Researchers looking to cure cancer are constantly searching for disease models that closely resemble human physiology for testing cancer-fighting drugs. Artificial Intelligence (AI) can help researchers sift through large amounts of available data, saving time and costs, to source the most appropriate disease model for oncology drug development.
In early drug development stages, the choice of the disease model used for drug discovery and drug testing can make or break a study. Drugs are only approved for studies in humans based on satisfactory safety and efficacy outcomes from experiments conducted using specific models. This ultimately leads to market approvals for commercializing the drug and helps deliver an optimized product for patients in need.
Using the ‘wrong’ model can be costly for a drug discovery project for multiple reasons. First, disease models themselves can be expensive to source, leaving scientists with less margin for error during selection because study budgets are often limited at this stage.
Second, characteristics of the chosen model, including specific mutations or biological pathways, can heavily influence the efficacy of a drug.
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