Comparative Atomistic Insights on Apo and ATP-I1171N/S/T in Nonsmall-Cell Lung Cancer

NVL-655 Is a Selective and Brain-Penetrant Inhibitor of Diverse ALK-Mutant Oncoproteins, Including Lorlatinib-Resistant Compound Mutations

Three generations of tyrosine kinase inhibitors (TKI) have been approved for anaplastic lymphoma kinase (ALK) fusion-positive non-small cell lung cancer. However, none address the combined need for broad resistance coverage, brain activity, and avoidance of clinically dose-limiting TRK inhibition. NVL-655 is a rationally designed TKI with >50-fold selectivity for ALK over 96% of the kinome tested. In vitro, NVL-655 inhibits diverse ALK fusions, activating alterations, and resistance mutations, showing ≥100-fold improved potency against ALKG1202R single and compound mutations over approved ALK TKIs. In vivo, it induces regression across 12 tumor models, including intracranial and patient-derived xenografts. NVL-655 inhibits ALK over TRK with 22-fold to >874-fold selectivity. These preclinical findings are supported by three case studies from an ongoing first-in-human phase I/II trial of NVL-655 which demonstrate preliminary proof-of-concept clinical activity in heavily pretreated patients with ALK fusion-positive non-small cell lung cancer, including in patients with brain metastases and single or compound ALK resistance mutations. Significance: By combining broad activity against single and compound ALK resistance mutations, brain penetrance, and selectivity, NVL-655 addresses key limitations of currently approved ALK inhibitors and has the potential to represent a distinct advancement as a fourth-generation inhibitor for patients with ALK-driven cancers.

Discovery of novel chemotype inhibitors targeting Anaplastic Lymphoma Kinase receptor through ligand-based pharmacophore modelling

Anaplastic Lymphoma Kinase (ALK) is a receptor tyrosine kinase within the insulin receptor superfamily. Alterations in ALK, such as rearrangements, mutations, or amplifications, have been detected in various tumours, including lymphoma, neuroblastoma, and non-small cell lung cancer. In this study, we outline a computational workflow designed to uncover new inhibitors of ALK. This process starts with a ligand-based exploration of the pharmacophoric space using 13 diverse sets of ALK inhibitors. Subsequently, quantitative structure-activity relationship (QSAR) modelling is employed in combination with a genetic function algorithm to identify the optimal combination of pharmacophores and molecular descriptors capable of elucidating variations in anti-ALK bioactivities within a compiled list of inhibitors. The successful QSAR model revealed three pharmacophores, two of which share three similar features, prompting their merger into a single pharmacophore model. The merged pharmacophore was used as a 3D search query to mine the National Cancer Institute (NCI) database for novel anti-ALK leads. Subsequent in vitro bioassay of the top 40 hits identified two compounds with low micromolar IC50 values. Remarkably, one of the identified leads possesses a novel chemotype compared to known ALK inhibitors.