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Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine

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Abstract

Personalized medicine strives to deliver the ‘right drug at the right dose’ by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

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References

  1. Noetzli M, Eap CB (2013) Pharmacodynamic, pharmacokinetic and pharmacogenetic aspects of drugs used in the treatment of Alzheimer’s disease. Clin Pharmacokinet 52(4):225–241. doi:10.1007/s40262-013-0038-9

    Article  CAS  PubMed  Google Scholar 

  2. Schork NJ (2015) Personalized medicine: time for one-person trials. Nature 520(7549):609–611. doi:10.1038/520609a

    Article  CAS  PubMed  Google Scholar 

  3. Lesko LJ, Schmidt S (2012) Individualization of drug therapy: history, present state, and opportunities for the future. Clin Pharmacol Ther 92(4):458–466. doi:10.1038/clpt.2012.113

    CAS  PubMed  Google Scholar 

  4. Waldman SA, Terzic A (2011) Patient-centric clinical pharmacology advances the path to personalized medicine. Biomark Med 5(6):697–700. doi:10.2217/bmm.11.78

    Article  PubMed  Google Scholar 

  5. Redekop WK, Mladsi D (2013) The faces of personalized medicine: a framework for understanding its meaning and scope. Value Health 16(6 Suppl):S4–9. doi:10.1016/j.jval.2013.06.005

    Article  PubMed  Google Scholar 

  6. Rowland M, Peck C, Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45–73. doi:10.1146/annurev-pharmtox-010510-100540

    Article  CAS  PubMed  Google Scholar 

  7. Chetty M, Rose RH, Abduljalil K, Patel N, Lu G, Cain T, Jamei M, Rostami-Hodjegan A (2014) Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability. Front Pharmacol 5:258. doi:10.3389/fphar.2014.00258

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. 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(2):225–237. doi:10.1208/s12248-009-9099-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chenel M, Bouzom F, Aarons L, Ogungbenro K (2008) Drug–drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 1: comparison of uniresponse and multiresponse designs using PopDes. J Pharmacokinet Pharmacodyn 35(6):635–659. doi:10.1007/s10928-008-9104-6

    Article  CAS  PubMed  Google Scholar 

  10. Jones HM, Mayawala K, Poulin P (2013) Dose selection based on physiologically based pharmacokinetic (PBPK) approaches. AAPS J 15(2):377–387. doi:10.1208/s12248-012-9446-2

    Article  CAS  PubMed  Google Scholar 

  11. Wagner C, Zhao P, Pan Y, Hsu V, Grillo J, Huang SM, Sinha V (2015) Application of physiologically based pharmacokinetic (PBPK) modeling to support dose selection: report of an FDA public workshop on PBPK. CPT Pharmacomet Syst Pharmacol 4(4):226–230. doi:10.1002/psp4.33

    Article  CAS  Google Scholar 

  12. Musib L, Choo E, Deng Y, Eppler S, Rooney I, Chan IT, Dresser MJ (2013) Absolute bioavailability and effect of formulation change, food, or elevated pH with rabeprazole on cobimetinib absorption in healthy subjects. Mol Pharm 10(11):4046–4054. doi:10.1021/mp400383x

    Article  CAS  PubMed  Google Scholar 

  13. Shono Y, Jantratid E, Kesisoglou F, Reppas C, Dressman JB (2010) Forecasting in vivo oral absorption and food effect of micronized and nanosized aprepitant formulations in humans. Eur J Pharm Biopharm 76(1):95–104. doi:10.1016/j.ejpb.2010.05.009

    Article  CAS  PubMed  Google Scholar 

  14. Pandey P, Hamey R, Bindra DS, Huang Z, Mathias N, Eley T, Crison J, Yan B, Perrone R, Vemavarapu C (2014) From bench to humans: formulation development of a poorly water soluble drug to mitigate food effect. AAPS Pharm Sci Tech 15(2):407–416. doi:10.1208/s12249-013-0069-4

    Article  CAS  Google Scholar 

  15. Olivares-Morales A, Kamiyama Y, Darwich AS, Aarons L, Rostami-Hodjegan A (2015) Analysis of the impact of controlled release formulations on oral drug absorption, gut wall metabolism and relative bioavailability of CYP3A substrates using a physiologically-based pharmacokinetic model. Eur J Pharm Sci 67:32–44. doi:10.1016/j.ejps.2014.10.018

    Article  CAS  PubMed  Google Scholar 

  16. Willmann S, Thelen K, Lippert J (2012) Integration of dissolution into physiologically-based pharmacokinetic models III: PK-Sim(R). J Pharm Pharmacol 64(7):997–1007. doi:10.1111/j.2042-7158.2012.01534.x

    Article  CAS  PubMed  Google Scholar 

  17. Kostewicz ES, Aarons L, Bergstrand M, Bolger MB, Galetin A, Hatley O, Jamei M, Lloyd R, Pepin X, Rostami-Hodjegan A, Sjogren E, Tannergren C, Turner DB, Wagner C, Weitschies W, Dressman J (2014) PBPK models for the prediction of in vivo performance of oral dosage forms. Eur J Pharm Sci 57:300–321. doi:10.1016/j.ejps.2013.09.008

    Article  CAS  PubMed  Google Scholar 

  18. Parrott N, Hainzl D, Scheubel E, Krimmer S, Boetsch C, Guerini E, Martin-Facklam M (2014) Physiologically based absorption modelling to predict the impact of drug properties on pharmacokinetics of bitopertin. AAPS J 16(5):1077–1084. doi:10.1208/s12248-014-9639-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kambayashi A, Blume H, Dressman JB (2014) Predicting the oral pharmacokinetic profiles of multiple-unit (pellet) dosage forms using a modeling and simulation approach coupled with biorelevant dissolution testing: case example diclofenac sodium. Eur J Pharm Biopharm 87(2):236–243. doi:10.1016/j.ejpb.2014.01.007

    Article  CAS  PubMed  Google Scholar 

  20. Mathias NR, Crison J (2012) The use of modeling tools to drive efficient oral product design. AAPS J 14(3):591–600. doi:10.1208/s12248-012-9372-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kesisoglou F, Wu Y (2008) Understanding the effect of API properties on bioavailability through absorption modeling. AAPS J 10(4):516–525. doi:10.1208/s12248-008-9061-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kambayashi A, Blume H, Dressman J (2013) Understanding the in vivo performance of enteric coated tablets using an in vitro-in silico-in vivo approach: case example diclofenac. Eur J Pharm Biopharm 85:1337–1347. doi:10.1016/j.ejpb.2013.09.009

    Article  CAS  PubMed  Google Scholar 

  23. Kesisoglou F, Balakrishnan A, Manser K (2015) Utility of PBPK absorption modeling to guide modified release formulation development of gaboxadol, a highly soluble compound with region-dependent absorption. J Pharm Sci 105:722–728. doi:10.1002/jps.24674

    Article  CAS  Google Scholar 

  24. Kesisoglou F, Chung J, van Asperen J, Heimbach T (2016) Physiologically based absorption modeling to impact biopharmaceutics and formulation strategies in drug development-industry case studies. J Pharm Sci. doi:10.1016/j.xphs.2015.11.034

