A novel series of pyridin-3-amine derivatives were designed, synthesized, and evaluated as multitargeted protein kinase inhibitors for the treatment of non-small cell lung cancer (NSCLC). Hit 1 was first disclosed by in silico screening against fibroblast growth factor receptors (FGFR), which was subsequently validated by in vitro experiments. The structure–activity relationship (SAR) of its analogues was then explored to afford novel FGFR inhibitors 2a–2p and 3a–3q. Among them, 3m showed potent inhibition against FGFR1, 2, and 3. Interestingly, compound 3m not only inhibited various phosphorylation and downstream signaling across different oncogenic forms in FGFR-overactivated cancer cells but also showed nanomolar level inhibition against several other NSCLC-related oncogene kinases, including RET, EGFR, EGFR/T790M/L858R, DDR2, and ALK. Finally, in vivo pharmacology evaluations of 3m showed significant antitumor activity (TGI = 66.1%) in NCI-H1581 NSCLC xenografts with a good pharmacokinetic profile.

Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4–ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD4i-likeness of a compound. BRD4LGR achieved a 20–30% higher AUC-ROC than that of Glide using the same test set. When conducting in vitro experiments against a library of previously untested, commercially available organic compounds, the second round of VS using BRD4LGR generated 15 new BRD4is. Moreover, inverting the machine-learning model provided easy access to structure–activity relationship (SAR) interpretation for hit-to-lead optimization.

Aldehyde oxidase (AOX) is an important drug-metabolizing enzyme. However, the current in vitro models for evaluating AOX metabolism are sometimes misleading, and preclinical animal models generally fail to predict human AOX-mediated metabolism. In this study, we report a combined computational and experimental investigation of drug-like molecules that are potential aldehyde oxidase substrates, of which multiple sites of metabolism (SOMs) mediated by AOX and their preferences for the reaction can be unambiguously identified. In addition, the proposed strategy was used to evaluate the metabolism of newly designed c-Met inhibitors, and a success switch-off of AOX metabolism was observed. Overall, this study provide useful information to guide lead optimization and drug discovery based on AOX-mediated metabolism.

The BET family of bromodomain-containing proteins (BRDs) is believed to be a promising drug target for therapeutic intervention in a number of diseases including cancer, inflammation and cardiovascular diseases. Hence, there is a great demand for novel chemotypes of BET inhibitors. The drug repurposing strategy offers great benefits to find inhibitors with known safety and pharmacokinetic profiles, thus increasing medicinal chemists’ interest in recent years. Using the drug repurposing strategy, a BRD4-specific score based virtual screening campaign on an in-house drug library was conducted followed by the ALPHA screen assay test. Nitroxoline, an FDA-approved antibiotic, was identified to effectively disrupt the interaction between the first bromodomain of BRD4 (bromodomain-containing protein 4) and acetylated H4 peptide with IC50 of 0.98 μM. Nitroxoline inhibited all BET family members with good selectivity against non-BET bromodomain-containing proteins, thus it is defined as a selective BET inhibitor. Based on the crystal structure of the nitroxoline-BRD4_BD1 complex, the mechanism of action as well as BET specificity of nitroxoline were determined. Since the anticancer activity of nitroxoline against MLL leukemia, one of the BET related diseases, has not been studied before, we tested whether nitroxoline might serve as a potential repurposing drug candidate for MLL leukemia. Nitroxoline effectively inhibited the proliferation of MLL leukemia cells by inducing cell cycle arrest and apoptosis. The profound efficacy is, at least in part, due to the inhibition of BET and downregulation of target gene transcription. Our discovery of nitroxoline as a BET inhibitor suggests potential application of nitroxoline and its derivatives for clinical translation in BET family related diseases.

