Abstract
Background
Prediction of pharmacokinetics in humans is essential for translating preclinical data to humans and planning safe and efficient clinical studies. The performance of various methods in extrapolation of preclinical pharmacokinetic data to humans is usually benchmarked by the fraction of predictions falling within a predefined interval that is centred on the value observed clinically. Recently, such an approach was used to compare physiologically based pharmacokinetic (PBPK) modelling and allometry in predicting the pharmacokinetics of a set of compounds in humans. Here, we present an analysis of the same dataset, focusing on predictions falling outside such a relatively narrow and centrally located interval. These are the main risk determinants in extrapolation of preclinical pharmacokinetic datato humans and should therefore be thoroughly understood in a risk mitigation approach to the design of early-phase human studies.
Methods
Values that had been previously predicted by allometry and by PBPK modelling in terms of the apparent total clearance after oral administration, apparent volume of distribution, area under the plasma concentration-time curve, maximum plasma drug concentration, time to reach the maximum plasma concentration and terminal elimination half-life in humans were used to generate a log-transformed dataset of predicted/observed ratios. The probabilities of mispredicting the values of these pharmacokinetic parameters using PBPK modelling and allometry were estimated by a bootstrap procedure on this set of ratios.
Results
Our results, albeit from a limited dataset, indicated that although PBPK modelling yielded higher fractions of satisfactory predictions than allometry, both methodologies were associated with a significant and occasionally high probability of obtaining mispredictions of pharmacokinetic parameters by factors of >2, >3 and >10. In line with recent proposals to extend the goals of early-phase human studies beyond safety and tolerability, and considering the need to mitigate risks in studies dealing with novel and highly potent drug candidates, we discuss these results in a pharmacological context.
Conclusions
Concise recommendations are given regarding the use of allometric and PBPK extrapolation methodologies in the translation process. The results presented here should alert clinical investigators to the limitations inherent in all approaches to prediction of human pharmacokinetics from preclinical data. We propose an adaptive approach to the design of early-phase clinical studies, particularly when dealing with compounds that are characterized by novel and only partially understood pharmacological profiles.
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References
Cohen A. Should we tolerate tolerability as an objective in early drug development? Br J Clin Pharmacol 2007; 64 (3): 249–52
Cohen A. Pharmacokinetic and pharmacodynamic data to be derived from early-phase drug development: designing informative human pharmacology studies. Clin Pharmacokinet 2008; 47: 373–81
Lowe PJ, Hijazi Y, Luttringer O, et al. On the anticipation of the human dose in first-in-man trials from preclinical and prior clinical information in early drug development. Xenobiotica 2007; 37 (10): 1331–54
Association of the British Pharmaceutical Industry. Guidelines for phase 1 clinical trials: 2007 edition. London: Association of the British Pharmaceutical Industry, 2007 [online]. Available from URL: http://www.abpi.org.uk/publications/pdfs/phase1_guidelines.pdf [Accessed 2010 Jun 24]
Khor SP, McCarthy K, DuPont M, et al. Pharmacokinetics, pharmacodynamics, allometry, and dose selection of rPSGL-Ig for phase I trial. J Pharmacol Exp Ther 2000; 293 (2): 618–24
de Giorgio-Miller A, Bungay P, Tutt M, et al. The translational pharmacology of a novel, potent, and selective non-steroidal progesterone receptor antagonist, 2-[4-(4-cyano-phenoxy)-3,5-dicyclopropyl-1H-pyrazol-1-yl]-N-methylacetamide (PF-02367982). J Pharmacol Exp Ther 2008; 327 (1): 78–87
