143D, a novel selective KRAS inhibitor exhibits potent antitumor activity in preclinical models.

The KRAS mutant has emerged as an important therapeutic target in recent years. Covalent inhibitors have shown promising antitumor activity against KRAS-mutant cancers in the clinic. In this study, a structure-based and focused chemical library analysis was performed, which led to the identification of 143D as a novel, highly potent and selective KRAS inhibitor.

BCL-2 inhibitor synergizes with PI3Kδ inhibitor and overcomes FLT3 inhibitor resistance in acute myeloid leukaemia.

Inhibitors targeting the antiapoptotic molecule BCL-2 have therapeutic potential for the treatment of acute myeloid leukaemia (AML); however, BCL-2 inhibitors such as venetoclax exhibit limited monotherapy efficacy in relapsed or refractory human AML. PI3Kδ/AKT signalling has been shown to be constitutively active in AML patients. Here, we demonstrate that the combination of BCL-2 and PI3Kδ inhibitors exerts synergistic antitumour effects both and in AML.

Discovery and identification of a novel small molecule BCL-2 inhibitor that binds to the BH4 domain.

The B-cell lymphoma 2 (BCL-2) protein family plays a pivotal role in regulating the apoptosis process. BCL-2, as an antiapoptotic protein in this family, mediates apoptosis resistance and is an ideal target for cell death strategies in cancer therapy. Traditional treatment modalities target BCL-2 by occupying the hydrophobic pocket formed by BCL-2 homology (BH) domains 1-3, while in recent years, the BH4 domain of BCL-2 has also been considered an attractive novel target.

A simple liquid chromatography tandem mass spectrometric method for fast detection of hydrogen sulfide based on thiolysis of 7-nitro-2, 1, 3-benzoxadiazole ether.

The long identified toxic gas, hydrogen sulfide (HS), which has also been confirmed as the third gaseous signaling molecule following NO and CO, plays important roles in various physiological and pathological process. The current most established quantification method for HS is HPLC method coupled with fluorescence detection after derivatization with a costly fluorescent reagent, Monobromobimane (MBB). However, The MBB method is characterized by strict reaction condition, long reaction time, tedious operation, and inconsistent reported results.

Discovery and characterization of natural products as novel indoleamine 2,3-dioxygenase 1 inhibitors through high-throughput screening.

Indoleamine 2,3-dioxygenase 1 (IDO1) is emerging as a promising therapeutic target for the treatment of malignant tumors characterized by dysregulated tryptophan metabolism. However, the antitumor efficacy of existing small-molecule IDO1 inhibitors is still unsatisfactory, and the underlying mechanism remains largely undefined. To identify novel IDO1 inhibitors, an in-house natural product library of 2000 natural products was screened for inhibitory activity against recombinant human IDO1.

Conformation and dynamics of the C-terminal region in human phosphoglycerate mutase 1.

Phosphoglycerate mutase 1 (PGAM1), an important enzyme in glycolysis, is overexpressed in a number of human cancers, thus has been proposed as a promising metabolic target for cancer treatments. The C-terminal portion of the available crystal structures of PGAM1 and its homologous proteins is partially disordered, as evidenced by weak electron density. In this study, we identified the conformational behavior of the C-terminal region of PGAM1 as well as its role during the catalytic cycle.

In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning.

Background: Chemical toxicity is an important reason for late-stage failure in drug R&D. However, it is time-consuming and expensive to identify the multiple toxicities of compounds using the traditional experiments. Thus, it is attractive to build an accurate prediction model for the toxicity profile of compounds.

Discovery of a new series of imidazo[1,2-a]pyridine compounds as selective c-Met inhibitors.

Aim: Aberrant c-Met activation plays a critical role in cancer formation, progression and dissemination, as well as in development of resistance to anticancer drugs. Therefore, c-Met has emerged as an attractive target for cancer therapy. The aim of this study was to develop new c-Met inhibitors and elaborate the structure-activity relationships of identified inhibitors.

Wedelolactone metabolism in rats through regioselective glucuronidation catalyzed by uridine diphosphate-glucuronosyltransferases 1As (UGT1As).

Background: Wedelolactone (WEL), a medicinal plant-derived coumestan, has been reported to exhibit a diverse range of pharmacological activities. However, the metabolism and disposition of WEL remain unexplored.

Purpose: The present study aims to investigate the metabolism of WEL in rats and identify the enzymes responsible for forming major WEL metabolites.

[Diversity-oriented synthesis and its application in drug discovery].

Diversity-oriented synthesis (DOS) aims to efficiently generate collections of small molecules with diverse appendages, functional groups, stereochemistry and skeletons, thus yielding diverse biological activities capable of modulating a wide variety of biological processes. In this review, we discussed the common strategies employed in DOS with specific examples from recent literature, including reagent-based approach, substrate-based approach, build-couple-pair strategy and privileged substructure-based DOS. The application of some DOS libraries in drug discovery is also presented.

Novel Bayesian classification models for predicting compounds blocking hERG potassium channels.

Aim: A large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels.

Methods: Doddareddy's hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets.

Prediction of cancer drugs by chemical-chemical interactions.

Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug.

Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways.

Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way.

Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions.

A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs.

Identifying chemicals with potential therapy of HIV based on protein-protein and protein-chemical interaction network.

Acquired immune deficiency syndrome (AIDS) is a severe infectious disease that causes a large number of deaths every year. Traditional anti-AIDS drugs directly targeting the HIV-1 encoded enzymes including reverse transcriptase (RT), protease (PR) and integrase (IN) usually suffer from drug resistance after a period of treatment and serious side effects. In recent years, the emergence of numerous useful information of protein-protein interactions (PPI) in the HIV life cycle and related inhibitors makes PPI a new way for antiviral drug intervention.

Predicting chemical toxicity effects based on chemical-chemical interactions.

Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound.

Binding sensitivity of adefovir to the polymerase from different genotypes of HBV: molecular modeling, docking and dynamics simulation studies.

Aim: To investigate the molecular mechanisms underlying the influence of DNA polymerase from different genotypes of hepatitis B virus (HBV) on the binding affinity of adefovir (ADV).

Methods: Computational approaches, including homology modeling, docking, MD simulation and MM/PBSA free energy analyses were used.

Results: Sequence analyses revealed that residue 238 near the binding pocket was not only a polymorphic site but also a genotype-specific site (His238 in genotype B; Asn238 in genotype C).

[Progress in the ligands and their complex structures of farnesoid X receptor].

Farnesoid X receptor (FXR) belongs to the nuclear receptor superfamily. It is highly related to the formation of metabolic syndrome and the glucose homeostasis, and therefore represents an important drug target against metabolic diseases and diabetes. In recent years, great progress has been made in the agonists, antagonists, and crystal structures of FXR.

3D-QSAR study of 20 (S)-camptothecin analogs.

Aim: To build up a quantitative structure-activity relationship (QSAR) model of 20 (S)-camptothecin (CPT) analogs for the prediction of the activity of new CPT analogs for drug design.

Methods: A training set of 43 structurally diverse CPT analogs which were inhibitors of topoisomerase I were used to construct a quantitative structure-activity relationship model with a comparative molecular field analysis (CoMFA). The QSAR model was optimized using partial least squares (PLS) analysis.