In silico resources help combat cancer drug resistance mediated by target mutations

Area of Science:

• Oncology
• Computational Biology
• Pharmacology

Background:

• Drug resistance is a significant cause of cancer treatment failure.
• Mutations in target proteins altering drug binding are a primary mechanism of cancer drug resistance (CDR).
• Existing CDR-related data, knowledge bases, and predictive tools are fragmented and underutilized.

Purpose of the Study:

• To examine and analyze computational resources for exploring CDR caused by target mutations.
• To evaluate these tools based on their characteristics, data, methodologies, and performance.
• To discuss the disadvantages of current resources and provide examples of CDR inhibitor discovery.

Main Methods:

• Systematic review and analysis of computational tools for CDR research.
• Evaluation criteria included functional characteristics, data capacity, data sources, methodologies, and performance.
• Case studies illustrating the discovery of potential CDR inhibitors using these resources.

Main Results:

• Identified and analyzed various computational resources for studying CDR.
• Highlighted the strengths and weaknesses of different tools.
• Demonstrated the utility of these resources in identifying potential CDR inhibitors.

Conclusions:

• Computational resources are valuable for exploring CDR mechanisms and predicting resistance.
• Improved utilization and integration of these tools can accelerate the discovery of novel therapeutic strategies.
• This work provides a guide for specialists and non-specialists to navigate CDR prediction resources effectively.