Clarke J, Leach W, Pippig S, et al. Evaluation of a surrogate antibody for preclinical safety testing of an anti-CD11a monoclonal antibody. Regul Toxicol Pharmacol 2004; 40 (3): 219–26
Kenter MJ, Cohen AF. Establishing risk of human experimentation with drugs: lessons from TGN1412. Lancet 2006; 368 (9544): 1387–91
Brightman FA, Leahy DE, Searle GE, et al. Application of a generic physiologically based pharmacokinetic model to the estimation of xenobiotic levels in human plasma. Drug Metab Dispos 2006; 34 (1): 94–101
Huang C, Zheng M, Yang Z, et al. Projection of exposure and efficacious dose prior to first-in-human studies: how successful have we been? Pharm Res 2008; 25 (4): 713–26
Ito K, Houston JB. Prediction of human drug clearance from in vitro and preclinical data using physiologically based and empirical approaches. Pharm Res 2005; 22 (1): 103–12
Jolivette LJ, Ward KW. Extrapolation of human pharmacokinetic parameters from rat, dog, and monkey data: molecular properties associated with extrapolative success or failure. J Pharm Sci 2005; 94 (7): 1467–83
Lave T, Coassolo P, Reigner B. Prediction of hepatic metabolic clearance based on interspecies allometric scaling techniques and in vitro-in vivo correlations. Clin Pharmacokinet 1999; 36 (3): 211–31
Lombardo F, Obach RS, DiCapua FM, et al. A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human. J Med Chem 2006; 49 (7): 2262–7
Mahmood I, Green MD, Fisher JE. Selection of the first-time dose in humans: comparison of different approaches based on interspecies scaling of clearance. J Clin Pharmacol 2003; 43 (7): 692–7
Mahmood I. Interspecies scaling of protein drugs: prediction of clearance from animals to humans. J Pharm Sci 2004; 93 (1): 177–85
Mahmood I. Interspecies scaling of biliary excreted drugs: a comparison of several methods. J Pharm Sci 2005; 94 (4): 883–92
Mahmood I. Prediction of drug clearance in children from adults: a comparison of several allometric methods. Br J Clin Pharmacol 2006; 61 (5): 545–57
Mahmood I. Application of allometric principles for the prediction of pharmacokinetics in human and veterinary drug development. Adv Drug Deliv Rev 2007; 59 (11): 1177–92
Shiran MR, Proctor NJ, Howgate EM, et al. Prediction of metabolic drug clearance in humans: in vitro-in vivo extrapolation vs allometric scaling. Xenobiotica 2006; 36 (7): 567–80
Sinha VK, De Buck SS, Fenu LA, et al. Predicting oral clearance in humans: how close can we get with allometry? Clin Pharmacokinet 2008; 47 (1): 35–45
Tang H, Mayersohn M. Accuracy of allometrically predicted pharmacokinetic parameters in humans: role of species selection. Drug Metab Dispos 2005; 33 (9): 1288–93
Tang H, Mayersohn M. A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry. J Pharm Sci 2006; 95 (8): 1783–99
Tang H, Hussain A, Leal M, et al. Interspecies prediction of human drug clearance based on scaling data from one or two animal species. Drug Metab Dispos 2007; 35 (10): 1886–93
De Buck SS, Sinha VK, Fenu LA, et al. Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs. Drug Metab Dispos 2007; 35 (10): 1766–80
Leahy DE. Integrating in vitro ADMET data through generic physiologically based pharmacokinetic models. Expert Opin Drug Metab Toxicol 2006; 2 (4): 619–28
Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies: II. Generic physiologically based pharmacokinetic models of drug disposition. J Pharm Sci 2002; 91 (5): 1358–70
Lave T, Schmitt-Hoffmann AH, Coassolo P, et al. A new extrapolation method from animals to man: application to a metabolized compound, mofarotene. Life Sci 1995; 56 (26): L473–8
Lave T, Dupin S, Schmitt M, et al. Interspecies scaling of tolcapone, a new inhibitor of catechol-O-methyltransferase (COMT): use of in vitro data from hepatocytes to predict metabolic clearance in animals and humans. Xenobiotica 1996; 26 (8): 839–51
