrolimus clearance: prospective investigation of blood cell distribution, sex and CYP3A5 expression as covariates and assessment of study power , 2011, Biopharmaceutics & drug disposition.

[22]  Amin Rostami-Hodjegan,et al.  Resurgence in the use of physiologically based pharmacokinetic models in pediatric clinical pharmacology: parallel shift in incorporating the knowledge of biological elements and increased applicability to drug development and clinical practice , 2011, Paediatric anaesthesia.

[23]  Shiew-Mei Huang,et al.  Quantitative Evaluation of Pharmacokinetic Inhibition of CYP3A Substrates by Ketoconazole: A Simulation Study , 2009, Journal of clinical pharmacology.

[24]  L Zhang,et al.  Predicting Drug Interaction Potential With a Physiologically Based Pharmacokinetic Model: A Case Study of Telithromycin, a Time‐Dependent CYP3A Inhibitor , 2012, Clinical pharmacology and therapeutics.

[25]  V. Armstrong,et al.  Quantification by liquid chromatography tandem mass spectrometry of mycophenolic acid and its phenol and acyl glucuronide metabolites. , 2006, Clinical chemistry.

[26]  John C Lipscomb,et al.  Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver. , 2007, Current drug metabolism.

[27]  P. Zhao,et al.  Bioavailability Considerations in Evaluating Drug‐Drug Interactions Using the Population Pharmacokinetic Approach , 2011, Journal of clinical pharmacology.

[28]  L. Paalzow.  Torsten Teorell, the father of pharmacokinetics. , 1995, Upsala journal of medical sciences.

[29]  Saeed Rezaee,et al.  Incorporating In Vitro Information on Drug Metabolism Into Clinical Trial Simulations to Assess the Effect of CYP2D6 Polymorphism on Pharmacokinetics and Pharmacodynamics: Dextromethorphan as a Model Application , 2007, Journal of clinical pharmacology.

[30]  Shiew-Mei Huang,et al.  Evaluation of Exposure Change of Nonrenally Eliminated Drugs in Patients With Chronic Kidney Disease Using Physiologically Based Pharmacokinetic Modeling and Simulation , 2012, Journal of clinical pharmacology.

[31]  K Rowland-Yeo,et al.  Prediction of in vivo drug clearance from in vitro data. II: Potential inter-ethnic differences , 2006, Xenobiotica; the fate of foreign compounds in biological systems.

[32]  G. Tucker,et al.  A Semi-Mechanistic Model to Predict the Effects of Liver Cirrhosis on Drug Clearance , 2010, Clinical pharmacokinetics.

[33]  Amin Rostami-Hodjegan,et al.  Sex differences in the clearance of CYP3A4 substrates: exploring possible reasons for the substrate dependency and lack of consensus. , 2012, Current drug metabolism.

[34]  Kairui Feng,et al.  The Simcyp population-based ADME simulator. , 2009, Expert opinion on drug metabolism & toxicology.

[35]  Masoud Jamei,et al.  Physiologically based mechanistic modelling to predict complex drug-drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut - the effect of diltiazem on the time-course of exposure to triazolam. , 2010, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[36]  Amin Rostami-Hodjegan,et al.  Physiologically based pharmacokinetic (PBPK) modeling: it is here to stay! , 2012, Biopharmaceutics & drug disposition.

[37]  G. Tucker,et al.  Predicting drug clearance from recombinantly expressed CYPs: intersystem extrapolation factors , 2004, Xenobiotica; the fate of foreign compounds in biological systems.

[38]  L. Berezhkovskiy,et al.  Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. , 2004, Journal of pharmaceutical sciences.

[39]  Patrick Poulin,et al.  Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. , 2002, Journal of pharmaceutical sciences.

[40]  E. Burchard,et al.  Effects of Genetic Variation in the Novel Organic Cation Transporter, OCTN1, on the Renal Clearance of Gabapentin , 2008, Clinical pharmacology and therapeutics.

[41]  Tetsuya Terasaki,et al.  Simultaneous Absolute Protein Quantification of Transporters, Cytochromes P450, and UDP-Glucuronosyltransferases as a Novel Approach for the Characterization of Individual Human Liver: Comparison with mRNA Levels and Activities , 2012, Drug Metabolism and Disposition.

[42]  Amin Rostami-Hodjegan,et al.  Simulation and prediction of in vivo drug metabolism in human populations from in vitro data , 2007, Nature Reviews Drug Discovery.

[43]  S-M Huang,et al.  Regulatory Experience With Physiologically Based Pharmacokinetic Modeling for Pediatric Drug Trials , 2012, Clinical pharmacology and therapeutics.

[44]  Sebastian Polak,et al.  Prediction of concentration-time profile and its inter-individual variability following the dermal drug absorption. , 2012, Journal of pharmaceutical sciences.

[45]  Masoud Jamei,et al.  Cytochrome p450 turnover: regulation of synthesis and degradation, methods for determining rates, and implications for the prediction of drug interactions. , 2008, Current drug metabolism.

[46]  Sebastian Polak,et al.  Population-Based Mechanistic Prediction of Oral Drug Absorption , 2009, The AAPS Journal.

[47]  J. Shin,et al.  Genetic Variants of the Organic Cation Transporter 2 Influence the Disposition of Metformin , 2008, Clinical pharmacology and therapeutics.

[48]  A J Atkinson,et al.  Systems Clinical Pharmacology , 2010, Clinical pharmacology and therapeutics.

