Impact of in vitro experimental variation in kinetic parameters on physiologically based kinetic (PBK) model simulations

Main Article Content

Ans Punt , Peter Bos, Betty Hakkert, Jochem Louisse
[show affiliations]

Abstract

In vitro toxicokinetic data are critical in meeting an increased regulatory need to improve chemical safety evaluations towards a better understanding of internal human chemical exposure and toxicity. In vitro intrinsic hepatic clearance (CLint), the fraction unbound in plasma (fup), and the intestinal apparent permeability (Papp) are important parameters as input in a physiologically based kinetic (PBK) model to make first estimates of internal exposure after oral dosing. In the present study we explored the experimental variation in the values for these parameters as reported in the literature. Furthermore, the impact that this experimental variation has on PBK model predictions of maximum plasma concentration (Cmax) and the area under the concentration time curve (AUC0-24h) was determined. As a result of the experimental variation in CLint, Papp, and fup, the predicted variation in Cmax for individual compounds ranged between 1.4- to 28-fold, and the predicted variation in AUC0-24h ranged between 1.4- and 23-fold. These results indicate that there are still some important steps to take to achieve robust data that can be used in regulatory applications. To gain regulatory acceptance of in vitro kinetic data and PBK models based on in vitro input data, the boundaries in experimental conditions as well as the applicability domain and the use of different in vitro kinetic models need to be described in guidance documents.

Article Details

How to Cite
Punt, A. (2023) “Impact of in vitro experimental variation in kinetic parameters on physiologically based kinetic (PBK) model simulations”, ALTEX - Alternatives to animal experimentation, 40(2), pp. 237–247. doi: 10.14573/altex.2202131.
Section
Articles
References

Andersen, M. E., McMullen, P. D., Phillips, M. B. et al. (2019). Developing context appropriate toxicity testing approaches using new alternative methods (NAMs). ALTEX 36, 523-534. doi:10.14573/altex.1906261

Arnesdotter, E., Rogiers, V., Vanhaecke, T. et al. (2021). An overview of current practices for regulatory risk assessment with lessons learnt from cosmetics in the European Union. Crit Rev Toxicol 51, 395-417. doi:10.1080/10408444.2021.1931027

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

Bessems, J. G., Loizou, G., Krishnan, K. et al. (2014). PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment. Recommendations from a joint EPAA-EURL ECVAM ADME workshop. Regul Toxicol Pharmacol 68, 119-139. doi:10.1016/j.yrtph.2013.11.008

Blaauboer, B. J. (2014). In vitro approaches to predictive biokinetics. In A. Bal-Price and P. Jennings (eds), In Vitro Toxicology Systems, Methods in Pharmacology and Toxicology (521-530). New York, NY USA: Humana Press. doi:10.1007/978-1-4939-0521-8_23

Black, S. R., Nichols, J. W., Fay, K. A. et al. (2021). Evaluation and comparison of in vitro intrinsic clearance rates measured using cryopreserved hepatocytes from humans, rats, and rainbow trout. Toxicology 457, 152819. doi:10.1016/j.tox.2021.152819

de Boer, A., Krul, L., Fehr, M. et al. (2020). Animal-free strategies in food safety & nutrition: What are we waiting for? Part I: Food safety. Trends Food Sci Technol 106, 469-484. doi:10.1016/j.tifs.2020.10.034

Cai, J. and Shalan, H. (2021). Assessment of cytochrome P450 metabolic clearance using hepatocyte suspension. In Z. Yan and G. W. Caldwell (eds), Cytochrome P450. Methods in Pharmacology and Toxicology (243-259). New York, NY, USA: Humana. doi:10.1007/978-1-0716-1542-3_15

Chen, Y.-C., Kenny, J. R., Wright, M. et al. (2019). Improving confidence in the determination of free fraction for highly bound drugs using bidirectional equilibrium dialysis. J Pharm Sci 108, 1296-1302. doi:10.1016/j.xphs.2018.10.011

Choi, G.-W., Lee, Y.-B. and Cho, H.-Y. (2019). Interpretation of non-clinical data for prediction of human pharmacokinetic parameters: In vitro-in vivo extrapolation and allometric scaling. Pharmaceutics 11, 168. doi:10.3390/pharmaceutics11040168

Coecke, S., Pelkonen, O., Leite, S. B. et al. (2013). Toxicokinetics as a key to the integrated toxicity risk assessment based primarily on non-animal approaches. Toxicol In Vitro 27, 1570-1577. doi:10.1016/j.tiv.2012.06.012

Deshmukh, S. V. and Harsch, A. (2011). Direct determination of the ratio of unbound fraction in plasma to unbound fraction in microsomal system (fup/fumic) for refined prediction of phase I mediated metabolic hepatic clearance. J Pharmacol Toxicol Methods 63, 35-39. doi:10.1016/j.vascn.2010.04.003

Elsby, R., Maggs, J. L., Ashby, J. et al. (2001). Comparison of the modulatory effects of human and rat liver microsomal metabolism on the estrogenicity of bisphenol A: Implications for extrapolation to humans. J Pharmacol Exp Ther 297, 103-113.

