How can we improve identification of toxicology-related risks in drug discovery?
Incorporating best practices and artificial intelligence into workflows improves success rates
Two factors largely drive the discontinuation of a drug development program, contributing to the low success rate for clinical trials: poor efficacy in humans and adverse events.
Although a variety of factors contribute to adverse outcomes for a given therapy, there is also a growing appreciation for the nuanced role of genetics in determining the extent of an adverse outcome for a given individual. Findings related to this role as well as the inherent properties of the drug itself can be used to improve the overall success rates of a drug development program.
In this report, we look at how each step in the drug discovery phase is crucial to improving success rates and explore best practices that could be incorporated into the workflow.
Although a variety of factors contribute to adverse outcomes for a given therapy, there is also a growing appreciation for the nuanced role of genetics in determining the extent of an adverse outcome for a given individual. Findings related to this role as well as the inherent properties of the drug itself can be used to improve the overall success rates of a drug development program.
In this report, we look at how each step in the drug discovery phase is crucial to improving success rates and explore best practices that could be incorporated into the workflow.
Report highlights:
The discovery stage
For successful drug development, we need to start as early as possible. Learn about what needs to be considered during the discovery stage that could impact safety, such as target liability; analysis; drug design, triage and hit; and drug repurposing
Personalized approaches
Because safety can vary by person and organ, we also need to identify how we can determine a drug’s potential toxic effects on specific organs and specific patients via toxicogenomics, pharmacogenomics and pharmacoepigenomics.
Artificial intelligence
To reach a deeper understanding of toxicity-related mechanisms and the effect of genetic differences, we need to access and process large quantities of experimental data. Learn how this can be achieved with AI and machine learning algorithms.
Future outlook
The future of economically viable drug development programs that deliver safe and efficacious therapies relies on changing our approach towards toxicology, including a move toward big data and artificial intelligence.
Key Point 6
Euismod in pellentesque massa placerat duis ultricies lacus sed. Sit amet luctus venenatis lectus magna fringilla urna porttitor rhoncus.