Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump
| Autor: | James M. Brase, Thomas S. Rush, Brian J. Bennion, Kevin McLoughlin, Thomas D. Sweitzer, Margaret J. Tse, Jonathan E. Allen, Stacie Calad-Thomson, Claire G. Jeong, Amanda Minnich |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Chemical Engineering
In silico Library and Information Sciences digestive system 01 natural sciences Quantitative Biology - Quantitative Methods Machine Learning 0103 physical sciences medicine Humans Quantitative Methods (q-bio.QM) ATP Binding Cassette Transporter Subfamily B Member 11 Liver injury Cholestasis 010304 chemical physics Chemistry Transporter General Chemistry medicine.disease Bile Salt Export Pump 0104 chemical sciences Computer Science Applications 010404 medicinal & biomolecular chemistry Biochemistry FOS: Biological sciences ATP-Binding Cassette Transporters Chemical and Drug Induced Liver Injury |
| Popis: | Drug-induced liver injury (DILI) is the most common cause of acute liver failure and a frequent reason for withdrawal of candidate drugs during preclinical and clinical testing. An important type of DILI is cholestatic liver injury, caused by buildup of bile salts within hepatocytes; it is frequently associated with inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. Unfortunately, models published to date have been insufficiently accurate to encourage wide adoption. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. Our model development leveraged the ATOM Modeling PipeLine (AMPL) developed by the ATOM Consortium, which enabled us to train and evaluate thousands of candidate models. In the course of model development, we assessed a variety of schemes for chemical featurization, dataset partitioning and class labeling, and identified those producing models that generalized best to novel chemical entities. Our best performing classification model was a neural network with ROC AUC = 0.88 on our internal test dataset and 0.89 on an independent external compound set. Our best regression model, the first ever reported for predicting BSEP IC50s, yielded a test set $R^2 = 0.56$ and mean absolute error 0.37, corresponding to a mean 2.3-fold error in predicted IC50s, comparable to experimental variation. These models will thus be useful as inputs to mechanistic predictions of DILI and as part of computational pipelines for drug discovery. |
| Databáze: | OpenAIRE |
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