EMA (2018). EMA Guideline on the reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-reporting-physiologically-based-pharmacokinetic-pbpk-modelling-simulation_en.pdf

Estudante, M., de Mello-Sampayo, C., Sahin, S. et al. (2015). The utility of in vitro trials that use Caco-2 cell systems as a replacement for animal intestinal permeability and human bioequivalence measurements in drug development. J Biomed Biopharm Res 12, 117-126. doi:10.19277/bbr.12.1.110

European Commission (2019). The European Green Deal. Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions. Brussels, 11-12-2019 640 final. doi:10.54648/eerr1996017

European Commission (2020). Chemicals strategy for sustainability – Towards a toxic-free environment, communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. Brussels, 14-10-2020 667 final. doi:10.54648/eerr1996017

Fagerholm, U., Spjuth, O. and Hellberg, S. (2021). Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma. Xenobiotica 51, 1095-1100. doi:10.1080/00498254.2021.1964044

Ferguson, K. C., Luo, Y. S., Rusyn, I. et al. (2019). Comparative analysis of rapid equilibrium dialysis (RED) and solid phase micro-extraction (SPME) methods for in vitro-in vivo extrapolation of environmental chemicals. Toxicol In Vitro 60, 245-251. doi:10.1016/j.tiv.2019.06.006

Fortaner, S., Mendoza-De Gyves, E., Cole, T. et al. (2021). Determination of in vitro metabolic hepatic clearance of valproic acid (VPA) and five analogues by UPLC-MS-QTOF, applicable in alternatives to animal testing. J Chromatogr B Anal Technol Biomed Life Sci 1181, 122893. doi:10.1016/j.jchromb.2021.122893

Gertz, M., Harrison, A., Houston, J. B. et al. (2010). Prediction of human intestinal first-pass metabolism of 25 CYP3A substrates from in vitro clearance and permeability data. Drug Metab Dispos 38, 1147-1158. doi:10.1124/dmd.110.032649

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

Hallifax, D., Turlizzi, E., Zanelli, U. et al. (2012). Clearance-dependent underprediction of in vivo intrinsic clearance from human hepatocytes: Comparison with permeabilities from artificial membrane (PAMPA) assay, in silico and caco-2 assay, for 65 drugs. Eur J Pharm Sci 45, 570-574. doi:10.1016/j.ejps.2011.12.010

Hanioka, N., Isobe, T., Tanaka-Kagawa, T. et al. (2020). In vitro glucuronidation of bisphenol A in liver and intestinal microsomes: Interspecies differences in humans and laboratory animals. Drug Chem Toxicol 45, 1565-1569. doi:10.1080/01480545.2020.1847133

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

Hubatsch, I., Ragnarsson, E. G. E. and Artursson, P. (2007). Determination of drug permeability and prediction of drug absorption in Caco-2 monolayers. Nat Protoc 2, 2111-2119. doi:10.1038/nprot.2007.303

Jones, H. 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

Jones, R. S., Chang, J. H., Flores, M. et al. (2021). Evaluation of a competitive equilibrium dialysis approach for assessing the impact of protein binding on clearance predictions. J Pharm Sci 110, 536-542. doi:10.1016/j.xphs.2020.09.012

Kulthong, K., Duivenvoorde, L., Mizera, B. Z. et al. (2018). Implementation of a dynamic intestinal gut-on-a-chip barrier model for transport studies of lipophilic dioxin congeners. RSC Adv 8, 32440-32453. doi:10.1039/c8ra05430d

Larregieu, C. A. and Benet, L. Z. (2014). Distinguishing between the permeability relationships with absorption and metabolism to improve BCS and BDDCS predictions in early drug discovery. Mol Pharm 11, 1335-1344. doi:10.1021/mp4007858

Lee, J. B., Zgair, A., Taha, D. A. et al. (2017). Quantitative analysis of lab-to-lab variability in Caco-2 permeability assays. Eur J Pharm Biopharm 114, 38-42. doi:10.1016/j.ejpb.2016.12.027

Li, C., Liu, T., Cui, X. et al. (2007). Development of in vitro pharmacokinetic screens using Caco-2, human hepatocyte, and Caco-2/human hepatocyte hybrid systems for the prediction of oral bioavailability in humans. J Biomol Screen 12, 1084-1091. doi:10.1177/1087057107308892

Loizou, G., McNally, K., Dorne, J.-L. C. M. et al. (2021). Derivation of a human in vivo benchmark dose for perfluorooctanoic acid from ToxCast in vitro concentration – Response data using a computational workflow for probabilistic quantitative in vitro to in vivo extrapolation. Front Pharmacol 12, 630457. doi:10.3389/fphar.2021.630457