Lave T, Coassolo P, Ubeaud G, et al. Interspecies scaling of bosentan, a new endothelin receptor antagonist and integration of in vitro data into allometric scaling. Pharm Res 1996; 13 (1): 97–101
Mahmood I, Balian JD. Interspecies scaling: predicting clearance of drugs in humans. Three different approaches. Xenobiotica 1996; 26 (9): 887–95
Adolph EF. Quantitative relations in the physiological constitutions of mammals. Science 1949; 109 (2841): 579–85
Agutter PS, Wheatley DN. Metabolic scaling: consensus or controversy? Theor Biol Med Model 2004; 1: 13–23
Boxenbaum H. Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J Pharmacokinet Biopharm 1982; 10 (2): 201–27
Boxenbaum H. Interspecies pharmacokinetic scaling and the evolutionarycomparative paradigm. Drug Metab Rev 1984; 15 (5–6): 1071–121
Demetrius L. The origin of allometric scaling laws in biology. J Theor Biol 2006; 243 (4): 455–67
Mordenti J. Man versus beast: pharmacokinetic scaling in mammals. J Pharm Sci 1986; 75 (11): 1028–40
Pilbeam D, Gould SJ. Size and scaling in human evolution. Science 1974; 186 (4167): 892–901
West GB, Brown JH, Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science 1997; 276 (5309): 122–6
West GB, Brown JH, Enquist BJ. The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science 1999; 284 (5420): 1677–9
West GB, Woodruff WH, Brown JH. Allometric scaling of metabolic rate from molecules and mitochondria to cells and mammals. Proc Natl Acad Sci USA 2002; 99 Suppl. 1: 2473–8
West GB, Brown JH. The origin of allometric scaling laws in biology from genomes to ecosystems: towards a quantitative unifying theory of biological structure and organization. J Exp Biol 2005; 208 (9): 1575–92
Bonate PL, Howard D. Prospective allometric scaling: does the emperor have clothes? J Clin Pharmacol 2000; 40 (4): 335–40
Tang H, Mayersohn M. A novel model for prediction of human drug clearance by allometric scaling. Drug Metab Dispos 2005; 33 (9): 1297–303
Tang H, Mayersohn M. On the observed large interspecies overprediction of human clearance (“vertical allometry”) of UCN-01: further support for a proposed model based on plasma protein binding. J Clin Pharmacol 2006; 46 (4): 398–400
Center for Drug Evaluation and Research, US FDA. Guidance for industry: estimating the maximum safe starting dose in initial clinical trials for therapeutics in adult healthy volunteers. Rockville (MD): FDA, 2005 Jul [online]. Available from URL: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm078932.pdf [Accessed 2010 Jun 24]
Brightman FA, Leahy DE, Searle GE, et al. Application of a generic physiologically based pharmacokinetic model to the estimation of xenobiotic levels in rat plasma. Drug Metab Dispos 2006; 34 (1): 84–93
Bernareggi A, Rowland M. Physiologic modeling of cyclosporin kinetics in rat and man. J Pharmacokinet Biopharm 1991; 19 (1): 21–50
Lupfert C, Reichel A. Development and application of physiologically based pharmacokinetic-modeling tools to support drug discovery. Chem Biodivers 2005; 2 (11): 1462–86
Nestorov I. Whole body pharmacokinetic models. Clin Pharmacokinet 2003; 42 (10): 883–908
Jones HM, Parrott N, Jorga K, et al. A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet 2006; 45 (5): 511–42
Parrott N, Paquereau N, Coassolo P, et al. An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery. J Pharm Sci 2005; 94 (10): 2327–43
Parrott N, Jones H, Paquereau N, et al. Application of full physiological models for pharmaceutical drug candidate selection and extrapolation of pharmacokinetics to man. Basic Clin Pharmacol Toxicol 2005; 96 (3): 193–9
Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies: 1. Mechanism-based prediction of volume of distribution. J Pharm Sci 2002; 91 (1): 129–56
Poulin P, Theil FP. A priori prediction of tissue: plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J Pharm Sci 2000; 89 (1): 16–35
Poulin P, Schoenlein K, Theil FP. Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs. J Pharm Sci 2001; 90 (4): 436–47
