Gut microbiota interactions with antitumor immunity in colorectal cancer: From understanding to application.

Colorectal cancer (CRC) is one of highly prevalent cancer. Immunotherapy with immune checkpoint inhibitors (ICIs) has dramatically changed the landscape of treatment for many advanced cancers, but CRC still exhibits suboptimal response to immunotherapy. The gut microbiota can affect both anti-tumor and pro-tumor immune responses, and further modulate the efficacy of cancer immunotherapy, particularly in the context of therapy with ICIs. Therefore, a deeper understanding of how the gut microbiota modulates immune responses is crucial to improve the outcomes of CRC patients receiving immunotherapy and to overcome resistance in nonresponders. The present review aims to describe the relationship between the gut microbiota, CRC, and antitumor immune responses, with a particular focus on key studies and recent findings on the effect of the gut microbiota on the antitumor immune activity. We also discuss the potential mechanisms by which the gut microbiota influences host antitumor immune responses as well as the prospective role of intestinal flora in CRC treatment. Furthermore, the therapeutic potential and limitations of different modulation strategies for the gut microbiota are also discussed. These insights may facilitate to better comprehend the interplay between the gut microbiota and the antitumor immune responses of CRC patients and provide new research pathways to enhance immunotherapy efficacy and expand the patient population that could be benefited by immunotherapy.

143D, a novel selective KRASG12C inhibitor exhibits potent antitumor activity in preclinical models.

The KRASG12C mutant has emerged as an important therapeutic target in recent years. Covalent inhibitors have shown promising antitumor activity against KRASG12C-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 KRASG12C inhibitor. The antitumor efficacy of 143D in vitro and in vivo was comparable with that of AMG510 and of MRTX849, two well-characterized KRASG12C inhibitors. At low nanomolar concentrations, 143D showed biochemical and cellular potency for inhibiting the effects of the KRASG12C mutation. 143D selectively inhibited cell proliferation and induced G1-phase cell cycle arrest and apoptosis by downregulating KRASG12C-dependent signal transduction. Compared with MRTX849, 143D exhibited a longer half-life and higher maximum concentration (Cmax) and area under the curve (AUC) values in mouse models, as determined by tissue distribution assays. Additionally, 143D crossed the blood‒brain barrier. Treatment with 143D led to the sustained inhibition of KRAS signaling and tumor regression in KRASG12C-mutant tumors. Moreover, 143D combined with EGFR/MEK/ERK signaling inhibitors showed enhanced antitumor activity both in vitro and in vivo. Taken together, our findings indicate that 143D may be a promising drug candidate with favorable pharmaceutical properties for the treatment of cancers harboring the KRASG12C mutation.

Discovery and characterization of a novel cGAS covalent inhibitor for the treatment of inflammatory bowel disease.

Cyclic GMP-AMP synthase (cGAS), a cytosolic DNA sensor, acts as a nucleotidyl transferase that catalyzes ATP and GTP to form cyclic GMP-AMP (cGAMP) and plays a critical role in innate immunity. Hyperactivation of cGAS-STING signaling contributes to hyperinflammatory responses. Therefore, cGAS is considered a promising target for the treatment of inflammatory diseases. Herein, we report the discovery and identification of several novel types of cGAS inhibitors by pyrophosphatase (PPiase)-coupled activity assays. Among these inhibitors, 1-(1-phenyl-3,4-dihydro-1H-pyrrolo[1,2-a]pyrazin-2-yl)prop-2-yn-1-one (compound 3) displayed the highest potency and selectivity at the cellular level. Compound 3 exhibited better inhibitory activity and pathway selectivity than RU.521, which is a selective cGAS inhibitor with anti-inflammatory effects in vitro and in vivo. Thermostability analysis, nuclear magnetic resonance and isothermal titration calorimetry assays confirmed that compound 3 directly binds to the cGAS protein. Mass spectrometry and mutation analysis revealed that compound 3 covalently binds to Cys419 of cGAS. Notably, compound 3 demonstrated promising therapeutic efficacy in a dextran sulfate sodium (DSS)-induced mouse colitis model. These results collectively suggest that compound 3 will be useful for understanding the biological function of cGAS and has the potential to be further developed for inflammatory disease therapies.

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. Herein, we describe the discovery and identification of DC-B01, a novel BCL-2 inhibitor targeting the BH4 domain, through virtual screening combined with biophysical and biochemical methods. Our results from surface plasmon resonance and cellular thermal shift assay confirmed that the BH4 domain is responsible for the interaction between BCL-2 and DC-B01. As evidenced by further cell-based experiments, DC-B01 induced cell killing in a BCL-2-dependent manner and triggered apoptosis via the mitochondria-mediated pathway. DC-B01 disrupted the BCL-2/c-Myc interaction and consequently suppressed the transcriptional activity of c-Myc. Moreover, DC-B01 inhibited tumor growth in vivo in a BCL­2­dependent manner. Collectively, these results indicate that DC-B01 is a promising BCL-2 BH4 domain inhibitor with the potential for further development.

