Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning

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

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes

シンジン カンゴシ ニ タイスル カンゴ ギジュツ ケンシュウ ノ ホウコク インシデント タイケンガタ シミュレーション ノ コウカ

これまでの医療安全に対する教育としては、看護手順に基づいて考えることを最優先に、机上の演習を取り入れてきた。しかし、医療安全の教育においても、シミュレーションという方法を用いることで、臨床でインシデントなどが起きた場合でも、新人看護師自身で複数の可能性を考えて行動できるようにすることを目的とした「インシデント体験型シミュレーション」を考案した。この研修の学習到達目標は、インシデント体験時の自己の傾向と医療安全に関する課題に気づくこととした。シミュレーションでは、敢えて失敗するようなシナリオ構成とし、自己の動画を振り返り素材として用い、シミュレーションにおける、自己の思考・判断・行動を一連のプロセスとして振り返ることができるようにした。その結果、インシデント体験時の自己の傾向に気づくことができた。ただ、新人看護師自身で自己の傾向に即した今後の対策につなげることは、難しいとわかった。資料Information