    Google Scholar 

  25. Shono Y, Jantratid E, Janssen N, Kesisoglou F, Mao Y, Vertzoni M, Reppas C, Dressman JB (2009) Prediction of food effects on the absorption of celecoxib based on biorelevant dissolution testing coupled with physiologically based pharmacokinetic modeling. Eur J Pharm Biopharm 73(1):107–114. doi:10.1016/j.ejpb.2009.05.009

    Article  CAS  PubMed  Google Scholar 

  26. Shepard T, Scott G, Cole S, Nordmark A, Bouzom F (2015) Physiologically Based Models in Regulatory Submissions: Output From the ABPI/MHRA Forum on Physiologically Based Modeling and Simulation. CPT Pharmacometrics Syst Pharmacol 4 (4):221-225. doi:10.1002/psp4.30

  27. Lionberger RA (2009) Regulatory applications of modelling and simulations at FDA. Paper presented at the physiologically based pharmacokinetic (PBPK) modeling in drug development and evaluation. Alexandria, April 6

  28. Luttringer O, Theil FP, Poulin P, Schmitt-Hoffmann AH, Guentert TW, Lave T (2003) Physiologically based pharmacokinetic (PBPK) modeling of disposition of epiroprim in humans. J Pharm Sci 92(10):1990–2007. doi:10.1002/jps.10461

    Article  CAS  PubMed  Google Scholar 

  29. Berlin M, Ruff A, Kesisoglou F, Xu W, Wang MH, Dressman JB (2015) Advances and challenges in PBPK modeling—analysis of factors contributing to the oral absorption of atazanavir, a poorly soluble weak base. Eur J Pharm Biopharm 93:267–280. doi:10.1016/j.ejpb.2015.03.031

    Article  CAS  PubMed  Google Scholar 

  30. Chowdhury MM, Kim DH, Ahn JK (2011) A physiologically based pharmacokinetic model for absorption and distribution of imatinib in human body. Bull Korean Chem Soc 32(11):3967–3972

    Article  CAS  Google Scholar 

  31. McNally K, Cotton R, Loizou GD (2011) A workflow for global sensitivity analysis of PBPK models. Front Pharmacol 2:31. doi:10.3389/fphar.2011.00031

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Evans MV, Andersen ME (2000) Sensitivity analysis of a physiological model for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD): assessing the impact of specific model parameters on sequestration in liver and fat in the rat. Toxicol Sci 54(1):71–80

    Article  CAS  PubMed  Google Scholar 

  33. Gueorguieva I, Nestorov IA, Rowland M (2006) Reducing whole body physiologically based pharmacokinetic models using global sensitivity analysis: diazepam case study. J Pharmacokinet Pharmacodyn 33(1):1–27. doi:10.1007/s10928-005-0004-8

    Article  CAS  PubMed  Google Scholar 

  34. Hamburg MA, Collins FS (2010) The path to personalized medicine. N Engl J Med 363(4):301–304. doi:10.1056/NEJMp1006304

    Article  CAS  PubMed  Google Scholar 

  35. Huang SM, Abernethy DR, Wang Y, Zhao P, Zineh I (2013) The utility of modeling and simulation in drug development and regulatory review. J Pharm Sci 102(9):2912–2923. doi:10.1002/jps.23570

    Article  CAS  PubMed  Google Scholar 

  36. Kang JS, Lee MH (2009) Overview of therapeutic drug monitoring. Korean J Intern Med 24(1):1–10. doi:10.3904/kjim.2009.24.1.1

    Article  PubMed  PubMed Central  Google Scholar 

  37. Strougo A, Yassen A, Krauwinkel W, Danhof M, Freijer J (2011) A semiphysiological population model for prediction of the pharmacokinetics of drugs under liver and renal disease conditions. Drug Metab Dispos 39(7):1278–1287. doi:10.1124/dmd.110.037838

    Article  CAS  PubMed  Google Scholar 

  38. Johansson F, Paterson R (2008) Physiologically based in silico models for prediction of oral drug absorption. Drug Absorpt Stud 21:486–509

    Article  Google Scholar 

  39. Nestorov I (2003) Whole body pharmacokinetic models. Clin Pharmacokinet 42(10):883–908. doi:10.2165/00003088-200342100-00002

    Article  CAS  PubMed  Google Scholar 

  40. Gupta S, Kesarla R, Omri A (2013) Formulation strategies to improve the bioavailability of poorly absorbed drugs with special emphasis on self-emulsifying systems. ISRN Pharm 2013:848043. doi:10.1155/2013/848043

  41. Yang R, Mayeno A, Liao K, Raeardon K, Reisfeld B (2005) Integration of PBPK and reaction network modelling: predictive xenobiotic metabolomics. Altex 6(2):373–379

    Google Scholar 

  42. Teorell T (1937) Kinetics of distribution of substances administered to the body. Arch Int Pharmacodyn Ther 57:205–240

    CAS  Google Scholar 

  43. Winkle HN (2007) Evolution of the global regulatory environment: a practical approach to change—implementing quality by design. In: PDA/FDA joint regulatory conference

  44. Kesisoglou F, Mitra A (2015) Application of absorption modeling in rational design of drug product under quality-by-design paradigm. AAPS J 17(5):1224–1236. doi:10.1208/s12248-015-9781-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhang X, Lionberger RA, Davit BM, Yu LX (2011) Utility of physiologically based absorption modeling in implementing quality by design in drug development. AAPS J 13(1):59–71. doi:10.1208/s12248-010-9250-9

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Jiang W, Kim S, Zhang X, Lionberger RA, Davit BM, Conner DP, Yu LX (2011) The role of predictive biopharmaceutical modeling and simulation in drug development and regulatory evaluation. Int J Pharm 418(2):151–160. doi:10.1016/j.ijpharm.2011.07.024

    Article  CAS  PubMed  Google Scholar 

  47. Peck CC (2010) Quantitative clinical pharmacology is transforming drug regulation. J Pharmacokinet Pharmacodyn 37(6):617–628. doi:10.1007/s10928-010-9171-3

    Article  CAS  PubMed  Google Scholar 

  48. Graf JF, Scholz BJ, Zavodszky MI (2012) BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems. J Pharmacokinet Pharmacodyn 39(1):37–54. doi:10.1007/s10928-011-9229-x

    Article  PubMed  Google Scholar 

  49. Grass GM, Sinko PJ (2002) Physiologically-based pharmacokinetic simulation modelling. Adv Drug Deliv Rev 54(3):433–451

    Article  CAS  PubMed  Google Scholar 

  50. Khalil F, Laer S (2011) Physiologically based pharmacokinetic modeling: methodology, applications, and limitations with a focus on its role in pediatric drug development. J Biomed Biotechnol 2011:907461. doi:10.1155/2011/907461

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Dressman JB, Thelen K, Willmann S (2011) An update on computational oral absorption simulation. Expert Opin Drug Metab Toxicol 7(11):1345–1364. doi:10.1517/17425255.2011.617743

