title: Quantitative prediction of fold resistance for inhibitors of EGFR.
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.
authors.affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA.
authors.forename: Trent E
authors.initials: TE
authors.lastname: Balius
authors.forename: Robert C
authors.initials: RC
authors.lastname: Rizzo
mesh_terms: D000818:Animals; D045744:Cell Line, Tumor; D003198:Computer Simulation; D019008:Drug Resistance, Neoplasm; D066246:ErbB Receptors / Q000037:antagonists & inhibitors* / Q000737:chemistry; D000069347:Erlotinib Hydrochloride; D000077156:Gefitinib; D006801:Humans; D008958:Models, Molecular; D011799:Quinazolines / Q000494:pharmacology
chemical_list: D011799:Quinazolines; D000069347:Erlotinib Hydrochloride; D066246:ErbB Receptors; D000077156:Gefitinib