Predictive performance of next generation human physiologically based kinetic (PBK) models based on in vitro and in silico input data
Article Sidebar
Main Article Content
Abstract
The goal of the present study was to assess the predictive performance of a generic human physiologically based kinetic (PBK) model based on in vitro and in silico input data and the effect of using different input approaches for chemical parameterization on those predictions. For this purpose, a dataset was created of 38,772 Cmax predictions for 44 compounds by applying different combinations of in vitro and in silico approaches for chemical parameterization, and these predicted Cmax values were compared to reported in vivo data. Best results were achieved when the hepatic clearance was parameterized based on in vitro (i.e., hepatocytes or liver S9) measured intrinsic clearance values, the method of Rodgers and Rowland for calculating tissue:plasma partition coefficients, and the method of Lobell and Sivarajah for calculating the fraction unbound in plasma. With these parameters, the median Cmax values of 34 out of the 44 compounds were predicted within 5-fold of the observed Cmax, and the Cmax values of 19 compounds were predicted within 2-fold. The median Cmax values of 10 compounds were more than 5-fold overestimated. Underestimations (> 5-fold) did not occur. A comparison of the current generic PBK model structure with chemical-specific PBK models available in literature was made to identify possible kinetic processes not included in the generic PBK model that might explain the overestimations. Overall, the results provide crucial insights into the predictive performance of PBK models based on in vitro and in silico input and the influence of different input approaches on the model predictions.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is appropriately cited (CC-BY). Copyright on any article in ALTEX is retained by the author(s).
Adiwidjaja, J., Boddy, A. V and McLachlan, A. J. (2020). Physiologically-based pharmacokinetic predictions of the effect of curcumin on metabolism of imatinib and bosutinib: In vitro and in vivo disconnect. Pharm Res 37, 128. doi:10.1007/s11095-020-02834-8
Barter, Z. E., Bayliss, M. K., Beaune, P. H. et al. (2007). 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. Curr Drug Metab 8, 33-45. doi:10.2174/138920007779315053
Barter, Z. E., Tucker, G. T. and Rowland-Yeo, K. (2013). Differences in cytochrome P450-mediated pharmacokinetics between Chinese and Caucasian populations predicted by mechanistic physiologically based pharmacokinetic modelling. Clin Pharmacokinet 52, 1085-1100. doi:10.1007/s40262-013-0089-y
Berezhkovskiy, L. M. (2004). Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J Pharm Sci 93, 1628-1640. doi:10.1002/jps.20073
Bi, Y., Lin, J., Mathialagan, S. et al. (2018). Role of hepatic organic anion transporter 2 in the pharmacokinetics of R- and S-warfarin: In vitro studies and mechanistic evaluation. Mol Pharm 15, 1284-1295. doi:10.1021/acs.molpharmaceut.7b01108
Blaauboer, B. J. (2010). Biokinetic modeling and in vitro-in vivo extrapolations. J Toxicol Environ Health B Crit Rev 13, 242-252. doi:10.1080/10937404.2010.483940
Boonpawa, R., Spenkelink, A., Punt, A. et al. (2017). In vitro-in silico -based analysis of the dose-dependent in vivo oestrogenicity of the soy phytoestrogen genistein in humans. Br J Pharmacol 174, 2739-2757. doi:10.1111/bph.13900
Bowman, C. M., Ma, F., Mao, J. et al. (2021). Examination of physiologically-based pharmacokinetic models of rosuvastatin. CPT Pharmacometrics Syst Pharmacol 10, 5-17. doi:10.1002/psp4.12571
Cao, Y. and Jusko, W. J. (2012). Applications of minimal physiologically-based pharmacokinetic models. J Pharmacokinet Pharmacodyn 39, 711-723. doi:10.1007/s10928-012-9280-2
Chan, J. (2019). Bottom-up physiologically-based biokinetic modelling as an alternative to animal testing. ALTEX 36, 597-612. doi:10.14573/altex.1812051
Chow, E. C. Y., Talattof, A., Tsakalozou, E. et al. (2016). Using physiologically based pharmacokinetic (PBPK) modeling to evaluate the impact of pharmaceutical excipients on oral drug absorption: Sensitivity analyses. AAPS J 18, 1500-1511. doi:10.1208/s12248-016-9964-4
Docci, L., Umehara, K., Krähenbühl, S. et al. (2020). Construction and verification of physiologically based pharmacokinetic models for four drugs majorly cleared by glucuronidation: Lorazepam, oxazepam, naloxone, and zidovudine. AAPS J 22, 128. doi:10.1208/s12248-020-00513-5