    Article  CAS  PubMed  Google Scholar 

  52. Paixao P, Gouveia LF, Morais JA (2012) Prediction of the human oral bioavailability by using in vitro and in silico drug related parameters in a physiologically based absorption model. Int J Pharm 429(1–2):84–98. doi:10.1016/j.ijpharm.2012.03.019

    Article  CAS  PubMed  Google Scholar 

  53. Zhao P, Zhang L, Grillo JA, Liu Q, Bullock JM, Moon YJ, Song P, Brar SS, Madabushi R, Wu TC, Booth BP, Rahman NA, Reynolds KS, Gil Berglund E, Lesko LJ, Huang SM (2011) Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin Pharmacol Ther 89(2):259–267. doi:10.1038/clpt.2010.298

    Article  CAS  PubMed  Google Scholar 

  54. Office of Clinical Pharmacology (OCP) (2015). http://www.fda.gov/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/ucm106189.htm. Accessed 15 June 2016

  55. Sinha V, Zhao P, Huang SM, Zineh I (2014) Physiologically based pharmacokinetic modeling: from regulatory science to regulatory policy. Clin Pharmacol Ther 95(5):478–480. doi:10.1038/clpt.2014.46

    Article  CAS  PubMed  Google Scholar 

  56. Zhao P, Rowland M, Huang SM (2012) Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther 92(1):17–20. doi:10.1038/clpt.2012.68

    Article  CAS  PubMed  Google Scholar 

  57. Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N, Peters SA, Snoeys J, Upreti VV, Zheng M, Hall SD (2015) Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther 97(3):247–262. doi:10.1002/cpt.37

    Article  CAS  PubMed  Google Scholar 

  58. Dickschen K, Willmann S, Thelen K, Lippert J, Hempel G, Eissing T (2012) Physiologically-based pharmacokinetic modeling of tamoxifen and its metabolites in women of different CYP2D6 phenotypes provides new insight into the tamoxifen mass balance. Front Pharmacol 3:92

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Almukainzi M, Jamali F, Aghazadeh-Habashi A, Löbenberg R (2016) Disease specific modeling: simulation of the pharmacokinetics of meloxicam and ibuprofen in disease state versus healthy conditions. Eur J Pharm Biopharm 100:77–84. doi:10.1016/j.ejpb.2015.12.004

    Article  CAS  PubMed  Google Scholar 

  60. Carrasco-Portugal MDC, Flores-Murrieta FJ (2011) Gender differences in the pharmacokinetics of oral drugs. Pharmacol Pharm 02(01):31–41. doi:10.4236/pp.2011.21004

    Article  CAS  Google Scholar 

  61. Khalil F, Laer S (2014) Physiologically based pharmacokinetic models in the prediction of oral drug exposure over the entire pediatric age range-sotalol as a model drug. AAPS J 16(2):226–239. doi:10.1208/s12248-013-9555-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wening K, Breitkreutz J (2011) Oral drug delivery in personalized medicine: unmet needs and novel approaches. Int J Pharm 404(1–2):1–9. doi:10.1016/j.ijpharm.2010.11.001

    Article  CAS  PubMed  Google Scholar 

  63. Abbiati RA, Manca D (2016) A modeling tool for the personalization of pharmacokinetic predictions. Comput Chem Eng 91:28–37. doi:10.1016/j.compchemeng.2016.03.008

    Article  CAS  Google Scholar 

  64. Androulakis IP (2015) Systems engineering meets quantitative systems pharmacology: from low-level targets to engaging the host defenses. Rev Syst Biol Med 7(3):101–112. doi:10.1002/wsbm.1294

    Article  CAS  Google Scholar 

  65. Tucker G, DeSilva B, Dressman J, Ito M, Kumamoto T, Mager D, Mahler HC, Maitland-van der Zee AH, Pauletti GM, Sasaki H, Shah V, Tang D, Ward M (2016) Current challenges and potential opportunities for the pharmaceutical sciences to make global impact: an FIP perspective. J Pharm Sci. doi:10.1016/j.xphs.2015.12.001

    PubMed  Google Scholar 

  66. Price PS, Conolly RB, Chaisson CF, Gross EA, Young JS, Mathis ET, Tedder DR (2010) Modeling interindividual variation in physiological factors used in PBPK models of humans. Crit Rev Toxicol 33(5):469–503. doi:10.1080/10408440390242324

    Article  Google Scholar 

  67. 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 1(3):161–169

    Article  CAS  Google Scholar 

  68. Barton HA, Chiu WA, Setzer RW, Andersen ME, Bailer AJ, Bois FY, Dewoskin RS, Hays S, Johanson G, Jones N, Loizou G, Macphail RC, Portier CJ, Spendiff M, Tan YM (2007) Characterizing uncertainty and variability in physiologically based pharmacokinetic models: state of the science and needs for research and implementation. Toxicol Sci 99(2):395–402. doi:10.1093/toxsci/kfm100

    Article  CAS  PubMed  Google Scholar 

  69. Franconi F, Brunelleschi S, Steardo L, Cuomo V (2007) Gender differences in drug responses. Pharmacol Res 55(2):81–95. doi:10.1016/j.phrs.2006.11.001

    Article  CAS  PubMed  Google Scholar 

  70. Fletcher CV, Acosta EP, Strykowski JM (1994) Gender differences in human pharmacokinetics and pharmacodynamics. J Adolesc Health 15:619–629

    Article  CAS  PubMed  Google Scholar 

  71. Whitley H, Lindsey W (2009) Sex-based differences in drug activity. Am Fam Physician 80(11):1254–1258

    PubMed  Google Scholar 

  72. Damoiseaux VA, Proost JH, Jiawan VC, Melgert BN (2014) Sex differences in the pharmacokinetics of antidepressants: influence of female sex hormones and oral contraceptives. Clin Pharmacokinet 53(6):509–519. doi:10.1007/s40262-014-0145-2

    Article  CAS  PubMed  Google Scholar 

  73. Regitz-Zagrosek V (2012) Sex and gender differences in pharmacology. Handbook of experimental pharmacology. Springer, Heidelberg

    Book  Google Scholar 

  74. Chen ML, Lee SC, Ng MJ, Schuirmann DJ, Lesko LJ, Williams RL (2000) Pharmacokinetic analysis of bioequivalence trials: implications for sex-related issues in clinical pharmacology and biopharmaceutics. Clin Pharmacol Ther 68(5):510–521. doi:10.1067/mcp.2000.111184

    Article  CAS  PubMed  Google Scholar 

  75. Greenblatt DJ, Harmatz JS, Roth T, Singh NN, Moline ML, Harris SC, Kapil RP (2013) Comparison of pharmacokinetic profiles of zolpidem buffered sublingual tablet and zolpidem oral immediate-release tablet: results from a single-center, single-dose, randomized, open-label crossover study in healthy adults. Clin Ther 35(5):604–611. doi:10.1016/j.clinthera.2013.03.007

