Pharmaceutical Research

, Volume 33, Issue 9, pp 2126–2139 | Cite as

Evaluation of the GastroPlus™ Advanced Compartmental and Transit (ACAT) Model in Early Discovery

  • N. Gobeau
  • R. Stringer
  • S. De Buck
  • T. Tuntland
  • B. Faller
Research Paper



The aim of this study was to evaluate the oral exposure predictions obtained early in drug discovery with a generic GastroPlus Advanced Compartmental And Transit (ACAT) model based on the in vivo intravenous blood concentration-time profile, in silico properties (lipophilicity, pKa) and in vitro high-throughput absorption-distribution-metabolism-excretion (ADME) data (as determined by PAMPA, solubility, liver microsomal stability assays).


The model was applied to a total of 623 discovery molecules and their oral exposure was predicted in rats and/or dogs. The predictions of Cmax, AUClast and Tmax were compared against the observations.


The generic model proved to make predictions of oral Cmax, AUClast and Tmax within 3-fold of the observations for rats in respectively 65%, 68% and 57% of the 537 cases. For dogs, it was respectively 77%, 79% and 85% of the 124 cases. Statistically, the model was most successful at predicting oral exposure of Biopharmaceutical Classification System (BCS) class 1 compounds compared to classes 2 and 3, and was worst at predicting class 4 compounds oral exposure.


The generic GastroPlus ACAT model provided reasonable predictions especially for BCS class 1 compounds. For compounds of other classes, the model may be refined by obtaining more information on solubility and permeability in secondary assays. This increases confidence that such a model can be used in discovery projects to understand the parameters limiting absorption and extrapolate predictions across species. Also, when predictions disagree with the observations, the model can be updated to test hypotheses and understand oral absorption.


advanced compartmental and transit (ACAT) drug discovery GastroPlus oral absorption physiologically based pharmacokinetics (PBPK) 



Advanced compartmental and transit


Absorption, distribution, metabolism, excretion

AFE abs

Absolute average fold error

AFE rel

Relative average fold error


Absorption scaling factor


Area under the curve


Area under the curve to the last time point


Biopharmaceutical classification system


Intrinsic clearance


Maximum concentration


Buffer solubility at a given pH


Solubility in the presence of bile salt at a given pH


Human fasted simulated intestinal fluid






Liquid chromatography tandem mass spectometry


liver weight


Madin Darby canine kidney


Microsomal protein per gram liver


Molecular weight


Novartis institutes for biomedical research


Parallel artifical membrane permeability assay


Physiologically based pharmacokinetics


Effective permeability




Per Os


Polar surface area


Hepatic blood flow


Aqueous solubilization ratio


Simulated intestinal fluid


Bile salt solubilization ratio


Time at which the concentration is maximum



The authors would like to thank all colleagues from Metabolism and Pharmacokinetics at NIBR and GNF, Global Discovery Chemistry and Analytical Sciences and Imaging who provided the critical data and their interpretation to build and evaluate the model. Also, the authors are grateful for the support and useful discussions with Pankaj Daga and Eric Martin, in NIBR Computer Aided Drug Discovery, and Gérard Flesch, in Novartis Pharma Integrated Quantitative Science.

This study was supported by NIBR. The authors have no conflicts of interest directly relevant to the content of this study.

Supplementary material

11095_2016_1951_MOESM1_ESM.docx (405 kb)
ESM 1 (DOCX 405 kb)
11095_2016_1951_MOESM2_ESM.xlsx (1.1 mb)
ESM 2 (XLSX 1.09 mb)


