BDDCS Class Prediction for New Molecular Entities

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 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 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 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 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.

生物药剂学药物处置分类系统（Biopharmaceutics Drug Disposition Classification System, BDDCS）已被成功用于针对药物代谢酶（drug metabolizing enzymes, DMEs）、药物转运体及其相互作用的药物相互作用（drug–drug interactions, DDIs）预测。BDDCS的核心假设为：在不涉及摄取性转运体或细胞旁转运的前提下，代谢程度（extent of metabolism, EoM）可用于区分肠道通透性的高低，反之亦然。我们团队近期发表了一套包含900余种已上市药物的BDDCS分类数据集。我们认为，一款结合体外实验的可靠BDDCS类别预测模型，能够提前预判新分子实体（new molecular entities, NMEs）的药物处置过程与潜在药物相互作用风险。本文介绍了一种基于分子结构预测BDDCS类别的计算流程。该模型以300种口服药物作为训练集，以379种口服药物作为外部验证集，采用VolSurf+软件计算得到的17种分子描述符进行建模。针对每个分子，模型将基于预测得到的代谢程度、FDA溶解度（FDA solubility, FDAS）及其置信度分数，输出其BDDCS类别归属概率。FDAS的预测准确率在训练集与验证集中分别为78%与77%；而EoM的预测准确率在训练集与外部验证集中分别为82%与79%。在验证集中，55%的分子的实际BDDCS类别与模型预测得分最高的类别一致，且超过92%的分子的实际类别位列模型预测的前两名。数据集的不均衡分层并未对预测结果产生负面影响；相较于占比最高的类别1，模型对类别2与类别3的预测准确率更高。针对类别4的药物，模型普遍存在预测能力不足的问题。线性判别分析（linear discriminant analysis, LDA）结果证实，不同BDDCS类别的预测准确率与数据集的结构特征密切相关。该模型可常规应用于药物发现早期阶段，用于为新分子实体的体外实验确定优先级（例如转运体亲和力、肠道代谢、肠道吸收与血浆蛋白结合率实验）。我们进一步将该BDDCS预测模型应用于包含3万余种化合物的大型药用化学数据集。基于该应用结果，我们认为溶解度而非肠道通透性是区分新分子实体与上市药物的核心差异。我们预计，在药物发现早期阶段开展BDDCS类别预测，可显著降低研发成本。