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 in vitro and in vivo in AML. Cotreatment with venetoclax and the specific PI3Kδ inhibitor idelalisib significantly enhanced antiproliferative effects and induced caspase-dependent apoptosis in a panel of AML cell lines. The synergistic effects were mechanistically based on the inactivation of AKT/4E-BP-1 signalling and the reduction of MCL-1 expression, which diminished the binding of Bim to MCL-1. Notably, compared with the parental FLT3-ITD-positive MV-4-11, the acquired FLT3 inhibitor quizartinib-resistant xenograft model carrying the F691L mutation, exhibited a markedly higher sensitivity to venetoclax. Furthermore, venetoclax combined with idelalisib led to tumour regression in all animals in this quizartinib-resistant AML model. Thus, these data indicate that combined inhibition of BCL-2 and PI3Kδ may be a promising strategy in AML, especially for patients with FLT3-ITD and/or FLT3-TKD mutations.

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 (H2S), 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 H2S 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. In this study, based on the thiolysis reaction of 7-nitro-2, 1, 3-benzoxadiazole (NBD) ether, the commonly used chromatographic modifier 4-chloro-7-nitro-2,1,3- benzoxadiazole (NBDCl) and four probes (NBDOMe, NBDOEt, NBDOTFE and NBDOCMR) synthesized from NBDCl were tested as alternatives for fast quantification of H2S by LC-MS/MS. The reaction product between NBD ethers/NBDCl and H2S showed special pink color visible to the naked eye and was easy to synthesize and separate in lab; it also showed good retention on common chromatographic columns and high instrument response; therefore it is a good determinand. After establishment of LC-MS/MS methods for all the related compounds, the reaction conditions were optimized for all the probes with H2S. Then the stability, selectivity, reaction rate, sensitivity and quantitative linear relationship between the reaction product and H2S concentration were studied for each probe. Finally, NBDOEt was selected for LC-MS/MS detection of H2S. In comparision with the MBB method, the established NBDOEt method showed matched sensitivity and linearity, better selectivity, and higher repeatability; and had the advantages of easy operation, simple reaction condition, and cheap raw materials. The method was successfully validated and applied to determination of Na2S content in Na2S∙9H2O bulk drug and injection. In conclusion, NBDOEt is a promising option for quantification of H2S in abiotic matrix.

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. High-throughput fluorescence-based screening identified 79 compounds with inhibitory activity > 30% at 20 µM. Nine natural products were further confirmed to inhibit IDO1 activity by > 30% using Ehrlich's reagent reaction. Compounds 2, 7, and 8 were demonstrated to inhibit IDO1 activity in a cellular context. Compounds 2 and 7 were more potent against IDO1 than TDO2 in the enzymatic assay. The kinetic studies showed that compound 2 exhibited noncompetitive inhibition, whereas compounds 7 and 8 were graphically well matched with uncompetitive inhibition. Compounds 7 and 8 were found to bind to the ferric-IDO1 enzyme. Docking stimulations showed that the naphthalene ring of compound 8 formed "T-shaped" π-π interactions with Phe-163 and that the 6-methyl-naphthalene group formed additional hydrophobic interactions with IDO1. Compound 8 was identified as a derivative of tanshinone, and preliminary SAR analysis indicated that tanshinone derivatives may be promising hits for the development of IDO1 inhibitors. This study provides new clues for the discovery of IDO1/TDO2 inhibitors with novel scaffolds.

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. Using the PONDR-FIT server, we demonstrated that the C-terminal region was intrinsically disordered. We applied the Monte Carlo (MC) method to explore the conformational space of the C-terminus and conducted a series of explicit-solvent molecular dynamics (MD) simulations, and revealed that the C-terminal region is inherently dynamic; large-scale conformational changes in the C-terminal segment led to the structural transition of PGAM1 from the closed state to the open state. Furthermore, the C-terminal segment influenced 2,3-bisphosphoglycerate (2,3-BPG) binding. The proposed swing model illustrated a critical role of the C-terminus in the catalytic cycle through the conformational changes. In conclusion, the C-terminal region induces large movements of PGAM1 from the closed state to the open state and influences cofactor binding during the catalytic cycle. This report describes the dynamic features of the C-terminal region in detail and should aid in design of novel and efficient inhibitors of PGAM1. A swing mechanism of the C-terminal region is proposed, to facilitate further studies of the catalytic mechanism and the physiological functions of its homologues.

