Advertisement

Variance based global sensitivity analysis of physiologically based pharmacokinetic absorption models for BCS I–IV drugs

  • Nicola MelilloEmail author
  • Leon Aarons
  • Paolo Magni
  • Adam S. Darwich
Original Paper
First Online:

Abstract

Regulatory agencies have a strong interest in sensitivity analysis for the evaluation of physiologically-based pharmacokinetic (PBPK) models used in pharmaceutical research and drug development and regulatory submissions. One of the applications of PBPK is the prediction of fraction absorbed and bioavailability for drugs following oral administration. In this context, we performed a variance based global sensitivity analysis (GSA) on in-house PBPK models for drug absorption, with the aim of identifying key parameters that influence the predictions of the fraction absorbed and the bioavailability for neutral, acidic and basic compounds. This analysis was done for four different classes of drugs, defined according to the Biopharmaceutics Classification System, differentiating compounds by permeability and solubility. For class I compounds (highly permeable, highly soluble), the parameters that mainly influence the fraction absorbed are related to the formulation properties, for class II compounds (highly permeable, lowly soluble) to the dissolution process, for class III (lowly permeable, highly soluble) to both absorption process and formulation properties and for class IV (lowly permeable, lowly soluble) to both absorption and dissolution processes. Considering the bioavailability, the results are similar to those for the fraction absorbed, with the addition that parameters related to gut wall and liver clearance influence as well the predictions. This work aimed to give a demonstration of the GSA methodology and highlight its importance in improving our understanding of PBPK absorption models and in guiding the choice of parameters that can safely be assumed, estimated or require data generation to allow informed model prediction.

Keywords

Drug absorption Oral bioavailability Global sensitivity analysis Variance based GSA PBPK BCS 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work is an in-kind contribution to the OrBiTo Project (http://www.imi.europa.eu/content/orbito).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10928_2018_9615_MOESM1_ESM.pdf (7.5 mb)
Supplementary material 1 (PDF 7641 kb)

