By C. J. De Mel, Jadetimes News
Uncover the role of artificial intelligence in reshaping scientific testing and the potential to reduce, if not end, animal experimentation.
The Role of AI in Analyzing Existing Data
One promising application of AI in this field is its ability to sift through existing global animal testing data to avoid redundant tests. Joseph Manuppello, a senior research analyst at the Physicians Committee of Responsible Medicine, explains the challenge: "It can be difficult for scientists to sift through decades of data to find and analyze exactly what they are after." He expresses enthusiasm for AI models like ChatGPT, which can extract and synthesize vast amounts of data efficiently.
AI in Toxicology and Drug Development
Thomas Hartung, a toxicology professor at Johns Hopkins University and director of the Center for Alternatives to Animal Testing, highlights AI's prowess: “AI is as good as a human, or better, at extracting information from scientific papers.” Hartung notes that the influx of over 1,000 new chemicals each year necessitates rigorous testing, a task increasingly being assisted by AI. Trained AI systems are now capable of making preliminary assessments of new chemical’s toxicity, offering significant advancements in both power and accuracy.
The Challenges and Biases of AI Systems
Despite AI’s capabilities, it is not without its flaws. Data bias, for example, can occur if an AI system is trained predominantly on health data from one ethnic group, leading to less accurate conclusions for other groups. Moreover, as Hartung points out, animal testing itself often yields unreliable results. He cites the case of Vioxx, a drug that passed animal tests but was later withdrawn due to serious health risks in humans. Conversely, some drugs like aspirin, which is toxic to rat embryos, might not have advanced if initially tested on animals.
AI Projects Aiming to Replace Animal Testing
AI projects such as AnimalGAN, developed by the US Food and Drug Administration, aim to predict how rats would react to various chemicals, using data from thousands of real rats across numerous treatment scenarios. Similarly, the Virtual Second Species project is developing an AI powered virtual dog to simulate historic dog test results, aiding the initial toxicity tests before human trials.
Regulatory and Ethical Considerations
Cathy Vickers, head of innovation at the UK’s National Centre for the Replacement, Refinement and Reduction of Animals in Research, acknowledges the challenge of achieving regulatory approval for AI based methods, emphasizing that full acceptance will take time. Emma Grange from Cruelty Free International stresses the need to phase out animal testing altogether, arguing that AI has the potential to replace outdated animal based methods.
The Future of Animal Testing
While AI offers promising alternatives, experts like Kerstin Kleinschmidt Dorr, chief veterinary officer at German pharmaceutical company Merck, caution that animal testing cannot disappear overnight. Merck, a sponsor of Virtual Second Species, advocates for a future where animal free solutions are found for the current challenges that necessitate animal testing.
The integration of AI in scientific testing marks a significant advancement towards reducing animal testing. Although AI is not yet a perfect solution, its potential to analyze vast amounts of data, predict chemical toxicity, and even assist in drug development suggests a future where reliance on animal testing could be significantly diminished, if not entirely eliminated.