The development of AI technologies promises to revolutionise every area of human endeavour – and toxicology is surely going to be no exception. Here we’ll examine how AI can be harnessed to improve efficiency and accuracy in toxicity assessment, risk evaluation and decision-making processes.

Drug safety

In the field of pharmaceuticals and drug safety, artificial intelligence is already being used to support the identification of potentially harmful substances. For example, the US-based Tox21 project has utilised Deep Learning AI algorithms to assess the safety of thousands of chemicals, by predicting their potential toxicity based on high-throughput screening data.

“We found that Deep Learning excelled in toxicity prediction and outperformed many other computational approaches like naive Bayes, support vector machines, and random forests,” reported the researchers.

AI is also employed to monitor and analyse adverse event reports for drug safety surveillance. In the US, for example, the FDA's Sentinel System utilises AI algorithms to analyse large healthcare databases and detect potential safety signals associated with medications.

Fascinatingly, AI is also being used to identify new uses for existing drugs. For example, AI algorithms have been employed to screen approved drugs and repurpose them for the treatment of different diseases. One notable example is the identification of the antidepressant drug trazodone as a potential candidate for treating Alzheimer's disease.

Pharmacogenomics: A personalised approach to medicine

Before the advent of AI, medical treatments were predominantly researched and designed based on population-level data and characteristics. But with artificial intelligence in toxicology, the technology can be employed to predict individual susceptibility to adverse drug reactions and to guide personalised medicine approaches. For example, in the field of pharmacogenomics, AI algorithms can analyse genetic data to identify genetic variants associated with drug metabolism and toxicity, helping tailor drug treatments to individual patients.

This has been demonstrated in the 100,000 Genomes Project launched by Genomics England in collaboration with the NHS. Within the scope of this project, AI algorithms have been used to interpret genomic data and identify specific genetic variants that may affect an individual's response to medications.

Food safety

Much research has already been done in this area, to determine how AI technologies can help detect and classify contaminants in food products.

By leveraging AI, food safety professionals can improve risk assessment, ensure the safety of food products and protect public health. It also has the potential to predict foodborne illness outbreaks, and facilitate rapid response and mitigation strategies.

One real-world example is the IBM Food Trust platform. This utilises AI and blockchain technology to trace and track the source of contaminated food, helping to quickly identify and prevent outbreaks.

Environmental toxicology

AI will be an increasingly valuable tool in predicting the toxicity of chemicals and assessing their impact on ecosystems. It can analyse large datasets on chemical properties, environmental parameters, and biological responses to model and simulate the behaviour and effects of pollutants in the environment. AI can also support the identification of emerging environmental hazards and guide pollution control and remediation efforts.

AI technology is already being applied in assessing the environmental impact of chemicals. For instance, AI models have been used to predict the toxicity of chemicals to aquatic organisms, aiding in the evaluation of chemical safety in the environment.

What does the future hold for AI in toxicology?

We have explored just a few areas in which artificial intelligence is beginning to have an influence.

As the technology develops, artificial intelligence tools will continue to support the management of toxicological risks. By integrating diverse datasets and leveraging big data analytics, it’s likely that AI will assist toxicologists in their identification of chemical hazards and respond to emerging toxicological risks more efficiently, and with greater accuracy.

While challenges and limitations remain, AI in toxicology has the potential to significantly enhance our efforts in ensuring the safety and risk assessment of chemicals and drugs in all areas of the field.

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