Systems Biology Framework for Predicting Drug Toxicity in Preclinical Studies - Aakansha Soy; Mrunal Salwadkar (Frontiers in Life Sciences Research, 2025)
Drug toxicity is a leading cause of late-stage drug development failures, representing one of the most significant barriers to efficient pharmaceutical innovation and clinical translation. Conventional preclinical models, such as in vitro assays and animal testing, often fail to adequately capture the complexity of human biological systems, resulting in poor predictive accuracy and high attrition rates. To address these limitations, this paper proposes a comprehensive systems biology framework for predicting drug toxicity by integrating multi-omics data, network biology, and computational modeling into a unified predictive platform. The framework begins with the integration of transcriptomic, proteomic, and metabolomic datasets, enabling the construction of holistic molecular interaction maps that reveal systemic perturbations induced by drug exposure. These datasets are harmonized and used to build gene–protein–metabolite networks, from which toxicity-related modules are identified through graph-theoretic clustering and pathway enrichment analysis. Predictive modeling is achieved by training advanced machine learning algorithms, including random forests and deep neural networks, on curated toxicogenomic databases to identify robust biomarkers that distinguish toxic from non-toxic compounds. Additionally, dynamic simulations of metabolic and signaling pathways using ordinary differential equation–based and agent-based models provide mechanistic insights into dose-dependent effects and temporal progression of toxicity. Case studies on hepatotoxicity (acetaminophen-induced liver injury) and cardiotoxicity (doxorubicin-associated cardiac dysfunction) are presented to demonstrate the framework’s predictive accuracy, mechanistic interpretability, and translational value. Results highlight that multi-omics integration improves prediction performance by 20–25% compared to single-omics approaches, while network-based visualization enhances interpretability of drug-induced adverse outcomes. This framework not only advances preclinical toxicology by providing mechanistic and predictive insights but also contributes to precision medicine through the identification of population-specific susceptibilities and biomarker-driven risk stratification. Ultimately, the systems biology–driven approach offers a scalable and robust pathway toward safer, more efficient drug development pipelines, reducing both cost and failure rates in pharmaceutical research.