Highlight report: Interspecies extrapolation by physiologically based pharmacokinetic modeling
Agata Widera11Leibniz Research Centre for Working Environment and Human Factors, IfADo - Ardeystr. 67, D-44139 Dortmund - Germany
EXCLI J 2015;14:Doc1014
Recently, several articles have been published questioning the usefulness of animal experiments for prediction of human toxicity (Leist and Hartung, 2013). For example, it has been reported that there is almost no correlation of gene expression alterations induced by inflammatory stimuli in humans and mice (Seok et al., 2013). However, a recent study of Thiel et al. (2015) demonstrates that this view may be too pessimistic. Based on pharmacokinetic modeling, Thiel et al. (2015) demonstrated a surprisingly precise mouse to human extrapolation for 10 exemplary pharmaceuticals. For interspecies modeling the authors adjusted four parameter domains in PBPK models: (i) Species-specific physiology, including more than 500 individual parameters, for example organ size and blood flow; (ii) the fraction of non-protein bound test compound; (iii) pharmacokinetic parameters, such as Km and Vmax for the predominant route of clearance, (iv) tissue specific gene expression of the most important genes responsible for elimination of the test compound (Thiel et al., 2015). Adjusting these parameter domains leads to a very good fit of mouse to human extrapolated plasma concentrations of the test compounds compared to measured human plasma concentrations. One of the limitations of the study of Thiel et al. (2015) is the use of gene expression data for simulation of the influence of metabolizing enzymes and carriers involved in clearance of the test compounds. Since the activities and not RNA levels are relevant in this context, the RNA based approximation can certainly be further improved. However, establishment of a tissue and species specific directory of all relevant metabolizing activities still represents an important future project.
Currently, much effort is invested in research on in vitro systems (Frey et al., 2014; Kim et al., 2015; Hammad and Ahmed, 2014), particularly in the fields of hepatotoxicity (Godoy et al., 2013; Grinberg et al., 2014; Ghallab, 2014; Schug et al., 2013), neurotoxicity (Balmer et al., 2014; Waldmann et al., 2014; Krug et al., 2013; Stöber, 2014) and nephrotoxicity (Giustarini et al., 2009; Faiz et al., 2011). These studies depend on knowledge of in vivo relevant concentrations which should be covered by in vitro testing. A precise extrapolation of doses in vivo to blood concentrations or even better compound concentrations at the target cells of toxicity is therefore critical for progress in the field of alternative methods and can best be achieved by systematic PBPK modeling (Mielke et al., 2011; Sterner et al., 2013; Strikwold et al., 2013; Wang et al., 2000). Further progress may be achieved by combining PBPK models with the recently established spatio-temporal models (Hoehme et al., 2010; Drasdo et al., 2014) which can simulate metabolism at the level of individual cells (Schliess et al., 2014; Drasdo et al., 2014; Widera, 2014). The here discussed study of Thiel and colleagues improves the reliability of extrapolating compound concentrations from mice to human by the systematic adaptation of species specific model parameters and therefore is of high relevance not only for the planning of first-in-man studies but also for an improved use of in vitro systems for prediction of human toxicity.
1. Balmer NV, Klima S, Rempel E, Ivanova VN, Kolde R, Weng MK, et al. From transient transcriptome responses to disturbed neurodevelopment: role of histone acetylation and methylation as epigenetic switch between reversible and irreversible drug effects. Arch Toxicol. 2014;88:1451-68.
2. Drasdo D, Bode J, Dahmen U, Dirsch O, Dooley S, Gebhardt R, et al.The virtual liver: state of the art and future perspectives. Arch Toxicol. 2014a;88:2071-5.
3. Drasdo D, Hoehme S, Hengstler JG. How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis. J Hepatol. 2014b;61:951-6.
4. Faiz H, Conjard-Duplany A, Boghossian M, Martin G, Baverel G, Ferrier B. Cadmium chloride inhibits lactate gluconeogenesis in isolated human renal proximal tubules: a cellular metabolomic approach with 13C-NMR. Arch Toxicol. 2011;85:1067-77.
5. Frey O, Misun PM, Fluri DA, Hengstler JG, Hierlemann A. Reconfigurable microfluidic hanging drop network for multi-tissue interaction and analysis. Nat Commun. 2014;5:4250.
