Unlocking precision medicine: Innovative strategies for druggable target identification and therapeutic enhancement

Abstract

Background: Precision medication emphasizes tailored treatment approaches based on individual patient characteristics, yet the limitations in current drug target identification hinder therapeutic advancements.

Objective: s This review aims to explore innovative strategies for identifying druggable targets (a biological target that is known to or is predicted to bind with high affinity to a drug) and enhancing their therapeutic potential within the framework of precision medication.

Methods: We examine various methodologies employed to assess the targets’ abilities in forming drugs, including computational modeling, network analysis, and multi-omics integration. Recent technological advancements in machine learning facilitate the extraction of relevant features from large datasets, improving target prioritization.

Results: Despite existing challenges in the landscape of targeted therapies—such as limited targets and high clinical failure rates—emerging data-driven approaches show promise in refining the drug discovery process. Enhanced validation frameworks are essential to mitigate risks associated with inadequate target assessment during early discovery phases.

Conclusions: The identification of viable druggable targets is critical for advancing personalized treatment options. By integrating diverse biological datasets and employing cutting-edge predictive tools, researchers can streamline drug development pathways, ultimately leading to more effective therapeutic interventions tailored to specific patient populations. This review highlights the need for innovative strategies in drug target discovery to unlock the full potential of precision medicine.

Keywords

Druggability / Precision medication / Target identification / Multi-omics / Therapeutic strategies / Machine learning / Drug discovery

Declarations

Not applicable.

Authors' contributions

W.Z.: Writing - original draft, Visualization, Methodology, Investigation, Data curation. X.H.: Writing - original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation. Z.Z.: Methodology, Investigation, Formal analysis. Z.F.: Writing - review & editing, Supervision, Project administration, Funding acquisition. Y.L.: Writing - original draft, Visualization, Methodology, Investigation, Data curation.

Ethics approval and consent to participate

Not applicable.

Consent for publication

The authors hereby consent to the publication of this manuscript in Precision Medication. All authors have reviewed and approved the final version of the manuscript. Additionally, the authors confirm that there are no conflicts of interest related to this work, and they have adhered to ethical guidelines in conducting the research presented in this review.

Availability of data and materials

Not applicable.

Funding

Not applicable.

Declaration of Competing Interest

The authors declare that they have no competing interests.

Acknowledgements

Not applicable.

Authors' other information

Not applicable.

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