[49]  C T Dollery.  The Challenge of Complexity , 2010, Clinical pharmacology and therapeutics.

[50]  Y. Lai,et al.  Interindividual Variability in Hepatic Expression of the Multidrug Resistance-Associated Protein 2 (MRP2/ABCC2): Quantification by Liquid Chromatography/Tandem Mass Spectrometry , 2012, Drug Metabolism and Disposition.

[51]  D. Ashcroft,et al.  Trends in oral drug bioavailability following bariatric surgery: examining the variable extent of impact on exposure of different drug classes. , 2012, British journal of clinical pharmacology.

[52]  G Levy,et al.  Effect of plasma protein binding on renal clearance of drugs. , 1980, Journal of pharmaceutical sciences.

[53]  Otto D. Payton,et al.  Implications for Clinical Practice , 1983 .

[54]  P Vicini,et al.  Multiscale Modeling in Drug Discovery and Development: Future Opportunities and Present Challenges , 2010, Clinical pharmacology and therapeutics.

[55]  Katrin Marcus,et al.  Mass spectrometry‐based absolute quantification of microsomal cytochrome P450 2D6 in human liver , 2009, Proteomics.

[56]  H. Buchwald.  Metabolic/bariatric surgery , 2012 .

[57]  P. McNamara,et al.  Protein binding predictions in infants , 2008, AAPS PharmSci.

[58]  M. Jamei,et al.  A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of 'bottom-up' vs 'top-down' recognition of covariates. , 2009, Drug metabolism and pharmacokinetics.

[59]  P. Preusch.  Integrative and organ systems pharmacology: a new initiative from the National Institute of General Medical Sciences. , 2004, Molecular interventions.

[60]  M. Jamei,et al.  Modeling and predicting drug pharmacokinetics in patients with renal impairment , 2011, Expert review of clinical pharmacology.

[61]  Mary F Paine,et al.  THE HUMAN INTESTINAL CYTOCHROME P450 “PIE” , 2006, Drug Metabolism and Disposition.

[62]  F Kesisoglou,et al.  Prediction of Phase I single-dose pharmacokinetics using recombinant cytochromes P450 and physiologically based modelling , 2009, Xenobiotica; the fate of foreign compounds in biological systems.

[63]  A. Rostami-Hodjegan,et al.  Determination of a quantitative relationship between hepatic CYP3A5*1/*3 and CYP3A4 expression for use in the prediction of metabolic clearance in virtual populations , 2010, Biopharmaceutics & drug disposition.

[64]  Kairui Feng,et al.  The Simcyp® Population-based ADME Simulator , 2009 .

[65]  François Bouzom,et al.  Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs? , 2012, Biopharmaceutics & drug disposition.

[66]  Amin Rostami-Hodjegan,et al.  The use of mechanistic DM-PK-PD modelling to assess the power of pharmacogenetic studies -CYP2C9 and warfarin as an example. , 2007, British journal of clinical pharmacology.

[67]  H. Buchwald,et al.  Metabolic/Bariatric Surgery Worldwide 2008 , 2009, Obesity surgery.

[68]  M. Jamei,et al.  A mechanistic pharmacokinetic model to assess modified oral drug bioavailability post bariatric surgery in morbidly obese patients: interplay between CYP3A gut wall metabolism, permeability and dissolution , 2012, The Journal of pharmacy and pharmacology.

[69]  R. Obach,et al.  Physicochemical determinants of human renal clearance. , 2009, Journal of medicinal chemistry.

[70]  Malcolm Rowland,et al.  Physiologically-based pharmacokinetics in drug development and regulatory science. , 2011, Annual review of pharmacology and toxicology.

[71]  Amin Rostami-Hodjegan,et al.  Assessing the efficiency of mixed effects modelling in quantifying metabolism based drug–drug interactions: using in vitro data as an aid to assess study power , 2009, Pharmaceutical statistics.

[72]  Guidance for Industry Drug Interaction Studies — Study Design , Data Analysis , Implications for Dosing , and Labeling Recommendations , 2022 .

[73]  Malcolm Rowland,et al.  Mechanistic Approaches to Volume of Distribution Predictions: Understanding the Processes , 2007, Pharmaceutical Research.

[74]  K. Stormark,et al.  Significantly Altered Systemic Exposure to Atorvastatin Acid Following Gastric Bypass Surgery in Morbidly Obese Patients , 2009, Clinical pharmacology and therapeutics.

[75]  A. Rostami-Hodjegan,et al.  'In silico' simulations to assess the 'in vivo' consequences of 'in vitro' metabolic drug-drug interactions. , 2004, Drug discovery today. Technologies.

[76]  R. Labadie,et al.  Influence of mild and moderate hepatic impairment on axitinib pharmacokinetics , 2010, Investigational New Drugs.

[77]  Amin Rostami-Hodjegan,et al.  Anatomical, Physiological and Metabolic Changes with Gestational Age during Normal Pregnancy , 2012, Clinical Pharmacokinetics.

[78]  K. Pang.  Modeling of intestinal drug absorption: roles of transporters and metabolic enzymes (for the Gillette Review Series). , 2003, Drug metabolism and disposition: the biological fate of chemicals.

[79]  J. Greef,et al.  Rescuing drug discovery: in vivo systems pathology and systems pharmacology , 2005, Nature Reviews Drug Discovery.