    Article  CAS  PubMed  Google Scholar 

  76. Xu H, Gan J, Liu X, Wu R, Jin Y, Li M, Yuan B (2013) Gender-dependent pharmacokinetics of lignans in rats after single and multiple oral administration of Schisandra chinensis extract. J Ethnopharmacol 147(1):224–231. doi:10.1016/j.jep.2013.03.008

    Article  CAS  PubMed  Google Scholar 

  77. Hu L, Jin Y, Li YG, Borel A (2015) Population pharmacokinetic/pharmacodynamic assessment of pharmacological effect of a selective estrogen receptor β agonist on total testosterone in healthy men. Clin Pharmacol Drug Dev 4(4):305–314. doi:10.1002/cpdd.184

    Article  CAS  PubMed  Google Scholar 

  78. Freire AC, Basit AW, Choudhary R, Piong CW, Merchant HA (2011) Does sex matter? The influence of gender on gastrointestinal physiology and drug delivery. Int J Pharm 415(1–2):15–28. doi:10.1016/j.ijpharm.2011.04.069

    Article  CAS  PubMed  Google Scholar 

  79. Marazziti D, Baroni S, Picchetti M, Piccinni A, Carlini M, Vatteroni E, Falaschi V, Lombardi A, Dell’Osso L (2013) Pharmacokinetics and pharmacodynamics of psychotropic drugs: effect of sex. CNS Spectr 18(3):118–127. doi:10.1017/S1092852912001010

    Article  PubMed  Google Scholar 

  80. Soldin OP, Chung SH, Mattison DR (2011) Sex differences in drug disposition. J Biomed Biotechnol 2011:187103. doi:10.1155/2011/187103

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Kashuba AD, Nafziger AN (1998) Physiological changes during the menstrual cycle and their effect on the pharmacokinetics and pharmacodynamics of drugs. Clin Pharmacokinet 34(3):203–218

    Article  CAS  PubMed  Google Scholar 

  82. Sheth AN, Lahiri CD, Ofotokun I (2015) Sex differences in metabolism and pharmacokinetics. In: Klein SL, Roberts CW (eds) Sex and gender differences in infection and treatments for infectious diseases. Springer, New York

    Google Scholar 

  83. Anderson GD (2008) Gender differences in pharmacological response. Int Rev Neurobiol 83:1–10. doi:10.1016/s0074-7742(08)00001-9

    Article  PubMed  Google Scholar 

  84. Waxman DJ, Holloway MG (2009) Sex differences in the expression of hepatic drug metabolizing enzymes. Mol Pharmacol 76(2):215–228. doi:10.1124/mol.109.056705

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Cummins CL, Wu CY, Benet LZ (2002) Sex-related differences in the clearance of cytochrome P450 3A4 substrates may be caused by P-glycoprotein. Clin Pharmacol Ther 72(5):474–489. doi:10.1067/mcp.2002.128388

    Article  CAS  PubMed  Google Scholar 

  86. Anderson GD (2005) Sex and racial differences in pharmacological response: where is the evidence? Pharmacogenetics, pharmacokinetics, and pharmacodynamics. J Women’s Health 14:19–29

    Article  Google Scholar 

  87. Benet LZ, Cummins CL, Wu CY (2004) Unmasking the dynamic interplay between efflux transporters and metabolic enzymes. Int J Pharm 277(1–2):3–9. doi:10.1016/j.ijpharm.2002.12.002

    Article  CAS  PubMed  Google Scholar 

  88. Ibarra M, Vazquez M, Fagiolino P, Derendorf H (2013) Sex related differences on valproic acid pharmacokinetics after oral single dose. J Pharmacokinet Pharmacodyn 40(4):479–486. doi:10.1007/s10928-013-9323-3

    Article  CAS  PubMed  Google Scholar 

  89. Worley RR, Fisher J (2015) Application of physiologically-based pharmacokinetic modeling to explore the role of kidney transporters in renal reabsorption of perfluorooctanoic acid in the rat. Toxicol Appl Pharmacol 289(3):428–441. doi:10.1016/j.taap.2015.10.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Heikkinen AT, Baneyx G, Caruso A, Parrott N (2012) Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates—an evaluation and case study using GastroPlus. Eur J Pharm Sci 47(2):375–386. doi:10.1016/j.ejps.2012.06.013

    Article  CAS  PubMed  Google Scholar 

  91. Mangoni AA, Jackson SH (2004) Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications. Br J Clin Pharmacol 57(1):6–14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Ingrande J, Lemmens HJ (2010) Dose adjustment of anaesthetics in the morbidly obese. Br J Anaesth 105(Suppl 1):i16–i23. doi:10.1093/bja/aeq312

    Article  CAS  PubMed  Google Scholar 

  93. Feng B, LaPerle JL, Chang G, Varma MV (2010) Renal clearance in drug discovery and development: molecular descriptors, drug transporters and disease state. Expert Opin Drug Metab Toxicol 6(8):939–952. doi:10.1517/17425255.2010.482930

    Article  CAS  PubMed  Google Scholar 

  94. Koren G, Nordeng H, MacLeod S (2013) Gender differences in drug bioequivalence: time to rethink practices. Clin Pharmacol Ther 93(3):260–262. doi:10.1038/clpt.2012.233

    Article  CAS  PubMed  Google Scholar 

  95. Ibarra M, Magallanes L, Lorier M, Vazquez M, Fagiolino P (2016) Sex-by-formulation interaction assessed through a bioequivalence study of efavirenz tablets. Eur J Pharm Sci 85:106–111. doi:10.1016/j.ejps.2016.02.001

    Article  CAS  PubMed  Google Scholar 

  96. Frost CE, Song Y, Shenker A, Wang J, Barrett YC, Schuster A, Harris SI, LaCreta F (2015) Effects of age and sex on the single-dose pharmacokinetics and pharmacodynamics of apixaban. Clin Pharmacokinet 54(6):651–662. doi:10.1007/s40262-014-0228-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Schwartz JB (2007) The current state of knowledge on age, sex, and their interactions on clinical pharmacology. Clin Pharmacol Ther 82(1):87–96. doi:10.1038/sj.clpt.6100226

    Article  CAS  PubMed  Google Scholar 

  98. Italiano D, Perucca E (2013) Clinical pharmacokinetics of new-generation antiepileptic drugs at the extremes of age: an update. Clin Pharmacokinet 52(8):627–645. doi:10.1007/s40262-013-0067-4

    Article  CAS  PubMed  Google Scholar 

  99. Reeve E, Wiese MD, Mangoni AA (2015) Alterations in drug disposition in older adults. Expert Opin Drug Metab Toxicol 11(4):491–508. doi:10.1517/17425255.2015.1004310

    Article  PubMed  Google Scholar 

  100. Maharaj AR, Barrett JS, Edginton AN (2013) A workflow example of PBPK modeling to support pediatric research and development: case study with lorazepam. AAPS J 15(2):455–464. doi:10.1208/s12248-013-9451-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Barrett JS, Della Casa Alberighi O, Laer S, Meibohm B (2012) Physiologically based pharmacokinetic (PBPK) modeling in children. Clin Pharmacol Ther 92(1):40–49. doi:10.1038/clpt.2012.64

