Ensemble Rule-Based Classification of Substrates of the Human ABC-Transporter ABCB1 Using Simple Physicochemical Descriptors.

Within the last decades, the detailed knowledge on the impact of membrane bound drug efflux transporters of the ATP binding cassette (ABC) protein family on the pharmacological profile of drugs has enormously increased. Especially, ABCB1 (P-glycoprotein, P-gp, MDR1) has attracted particular interest in medicinal chemistry, since it determines the clinical efficacy, side effects and toxicity risks of drug candidates. Based on this, the development of in silico models that provide rapid and cost-effective screening tools for the classification of substrates and nonsubstrates of ABCB1 is an urgent need in contemporary ADMET profiling. A characteristic hallmark feature of this transporter is its polyspecific ligand recognition pattern. In this study we describe a method for classifying ABCB1 ligands in terms of simple, conjunctive rules (RuleFit) based on interpretable ADMET features. The retrieved results showed that models based on large, very diverse data sets gave better classification performance than models based on smaller, more homogenous training sets. The best model achieved gave a correct classification rate of 0.90 for an external validation set. Furthermore, from the interpretation of the best performing model it could be concluded that in comparison to nonsubstrates ABCB1 substrates generally show a higher number of hydrogen-bond acceptors, are more flexible and exhibit higher logP values.

Predicting ligand interactions with ABC transporters in ADME.

ABC-type drug efflux pumps, e.g., ABCB1 (=P-glycoprotein, =MDR1), ABCC1 (=MRP1), and ABCG2 (=MXR, =BCRP), confer a multi-drug resistance (MDR) phenotype to cancer cells. Furthermore, the important contribution of ABC transporters for bioavailability, distribution, elimination, and blood-brain barrier permeation of drug candidates is increasingly recognized. This review presents an overview on the different computational methods and models pursued to predict ABC transporter substrate properties of drug-like compounds. They encompass ligand-based approaches ranging from 'simple rule'-based efforts to sophisticated machine learning methods. Many of these models show excellent performance for the data sets used. However, due to the complex nature of the applied methods, useful interpretation of the models that can be directly translated into chemical structures by the medicinal chemist is rather difficult. Additionally, very recent and promising attempts in the field of structure-based modeling of ABC transporters, which embody homology modeling as well as recently published X-ray structures of murine ABCB1, will be discussed.

In silico prediction of substrate properties for ABC-multidrug transporters.

Overexpression of ABC (ATP-binding cassette)-type drug efflux pumps, such as ABCB1, ABCC1 and ABCG2 in cancer cells confers multi-drug resistance (MDR) and represents a major cause of treatment failures in cancer therapy. Furthermore, there is increasing evidence for the important contribution of ABC-transporters to bioavailability, distribution, elimination and blood-brain barrier permeation of drug candidates. This review presents an overview on the different computational methods and models pursued to predict ABC-transporter substrate properties of drug-like compounds. They range from linear discriminant analysis to pharmacophore modelling and machine learning algorithms. Many of these models show a satisfying performance within the study-specific, defined chemical space but general applicability for the whole drug-like chemical space still needs to be proven. First attempts aiming towards selectivity profiling for ligands of the two polyspecific transporters ABCB1 and ABCG2 is also discussed. This might pave the way for a pharmacological profiling of compound series with special focus on their ADMET (absorption, distribution, metabolism, excretion and toxicity) properties.