Background Alectinib has shown a greater efficacy to ALK -rearranged non-small-cell lung cancers in first-line setting; however, most patients relapse due to acquired resistance, such as secondary mutations in ALK including I1171N and G1202R. Although ceritinib or lorlatinib was shown to be effective to these resistant mutants, further resistance often emerges due to ALK-compound mutations in relapse patients following the use of ceritinib or lorlatinib. However, the drug for overcoming resistance has not been established yet. Methods We established lorlatinib-resistant cells harboring ALK-I1171N or -G1202R compound mutations by performing ENU mutagenesis screening or using an in vivo mouse model. We performed drug screening to overcome the lorlatinib-resistant ALK-compound mutations. To evaluate these resistances in silico , we developed a modified computational molecular dynamic simulation (MP-CAFEE). Findings We identified 14 lorlatinib-resistant ALK-compound mutants, including several mutants that were recently discovered in lorlatinib-resistant patients. Some of these compound mutants were found to be sensitive to early generation ALK-TKIs and several BCR-ABL inhibitors. Using our original computational simulation, we succeeded in demonstrating a clear linear correlation between binding free energy and in vitro experimental IC 50 value of several ALK-TKIs to single- or compound-mutated EML4-ALK expressing Ba/F3 cells and in recapitulating the tendency of the binding affinity reduction by double mutations found in this study. Computational simulation revealed that ALK-L1256F single mutant conferred resistance to lorlatinib but increased the sensitivity to alectinib. Interpretation We discovered lorlatinib-resistant multiple ALK-compound mutations and an L1256F single mutation as well as the potential therapeutic strategies for these ALK mutations. Our original computational simulation to calculate the binding affinity may be applicable for predicting resistant mutations and for overcoming drug resistance in silico. Fund This work was mainly supported by MEXT/JSPS KAKENHI Grants and AMED Grants.
ALK gene rearrangement was observed in 3%–5% of non-small cell lung cancer patients, and multiple ALK-tyrosine kinase inhibitors (TKIs) have been sequentially used. Multiple ALK-TKI resistance mutations have been identified from the patients, and several compound mutations, such as I1171N + F1174I or I1171N + L1198H are resistant to all the approved ALK-TKIs. In this study, we found that gilteritinib has an inhibitory effect on ALK-TKI–resistant single mutants and I1171N compound mutants in vitro and in vivo. Surprisingly, EML4-ALK I1171N + F1174I compound mutant-expressing tumors were not completely shrunk but regrew within a short period of time after alectinib or lorlatinib treatment. However, the relapsed tumor was markedly shrunk after switching to the gilteritinib in vivo model. In addition, gilteritinib was effective against NTRK-rearranged cancers including entrectinib-resistant NTRK1 G667C-mutant and ROS1 fusion-positive cancer.
Recently, molecular generation models based on deep learning have attracted significant attention in drug discovery. However, most existing molecular generation models have a serious limitation in the context of drug design wherein they do not sufficiently consider the effect of the three-dimensional (3D) structure of the target protein in the generation process. In this study, we developed a new deep learning-based molecular generator, SBMolGen, that integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations. The results of an evaluation using four target proteins (two kinases and two G protein-coupled receptors) showed that the generated molecules had a better binding affinity score (docking score) than the known active compounds, and they possessed a broader chemical space distribution. SBMolGen not only generates novel binding active molecules but also presents 3D docking poses with target proteins, which will be useful in subsequent drug design. File list (2)download file view on ChemRxiv SBMolGen_manuscript_ChemRxiv.pdf (13.43 MiB) download file view on ChemRxiv SBMolGen_manuscript_Supporting_Information_ChemR...
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