Simulation and prediction of in vivo drug metabolism in human populations from in vitro data

The perceived failure of new drug development has been blamed on deficiencies in in vivo studies of drug efficacy and safety. Prior simulation of the potential exposure of different individuals to a given dose might help to improve the design of such studies. This should also help researchers to focus on the characteristics of individuals who present with extreme reactions to therapy. An effort to build virtual populations using extensive demographic, physiological, genomic and in vitro biochemical data to simulate and predict drug disposition from routinely collected in vitro data is outlined.

[1]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[2]  R. Juchems,et al.  [Hemodynamics in posture changes]. , 1970, Zeitschrift fur Kreislaufforschung.

[3]  C Maxwell,et al.  Instant experience in clinical trials: a novel aid to teaching by simulation. , 1971, The Journal of clinical pharmacology and new drugs.

[4]  G R Wilkinson,et al.  Commentary: a physiological approach to hepatic drug clearance. , 1975, Clinical pharmacology and therapeutics.

[5]  Grant R. Wilkinson,et al.  A physiological approach to hepatic drug clearance , 1975 .

[6]  B T Marsh.  The planning of a clinical trials workshop. , 1975, British journal of clinical pharmacology.

[7]  T L Woodings,et al.  Clinical trial experience by simulation: a workshop report. , 1978, British medical journal.

[8]  G. Tucker,et al.  Polymorphic drug oxidation: pharmacokinetic basis and comparison of experimental indices. , 1986, British journal of clinical pharmacology.

[9]  J M Bland,et al.  Computer simulation of a clinical trial as an aid to teaching the concept of statistical significance. , 1986, Statistics in medicine.

[10]  D. Cortese,et al.  Severe hypoxemia and liver disease. , 1989, The American review of respiratory disease.

[11]  R. Kato,et al.  Polymorphism in hydroxylation of mephenytoin and hexobarbital stereoisomers in relation to hepatic P‐450 human‐2 , 1990, Clinical pharmacology and therapeutics.

[12]  G T Tucker,et al.  Pharmacokinetic-pharmacogenetic modelling in the detection of polymorphisms in xenobiotic metabolism. , 1990, The Annals of occupational hygiene.

[13]  C. Mathias.  Effect of food intake on cardiovascular control in patients with impaired autonomic function , 1990, Journal of Neuroscience Methods.

[14]  G. Tucker,et al.  Backtracking booze with Bayes--the retrospective interpretation of blood alcohol data. , 1991, British journal of clinical pharmacology.

[15]  F. Trevisani,et al.  The hemodynamic status of preascitic cirrhosis: An evaluation under steady‐state conditions and after postural change , 1992, Hepatology.

[16]  Tucker Gt,et al.  The rational selection of drug interaction studies: implications of recent advances in drug metabolism. , 1992 .

[17]  R. Kato,et al.  Cytochrome P450 mediated metabolism of diazepam in human and rat: involvement of human CYP2C in N-demethylation in the substrate concentration-dependent manner. , 1993, Pharmacogenetics.

[18]  S. Ohmori,et al.  Characterization of human liver microsomal cytochrome P450 involved in the reductive metabolism of zonisamide. , 1993, Molecular pharmacology.

[19]  M. Chiba,et al.  Metabolism: Scaling-up from In Vitro to Organ and Whole Body , 1994 .

[20]  G. Tucker,et al.  Sensitivity of indirect metrics for assessing "rate" in bioequivalence studies--moving the "goalposts" or changing the "game". , 1994, Journal of pharmaceutical sciences.

[21]  G. Tucker,et al.  Caffeine urinary metabolite ratios as markers of enzyme activity: a theoretical assessment. , 1996, Pharmacogenetics.

[22]  T Sakaki,et al.  Multiple forms of human P450 expressed in Saccharomyces cerevisiae. Systematic characterization and comparison with those of the rat. , 1996, Biochemical pharmacology.

[23]  L. Blendis,et al.  The effect of posture on central blood volume in patients with preascitic cirrhosis on a sodium‐restricted diet , 1996, Hepatology.

[24]  J B Houston,et al.  Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. , 1997, Drug metabolism reviews.

[25]  S. Imaoka,et al.  A comparison of hepatic cytochrome P450 protein expression between infancy and postinfancy. , 1997, Life sciences.

[26]  S. Yamaguchi,et al.  No ethnic difference between Caucasian and Japanese hepatic samples in the expression frequency of CYP3A5 and CYP3A7 proteins. , 1999, Biochemical pharmacology.

[27]  T Lavé,et al.  Prediction of Hepatic Metabolic Clearance Based on Interspecies Allometric Scaling Techniques and In Vitro-In Vivo Correlations , 1999, Clinical pharmacokinetics.

