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• W2048397943 abstract "Transfer of knowledge along the different phases of drug development is a fundamental process in pharmaceutical research. In particular, cross-species extrapolation between different laboratory animals and further on to first-in-human trials is challenging because of the uncertain comparability of physiological processes. Physiologically based pharmacokinetic (PBPK) modeling allows translation of mechanistic knowledge from one species to another by specifically considering physiological and biochemical differences in between. We here evaluated different knowledge-driven approaches for cross-species extrapolation by systematically incorporating specific model parameter domains of a target species into the PBPK model of a reference species. Altogether, 15 knowledge-driven approaches were applied to murine and human PBPK models of 10 exemplary drugs resulting in 300 different extrapolations. Statistical analysis of the quality of the different extrapolations revealed not only species-specific physiology as the key determinant in cross-species extrapolation but also identified a synergistic effect when considering both kinetic rate constants and gene expression profiles of relevant enzymes and transporters. Moreover, we show that considering species-specific physiology, plasma protein binding, enzyme and transport kinetics, as well as tissue-specific gene expression profiles in PBPK modeling increases accuracy of cross-species extrapolations and thus supports first-in-human trials based on prior preclinical knowledge. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:191–206, 2015 Transfer of knowledge along the different phases of drug development is a fundamental process in pharmaceutical research. In particular, cross-species extrapolation between different laboratory animals and further on to first-in-human trials is challenging because of the uncertain comparability of physiological processes. Physiologically based pharmacokinetic (PBPK) modeling allows translation of mechanistic knowledge from one species to another by specifically considering physiological and biochemical differences in between. We here evaluated different knowledge-driven approaches for cross-species extrapolation by systematically incorporating specific model parameter domains of a target species into the PBPK model of a reference species. Altogether, 15 knowledge-driven approaches were applied to murine and human PBPK models of 10 exemplary drugs resulting in 300 different extrapolations. Statistical analysis of the quality of the different extrapolations revealed not only species-specific physiology as the key determinant in cross-species extrapolation but also identified a synergistic effect when considering both kinetic rate constants and gene expression profiles of relevant enzymes and transporters. Moreover, we show that considering species-specific physiology, plasma protein binding, enzyme and transport kinetics, as well as tissue-specific gene expression profiles in PBPK modeling increases accuracy of cross-species extrapolations and thus supports first-in-human trials based on prior preclinical knowledge. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 104:191–206, 2015 Development of novel drugs is a time-consuming and laborious process. In particular, the translation of preclinical knowledge generated in laboratory animals to first-in-human studies is a critical step with attrition rates above 30%.1.Paul S.M. Mytelka D.S. Dunwiddie C.T. Persinger C.C. Munos B.H. Lindborg S.R. Schacht A.L. How to improve R&D productivity: The pharmaceutical industry’s grand challenge.Nat Rev Drug Discov. 2010; 9: 203-214PubMed Google Scholar In this regard, reliable cross-species extrapolations are needed to guarantee safety in human clinical trials. Current approaches for a cross-species extrapolation are often based on empirical allometric scaling techniques.2.Boxenbaum H. Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics.J Pharmacokinet Biopharm. 