References

  1. 1.
    Margolskee A, Darwich AS, Pepin X, Pathak SM, Bolger MB, Aarons L, Rostami-Hodjegan A, Angstenberger J, Graf F, Laplanche L, Müller T, Carlert S, Daga P, Murphy D, Tannergren C, Yasin M, Greschat-Schade S, Mück W, Muenster U, van der Mey D, Frank KJ, Lloyd R, Adriaenssen L, Bevernage J, De Zwart L, Swerts D, Tistaert C, Van Den Bergh A, Van Peer A, Beato S, Nguyen-Trung A-T, Bennett J, McAllister M, Wong M, Zane P, Ollier C, Vicat P, Kolhmann M, Marker A, Brun P, Mazuir F, Beilles S, Venczel M, Boulenc X, Loos P, Lennernäs H, Abrahamsson B (2017) IMI—oral biopharmaceutics tools project—evaluation of bottom-up PBPK prediction success part 1: characterisation of the OrBiTo database of compounds. Eur J Pharm Sci 96:598–609.  https://doi.org/10.1016/j.ejps.2016.09.027 CrossRefGoogle Scholar
  2. 2.
    Amidon GL, Lennernäs H, Shah VP, Crison JR (1995) A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res 12:413–420.  https://doi.org/10.1023/A:1016212804288 CrossRefGoogle Scholar
  3. 3.
    Dahan A, Miller JM, Amidon GL (2009) Prediction of solubility and permeability class membership: provisional BCS classification of the world’s top oral drugs. AAPS J 11:740–746.  https://doi.org/10.1208/s12248-009-9144-x CrossRefGoogle Scholar
  4. 4.
    CDER (FDA) (2000) Guidance for industry: waiver of in vivo bioavailability and bioequivalence studies for immediate-release solid oral dosage forms based on a Biopharmaceutics Classification System. Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), RockvilleGoogle Scholar
  5. 5.
    Lennernäs H (2014) Human in vivo regional intestinal permeability: importance for pharmaceutical drug development. Mol Pharm 11:12–23.  https://doi.org/10.1021/mp4003392 CrossRefGoogle Scholar
  6. 6.
    Agoram B, Woltosz WS, Bolger MB (2001) Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev 50:S41–S67.  https://doi.org/10.1016/S0169-409X(01)00179-X CrossRefGoogle Scholar
  7. 7.
    Cong D, Doherty M, Pang KS (2000) A new physiologically based, segregated-flow model to explain route-dependent intestinal metabolism. Drug Metab Dispos Biol Fate Chem 28:224–235Google Scholar
  8. 8.
    Darwich AS, Neuhoff S, Jamei M, Rostami-Hodjegan A (2010) Interplay of metabolism and transport in determining oral drug absorption and gut wall metabolism: a simulation assessment using the “Advanced Dissolution, Absorption, Metabolism (ADAM)” model. Curr Drug Metab 11:716–729.  https://doi.org/10.2174/138920010794328913 CrossRefGoogle Scholar
  9. 9.
    Gertz M, Houston JB, 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.  https://doi.org/10.1124/dmd.111.039248 CrossRefGoogle Scholar
  10. 10.
    Jamei M, Turner D, Yang J, Neuhoff S, Polak S, Rostami-Hodjegan A, Tucker G (2009) Population-based mechanistic prediction of oral drug absorption. AAPS J 11:225–237.  https://doi.org/10.1208/s12248-009-9099-y CrossRefGoogle Scholar
  11. 11.
    Willmann S, Schmitt W, Keldenich J, Lippert J, Dressman JB (2004) A physiological model for the estimation of the fraction dose absorbed in humans. J Med Chem 47:4022–4031.  https://doi.org/10.1021/jm030999b CrossRefGoogle Scholar
  12. 12.
    Parrott N, Lave T (2008) Applications of physiologically based absorption models in drug discovery and development. Mol Pharm 5:760–775.  https://doi.org/10.1021/mp8000155 CrossRefGoogle Scholar
  13. 13.
    Jamei M (2016) Recent advances in development and application of physiologically-based pharmacokinetic (PBPK) models: a transition from academic curiosity to regulatory acceptance. Curr Pharmacol Rep 2:161–169.  https://doi.org/10.1007/s40495-016-0059-9 CrossRefGoogle Scholar
  14. 14.
    Jones HM, Dickins M, Youdim K, Gosset JR, Attkins NJ, Hay TL, Gurrell IK, Logan YR, Bungay PJ, Jones BC, Gardner IB (2012) Application of PBPK modelling in drug discovery and development at Pfizer. Xenobiotica 42:94–106.  https://doi.org/10.3109/00498254.2011.627477 CrossRefGoogle Scholar
  15. 15.
    Lennernäs H, Aarons L, Augustijns P, Beato S, Bolger M, Box K, Brewster M, Butler J, Dressman J, Holm R, Julia Frank K, Kendall R, Langguth P, Sydor J, Lindahl A, McAllister M, Muenster U, Müllertz A, Ojala K, Pepin X, Reppas C, Rostami-Hodjegan A, Verwei M, Weitschies W, Wilson C, Karlsson C, Abrahamsson B (2014) Oral biopharmaceutics tools—time for a new initiative—an introduction to the IMI project OrBiTo. Eur J Pharm Sci 57:292–299.  https://doi.org/10.1016/j.ejps.2013.10.012 CrossRefGoogle Scholar