[28]  R. Weaver,et al.  Prediction of interindividual variation in drug plasma levels in vivo from individual enzyme kinetic data and physiologically based pharmacokinetic modeling. , 2000, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[29]  J. Lin,et al.  Sense and nonsense in the prediction of drug-drug interactions. , 2000, Current drug metabolism.

[30]  N H Holford,et al.  Simulation of clinical trials. , 2000, Annual review of pharmacology and toxicology.

[31]  D. Lewis,et al.  Structural characteristics of human P450s involved in drug metabolism: QSARs and lipophilicity profiles. , 2000, Toxicology.

[32]  M. Wilson,et al.  Regulation of intestinal blood flow. , 2000, The Journal of surgical research.

[33]  P. Jeffrey,et al.  Utility of metabolic stability screening: comparison of in vitro and in vivo clearance , 2001, Xenobiotica; the fate of foreign compounds in biological systems.

[34]  H. Zhou,et al.  Interindividual variations in levels and activities of cytochrome P-450 in liver microsomes of Chinese subjects. , 2001, Acta pharmacologica Sinica.

[35]  S. Ekins,et al.  Application of in silico approaches to predicting drug--drug interactions. , 2001, Journal of pharmacological and toxicological methods.

[36]  T Lavé,et al.  Prediction of Hepatic Metabolic Clearance , 2001, Clinical pharmacokinetics.

[37]  A. J. MacDonald,et al.  Analysis of solvent central nervous system toxicity and ethanol interactions using a human population physiologically based kinetic and dynamic model. , 2002, Regulatory toxicology and pharmacology : RTP.

[38]  H. Jick,et al.  Statins and the risk of idiopathic venous thromboembolism. , 2002, British journal of clinical pharmacology.

[39]  E Niclas Jonsson,et al.  More efficient clinical trials through use of scientific model‐based statistical tests , 2002, Clinical pharmacology and therapeutics.

[40]  Malcolm Rowland,et al.  Incorporating measures of variability and uncertainty into the prediction of in vivo hepatic clearance from in vitro data. , 2002, Drug metabolism and disposition: the biological fate of chemicals.

[41]  T. Bjornsson,et al.  A Review and Assessment of Potential Sources of Ethnic Differences in Drug Responsiveness , 2003, Journal of clinical pharmacology.

[42]  Gunnar Johanson,et al.  The Bayesian population approach to physiological toxicokinetic-toxicodynamic models--an example using the MCSim software. , 2003, Toxicology letters.

[43]  Jakob Ribbing,et al.  Power, Selection Bias and Predictive Performance of the Population Pharmacokinetic Covariate Model , 2004, Journal of Pharmacokinetics and Pharmacodynamics.

[44]  Tommy B Andersson,et al.  An evaluation of the in vitro metabolism data for predicting the clearance and drug-drug interaction potential of CYP2C9 substrates. , 2004, Drug Metabolism And Disposition.

[45]  Kiyomi Ito,et al.  Database analyses for the prediction of in vivo drug-drug interactions from in vitro data. , 2004, British journal of clinical pharmacology.

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

[47]  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.

[48]  Kiyomi Ito,et al.  DRUG-DRUG INTERACTIONS : CYP 2 D 6 PARADIGM , 2005 .

[49]  M. Jamei,et al.  Kinetic values for mechanism-based enzyme inhibition: assessing the bias introduced by the conventional experimental protocol. , 2005, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[50]  Amin Rostami-Hodjegan,et al.  Changes in liver volume from birth to adulthood: A meta‐analysis , 2005, Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society.

[51]  Draft Guidance,et al.  Guidance for Industry Drug Interaction Studies — Study Design , Data Analysis , and Implications for Dosing and Labeling DRAFT GUIDANCE , 2006 .

[52]  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.

[53]  Amir Heydari,et al.  Implications of mechanism-based inhibition of CYP2D6 for the pharmacokinetics and toxicity of MDMA , 2006, Journal of psychopharmacology.

[54]  K Rowland-Yeo,et al.  Prediction of metabolic drug clearance in humans: In vitro–in vivo extrapolation vs allometric scaling , 2006, Xenobiotica; the fate of foreign compounds in biological systems.

[55]  Kiyomi Ito,et al.  Determination of a Human Hepatic Microsomal Scaling Factor for Predicting in Vivo Drug Clearance , 2006, Pharmaceutical Research.

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

[57]  G. Tucker,et al.  Prediction of in vivo drug clearance from in vitro data. I: Impact of inter-individual variability , 2006, Xenobiotica; the fate of foreign compounds in biological systems.

[58]  J. Dearden.  In silico prediction of aqueous solubility , 2006, Expert opinion on drug discovery.

[59]  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.

[60]  N. Thomas.  A Model Patient , 1861, BMJ : British Medical Journal.

[61]  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.

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