Evaluation of Solubility-Limited Absorption as a Surrogate to Predicting Positive Food Effect of BCS II/IV Drugs.

Karine Rodriguez-Fernandez, José David Gómez-Mantilla, Suneet Shukla, Peter Stopfer, Peter Sieger, Victor Mangas-Sanjuán, Sheila Annie Peters
Author Information
  1. Karine Rodriguez-Fernandez: Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain. ORCID
  2. José David Gómez-Mantilla: Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Binger Straße 173, 55216, Ingelheim am Rhein, Germany.
  3. Suneet Shukla: Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Binger Straße 173, 55216, Ingelheim am Rhein, Germany.
  4. Peter Stopfer: Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Binger Straße 173, 55216, Ingelheim am Rhein, Germany.
  5. Peter Sieger: Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397, Biberach a.d. Riss, Germany.
  6. Victor Mangas-Sanjuán: Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, Valencia, Spain. ORCID
  7. Sheila Annie Peters: Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Binger Straße 173, 55216, Ingelheim am Rhein, Germany. sheila.peters@boehringer-ingelheim.com.

Abstract

INTRODUCTION AND OBJECTIVE: Physiologically based pharmacokinetic (PBPK) models are increasingly used to predict food effect (FE) but model parameterization is challenged by in vitro-in vivo (IVIV) disconnect and/or parameter nonidentifiability. To overcome these issues, we propose a simplified PBPK model, in which all solubility-driven processes are lumped into a single parameter, solubility, which is optimized against observed concentration-time data.
METHODS: A set of commercially available biopharmaceutical classification system (BCS) II/IV compounds was selected to measure the solubility in a fasted state simulated intestinal fluid (FaSSIF) medium. The compounds were ranked from the lowest to the highest dose-adjusted FaSSIF solubility (FaSSIF/D) value and subdivided into three areas based on an upper and a lower limit: drugs with FaSSIF/D > upper limit having no FE, drugs with FaSSIF/D < lower limit having FE, and drugs between the limits said to be in the sensitivity range (SR), for which we tested the hypothesis that solubility-limited absorption (SLA) identified by simplified PBPK model can reliably predict positive FE if their exposures are not impacted by gut efflux or gut metabolism.
RESULTS: We demonstrate, using a subset of drugs within SR for which PBPK models were available, that drugs with SLA exhibited a positive FE, while those with no SLA did not show FE.
CONCLUSIONS: This proposal allows for a reliable binary prediction of FE to enable timely decisions on the need for pilot FE studies as well as the timing of pivotal FE studies.

