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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Lu AT, Frisella ME, Johnson KC. Dissolution modeling: factors affecting the dissolution rates of polydisperse powders. Pharm Res. 1993;10(9):1308–14.
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.
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.
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.
Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS J. 2011;13(4):519–47.
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.
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.
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.
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.
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.
Kesisoglou F, Wu YH. Understanding the effect of API properties on bioavailability through absorption modeling. AAPS J. 2008;10(4):516–25.
Author information
Authors and Affiliations
Corresponding author
Additional information
Binfeng Xia and Hefei Zhang contributed equally to this work.
Rights 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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1208/s12249-014-0075-1