References
- Arundel P. (1997). A multi-compartment model generally applicable to physiologically-based pharmacokinetics. 3rd IFAC Symposium on modelling and control in biomedical systems; 1997 Mar 23–26; Warwick, UK.
- Berezhkovskiy LM. (2004). Determination of volume of distribution at steady state with complete consideration of the kinetics of protein and tissue binding in linear pharmacokinetics. J Pharm Sci 93:364–74.
- Berry LM, Li C, Zhao Z. (2011). Species differences in distribution and prediction of human V(ss) from preclinical data. Drug Metab Dispos 39:2103–16.
- Bjorkman S. (2002). Prediction of the volume of distribution of a drug: which tissue-plasma partition coefficients are needed? J Pharm Pharmacol 54:1237.
- Boxenbaum H. (1982). Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J Pharmacokinet Biopharm 10:201–27.
- Caldwell GW, Masucci JA, Yan Z, Hageman W. (2004). 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 29:133–43.
- Chan R, De Bruyn T, Wright M, Broccatelli F. (2018). Comparing mechanistic and preclinical predictions of volume of distribution on a large set of drugs. J Pharm Res 35:87.
- Cook D, Brown D, Alexander R, et al. (2014). Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 13:419–31.
- Dolgos H, Trusheim M, Gross D, et al. (2016). Translational medicine guide transforms drug development processes: the recent Merck experience. Drug Discov Today 21:517–26.
- Hosea NA, Collard WT, Cole S, et al. (2009). Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction approaches. J Clin Pharmacol 49:513–33.
- Jones RD, Jones HM, Rowland M, et al. (2011). PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: Comparative assessment of prediction methods of human volume of distribution. J Pharm Sci 100:4074–89.
- Leeson PD, Springthorpe B. (2007). The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6:881–90.
- Li R, Ghosh A, Maurer TS, et al. (2014). Physiologically based pharmacokinetic prediction of telmisartan in human. Drug Metab Dispos 42:1646–55.
- Lombardo F, Waters NJ, Argikar UA, et al. (2013). Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: volume of distribution at steady state. J Clin Pharmacol 53:167–77.
- Miller NA, Reddy MB, Heikkinen AT, Lukacova Vand Parrott N. (2019). Physiologically based pharmacokinetic modelling for first-in-human predictions: an updated model building strategy illustrated with challenging industry case studies. Clin Pharmacokinet 58:727–46.
- Nigade PB, Gundu J, Pai KS, et al. (2019). Prediction of volume of distribution in preclinical species and humans: application of simplified physiologically based algorithms. Xenobiotica 49:528–39.
- Obach RS, Baxter JG, Liston TE, et al. (1997). The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther 283:46–46.
- Obach RS, Lombardo F, Waters NJ. (2008). Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab Dispos 36:1385–405.
- Øie S, Tozer TN. (1979). Effect of altered plasma protein binding on apparent volume of distribution. J Pharm Sci 68:1203–5.
- Peters SA. (2012). Physiologically‐based pharmacokinetic (PBPK) modelling and simulations: principles, methods and applications in the pharmaceutical industry. Hoboken, NJ: John Wiley & Sons.
- Poulin P, Collet SH, Atrux-Tallau N, et al. (2019). Application of the tissue composition-based model to minipig for predicting the volume of distribution at steady state and dermis-to-plasma partition coefficients of drugs used in the physiologically based pharmacokinetics model in dermatology. J Pharm Sci 108:603–19.
- Poulin P, Haddad S. (2012). Advancing prediction of tissue distribution and volume of distribution of highly lipophilic compounds from a simplified tissue-composition-based model as a mechanistic animal alternative method. J Pharm Sci 101:2250–61.
- Poulin P, Theil FP. (2002). Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J Pharm Sci 91:129–56.
- R Development Core Team. (2017). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Development Core Team.
- Rodgers T, Jones HM, Rowland M. (2012). Tissue lipids and drug distribution: dog versus rat. J Pharm Sci 101:4615–25.
- Rodgers T, Rowland M. (2007). Mechanistic approaches to volume of distribution predictions: understanding the processes. Pharm Res 24:918–33.
- Smith DA, Di L, Kerns EH. (2010). The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nat Rev Drug Disc 9:929–39.
- Sohlenius-Sternbeck A-K, Jones C, Ferguson D, et al. (2012). Practical use of the regression offset approach for the prediction of in vivo intrinsic clearance from hepatocytes. Xenobiotica 42:841–53.
- Tang H, Hussain A, Leal M, et al. (2007). Interspecies prediction of human drug clearance based on scaling data from one or two animal species. Drug Metab Dispos 35:1886–93.
- Wang L, Prasad B, Salphati L, et al. (2015). Interspecies variability in expression of hepatobiliary transporters across human, dog, monkey, and rat as determined by quantitative proteomics. Drug Metab Dispos 43:367–74.
- Waters NJ, Lombardo F. (2010). Use of the Øie-Tozer model in understanding mechanisms and determinants of drug distribution. Drug Metab Dispos 38:1159–65.
- Ye M, Nagar S, Korzekwa K. (2016). A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding. Biopharm Drug Dispos 37:123–41.