    Article  CAS  PubMed  Google Scholar 

  102. Yoon M, Clewell HJ (2016) Addressing early life sensitivity using physiologically based pharmacokinetic modeling and in vitro to in vivo extrapolation. Toxicol Res 32(1):15–20. doi:10.5487/TR.2016.32.1.015

    Article  PubMed  PubMed Central  Google Scholar 

  103. Yang F, Tong X, McCarver DG, Hines RN, Beard DA (2006) Population-based analysis of methadone distribution and metabolism using an age-dependent physiologically based pharmacokinetic model. J Pharmacokinet Pharmacodyn 33(4):485–518. doi:10.1007/s10928-006-9018-0

    Article  CAS  PubMed  Google Scholar 

  104. Huang SM (2012) Advisory committee for pharmaceutical science and clinical pharmacology meeting. National Harbor, March 12

  105. Villiger A, Stillhart C, Parrott N, Kuentz M (2016) Using physiologically based pharmacokinetic (PBPK) modeling to gain insights into the effect of physiological factors on oral absorption in pediatric populations. AAPS J 18(4):933–947

    Article  CAS  PubMed  Google Scholar 

  106. Cotreau M, von Moltke L, Greenblatt D (2005) The influence of age and sex on the clearance of cytochrome P450 3A substrates. Clin Pharmacokinet 44:33–60

    Article  CAS  PubMed  Google Scholar 

  107. Johnson TN, Zhou D, Bui KH (2014) Development of physiologically based pharmacokinetic model to evaluate the relative systemic exposure to quetiapine after administration of IR and XR formulations to adults, children and adolescents. Biopharm Drug Dispos 35(6):341–352. doi:10.1002/bdd.1899

    Article  CAS  PubMed  Google Scholar 

  108. Nicolas JM, Espie P, Molimard M (2009) Gender and interindividual variability in pharmacokinetics. Drug Metab Rev 41(3):408–421. doi:10.1080/10837450902891485

    Article  CAS  PubMed  Google Scholar 

  109. Morrissey KM, Stocker SL, Wittwer MB, Xu L, Giacomini KM (2013) Renal transporters in drug development. Annu Rev Pharmacol Toxicol 53:503–529. doi:10.1146/annurev-pharmtox-011112-140317

    Article  CAS  PubMed  Google Scholar 

  110. Kubitza D, Becka M, Roth A, Mueck W (2013) The influence of age and gender on the pharmacokinetics and pharmacodynamics of rivaroxaban—an oral, direct factor Xa inhibitor. J Clin Pharmacol 53(3):249–255. doi:10.1002/jcph.5

    Article  PubMed  CAS  Google Scholar 

  111. Zhao W, Zhang D, Storme T, Baruchel A, Decleves X, Jacqz-Aigrain E (2015) Population pharmacokinetics and dosing optimization of teicoplanin in children with malignant haematological disease. Br J Clin Pharmacol 80(5):1197–1207. doi:10.1111/bcp.12710

    Article  CAS  PubMed  Google Scholar 

  112. Grillo JA, Zhao P, Bullock JM, Booth BP, Lu M, Robie-Suh K, Berglund EG, Pang KS, Rahman A, Zhang L, Lesko LJ, Huang SM (2012) Utility of a physiologically-based pharmacokinetic (PBPK) modeling appraoch to quantitatively predict a complex drug-drug-disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice. Biopharm Drug Dispos 33:99–110. doi:10.1038/10.1002/bdd

    Article  CAS  PubMed  Google Scholar 

  113. Thompson CM, Johns DO, Sonawane B, Barton HA, Hattis D, Tardif R, Krishnan K (2009) Database for physiologically based pharmacokinetic (PBPK) modeling: physiological data for healthy and health-impaired elderly. J Toxicol Environ Health B 12(1):1–24. doi:10.1080/10937400802545060

    Article  CAS  Google Scholar 

  114. Peterson JK, Houghton PJ (2004) Integrating pharmacology and in vivo cancer models in preclinical and clinical drug development. Eur J Cancer 40(6):837–844. doi:10.1016/j.ejca.2004.01.003

    Article  CAS  PubMed  Google Scholar 

  115. Vizirianakis IS, Mystridis GA, Avgoustakis K, Fatouros DG, Spanakis M (2016) Enabling personalized cancer medicine decisions: the challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (review). Oncol Rep 35(4):1891–1904. doi:10.3892/or.2016.4575

    PubMed  Google Scholar 

  116. Barbolosi D, Ciccolini J, Lacarelle B, Barlesi F, Andre N (2016) Computational oncology—mathematical modelling of drug regimens for precision medicine. Nat Rev Clin Oncol 13(4):242–254. doi:10.1038/nrclinonc.2015.204

    Article  PubMed  Google Scholar 

  117. Cheeti S, Budha NR, Rajan S, Dresser MJ, Jin JY (2013) A physiologically based pharmacokinetic (PBPK) approach to evaluate pharmacokinetics in patients with cancer. Biopharm Drug Dispos 34(3):141–154. doi:10.1002/bdd.1830

    Article  CAS  PubMed  Google Scholar 

  118. Machavaram KK, Almond LM, Rostami-Hodjegan A, Gardner I, Jamei M, Tay S, Wong S, Joshi A, Kenny JR (2013) A physiologically based pharmacokinetic modeling approach to predict disease-drug interactions: suppression of CYP3A by IL-6. Clin Pharmacol Ther 94(2):260–268. doi:10.1038/clpt.2013.79

    Article  CAS  PubMed  Google Scholar 

  119. Pai MP (2010) Estimating the glomerular filtration rate in obese adult patients for drug dosing. Adv Chronic Kidney Dis 17(5):e53–62. doi:10.1053/j.ackd.2010.05.010

    Article  PubMed  Google Scholar 

  120. Levitt DG, Schnider TW (2005) Human physiologically based pharmacokinetic model for propofol. BMC Anesthesiol 5(1):4. doi:10.1186/1471-2253-5-4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  121. Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A (2009) The simcyp(R) population-based ADME simulator. Expert Opin Drug Metab Toxicol 5(2):211–223

    Article  CAS  PubMed  Google Scholar 

  122. Ghobadi C, Johnson TN, Aarabi M, Almond LM, Allabi AC, Rowland-Yeo K, Jamei M, Rostami-Hodjegan A (2011) Application of a systems approach to the bottom-up assessment of pharmacokinetics in obese patients: expected variations in clearance. Clin Pharmacokinet 50(12):809–822. doi:10.2165/11594420-000000000-00000

    Article  CAS  PubMed  Google Scholar 

  123. Wellen KE, Hotamisligil GS (2003) Obesity-induced inflammatory changes in adipose tissue. J Clin Invest 112(12):1785–1788. doi:10.1172/JCI20514

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Das UN (2001) Is obesity an inflammatory condition? Nutrition 17(11–12):953–966

