Integrating precision medicine and artificial intelligence to prevent cardiotoxicity in cardiovascular drug therapy

Abstract

Precision medicine refers to tailoring therapeutic interventions to an individual’s genetic, molecular and phenotypic characteristics, while multi-omics integrates genomics, proteomics and metabolomics data to provide a systems-level view of disease. Together with artificial intelligence (AI) driven predictive modeling, these approaches enable early identification of cardiotoxic risk and optimization of drug therapy in cardiovascular diseases. The present review explores the possibility of precision medicine to overcome cardiotoxicity associated with conventional cardiovascular disease (CVD) treatments. It highlights the integration of biomarker-driven therapies, pharmacogenomics, and multi-omics technologies to improve therapeutic efficacy and minimize the risk of adverse drug reactions. Additionally, the review assesses the emerging contributions of artificial intelligence (AI) and network medicine in improving cardiovascular diagnostics and developing personalized treatment regimens. The discovery of genomics, proteomics, metabolomics into cardiovascular research has significantly increased our understanding in disease etiology and variability in response of drug. Furthermore, AI-driven predictive models and machine learning algorithms play key role in minimizing clinical risk and support precision-guided decision, ultimately enhance patient outcomes. The advancement in omics technology, AI and customized therapy is expected to revolutionize cardiovascular care, despite current challenges in clinical implementation. The integration of cutting-edge approaches into standard clinical practice would maximize treatment effectiveness and guarantee patient safety.

Keywords

Cardiovascular disease / Precision medicine / Pharmacogenomics / Artificial intelligence / Multi-omics / Personalized therapy

Declarations

Not applicable.

CRediT authorship contribution statement

Akrati Pathak: Writing - original draft, Visualization, Validation, Software, Resources. Tarique Anwer: Writing - original draft, Supervision, Resources, Project administration, Conceptualization. Ankit Verma: Writing - original draft, Visualization, Validation, Software, Resources. Muhanad Alhujaily: Writing - review & editing, Visualization, Validation. Mushabbab Alahmari: Writing - review & editing, Visualization, Software, Resources. Saeed Alshahrani: Writing - review & editing, Visualization, Software. Nawazish Alam: Writing - review & editing, Validation, Resources. Yousra Nomier: Writing - review & editing, Visualization, Validation. Mohammad Firoz Alam: Writing - original draft, Validation, Software, Resources.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors have read and agreed to the published version of the manuscript and give their consent for publication in this journal.

Data availability

Not applicable.

Funding

Not applicable.

Declaration of Generative AI and AI-assisted technologies in the writing process

Authors have used AI tools such as ChatGPT, OpenAI, DeepSeek, to get assistance in the language editing and improvement of this manuscript.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Not applicable.

Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.prmedi.2026.100078.

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