BDDCS Class Prediction
for New Molecular Entities
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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 <i>vice versa</i>, at least when uptake transporters
or paracellular transport is 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 <i>in vitro</i> 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 time. The unbalanced stratification of
the data set did not 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) confirming the degree of accuracy
for the prediction of the different BDDCS classes is tied to the structure
of the data set. This model could routinely be used in early drug
discovery to prioritize <i>in vitro</i> 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