  1. 1.
    Ballard P, Brassil P, Bui KH, Dolgos H, Petersson C, Tunek A, et al. The right compound in the right assay at the right time: an integrated discovery DMPK strategy. Drug Metab Rev. 2012;44(3):224–52.CrossRefPubMedGoogle Scholar
  2. 2.
    Parrott N, Paquereau N, Coassolo P, Lave T. An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery. J Pharm Sci. 2005;94(10):2327–43.CrossRefPubMedGoogle Scholar
  3. 3.
    Poulin P, Haddad S. 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. 2012;101(6):2250–61.CrossRefPubMedGoogle Scholar
  4. 4.
    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(6):1259–76.CrossRefPubMedGoogle Scholar
  5. 5.
    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(6):1238–57.CrossRefPubMedGoogle Scholar
  6. 6.
    Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N, Peters SA, et al. Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther. 2015;97(3):247–62.CrossRefPubMedGoogle Scholar
  7. 7.
    Di L, Feng B, Goosen TC, Lai Y, Steyn SJ, Varma MV, et al. A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development. Drug Metab Dispos. 2013;41(12):1975–93.CrossRefPubMedGoogle Scholar
  8. 8.
    Musther H, Olivares-Morales A, Hatley OJD, Liu B, Rostami Hodjegan A. Animal versus human oral drug bioavailability: do they correlate? Eur J Pharm Sci. 2014;57:280–91.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Wajima T, Yano Y, Fukumura K, Oguma T. Prediction of human pharmacokinetic profile in animal scale up based on normalizing time course profiles. J Pharm Sci. 2004;93(7):1890–900.CrossRefPubMedGoogle Scholar
  10. 10.
    Vuppugalla R, Marathe P, He H, Jones RDO, Yates JWT, Jones HM, et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 4: prediction of plasma concentration-time profiles in human from in vivo preclinical data by using the Wajima approach. J Pharm Sci. 2011;100(10):4111–26.CrossRefPubMedGoogle Scholar
  11. 11.
    Kostewicz ES, Aarons L, Bergstrand M, Bolger MB, Galetin A, Hatley O, et al. PBPK models for the prediction of in vivo performance of oral dosage forms. Eur J Pharm Sci. 2014;57:300–21.CrossRefPubMedGoogle Scholar
  12. 12.
    De Buck SS, Sinha VK, Fenu LA, Gilissen RA, Mackie CE, Nijsen MJ. The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools. Drug Metab Dispos. 2007;35(4):649–59.CrossRefPubMedGoogle Scholar
  13. 13.
    De Buck SS, Sinha VK, Fenu LA, Nijsen MJ, Mackie CE, Gilissen RAHJ. Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs. Drug Metab Dispos. 2007;35(10):1766–80.CrossRefPubMedGoogle Scholar
  14. 14.
    Poulin P, Jones RDO, Jones HM, Gibson CR, Rowland M, Chien JY, et al. PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: prediction of plasma concentration-time profiles in human by using the physiologically-based pharmacokinetic modeling approach. J Pharm Sci. 2011;100(10):4127–57.CrossRefPubMedGoogle Scholar
  15. 15.
    Parrott N, Lave T. Applications of physiologically based absorption models in drug discovery and development. Mol Pharm. 2008;5(5):760–75.CrossRefPubMedGoogle Scholar
  16. 16.
    Jones HM, Gardner IB, Watson KJ. Modelling and PBPK simulation in drug discovery. AAPS J. 2009;11(1):155–66.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Peters SA. Evaluation of a generic physiologically based pharmacokinetic model for lineshape analysis. Clin Pharmacokinet. 2008;47(4):261–75.CrossRefPubMedGoogle Scholar
  18. 18.
    Sjogren E, Westergren J, Grant I, Hanisch G, Lindfors L, Lennernas H, et al. In silico predictions of gastrointestinal drug absorption in pharmaceutical product development: application of the mechanistic absorption model GI-Sim. Eur J Pharm Sci. 2013;49(4):679–98.CrossRefPubMedGoogle Scholar
  19. 19.
    Faller B, Ottaviani G, Ertl P, Berellini G, Collis A. Evolution of the physicochemical properties of marketed drugs: can history foretell the future? Drug Discov Today. 2011;16(21–22):976–84.CrossRefPubMedGoogle Scholar
  20. 20.
    Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev. 2001;50 Suppl 1:S41–67.CrossRefPubMedGoogle Scholar
  21. 21.
    Ungell AL, Nylander S, Bergstrand S, Sjoberg A, Lennernas H. Membrane transport of drugs in different regions of the intestinal tract of the rat. J Pharm Sci. 1998;87(3):360–6.CrossRefPubMedGoogle Scholar
