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Computational Approaches in Antibody-drug Conjugate Optimization for Targeted Cancer Therapy
Rita Melo1,2, Agostinho Lemos3, Antonio J Preto2
1Centro de Ciencias e Tecnologias Nucleares, Instituto Superior Tecnico, Universidade de Lisboa, CTN, Estrada Nacional 10 (km 139,7), 2695-066 Bobadela LRS, Portugal.
Abstract:
Cancer has become one of the main leading causes of morbidity and mortality worldwide. One of the critical drawbacks of current cancer therapeutics has been the lack of the target-selectivity, as these drugs should have an effect exclusively on cancer cells while not perturbing healthy ones. In addition, their mechanism of action should be sufficiently fast to avoid the invasion of neighbouring healthy tissues by cancer cells. The use of conventional chemotherapeutic agents and other traditional therapies, such as surgery and radiotherapy, leads to off-target interactions with serious side effects. In this respect, recently developed target-selective Antibody-Drug Conjugates (ADCs) are more effective than traditional therapies, presumably due to their modular structures that combine many chemical properties simultaneously. In particular, ADCs are made up of three different units: a highly selective Monoclonal antibody (Mab) which is developed against a tumour-associated antigen, the payload (cytotoxic agent), and the linker. The latter should be stable in circulation while allowing the release of the cytotoxic agent in target cells. The modular nature of these drugs provides a platform to manipulate and improve selectivity and the toxicity of these molecules independently from each other. This in turn leads to generation of second- and third-generation ADCs, which have been more effective than the previous ones in terms of either selectivity or toxicity or both. Development of ADCs with improved efficacy requires knowledge at the atomic level regarding the structure and dynamics of the molecule. As such, we reviewed all the most recent computational methods used to attain all-atom description of the structure, energetics and dynamics of these systems. In particular, this includes homology modelling, molecular docking and refinement, atomistic and coarse-grained molecular dynamics simulations, principal component and cross-correlation analysis. The full characterization of the structure-activity relationship devoted to ADCs is critical for antibody-drug conjugate research and development.
Insights
Antibody-drug conjugates (ADCs) offer targeted cancer therapy by combining monoclonal antibodies with cytotoxic agents. Computational methods are crucial for understanding ADC structure and dynamics to improve efficacy and reduce side effects.
Area of Science:
- Biochemistry
- Computational Biology
- Pharmacology
Background:
- Cancer remains a leading cause of death globally, with conventional therapies causing significant side effects due to lack of target selectivity.
- Current treatments like chemotherapy, surgery, and radiotherapy often affect healthy cells, leading to severe adverse events.
- Antibody-drug conjugates (ADCs) represent a promising advancement, offering targeted delivery of cytotoxic agents to cancer cells.
Purpose of the Study:
- To review computational methods for characterizing the all-atom structure, energetics, and dynamics of Antibody-Drug Conjugates (ADCs).
- To highlight the importance of understanding ADC molecular properties for improving drug efficacy and selectivity.
- To provide insights into structure-activity relationships for the development of next-generation ADCs.
Main Methods:
- Homology modeling for structural prediction.
- Molecular docking and refinement for binding site analysis.
- Atomistic and coarse-grained molecular dynamics simulations for dynamic behavior.
- Principal component and cross-correlation analysis for system dynamics.
Main Results:
- Computational approaches enable a detailed, all-atom understanding of ADC structure and dynamics.
- These methods facilitate the independent manipulation of ADC selectivity and toxicity.
- The review covers essential computational techniques for ADC characterization.
Conclusions:
- A comprehensive understanding of ADC structure-activity relationships is critical for advancing ADC research and development.
- Computational modeling provides essential atomic-level insights for designing more effective and safer ADCs.
- Further application of these computational methods will drive the creation of improved second- and third-generation ADCs.
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