Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning
| Autor: | Koichi Handa, Takahisa Motomura, Takeshi Fujiwara, Tatsuru Matsuo, Hiroaki Iwata, Maeda Kazuya, Hideaki Mamada, Mayumi Matsushita |
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| Rok vydání: | 2020 |
| Předmět: |
Process (engineering)
Computer science Metabolic Clearance Rate Pharmaceutical Science Inference 02 engineering and technology Drug Elimination Routes Machine learning computer.software_genre 030226 pharmacology & pharmacy Models Biological 03 medical and health sciences Animal data 0302 clinical medicine Deep Learning Pharmacokinetics Total drug clearance Species Specificity Drug Discovery Animals Humans business.industry Drug discovery Deep learning 021001 nanoscience & nanotechnology Rats Multimodal learning Pharmaceutical Preparations Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | Journal of pharmaceutical sciences. 110(4) |
| ISSN: | 1520-6017 |
| Popis: | Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists. |
| Databáze: | OpenAIRE |
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