    Article  CAS  PubMed  Google Scholar 

  125. Cancello R, Clement K (2006) Is obesity an inflammatory illness? Role of low-grade inflammation and macrophage infiltration in human white adipose tissue. BJOG 113(10):1141–1147. doi:10.1111/j.1471-0528.2006.01004.x

    Article  CAS  PubMed  Google Scholar 

  126. Zhao P, Vieira Mde L, Grillo JA, Song P, Wu TC, Zheng JH, Arya V, Berglund EG, Atkinson AJ Jr, Sugiyama Y, Pang KS, Reynolds KS, Abernethy DR, Zhang L, Lesko LJ, Huang SM (2012) Evaluation of exposure change of nonrenally eliminated drugs in patients with chronic kidney disease using physiologically based pharmacokinetic modeling and simulation. J Clin Pharmacol 52(1 Suppl):91S–108S. doi:10.1177/0091270011415528

    Article  CAS  PubMed  Google Scholar 

  127. Duong JK, Kumar SS, Kirkpatrick CM, Greenup LC, Arora M, Lee TC, Timmins P, Graham GG, Furlong TJ, Greenfield JR, Williams KM, Day RO (2013) Population pharmacokinetics of metformin in healthy subjects and patients with type 2 diabetes mellitus: simulation of doses according to renal function. Clin Pharmacokinet 52(5):373–384. doi:10.1007/s40262-013-0046-9

    Article  CAS  PubMed  Google Scholar 

  128. Rabinovich-Guilatt L, Siegler KE, Schultz A, Halabi A, Rembratt A, Spiegelstein O (2016) The effect of mild and moderate renal impairment on the pharmacokinetics of pridopidine, a new drug for Huntington’s disease. Br J Clin Pharmacol 81(2):246–255. doi:10.1111/bcp.12792

    Article  CAS  PubMed  Google Scholar 

  129. Chow CYE, Sandy Pang K (2013) Why we need proper PBPK models to examine intestine and liver oral drug absorption. Curr Drug Metab 14(1):57–79. doi:10.2174/138920013804545124

    Article  CAS  PubMed  Google Scholar 

  130. Mittal R, Coopersmith CM (2014) Redefining the gut as the motor of critical illness. Trends Mol Med 20(4):214–223. doi:10.1016/j.molmed.2013.08.004

    Article  PubMed  Google Scholar 

  131. Pfeiffer RF (2003) Gastrointestinal dysfunction in Parkinson’s disease. Lancet Neurol 2(2):107–116. doi:10.1016/s1474-4422(03)00307-7

    Article  PubMed  Google Scholar 

  132. Baraldo M (2008) The influence of circadian rhythms on the kinetics of drugs in humans. Expert Opin Drug Metab Toxicol 4(2):175–192. doi:10.1517/17425255.4.2.175

    Article  CAS  PubMed  Google Scholar 

  133. Binkhorst L, Kloth JS, de Wit AS, de Bruijn P, Lam MH, Chaves I, Burger H, van Alphen RJ, Hamberg P, van Schaik RH, Jager A, Koch BC, Wiemer EA, van Gelder T, van der Horst GT, Mathijssen RH (2015) Circadian variation in tamoxifen pharmacokinetics in mice and breast cancer patients. Breast Cancer Res Treat 152(1):119–128. doi:10.1007/s10549-015-3452-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Dallmann R, Okyar A, Levi F (2016) Dosing-time makes the poison: circadian regulation and pharmacotherapy. Trends Mol Med 22(5):430–445. doi:10.1016/j.molmed.2016.03.004

    Article  CAS  PubMed  Google Scholar 

  135. Rao RT, DuBois DC, Almon RR, Jusko WJ, Androulakis IP (2016) Mathematical modeling of the circadian dynamics of the neuroendocrine-immune network in experimentally induced arthritis. Am J Physiol Endocrinol Metab 00006:02016. doi:10.1152/ajpendo.00006.2016

    Google Scholar 

  136. Ovacik MA, Sukumaran S, Almon RR, DuBois DC, Jusko WJ, Androulakis IP (2010) Circadian signatures in rat liver: from gene expression to pathways. BMC Bioinform 11:540. doi:10.1186/1471-2105-11-540

    Article  Google Scholar 

  137. Almon RR, Yang E, Lai W, Androulakis IP, Ghimbovschi S, Hoffman EP, Jusko WJ, Dubois DC (2008) Relationships between circadian rhythms and modulation of gene expression by glucocorticoids in skeletal muscle. Am J Physiol Regul Integr Comp Physiol 295(4):R1031–1047. doi:10.1152/ajpregu.90399.2008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Almon RR, Yang E, Lai W, Androulakis IP, DuBois DC, Jusko WJ (2008) Circadian variations in rat liver gene expression: relationships to drug actions. J Pharmacol Exp Ther 326(3):700–716. doi:10.1124/jpet.108.140186

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Pierre K, Schlesinger N, Androulakis IP (2016) The role of the hypothalamic-pituitary-adrenal axis in modulating seasonal changes in immunity. Physiol Genomics 00006:02016. doi:10.1152/physiolgenomics.00006.2016

    Google Scholar 

  140. Mavroudis PD, Corbett SA, Calvano SE, Androulakis IP (2015) Circadian characteristics of permissive and suppressive effects of cortisol and their role in homeostasis and the acute inflammatory response. Math Biosci 260:54–64. doi:10.1016/j.mbs.2014.10.006

    Article  CAS  PubMed  Google Scholar 

  141. Mavroudis PD, Corbett SA, Calvano SE, Androulakis IP (2014) Mathematical modeling of light-mediated HPA axis activity and downstream implications on the entrainment of peripheral clock genes. Physiol Genomics 46(20):766–778. doi:10.1152/physiolgenomics.00026.2014

    Article  CAS  PubMed  Google Scholar 

  142. Mavroudis PD, Scheff JD, Calvano SE, Androulakis IP (2013) Systems biology of circadian–immune interactions. J Innate Immun 5(2):153–162. doi:10.1159/000342427

    Article  CAS  PubMed  Google Scholar 

  143. Mavroudis PD, Scheff JD, Calvano SE, Lowry SF, Androulakis IP (2012) Entrainment of peripheral clock genes by cortisol. Physiol Genomics 44(11):607–621. doi:10.1152/physiolgenomics.00001.2012

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Sajan J, Cinu T, Chacko A, Litty J, Jaseeda T (2009) Chronotherapeutics and chronotherapeutic drug delivery systems. Trop J Pharm Res 8(5):467–475

    Article  CAS  Google Scholar 

  145. Haus E, Sackett-Lundeen L, Smolensky MH (2012) Rheumatoid arthritis: circadian rhythms in disease activity, signs and symptoms, and rationale for chronotherapy with corticosteroids and other medications. Bull NYU Hosp Jt Dis 70(Suppl 1):3–10

    PubMed  Google Scholar 

  146. Cutolo M, Straub RH (2008) Circadian rhythms in arthritis: hormonal effects on the immune/inflammatory reaction. Autoimmun Rev 7(3):223–228. doi:10.1016/j.autrev.2007.11.019

