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Computationally guided design of N4-(2-methyl-2H-indazol-6-yl)-N2-phenylpyrimidine-2,4-diamine inhibitors of EGFR kinase targeting Cys797
(2026-03-01)
Konsue, Adchata
;
Gleeson, Duangkamol
;
Choowongkomon, Kiattawee
;
Jones, Donald J.L.
;
Hannanta-anan, Pimkhuan
The epidermal growth factor receptor kinase (EGFR) is a tyrosine kinase (TK) implicated in the uncontrolled growth of non-small cell lung cancer. EGFR-TK inhibitors have been used extensively, however inhibitor resistance often develops leading to disease progression. In this work, we report the computationally guided design and preparation of novel covalent 2,4-diaminopyrimidine EGFR-TK inhibitors, inspired by Osimertinib. Molecular dynamics simulations and quantum mechanical (QM) calculations were performed on novel designs incorporating a 2-methyl-2H-indazol-6-amine at the 4-position of pyrimidine as well as various linkers and electrophiles. Calculations suggested swapping the 5-pyrimidine -H atom for -Cl would lead to a preferential “out” ligand conformation that favored T790M enzyme which was later confirmed experimentally. Compound 19 was the most potent inhibitor of WT EGFR (3.0 nM) observed, more potent than the EGFR WT inhibitor Erlotinib (5.9 nM). Compounds 48 and 49 demonstrated better activity for the double-mutant EGFR (3.0 & 2.0 nM, respectively) than Osimertinib (12.8 nM). The selectivity of these compounds for the DM was found to be comparable to Osimertinib (∼20 fold) while their phosphate buffer solubilities were > 50-fold better than both marketed drugs. Kinetic evaluation of 48 (propenamide moiety) vs 49 (acrylamide electrophile) confirms k<inf>inact</inf>/K<inf>i</inf> values consistent with a covalent mode of action for the latter, but not the former. 2009 Elsevier Ltd. All rights reserved.
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Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies
(2019-08-01)
Simeon, Saw
;
Jongkon, Nathjanan
;
Chotpatiwetchkul, Warot
;
Gleeson, M. Paul
Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure–activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r<sup>2</sup> ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.

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