6. Ghallab A. The rediscovery of HepG2 cells for prediction of drug induced liver injury (DILI). EXCLI J. 2014;13:1286-8.
7. Giustarini D, Dalle-Donne I, Paccagnini E, Milzani A, Rossi R. Carboplatin-induced alteration of the thiol homeostasis in the isolated perfused rat kidney. Arch Biochem Biophys. 2009;488:83-9.
8. Godoy P, Hewitt NJ, Albrecht U, Andersen ME, Ansari N, Bhattacharya S, et al. Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch Toxicol. 2013;87:1315-530.
9. Grinberg M, Stöber RM, Edlund K, Rempel E, Godoy P, Reif R, et al. Toxicogenomics directory of chemically exposed human hepatocytes. Arch Toxicol. 2014;88:2261-87.
10. Hammad S, Ahmed H. Biomarker: the universe of chemically induced gene expression alterations in human hepatocyte. EXCLI J. 2014;13:1275-7.
11. Hoehme S, Brulport M, Bauer A, Bedawy E, Schormann W, Hermes M, et al. Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration. Proc Natl Acad Sci U S A. 2010;107:10371-6.
12. Kim JY, Fluri DA, Marchan R, Boonen K, Mohanty S, Singh P, et al. 3D spherical microtissues and microfluidic technology for multi-tissue experiments and analysis. J Biotechnol. 2015;205:24-35.
13. Krug AK, Kolde R, Gaspar JA, Rempel E, Balmer NV, Meganathan K, et al. Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomics approach. Arch Toxicol. 2013;87:123-43.
14. Leist M, Hartung T. Inflammatory findings on species extrapolations: humans are definitely no 70-kg mice. Arch Toxicol. 2013;87:563-7.
15. Mielke H, Anger LT, Schug M, Hengstler JG, Stahlmann R, Gundert-Remy U. A physiologically based toxicokinetic modelling approach to predict relevant concentrations for in vitro testing. Arch Toxicol. 2011;85:555-63.
16. Schliess F, Hoehme S, Henkel SG, Ghallab A, Driesch D, Böttger J, et al. Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration. Hepatology. 2014;60:2040-51.
17. Schug M, Stöber R, Heise T, Mielke H, Gundert-Remy U, Godoy P, et al. Pharmacokinetics explain in vivo/in vitro discrepancies of carcinogen-induced gene expression alterations in rat liver and cultivated hepatocytes. Arch Toxicol. 2013;87:337-45.
18. Seok J, Warren HS, Cuenca AG, Mindrinos MN, Baker HV, Xu W, et al. Inflammation and host response to injury, large scale collaborative research program. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc Natl Acad Sci U S A. 2013;110:3507-12.
19. Sterner TR, Ruark CD, Covington TR, Yu KO, Gearhart JM. A physiologically based pharmacokinetic model for the oxime TMB-4: simulation of rodent and human data. Arch Toxicol. 2013;87:661-80.
20. Stöber R. Transcriptome based differentiation of harmless, teratogenetic and cytotoxic concentration ranges of valproic acid. EXCLI J. 2014;13:1281-2.
21. Strikwold M, Spenkelink B, Woutersen RA, Rietjens IM, Punt A. Combining in vitro embryotoxicity data with physiologically based kinetic (PBK) modelling to define in vivo dose-response curves for developmental toxicity of phenol in rat and human. Arch Toxicol. 2013;87:1709-23.
22. Thiel C, Schneckener S, Krauss M, Ghallab A, Hofmann U, Kanacher T, et al. A systematic evaluation of the use of physiologically based pharmacokinetic modeling for cross-species extrapolation J Pharm Sci. 2015;104:191-206.
23. Waldmann T, Rempel E, Balmer NV, König A, Kolde R, Gaspar JA, et al. Design principles of concentration-dependent transcriptome deviations in drug-exposed differentiating stem cells. Chem Res Toxicol. 2014;27:408-20.
24. Wang X, Santostefano MJ, DeVito MJ, Birnbaum LS. Extrapolation of a PBPK model for dioxins across dosage regimen, gender, strain, and species. Toxicol Sci. 2000;56:49-60.
25. Widera A. Integrated spatiotemporal-metabolic modelling bridges the gap between metabolism on the cellular level and organ function. EXCLI J. 2014;13:1289-91.