Abstract
Modeling and simulation of drug dissolution and oral absorption has been increasingly used over the last decade to understand drug behavior in vivo based on the physicochemical properties of Active Pharmaceutical Ingredients (API) and dosage forms. As in silico and in vitro tools become more sophisticated and our knowledge of physiological processes has grown, model simulations can provide a valuable confluence, tying-in in vitro data with in vivo data while offering mechanistic insights into clinical performance. To a formulation scientist, this unveils not just the parameters that are predicted to significantly impact dissolution/absorption, but helps probe explanations around drug product performance and address specific in vivo mechanisms. In formulation, development, in silico dissolution–absorption modeling can be effectively used to guide: API selection (form comparison and particle size properties), influence clinical study design, assess dosage form performance, guide strategy for dosage form design, and breakdown clinically relevant conditions on dosage form performance (pH effect for patients on pH-elevating treatments, and food effect). This minireview describes examples of these applications in guiding product development including those with strategies to mitigate observed clinical exposure liability or mechanistically probe product in vivo performance attributes.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1208%2Fs12248-012-9372-3/MediaObjects/12248_2012_9372_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1208%2Fs12248-012-9372-3/MediaObjects/12248_2012_9372_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1208%2Fs12248-012-9372-3/MediaObjects/12248_2012_9372_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1208%2Fs12248-012-9372-3/MediaObjects/12248_2012_9372_Fig4_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1208%2Fs12248-012-9372-3/MediaObjects/12248_2012_9372_Fig5_HTML.gif)
Similar content being viewed by others
References
Agoram B, Woltosz WS, Bolger MB. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv Drug Deliv Rev. 2001;50:S41–67.
Sugano K. Introduction to computational oral absorption simulation. Expert Opin Drug Metab Toxicol. 2009;5(3):25–293.
Dressman JB, Thelen K, Willmann S. An update on computational oral absorption simulation. Expert Opin Drug Metab Toxicol. 2011;7(11):1345–64.
Willmann S, Lippert J, Schmitt W. From physicochemistry to absorption and distribution: predictive mechanistic modeling and computational tools. Expert Opin Drug Metab Toxicol. 2005;1(1):159–68.
Parrott N, Lave T. Application of physiologically based absorption models in drug discovery and development. Mol Pharm. 2008;5(5):760–75.
Parrott N, Pasquereau N, Coassolo P, Lave T. An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery. J Pharm Sci. 2005;94:2327–43.
Kuentz M, Nick S, Parrott N, Rothlisberger D. A strategy for preclinical formulation development using GastroPlus as pharmacokinetic simulation tool and a statistical screening design applied to a dog study. Eur J Pharm Sci. 2006;27:91–9.
Chen Y, Jin JY, Mukadam S, Malhi V, Kenny JR. Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies. Biopharm Drug Dispos. 2012. doi:10.1002/bdd.1769.
Jones H, Gardner IB, Collard WT, Stanley PJ, Oxley P, Hosea NA, Plowchalk D, Gernhardt Lin S, Dickins M, Rahavendran SR, Jones BC, Watson KJ, Pertinez H, Kumar V, Cole S. Simulation of human intravenous and oral pharmacokinetics of 21 diverse compounds using physiologically based pharmacokinetic modeling. Clin Pharmacokinet. 2011;50(5):331–47.
Parrott N, Lukacova V, Fraczkiewicz G, Bolger MB. Predicting pharmacokinetics of drugs using physiologically based modeling—application to food-effects. AAPS J. 2009;11:45–53.
Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol. 2011;51:45–73.
Gao Y, Carr RA, Spence JK, Wang WW, Turner TM, Lipari JM, Miller JM. A pH-dilution method for estimation of biorelevant drug solubility along the gastrointestinal tract: application to physiologically based pharmacokinetic modeling. Mol Pharm. 2010;96(4):1516–26.
Battachar SN, Perkins EJ, Tan JS, Burns LJ. Effect of gastric pH on the pharmacokinetics of a BCS class II compound in dogs: utilization of an artificial stomach and duodenum dissolution model and GastroPlus simulation to predict absorption. J Pharm Sci. 2011;100(11):4756–65.
Kesisoglou Fand Wu Y. Understanding the effect of API properties on bioavailability through absorption modeling. AAPS J. 2008;10(4):516–25.
