Drugs acting on central nervous system (CNS) may take longer duration to reach the market as these compounds have a higher attrition rate in clinical trials due to the complexity of the brain, side effects, and poor blood-brain barrier (BBB) permeability compared to non-CNS-acting compounds. The roles of active efflux transporters with BBB are still unclear. The aim of the present work was to develop a predictive model for BBB permeability that includes the MRP-1 transporter, which is considered as an active efflux transporter. A support vector machine model was developed for the classification of MRP-1 substrates and non-substrates, which was validated with an external data set and Y-randomization method. An artificial neural network model has been developed to evaluate the role of MRP-1 on BBB permeation. A total of nine descriptors were selected, which included molecular weight, topological polar surface area, ClogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, P-gp, BCRP, and MRP-1 substrate probabilities for model development. We identified 5 molecules that fulfilled all criteria required for passive permeation of BBB, but they all have a low logBB value, which suggested that the molecules were effluxed by the MRP-1 transporter.
Prediction of biological and toxicological properties of small molecules using in silico approaches has become a wide practice in pharmaceutical research to lessen the cost and enhance productivity. The development of a tool “ChemSuite,” a stand‐alone application for chemoinformatics calculations and machine‐learning model development, is reported. Availability of multi‐functional features makes it widely acceptable in various fields. Force field such as UFF is incorporated in tool for optimization of molecules. Packages like RDKit, PyDPI and PaDEL help to calculate 1D, 2D and 3D descriptors and more than 10 types of fingerprints. MinMax Scaler and Z‐Score algorithms are available to normalize descriptor values. Varied descriptor selection and machine‐learning algorithms are available for model development. It allows the user to add their own algorithm or extend the software for various scientific purposes. It is free, open source and has user‐friendly graphical interface, and it can work on all major platforms.
Background:
The efflux transporter multidrug resistance associated protein-2 belongs to
ATP-binding cassette superfamily which plays an important role in multidrug resistance and drugdrug
interactions. Efflux transporters are considered to be important targets for increasing the
efficacy of drugs and importance of computational study of efflux transporters for predicting
substrates, non-substrates, inhibitors and non-inhibitors is well documented. Previous work on
predictive models for inhibitors of multidrug resistance associated Protein-2 efflux transporter
showed that machine learning methods produced good results.
Objective:
The aim of the present work was to develop a machine learning predictive model to
classify inhibitors and non-inhibitors of multidrug resistance associated protein-2 transporter using a
well refined dataset.
Method:
In this study, the various algorithms of machine learning were used to develop the
predictive models i.e. support vector machine, random forest and k-nearest neighbor. The methods
like variance threshold, SelectKBest, random forest, and recursive feature elimination were used to
select the features generated by PyDPI. A total of 239 molecules consisting of 124 inhibitors and 115
non-inhibitors were used for model development.
Results:
The best multidrug resistance associated protein-2 inhibitor model showed prediction
accuracies of 0.76, 0.72 and 0.79 for training, 5-fold cross-validation and external sets, respectively.
Conclusion:
It was observed that support vector machine model built on features selected using
recursive feature elimination method shows the best performance. The developed model can be used
in the early stages of drug discovery for identifying the inhibitors of multidrug resistance associated
protein-2 efflux transporter.
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