  16. 16.
    Darwich AS, Margolskee A, Pepin X, Aarons L, Galetin A, Rostami-Hodjegan A, Carlert S, Hammarberg M, Hilgendorf C, Johansson P, Karlsson E, Murphy D, Tannergren C, Thörn H, Yasin M, Mazuir F, Nicolas O, Ramusovic S, Xu C, Pathak SM, Korjamo T, Laru J, Malkki J, Pappinen S, Tuunainen J, Dressman J, Hansmann S, Kostewicz E, He H, Heimbach T, Wu F, Hoft C, Pang Y, Bolger MB, Huehn E, Lukacova V, Mullin JM, Szeto KX, Costales C, Lin J, McAllister M, Modi S, Rotter C, Varma M, Wong M, Mitra A, Bevernage J, Biewenga J, Van Peer A, Lloyd R, Shardlow C, Langguth P, Mishenzon I, Nguyen MA, Brown J, Lennernäs H, Abrahamsson B (2017) IMI—oral biopharmaceutics tools project—evaluation of bottom-up PBPK prediction success part 3: identifying gaps in system parameters by analysing In Silico performance across different compound classes. Eur J Pharm Sci 96:626–642.  https://doi.org/10.1016/j.ejps.2016.09.037 CrossRefGoogle Scholar
  17. 17.
    Margolskee A, Darwich AS, Pepin X, Aarons L, Galetin A, Rostami-Hodjegan A, Carlert S, Hammarberg M, Hilgendorf C, Johansson P, Karlsson E, Murphy D, Tannergren C, Thörn H, Yasin M, Mazuir F, Nicolas O, Ramusovic S, Xu C, Pathak SM, Korjamo T, Laru J, Malkki J, Pappinen S, Tuunainen J, Dressman J, Hansmann S, Kostewicz E, He H, Heimbach T, Wu F, Hoft C, Laplanche L, Pang Y, Bolger MB, Huehn E, Lukacova V, Mullin JM, Szeto KX, Costales C, Lin J, McAllister M, Modi S, Rotter C, Varma M, Wong M, Mitra A, Bevernage J, Biewenga J, Van Peer A, Lloyd R, Shardlow C, Langguth P, Mishenzon I, Nguyen MA, Brown J, Lennernäs H, Abrahamsson B (2017) IMI—oral biopharmaceutics tools project—evaluation of bottom-up PBPK prediction success part 2: an introduction to the simulation exercise and overview of results. Eur J Pharm Sci 96:610–625.  https://doi.org/10.1016/j.ejps.2016.10.036 CrossRefGoogle Scholar
  18. 18.
    Zhang X-Y, Trame M, Lesko L, Schmidt S (2015) Sobol sensitivity analysis: a tool to guide the development and evaluation of systems pharmacology models. CPT Pharmacomet Syst Pharmacol 4:69–79.  https://doi.org/10.1002/psp4.6 CrossRefGoogle Scholar
  19. 19.
    Saltelli A, Tarantola S, Campolongo F, Ratto M (2004) Sensitivity analysis in practice: a guide to assessing scientific models. Wiley, San FranciscoGoogle Scholar
  20. 20.
    Borgonovo E, Plischke E (2016) Sensitivity analysis: a review of recent advances. Eur J Oper Res 248:869–887.  https://doi.org/10.1016/j.ejor.2015.06.032 CrossRefGoogle Scholar
  21. 21.
    Iooss B, Lemaître P (2015) A review on global sensitivity analysis methods. In: Meloni C, Dellino G (eds) Uncertainty management in simulation-optimization of complex systems: algorithms and applications. Springer, New YorkGoogle Scholar
  22. 22.
    Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, Wagener T (2016) Sensitivity analysis of environmental models: a systematic review with practical workflow. Environ Model Softw 79:214–232.  https://doi.org/10.1016/j.envsoft.2016.02.008 CrossRefGoogle Scholar
  23. 23.
    Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global sensitivity analysis. The Primer. Wiley, ChichesterGoogle Scholar
  24. 24.
    Fenneteau F, Li J, Nekka F (2009) Assessing drug distribution in tissues expressing P-glycoprotein using physiologically based pharmacokinetic modeling: identification of important model parameters through global sensitivity analysis. J Pharmacokinet Pharmacodyn 36:495.  https://doi.org/10.1007/s10928-009-9134-8 CrossRefGoogle Scholar
  25. 25.
    CDER (FDA) (2016) Physiologically Based Pharmacokinetic analyses—format and content: guidance for industry, draft. Center for Drug Evaluation and Research (CDER), U.S. Food and Drug Administration (FDA), RockvilleGoogle Scholar
  26. 26.
    CHMP (EMA) (2016) Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation—draft. Committee for Medicinal Products for Human Use (CHMP), European Medicines Agency (EMA), LondonGoogle Scholar
  27. 27.
    Dressman JB, Fleisher D (1986) Mixing-tank model for predicting dissolution rate control of oral absorption. J Pharm Sci 75:109–116.  https://doi.org/10.1002/jps.2600750202 CrossRefGoogle Scholar
  28. 28.
    Yu LX, Amidon GL (1999) A compartmental absorption and transit model for estimating oral drug absorption. Int J Pharm 186:119–125.  https://doi.org/10.1016/S0378-5173(99)00147-7 CrossRefGoogle Scholar
  29. 29.
