EXEL-7647 inhibits mutant forms of ErbB2 associated with lapatinib resistance and neoplastic transformation.

PURPOSE: Mutations associated with resistance to kinase inhibition are an important mechanism of intrinsic or acquired loss of clinical efficacy for kinase-targeted therapeutics. We report the prospective discovery of ErbB2 mutations that confer resistance to the small-molecule inhibitor lapatinib. EXPERIMENTAL DESIGN: We did in vitro screening using a randomly mutagenized ErbB2 expression library in Ba/F3 cells, which were dependent on ErbB2 activity for survival and growth. RESULTS: Lapatinib resistance screens identified mutations at 16 different ErbB2 amino acid residues, with 12 mutated amino acids mapping to the kinase domain. Mutations conferring the greatest lapatinib resistance cluster in the NH2-terminal kinase lobe and hinge region. Structural computer modeling studies suggest that lapatinib resistance is caused by multiple mechanisms; including direct steric interference and restriction of conformational flexibility (the inactive state required for lapatinib binding is energetically unfavorable). ErbB2 T798I imparts the strongest lapatinib resistance effect and is analogous to the epidermal growth factor receptor T790M, ABL T315I, and cKIT T670I gatekeeper mutations that are associated with clinical drug resistance. ErbB2 mutants associated with lapatinib resistance transformed NIH-3T3 cells, including L755S and T733I mutations known to occur in human breast and gastric carcinomas, supporting a direct mechanism for lapatinib resistance in ErbB2-driven human cancers. The epidermal growth factor receptor/ErbB2/vascular endothelial growth factor receptor inhibitor EXEL-7647 was found to inhibit almost all lapatinib resistance-associated mutations. Furthermore, no ErbB2 mutations were found to be associated with EXEL-7647 resistance and lapatinib sensitivity. CONCLUSIONS: Taken together, these data suggest potential target-based mechanisms of resistance to lapatinib and suggest that EXEL-7647 may be able to circumvent these effects.

The use of computational methods in the discovery and design of kinase inhibitors.

The recent success of the first FDA-approved small-molecule tyrosine kinase inhibitor Gleevec (STI-571, imatinib mesylate) in the treatment of chronic myelogenous leukemia (CML) has focused attention on the potential therapeutic usefulness of inhibitors of other kinase targets. This review shall highlight recent applications of computational chemistry methods, comprising both ligand-based and structure-based approaches, in the discovery and design of kinase inhibitors. In particular, we will focus on ATP-competitive inhibitors of selected kinase targets of therapeutic importance.

Structure-activity relationships of the antimalarial agent artemisinin. 6. The development of predictive in vitro potency models using CoMFA and HQSAR methodologies.

Artemisinin (1) is a unique sesquiterpene peroxide occurring as a constituent of Artemisia annua L. Because of the effectiveness of Artemisinin in the treatment of drug-resistant Plasmodium falciparum and its rapid clearance of cerebral malaria, development of clinically useful semisynthetic drugs for severe and complicated malaria (artemether, artesunate) was prompt. However, recent reports of fatal neurotoxicity in animals with dihydroartemisinin derivatives such as artemether have spawned a renewed effort to develop nontoxic analogues of artemisinin. In our effort to develop more potent, less neurotoxic agents for the oral treatment of drug-resistant malaria, we utilized comparative molecular field analysis (CoMFA) and hologram QSAR (HQSAR), beginning with a series of 211 artemisinin analogues with known in vitro antimalarial activity. CoMFA models were based on two conformational hypotheses: (a) that the X-ray structure of artemisinin represents the bioactive shape of the molecule or (b) that the hemin-docked conformation is the bioactive form of the drug. In addition, we examined the effect of inclusion or exclusion of racemates in the partial least squares (pls) analysis. Databases derived from the original 211 were split into chiral (n = 157), achiral (n = 34), and mixed databases (n = 191) after leaving out a test set of 20 compounds. HQSAR and CoMFA models were compared in terms of their potential to generate robust QSAR models. The r(2) and q(2) (cross-validated r(2)) were used to assess the statistical quality of our models. Another statistical parameter, the ratio of the standard error to the activity range (s/AR), was also generated. CoMFA and HQSAR models were developed having statistically excellent properties, which also possessed good predictive ability for test set compounds. The best model was obtained when racemates were excluded from QSAR analysis. Thus, CoMFA of the n = 157 database gave excellent predictions with outstanding statistical properties. HQSAR did an outstanding job in statistical analysis and also handled predictions well.