Prediction of Biopharmaceutical Drug Disposition Classification System (BDDCS) by Structural Parameters
DOI:
https://doi.org/10.18433/jpps30271Abstract
Modeling of physicochemical and pharmacokinetic properties is important for the prediction and mechanism characterization in drug discovery and development. Biopharmaceutics Drug Disposition Classification System (BDDCS) is a four-class system based on solubility and metabolism. This system is employed to delineate the role of transporters in pharmacokinetics and their interaction with metabolizing enzymes. It further anticipates drug disposition and potential drug-drug interactions in the liver and intestine. According to BDDCS, drugs are classified into four groups in terms of the extent of metabolism and solubility (high and low). In this study, structural parameters of drugs were used to develop classification-based models for the prediction of BDDCS class. Reported BDDCS data of drugs were collected from the literature, and structural descriptors (Abraham solvation parameters and octanol–water partition coefficient (log P)) were calculated by ACD/Labs software. Data were divided into training and test sets. Classification-based models were then used to predict the class of each drug in BDDCS system using structural parameters and the validity of the established models was evaluated by an external test set. The results of this study showed that log P and Abraham solvation parameters are able to predict the class of solubility and metabolism in BDDCS system with good accuracy. Based on the developed methods for prediction solubility and metabolism class, BDDCS could be predicted in the correct with an acceptable accuracy. Structural properties of drugs, i.e. logP and Abraham solvation parameters (polarizability, hydrogen bonding acidity and basicity), are capable of estimating the class of solubility and metabolism with an acceptable accuracy.
Downloads
References
Amidon GL, Lennernäs 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.
Yu LX, Amidon GL, Polli JE, Zhao H, Mehta MU, Conner DP, et al. Biopharmaceutics classification system: The scientific basis for biowaiver extensions. Pharm Res, 2002; 19(7):921-5.
Benet LZ, Amidon GL, Barends DM, Lennernäs H, Polli JE, Shah VP, et al. The use of BDDCS in classifying the permeability of marketed drugs. Pharm Res, 2008; 25(3):483-8.
Wu C-Y, 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.
Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS J, 2011; 13(4):519-47.
Benet L, Larregieu C. The FDA should eliminate the ambiguities in the current BCS biowaiver guidance and make public the drugs for which BCS biowaivers have been granted. Clin Pharmacol Ther, 2010; 88(3):405-7.
Hosmer Jr DW, Lemeshow S, Sturdivant RX. Applied logistic regression. John Wiley & Sons; 2013.
Ren YY, Zhou LC, Yang L, Liu PY, Zhao BW, Liu HX. Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis. SAR QSAR Environ Res, 2016; 27(9):721-46.
Smith CJ, Perfetti TA, Ko GM, Garg R. Ames mutagenicity, structural alerts of carcinogenicity, Hansch QSAR parameters (ClogP, CMR, MgVol), tumor site concordance/multiplicity, and tumorigenicity rank in NTP 2-year rodent studies. Toxicol Res Appl, 2018;2:2397847318759327.
Faller B, Ertl P. Computational approaches to determine drug solubility. Adv Drug Deliv Rev, 2007; 59(7):533-45.
Salahinejad M, Le TC, Winkler DA. Aqueous solubility prediction: Do crystal lattice interactions help? Mol Pharm, 2013; 10(7):2757-66.
Louis B, Agrawal VK, Khadikar PV. Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses. Eur J Med Chem, 2010; 45(9):4018-25.
Machatha SG, Yalkowsky SH. Comparison of the octanol/water partition coefficients calculated by ClogP®, ACDlogP and KowWin® to experimentally determined values. Int J Pharm, 2005; 294(1-2):185-92.
Jouyban A. Solubility prediction of drugs in water–polyethylene glycol 400 mixtures using Jouyban–Acree model. Chem Pharm Bull, 2006; 54(11):1561-6.
Acree WE, Grubbs LM, Abraham MH. Prediction of partition coefficients and permeability of drug molecules in biological systems with Abraham model solute descriptors derived from measured solubilities and water-to-organic solvent partition coefficients. Toxicity and drug testing. InTech; 2012.
Abraham MH. Human intestinal absorption - Neutral molecules and ionic species. J Pharm Sci, 2014; 103(7):1956-66.
Abraham MH, Smith RE, Luchtefeld R, Boorem AJ, Lou R, Acree Jr WE. Prediction of solubility of drugs and other compounds in organic solvents. J Pharm Sci, 2010; 99(3):1500-15.
Shayanfar A, Fakhree M, Jouyban A. A simple QSPR model to predict aqueous solubility of drugs. J Drug Deliv Sci Tec, 2010; 20(6):467-76.
Jouyban A. Review of the cosolvency models for predicting solubility of drugs in water-cosolvent mixtures. J Pharm Pharm Sci, 2008; 11(1):32-58.
Downloads
Published
How to Cite
Issue
Section
License
This is an open access journal with free of charge non-commercial download. At the time of submission, authors will be asked to transfer the copyright to the accepted article to the Journal of Pharmacy and Pharmaceutical Sciences. The author may purchase the copyright for $500 upon which he/she will have the exclusive copyright to the article. Nevertheless, acceptance of a manuscript for publication in the Journal is with the authors' approval of the terms and conditions of the Creative Commons copyright license Creative Common license (Attribution-ShareAlike) License for non-commercial uses.
CLOCKSS system has permission to collect, preserve, and serve this Archival Unit.
