Targeted drug conjugates in cancer therapy: Challenges and opportunities

Abstract

Traditional chemotherapy is often accompanied by off-target toxicity, resulting in adverse side effects and driving the development of targeted therapies. Targeted drug conjugates (TDCs) typically comprise targeting ligands, such as specific antibodies, peptides, or small molecules, attached to a cytotoxic agent via a chemical linker. In this study, we briefly discussed the molecular aspects of the key components of TDCs and the mechanisms by which these key factors exert their activity. Moreover, we reviewed FDA-approved TDCs and promising candidates in clinical trials and discussed current challenges and future directions for TDC development, providing insights for the research and development of novel cancer therapeutics using TDCs. TDCs combine the advantages of highly specific targeting and a potent killing effect, enabling accurate and efficient cancer cell elimination. Food and Drug Administration (FDA)-approved antibody-drug conjugates (ADCs) have shown good efficacy in treating various cancers; however, they still present limitations such as immunogenicity, hematotoxicity, and complex pharmacokinetics. Smaller peptide-drug conjugates (PDCs) and small molecule-drug conjugates (SMDCs) may combine the advantages of ADCs while overcoming some of their limitations, thereby presenting more efficacious and safer alternatives. TDCs enhance the therapeutic effects of cytotoxic agents and reduce their adverse effects. However, tumor heterogeneity, limited transmembrane permeability, and drug resistance pose significant challenges for TDCs, potentially affecting their therapeutic efficacy. Nevertheless, TDCs are a promising therapeutic approach for cancer treatment, achieving precise drug delivery while minimizing toxicity and side effects on normal cells.

Keywords

Antibody-drug conjugate / Peptide-drug conjugate / Small molecule-drug conjugate / Tumor / Targeted therapy

Data availability

Not applicable.

Ethics approval

Not applicable.

Funding information

This work was supported by the Taishan Scholars Program (tsqn202211296), Fundamental Research Funds for the Central Universities Ocean University of China, and Science Foundation for Excellent Young Scholars of Shandong Province (ZR2023YQ062).

Declaration of generative AI in scientific writing

In the preparation of this work, no generative AI tools or services were utilized for drafting, editing, or revising the content. All analysis, and writing were conducted solely by the authors, ensuring the integrity and originality of the work presented.

CRediT authorship contribution statement

Geng Jia: Writing - original draft, Resources. Yuqi Jiang: Writing - review & editing. Xiaoyang Li: Writing - review & editing, Supervision, Conceptualization.

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

Acknowledgments

Graphic materials were provided by Figdraw (www.figdraw.com), and part of the drawing work was completed in PowerPoint.

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