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Machine Learning Models Identify New Inhibitors for Human OATP1B1.

Thomas R Lane¹, Fabio Urbina¹, Xiaohong Zhang²

¹Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510 Raleigh, North Carolina 27606, United States.

Molecular Pharmaceutics

|October 21, 2022

Summary

Machine learning models accurately predict OATP1B1 drug interactions. New inhibitors of the OATP1B1 transporter were identified, aiding in the prediction of drug-drug interactions and improving drug safety.

Keywords:

MegaTrans OATP1B1 deep learning drug discovery machine learning support vector machine transporters

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Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

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Published on: October 23, 2019

Area of Science:

Pharmacology
Computational Chemistry
Biochemistry

Background:

The OATP1B1 transporter is crucial for drug uptake in the liver.
OATP1B1 is implicated in significant drug-drug interactions.
Computational models can predict transporter substrates and inhibitors.

Purpose of the Study:

To generate in vitro inhibition data for OATP1B1 transporter.
To build and compare machine learning models for predicting OATP1B1 inhibition.
To identify novel OATP1B1 inhibitors.

Main Methods:

In vitro inhibition assays using [3H]estrone-3-sulfate (E3S) transport in CHO cells.
Development of machine learning models (SVC, XGBoost, etc.) using ECFP6, 3D pharmacophores, and chemical descriptors.
Nested cross-validation and external/prospective validation testing.

Main Results:

Several machine learning algorithms (SVC, XGBoost, logistic regression, k-nearest neighbors) showed high accuracy, AUC, and specificity.
Support Vector Classifier (SVC) models demonstrated superior performance on an external test set.
Prospective validation confirmed 84% accuracy in predicting OATP1B1 inhibition, identifying six potential novel inhibitors.

Conclusions:

Validated machine learning models can effectively predict OATP1B1 inhibition and potential drug-drug interactions.
The identified novel inhibitors expand the understanding of OATP1B1 modulation.
These models enhance drug development safety through predictive toxicology tools like MegaTrans software.