The application of in silico drug-likeness predictions in pharmaceutical research

Integrated meta-analysis, network pharmacology, and molecular docking to investigate the efficacy and potential pharmacological mechanism of Kai-Xin-San on Alzheimer's disease

CONTEXT

Kai-Xin-San (KXS) has been used to treat Alzheimer's disease (AD) for thousands of years. However, no quantitative data regarding AD treatment using KXS are available. Moreover, its active compounds and mechanism of action for the treatment of AD remain largely unclear.

OBJECTIVES

To evaluate the efficacy and the potential pharmacological mechanisms of KXS in AD treatment.

MATERIALS AND METHODS

A systematic collection of KXS experiments was conducted from PubMed, Web of Science, Embase, CNKI, VIP, and Wanfang Data up to February, 2020. Review Manager 5 software was used for meta-analysis. In network pharmacology, components of KXS were screened, AD-related genes were then identified and the 'component-target-pathway' network constructed. Molecular docking was finally employed for in silico simulation matching between representative KXS compounds and their target genes.

RESULTS

Meta-analysis revealed that KXS improves the cognitive benefits in AD models by reducing the time of escape latency (SMD = -16.84) as well as increasing the number of cross-platform (SMD = 2.56) and proportion of time in the target quadrant (SMD = 7.52). Network pharmacology identified 25 KXS active compounds and 44 genes targets. DRD2, MAOA, ACHE, ADRA2A and CHRM2 were core target proteins. Besides, 22 potential pathways of KXS were identified, like cholinergic synapses, the cGMP/PKG pathway and calcium signalling. Molecular docking showed that stigmasterol, aposcopolamine and inermin can closely bind three targets (ACHE, ADRA2A and CHRM2).

DISCUSSION AND CONCLUSION

These findings suggest that KXS exerts effect on AD through multi-target, multi-component and multi-pathway mechanism. Future studies may explore the active components of KXS.

Exploring active ingredients and function mechanisms of Ephedra-bitter almond for prevention and treatment of Corona virus disease 2019 (COVID-19) based on network pharmacology

BACKGROUND

COVID-19 has caused a global pandemic, and there is no wonder drug for epidemic control at present. However, many clinical practices have shown that traditional Chinese medicine has played an important role in treating the outbreak. Among them, ephedra-bitter almond is a common couplet medicine in anti-COVID-19 prescriptions. This study aims to conduct an exploration of key components and mechanisms of ephedra-bitter almond anti-COVID-19 based on network pharmacology.

MATERIAL AND METHODS

We collected and screened potential active components of ephedra-bitter almond based on the TCMSP Database, and we predicted targets of the components. Meanwhile, we collected relevant targets of COVID-19 through the GeneCards and CTD databases. Then, the potential targets of ephedra-bitter almond against COVID-19 were screened out. The key components, targets, biological processes, and pathways of ephedra-bitter almond anti-COVID-19 were predicted by constructing the relationship network of herb-component-target (H-C-T), protein-protein interaction (PPI), and functional enrichment. Finally, the key components and targets were docked by AutoDock Vina to explore their binding mode.

RESULTS

Ephedra-bitter almond played an overall regulatory role in anti-COVID-19 via the patterns of multi-component-target-pathway. In addition, some key components of ephedra-bitter almond, such as β-sitosterol, estrone, and stigmasterol, had high binding activity to 3CL and ACE2 by molecular docking simulation, which provided new molecular structures for new drug development of COVID-19.

CONCLUSION

Ephedra-bitter almonds were used to prevent and treat COVID-19 through directly inhibiting the virus, regulating immune responses, and promoting body repair. However, this work is a prospective study based on data mining, and the findings need to be interpreted with caution.

Discovery of novel 1,3,5-triazine as adenosine A2A receptor antagonist for benefit in Parkinson's disease

Parkinson's disease (PD) is a chronic neuro-degenerative ailment characterized by impairment in various motor and nonmotor functions of the body. In the past few years, adenosine A₂ A receptor (A₂ AR) antagonists have attracted much attention due to significant relief in PD. Therefore, in the current study, we intend to disclose the development of novel 1,3,5-triazines as A₂ AR antagonist. The radioligand binding and selectivity of analogs were tested in HEK293 (human embryonic kidney) and the cells were transfected with pcDNA 3.1(+) containing full-length human A₂ AR cDNA and pcDNA 3.1(+) containing full-length human A₁ R cDNA, where they exhibit selective affinity for A₂ AR. Molecular docking analysis was also conducted to rationalize the probable mode of action, binding affinity, and orientation of the most potent molecule (7c) at the active site of A₂ AR. It has been shown that compound 7c form numerous nonbonded interactions in the active site of A₂ AR by interacting with Ala59, Ala63, Ile80, Val84 Glu169, Phe168, Met270, and Ile274. The study revealed 1,3,5-triazines as a novel class of A₂ AR antagonists.

