Epidermal growth factor receptor (EGFR) mutations typically occur in exons 18-21 and are established driver mutations in non-small cell lung cancer (NSCLC)
. Targeted therapies are approved for ...patients with 'classical' mutations and a small number of other mutations
. However, effective therapies have not been identified for additional EGFR mutations. Furthermore, the frequency and effects of atypical EGFR mutations on drug sensitivity are unknown
. Here we characterize the mutational landscape in 16,715 patients with EGFR-mutant NSCLC, and establish the structure-function relationship of EGFR mutations on drug sensitivity. We found that EGFR mutations can be separated into four distinct subgroups on the basis of sensitivity and structural changes that retrospectively predict patient outcomes following treatment with EGFR inhibitors better than traditional exon-based groups. Together, these data delineate a structure-based approach for defining functional groups of EGFR mutations that can effectively guide treatment and clinical trial choices for patients with EGFR-mutant NSCLC and suggest that a structure-function-based approach may improve the prediction of drug sensitivity to targeted therapies in oncogenes with diverse mutations.
•The use of AI-driven solutions to enable pre-clinical drug discovery is growing steadily within the pharmaceutical industry.•AI technologies can be leveraged across the drug discovery value ...chain.•Artificial intelligence may transform the way we do drug discovery, but certainly cannot replace human ingenuity.•AI may become a disruptive technology for drug discovery in the future, but there is still room for healthy skepticism as the field matures.
Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.
We report a phase II study of 50 advanced non-small cell lung cancer (NSCLC) patients with point mutations or insertions in EGFR exon 20 treated with poziotinib (NCT03066206). The study achieved its ...primary endpoint, with confirmed objective response rates (ORRs) of 32% and 31% by investigator and blinded independent review, respectively, with a median progression-free survival of 5.5 months. Using preclinical studies, in silico modeling, and molecular dynamics simulations, we found that poziotinib sensitivity was highly dependent on the insertion location, with near-loop insertions (amino acids A767 to P772) being more sensitive than far-loop insertions, an observation confirmed clinically with ORRs of 46% and 0% observed in near versus far-loop, respectively (p = 0.0015). Putative mechanisms of acquired resistance included EGFR T790M, MET amplifications, and epithelial-to-mesenchymal transition (EMT). Our data demonstrate that poziotinib is active in EGFR exon 20-mutant NSCLC, although this activity is influenced by insertion location.
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•Poziotinib yields a 32% response rate in EGFR exon 20-mutant NSCLC•Poziotinib sensitivity is highly dependent on insertion location•Near-loop exon 20 insertions are more sensitive to poziotinib than far-loop insertions•Mechanisms of acquired poziotinib resistance include EGFR T790M and MET amplifications
Elamin et al. show that poziotinib is active in EGFR exon 20-mutant non-small cell lung cancer. The activity of poziotinib is influenced by insertion location in exon 20, with near-loop insertion being more sensitive than far-loop insertion. Poziotinib acquired resistance is mediated via EGFR-dependent and -independent mechanisms.
We characterized the landscape and drug sensitivity of ERBB2 (HER2) mutations in cancers. In 11 datasets (n = 211,726), ERBB2 mutational hotspots varied across 25 tumor types. Common HER2 mutants ...yielded differential sensitivities to eleven EGFR/HER2 tyrosine kinase inhibitors (TKIs) in vitro, and molecular dynamics simulations revealed that mutants with a reduced drug-binding pocket volume were associated with decreased affinity for larger TKIs. Overall, poziotinib was the most potent HER2 mutant-selective TKI tested. Phase II clinical testing in ERBB2 exon 20-mutant non-small cell lung cancer resulted in a confirmed objective response rate of 42% in the first 12 evaluable patients. In pre-clinical models, poziotinib upregulated HER2 cell-surface expression and potentiated the activity of T-DM1, resulting in complete tumor regression with combination treatment.
