[Adverse Effect Predictions Based on Computational Toxicology Techniques and Large-scale Databases]

Efficiency of pharmaceutical toxicity prediction in computational toxicology

The adverse effects and toxicity of chemical substances pose substantial challenges in drug discovery and environmental science. Their management, most especially in the early development stage, is crucial in preventing costly failures in clinical trials. Predictive methodologies, such as computational toxicology, offer an effective means of managing risks, particularly for new compounds with insufficient post-marketing surveillance and those lacking information on adverse effects. Computational approaches have become increasingly important in environmental science, in which the sheer number and diversity of chemicals present similar challenges to toxicity control. Traditional animal-based evaluation methods are resource intensive, time consuming, and ethically problematic, making them unsuitable for use in assessing the vast compound range. It is an urgent task for the academic community to minimize the risks associated with drug discovery and environmental exposure. This study focuses on systems used to predict toxicity from chemical structure information and outlines the prediction accuracy and systems developed in Japan.

[Potential for Big Data Analysis Using AI in the Field of Clinical Pharmacy]

Industrial reforms utilizing artificial intelligence (AI) have advanced remarkably in recent years. The application of AI to big data analysis in the medical information field has also been advancing and is expected to be used to find drug adverse effects that cannot be predicted by conventional methods. We have developed an adverse drug reactions analysis system that uses machine learning and data from the Japanese Adverse Drug Event Report (JADER) database. The system was developed using the C# programming language and incorporates the open source machine learning library Accord.Net. Potential analytical capabilities of the system include discovering unknown drug adverse effects and evaluating drug-induced adverse events in pharmaceutical management. However, to apply the system to pharmaceutical management, it is important to examine the characteristics and suitability of the level of AI used in the system and to select statistical methods or machine learning when appropriate. If these points are addressed, there is potential for pharmaceutical management to be individualized and optimized in the clinical setting by using the developed system to analyze big data. The system also has the potential to allow individual healthcare facilities such as hospitals and pharmacies to contribute to drug repositioning, including the discovery of new efficacies, interactions, and drug adverse events.

[AI-based QSAR Modeling for Prediction of Active Compounds in MIE/AOP]

Toxicity testing is critical for new drug and chemical development process. A clinical study, experimental animal models, and in vitro study are performed to evaluate the safety of a new drug. The limitations of these methods include extensive time for toxicity testing, an ethical problem, and high costs of experimentation. Therefore computational methods are considered useful for estimating chemical toxicity. In silico toxicity prediction is one of the toxicity assessments that uses computational methods to predict and stimulate the toxicity of chemicals. In silico study aims to contribute to effective development of new drug and chemical design. In this study, quantitative structure-activity relationship (QSAR) models will be used to predict toxicities based on chemical structural parameters. Because toxicities are complicated physiological phenomena, a similar toxicity expression might cause a different pathway. Also, since many drugs with unknown mechanisms of actions are available, the application of artificial intelligence (AI)-which uses sophisticated algorithms- is increasingly used to predict toxicities. Recently, the QSAR model was applied to determine complex relations between chemical structures and toxicities. However, accuracy of QSAR for toxicity prediction remains an important issue. International competitions funded by public institutions can address this issue. Two important toxicity challenges were organized in the past decade; this article presents issues of toxicity based on these challenges.