In silico design and analysis of NS4B inhibitors against hepatitis C virus

The hepatitis C virus is a communicable disease that gradually harms the liver leading to cirrhosis and hepatocellular carcinoma. Important therapeutic interventions have been reached since the discovery of the disease. However, its resurgence urges the need for new approaches against this malady. The NS4B receptor is one of the important proteins for Hepatitis C Virus RNA replication that acts by mediating different viral properties. In this work, we opt to explore the relationships between the molecular structures of biologically tested NS4B inhibitors and their corresponding inhibitory activities to assist the design of novel and potent NS4B inhibitors. For that, a set of 115 indol-2-ylpyridine-3-sulfonamides (IPSA) compounds with inhibitory activity against NS4B is used. A hybrid genetic algorithm combined with multiple linear regressions (GA-MLR) was implemented to construct a predictive model. This model was further used and applied to a set of compounds that were generated based on a pharmacophore modeling study combined with virtual screening to identify structurally similar lead compounds. Multiple filtrations were implemented for selecting potent hits. The selected hits exhibited advantageous molecular features, allowing for favorable inhibitory activity against HCV. The results showed that 7 out of 1285 screened compounds, were selected as potent candidate hits where Zinc14822482 exhibits the best predicted potency and pharmacophore features. The predictive pharmacokinetic analysis further justified the compounds as potential hit molecules, prompting their recommendation for a confirmatory biological evaluation. We believe that our strategy could help in the design and screening of potential inhibitors in drug discovery.Communicated by Ramaswamy H. Sarma.

Virtual Screening and Bioactivity Evaluation of Novel Androgen Receptor Antagonists From Anti-PCa Traditional Chinese Medicine Prescriptions

Prostate cancer (PCa), a type of malignancy that arises in the prostate gland, is the most commonly diagnosed neoplasm and the second leading cause of cancer-related deaths in men. Acquisition of resistance to conventional therapy is a major problem for PCa patient treatment. Androgen receptor (AR) signaling pathway is necessary in the pathogenesis of prostate cancer, and there is a heightened interest in finding novel AR antagonists that target AR and its regulatory pathways. In our search for novel androgen receptor antagonists, we focus on the Traditional Chinese Medicine (TCM), which has been used for thousands of years to prove effective in the treatment of cancer. In this study, we collected 653 traditional Chinese medicine prescriptions that have certain therapeutic effect to prostate cancer, including the prescriptions and even the folk prescriptions. After summarizing the frequency of herbs and gathering the natural products contained in these prescriptions, we built a natural products database to do computer-aided virtual screening and drug-like evaluation to find potential AR antagonists. Totally 25 compounds were submitted to experimental biological activity tests. Through the MTT cell proliferation experiment, 5 chemicals were found to inhibit the proliferation of LNCaP cells in a concentration-dependent manner. Especially, MoL_11 was found to have good antagonistic activity and significantly inhibit fluorescence enzyme activity by the AR reporter gene experiment. Finally, the molecular dynamics simulation method was used to study the interaction between the most active compound MoL_11 and the wild-type and F876L mutant androgen receptor (WT/F876L AR), and it was found that F876L AR could not cause resistance to MoL_11.

Study on the Mechanisms of Banxia Xiexin Decoction in Treating Diabetic Gastroparesis Based on Network Pharmacology

In China, Banxia Xiexin decoction (BXD) is applied to treat diabetic gastroparesis (DGP), but its key active ingredients and mechanisms against DGP are unclear. This study is designated to reveal the molecular mechanisms of BXD in treating DGP by adopting a creative approach known as network pharmacology to explore the active ingredients and therapeutic targets of BXD. In our study, 730 differentially expressed genes of DGP were obtained, and 30 potential targets of BXD against DGP were screened out (including ADRB2, DRD1, FOS, MMP9, FOSL1, FOSL2, JUN, MAP2, DRD2, MYC, F3, CDKN1A, IL6, NFKBIA, ICAM1, CCL2, SELE, DUOX2, MGAM, THBD, SERPINE1, ALOX5, CXCL11, CXCL2, CXCL10, RUNX2, CD40LG, C1QB, MCL1, and ADCYAP1). Based on the findings, BXD contains 60 compounds with therapeutic effect on DGP, including the key active ingredients such as quercetin, wogonin, baicalein, beta-sitosterol, and kaempferol. Sixty-eight pathways including TNF signaling pathway, IL-17 signaling pathway, and AGE-RAGE signaling pathway were significantly enriched. In this study, the mechanisms of BXD in treating DGP are affirmed to be a complex network with multi-target and multi-pathway, which provides a reference for future experimental studies.

