The principle of safety evaluation in medicinal drug - how can toxicology contribute to drug discovery and development as a multidisciplinary science?

[Computational toxicology in drug safety research]

The progress of computational toxicology (CompTox) in drug safety research is highly anticipated. CompTox provides toxicity screening methods for drug discovery in the early stages. CompTox also contributes to fostering the application of the principles of the 3Rs in toxicity testing by expanding non-animal test methods. The mechanism of toxicity is complex and varied, and drug discovery modalities are becoming more diverse. Consequently, the research is considered necessary to predict toxicity using not only chemical structures but experimental data as well. Additionally, various perspectives, such as interpretation of toxicity mechanisms and species differences, must be considered in risk assessment and management in drug safety research. Therefore, it is important to construct a comprehensive CompTox system that not only presents toxicity prediction results but also provides much information related to the relationship between drug candidate substances and living organisms. In this review paper, CompTox is positioned as a discipline of toxicology that applies computer-based technology, including AI (artificial intelligence). I also introduce toxicity prediction systems based on experimental data and an ontology system that supports the interpretation of toxicity prediction results as examples of research on constructing the foundation of a comprehensive CompTox system.

Epigenetic effects of graphene oxide and its derivatives: A mini-review

Graphene oxide (GO), an engineered nanomaterial, has a two-dimensional structure with carbon atoms arranged in a hexagonal array. While it has been widely used in many industries, such as biomedicine, electronics, and biosensors, there are still concerns over its safety. Recently, many studies have focused on the potential toxicity of GO. Epigenetic toxicity is an important aspect of a material's toxicological profile, since changes in gene expression have been associated with carcinogenicity and disease progression. In this review, we focus on the epigenetic alterations caused by GO, including DNA methylation, histone modification, and altered expression of non-coding RNAs. GO can affect DNA methyltransferase activity and disrupt the methylation of cytosine bases in DNA strands, leading to alteration of genome expression. Modulation of histones by GO, targeting histone deacetylase and demethylase, as well as dysregulation of miRNA and lncRNA expression have been reported. Further studies are required to determine the mechanisms of GO-induced epigenetic alterations.

Toxicity testing is evolving!

The efficient management of the continuously increasing number of chemical substances used in today's society is assuming greater importance than ever before. Toxicity testing plays a key role in the regulatory decisions of agencies and governments that aim to protect the public and the environment from the potentially harmful or adverse effects of these multitudinous chemicals. Therefore, there is a critical need for reliable toxicity-testing methods to identify, assess and interpret the hazardous properties of any substance. Traditionally, toxicity-testing approaches have been based on studies in experimental animals. However, in the last 20 years, there has been increasing concern regarding the sustainability of these methodologies. This has created a real need for the development of new approach methodologies (NAMs) that satisfy the regulatory requirements and are acceptable and affordable to society. Numerous initiatives have been launched worldwide in attempts to address this critical need. However, although the science to support this is now available, the legislation and the pace of NAMs acceptance is lagging behind. This review will consider some of the various initiatives in Europe to identify NAMs to replace or refine the current toxicity-testing methods for pharmaceuticals. This paper also presents a novel systematic approach to support the desired toxicity-testing methodologies that the 21st century deserves.

Current status and future perspective of computational toxicology in drug safety assessment under ontological intellection

For the safety assessment of pharmaceuticals, initial data management requires accurate toxicological data acquisition, which is based on regulatory safety studies according to guidelines, and computational systems have been developed under the application of Good Laboratory Practice (GLP). In addition to these regulatory toxicology studies, investigative toxicological study data for the selection of lead compound and candidate compound for clinical trials are directed to estimation by computational systems such as Quantitative Structure-Activity Relationship (QSAR) and related expert systems. Furthermore, in the "Go" or "No-Go" decision of drug development, supportive utilization of a scientifically interpretable computational toxicology system is required for human safety evaluation. A pharmaceutical safety evaluator as a related toxicologist who is facing practical decision needs not only a data-driven Artificial Intelligence (AI) system that calls for the final consequence but also an explainable AI that can provide comprehensive information necessary for evaluation and can help with decision making. Through the explication and suggestion of information on the mechanism of toxic effects to safety assessment scientists, a subsidiary partnership system for risk assessment is ultimately to be a powerful tool that can indicate project-vector with data weight for the corresponding counterparts. To bridge the gaps between big data and knowledge, multi-dimensional thinking based on philosophical ontology theory is necessary for handling heterogeneous data for integration. In this review, we will explain the current state and future perspective of computational toxicology related to drug safety assessment from the viewpoint of ontology thinking.

The Adverse Outcome Pathway: A Multifaceted Framework Supporting 21st Century Toxicology

The adverse outcome pathway (AOP) framework serves as a knowledge assembly, interpretation, and communication tool designed to support the translation of pathway-specific mechanistic data into responses relevant to assessing and managing risks of chemicals to human health and the environment. As such, AOPs facilitate the use of data streams often not employed by risk assessors, including information from in silico models, in vitro assays and short-term in vivo tests with molecular/biochemical endpoints. This translational capability can increase the capacity and efficiency of safety assessments both for single chemicals and chemical mixtures. Our mini-review describes the conceptual basis of the AOP framework and aspects of its current status relative to use by toxicologists and risk assessors, including four illustrative applications of the framework to diverse assessment scenarios.