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•ERBB2 mutations occur in at least 25 tumor types with varying patterns of mutations•Mutation-induced changes in drug-binding pocket volume dictate drug sensitivity•Poziotinib inhibits mutant HER2, yielding a 42% response rate in NSCLC patients•Combination of poziotinib with T-DM1 potentiates antitumor activity of both agents
Robichaux et al. show that ERBB2 mutation hotspots vary across human tumor types, which affect the volume of the HER2 TKI binding pocket and dictate drug sensitivity. Poziotinib is the most potent HER2 TKI among those tested. Moreover, poziotinib enhances T-DM1 efficacy by increasing the cell-surface HER2 level.
Molecular docking programs are widely used modeling tools for predicting ligand binding modes and structure based virtual screening. In this study, six molecular docking programs (DOCK, FlexX, GLIDE, ...ICM, PhDOCK, and Surflex) were evaluated using metrics intended to assess docking pose and virtual screening accuracy. Cognate ligand docking to 68 diverse, high-resolution X-ray complexes revealed that ICM, GLIDE, and Surflex generated ligand poses close to the X-ray conformation more often than the other docking programs. GLIDE and Surflex also outperformed the other docking programs when used for virtual screening, based on mean ROC AUC and ROC enrichment values obtained for the 40 protein targets in the Directory of Useful Decoys (DUD). Further analysis uncovered general trends in accuracy that are specific for particular protein families. Modifying basic parameters in the software was shown to have a significant effect on docking and virtual screening results, suggesting that expert knowledge is critical for optimizing the accuracy of these methods.
Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of ...biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.
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Structure based design of a novel class of aminopyrimidine MTH1 (MutT homolog 1) inhibitors is described. Optimization led to identification of IACS-4759 (compound 5), a sub-nanomolar ...inhibitor of MTH1 with excellent cell permeability and good metabolic stability in microsomes. This compound robustly inhibited MTH1 activity in cells and proved to be an excellent tool for interrogation of the utility of MTH1 inhibition in the context of oncology.
Src homology 2 (SH2) domain-containing phosphatase 2 (SHP2) plays a role in receptor tyrosine kinase (RTK), neurofibromin-1 (NF-1), and Kirsten rat sarcoma virus (KRAS) mutant-driven cancers, as well ...as in RTK-mediated resistance, making the identification of small-molecule therapeutics that interfere with its function of high interest. Our quest to identify potent, orally bioavailable, and safe SHP2 inhibitors led to the discovery of a promising series of pyrazolopyrimidinones that displayed excellent potency but had a suboptimal in vivo pharmacokinetic (PK) profile. Hypothesis-driven scaffold optimization led us to a series of pyrazolopyrazines with excellent PK properties across species but a narrow human Ether-à-go-go-Related Gene (hERG) window. Subsequent optimization of properties led to the discovery of the pyrimidinone series, in which multiple members possessed excellent potency, optimal in vivo PK across species, and no off-target activities including no hERG liability up to 100 μM. Importantly, compound 30 (IACS-15414) potently suppressed the mitogen-activated protein kinase (MAPK) pathway signaling and tumor growth in RTK-activated and KRASmut xenograft models in vivo.
Indoleamine 2,3-dioxygenase 1 (IDO1), a heme-containing enzyme that mediates the rate-limiting step in the metabolism of l-tryptophan to kynurenine, has been widely explored as a potential ...immunotherapeutic target in oncology. We developed a class of inhibitors with a conformationally constrained bicyclo3.1.0hexane core. These potently inhibited IDO1 in a cellular context by binding to the apoenzyme, as elucidated by biochemical characterization and X-ray crystallography. A SKOV3 tumor model was instrumental in differentiating compounds, leading to the identification of IACS-9779 (62) and IACS-70465 (71). IACS-70465 has excellent cellular potency, a robust pharmacodynamic response, and in a human whole blood assay was more potent than linrodostat (BMS-986205). IACS-9779 with a predicted human efficacious once daily dose below 1 mg/kg to sustain >90% inhibition of IDO1 displayed an acceptable safety margin in rodent toxicology and dog cardiovascular studies to support advancement into preclinical safety evaluation for human development.
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