1982; 10: 201-227Crossref PubMed Scopus (547) Google Scholar In this context, pharmacokinetic (PK) parameters such as the clearance of administered drugs are correlated to the body weight by using a power law function. This, however, requires observations of that parameter for a series of reference species.3.Riviere J.E. Martin-Jimenez T. Sundlof S.F. Craigmill A.L. Interspecies allometric analysis of the comparative pharmacokinetics of 44 drugs across veterinary and laboratory animal species.J Vet Pharmacol Ther. 1997; 20: 453-463Crossref PubMed Scopus (119) Google Scholar In a similar approach, Dedrick plots may be used to predict the plasma drug concentration–time profile based on simple dose normalizations and species-invariant time methods.4.Dedrick R.L. Animal scale-up.J Pharmacokinet Pharmacodyn. 1973; 1: 435-461Crossref Scopus (331) Google Scholar In addition, allometric scaling laws have been developed by taking into account the brain weight or the maximum life span potential in order to improve the predictive accuracy.5.Mahmood I. Balian J.D. Interspecies scaling: Predicting clearance of drugs in humans. Three different approaches.Xenobiotica. 1996; 26: 887-895Crossref PubMed Scopus (217) Google Scholar Such approaches have been used successfully for single compounds such as dolasetron.6.Sanwald-Ducray P. Dow J. Prediction of the pharmacokinetic parameters of reduced-dolasetron in man using in vitro–in vivo and interspecies allometric scaling.Xenobiotica. 1997; 27: 189-201Crossref PubMed Scopus (23) Google Scholar However, limitations of allometric scaling techniques have also been shown.7.Pavankuamr V.V. Vinu C.A. Mullangi R. Srinivas N.R. Preclinical pharmacokinetics and interspecies scaling of ragaglitazar, a novel biliary excreted PPAR dual activator.Eur J Drug Metab Pharmacokinet. 2007; 32: 29-37Crossref PubMed Scopus (14) Google Scholar, 8.Ward K.W. Azzarano L.M. Bondinell W.E. Cousins R.D. Huffman W.F. Jakas D.R. Keenan R.M. Ku T.W. Lundberg D. Miller W.H. Mumaw J.A. Newlander K.A. Pirhalla J.L. Roethke T.J. Salyers K.L. Souder P.R. Stelman G.J. Smith B.R. Preclinical pharmacokinetics and interspecies scaling of a novel vitronectin receptor antagonist.Drug Metab Dispos. 1999; 27: 1232-1241PubMed Google Scholar Recent approaches incorporate physiologically based pharmacokinetic (PBPK) models to extrapolate between species.9.Hall C. Lueshen E. Mošat’ A. Linninger A.A. Interspecies scaling in pharmacokinetics: A novel whole-body physiologically based modeling framework to discover drug biodistribution mechanisms in vivo.J Pharm Sci. 2012; 101: 1221-1241Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar, 10.Bradshaw Pierce E.L. Eckhardt S.G. Gustafson D. A physiologically based pharmacokinetic model of docetaxel disposition: From mouse to man.Clin Cancer Res. 2007; 13: 2768-2776Crossref PubMed Scopus (60) Google Scholar PBPK models describe physiological processes governing the fate of a drug in the body. In PBPK models, relevant tissues and organs of an organism are represented as compartments which are connected by blood flow. Organs are further subdivided into more detailed subcompartments such as blood cells, plasma, interstitium, and intracellular space. Notably, PBPK models are based on prior information regarding species-specific physiology (SP).11.Willmann S.L.J. Sevestre M. Solodenko J. Fois F. Schmitt W. PK-Sim®: A physiologically based pharmacokinetic ‘whole-body’ model.Biosilico. 2003; 1: 121-124Crossref Google Scholar Mass transfer is described by using so called distribution models which are parameterized based on the physicochemical properties of a drug.12.Rodgers T. Rowland M. Physiologically based pharmacokinetic modelling 2: Predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions.J Pharm Sci. 2006; 95: 1238-1257Abstract Full Text Full Text PDF PubMed Scopus (599) Google Scholar, 13.Rodgers T. Leahy D. Rowland M. Physiologically based pharmacokinetic modeling 1: Predicting the tissue distribution of moderate-to-strong bases.J Pharm Sci. 2005; 94: 1259-1276Abstract Full Text Full Text PDF PubMed Scopus (527) Google Scholar, 14.Schmitt W. General approach for the calculation of tissue to plasma partition coefficients.Toxicol In Vitro. 2008; 22: 457-467Crossref PubMed Scopus (209) Google Scholar, 15.Willmann S. Lippert J. Schmitt W. From physicochemistry to absorption and distribution: Predictive mechanistic modelling and computational tools.Expert Opin Drug Metab Toxicol. 