Identification and characterization of the in-vivo metabolites of the novel soluble epoxide hydrolase inhibitor EC5026 using liquid chromatography quadrupole time of flight mass spectrometry.

EC5026 is a novel soluble epoxide hydrolase inhibitor being developed clinically to treat neuropathic pain and inflammation. In the current study, we employed the LC-ESI-Q-TOF-MS/MS technique to identify four in-vivo phase-I metabolites of EC5026 in rat model, out of which three were found to be novel. The identified metabolites include aliphatic hydroxylation, di-hydroxylation, terminal desaturation, and carboxylation. No phase-II metabolites were found. The pharmacokinetic profile of identified metabolites was established after a single oral dose of EC5026 to Wistar rats. The Tmax of the drug and metabolites were found to be in the range of 1-2 hours and 4-12 hours, respectively. The major metabolites M1 and M2 were found to have more than 2-fold (263.87% AUC) and equivalent exposure (96.33% AUC) compared to the parent drug, respectively. Further, the docking study revealed that the mono-hydroxylated and terminally desaturated metabolites possess better binding affinity than the parent drug. Therefore, these metabolites may hold sEH inhibition potential and can be followed through future research.

Machine Learning, Molecular Docking, and Dynamics-Based Computational Identification of Potential Inhibitors against Lung Cancer.

Lung cancer is the most prevalent cause of cancer deaths worldwide. However, its treatment faces a significant hurdle due to the development of resistance. Phytomolecules are an important source of new chemical entities due to their rich chemical diversity. Therefore, a machine learning (ML) model was developed to computationally identify potential inhibitors using a curated data set of 649 phytomolecules with inhibitory activity against lung cancer cell lines. Four distinct ML approaches, including k-nearest neighbor, random forest, support vector machine, and extreme gradient boosting, were used in conjugation with MACCS and Morgan2 fingerprints to generate the models. It was observed that the random forest model developed by using the MACCS fingerprint shows the best performance. To further explore the chemical space and feature importance, k-means clustering, t-SNE analysis, and mean decrease in impurity had been calculated. Simultaneously, ∼400 000 natural products (NPs) retrieved from the COCONUT database were filtered for pharmacokinetic properties and taken for a multistep screening using docking against epidermal growth factor receptor (EGFR) mutant, a therapeutic drug target of lung cancer. Thereafter, the best-performing random forest model was used to predict the antilung cancer potential of the NPs having binding affinity better than the cocrystal ligand. This allowed the identification of 205 potential inhibitors, wherein the molecules with an indolocarbazole scaffold were enriched in top-scoring molecules. The top three indolocarbazole molecules with the lowest binding energy were further evaluated through 100 ns molecular dynamics (MD) simulations, which suggested that these molecules are strong binders. Also, structural similarity analysis against known drugs revealed that these NPs are similar to staurosporine, which demonstrates potent and selective activity against EGFR mutants. Thereby, the consensus analysis employing ML, molecular docking, and dynamics revealed that the molecules having an indolocarbazole scaffold are the most promising NPs that can act as potential inhibitors against lung cancer.

A pharmacokinetic study to correlate the hypoglycemic effect of phlorizin in rats: Identification of metabolites as inhibitors of sodium/glucose cotransporters.

