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Quantitative prediction of fold resistance for inhibitors of EGFR
Trent E Balius1, Robert C Rizzo
1Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA.
Abstract:
Clinical use of ATP-competitive inhibitors of the epidermal growth factor receptor (EGFR) kinase domain can lead to an acquired drug resistant mutant L858R&T790M which dramatically reduces binding affinity relative to a prevalent cancer causing mutation L858R. In this study, we have used molecular dynamics (MD) computer simulations, free energy calculations (MM-GBSA method), and per-residue footprint analysis to characterize binding of three inhibitors (erlotinib, gefitinib, and AEE788) with wildtype EGFR and three mutants. The goal is to characterize how variation in structure and energy correlate with changes in experimental activities and to deduce origins of drug resistance. For seven fold resistance values, each computed from the difference of two independent computer simulations, excellent agreement was obtained with available experimental data (r2 = 0.84). Importantly, the results correctly predict that affinity will increase as a result of L858R and decrease due to L858R&T790M. Per-residue analysis shows an increase in favorable packing at the site of the methionine mutation reaffirming that a steric clash hypothesis is unlikely; however, large losses in van der Waals, Coulombic, and H-bond interactions strongly suggest that resistance is not due solely to changes in affinity for the native substrate ATP as recently proposed. Instead, the present results indicate that drug resistance more likely involves disruption of favorable interactions, including a water-mediated H-bond network between the ligands and residues T854, T790, and Q791, which could have important implication for guiding rational design of inhibitors with improved resistance profiles.
Insights
Drug resistance in cancer treatment arises from mutations like L858R and T790M in epidermal growth factor receptor (EGFR). This study used computer simulations to reveal that resistance stems from disrupted interactions, not just altered ATP binding, guiding future drug design.
Area of Science:
- Biochemistry
- Computational Chemistry
- Molecular Biology
Background:
- Acquired resistance to epidermal growth factor receptor (EGFR) inhibitors is a significant clinical challenge.
- The L858R and T790M mutations in EGFR confer resistance by reducing drug binding affinity.
- Understanding the molecular basis of this resistance is crucial for developing effective cancer therapies.
Purpose of the Study:
- To computationally characterize the binding of three EGFR inhibitors (erlotinib, gefitinib, AEE788) to wildtype EGFR and key resistant mutants (L858R, L858R&T790M).
- To correlate structural and energetic variations with experimental drug activities and elucidate the origins of drug resistance.
- To provide insights for the rational design of next-generation EGFR inhibitors with improved resistance profiles.
Main Methods:
- Molecular dynamics (MD) simulations were employed to model inhibitor-EGFR interactions.
- Free energy calculations using the MM-GBSA method quantified binding affinities.
- Per-residue footprint analysis identified key interactions contributing to binding and resistance.
Main Results:
- The study achieved excellent agreement (r2 = 0.84) between computed and experimental resistance values.
- Simulations correctly predicted increased affinity for L858R and decreased affinity for L858R&T790M mutants.
- Analysis revealed that drug resistance is primarily due to the disruption of favorable interactions, including a water-mediated hydrogen bond network, rather than solely altered affinity for ATP.
Conclusions:
- Drug resistance to EGFR inhibitors is complex, involving significant disruption of ligand-protein interactions.
- The findings challenge the hypothesis that resistance is solely due to changes in affinity for the native substrate ATP.
- Understanding the role of specific interactions, like the water-mediated network, is vital for designing inhibitors that overcome resistance mechanisms.
