BDDCS Class Prediction for New Molecular Entities

Abstract

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport are not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the times. The unbalanced stratification of the dataset didn’t affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirmed the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the dataset. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.

BDDCS Predictions, Self-Correcting Aspects of BDDCS Assignments, BDDCS Assignment Corrections, and Classification for more than 175 Additional Drugs

“…BDDCS has linked a major role of intestinal metabolism and intestinal transporters in drug-induced toxicity. For example, BDDCS helped schematize for which drugs hERG (human Ether-à-go-go Related Gene) voltage-gated potassium channel inhibition is likely to result in TdP (22,23) from drug-drug interactions due to CYP or P-gp inhibition (24). For BDDCS class 2 hERG inhibitors that are also substrates of both CYP and P-gp, the dual inhibition of metabolism and transport could significantly increase the plasma concentration leading to more cases of severe toxicity.…”

“…To expand predictability between labs, we recently prioritized high permeability rate drugs to be used as standards in predicting the extent of metabolism: labetalol in Caco-2, zidovudine in MDCK, and theophylline in PAMPA (26). In silico, Broccatelli et al (24) correctly predicted the BDDCS class for 55% of molecules, though 92% of the molecules were predicted by one of the top two ranked classes. Benet et al (30) showed that in silico predictions of the minimum solubility of drugs over the pH range 3-7.5 are well segregated between class 2 and 3 drugs, but are unexpectedly similar when comparing class 1 and 4 drugs.…”

“…We realized that this compound was eliminated in the bile (Table I). Importantly, colchicine was also identified as the sole false negative of highly permeable BDDCS class 1 compounds that were P-gp substrates when predicting CNS exposure (24). Aliskiren and cefoperazone are poorly permeable and eliminated in the bile (39,40), although we initially classified them as extensively metabolized/highly permeable.…”

Dedication to Professor Leslie Z. Benet: 50 Years of Scientific Excellence and Still Going Strong!

“…During this period, of particular note are the metabolic enzyme-transporter interaction papers that focus on the cellular availability of drug to metabolic processes in the cell and to the polarity and orientation of the epithelial cell in the intestine, liver, and kidney (22)(23)(24)(25), and to the Biopharmaceutics Drug Disposition Classification System (BDDCS) series of papers (26)(27)(28). These papers truly encompass major conceptual leaps in biopharmaceutics and pharmacokinetics, and our understanding of absorption, distribution, metabolism, and excretion (ADME) drug properties.…”

“…The insight to see that Class I and II drugs of the Biopharmaceutics Classification System (BCS) are eliminated by metabolism and that Class III and IV drugs of the BCS are eliminated by biliary and/or renal excretion is a concept that allows us to look at a drug molecule and with computational systems "in silico" form hypotheses as to how that potential drug will be handled by the body. In fact, Les has done this with over 900 new molecular entities, "drugs" (28)(29)(30)(31)(32). To see this ADME organizing principle required a scientist with a very broad knowledge and experience in drug metabolism and pharmacokinetic elimination.…”