Multiscale computational modelling and simulation methods have become increasingly important in pharmacology, particularly in drug development, design, and delivery. Understanding drug mechanisms and safety requires an understanding of complex intra- and inter-cellular networks comprising proteins, RNA, and metabolites, and the complex interactions between them. Traditional drug design methods that rely on a one drug-one target model do not account for system-wide effects, particularly at different time scales, that can lead to lack of efficacy or adverse effects, which account for approximately 30% of drug development failures.
Approximately 2 million US patients are affected by drug-related adverse effects annually, resulting in approximately 100,000 deaths. To address these issues, researchers are embracing multiscale mechanistic modeling approaches that consider the interaction between drugs, proteins, RNA, metabolites, and biological pathways. By predicting adverse effects and systems-wide effects, we can create new tools that allow for more predictive drug design and personalized medicine.
To address this issue, this research topic aims to advance our understanding of drug responses beyond the one drug-one target model and consider off-target effects using systems biology approaches.

We welcome original research, review manuscripts, and methods articles on this critical topic, including, but not limited to:
- Novel algorithms for network analysis of protein-protein interactions
- Generation of off-target binding networks in drug development
- Predictive drug-response models based on chemical similarity, target- structural similarity and side-effect similarity
- Studies linking cellular drug response to organ or system-level biomarker responses

Topic Editor K. Melissa Hallow is a consultant for Boehringer Ingelheim, serves on the advisory board for Simulations Plus Inc. and receives funding from AstraZeneca and Eli Lilly Pharmaceuticals. All other Topic Editors declare no conflicts with the Topic.

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Frontiers in Systems Biology

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