Computational modelling and network medicine in drug toxicology and clinical pharmacovigilance

Drug toxicology and clinical pharmacovigilance are essential fields focused on ensuring the safety and effectiveness of medicinal products. As the complexity of new drug candidates grows, so does the difficulty of predicting, identifying, and managing adverse drug reactions (ADRs). Traditional approaches often fail to fully capture the complex, systems-level interactions that form the basis of drug toxicity.

Fortunately, the development of advanced computational methods and the network-focused perspective of biological systems provide powerful new ways to address these challenges. Computational modelling enables the simulation and prediction of drug-target interactions, toxicity pathways, and patient-specific responses. At the same time, network medicine offers a framework for understanding diseases and drug effects as disturbances within complex biological networks, uncovering new biomarkers and mechanisms of toxicity that are hidden from single-target approaches.

This article collection, Computational Modelling and Network Medicine in Drug Toxicology and Clinical Pharmacovigilance, aims to gather cutting-edge research that applies these modern, high-dimensional methodologies to solve urgent questions in drug safety. We want to highlight studies that connect theoretical computational models with practical clinical applications.

We seek contributions that demonstrate how integrative modelling, network pharmacology, and multi-scale data analysis can advance the prediction, understanding, and management of drug-related adverse effects, ultimately contributing to safer and more effective therapeutics. Topics of interest for this collection include, but are not limited to:

Predictive toxicology: In silico models for predicting toxicity profiles, including ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, drug-induced organ injury, and idiosyncratic adverse events.

Network-based drug safety: Application of network pharmacology, pathway analysis, and network-based biomarkers to elucidate the mechanisms of adverse drug reactions.

Clinical pharmacovigilance: Integration of computational tools, such as machine learning and natural language processing, to analyze electronic health records, spontaneous reporting systems, and large-scale clinical data for early signal detection and risk assessment.

Personalized safety: Development of computational models to predict individual patient susceptibility to adverse drug reactions based on genetic, environmental, and clinical factors.

High-dimensional data integration: Studies utilizing multi-omics data (genomics, proteomics, metabolomics) in conjunction with network models to enhance drug toxicity screening.

Application of LLM for summarizing the data and performing initial analysis for lab testing.

In summary, this article collection highlights the transformative power of computational modelling and network medicine in reshaping drug toxicology and pharmacovigilance. The collected works demonstrate significant strides towards more predictive, precise, and patient-centric approaches. We warmly invite researchers from across computational biology, systems pharmacology, toxicology, and clinical medicine to contribute their original work to this ongoing special issue. It is our hope that these insights will catalyze further innovation, fostering safer and more effective therapeutic strategies for all patients.

Author Contribution

All the authors contributed to the conception, writing, and revision of the manuscript.

Declarations

Competing interests

The author serves on the editorial board of JTRM. QZ is the section associate editor of Network Medicines, XL is the section associate editor of Computational Modelling and Epidemiology, VB is the section editor of Computational Modelling and Epidemiology.