Emami Riedmaier, A., Burt, H., Abduljalil, K. et al. (2016). More power to OATP1B1: An evaluation of sample size in pharmacogenetic studies using a rosuvastatin PBPK model for intestinal, hepatic, and renal transporter-mediated clearances. J Clin Pharmacol 56, Suppl 7, S132-S142. doi:10.1002/jcph.669
Fabian, E., Gomes, C., Birk, B. et al. (2019). In vitro-to-in vivo extrapolation (IVIVE) by PBTK modeling for animal-free risk assessment approaches of potential endocrine-disrupting compounds. Arch Toxicol 93, 401-416. doi:10.1007/s00204-018-2372-z
Frank, D., Jaehde, U. and Fuhr, U. (2007). Evaluation of probe drugs and pharmacokinetic metrics for CYP2D6 phenotyping. Eur J Clin Pharmacol 63, 321-333. doi:10.1007/s00228-006-0250-8
German, C., Pilvankar, M. and Przekwas, A. (2019). Computational framework for predictive PBPK-PD-Tox simulations of opioids and antidotes. J Pharmacokinet Pharmacodyn 46, 513-529. doi:10.1007/s10928-019-09648-1
Gertz, M., Houston, J. B. and Galetin, A. (2011). Physiologically based pharmacokinetic modeling of intestinal first-pass metabolism of CYP3A substrates with high intestinal extraction. Drug Metab Dispos 39, 1633-1642. doi:10.1124/dmd.111.039248
Ghoneim, A. M. and Mansour, S. M. (2020). The effect of liver and kidney disease on the pharmacokinetics of clozapine and sildenafil: A physiologically based pharmacokinetic modeling. Drug Des Devel Ther 14, 1469-1479. doi:10.2147/DDDT.S246229
Gouliarmou, V., Lostia, A. M., Coecke, S. et al. (2018). Establishing a systematic framework to characterise in vitro methods for human hepatic metabolic clearance. Toxicol In Vitro 53, 233-244. doi:10.1016/j.tiv.2018.08.004
Grandoni, S., Cesari, N., Brogin, G. et al. (2019). Building in-house PBPK modelling tools for oral drug administration from literature information. ADMET DMPK 7, 4-21. doi:10.5599/admet.638
Groothuis, F. A., Heringa, M. B., Nicol, B. et al. (2015). Dose metric considerations in in vitro assays to improve quantitative in vitro-in vivo dose extrapolations. Toxicology 332, 30-40. doi:10.1016/j.tox.2013.08.012
Hallifax, D. and Houston, J. B. (2006). Binding of drugs to hepatic microsomes: Comment and assessment of current prediction methodology with recommendation for improvement. Drug Metab Dispos 34, 724-726. doi:10.1124/dmd.105.007658
Harwood, M. D., Neuhoff, S., Carlson, G. L. et al. (2013). Absolute abundance and function of intestinal drug transporters: A prerequisite for fully mechanistic in vitro-in vivo extrapolation of oral drug absorption. Biopharm Drug Dispos 34, 2-28. doi:10.1002/bdd.1810
Heikkinen, A. T., Baneyx, G., Caruso, A. et al. (2012). Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates – An evaluation and case study using GastroPlus™. Eur J Pharm Sci 47, 375-386. doi:10.1016/j.ejps.2012.06.013
Hou, T. J., Zhang, W., Xia, K. et al. (2004). ADME evaluation in drug discovery. 5. Correlation of caco-2 permeation with simple molecular properties. J Chem Inf Comput Sci 44, 1585-1600. doi:10.1021/ci049884m
Houston, J. and Galetin, A. (2008). Methods for predicting in vivo pharmacokinetics using data from in vitro assays. Curr Drug Metab 9, 940-951. doi:10.2174/138920008786485164
Ito, K., Brown, H. S. and Houston, J. B. (2004). Database analyses for the prediction of in vivo drug-drug interactions from in vitro data. Br J Clin Pharmacol 57, 473-486. doi:10.1111/j.1365-2125.2003.02041.x
Jamei, M., Turner, D., Yang, J. et al. (2009). Population-based mechanistic prediction of oral drug absorption. AAPS J 11, 225-237. doi:10.1208/s12248-009-9099-y
Jones, H. M., Barton, H. A., Lai, Y. et al. (2012). Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data. Drug Metab Dispos 40, 1007-1017. doi:10.1124/dmd.111.042994
Jones, H. M. and Rowland-Yeo, K. (2013). Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol 2, e63. doi:10.1038/psp.2013.41
Karlsson, F. H., Bouchene, S., Hilgendorf, C. et al. (2013). Utility of in vitro systems and preclinical data for the prediction of human intestinal first-pass metabolism during drug discovery and preclinical development. Drug Metab Dispos 41, 2033-2046. doi:10.1124/dmd.113.051664
Kawamoto, Y., Matsuyama, W., Wada, M. et al. (2007). Development of a physiologically based pharmacokinetic model for bisphenol A in pregnant mice. Toxicol Appl Pharmacol 224, 182-191. doi:10.1016/j.taap.2007.06.023
Kilford, P. J., Gertz, M., Houston, J. B. et al. (2008). Hepatocellular binding of drugs: Correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data. Drug Metab Dispos 36, 1194-1197. doi:10.1124/dmd.108.020834
Li, R., Barton, H. and Maurer, T. (2015). A mechanistic pharmacokinetic model for liver transporter substrates under liver cirrhosis conditions. CPT Pharmacometrics Syst Pharmacol 4, 338-349. doi:10.1002/psp4.39
