frontier-banner
前沿速递
首页>前沿速递>

Acta Pharmaceutica Sinica B | APRIL-driven Dual-Targeted Ligand-Drug Conjugates Overcome Therapeutic Resistance in Multiple Myeloma

Acta Pharmaceutica Sinica B | APRIL-driven Dual-Targeted Ligand-Drug Conjugates Overcome Therapeutic Resistance in Multiple Myeloma
--

This study provides a novel dual-targeted therapeutic strategy for refractory and relapsed multiple myeloma, offering direct inspiration for experimental directions aimed at overcoming BCMA antigen loss.

 

Literature Overview

The article titled "APRIL-driven BCMA/TACI dual-targeted ligand–drug conjugates for selective and potent therapy of multiple myeloma," published in Acta Pharmaceutica Sinica B, systematically explores the utilization of the natural bispecificity of Proliferation-Inducing Ligand (APRIL) to construct ligand-drug conjugates (LDCs) targeting both BCMA and TACI. This approach aims to address drug resistance caused by antigen downregulation in single-target therapies.

Background Knowledge

Multiple myeloma, a hematologic malignancy, often recurs due to disease progression despite the availability of multiple approved therapies. BCMA, as a key target, is an ideal immunotherapeutic candidate due to its high expression. However, in clinical practice, the downregulation, shedding of BCMA antigen, and tumor escape mechanisms severely limit the long-term efficacy of antibody-drug conjugates (ADCs) and CAR-T therapies. Current single-target strategies struggle to cope with the challenges posed by tumor heterogeneity and antigen loss. The focus of this study lies in leveraging APRIL, a natural ligand capable of high-affinity binding to both BCMA and TACI. By exploiting the synergistic effect of dual receptors to enhance endocytosis efficiency, this study attempts to maintain drug delivery via TACI even in the absence of BCMA, thereby overcoming the bottlenecks of traditional therapies.

 

 

Research Methods and Experiments

The authors constructed a hexavalent fusion protein (APRILFc) based on murine APRIL fused with the human IgG1 Fc domain. Site-directed mutagenesis was employed to introduce cysteine residues, enabling the conjugation of MMAE or SN-38 toxins to produce LDCs. The experimental system utilized various multiple myeloma cell lines (e.g., RPMI8226, H929, MM.1S) and BCMA-knockout (BCMA-KO) cell models generated via CRISPR/Cas9 technology. In vivo experiments included subcutaneous xenograft models, tibial orthotopic tumor models, and systemic dissemination models of multiple myeloma using NCG and NOD/SCID animals. Key evidence demonstrated that APRILFc exhibited significantly faster endocytosis rates on the cell surface compared to monoclonal antibodies. Furthermore, even in BCMA-knockout cells, APRILFc maintained efficient endocytosis and toxin delivery mediated by TACI, leading to apoptosis.

Key Conclusions and Perspectives

  • APRIL-LDCs exhibited superior cytotoxicity compared to traditional ADCs in cells positive for both BCMA and TACI, with significantly accelerated endocytosis kinetics. This provides data support for the future development of rapid-acting drug delivery systems.
  • In BCMA-knockout models, APRIL-LDCs retained significant anti-tumor activity, demonstrating the critical role of TACI as a backup target in overcoming antigen escape. This guides future experimental directions to prioritize dual-target synergistic mechanisms.
  • In vivo experiments confirmed that APRIL-MMAE effectively inhibited multiple myeloma tumor growth and extended survival, remaining effective even in environments with BCMA shedding or low expression. This suggests the strategy could address the challenge of resistant recurrence in clinical translation.
  • Safety assessments indicated that although APRIL possesses natural bioactivity, the conjugated LDCs did not cause significant toxicity to normal B cells or systemic toxicity, suggesting that engineering modifications can achieve highly selective targeted therapy.

Research Significance and Prospects

This discovery holds significant importance for the field of drug development, indicating that the multivalent binding characteristics of natural ligands can replace the construction of complex bispecific antibodies, simplifying production processes and improving binding affinity. For clinical monitoring, this study suggests that dual-targeted therapies may be an effective salvage treatment option when patients experience BCMA antigen loss. Additionally, in disease modeling, establishing multiple myeloma models that incorporate TACI expression characteristics will help more accurately predict the efficacy of dual-targeted drugs.

 

 

Conclusion

By ingeniously leveraging the natural bispecificity of APRIL, this study successfully developed a novel LDC platform for multiple myeloma, providing a highly promising solution to overcome antigen loss and drug resistance in BCMA-targeted therapies. From laboratory construction to in vivo validation, this research not only elucidated the molecular mechanism of dual-receptor synergistic endocytosis but also confirmed its卓越的 anti-tumor activity in BCMA-deficient models, demonstrating the necessity of transitioning from single-target to dual-target strategies. This achievement lays a solid foundation for the precise treatment of multiple myeloma in the future. Particularly in addressing disease heterogeneity and recurrence/metastasis, it is expected to drive the clinical translation of next-generation immunotherapies and improve long-term patient outcomes.

 

Reference:
Linling Zhou, Yanping Sun, Jie Bi, Jisheng Wang, and Liqiang Pan. APRIL-driven BCMA/TACI dual-targeted ligand–drug conjugates for selective and potent therapy of multiple myeloma. Acta Pharmaceutica Sinica. B.
Antibody Viscosity Prediction
High-concentration antibody solutions are essential for the development of subcutaneous injectable formulations, but they often exhibit high viscosity, which poses challenges to antibody drug development, production, and administration. Previous computational models have been limited to training on only a few dozen data points, which is a bottleneck for generalization.