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The AAPS Journal

, Volume 15, Issue 1, pp 143–158 | Cite as

Case Studies for Practical Food Effect Assessments across BCS/BDDCS Class Compounds using In Silico, In Vitro, and Preclinical In Vivo Data

  • Tycho Heimbach
  • Binfeng Xia
  • Tsu-han Lin
  • Handan He
Research Article

Abstract

Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration–time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1–4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound’s BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions.

KEY WORDS

absorption modeling BCS/BDDCS food effect prediction human PBPK model oral bioavailability 

Notes

Acknowledgement

The authors would like to thank Dr. Akash Jain and the members of the Novartis Food Effect Quality Plus team for many helpful discussions.

Conflict of Interests

None.

Supplementary material

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References

  1. 1.
    Fleisher D, Li C, Zhou Y, Pao LH, Karim A. Drug, meal and formulation interactions influencing drug absorption after oral administration. Clinical implications. Clin Pharmacokinet. 1999;36(3):233–54.PubMedCrossRefGoogle Scholar
  2. 2.
    US FDA. Food–effect bioavailability and fed bioequivalence studies. In: Guidance for industry. http://www.fda.gov/downloads/regulatoryinformation/guidances/ucm126833.pdf. 2002. Accessed 02 Jun 2012.
  3. 3.
    Zhang X, Lionberger RA, Davit BM, Yu LX. Utility of physiologically based absorption modeling in implementing quality by design in drug development. AAPS J. 2011;13(1):59–71. doi: 10.1208/s12248-010-9250-9.PubMedCrossRefGoogle Scholar
  4. 4.
    Hendeles L, Weinberger M, Milavetz G, Hill 3rd M, Vaughan L. Food-induced “dose-dumping” from a once-a-day theophylline product as a cause of theophylline toxicity. Chest. 1985;87(6):758–65.PubMedCrossRefGoogle Scholar
  5. 5.
    Wilder BJ, Leppik I, Hietpas TJ, Cloyd JC, Randinitis EJ, Cook J. Effect of food on absorption of dilantin kapseals and mylan extended phenytoin sodium capsules. Neurology. 2001;57(4):582–9.PubMedCrossRefGoogle Scholar
  6. 6.
    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. Pharmaceut Res. 1995;12(3):413–20.CrossRefGoogle Scholar
  7. 7.
    Wu CY, Benet LZ. Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm Res. 2005;22(1):11–23. doi: 10.1007/s11095-004-9004-4.PubMedCrossRefGoogle Scholar
  8. 8.
    Benet L. Z. WCY. Using a biopharmaceutics drug disposition classification system to predict bioavailability and elimination characteristics of new molecular entities. Somerset, NJ: NJDMDG. 2006.Google Scholar
  9. 9.
    Custodio JM, Wu C-Y, Benet Leslie Z. Predicting drug disposition, absorption/elimination/transporter interplay and the role of food on drug absorption. Adv Drug Deliv Rev. 2008;60(6):717–33.PubMedCrossRefGoogle Scholar
  10. 10.
    Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS J. 2011;13(4):519–47. doi: 10.1208/s12248-011-9290-9.PubMedCrossRefGoogle Scholar
  11. 11.
    Singh A, Worku ZA, Van den Mooter G. Oral formulation strategies to improve solubility of poorly water-soluble drugs. Expert Opin Drug Deliv. 2011;8(10):1361–78. doi: 10.1517/17425247.2011.606808.PubMedCrossRefGoogle Scholar
  12. 12.
    Lui CY, Amidon GL, Berardi RR, Fleisher D, Youngberg C, Dressman JB. Comparison of gastrointestinal Ph in dogs and humans—implications on the use of the beagle dog as a model for oral absorption in humans. J Pharm Sci. 1986;75(3):271–4.PubMedCrossRefGoogle Scholar
  13. 13.
    Meyer JH, Dressman J, Fink A, Amidon G. Effect of size and density on canine gastric-emptying of nondigestible solids. Gastroenterology. 1985;89(4):805–13.PubMedGoogle Scholar
  14. 14.
    Akimoto M, Nagahata N, Furuya A, Fukushima K, Higuchi S, Suwa T. Gastric pH profiles of beagle dogs and their use as an alternative to human testing. Eur J Pharm Biopharm. 2000;49(2):99–102.PubMedCrossRefGoogle Scholar
  15. 15.