    Article  CAS  PubMed  Google Scholar 

  147. Lemmer B (1999) Chronopharmacokinetics: implications for drug treatment. J Pharm Pharmacol 51(8):887–890. doi:10.1211/0022357991773294

    Article  CAS  PubMed  Google Scholar 

  148. Lemmer B (2012) The importance of biological rhythms in drug treatment of hypertension and sex-dependent modifications. Chronophysiol Ther 2:9–18. doi:10.2147/cpt.s21861

    Article  CAS  Google Scholar 

  149. Erkekoglu P, Baydar T (2012) Chronopharmacokinetics of drugs in toxicological aspects: a short review for pharmacy practitioners. J Res Pharm Pract 1(1):3–9. doi:10.4103/2279-042X.99670

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Okyar A, Dressler C, Hanafy A, Baktir G, Lemmer B, Spahn-Langguth H (2012) Circadian variations in exsorptive transport: in situ intestinal perfusion data and in vivo relevance. Chronobiol Int 29(4):443–453. doi:10.3109/07420528.2012.668996

    Article  CAS  PubMed  Google Scholar 

  151. Iwasaki M, Koyanagi S, Suzuki N, Katamune C, Matsunaga N, Watanabe N, Takahashi M, Izumi T, Ohdo S (2015) Circadian modulation in the intestinal absorption of P-glycoprotein substrates in monkeys. Mol Pharmacol 88(1):29–37. doi:10.1124/mol.114.096735

    Article  CAS  PubMed  Google Scholar 

  152. Iwasaki M, Koyanagi S, Suzuki N, Katamune C, Matsunaga N, Watanabe N, Takahashi M, Izumi T, Ohdo S (2015) Circadian modulation in the intestinal absorption of P-glycoprotein substrates in monkeys. Mol Pharmacol 88:29–37. doi: 10.1124/mol.114.096735 

    Article  PubMed  CAS  Google Scholar 

  153. Adil MS, Arshad HM, Ilyaz M, Haadi A, Nematullah M (2014) Chronotherapeutics: targeting the disease at its ideal time. Pharma Innov J 2:12

    Google Scholar 

  154. Smolensky MH, Peppas NA (2007) Chronobiology, drug delivery, and chronotherapeutics. Adv Drug Deliv Rev 59(9–10):828–851. doi:10.1016/j.addr.2007.07.001

    Article  CAS  PubMed  Google Scholar 

  155. Liu Q, Gong Y, Shi Y, Jiang L, Zheng C, Ge L, Liu J, Zhu J (2013) A novel multi-unit tablet for treating circadian rhythm diseases. AAPS Pharm Sci Tech 14(2):861–869. doi:10.1208/s12249-013-9975-8

    Article  CAS  Google Scholar 

  156. Peng HT, Bouak F, Vartanian O, Cheung B (2013) A physiologically based pharmacokinetics model for melatonin—effects of light and routes of administration. Int J Pharm 458(1):156–168. doi:10.1016/j.ijpharm.2013.09.033

    Article  CAS  PubMed  Google Scholar 

  157. Tuck CH, Holleran S, Berglund L (1997) Hormonal regulation of lipoprotein(a) levels: effects of estrogen replacement therapy on lipoprotein(a) and acute phase reactants in postmenopausal women. Arterioscler Thromb Vasc Biol 17(9):1822–1829. doi:10.1161/01.atv.17.9.1822

    Article  CAS  PubMed  Google Scholar 

  158. Bisdee J, Garlick P, James W (1989) Metabolic changes during the menstrual cycle. Br J Nutr 61:641–650

    Article  CAS  PubMed  Google Scholar 

  159. Chen A, Yarmush ML, Maguire T (2012) Physiologically based pharmacokinetic models: integration of in silico approaches with micro cell culture analogues. Curr Drug Metab 13(6):863–880

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Jinno J, Oh D, Crison JR, Amidon GL (2000) Dissolution of ionizable water-insoluble drugs: the combined effect of pH and surfactant. J Pharm Sci 89(2):268–274. doi:10.1002/(SICI)1520-6017(200002)89:2<268:AID-JPS14>3.0.CO;2-F

    Article  CAS  PubMed  Google Scholar 

  161. Savjani KT, Gajjar AK, Savjani JK (2012) Drug solubility: importance and enhancement techniques. ISRN Pharm 2012:195727. doi:10.5402/2012/195727

  162. Takano R, Sugano K, Higashida A, Hayashi Y, Machida M, Aso Y, Yamashita S (2006) Oral absorption of poorly water-soluble drugs: computer simulation of fraction absorbed in humans from a miniscale dissolution test. Pharm Res 23(6):1144–1156. doi:10.1007/s11095-006-0162-4

    Article  CAS  PubMed  Google Scholar 

  163. Hogben CA, Tocco DJ, Brodie BB, Schanker LS (1959) On the mechanism of intestinal absorption of drugs. J Pharmacol Exp Ther 125 (4):275-282

  164. Magallanes L, Fotaki N, Bertola V, Barindelli A, Vazquez M, Fagiolio P (2015) Biorelevant in vitro dissolution testing to predict in vivo absorption of furosemide after oral administration. In: AAPS Annual Meeting

  165. Testa B, Crivori P, Reist M, Carrupt P (2000) The influence of lipophilicity on the pharmacokinetic behavior of drugs: concepts and examples. Perspect Drug Discov Des 19:179–211. doi:10.1023/A:1008741731244

    Article  CAS  Google Scholar 

  166. Hopkins AL (2009) Drug discovery: predicting promiscuity. Nature 462(7270):167–168. doi:10.1038/462167a

    Article  CAS  PubMed  Google Scholar 

  167. Arnott JA, Kumar R, Planey SL (2013) Lipophilicity indices for drug development. J Appl Biopharm Pharmacokinet 1:31–38

    Google Scholar 

  168. Wils P, Warnery A, Phung-Ba V, Legrain S, Scherman D (1994) High lipophilicity decreases drug transport across intestinal epithelial cells. J Pharmacol Exp Ther 269(2):654–658

    CAS  PubMed  Google Scholar 

  169. Shah VP, Amidon GL, Amidon GL, Lennernas H, Shah VP, Crison JR (2014) 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. doi:10.1208/s12248-014-9620-9

    Google Scholar 

  170. Gray V, Kelly G, Xia M, Butler C, Thomas S, Mayock S (2009) The science of USP 1 and 2 dissolution: present challenges and future relevance. Pharm Res 26(6):1289–1302. doi:10.1007/s11095-008-9822-x

    Article  CAS  PubMed  Google Scholar 

  171. Mirza T, Bykadi SA, Ellison CD, Yang Y, Davit BM, Khan MA (2013) Use of in vitro-in vivo correlation to predict the pharmacokinetics of several products containing a BCS class 1 drug in extended release matrices. Pharm Res 30(1):179–190. doi:10.1007/s11095-012-0861-y

    Article  CAS  PubMed  Google Scholar 

  172. Gonzalez-Garcia I, Mangas-Sanjuan V, Merino-Sanjuan M, Bermejo M (2015) In vitro-in vivo correlations: general concepts, methodologies and regulatory applications. Drug Dev Ind Pharm 41(12):1935–1947. doi:10.3109/03639045.2015.1054833