Phlorizin (PRZ) is a natural product that belongs to a class of dihydrochalcones. The unique pharmacological property of PRZ is to block glucose absorption or reabsorption through specific and competitive inhibitors of the sodium/glucose cotransporters (SGLTs) in the intestine (SGLT1) and kidney (SGLT2). This results in glycosuria by inhibiting renal reabsorption of glucose and can be used as an adjuvant treatment for type 2 diabetes. The pharmacokinetic profile, metabolites of the PRZ, and efficacy of metabolites towards SGLTs are unknown. Therefore, the present study on the characterization of hitherto unknown in vivo metabolites of PRZ and pharmacokinetic profiling using liquid chromatography-electrospray ionization tandem mass spectrometry (LC/ESI/MS/MS) and accurate mass measurements is undertaken. Plasma, urine, and feces samples were collected after oral administration of PRZ to Sprague-Dawley rats to identify in vivo metabolites. Furthermore, in silico efficacy of the identified metabolites was evaluated by docking study. PRZ at an intraperitoneal dose of 400 mg/kg showed maximum concentration in the blood to 439.32 ± 8.84 ng/mL at 1 h, while phloretin showed 14.38 ± 0.33 ng/mL at 6 h. The pharmacokinetic profile of PRZ showed that the maximum concentration lies between 1 and 2 h after dosing. Decreased blood glucose levels and maximum excretion of glucose in the urine were observed when the PRZ and metabolites were observed in plasma. The identification and characterization of PRZ metabolites by LC/ESI/MS/MS further revealed that the phase I metabolites of PRZ are hydroxy (mono-, di-, and tri-) and reduction. Phase II metabolites are O-methylated, O-acetylated, O-sulfated, and glucuronide metabolites of PRZ. Further docking study revealed that the metabolites diglucuronide metabolite of mono-hydroxylated PRZ and mono-glucuronidation of PRZ could be considered novel inhibitors of SGLT1 and SGLT2, respectively, which show better binding affinities than their parent compound PRZ and the known inhibitors.

A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures.

Virtual screening (VS) is a routine method to evaluate chemical libraries for lead identification. Therefore, the selection of appropriate protein structures for VS is an essential prerequisite to identify true actives during docking. But the presence of several crystal structures of the same protein makes it difficult to select one or few structures rationally for screening. Therefore, a computational prioritization protocol has been developed for shortlisting crystal structures that identify true active molecules with better efficiency. As identification of small-molecule inhibitors is an important clinical requirement for the T790M/L858R (TMLR) EGFR mutant, it has been selected as a case study. The approach involves cross-docking of 21 co-crystal ligands with all the structures of the same protein to select structures that dock non-native ligands with lower RMSD. The cross docking performance was then correlated with ligand similarity and binding-site conformational similarity. Eventually, structures were shortlisted by integrating cross-docking performance, and ligand and binding-site similarity. Thereafter, binding pose metadynamics was employed to identify structures having stable co-crystal ligands in their respective binding pockets. Finally, different enrichment metrics like BEDROC, RIE, AUAC, and EF1% were evaluated leading to the identification of five TMLR structures (5HCX, 5CAN, 5CAP, 5CAS, and 5CAO). These structures docked a number of non-native ligands with low RMSD, contain structurally dissimilar ligands, have conformationally dissimilar binding sites, harbor stable co-crystal ligands, and also identify true actives early. The present approach can be implemented for shortlisting protein targets of any other important therapeutic kinases.

Meta-Analysis of 49 SNPs Covering 25,446 Cases and 41,106 Controls Identifies Polymorphisms in Hormone Regulation and DNA Repair Genes Associated with Increased Endometrial Cancer Risk.

Endometrial cancer (EC) is among the most common gynecological disorders globally. As single nucleotide polymorphisms (SNPs) play an important role in the causation of EC, therefore, a comprehensive meta-analysis of 49 SNPs covering 25,446 cases and 41,106 controls was performed to identify SNPs significantly associated with increased EC risk. PubMed was searched to identify case control studies and meta-analysis was performed to compute the pooled odds ratio (OR) at 95% confidence interval (CI). Cochran's Q-test and I2 were used to study heterogeneity, based on which either a random or a fixed effect model was implemented. The meta-analysis identified 11 SNPs (from 10 genes) to be significantly associated with increased EC risk. Among these, seven SNPs were significant in at least three of the five genetic models, as well as three of the polymorphisms (rs1801320, rs11224561, and rs2279744) corresponding to RAD51, PGR, and MDM2 genes, which contained more than 1000 EC cases each and exhibited increased risk. The current meta-analysis indicates that polymorphisms associated with various hormone related genes-SULT1A1 (rs1042028), PGR (rs11224561), and CYP19A1 (rs10046 and rs4775936); DNA repair genes-ERCC2 (rs1799793), OGG1 (rs1052133), MLH1 (rs1800734), and RAD51 (rs1801320) as well as genes like MDM2 (rs2279744), CCND1 (rs9344), and SERPINE1 (rs1799889), are significantly associated with increased EC risk.

