The role of 'big data' and 'in silico' New Approach Methodologies (NAMs) in ending animal use - A commentary on progress

In silico (computational) methods continue to evolve as part of a robust 21st century public health strategy in risk assessment, relevant to all sectors of chemical safety including preclinical drug discovery, industrial chemicals testing, food and cosmetics. Alongside in vitro methods as components of intelligent testing and pathway driven strategies, in silico models provide the potential for more human relevant solutions to the use of animals in safety testing and biomedical research. These are often termed 'New Approach Methodologies' (NAMs). Some NAMs incorporate the use of 'big data', for example the information provided from high throughput or high content in vitro screening assays or 'omics' technologies. Big data has increasing relevance to predictive toxicology but must be appropriately defined, particularly with regard to 'quality vs quantity'. The purpose of this article is to provide a commentary on the progress of in silico human-based research methods within the context of NAMs, as well as discussion of the emerging use of big data with relevance to safety assessment. The current status of in silico methods is discussed, with input from researchers in the field. Scientific and legislative drivers for change are also considered, along with next steps to address challenges in funding and recognition, to achieve regulatory acceptance and uptake within the research community. To provide some wider context, the use of in silico methods alongside other relevant approaches (e.g., human-based in vitro) is also discussed.

Automated summary

In silico methods play a crucial role in public health risk assessment and have the potential to provide more human-relevant solutions to safety testing and biomedical research, thereby reducing the reliance on animal experimentation. The use of big data in predictive toxicology is also discussed, emphasizing the need for appropriate definition and quality control. This article provides a commentary on the progress of in silico human-based research methods and explores the emerging use of big data in safety assessment.