References

  1. J Pharm Pharmacol. 2019 Apr;71(4):510-535 [PMID: 29956330]
  2. Pharm Res. 2007 Jun;24(6):1118-30 [PMID: 17385020]
  3. Nucleic Acids Res. 2024 Jan 5;52(D1):D1265-D1275 [PMID: 37953279]
  4. Br J Clin Pharmacol. 1998 Aug;46(2):101-10 [PMID: 9723817]
  5. Pharm Res. 1995 Mar;12(3):413-20 [PMID: 7617530]
  6. Clin Pharmacol Ther. 1978 Mar;23(3):315-9 [PMID: 627138]
  7. Biopharm Drug Dispos. 1996 Aug;17(6):511-9 [PMID: 8866042]
  8. Clin Pharmacol Ther. 2015 Mar;97(3):247-62 [PMID: 25670209]
  9. J Pharm Sci. 1986 Sep;75(9):891-3 [PMID: 3783459]
  10. Clin Pharmacokinet. 2025 Mar;64(3):369-372 [PMID: 39899202]
  11. J Pharmacokinet Biopharm. 1977 Aug;5(4):291-334 [PMID: 330836]
  12. AAPS J. 2008 Jun;10(2):282-8 [PMID: 18500565]
  13. Drug Metab Pharmacokinet. 2007 Aug;22(4):225-54 [PMID: 17827779]
  14. Clin Pharmacokinet. 2019 Nov;58(11):1355-1371 [PMID: 31236775]
  15. AAPS J. 2023 Jun 15;25(4):60 [PMID: 37322223]
  16. Chem Rev. 1998 Apr 2;98(2):391-408 [PMID: 11848905]
  17. J Med Chem. 2022 Feb 10;65(3):1685-1694 [PMID: 35060378]
  18. AAPS J. 2021 Dec 27;24(1):16 [PMID: 34961909]
  19. Eur J Clin Pharmacol. 1988;34(3):315-7 [PMID: 3396623]
  20. Drugs. 1987;34 Suppl 3:16-27 [PMID: 3327676]
  21. Am J Health Syst Pharm. 2010 Feb 1;67(3):217-22 [PMID: 20101064]
  22. CPT Pharmacometrics Syst Pharmacol. 2018 Feb;7(2):82-89 [PMID: 29168611]
  23. Am J Cardiol. 2001 Feb 15;87(4):432-5 [PMID: 11179527]
  24. Clin Pharmacol Ther. 2012 Mar;91(3):483-8 [PMID: 22278332]
  25. Clin Ther. 2006 Sep;28(9):1308-17 [PMID: 17062304]
  26. Pharmaceutics. 2020 Jul 17;12(7): [PMID: 32708881]
  27. Eur J Clin Pharmacol. 1979 May 21;15(4):269-74 [PMID: 477711]
  28. Clin Pharmacokinet. 2006;45(12):1213-26 [PMID: 17112297]
  29. J Pharm Pharmacol. 2007 Oct;59(10):1335-43 [PMID: 17910807]
  30. Br J Clin Pharmacol. 2004 Dec;58(7):S831-40; discussion S841-3 [PMID: 15595979]
  31. Drug Metab Dispos. 2020 Nov;48(11):1169-1182 [PMID: 32862146]
  32. Biopharm Drug Dispos. 1996 Mar;17(2):135-43 [PMID: 8907720]
  33. AAPS J. 2020 Sep 27;22(6):123 [PMID: 32981010]
  34. Clin Pharmacokinet. 2005;44(11):1165-77 [PMID: 16231967]
  35. Clin Pharmacol Ther. 2020 Mar;107(3):650-661 [PMID: 31608434]
  36. Eur J Pharm Biopharm. 2011 Oct;79(2):349-56 [PMID: 21527341]
  37. Int J Pharm. 2023 Mar 25;635:122758 [PMID: 36801481]
  38. J Clin Pharmacol. 2015 Jan;55(1):104-13 [PMID: 24990113]
  39. Arzneimittelforschung. 2006;56(11):735-9 [PMID: 17220050]
  40. CPT Pharmacometrics Syst Pharmacol. 2022 Jul;11(7):805-821 [PMID: 35344639]
  41. Pharmaceutics. 2022 Aug 27;14(9): [PMID: 36145555]
  42. Pharmacol Res Perspect. 2022 Apr;10(2):e00946 [PMID: 35307978]
  43. J Clin Pharmacol. 2020 Oct;60 Suppl 1:S98-S104 [PMID: 33205433]
  44. Drugs. 2017 Nov;77(17):1833-1855 [PMID: 29076109]
  45. J Clin Pharmacol. 2001 Feb;41(2):183-6 [PMID: 11210399]
  46. J Pharm Sci. 2019 Jan;108(1):592-602 [PMID: 29906472]
  47. Clin Pharmacol Ther. 1986 Nov;40(5):531-6 [PMID: 3769384]
  48. Clin Pharmacokinet. 2016 Jun;55(6):673-96 [PMID: 26895020]
  49. Cancer Chemother Pharmacol. 2015 May;75(5):907-16 [PMID: 25724156]

MeSH Term

Solubility
Food-Drug Interactions
Humans
Models, Biological
Intestinal Absorption
Pharmaceutical Preparations
Fasting
Biopharmaceutics

Chemicals

Pharmaceutical Preparations

Word Cloud

Created with Highcharts 10.0.0FEdrugsPBPKmodelsolubilityFaSSIF/DSLAbasedmodelspredictparametersimplifiedavailableBCSII/IVcompoundsFaSSIFupperlowerlimitSRpositivegutstudiesINTRODUCTIONANDOBJECTIVE:Physiologicallypharmacokineticincreasinglyusedfoodeffectparameterizationchallengedvitro-invivoIVIVdisconnectand/ornonidentifiabilityovercomeissuesproposesolubility-drivenprocesseslumpedsingleoptimizedobservedconcentration-timedataMETHODS:setcommerciallybiopharmaceuticalclassificationsystemselectedmeasurefastedstatesimulatedintestinalfluidmediumrankedlowesthighestdose-adjustedvaluesubdividedthreeareaslimit:><limitssaidsensitivityrangetestedhypothesissolubility-limitedabsorptionidentifiedcanreliablyexposuresimpactedeffluxmetabolismRESULTS:demonstrateusingsubsetwithinexhibitedshowCONCLUSIONS:proposalallowsreliablebinarypredictionenabletimelydecisionsneedpilotwelltimingpivotalEvaluationSolubility-LimitedAbsorptionSurrogatePredictingPositiveFoodEffectDrugs

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