  22. 22.
    Gastroplus simulation software for drug discovery and development version 8.5, Ed. Simulations Plus. 2013.Google Scholar
  23. 23.
    Lu AT, Frisella ME, Johnson KC. Dissolution modeling: factors affecting the dissolution rates of polydisperse powders. Pharm Res. 1993;10(9):1308–14.CrossRefPubMedGoogle Scholar
  24. 24.
    Gedeck P, Lu Y, Skolnik S, Rodde S, Dollinger G, Jia W, et al. Benefit of retraining pKa models studied using internally measured data. J Chem Inf Model. 2015;55(7):1449–59.CrossRefPubMedGoogle Scholar
  25. 25.
    Jantratid E, Janssen N, Reppas C, Dressman JB. Dissolution media simulating conditions in the proximal human gastrointestinal tract: an update. Pharm Res. 2008;25(7):1663–76.CrossRefPubMedGoogle Scholar
  26. 26.
    Mithani SD, Bakatselou V, TenHoor CN, Dressman JB. Estimation of the increase in solubility of drugs as a function of bile salt concentration. Pharm Res. 1996;13(1):163–7.CrossRefPubMedGoogle Scholar
  27. 27.
    Wohnsland F, Faller B. High-throughput permeability pH profile and high-throughput alkane/water log P with artificial membranes. J Med Chem. 2001;44(6):923–30.CrossRefPubMedGoogle Scholar
  28. 28.
    Ito K, Houston JB. Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm Res. 2004;21(5):785–92.CrossRefPubMedGoogle Scholar
  29. 29.
    Davies B, Morris T. Physiological parameters in laboratory animals and humans. Pharm Res. 1993;10(7):1093–5.CrossRefPubMedGoogle Scholar
  30. 30.
    Amidon GL, Lennernas H, Shah VP, Crison JR. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res. 1995;12(3):413–20.CrossRefPubMedGoogle Scholar
  31. 31.
    Zaki NM, Artursson P, Bergstrom CAS. A modified physiological BCS for prediction of intestinal absorption in drug discovery. Mol Pharm. 2010;7(5):1478–87.CrossRefPubMedGoogle Scholar
  32. 32.
    Heikkinen AT, Baneyx G, Caruso A, Parrott N. Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates - an evaluation and case study using GastroPlus. Eur J Pharm Sci. 2012;47(2):375–86.CrossRefPubMedGoogle Scholar
  33. 33.
    Heikkinen AT, Fowler S, Gray L, Li J, Peng Y, Yadava P, et al. In vitro to in vivo extrapolation and physiologically based modeling of cytochrome P450 mediated metabolism in beagle dog gut wall and liver. Mol Pharm. 2013;10(4):1388–99.CrossRefPubMedGoogle Scholar
  34. 34.
    Tanaka Y, Waki R, Nagata S. Species differences in the dissolution and absorption of griseofulvin and albendazole, biopharmaceutics classification system class II drugs, in the gastrointestinal tract. Drug Metab Pharmacokinet. 2013;28(6):485–90.CrossRefPubMedGoogle Scholar
  35. 35.
    Tanaka Y, Baba T, Tagawa K, Waki R, Nagata S. Prediction of oral absorption of low-solubility drugs by using rat simulated gastrointestinal fluids: the importance of regional differences in membrane permeability and solubility. J Pharm Pharm Sci. 2014;17(1):106–20.CrossRefPubMedGoogle Scholar
  36. 36.
    Gao Y, Carr RA, Spence JK, Wang WW, Turner TM, Lipari JM, et al. A pH-dilution method for estimation of biorelevant drug solubility along the gastrointestinal tract: application to physiologically based pharmacokinetic modeling. Mol Pharm. 2010;7(5):1516–26.CrossRefPubMedGoogle Scholar
  37. 37.
    Berghausen J, Seiler FH, Gobeau N, Faller B. Simulated rat intestinal fluid improves oral exposure prediction for poorly soluble compounds over a wide dose range. ADMET DMPK. 2016;4(1):35–53.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • N. Gobeau
    • 1
    • 2
  • R. Stringer
    • 1
  • S. De Buck
    • 3
  • T. Tuntland
    • 4
  • B. Faller
    • 1
  1. 1.Metabolism and Pharmacokinetics (MAP) DepartmentNovartis Institutes for Biomedical ResearchBaselSwitzerland
  2. 2.Medicines for Malaria VentureGeneva 15Switzerland
  3. 3.Drug Metabolism and Pharmacokinetics (DMPK) DepartmentNovartis Institutes for Biomedical ResearchBaselSwitzerland
  4. 4.Metabolism and Pharmacokinetics (MAP) DepartmentGenomics Institute of the Novartis Foundation, Novartis Institutes for Biomedical ResearchSan DiegoUSA

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