2005; 1: 159-168Crossref PubMed Scopus (89) Google Scholar Because of the large degree of mechanistic information included in PBPK models, they are particularly well suited for extrapolation to new treatment scenarios or specific subgroups of patients. Human PBPK models have been used before, for instance, for pediatric scaling, dose extrapolation, and prediction of adverse events in high-risk subgroups of patients.16.Maharaj A.R. Barrett J.S. Edginton A.N. A workflow example of PBPK modeling to support pediatric research and development: Case study with lorazepam.AAPS J. 2013; 15: 455-464Crossref PubMed Scopus (76) Google Scholar, 17.Tardif R. Charest-Tardif G. Brodeur J. Krishnan K. Physiologically based pharmacokinetic modeling of a ternary mixture of alkyl benzenes in rats and humans.Toxicol Appl Pharmacol. 1997; 144: 120-134Crossref PubMed Scopus (139) Google Scholar, 18.Lippert J. Brosch M. von Kampen O. Meyer M. Siegmund H.U. Schafmayer C. Becker T. Laffert B. Gorlitz L. Schreiber S. Neuvonen P.J. Niemi M. Hampe J. Kuepfer L. A mechanistic, model-based approach to safety assessment in clinical development.CPT Pharmacometrics Syst Pharmacol. 2012; 1: e13Crossref PubMed Scopus (33) Google Scholar PBPK models can also be used for cross-species extrapolation from one species to another. For cyclosporine, a PBPK modeling framework combined with physiological scaling laws has been used successfully to extrapolate from rats to pigs, monkeys, and humans.9.Hall C. Lueshen E. Mošat’ A. Linninger A.A. Interspecies scaling in pharmacokinetics: A novel whole-body physiologically based modeling framework to discover drug biodistribution mechanisms in vivo.J Pharm Sci. 2012; 101: 1221-1241Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar Also, scaling of physiological, metabolic, and excretory variables from a mouse PBPK model of docetaxel was used for the prediction of drug plasma levels in men.10.Bradshaw Pierce E.L. Eckhardt S.G. Gustafson D. A physiologically based pharmacokinetic model of docetaxel disposition: From mouse to man.Clin Cancer Res. 2007; 13: 2768-2776Crossref PubMed Scopus (60) Google Scholar Although both approaches yielded reasonable predictions for single compounds, a more generalized investigation of the benefit of using PBPK modeling for extrapolation from one species to another is still missing. We here systematically evaluated different approaches for cross-species extrapolation by using murine and human PBPK models for 10 exemplary drugs. PK of a target species was predicted by stepwise adjusting physiological and biochemical parameters in a reference PBPK model of another species. Thereby, the improvement in model accuracy for different model parameter domains reflecting different degrees of prior information was quantified. We first established a naive model, which neglects any knowledge about interspecies differences and which we henceforth used as a benchmark model. Secondly, we generated various extrapolated models based on different degrees of prior knowledge and quantified the relative improvement in model error (ME) in these models with respect to the naive benchmark model. This systematic workflow allowed us to assess the benefit of different knowledge-driven approaches for cross-species extrapolation, which predict pharmacokinetic profiles in a target species based on mechanistic knowledge obtained in a reference species. The software tool PK-Sim® (version 5.1; Bayer Technology Services GmbH, Leverkusen, Germany) was used to build the PBPK models for the considered drugs.11.Willmann S.L.J. Sevestre M. Solodenko J. Fois F. Schmitt W. PK-Sim®: A physiologically based pharmacokinetic ‘whole-body’ model.Biosilico. 2003; 1: 121-124Crossref Google Scholar, 15.Willmann S. Lippert J. Schmitt W. From physicochemistry to absorption and distribution: Predictive mechanistic modelling and computational tools.Expert Opin Drug Metab Toxicol. 2005; 1: 159-168Crossref PubMed Scopus (89) Google Scholar, 19.Eissing T. Kuepfer L. Becker C. Block M. Coboeken K. Gaub T. Goerlitz L. Jaeger J. Loosen R. Ludewig B. Meyer M. Niederalt C. Sevestre M. Siegmund H.U. Solodenko J. Thelen K. Telle U. Weiss W. Wendl T. Willmann S. Lippert J. A computational systems biology software platform for multiscale modeling and simulation: Integrating whole-body physiology, disease biology, and molecular reaction networks.Front Physiol. 