Lobell, M. and Sivarajah, V. (2003). In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values. Mol Divers 7, 69-87. doi:10.1023/B:MODI.0000006562.93049.36
Lombardo, F., Berellini, G. and Obach, R. S. (2018). Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 1352 drug compounds. Drug Metab Dispos 46, 1466-1477. doi:10.1124/dmd.118.082966
Louisse, J., Beekmann, K. and Rietjens, I. M. C. M. (2017). Use of physiologically based kinetic modeling-based reverse dosimetry to predict in vivo toxicity from in vitro data. Chem Res Toxicol 30, 114-125. doi:10.1021/acs.chemrestox.6b00302
Louisse, J., Alewijn, M., Peijnenburg, A. A. C. M. et al. (2020). Towards harmonization of test methods for in vitro hepatic clearance studies. Toxicol In Vitro 63, 104722. doi:10.1016/j.tiv.2019.104722
Nguyen, H. Q., Kimoto, E., Callegari, E. et al. (2016). Mechanistic modeling to predict midazolam metabolite exposure from in vitro data. Drug Metab Dispos 44, 781-791. doi:10.1124/dmd.115.068601
Obach, R. S. (1999). Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: An examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Metab Dispos 27, 1350-1359.
OECD (2021). Guidance Document on the Characterisation, Validation and Reporting of Physiologically Based Kinetic (PBK) Models for Regulatory Purposes. Series on Testing and Assessment No. 331.
Paini, A., Leonard, J. A., Joossens, E. et al. (2019). Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making. Comput Toxicol 9, 61-72. doi:10.1016/j.comtox.2018.11.002
Pearce, R. G., Setzer, R. W., Strope, C. L. et al. (2017). httk: R package for high-throughput toxicokinetics. J Stat Softw 79, 1-26. doi:10.18637/jss.v079.i04
Peters, S. A. and Dolgos, H. (2019). Requirements to establishing confidence in physiologically based pharmacokinetic (PBPK) models and overcoming some of the challenges to meeting them. Clin Pharmacokinet 58, 1355-1371. doi:10.1007/s40262-019-00790-0
Posada, M. M., Morse, B. L., Turner, P. K. et al. (2020). Predicting clinical effects of CYP3A4 modulators on abemaciclib and active metabolites exposure using physiologically based pharmacokinetic modeling. J Clin Pharmacol 60, 915-930. doi:10.1002/jcph.1584
Poulin, P. and Theil, F.-P. (2002). Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition. J Pharm Sci 91, 1358-1370. doi:10.1002/jps.10128
Punt, A., Aartse, A., Bovee, T. F. H. et al. (2019). Quantitative in vitro-to-in vivo extrapolation (QIVIVE) of estrogenic and anti-androgenic potencies of BPA and BADGE analogues. Arch Toxicol 93, 1941-1953. doi:10.1007/s00204-019-02479-6
Punt, A., Pinckaers, N., Peijnenburg, A. et al. (2021). Development of a web-based toolbox to support quantitative in-vitro-to-in-vivo extrapolations (QIVIVE) within nonanimal testing strategies. Chem Res Toxicol 34, 460-472. doi:10.1021/acs.chemrestox.0c00307
Punt, A., Louisse, J., Pinckaers, N. et al. (2022). Predictive performance of next generation physiologically based kinetic (PBK)-model predictions in rats based on in vitro and in silico input data. Toxicol Sci, kfab150. doi:10.1093/toxsci/kfab150
Rodgers, T. and Rowland, M. (2006). Physiologically based pharmacokinetic modelling 2: Predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci 95, 1238-1257. doi:10.1002/jps.20502
Sager, J. E., Yu, J., Ragueneau-Majlessi, I. et al. (2015). Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: A systematic review of published models, applications, and model verification. Drug Metab Dispos 43, 1823-1837. doi:10.1124/dmd.115.065920
Schmitt, M. V., Reichel, A., Liu, X. et al. (2021). Extension of the mechanistic tissue distribution model of Rodgers and Rowland by systematic incorporation of lysosomal trapping: Impact on unbound partition coefficient and volume of distribution predictions in the rat. Drug Metab Dispos 49, 53-61. doi:10.1124/dmd.120.000161
Schmitt, W. (2008). General approach for the calculation of tissue to plasma partition coefficients. Toxicol In Vitro 22, 457-467. doi:10.1016/J.TIV.2007.09.010
Scotcher, D., Jones, C., Rostami-Hodjegan, A. et al. (2016). Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance. Eur J Pharm Sci 94, 59-71. doi:10.1016/j.ejps.2016.03.018
Shebley, M., Sandhu, P., Emami Riedmaier, A. et al. (2018). Physiologically based pharmacokinetic model qualification and reporting procedures for regulatory submissions: A consortium perspective. Clin Pharmacol Ther 104, 88-110. doi:10.1002/cpt.1013
Shibata, Y., Takahashi, H. and Ishii, Y. (2000). A convenient in vitro screening method for predicting in vivo drug metabolic clearance using isolated hepatocytes suspended in serum. Drug Metab Dispos 28, 1518-1523.