In-Silico molecular screening of natural compounds as a potential therapeutic inhibitor for Methicillin-resistant Staphylococcus aureus inhibition.

Methicillin-resistant Staphylococcus aureus (MRSA) is a life-threatening superbug causing infectious diseases such as pneumonia, endocarditis, osteomyelitis, etc. Conventional antibiotics are ineffective against MRSA infections due to their resistance mechanism against the antibiotics. The Penicillin Binding Protein (PBP2a) inhibits the activity of antibiotics by hydrolyzing the ß-lactam ring. Thus, alternate treatment methods are needed for the treatment of MRSA infections. Natural bioactive compounds exhibit good inhibition efficiency against MRSA infections by hindering its enzymatic mechanism, efflux pump system, etc. The present work deals with identifying potential and non-toxic natural bioactive compounds (ligands) through molecular docking studies through StarDrop software. Various natural bioactive compounds which are effective against MRSA infections were docked with the protein (6VVA). The ligands having good binding energy values and pharmacokinetic and drug-likeness properties have been illustrated as potential ligands for treating MRSA infections. From this exploration, Luteolin, Kaempferol, Chlorogenic acid, Sinigrin, Zingiberene, 1-Methyl-4-(6-methylhepta-1,5-dien-2-yl)cyclohex-1-ene, and Curcumin have found with good binding energies of -8.6 kcal/mol, -8.4 kcal/mol, -8.2 kcal/mol, -7.5 kcal/mol, -7.4 kcal/mol, -7.3 kcal/mol, and -7.2 kcal/mol, respectively.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches.

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules. Reviewed the niche area of phytomolecule-based anti-cancer drug discovery with respect to current trends including machine learning.

Elucidation of Increased Cervical Cancer Risk Due to Polymorphisms in XRCC1 (R399Q and R194W), ERCC5 (D1104H), and NQO1 (P187S).

Genetic variations like single nucleotide polymorphisms (SNPs) are associated with cervical carcinogenesis. In this study, SNPs have been identified that contribute toward changes in the function and stability of the proteins and show association with cervical cancer. Initially, literature mining identified 114 protein-coding polymorphisms with population-based evidence in cervical cancer. Subsequently, the functional assessment was performed using sequence-dependent tools, and thereafter, protein stability was analyzed using sequence and structural data. Twenty-three non-synonymous SNPs (nsSNPs) found to be damaging and destabilizing were then analyzed to check their risk association at the population level. The meta-analysis indicated that polymorphisms in DNA damage repair genes XRCC1 (rs25487 and rs1799782), ERCC5 (rs17655), and oxidative stress-related gene NQO1 (rs1800566) are significantly associated with increased cervical cancer risk. The XRCC1 rs25487 and rs1799782 polymorphisms showed the highest risk of cervical cancer in the homozygous model having odds ratio (OR) = 1.85, 95% confidence interval (CI) = 1.17-2.92, p = 0.01, and recessive model with OR = 1.81, 95% CI = 1.01-3.24, and p = 0.04 respectively. Similarly, rs17655 polymorphism of ERCC5 and rs1800566 polymorphism of NQO1 showed the highest pooled OR in the homozygous (OR = 1.70, 95% CI = 1.32-2.19, p = 0.00004) and heterozygous model (OR = 1.3, 95% CI = 1.06-1.58, p = 0.01) respectively. Thus, in this study, a comprehensive collection of nsSNPs was collated and assessed, leading to the identification of polymorphisms in DNA damage repair and oxidative stress-related genes, that destabilize the protein and shows increased risk associated with cervical cancer.