The disruptor of telomeric silencing 1-like (DOT1L) protein is a histone H3K79 methyltransferase that plays a key role in transcriptional elongation and cell cycle regulation and is required for the development and maintenance of MLL-rearranged mixed lineage leukemia. Much effort has been dedicated toward discovering novel scaffold DOT1L inhibitors using different strategies. Here, we report the development and application of a target-specific scoring function, the SAM score, for (S)-adenosyl-l-methionine (SAM)-dependent methyltransferases, for the discovery of novel DOT1L inhibitors. On the basis of the SAM score, we successfully identified a novel class of DOT1L inhibitors with a scaffold of [1,2,4]-triazolo-[3,4-b][1,3,4]-thiadiazole, in which compound 6 exhibits an IC50 value of 8.3 μM with selectivity versus other tested SAM-dependent methyltransferases. In cellular studies, 6 selectively targets DOT1L, blocks the proliferation of mixed lineage leukemia cell lines, and causes cell cycle arrest and apoptosis. Moreover, we analyzed the putative binding modes of 6 and its analogues obtained by molecular docking, which may assist with the future development of DOT1L inhibitors with improved potency and selectivity profiles.

Protein arginine methyltransferase 5 (PRMT5) is a type II PRMT enzyme critical for diverse cellular processes and different types of cancers. Many efforts have been made to discover novel scaffold PRMT5 inhibitors. Herein, we report the discovery of DC_P33 as a hit compound of PRMT5 inhibitor, identified by molecular docking based virtual screening and 3H-labeled radioactive methylation assays. Structure–activity relationship (SAR) analysis was performed on the analogs of DC_P33 and then structural modifications were done to improve its activity. Among the derivatives, the compound DC_C01 displayed an IC50 value of 2.8 μM, and good selectivity toward PRMT1, EZH2 and DNMT3A. Moreover, DC_C01 exhibited anti-proliferation activities against Z-138, Maver-1, and Jeko-1 cancer cells with EC50 values of 12 μM, 12 μM, and 10.5 μM, respectively. Taken together, these results contribute to the development of specific inhibitors against PRMT5 and cancer therapy.

Disrupting the interaction between mixed lineage leukemia (MLL) fusion protein and menin provides a therapeutic approach for MLL-mediated leukemia. Here, we aim to discover novel inhibitors targeting the menin-MLL interface with virtual screening. Both structure-based molecular docking and ligand-based pharmacophore models were established, and the models used for compound screening show a remarkable ability to retrieve known active ligands from decoy molecules. Verified by a fluorescence polarization assay, five hits with novel scaffolds were identified. Among them, DCZ_M123 exhibited potent inhibitory activity with an IC50 of 4.71 ± 0.12 μM and a KD of 14.70 ± 2.13 μM, and it can effectively inhibit the human MLL-rearranged leukemia cells MV4;11 and KOPN8 with GI50 values of 0.84 μM and 0.54 μM, respectively.

The DNA methyltransferases (DNMTs) found in mammals include DNMT1, DNMT3A, and DNMT3B and are attractive targets in cancer chemotherapy. DNMT1 was the first among the DNMTs to be characterized, and it is responsible for maintaining DNA methylation patterns. A number of DNMT inhibitors have been reported, but most of them are nucleoside analogs that can lead to toxic side effects and lack specificity. By combining docking-based virtual screening with biochemical analyses, we identified a novel compound, DC_05. DC_05 is a non-nucleoside DNMT1 inhibitor with low micromolar IC50 values and significant selectivity toward other AdoMet-dependent protein methyltransferases. Through a process of similarity-based analog searching, compounds DC_501 and DC_517 were found to be more potent than DC_05. These three potent compounds significantly inhibited cancer cell proliferation. The structure–activity relationship (SAR) and binding modes of these inhibitors were also analyzed to assist in the future development of more potent and more specific DNMT1 inhibitors.

Protein arginine methyltransferase 5 (PRMT5) is a type II PRMT enzyme critical for diverse cellular processes and different types of cancers. Many efforts have been made to discover novel scaffold PRMT5 inhibitors. Herein, we report the discovery of DC_P33 as a hit compound of PRMT5 inhibitor, identified by molecular docking based virtual screening and 3H-labeled radioactive methylation assays. Structure–activity relationship (SAR) analysis was performed on the analogs of DC_P33 and then structural modifications were done to improve its activity. Among the derivatives, the compound DC_C01 displayed an IC50 value of 2.8 μM, and good selectivity toward PRMT1, EZH2 and DNMT3A. Moreover, DC_C01 exhibited anti-proliferation activities against Z-138, Maver-1, and Jeko-1 cancer cells with EC50 values of 12 μM, 12 μM, and 10.5 μM, respectively. Taken together, these results contribute to the development of specific inhibitors against PRMT5 and cancer therapy.