Soetaert, K., Petzoldt, T. and Setzer, R. W. (2010). Solving differential equations in R: Package deSolve. J Stat Softw 33, 1-25. doi:10.18637/jss.v033.i09
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, 841-853. doi:10.3109/00498254.2012.669080
Sun, D., Lennernas, H., Welage, L. S. et al. (2002). Comparison of human duodenum and Caco-2 gene expression profiles for 12,000 gene sequences tags and correlation with permeability of 26 drugs. Pharm Res 19, 1400-1416. doi:10.1023/a:1020483911355
Teeguarden, J. G., Waechter Jr., J. M., Clewell III, H. J. et al. (2005). Evaluation of oral and intravenous route pharmacokinetics, plasma protein binding, and uterine tissue dose metrics of bisphenol A: A physiologically based pharmacokinetic approach. Toxicol Sci 85, 823-838. doi:10.1093/toxsci/kfi135
Tsamandouras, N., Rostami-Hodjegan, A. and Aarons, L. (2015). Combining the “bottom up” and “top down” approaches in pharmacokinetic modelling: Fitting PBPK models to observed clinical data. Br J Clin Pharmacol 79, 48-55. doi:10.1111/bcp.12234
Utsey, K., Gastonguay, M. S., Russell, S. et al. (2020). Quantification of the impact of partition coefficient prediction methods on physiologically based pharmacokinetic model output using a standardized tissue composition. Drug Metab Dispos 48, 903-916. doi:10.1124/DMD.120.090498
Wambaugh, J. F., Hughes, M. F., Ring, C. L. et al. (2018). Evaluating in vitro-in vivo extrapolation of toxicokinetics. Toxicol Sci 163, 152-169. doi:10.1093/toxsci/kfy020
Wambaugh, J. F., Wetmore, B. A., Ring, C. L. et al. (2019). Assessing toxicokinetic uncertainty and variability in risk prioritization. Toxicol Sci 172, 235-251. doi:10.1093/toxsci/kfz205
Wetmore, B. A., Wambaugh, J. F., Allen, B. et al. (2015). Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing. Toxicol Sci 148, 121-136. doi:10.1093/toxsci/kfv171
Yang, Y., Li, P., Zhang, Z. et al. (2020). Prediction of cyclosporin-mediated drug interaction using physiologically based pharmacokinetic model characterizing interplay of drug transporters and enzymes. Int J Mol Sci 21, 7023. doi:10.3390/ijms21197023
Yoon, M., Blaauboer, B. J. and Clewell, H. J. (2015). Quantitative in vitro to in vivo extrapolation (QIVIVE): An essential element for in vitro-based risk assessment. Toxicology 332, 1-3. doi:10.1016/j.tox.2015.02.002
Yu, L. X. and Amidon, G. L. (1999). A compartmental absorption and transit model for estimating oral drug absorption. Int J Pharm 186, 119-125. doi:10.1016/S0378-5173(99)00147-7
Yun, Y. E. and Edginton, A. N. (2013). Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters. Xenobiotica 43, 839-852. doi:10.3109/00498254.2013.770182
Zhang, X., Quinney, S. K., Gorski, J. C. et al. (2009). Semiphysiologically based pharmacokinetic models for the inhibition of midazolam clearance by diltiazem and its major metabolite. Drug Metab Dispos 37, 1587-1597. doi:10.1124/dmd.109.026658