    Article  CAS  PubMed  Google Scholar 

  173. Khan GM, Jiabi Z (1998) Formulation and in vitro evaluation of ibuprofen-carbopol® 974P-NF controlled release matrix tablets III: influence of co-excipients on release rate of the drug. J Controll Release 54(2):185–190. doi:10.1016/s0168-3659(97)00225-3

    Article  CAS  Google Scholar 

  174. Shoaib MH, Tazeen J, Merchant HA, Yousuf RI (2006) Evaluation of drug release kinetics from ibuprofen matrix tablets using HPMC. Pak J Pharm Sci 19(2):119–124

    CAS  PubMed  Google Scholar 

  175. Yasmin D, Rahman R, Akter M (2013) Formulation development of directly compressed naproxen SR tablet using kollidon SR and avicel PH 102 polymer. Int Curr Pharm J 2(6):112–114

    Article  CAS  Google Scholar 

  176. Pouton CW (2006) Formulation of poorly water-soluble drugs for oral administration: physicochemical and physiological issues and the lipid formulation classification system. Eur J Pharm Sci 29(3–4):278–287. doi:10.1016/j.ejps.2006.04.016

    Article  CAS  PubMed  Google Scholar 

  177. Serajuddin AT (2007) Salt formation to improve drug solubility. Adv Drug Deliv Rev 59(7):603–616. doi:10.1016/j.addr.2007.05.010

    Article  CAS  PubMed  Google Scholar 

  178. Yu LX (2008) Pharmaceutical quality by design: product and process development, understanding, and control. Pharm Res 25(4):781–791. doi:10.1007/s11095-007-9511-1

    Article  CAS  PubMed  Google Scholar 

  179. Wu CY, Benet LZ (2005) Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm Res 22(1):11–23

    Article  CAS  PubMed  Google Scholar 

  180. GastroPlus User Manual—Simulation Software for Drug Discovery and Development (2015) Version 9.0 edn. Simulations plus

  181. Certara (2015) Hands-on experience with model-based drug development: incorporating population variability into mechanistic prediction of PK and modeling PK-PD, Princeton

  182. Wang HY, Chen X, Jiang J, Shi J, Hu P (2016) Evaluating a physiologically based pharmacokinetic model for predicting the pharmacokinetics of midazolam in Chinese after oral administration. Acta Pharmacol Sin 37(2):276–284. doi:10.1038/aps.2015.122

    Article  CAS  PubMed  Google Scholar 

  183. Barter ZE, Tucker GT, Rowland-Yeo K (2013) Differences in cytochrome p450-mediated pharmacokinetics between chinese and caucasian populations predicted by mechanistic physiologically based pharmacokinetic modelling. Clin Pharmacokinet 52(12):1085–1100. doi:10.1007/s40262-013-0089-y

    Article  CAS  PubMed  Google Scholar 

  184. Honorio Tda S, Pinto EC, Rocha HV, Esteves VS, dos Santos TC, Castro HC, Rodrigues CR, de Sousa VP, Cabral LM (2013) In vitro-in vivo correlation of efavirenz tablets using GastroPlus(R). AAPS PharmSciTech 14 (3):1244-1254. doi:10.1208/s12249-013-0016-4

  185. Mahmood AH, Liu X, Grice JE, Medley GA, Roberts MS (2015) Using deconvolution to understand the mechanism for variable plasma concentration-time profiles after intramuscular injection. Int J Pharm 481(1–2):71–78. doi:10.1016/j.ijpharm.2015.01.046

    Article  CAS  PubMed  Google Scholar 

  186. Kesisoglou F, Xia B, Agrawal NG (2015) Comparison of deconvolution-based and absorption modeling IVIVC for extended release formulations of a BCS III drug development candidate. AAPS J 17(6):1492–1500. doi:10.1208/s12248-015-9816-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. Wray S, Fox NC (2016) Stem cell therapy for Alzheimer’s disease: hope or hype? Lancet Neurol 15(2):133–135. doi:10.1016/s1474-4422(15)00382-8

    Article  Google Scholar 

  188. Paixao P, Gouveria, LF, Morais JAG (2012) Prediction of human oral bioavailability by using in vitro and in silico drug related parameters in a physiologically based absorption model. Int J Pharm 429:84–98

    Article  PubMed  CAS  Google Scholar 

  189. Mordenti J (1985) Pharmacokinetic scale-up: accurate prediction of human pharmacokinetic profiles from animal data. J Pharm Sci 74(10):1097–1099

    Article  CAS  PubMed  Google Scholar 

  190. Lombardo F, Waters NJ, Argikar UA, Dennehy MK, Zhan J, Gunduz M, Harriman SP, Berellini G, Liric Rajlic I, Obach RS (2013) Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 2: clearance. J Clin Pharmacol 53(2):178–191. doi:10.1177/0091270012440282

    Article  CAS  PubMed  Google Scholar 

  191. Lombardo F, Waters NJ, Argikar UA, Dennehy MK, Zhan J, Gunduz M, Harriman SP, Berellini G, Rajlic IL, Obach RS (2013) Comprehensive assessment of human pharmacokinetic prediction based on in vivo animal pharmacokinetic data, part 1: volume of distribution at steady state. J Clin Pharmacol 53(2):167–177. doi:10.1177/0091270012440281

    Article  CAS  PubMed  Google Scholar 

  192. Gueorguieva II, Nestorov IA, Rowland M (2004) Fuzzy simulation of pharmacokinetic models: case study of whole body physiologically based model of diazepam. J Pharmacokinet Pharmacodyn 31(3):185–213

    Article  CAS  PubMed  Google Scholar 

  193. Gueorguieva I, Nestorov I, Rowland M (2002) Reducing PBPK models using global sensitivity approach and benefit/cost criterion. Paper presented at the population approach group Europe, Paris

  194. Haas DM, Hebert MF, Soldin OP, Flockhart DA, Madadi P, Nocon JJ, Chambers CD, Hankins GD, Clark S, Wisner KL, Li L, Renbarger JL, Learman LA (2009) Pharmacotherapy and pregnancy: highlights from the second international conference for individualized pharmacotherapy in pregnancy. Clin Transl Sci 2(6):439–443. doi:10.1111/j.1752-8062.2009.00166.x

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

CH is supported by a US Department of Education GAANN Grant to the Department of Biomedical Engineering at Rutgers University (P200A150131). MS is supported by a Bristol-Myers Squibb Doctoral Fellowship. IPA acknowledges support from NIH Grant GM 24211.

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  1. Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ, 08854, USA

    Clara Hartmanshenn, Megerle Scherholz & Ioannis P. Androulakis

  2. Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ, 08854, USA

    Ioannis P. Androulakis

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Hartmanshenn, C., Scherholz, M. & Androulakis, I.P. Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine. J Pharmacokinet Pharmacodyn 43, 481–504 (2016). https://doi.org/10.1007/s10928-016-9492-y

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  • DOI: https://doi.org/10.1007/s10928-016-9492-y

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