    Lentz KA, Quitko M, Morgan DG, Grace JE. Development and validation of a preclinical food effect model. J Pharm Sci. 2007;96(2):459–72. doi: 10.1002/Jps.20767.PubMedCrossRefGoogle Scholar
  16. 16.
    Russell WMS, Burch RL. The principles of humane experimental technique. London: Methuen & Co. Special edition published by Universities Federation for Animal Welfare (UFAW), 1992; 1959.Google Scholar
  17. 17.
    Huang SM. PBPK as a tool in regulatory review. Biopharm Drug Dispos. 2012;33(2):51–2. doi: 10.1002/Bdd.1777.PubMedCrossRefGoogle Scholar
  18. 18.
    Lukacova V, Woltosz WS, Bolger MB. Prediction of modified release pharmacokinetics and pharmacodynamics from in vitro, immediate release, and intravenous data. AAPS J. 2009;11(2):323–34. doi: 10.1208/s12248-009-9107-2.PubMedCrossRefGoogle Scholar
  19. 19.
    Parrott N, Lukacova V, Fraczkiewicz G, Bolger MB. Predicting pharmacokinetics of drugs using physiologically based modeling—application to food effects. AAPS J. 2009;11(1):45–53. doi: 10.1208/s12248-008-9079-7.PubMedCrossRefGoogle Scholar
  20. 20.
    Vieira MLT, Zhao P, Berglund EG, Reynolds KS, Zhang L, Lesko LJ, et al. Predicting drug interaction potential with a physiologically based pharmacokinetic model: a case study of telithromycin, a time-dependent CYP3A inhibitor. Clin Pharmacol Ther. 2012;91(4):700–8. doi: 10.1038/clpt.2011.305.PubMedCrossRefGoogle Scholar
  21. 21.
    Shaffer CL, Scialis RJ, Rong HJ, Obach RS. Using Simcyp to project human oral pharmacokinetic variability in early drug research to mitigate mechanism-based adverse events. Biopharm Drug Dispos. 2012;33(2):72–84. doi: 10.1002/Bdd.1768.PubMedCrossRefGoogle Scholar
  22. 22.
    Shono Y, Jantratid E, Dressman JB. Precipitation in the small intestine may play a more important role in the in vivo performance of poorly soluble weak bases in the fasted state: case example nelfinavir. Eur J Pharm Biopharm. 2011;79(2):349–56. doi: 10.1016/j.ejpb.2011.04.005.PubMedCrossRefGoogle Scholar
  23. 23.
    Shono Y, Jantratid E, Janssen N, Kesisoglou F, Mao Y, Vertzoni M, et al. Prediction of food effects on the absorption of celecoxib based on biorelevant dissolution testing coupled with physiologically based pharmacokinetic modeling. Eur J Pharm Biopharm. 2009;73(1):107–14. doi: 10.1016/j.ejpb.2009.05.009.PubMedCrossRefGoogle Scholar
  24. 24.
    Shono Y, Jantratid E, Kesisoglou F, Reppas C, Dressman JB. Forecasting in vivo oral absorption and food effect of micronized and nanosized aprepitant formulations in humans. Eur J Pharm Biopharm. 2010;76(1):95–104. doi: 10.1016/j.ejpb.2010.05.009.PubMedCrossRefGoogle Scholar
  25. 25.
    Nicolaides E, Symillides M, Dressman JB, Reppas C. Biorelevant dissolution testing to predict the plasma profile of lipophilic drugs after oral administration. Pharm Res. 2001;18(3):380–8.PubMedCrossRefGoogle Scholar
  26. 26.
    Dressman JB, Reppas C. In vitro-in vivo correlations for lipophilic, poorly water-soluble drugs. Eur J Pharm Sci. 2000;11:S73–80.PubMedCrossRefGoogle Scholar
  27. 27.
    Parrott N, Lave T. Prediction of intestinal absorption: comparative assessment of GASTROPLUS (TM) and IDEA (TM). Eur J Pharm Sci. 2002;17(1–2):51–61.PubMedCrossRefGoogle Scholar
  28. 28.
    Kuentz M, Nick S, Parrott N, Rothlisberger D. A strategy for preclinical formulation development using GastroPlus (TM) as pharmacokinetic simulation tool and a statistical screening design applied to a dog study. Eur J Pharm Sci. 2006;27(1):91–9. doi: 10.1016/j.ejps.2005.08.011.PubMedCrossRefGoogle Scholar
  29. 29.
    Yu LX, Amidon GL. Characterization of small intestinal transit time distribution in humans. Int J Pharm. 1998;171(2):157–63.CrossRefGoogle Scholar
  30. 30.
    Heimbach T, Lakshminarayana SB, Hu WY, He HD. Practical anticipation of human efficacious doses and pharmacokinetics using in vitro and preclinical in vivo Data. AAPS J. 2009;11(3):602–14. doi: 10.1208/s12248-009-9136-x.PubMedCrossRefGoogle Scholar
  31. 31.