2011; 2: 4Crossref PubMed Scopus (146) Google Scholar, 20.Willmann S. Solodenko J. Sevestre M. Lippert J. Schmitt W. A pharmacodynamic extension for the physiology-based pharmacokinetic whole-body model PK-Sim((R)).Eur J Pharm Sci. 2004; 23 (S75): S75Google Scholar The different knowledge-driven approaches for cross-species extrapolation were implemented in MATLAB (version 7.11.0; The MathWorks, Inc., Natick, Massachusetts) by use of the MoBi®Toolbox for MATLAB (version 2.3; Bayer Technology Services GmbH). Statistical computations are performed using R (version 3.0.2, 2013; R Core Team, http://www.R-project.org). In total, PBPK models for 10 different drugs (torsemide, talinolol, midazolam, caffeine, pravastatin, morphine, docetaxel, dextromethorphan, cyclosporine, and erythromycin) were developed. Only intravenous administration was considered. The drugs have been selected such that the corresponding route of degradation is mainly governed by a single reaction, that is, either enzyme-catalyzed metabolization or active drug transport. This selection criterion ensures that the PBPK models developed have a comparable structural complexity. Major physicochemical properties of the considered drugs are listed in Table 1. The octanol/water partition coefficient (log P) ranges from −0.07 (caffeine) to 3.64 (cyclosporine). The majority of the molecular weights (MWs) are between 190 and 430 g/mol except for erythromycin (733.92 g/mol), docetaxel (807.88 g/mol), and cyclosporine (1202.60 g/mol). Acid dissociation constants (pKa) lie in the range of 4.56 (pravastatin) to 11.83 (cyclosporine).Table 1Physicochemical PropertiesDrugLog PMW (g/mol)pKaTorsemide21.Brater D.C. Leinfelder J. Anderson S.A. Clinical pharmacology of torasemide, a new loop diuretic.Clin Pharmacol Ther. 1987; 42: 187-192Crossref PubMed Scopus (56) Google Scholar0.5722.Knauf H. Mutschler E. Clinical pharmacokinetics and pharmacodynamics of torasemide.Clin Pharmacokinet. 1998; 34: 1-24Crossref PubMed Scopus (79) Google Scholar348.4022.Knauf H. Mutschler E. Clinical pharmacokinetics and pharmacodynamics of torasemide.Clin Pharmacokinet. 1998; 34: 1-24Crossref PubMed Scopus (79) Google Scholar7.1022.Knauf H. Mutschler E. Clinical pharmacokinetics and pharmacodynamics of torasemide.Clin Pharmacokinet. 1998; 34: 1-24Crossref PubMed Scopus (79) Google ScholarTalinolol23.Assmann I. The actions of talinolol, a beta 1-selective beta blocker, in cardiac arrhythmia and acute myocardial infarction.Curr Med Res Opin. 1995; 13: 325-342Crossref PubMed Scopus (14) Google Scholar2.3024.Wishart D.S. Tzur D. Knox C. Eisner R. Guo A.C. Young N. Cheng D. Jewell K. Arndt D. Sawhney S. Fung C. Nikolai L. Lewis M. Coutouly M.-A. Forsythe I. Tang P. Shrivastava S. Jeroncic K. Stothard P. Amegbey G. Block D. Hau D.D. Wagner J. Miniaci J. Clements M. Gebremedhin M. Guo N. Zhang Y. Duggan G.E. Macinnis G.D. Weljie A.M. Dowlatabadi R. Bamforth F. Clive D. Greiner R. Li L. Marrie T. Sykes B.D. Vogel H.J. Querengesser L. HMDB: The Human Metabolome Database.Nucleic Acids Res. 2007; 35: D521-D526Crossref PubMed Scopus (2066) Google Scholar363.5025.Tubic M. Wagner D. Spahn-Langguth H. Bolger M.B. Langguth P. In silico modeling of non-linear drug absorption for the P-gp substrate talinolol and of consequences for the resulting pharmacodynamic effect.Pharm Res. 2006; 23: 1712-1720Crossref PubMed Scopus (64) Google Scholar9.4325.Tubic M. Wagner D. Spahn-Langguth H. Bolger M.B. Langguth P. In silico modeling of non-linear drug absorption for the P-gp substrate talinolol and of consequences for the resulting pharmacodynamic effect.Pharm Res. 2006; 23: 1712-1720Crossref PubMed Scopus (64) Google ScholarMidazolam26.Kanto J.H. Midazolam: The first water-soluble benzodiazepine. Pharmacology, pharmacokinetics and efficacy in insomnia and anesthesia.Pharmacotherapy. 1985; 5: 138-155Crossref PubMed Scopus (140) Google Scholar2.7027.Mulla H. McCormack P. Lawson G. Firmin R.K. Upton D.R. 