Boxenbaum H, Ronfeld R. Interspecies pharmacokinetic scaling and the Dedrick plots. Am J Physiol 1983; 245 (6): R768–75
Dedrick R, Bischoff KB, Zaharko DS. Interspecies correlation of plasma concentration history of methotrexate (NSC-740). Cancer Chemother Rep 1970; 54 (2): 95–101
Gabrielsson J, Weiner DL. Pharmacokinetic and pharmacodynamic data analysis: concepts and applications. 4th ed. Stockholm: Swedish Pharmaceutical Press, 2007
Efron B, Tibshirani R. An introduction to the bootstrap. New York: Chapman & Hall/CRC, 1993
Dollery CT. Clinical pharmacology in the molecular era. Clin Pharmacol Ther 2008; 83 (2): 220–5
Mignot A. High-risk molecules or insufficient scientific data? Clin Pharmacol Ther 2007; 83 (2): 365–7
Committee for Medicinal Products for Human Use, European Medicines Agency. Guideline on strategies to identify and mitigate risks for firstin-human clinical trials with investigational medicinal products [document reference EMEA/CHMP/SWP/28367/07]. London: European Medicines Agency, 2007 Jul 19 [online]. Available from URL: http://www.ema.europa.eu/pdfs/human/swp/2836707enfin.pdf [Accessed 2010 Jun 24]
Caldwell GW, Masucci JA, Yan Z, et al. Allometric scaling of pharmacokinetic parameters in drug discovery: can human CL, Vss and t1/2 be predicted from in-vivo rat data? Eur J Drug Metab Pharmacokinet 2004; 29 (2): 133–43
Evans CA, Jolivette LJ, Nagilla R, et al. Extrapolation of preclinical pharmacokinetics and molecular feature analysis of “discovery-like” molecules to predict human pharmacokinetics. Drug Metab Dispos 2006; 34 (7): 1255–65
Gomase VS, Tagore S. Species scaling and extrapolation. Curr Drug Metab 2008; 9 (3): 193–8
Mahmood I, Balian JD. Interspecies scaling: a comparative study for the prediction of clearance and volume using two or more than two species. Life Sci 1996; 59 (7): 579–85
Mahmood I. Interspecies scaling: predicting volumes, mean residence time and elimination half-life. Some suggestions. J Pharm Pharmacol 1998; 50 (5): 493–9
Ward KW, Smith BR. A comprehensive quantitative and qualitative evaluation of extrapolation of intravenous pharmacokinetic parameters from rat, dog, and monkey to humans: II. Volume of distribution and mean residence time. Drug Metab Dispos 2004; 32 (6): 612–9
Goodyear M. Learning from the TGN1412 trial. BMJ 2006; 332 (7543): 677–8
Expert Scientific Group on Phase One Clinical Trials. Final report. London: the Stationery Office, 2006 Nov 30 [online]. Available from URL: http://www.trialformsupport.com/business/doc/Final_Report_of_the_Expert_Scientific_Group_(ESG).pdf [Accessed 2010 Jun 24]
Aarons L, Karlsson MO, Mentre F, et al. Role of modelling and simulation in phase I drug development. Eur J Pharm Sci 2001; 13 (2): 115–22
Derendorf H, Lesko LJ, Chaikin P, et al. Pharmacokinetic/pharmacodynamic modeling in drug research and development. J Clin Pharmacol 2000; 40 (12 Pt 2): 1399–418
Bhogal N, Combes R. TGN1412: time to change the paradigm for the testing of new pharmaceuticals. Altern Lab Anim 2006; 34 (2): 225–39
Farzaneh L, Kasahara N, Farzaneh F. The strange case of TGN1412. Cancer Immunol Immunother 2007; 56 (2): 129–34
Hansen S, Leslie RG. TGN1412: scrutinizing preclinical trials of antibodybased medicines. Nature 2006; 441 (7091): 282
Acknowledgements
The work was conducted during Dr Teitelbaum’s sabbatical leave from the Israel Institute for Biological Research (Ness Ziona, Israel). No source of funding was used to assist in the preparation of this study. The authors have no conflicts of interest that are directly relevant to the content of this study.
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Teitelbaum, Z., Lave, T., Freijer, J. et al. Risk Assessment in Extrapolation of Pharmacokinetics from Preclinical Data to Humans. Clin Pharmacokinet 49, 619–632 (2010). https://doi.org/10.2165/11533760-000000000-00000
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DOI: https://doi.org/10.2165/11533760-000000000-00000