Predictive Toxicology and Artificial Intelligence

Regulatory agencies all over the world are advocating for less animal testing and more use of emerging alternative technologies, while also demanding more substance toxicity evaluation and characterization than ever before. Predictive toxicology is a multidisciplinary approach to chemical toxicity assessment that employs a variety of non-animal testing methods to predict a chemical's effects on biological systems. This method has several advantages over traditional hazard assessment approaches, including the elimination of the need for extensive animal experiments and their accompanying expenditures. Over the last few decades, governmental and private organisations have been rapidly increasing their use of computational methodologies for modelling pharmacological and toxicological data paired with strong data mining algorithms. The Predictive Toxicological Roadmap also outlines toxicology domains that could benefit from increased predictability, as well as promising emerging technologies that could satisfy these demands while simultaneously supporting the 3Rs for animals (Replacement, Reduction, and Refinement).

In the pharmaceutical industry, artificial intelligence refers to the use of automated algorithms to do tasks that formerly required human intelligence. Artificial intelligence has revolutionised how scientists discover new treatments, combat disease, and more in the pharmaceutical and biotech industries during the last five years. In the pharmaceutical sector, artificial intelligence and machine learning are crucial. According to industry stakeholders, the best use cases for these technologies are drug development, drug manufacturing, diagnostic aid, and enhancing medical treatment operations. By 2025, over half of all global healthcare corporations will have implemented artificial intelligence plans, according to some experts, and it will be critical for how businesses operate in the future.

• Artificial Intelligence
• Big Data
• Chemical Structure
• Deep Learning
• High Throughput Screening
• Image Analysis
• Machine Learning
• Toxicogenomics