Skip to main content
Log in

Application of Physiologically Based Absorption Modeling to Formulation Development of a Low Solubility, Low Permeability Weak Base: Mechanistic Investigation of Food Effect

  • Research Article
  • Published:
AAPS PharmSciTech Aims and scope Submit manuscript

Abstract

Physiologically based pharmacokinetic (PBPK) modeling has been broadly used to facilitate drug development, hereby we developed a PBPK model to systematically investigate the underlying mechanisms of the observed positive food effect of compound X (cpd X) and to strategically explore the feasible approaches to mitigate the food effect. Cpd X is a weak base with pH-dependent solubility; the compound displays significant and dose-dependent food effect in humans, leading to a nonadherence of drug administration. A GastroPlus Opt logD Model was selected for pharmacokinetic simulation under both fasted and fed conditions, where the biopharmaceutic parameters (e.g., solubility and permeability) for cpd X were determined in vitro, and human pharmacokinetic disposition properties were predicted from preclinical data and then optimized with clinical pharmacokinetic data. A parameter sensitivity analysis was performed to evaluate the effect of particle size on the cpd X absorption. A PBPK model was successfully developed for cpd X; its pharmacokinetic parameters (e.g., C max, AUCinf, and t max) predicted at different oral doses were within ±25% of the observed mean values. The in vivo solubility (in duodenum) and mean precipitation time under fed conditions were estimated to be 7.4- and 3.4-fold higher than those under fasted conditions, respectively. The PBPK modeling analysis provided a reasonable explanation for the underlying mechanism for the observed positive food effect of the cpd X in humans. Oral absorption of the cpd X can be increased by reducing the particle size (<100 nm) of an active pharmaceutical ingredient under fasted conditions and therefore, reduce the cpd X food effect correspondingly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

ACAT:

advanced compartmental absorption and transit

AUC:

area under the plasma concentration–time curve

BCS:

Biopharmaceutics Classification System

CAT:

compartmental absorption and transit

C max :

maximum plasma concentration

ECG:

electrocardiogram

FaSSIF:

fasted-state simulating intestinal fluid

FeSSIF:

fed-state simulating intestinal fluid

GI:

gastrointestinal

IR:

immediate release

PBPK:

physiologically based pharmacokinetics

PSA:

parameter sensitivity analysis

PK:

pharmacokinetics

SGF:

simulated gastric fluid

t max :

time to reach maximum concentration

F a :

fraction of absorption

API:

active pharmaceutical ingredients

NFE:

Nanoparticle factor effect

REFERENCES

  1. Yu LX, Lipka E, Crison JR, Amidon GL. Transport approaches to the biopharmaceutical design of oral drug delivery systems: prediction of intestinal absorption. Adv Drug Deliv Rev. 1996;19(3):359–76.

    Article  PubMed  CAS  Google Scholar 

  2. Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev. 2001;50 Suppl 1:S41–67.

    Article  PubMed  CAS  Google Scholar 

  3. Amidon GL, Lennernas H, Shah VP, Crison JR. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res. 1995;12(3):413–20.

    Article  PubMed  CAS  Google Scholar 

  4. Fleisher D, Li C, Zhou Y, Pao L-H, Karim A. Drug, meal and formulation interactions influencing drug absorption after oral administration. Clinical implications. Clin Pharmacokinet. 1999;36(3):233–54.

    Article  PubMed  CAS  Google Scholar 

  5. 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.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  6. Gu C-H, Li H, Levons J, Lentz K, Gandhi RB, Raghavan K, et al. Predicting effect of food on extent of drug absorption based on physicochemical properties (Pharmaceutical Research (2007) DOI: 10.1007/s11095-007-9236-1). Pharm Res. 2008;25(4):979.