Prioritization and Meta-analysis of regulatory SNPs identified IL6, TGFB1, TLR9 and MMP7 as significantly associated with cervical cancer.

Cervical cancer is a leading women cancer globally with respect to both incidence and mortality. Its increased risk has been linked with HPV infection and genetic variations like single nucleotide polymorphisms (SNPs). Although, studies have been published which evaluates the effect of SNPs in a few candidate genes, however the role of number of regulatory SNPs (rSNPs) in cervical cancer is not available. As literature evidence has shown that non-coding rSNPs are related with increasing cervical cancer risk, we undertook this study to prioritize the important rSNPs and elucidate their role. A search was conducted in PubMed up to December 2020, which led to the identification of 263 articles and 969 SNPs in the non-coding region. These 969 SNPs were analysed through rSNPBase and RegulomeDB, leading to identification of 105 rSNPs. Afterwards, a regulatory module was constructed using protein-protein interaction data and a hub of highly interacting 23 target genes (corresponding to 34 rSNPs) was identified using MCODE. To further understand the mechanism of action of the 34 rSNPs, their transcription factor information with respect to cervical cancer was retrieved. To evaluate the pooled effect of these prioritized polymorphisms in cervical cancer patients, a meta-analysis was performed on 10,537 cases and 11,252 controls from 30 studies corresponding to 8 rSNPs. It led to identification of polymorphisms in IL6 (rs2069837), TGFB1 (rs1800469), TLR9 (rs187084) and MMP7 (rs11568818) which are significantly (p < 0.05) associated with increased cervical cancer risk at the population level. Overall, the study demonstrates that rSNPs targeting immune and inflammatory genes (IL1B, IL6, IL10, IL18, TGFB1, CCR5, CD40, TLR9, and MMP7) are associated with cervical cancer.

A comprehensive meta-analysis of non-coding polymorphisms associated with precancerous lesions and cervical cancer.

OBJECTIVES: To study the risk of polymorphisms present in the non-coding regions of genes related with cervical cancer. METHODS: The PubMed database was extensively searched using text-mining techniques to identify literature containing the association of single nucleotide polymorphisms and cervical cancer. Case-control studies published till June 2020 were considered for the meta-analysis if they fulfilled the selection criteria. The polymorphisms within each case-control study were checked for the presence of genotype data and then divided into groups based on the precancerous and cancerous conditions of the cervix. Odds ratio and 95% confidence intervals (CI) were used to study the effects of polymorphisms with the help of different genetic models (allele, dominant, recessive, heterozygous and homozygous). Also checked heterogeneity along with publication bias and statistical significance using the p-value. RESULTS: 120 papers covering 48 unique non-coding SNPs having 37,123 cases and 39,641 control data was considered for the meta-analysis. The genotype data was categorised into Cancer, Precancer and "Cancer + Precancer" groups, for 43, 8 and 11 SNPs respectively. The meta-analysis identified 21 and 1 SNPs as significant in the Cancer and "Cancer + Precancer" groups. Among all the polymorphisms, rs1143627 (IL1B), rs1800795 (IL6), rs1800871 (IL10), rs568408 (IL12A), rs3312227 (IL12B), rs2275913 (IL17A), rs5742909 (CTLA4), rs1800629 (TNFα), and rs4646903 (CYP1A1) were found to increase risk of cervical cancer in at least three of the five genetic models. CONCLUSION: We identified potential non-coding SNPs corresponding to various cytokines like interleukins (ILs), tumor necrosis factor (TNF), interferon (IFN) and other immune related genes like toll like receptor (TLR), cytotoxic T-lymphocyte associated protein (CTLA) and matrix metalloproteinase (MMP), as significant with increased pooled OR in this meta-analysis pointing to risk association of the immune-related genes in cervical carcinogenesis.