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3Rs principle
The scientific community faces ongoing public criticism for utilizing animal models in preclinical testing to replicate human biology. In the development of new pharmacological compounds, a gradual approach begins with simpler models like rodents and progresses to more complex ones such as dogs and primates. Criticism intensifies, particularly concerning advanced models, as stress and pain become apparent. Stress can significantly weaken the animal organism, thereby affecting the collected data, rendering the test inefficient and complicating outcomes. To address this, the 3Rs principle (Reduction, Refinement, and Replacement) minimizes animal use, enhances welfare by alleviating stress and pain, and explores alternative testing methods. This approach is now reinforced and made more feasible with the use of artificial intelligence.
AI Advantages in Pharmacological Research
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Drug Development & Discovery
AI is actively involved in the discovery of new drugs by generating and evaluating various drug candidates that meet specific molecular criteria. This process, which previously required collaboration among multiple laboratories, has now been dramatically accelerated to optimize a drug pipeline. As a result, virtual screening immediately improves the utilization of animal models, which would have otherwise undergone unsatisfactory testing, leading to adverse consequences on animal safety.
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Digital Twins & Diseases Models
The potential of AI also lies in its capacity to generate virtual models that mimic animal biology, resulting in what are known as 'Animal Digital Twins,' already utilized in molecular imaging within preclinical settings for pharmaceutical development. This approach is also applied in modeling pathological states and their progression to investigate the efficacy of new therapies within more complex biological contexts, similar to human biology. This promotes limitations on tests conducted through genetically modified organisms to replicate pathological states, which are considered unethical or potentially dangerous according to public opinion.
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Organ-on-a-chip Models
The integration of AI with advanced technology such as organ-on-a-chip presents a promising avenue for limiting animal testing. These advanced systems faithfully mimic the anatomical and physiological characteristics of human organs, providing a highly realistic environment for substance testing. By harnessing AI algorithms, the intricate datasets produced by these models can be analyzed to enhance predictive accuracy, thereby reducing the need for animal sacrifice.
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Multi-omics Data Models
The integration of multi-omics data involves analyzing various types of biological data at the molecular level with the assistance of AI, which facilitates the integration of genomics, transcriptomics, proteomics, and metabolomics data, to make possible the study of highly complex biological systems. This allows researchers to identify biomarkers, therapeutic targets, and anticipate responses to the treatments under investigation. This scenario could lead to a significant reduction in the use of animals in research.
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Toxicity
AI can analyze vast amounts of data to identify predictive patterns and correlations between chemical properties and toxicity outcomes. These advanced analytical capabilities enable AI to develop accurate toxicity models, which can effectively predict the potential harm of substances without the need for extensive animal testing. This approach not only enhances efficiency but also reduces ethical concerns associated with traditional testing methods.
AI Actual Limitations
While some might view AI as the solution to research challenges and the eventual replacement of animal models, this is not the case. Currently, a major obstacle is fully replicating human biology. Creating a "Human Digital Twin" requires vast biological, genetic, demographic, and environmental data, which AI can process for accurate analysis. However, obtaining such data poses legal challenges due to privacy restrictions, and there is a shortage of human-derived data compared to animal data, leading to bias and reduced accuracy in AI-processed datasets. To tackle this, regulation is needed to improve data availability and validate AI to minimize risks associated with incomplete assessments, and to enhance public perception of AI itself.
Conclusions
The conscious and regulated utilization of validated artificial intelligence is poised to serve as a cornerstone in advancing scientific research aimed at ensuring the welfare of animal models within the framework of the 3Rs principle. This approach advocates for ethically sound and easily understandable strategies, catering to a growing public consciousness regarding research needs.