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Why next-generation mechanistic models will transform drug discovery: integrating efficacy and safety
Gustavo Santos Sandes Felizardo1, Vinícius Alexandre Fiaia Costa1, Eder Soares de Almeida Santos1
1Laboratory of Cheminformatics, Faculty of Pharmacy, Universidade Federal de Goiás, Goiás, Brazil.
Expert Opinion on Drug Discovery
|July 4, 2026
Summary
Next-generation mechanistic models can improve drug discovery by integrating exposure, efficacy, and safety data. Key advancements require standardized data, validation, and model integration for a more predictive pipeline.
Area of Science:
- Pharmacology and Drug Development
- Computational Biology
- Systems Biology
Background:
- Drug discovery faces high attrition rates due to fragmented evaluation of exposure, efficacy, and safety.
- Mechanistic models offer a biologically grounded framework to connect these determinants across biological levels.
- Improved translational decision-making can be achieved through earlier, integrated assessment of candidate progression.
Purpose of the Study:
- To review the conceptual basis and role of next-generation mechanistic models in drug discovery.
- To highlight how these models connect efficacy and safety across biological scales.
- To examine approaches like physiologically based pharmacokinetic models, virtual cell-based assays, and AI-augmented models.
Main Methods:
- Narrative review of literature.
- PubMed searches conducted up to May 25, 2026.
- Emphasis on specific next-generation mechanistic modeling approaches.
Main Results:
- Next-generation mechanistic models can support in vitro-to-in vivo extrapolation and incorporate in silico predictions.
- These models can improve candidate prioritization by linking efficacy and safety across biological scales.
- AI-augmented mechanistic models and virtual-cell frameworks are emerging tools.
Conclusions:
- Transforming drug discovery requires more than just biological detail or computational power.
- Progress depends on standardized data, robust validation, model calibration, reproducibility, and integration.
- Mechanistic frameworks can become crucial for a more predictive and less attrition-prone drug discovery pipeline.
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