Abstract. Here, we compile the Biopharmaceutics Drug Disposition Classification System (BDDCS) classification for 927 drugs, which include 30 active metabolites. Of the 897 parent drugs, 78.8% (707) are administered orally. Where the lowest measured solubility is found, this value is reported for 72.7% (513) of these orally administered drugs and a dose number is recorded. The measured values are reported for percent excreted unchanged in urine, LogP, and LogD 7.4 when available. For all 927 compounds, the in silico parameters for predicted Log solubility in water, calculated LogP, polar surface area, and the number of hydrogen bond acceptors and hydrogen bond donors for the active moiety are also provided, thereby allowing comparison analyses for both in silico and experimentally measured values. We discuss the potential use of BDDCS to estimate the disposition characteristics of novel chemicals (new molecular entities) in the early stages of drug discovery and development. Transporter effects in the intestine and the liver are not clinically relevant for BDDCS class 1 drugs, but potentially can have a high impact for class 2 (efflux in the gut, and efflux and uptake in the liver) and class 3 (uptake and efflux in both gut and liver) drugs. A combination of high dose and low solubility is likely to cause BDDCS class 4 to be underpopulated in terms of approved drugs (N=53 compared with over 200 each in classes 1-3). The influence of several measured and in silico parameters in the process of BDDCS category assignment is discussed in detail.KEY WORDS: BDDCS; biowaiver; dose number; extent of metabolism; permeability rate.In 2005, Wu and Benet (1) introduced the Biopharmaceutics Drug Disposition Classification System (BDDCS). Wu and Benet recognized that there was a very strong correlation between the intestinal permeability rate and the extent of metabolism. For example, Benet et al. (2) noted that for the 29 drugs and endogenous substances for which human jejunal permeability rate measurements were available, there was an excellent correlation between these permeability rate measurements and the extent of drug metabolism in humans. Fourteen of the 16 drugs exhibiting human intestinal permeability rates greater than metoprolol were extensively metabolized, while 11 of 12 drugs showing permeability rates less than metoprolol were poorly metabolized. Two drugs showing disparity between the permeability rate and metabolism, cephalexin and losartan, exhibit permeability rates that differ by no more than 16% from metoprolol (2). Since the coefficients of variation for the human permeability parameters range from 29% to 130%, these borderline compounds may in fact also have followed the correlation. The correlation between the extent of metabolism and human intestinal jejunal permeability was markedly better than that observed for intestinal jejunal permeability and partition coefficient by Takagi et al. (3), who noted that Log P measured and calculated correctly predict high versus low permeability only about two thir...

P-glycoprotein (Pgp or ABCB1) is an ABC transporter protein involved in intestinal absorption, drug metabolism and brain penetration, and its inhibition can seriously alter a drug's bioavailability and safety. In addition, inhibitors of Pgp can be used to overcome multidrug resistance. Given this dual-purpose, reliable in silico procedures to predict Pgp inhibition are of great interest. A large and accurate literature collection yielded more than 1200 structures; a model was then constructed using various MIF-based technologies, considering pharmacophoric features and those physico-chemical properties related to membrane partitioning. High accuracy was demonstrated internally, with two different validation sets, and moreover using a number of molecules, for which Pgp inhibition was not experimentally available but was evaluated `inhouse'. All the validations confirmed the robustness of the model and its suitability to help medicinal chemists in drug discovery. The information derived from the model was rationalized as a pharmacophore for competitive Pgp inhibition.

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.