Understanding the enzymatic inhibition of intestinal alkaline phosphatase by aminophenazone-derived aryl thioureas with aided computational molecular dynamics simulations: synthesis, characterization, SAR and kinetic profiling

The work presented in this paper aims toward the synthesis of aryl thiourea derivatives 4a-l of pyrazole based nonsteroidal anti-inflammatory drug named 4-aminophenazone, as potential inhibitors of intestinal alkaline phosphatase enzyme. The screening of synthesized target compounds 4a-l for unraveling the anti-inflammatory potential against calf intestinal alkaline phosphatase gives rise to lead member 4c possessing IC₅₀ value 0.420 ± 0.012 µM, many folds better than reference standard used (KH₂PO₄ IC₅₀ = 2.8 ± 0.06 µM and L-phenylalanine IC₅₀ = 100 ± 3.1 µM). SAR for unfolding the active site binding pocket interaction along with the mode of enzyme inhibition based on kinetic studies is carried out which showed non-competitive binding mode. The enzyme inhibition studies were further supplemented by molecular dynamic simulations for predicting the protein behavior against active inhibitors 4c and 4g during docking analysis. The preliminary toxicity of the synthesized compounds was determined by using brine shrimp assay. This work also includes detailed biochemical analysis along with RO5 parameters for all the newly synthesized drug derivatives 4a-l.

Network Pharmacology to Identify the Pharmacological Mechanisms of a Traditional Chinese Medicine Derived from Trachelospermum jasminoides in Patients with Rheumatoid Arthritis

BACKGROUND This study used a network pharmacology approach to identify the pharmacological mechanisms of a traditional Chinese medicine derived from Trachelospermum jasminoides (Lindl.) Lem. in patients with rheumatoid arthritis (RA). MATERIAL AND METHODS Known compounds of T. jasminoides were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Shanghai Institute of Organic Chemistry of Chinese Academy of Science, Chemistry (CASC) database, and a literature search. Putative targets of identified compounds were predicted by SwissTargetPrediction. RA-related targets were achieved from the Therapeutic Target database, Drugbank database, Pharmacogenomics Knowledgebase, and Online Mendelian Inheritance in Man database. The protein-protein interaction (PPI) network was built by STRING. CluGO was utilized for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis. RESULTS A total of 354 potential targets were predicted for the 17 bioactive compounds in T. jasminoides; 69 of these targets overlapped with RA-related targets. A PPI network was composed and 2 clusters of 59 and 42 nodes each were excavated. GO and KEGG enrichment analysis of the overlapping targets and the 2 clusters was mainly grouped into immunity, inflammation, estrogen, anxiety, and depression processes. CONCLUSIONS Our study illustrated that T. jasminoides alleviates RA through the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway, and other immune and inflammatory-related processes. It also may exert effects in regulating cell differentiation and potentially has anti-anxiety, anti-depression, and estrogen-like effects.

Modelling the Anti-Methicillin-Resistant Staphylococcus Aureus (MRSA) Activity of Cannabinoids: A QSAR and Docking Study

Twenty-four cannabinoids active against MRSA SA1199B and XU212 were optimized at WB97XD/6-31G(d,p), and several molecular descriptors were obtained. Using a multiple linear regression method, several mathematical models with statistical significance were obtained. The robustness of the models was validated, employing the leave-one-out cross-validation and Y-scrambling methods. The entire data set was docked against penicillin-binding protein, iso-tyrosyl tRNA synthetase, and DNA gyrase. The most active cannabinoids had high affinity to penicillin-binding protein (PBP), whereas the least active compounds had low affinities for all of the targets. Among the cannabinoid compounds, Cannabinoid 2 was highlighted due to its suitable combination of both antimicrobial activity and higher scoring values against the selected target; therefore, its docking performance was compared to that of oxacillin, a commercial PBP inhibitor. The 2D figures reveal that both compounds hit the protein in the active site with a similar type of molecular interaction, where the hydroxyl groups in the aromatic ring of cannabinoids play a pivotal role in the biological activity. These results provide some evidence that the anti-Staphylococcus aureus activity of these cannabinoids may be related to the inhibition of the PBP protein; besides, the robustness of the models along with the docking and Quantitative Structure–Activity Relationship (QSAR) results allow the proposal of three new compounds; the predicted activity combined with the scoring values against PBP should encourage future synthesis and experimental testing.