    Saltelli A (2002) Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145:280–297.  https://doi.org/10.1016/S0010-4655(02)00280-1 CrossRefGoogle Scholar
  30. 30.
    Sobol IM (1993) Sensitivity estimates for nonlinear mathematical models. Math Model Comput Exp 1:407–414Google Scholar
  31. 31.
    Saltelli A, Annoni P (2010) How to avoid a perfunctory sensitivity analysis. Environ Model Softw 25:1508–1517.  https://doi.org/10.1016/j.envsoft.2010.04.012 CrossRefGoogle Scholar
  32. 32.
    MATLAB R2017b (2017) The MahWorks, Inc., Natick, MAGoogle Scholar
  33. 33.
    Archer GEB, Saltelli A, Sobol IM (1997) Sensitivity measures, Anova-like techniques and the use of bootstrap. J Stat Comput Simul 58:99–120.  https://doi.org/10.1080/00949659708811825 CrossRefGoogle Scholar
  34. 34.
    Lennernäs H, Palm K, Fagerholm U, Artursson P (1996) Comparison between active and passive drug transport in human intestinal epithelial (CACO-2) cells in vitro and human jejunum in vivo. Int J Pharm 127:103–107.  https://doi.org/10.1016/0378-5173(95)04204-0 CrossRefGoogle Scholar
  35. 35.
    Scherholz ML, Forder J, Androulakis IP (2018) A framework for 2-stage global sensitivity analysis of GastroPlus™ compartmental models. J Pharmacokinet Pharmacodyn 45:309–327.  https://doi.org/10.1007/s10928-018-9573-1 CrossRefGoogle Scholar
  36. 36.
    McNally K, Cotton R, Loizou GD (2011) A workflow for global sensitivity analysis of PBPK models. Front Pharmacol 2:31.  https://doi.org/10.3389/fphar.2011.00031 CrossRefGoogle Scholar
  37. 37.
    Daga PR, Bolger MB, Haworth IS, Clark RD, Martin EJ (2018) Physiologically based pharmacokinetic modeling in lead optimization. 2. Rational bioavailability design by global sensitivity analysis to identify properties affecting bioavailability. Mol Pharm 15:831–839.  https://doi.org/10.1021/acs.molpharmaceut.7b00973 CrossRefGoogle Scholar
  38. 38.
    Simcyp (2017) Simcyp Simulator—Version 17. CERTARA, L. P., SheffieldGoogle Scholar
  39. 39.
    Bu H-Z (2006) A literature review of enzyme kinetic parameters for CYP3A4-mediated metabolic reactions of 113 drugs in human liver microsomes: structure–kinetics relationship assessment. Curr Drug Metab 7:231–249.  https://doi.org/10.2174/138920006776359329 CrossRefGoogle Scholar
  40. 40.
    Sugano K (2008) Theoretical comparison of hydrodynamic diffusion layer models used for dissolution simulation in drug discovery and development. Int J Pharm 363:73–77.  https://doi.org/10.1016/j.ijpharm.2008.07.002 CrossRefGoogle Scholar
  41. 41.
    Manallack DT (2007) The pKa distribution of drugs: application to drug discovery. Perspect Med Chem.  https://doi.org/10.1177/1177391x0700100003 Google Scholar
  42. 42.
    Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25.  https://doi.org/10.1016/S0169-409X(96)00423-1 CrossRefGoogle Scholar
  43. 43.
    Valetin J (2006) Human alimentary tract model for radiological protection. Ann Int Comm Radiol Prot (ICRP) 36(1–2):25–327Google Scholar
  44. 44.
    Gastroplus (2017) Gastroplus—Version 9.5. Simulations Plus, LancasterGoogle Scholar
  45. 45.
    Snyder WS, Cook MJ, Nasset ES, Karhausen LR, Parry Howells G, Tipton IH (1975) Report of the task group on reference man. Ann Int Comm Radiol Prot (ICRP) 100:100.  https://doi.org/10.1016/0146-6453(79)90123-4 Google Scholar
  46. 46.
    Olivares-Morales A, Ghosh A, Aarons L, Rostami-Hodjegan A (2016) Development of a novel simplified PBPK absorption model to explain the higher relative bioavailability of the OROS® formulation of oxybutynin. AAPS J 18:1532–1549.  https://doi.org/10.1208/s12248-016-9965-3 CrossRefGoogle Scholar
  47. 47.
    Valetin J (2002) Basic anatomical and physiological data for use in radiological protection: reference values. Int Comm Radiol Prot (ICRP) 32(3):1–277Google Scholar
  48. 48.
    Brown RP, Delp MD, Lindstedt SL, Rhomberg LR, Beliles RP (1997) Physiological parameter values for physiologically based pharmacokinetic models. Toxicol Ind Health 13:407–484.  https://doi.org/10.1177/074823379701300401 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Bioinformatics, Mathematical Modelling and Synthetic Biology, Department of Electrical, Computer and Biomedical EngineeringUniversità degli Studi di PaviaPaviaItaly
  2. 2.Centre for Applied Pharmacokinetic Research, Division of Pharmacy & OptometryThe University of ManchesterManchesterUK

Personalised recommendations

Cite article

Buy options