C-H chlorination of (hetero)anilines via photo/organo co-catalysis.

Chlorinated (hetero)anilines are a class of important structural motifs that are widely present in synthetic building blocks and pharmaceuticals. Despite recent advancements, direct aniline chlorination still suffers from ortho/para and mono/poly chlorination selectivity problems. Herein, we disclose a photo-redox and organo co-catalyzed chlorination method for anilines. This method has great substrate generality and excellent mono-chlorination selectivity. Another merit of this method is the late-stage modification of drug molecules, which would be useful in medicinal chemistry.

Discovery of Dual FGFR4 and EGFR Inhibitors by Machine Learning and Biological Evaluation.

Kinase inhibitors are widely used in antitumor research, but there are still many problems such as drug resistance and off-target toxicity. A more suitable solution is to design a multitarget inhibitor with certain selectivity. Herein, computational and experimental studies were applied to the discovery of dual inhibitors against FGFR4 and EGFR. A quantitative structure-property relationship (QSPR) study was carried out to predict the FGFR4 and EGFR activity of a data set consisting of 843 and 5088 compounds, respectively. Four different machine learning methods including support vector machine (SVM), random forest (RF), gradient boost regression tree (GBRT), and XGBoost (XGB) were built using the most suitable features selected by the mutual information algorithm. As for FGFR4 and EGFR, SVM showed the best performance with R2test-FGFR4 = 0.80 and R2test-EGFR = 0.75, demonstrating excellent model stability, which was used to predict the activity of some compounds from an in-house database. Finally, compound 1 was selected, which exhibits inhibitory activity against FGFR4 (IC50 = 86.2 nM) and EGFR (IC50 = 83.9 nM) kinase, respectively. Furthermore, molecular docking and molecular dynamics simulations were performed to identify key amino acids for the interaction of compound 1 with FGFR4 and EGFR. In this paper, the machine-learning-based QSAR models were established and effectively applied to the discovery of dual-target inhibitors against FGFR4 and EGFR, demonstrating the great potential of machine learning strategies in dual inhibitor discovery.

Investigation of Crystal Structures in Structure-Based Virtual Screening for Protein Kinase Inhibitors.

Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.

Preparation of 5'-deoxy-5'-amino-5'-C-methyl adenosine derivatives and their activity against DOT1L.

From a readily available 5-C-Me ribofuranoside, we have realized a reliable route to valuable 5'-deoxy-5'-amino-5'-C-methyl adenosine derivatives at gram scale with confirmed stereochemistry. These adenosine derivatives are useful starting materials for the preparation of 5'-deoxy-5'-amino-5'-C-methyl adenosine derivatives with higher complexity. From one of the new adenosine derivatives, some 5'-deoxy-5'-amino-5'-C-methyl adenosine DOT1L inhibitors were prepared in several steps. Data from DOT1L assay indicated that additional 5'-C-Me group improved the enzyme inhibitory activity.

Discovery of 2-Amino-7-sulfonyl-7H-pyrrolo[2,3-d]pyrimidine Derivatives as Potent Reversible FGFR Inhibitors with Gatekeeper Mutation Tolerance: Design, Synthesis, and Biological Evaluation.

Fibroblast growth factor receptors (FGFRs) play key roles in promoting cancer cell proliferation, differentiation, and migration. However, acquired resistance to FGFR inhibitors has become an emerging challenge in long-term cancer therapies, especially for hepatocellular carcinoma (HCC). Gatekeeper (GK) mutations are the main mechanism of resistance. Herein, we describe the discovery of a series of reversible FGFR inhibitors, particularly for GK mutations with the 2-amino-7-sulfonyl-7H-pyrrolo[2,3-d]pyrimidine scaffold. Rational design, optimization, and pharmacokinetic screening provided representative compound 19 with potent FGFR inhibition in vitro, high bioavailability, and an acceptable half-life. GK mutation tolerance was supported by assays against FGFR4V550L and Ba/F3-TEL-FGFR4V550L cells. Moreover, compound 19 exhibited potent antitumor potency in HUH7 xenograft mouse models with no obvious toxicity observed. Compound 19 was identified as a potential candidate for overcoming GK mutations for HCC treatment.

Recent advance in the development of novel, selective and potent FGFR inhibitors.

Mutation or abnormal expression of protein tyrosine kinases (PTKs) is one of the main causes of cancer. Fibroblast growth factor receptors (FGFRs) are a subfamily of tyrosine kinase receptors, which have four subtypes including FGFR1, FGFR2, FGFR3 and FGFR4. Their abnormal expression in cells is considered to be the main cause of tumorigenesis, so inhibiting FGFRs is thought to be important targets for cancer treatment. This article mainly summarizes the recent development of FGFR inhibitors in the past 5 years, and hopes to guide the future research on the design and synthesis of FGFR inhibitors.

Protein-ligand interaction-guided discovery of novel VEGFR-2 inhibitors.

As an effective target in abnormal angiogenesis-related tumor treatment, VEGFR-2 has small-molecule inhibitors of various scaffolds being approved for treating diseases such as renal carcinoma, non-small cell lung cancer, etc. However, endogenous and acquired drug resistance are still considered to be the main contributors for the failure of VEGFR-2 clinical candidates. Therefore, development of novel VEGFR-2 inhibitors is still urgently needed in the market but also challenging. In this work, residues including Asp1046, Ile1025, HIS1026, Cys919 and Lys868 were identified as the most important residues for Hbonded interaction, while His1026, Asp1046, Glu885, Ile1025 and Leu840 exhibited critical role for the nonbonded interactions through a comprehensive analysis of protein-ligand interactions, which plays critical roles in the binding of compounds and targets. Guided by the analysis of binding interactions, a total of 10 novel VEGFR-2 inhibitors based on N-methyl-4-oxo-N-propyl-1,4-dihydroquinoline-2-carboxamide scaffold were discovered through fragment-based drug design and structure-based virtual screening, which expands the chemical space of current VEGFR-2 inhibitors. Biological activity evaluation showed that even though the enzymatic activity of these compounds against VEGFR-2 were inferior to that of the positive controls sorafenib and motesanib, compound I-10 showed moderate HepG2 cell inhibitory activity with an IC50 value of 33.65 µM and eight compounds exhibited moderate or higher HUVEC inhibitory activity in the range of 19.54-57.98 µM compared to the controls. Particularly, the HUVEC inhibitory activity of compound I-6 (IC50 = 19.54 µM) outperformed motesanib and can be used as starting points for further optimization and development for cancer treatment.Communicated by Ramaswamy H. Sarma.