Curcumin analogs as the inhibitors of TLR4 pathway in inflammation and their drug like potentialities: a computer-based study

Toll-like receptor 4 (TLR4) pathway is one of the major pathways that mediate the inflammation in human body. There are different anti-inflammatory drugs available in the market which specifically act on different signaling proteins of TLR4 pathway but they do have few side effects and other limitations for intended use in human body. In this study, Curcumin and its different analogs have been analyzed as the inhibitors of signaling proteins, i.e. Cycloxygenase-2 (COX-2), inhibitor of kappaβ kinase (IKK) and TANK binding kinase-1 (TBK-1) of TLR4 pathway using different computational tools. Initially, three compounds were selected for respective target based on free binding energy among which different compounds were reported to have better binding affinity than commercially available drug (control). Upon continuous computational exploration with induced fit docking (IFD), 6-Gingerol, Yakuchinone A and Yakuchinone B were identified as the best inhibitors of COX-2, IKK, and TBK-1 respectively. Then their drug-like potentialities were analyzed in different experiments where they were also predicted to perform well. Hopefully, this study will uphold the efforts of researchers to identify anti-inflammatory drugs from natural sources.

Network Pharmacology-Based Strategy for Predicting Active Ingredients and Potential Targets of Gegen Qinlian Decoction for Rotavirus Enteritis

Materials and Methods

In this study, a network pharmacology-based strategy was used to elucidate the mechanism of GGQLD for the treatment of RVE. Oral bioavailability and drug-likeness were taken as the judgment criteria to search the active ingredients of GGQLD in traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). The affinity between protein and ingredients was further determined using the similarity ensemble approach to find the corresponding targets. According to the genes related to enteritis in GeneCards database, the key targets were screened by intersections between drug and disease targets. And the therapeutic mechanism was predicted using the protein-protein interactions (PPIs), the Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, which was verified by detecting calcium ion concentration with the fluorescent probe.

Result

130 active ingredients were screened from GGQLD, including (R)-canadine, moupinamide, formononetin, and other flavonoids. They act on a total of 366 targets, which is mainly distributed in the biological process of hormone binding or signaling pathways of neuroactive ligand receptor interaction, serotonergic synapse, and calcium signaling pathway. Furthermore, serotonin receptors, adrenergic receptors, cholinergic receptors, and dopamine receptors in the enteric nervous system may be the key targets of RVE treatment by GGQLD.

Conclusion

This study demonstrated that the potential mechanism that GGQLD can effectively improve the symptoms of RVE may depend on the regulation of calcium ions, serotonin, and gastrointestinal hormone ion that could mutually affect the intestinal nervous system.

Network Pharmacology-Based Identification of the Mechanisms of Shen-Qi Compound Formula in Treating Diabetes Mellitus

Aim

The purpose of this research is to identify the mechanisms of Shen-Qi compound formula (SQC), a traditional Chinese medicine (TCM), for treating diabetes mellitus (DM) using system pharmacology.

Methods

The active components and therapeutic targets were identified, and these targets were analyzed using gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) analysis. Finally, an integrated pathway was constructed to show the mechanisms of SQC.

Results

A total of 282 active components and 195 targets were identified through a database search. The component-target network was constructed, and the key components were screened out according to their degree. Through the GO, PPI, and KEGG analyses, the mechanism network of SQC treating DM was constructed.

Conclusions

This study shows that the mechanisms of SQC treating DM are related to various pathways and targets. This study provides a good foundation and basis for further in-depth verification and clinical application.

In silico Discovery of Resveratrol Analogues as Potential Agents in Treatment of Metabolic Disorders

BACKGROUND

Resveratrol was demonstrated to act as partial agonist of PPAR-γ receptor, which opens up the possibility for its use in the treatment of metabolic disorders. Considering the poor bioavailability of resveratrol, particularly due to its low aqueous solubility, we aimed to identify analogues of resveratrol with improved pharmacokinetic properties and higher binding affinities towards PPAR-γ.

METHODS

3D structures of resveratrol and its analogues were retrieved from ZINC database, while PPAR-γ structure was obtained from Protein Data Bank. Docking studies were performed using Molegro Virtual Docker software. Molecular descriptors relevant to pharmacokinetics were calculated from ligand structures using VolSurf+ software.