    Xia B, Heimbach T, Lin TH, He H, Wang Y, Tan E. Novel physiologically based pharmacokinetic modeling of patupilone for human pharmacokinetic predictions. Canc Chemother Pharmacol. 2012;69(6):1567–82. doi: 10.1007/s00280-012-1863-5.CrossRefGoogle Scholar
  32. 32.
    Kesisoglou F, Wu YH. Understanding the effect of API properties on bioavailability through absorption modeling. AAPS J. 2008;10(4):516–25. doi: 10.1208/s12248-008-9061-4.PubMedCrossRefGoogle Scholar
  33. 33.
    Jones HM, Parrott N, Ohlenbusch G, Lave T. Predicting pharmacokinetic food effects using biorelevant solubility media and physiologically based modelling. Clin Pharmacokinet. 2006;45(12):1213–26.PubMedCrossRefGoogle Scholar
  34. 34.
    Kostewicz ES, Wunderlich M, Brauns U, Becker R, Bock T, Dressman JB. Predicting the precipitation of poorly soluble weak bases upon entry in the small intestine. J Pharm Pharmacol. 2004;56(1):43–51. doi: 10.1211/0022357022511.PubMedCrossRefGoogle Scholar
  35. 35.
    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. doi: 10.1124/dmd.107.015644.PubMedCrossRefGoogle Scholar
  36. 36.
    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. doi: 10.1124/dmd.106.014027.PubMedCrossRefGoogle Scholar
  37. 37.
    Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition. J Pharm Sci. 2002;91(5):1358–70. doi: 10.1002/jps.10128.PubMedCrossRefGoogle Scholar
  38. 38.
    Meier Y, Eloranta JJ, Darimont J, Ismair MG, Hiller C, Fried M, et al. Regional distribution of solute carrier mRNA expression along the human intestinal tract. Drug Metab Dispos. 2007;35(4):590–4. doi: 10.1124/dmd.106.013342.PubMedCrossRefGoogle Scholar
  39. 39.
    Chen ML, Yu L. The use of drug metabolism for prediction of intestinal permeability. Mol Pharmaceut. 2009;6(1):74–81. doi: 10.1021/Mp8001864.CrossRefGoogle Scholar
  40. 40.
    Mithani SD, Bakatselou V, TenHoor CN, Dressman JB. Estimation of the increase in solubility of drugs as a function of bile salt concentration. Pharmaceut Res. 1996;13(1):163–7.CrossRefGoogle Scholar
  41. 41.
    Litman T, Druley TE, Stein WD, Bates SE. From MDR to MXR: new understanding of multidrug resistance systems, their properties and clinical significance. Cell Mol Life Sci. 2001;58(7):931–59.PubMedCrossRefGoogle Scholar
  42. 42.
    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.PubMedCrossRefGoogle Scholar
  43. 43.
    Lentz KA. Current methods for predicting human food effect. AAPS J. 2008;10(2):282–8. doi: 10.1208/s12248-008-9025-8.PubMedCrossRefGoogle Scholar
  44. 44.
    US FDA. Waiver of in vivo bioavailability and bioequivalence studies for immediate-release solid oral dosage forms based on a biopharmaceutics classification system. In: Guidance for industry. 2000. http://www.fda.gov/downloads/Drugs/…/Guidances/ucm070246.pdf. Accessed 02 Jun 2012.
  45. 45.
    Fagerholm U, Johansson M, Lennernas H. Comparison between permeability coefficients in rat and human jejunum. Pharm Res. 1996;13(9):1336–42.PubMedCrossRefGoogle Scholar
  46. 46.
    Tse FLS. Pharmacokinetics in drug discovery and development: nonclinical studies. In: Welling PG, Tse FLS, editors. Pharmacokinetics: regulatory, industrial, academic perspectives. 2nd ed. New York: Dekker; 1995. p. 300–6.Google Scholar
  47. 47.
    Jones RD, Jones HM, Rowland M, Gibson CR, Yates JW, Chien JY, et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: comparative assessment of prediction methods of human volume of distribution. J Pharm Sci. 2011. doi: 10.1002/jps.22553.
  48. 48.
    Ring BJ, Chien JY, Adkison KK, Jones HM, Rowland M, Jones RD, et al. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: Comparative assessement of prediction methods of human clearance. J Pharm Sci. 2011. doi: 10.1002/jps.22552.

Copyright information

© American Association of Pharmaceutical Scientists 2012

Authors and Affiliations

  • Tycho Heimbach
    • 1
  • Binfeng Xia
    • 1
  • Tsu-han Lin
    • 1
  • Handan He
    • 1
  1. 1.Novartis Institutes for BioMedical Research, DMPKEast HanoverUSA

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