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Stothard P. Chang Z. Woolsey J. DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2338) Google Scholar194.2028.Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2338) Google Scholar10.4028.Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2338) Google ScholarMorphine30.Wang J.K. Nauss L.A. Thomas J.E. Pain relief by intrathecally applied morphine in man.Anesthesiology. 1979; 50: 149-151Crossref PubMed Scopus (653) Google Scholar0.8928.Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. 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DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2338) Google Scholar807.8828.Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2338) Google Scholar10.9628.Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2338) Google ScholarDextromethorphan32.Bem J.L. Peck R. Dextromethorphan.Drug Saf. 1992; 7: 190-199Crossref PubMed Scopus (202) Google Scholar3.6028.Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. 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DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2338) Google Scholar424.5328.Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2338) Google Scholar4.5628.Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2338) Google ScholarMolecular weight (MW), acid dissociation constant (pKa), and the octanol/water partition coefficient (log P) for the 10 considered drugs. Open table in a new tab Molecular weight (MW), acid dissociation constant (pKa), and the octanol/water partition coefficient (log P) for the 10 considered drugs. The developed PBPK models for mouse and human are validated by comparing the simulated concentration–time profiles with measured data of intravenous studies obtained from the literature. For torsemide, talinolol, and pravastatin, own experimental data were measured quantifying pharmacokinetic profiles in mice. Table 2 provides an overview of the administration route, the dose, the measuring and infusion time, as well as the sampling site from which the blood samples were collected.Table 2Experimental ConditionsDrugSpeciesAdministration RouteSampling SiteDose (mg/kg)T Infusion (min)T Measured (h)ReferenceTorsemideHumanIV InfusionVenous bloodaPeripheral venous blood.0.27602436.Neugebauer G. Besenfelder E. von Möllendorff E. Pharmacokinetics and metabolism of torasemide in man.Arzneimittelforschung. 1988; 38: 164-166PubMed Google ScholarMouseIV BolusPortal vein5.00–2bOwn data (see Experimental Procedure).TalinololHumanIV InfusionVenous bloodaPeripheral venous blood.0.39302437.Schwarz U.I. Hanso H. Oertel R. Miehlke S. Kuhlisch E. Glaeser H. Hitzl M. Dresser G.K. Kim R.B. Kirch W. Induction of intestinal P-glycoprotein by St John’s wort reduces the oral bioavailability of talinolol.Clin Pharmacol Ther. 2007; 81: 669-678Crossref PubMed Scopus (116) Google ScholarMouseIV BolusPortal vein10.00–2bOwn data (see Experimental Procedure).MidazolamHumanIV BolusVenous bloodaPeripheral venous blood.0.15–1238.Heizmann P. Eckert M. Ziegler W.H. Pharmacokinetics and bioavailability of midazolam in man.Br J Clin Pharmacol. 1983; 16: 43S-49SCrossref PubMed Scopus (229) Google ScholarMouseIV BolusVenous blood2.50–239.Kuze J. Mutoh T. Takenaka T. Morisaki K. Nakura H. Hanioka N. Narimatsu S. Separate evaluation of intestinal and hepatic metabolism of three benzodiazepines in rats with cannulated portal and jugular veins: Comparison with the profile in non-cannulated mice.Xenobiotica. 2009; 39: 871-880Crossref PubMed Scopus (13) Google ScholarCaffeineHumanIV InfusionVenous bloodaPeripheral venous blood.5.00302440.Blanchard J. Sawers S.J. Comparative pharmacokinetics of caffeine in young and elderly men.J Pharmacokinet Biopharm. 1983; 11: 109-126Crossref PubMed Scopus (107) Google ScholarMouseIV BolusVenous blood5.00–241.Jun Tang J.O. Xiao Yun Kazavchinskaya Polina Stringham Kim Trinh Ann Kennedy Kevin Wu C. Study of mouse pharmacokinetics using serial blood sampling technique.in: ISSX 15th North American Regional Meeting CEREP, San Diego, California2008Google ScholarMorphineHumanIV BolusVenous bloodaPeripheral venous blood.22.80–842.Stuart-Harris R. Joel S.P. McDonald P. Currow D. Slevin M.L. 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