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. MATERIALS AND METHODS: In this study, we carried out a research on six types of toxicities: (I) Acute Toxicity; (II) Mutagenicity; (III) Tumorigenicity; (IV) Skin and Eye Irritation; (V) Reproductive Effects; (VI) Multiple Dose Effects, using local lazy learning (LLL) method for multi-label learning. 17,120 compounds were split into the training set and the test set as a ratio of 4:1 by using the Kennard-Stone algorithm. Four types of properties, including molecular fingerprints (ECFP_4 and FCFP_4), descriptors, and chemical-chemical-interactions, were adopted for model building. RESULTS: The model 'ECFP_4+LLL' yielded the best performance for the test set, while balanced accuracy (BACC) reached 0.692, 0.691, 0.666, 0.680, 0.631, 0.599 for six types of toxicities, respectively. Furthermore, some essential toxicophores for six types of toxicities were identified by using the Laplacian-modified Bayesian model. CONCLUSION: The accurate prediction model and the chemical toxicophores can provide some guidance for designing drugs with low toxicity.

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. METHODS: Based on the predicted binding modes of Compounds 5 and 14 in docking studies, a new series of c-Met inhibitor-harboring 3-((1H-pyrrolo[3,2-c]pyridin-1-yl)sulfonyl)imidazo[1,2-a]pyridine scaffolds was discovered. Potent inhibitors were identified through extensive optimizations combined with enzymatic and cellular assays. A promising compound was further investigated in regard to its selectivity, its effects on c-Met signaling, cell proliferation and cell scattering in vitro. RESULTS: The most potent Compound 31 inhibited c-Met kinase activity with an IC50 value of 12.8 nmol/L, which was >78-fold higher than those of a panel of 16 different tyrosine kinases. Compound 31 (8, 40, 200 nmol/L) dose-dependently inhibited the phosphorylation of c-Met and its key downstream Akt and ERK signaling cascades in c-Met aberrant human EBC-1 cancer cells. In 12 human cancer cell lines harboring different background levels of c-Met expression/activation, Compound 31 potently inhibited c-Met-driven cell proliferation. Furthermore, Compound 31 dose-dependently impaired c-Met-mediated cell scattering of MDCK cells. CONCLUSION: This series of c-Met inhibitors is a promising lead for development of novel anticancer drugs.

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. METHODS: Plasma, urine, feces, and bile samples were collected before and after 50 mg/kg WEL was orally administered to rats. Metabolites were profiled by ultrahigh performance liquid chromatography/quadrupole time-of-flight mass spectrometry and identified by high-performance liquid chromatography-solid-phase extraction-nuclear magnetic resonance spectroscopy. The in vitro WEL glucuronidation activities of human liver microsomes, human kidney microsomes, human intestine microsomes, and 12 recombinant human uridine diphosphate-glucuronosyltransferase (UGT) isoforms were screened. Molecular docking simulation of the interaction between WEL and UGT1A9 was conducted. RESULTS: WEL underwent extensive metabolism, and 17 metabolites were identified. The major metabolic pathways observed were glucuronidation and methylation. Glucuronic acid was preferentially introduced into 5-OH, whereas no obvious regioselectivity was observed in the methylation of 11-OH and 12-OH. Multiple UGTs, including UGT1A1, UGT1A3, UGT1A6, UGT1A7, UGT1A8, UGT1A9, and UGT1A10, were involved in forming WEL glucuronides and O-methylated WEL glucuronides. CONCLUSION: The extensive glucuronidation and methylation is responsible for the low oral bioavailability of WEL in rats. UGT1A1 and UGT1A9 were the major enzymes involved in the glucuronidation of WEL and O-methylated WEL. Molecular docking studies revealed that 5-OH was accessible to the catalytic domain of UGT1As; therefore, 5-OH exhibited a high probability of glucuronidation.

[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. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation. RESULTS: A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation. CONCLUSION: The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.

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. The 1(st) order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the 'wrong' predicted indications, indicating that some 'wrong' drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.

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. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

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. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.

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. In this study, we identified 26 core human proteins involved in PPI between HIV-1 and host, that have great potential for HIV therapy. In addition, 280 chemicals that interact with three HIV drugs targeting human proteins can also interact with these 26 core proteins. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying novel anti-HIV drugs.

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. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1(st) order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.

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). The calculated binding free-energy supported the hypothesis that the polymerase from HBV genotype C was more sensitive to ADV than that from genotype B. By using MD simulation trajectory analysis, binding free energy decomposition and alanine scanning, some energy variation in the residues around the binding pocket was observed. Both the alanine mutations at residues 236 and 238 led to an increase of the energy difference between genotypes C and B (ΔΔG(C-B)), suggesting that these residues contributed to the genotype-associated antiviral variability with regard to the interaction with ADV. CONCLUSION: The results support the hypothesis that the HBV genotype C polymerase is more sensitive to ADV than that from genotype B. Moreover, residue N236 and the polymorphic site 238 play important roles in contributing to the higher sensitivity of genotype C over B in the interaction with ADV.

[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. The diverse FXR ligands and their structure-activity relationship are reviewed in this article. The advances in the crystal structures of FXR in complex with different ligands are also introduced.