Willmann S, Thelen K, Becker C, Dressman JB, Lippert J. Mechanism-based prediction of particle size dependent dissolution and absorption: cilostazol pharmacokinetics in dogs. Eur J Pharm Biopharm. 2010;76(1):83–94.
Mitra A, Kesisoglou F, Beauchamp M, Zhu W, Chiti F, Wu Y. Using absorption simulation of gastric pH modulated dog model for formulation development to overcome achlorhydria effect. Mol Pharm. 2011;8:2216–23.
Lukacova M, 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.
Tsume Y, Amidon GL. The biowaiver extension for BCS class III drugs: the effect of dissolution rate on the bioequivalence of BCS class III immediate release drugs predicted by computer simulation. Mol Pharm. 2010;7(4):1235–43.
Okumo A, DiMaso M, Löbenberg R. Computer simulations using GastroPlus to justify a biowaiver for etoricoxib solid oral products. Eur J Pharm Biopharm. 2009;72(1):91–8.
Zhang X, Lionberger RA, Davit BA, Yu LX. Utility of physiologically based absorption modeling in implementation quality by design in drug development. AAPS J. 2011;13(1):59–71.
Crison J, Timmins P, Keung A, Upreti V, Boulton D, Scheer B. Biowaiver approach for biopharmaceutics classification system class 3 compound metformin hydrochloride using in silico modeling. J Pharm Sci. 2012;101(5):1773–82.
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;19:359–76.
Lucker P, Moore G, Wieselgren I, Olofsson O, Bergstrand R. Pharmacokinetic and pharmacodynamic comparison of Metoprolol CR/ZOK once daily with conventional tablets once daily and in divided doses. J Clin Pharmacol. 1990;30:S17–27.
Larsson M, Landahl S, Lundborg P, Regardh C. Pharmacokinetics of Metoprolol in healthy, elderly, non-smoking individuals after a single dose and two weeks of treatment. Eur J Clin Pharmacol. 1984;27:217–22.
Takagi T, Chandrasekharen R, Bermejo M, Yamashita S, Yu LX, Amidon GL. A provisional biopharmaceutical classification of the top 200 drug products in the United States, Great Britain. Spain Japan Mol Pharm. 2006;3(6):631–43.
Fagerholm U, Johansson M, Lennernas H. Comparison between permeability coefficients in the rat and human jejunum. Pharm Res. 1996;13(9):1336–42.
Fu XC, Chen CX, Wang GP, Liang WQ, Yu QS. Prediction of human intestinal absorption using an artificial neural network. Pharmazie. 2005;60(9):674–6.
Hussain AS, Shivanand P, Johnson RD. Application of neural computing in pharmaceutical product development: computer aided formulation design. Drug Dev Indust Pharm. 1994;20(10):1739–52.
Aguilar-Diaz JE, Garcia-Montoya E, Sune-Negre JM, Perez-Lozao P, Minarro M, Tico JR. Predicting orally disintegrating tablets formulations of ibuprofen tablets: an application of the new SeDeM-ODT expert system. Eur J Pharm Biopharm. 2012;80:638–48.
Wilson WI, Peng Y, Ausberger LL. Generalization of a prototype intelligent hybrid system for hard gelatin capsule formulation development. AAPS PharmSciTech. 2005;6(3):E449–7.
Admet Predictor. http://www.simulations-plus.com/
Discover and Material Studio. http://accelrys.com/
Acknowledgments
The authors would like to thank the following individuals for their support in providing data for their projects: Rhye Hamey, Robert Perrone, Chandra Vema-Verappu, and Monica Adams.
Author information
Authors and Affiliations
Corresponding author
Additional information
Guest Editors: James Polli, Jack Cook, Barbara Davit, and Paul Dickinson
AAPS Journal Themed Issue Manuscript—Facilitation oral product development and reducing regulatory burden through novel approaches to assess bioavailability/bioequivalence; based on an AAPS workshop held on October 22–23, 2011.
Rights and permissions
About this article
Cite this article
Mathias, N.R., Crison, J. The Use of Modeling Tools to Drive Efficient Oral Product Design. AAPS J 14, 591–600 (2012). https://doi.org/10.1208/s12248-012-9372-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1208/s12248-012-9372-3