    Article  CAS  Google Scholar 

  7. Heimbach T, Xia BF, Lin TH, He HD. Case studies for practical food effect assessments across BCS/BDDCS class compounds using in silico, in vitro, and preclinical in vivo data. AAPS J. 2013;15(1):143–58.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  8. Wagner C, Jantratid E, Kesisoglou F, Vertzoni M, Reppas C, Dressman JB. Predicting the oral absorption of a poorly soluble, poorly permeable weak base using biorelevant dissolution and transfer model tests coupled with a physiologically based pharmacokinetic model. Eur J Pharm Biopharm. 2012;82(1):127–38.

    Article  PubMed  CAS  Google Scholar 

  9. Xia BF, Heimbach T, Lin TH, Li SF, Zhang HF, Sheng J, et al. Utility of physiologically based modeling and preclinical in vitro/in vivo data to mitigate positive food effect in a BCS class 2 compound. AAPS Pharmscitech. 2013;14(3):1255–66.

    Article  PubMed  CAS  Google Scholar 

  10. Usansky HH, Sinko PJ. Estimating human drug oral absorption kinetics from Caco-2 permeability using an absorption-disposition model: model development and evaluation and derivation of analytical solutions for k(a) and F(a). J Pharmacol Exp Ther. 2005;314(1):391–9.

    Article  PubMed  CAS  Google Scholar 

  11. Lu AT, Frisella ME, Johnson KC. Dissolution modeling: factors affecting the dissolution rates of polydisperse powders. Pharm Res. 1993;10(9):1308–14.

    Article  PubMed  CAS  Google Scholar 

  12. Gardner JD, Ciociola AA, Robinson M. Measurement of meal-stimulated gastric acid secretion by in vivo gastric autotitration. J Appl Physiol. 2002;92(2):427–34.

    PubMed  CAS  Google Scholar 

  13. Dressman JB, Berardi RR, Dermentzoglou LC, Russell TL, Schmaltz SP, Barnett JL, et al. Upper gastrointestinal (GI) Ph in young, healthy men and women. Pharm Res. 1990;7(7):756–61.

    Article  PubMed  CAS  Google Scholar 

  14. Muller RH, Peters K. Nanosuspensions for the formulation of poorly soluble drugs: I. Preparation by a size-reduction technique. Int J Pharm. 1998;160(2):229–37.

    Article  CAS  Google Scholar 

  15. Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS J. 2011;13(4):519–47.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  16. 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.

    Article  PubMed  CAS  Google Scholar 

  17. 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.

    Article  PubMed  CAS  Google Scholar 

  18. Nicolaides E, Galia E, Efthymiopoulos C, Dressman JB, Reppas C. Forecasting the in vivo performance of four low solubility drugs from their in vitro dissolution data. Pharm Res. 1999;16(12):1876–82.

    Article  PubMed  CAS  Google Scholar 

  19. Jinno J, Kamada N, Miyake M, Yamada K, Mukai T, Odomi M, et al. Effect of particle size reduction on dissolution and oral absorption of a poorly water-soluble drug, cilostazol, in beagle dogs. J Control Release. 2006;111(1–2):56–64.

    Article  PubMed  CAS  Google Scholar 

  20. Wu Y, Loper A, Landis E, Hettrick L, Novak L, Lynn K, et al. The role of biopharmaceutics in the development of a clinical nanoparticle formulation of MK-0869: a Beagle dog model predicts improved bioavailability and diminished food effect on absorption in human. Int J Pharm. 2004;285(1–2):135–46.

    Article  PubMed  CAS  Google Scholar 

  21. Kesisoglou F, Wu YH. Understanding the effect of API properties on bioavailability through absorption modeling. AAPS J. 2008;10(4):516–25.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hefei Zhang.

Additional information

Binfeng Xia and Hefei Zhang contributed equally to this work.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, H., Xia, B., Sheng, J. et al. Application of Physiologically Based Absorption Modeling to Formulation Development of a Low Solubility, Low Permeability Weak Base: Mechanistic Investigation of Food Effect. AAPS PharmSciTech 15, 400–406 (2014). https://doi.org/10.1208/s12249-014-0075-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1208/s12249-014-0075-1

KEY WORDS

Navigation