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

Maas, W. J. M., de Graaf, I. A. M., Schoen, E. D. et al. (2000). Assessment of some critical factors in the freezing technique for the cryopreservation of precision-cut rat liver slices. Cryobiology 40, 250-263. doi:10.1006/cryo.2000.2246

Mazur, C. S., Kenneke, J. F., Hess-Wilson, J. K. et al. (2010). Differences between human and rat intestinal and hepatic bisphenol a glucuronidation and the influence of alamethicin on in vitro kinetic measurements. Drug Metab Dispos 38, 2232-2238. doi:10.1124/dmd.110.034819

McNally, K., Hogg, A. and Loizou, G. (2018). A computational workflow for probabilistic quantitative in vitro to in vivo extrapolation. Front Pharmacol 9, 508. doi:10.3389/fphar.2018.00508

Neuhoff, S., Ungell, A. L., Zamora, I. et al. (2003). pH-dependent bidirectional transport of weakly basic drugs across Caco-2 monolayers: Implications for drug-drug interactions. Pharm Res 20, 1141-1148. doi:10.1023/a:1025032511040

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., Tan, Y. M., Sachana, M. et al. (2021). Gaining acceptance in next generation PBK modelling approaches for regulatory assessments – An OECD international effort. Comput Toxicol 18, 100163. doi:10.1016/j.comtox.2021.100163

Paul Friedman, K., Gagne, M., Loo, L.-H. et al. (2020). Utility of in vitro bioactivity as a lower bound estimate of in vivo adverse effect levels and in risk-based prioritization. Toxicol Sci 173, 202-225. doi:10.1093/toxsci/kfz201

Peters, S. A. (2012). Review of pharmacokinetic principles. In S. A. Peters, Physiologically-Based Pharmacokinetic (PBPK) Modeling and Simulations (Chapter 3, 17-42). Hoboken, NJ, USA: John Wiley & Sons, Inc. doi:10.1002/9781118140291.ch3

Punt, A., Peijnenburg, A. A. C. M., Hoogenboom, R. L. A. P. et al. (2017). Non-animal approaches for toxicokinetics in risk evaluations of food chemicals. ALTEX 34, 501-514. doi:10.14573/altex.1702211

Punt, A., Bouwmeester, H., Blaauboer, B. J. et al. (2020). New approach methodologies (NAMs) for human-relevant biokinetics predictions. Meeting the paradigm shift in toxicology towards an animal-free chemical risk assessment. ALTEX 37, 607-622. doi:10.14573/altex.2003242

Punt, A., Louisse, J., Beekmann, K. et al. (2022). Predictive performance of next generation human physiologically based kinetic (PBK) models based on in vitro and in silico input data. ALTEX 39, 221-234. doi:10.14573/altex.2108301

R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

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

SCCS (2018). The SCCS Notes of Guidance for the Ttesting of Cosmetic Ingredients and their Safety Evaluation. 10th revision. Section 3-4.12.1.

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

Seibert, E. and Tracy, T. S. (2014). Fundamentals of enzyme kinetics. Methods Mol Biol 1113, 9-22. doi:10.1007/978-1-62703-758-7_2

Srivastava, A., Pike, A., Williamson, B. et al. (2021). A novel method for preventing non-specific binding in equilibrium dialysis assays using Solutol® as an additive. J Pharm Sci 110, 1412-1417. doi:10.1016/j.xphs.2020.11.018

Usansky, H. H. and Sinko, P. J. (2005). Estimating human drug oral absorption kinetics from Caco-2 permeability using an absorption-disposition model: Model development and evaluation and derivation of analytical solutions for ka and Fa. J Pharmacol Exp Ther 314, 391-399. doi:10.1124/jpet.104.076182

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

Wang, H., Zrada, M., Anderson, K. et al. (2014). Understanding and reducing the experimental variability of in vitro plasma protein binding measurements. J Pharm Sci 103, 3302-3309. doi:10.1002/jps.24119

Watanabe, R., Esaki, T., Kawashima, H. et al. (2018). Predicting fraction unbound in human plasma from chemical structure: Improved accuracy in the low value ranges. Mol Pharm 15, 5302-5311. doi:10.1021/acs.molpharmaceut.8b00785

Wilk-Zasadna, I., Bernasconi, C., Pelkonen, O. et al. (2015). Biotransformation in vitro: An essential consideration in the quantitative in vitro-to-in vivo extrapolation (QIVIVE) of toxicity data. Toxicology 332, 8-19. doi:10.1016/j.tox.2014.10.006

Ye, M., Nagar, S. and 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, 123-141. doi:10.1002/bdd.1996

Zare Jeddi, M., Hopf, N. B., Viegas, S. et al. (2021). Towards a systematic use of effect biomarkers in population and occupational biomonitoring. Environ Int 146, 106257. doi:10.1016/j.envint.2020.106257

Most read articles by the same author(s)