Objective:

To discuss the evolving role of analytical science in drug discovery, including the challenges and opportunities presented by more physiologically relevant models and automation, as well as the trade-offs involved.

Approach:

• Data Generation: Analytical science is increasingly generating larger datasets that inform decisions earlier in the drug discovery process.
• Complex Models: The use of more physiologically relevant models introduces variability and challenges in data consistency and reproducibility.
• Automation: Automation is utilized to manage variability and improve the practicality of complex models in drug discovery.

Key Findings:

• Advancements in analytical technologies enhance data consistency and reliability.
• Complex models introduce greater variability and challenges in scaling and maintaining data reproducibility.
• Simpler assays can provide valuable insights early in the discovery process.

Interpretation:

Balancing biological complexity with practical assay design is crucial, with the choice of model depending on the specific scientific question and stage of discovery.

Limitations:

• Complex models introduce inherent variability that cannot be fully controlled.
• More sophisticated models require advanced handling and maintenance, impacting data reproducibility.

Conclusion:

Automation is essential for managing the complexity of models, allowing researchers to concentrate on data insights rather than workflow management.

Sources:

• Society for Laboratory Automation and Screening (SLAS)