A Computational Approach for Prioritizing Selection of Therapies Targeting Drug Resistant Variation in Anaplastic
Matthew D McCoy1, Subha Madhavan1
1Innovation Center for Biomedical Informatics, Georgetown University Medical Center,Washington, DC, United States of America.
Abstract:
Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase implicated as a driver of a number of cancer types, and activates cellular pathways involved in cell proliferation and differentiation. Tyrosine kinase inhibitors (TKIs) are a small molecule therapeutic that blocks ALK function, but tumor evolution leads to the rapid emergence of drug resistant somatic variation and necessitates selection of a new treatment strategy. Computational simulations of protein:drug interactions were used to investigate the impact of seven drug resistant mutations on binding to eleven TKIs approved, or under investigation, for treatment of ALK positive cancers. The results show variant specific disruptions to TKI molecular interactions, and demonstrate the potential to aid prioritization of therapeutic interventions. Validation remains a challenge due to the complex dependence of biomolecular interactions on the local biophysical environment, but improvements to the underlying structural model and continued curation efforts will improve the clinical utility of computational predictions.
Insights
Anaplastic lymphoma kinase (ALK) inhibitors face resistance due to cancer mutations. Computational simulations predict how these mutations affect drug binding, aiding the selection of effective tyrosine kinase inhibitors (TKIs) for ALK-positive cancers.
Area of Science:
- Oncology
- Pharmacology
- Computational Biology
Background:
- Anaplastic lymphoma kinase (ALK) is a driver in various cancers, regulating cell proliferation.
- Tyrosine kinase inhibitors (TKIs) target ALK, but drug resistance emerges due to somatic mutations.
- New treatment strategies are needed to overcome resistance in ALK-positive cancers.
Purpose of the Study:
- To investigate the impact of drug-resistant mutations on the binding of ALK inhibitors.
- To evaluate the efficacy of approved and investigational TKIs against resistant ALK variants.
- To demonstrate the potential of computational simulations in guiding therapeutic decisions.
Main Methods:
- Utilized computational simulations of protein:drug interactions.
- Analyzed the binding of seven drug-resistant mutations to eleven TKIs.
- Focused on TKIs for ALK-positive cancers.
Main Results:
- Observed variant-specific disruptions in molecular interactions between TKIs and ALK mutations.
- Identified potential for computational predictions to prioritize therapeutic interventions.
- Highlighted challenges in validation due to environmental dependencies.
Conclusions:
- Computational simulations can predict the impact of ALK mutations on TKI efficacy.
- These predictions can aid in selecting treatments for resistant ALK-positive cancers.
- Further model improvements and data curation are necessary for enhanced clinical utility.
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