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Comparison of Predictions by BCS, rDCS and Machine Learning for the Effect of Food on Oral Drug Absorption Based on Features Calculated In silico

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Abstract

In this study, observed food effects of 473 drugs were categorized into positive, negative, or no effects and compared with the predictions made by machine learning (ML), the Biopharmaceutics Classification System (BCS) and refined Developability Classification System (rDCS). All methods used primarily in silico estimates for prediction, and for ML, four algorithms were evaluated using nested cross-validation to select important information from 371 features calculated based on the chemical structure. Approximately 18 features, including estimated solubility in biorelevant media, were selected as important, and the random forest classifier was the best among four algorithms with 36.6% error rate (ER) and 10.8% opposite prediction rate (OPR). The prediction by rDCS utilizing solubility in a biorelevant medium was somewhat inferior, but not by much; 41.0% ER and 11.4% OPR. Compared with these two methods, the prediction by BCS was inferior; 54.5% ER and 21.4% OPR. ER was improved modestly by using measured features instead of in silico estimates when BCS was applied to a subset of 151 drugs (46.4% from 55.0%). ML and rDCS predicted the food effects of the same subset using in silico estimates with ERs of 37.7% and 42.4%, respectively, suggesting that the predictions by ML and rDCS using in silico features are similar or more accurate than those by BCS using measured features. These results suggest that ML was useful in revealing essential features from complex information and, together with rDCS, is effective in predicting food effects during drug development, including early drug discovery.

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Acknowledgements

This work was supported by AMED under Grant Number JP20be0304203.

Funding

This work was supported by AMED under Grant Number JP20be0304203.

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Authors and Affiliations

  1. Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8675, Japan

    Yusuke Hoshino & Akihiro Hisaka

  2. Toxicology & Pharmacokinetics Research, Central Research Laboratories, Zeria Pharmaceutical Co., Ltd, 2512-1 Numagami, Oshikiri, Kumagaya-shi, Saitama, 360-0111, Japan

    Yusuke Hoshino

  3. Department of Clinical Medicine, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8575, Japan

    Hideki Yoshioka

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  1. Yusuke Hoshino
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Contributions

Substantial contributions to the conception or design of the work, Y.H., H.Y, and A.H.; Drafting the work or revising it critically for important intellectual content, Y.H. and A.H.; Final approval of the version to be published, Y.H., H.Y, and A.H., Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved, Y.H., H.Y, and A.H.

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Correspondence to Akihiro Hisaka.

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Conflict of interest

The authors declare no conflicts of interest associated with this manuscript. Yusuke Hoshino is an employee of Zeria Pharmaceutical Co., Ltd. However, Zeria Pharmaceutical Co., Ltd. did not contribute toward the analysis in this study.

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Hoshino, Y., Yoshioka, H. & Hisaka, A. Comparison of Predictions by BCS, rDCS and Machine Learning for the Effect of Food on Oral Drug Absorption Based on Features Calculated In silico. AAPS J 24, 10 (2022). https://doi.org/10.1208/s12248-021-00664-z

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