RESULTS

Using structural similarity search method, 56 analogues of resveratrol were identified and subjected to docking analyses. Binding energies were ranged from -136.69 to -90.89 kcal/mol, with 16 analogues having higher affinities towards PPAR-γ in comparison to resveratrol. From the calculated values of SOLY descriptor, 23 studied compounds were shown to be more soluble in water than resveratrol. However, only two tetrahydroxy stilbene derivatives, piceatannol and oxyresveratrol, had both better solubility and affinity towards PPAR-γ. These compounds also had more favorable ADME profile, since they were shown to be more metabolically stable and wider distributed in body than resveratrol.

CONCLUSION

Piceatannol and oxyresveratrol should be considered as potential lead compounds for further drug development. Although experimental validation of obtained in silico results is required, this work can be considered as a step toward the discovery of new natural and safe drugs in treatment of metabolic disorders.

Discovery of Targeted Covalent Natural Products against PLK1 by Herb-Based Screening

Natural products (NPs) are a rich source of drug discovery, and some of them act by covalently binding to the targets. Recently, targeted covalent natural product (TCNP) design has gained considerable attention since this approach offers significant benefits in improving biological efficacy and decreasing the off-target side effects. However, most of the known TCNPs were discovered by chance. Rational approaches for a systematic screen of TCNPs are much needed. Here, we developed a combined computational and experimental approach to carry out herb-based screening to identify TCNPs against proper cysteine residues in the target proteins. The herb-based TCNP screening approach (HB-TCNP) starts from a druggable pocket and cysteine residue prediction, followed by virtual screening of a covalent NP database and herb-based mapping to identify candidate herbs for experimental validation. Herbs with time-dependent activity are selected, and their NPs are experimentally tested to further screen covalent NPs. We have successfully applied HB-TCNP to screen anti-PLK1 herbs and NPs with high efficacy. Cys67 and Cys133 in the ATP binding pocket of PLK1 were used in the search. Five herbs were tested and exhibited PLK1 inhibition activity to some extent, among which Scutellaria baicalensis showed the most potent activity with time dependency. Further experimental studies showed that the main active compounds in Scutellaria baicalensis, baicalein and baicalin, covalently bind PLK1 through Cys133. Our study provided an efficient way to rationally design TCNPs and to make better use of herb medicines. The Cys133 residue in PLK1 serves as a novel covalent site for further drug discovery against PLK1.

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning

Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug–drug interactions. The prediction of BCRP inhibition can facilitate evaluating potential drug resistance and drug–drug interactions in early stage of drug discovery. Here we reported a structurally diverse dataset consisting of 1098 BCRP inhibitors and 1701 non-inhibitors. Analysis of various physicochemical properties illustrates that BCRP inhibitors are more hydrophobic and aromatic than non-inhibitors. We then developed a series of quantitative structure–activity relationship (QSAR) models to discriminate between BCRP inhibitors and non-inhibitors. The optimal feature subset was determined by a wrapper feature selection method named rfSA (simulated annealing algorithm coupled with random forest), and the classification models were established by using seven machine learning approaches based on the optimal feature subset, including a deep learning method, two ensemble learning methods, and four classical machine learning methods. The statistical results demonstrated that three methods, including support vector machine (SVM), deep neural networks (DNN) and extreme gradient boosting (XGBoost), outperformed the others, and the SVM classifier yielded the best predictions (MCC = 0.812 and AUC = 0.958 for the test set). Then, a perturbation-based model-agnostic method was used to interpret our models and analyze the representative features for different models. The application domain analysis demonstrated the prediction reliability of our models. Moreover, the important structural fragments related to BCRP inhibition were identified by the information gain (IG) method along with the frequency analysis. In conclusion, we believe that the classification models developed in this study can be regarded as simple and accurate tools to distinguish BCRP inhibitors from non-inhibitors in drug design and discovery pipelines.

Integrated network pharmacology analysis and serum metabolomics to reveal the cognitive improvement effect of Bushen Tiansui formula on Alzheimer's disease

ETHNOPHARMACOLOGICAL RELEVANCE

Bushen Tiansui Formula (BSTSF) is a traditional Chinese medicine formula used clinically to treat Alzheimer's disease (AD) for many years. Previously, we have partially elucidated the mechanisms involved in the therapeutic effects of BSTSF on AD. However, the underlying mechanisms remain largely unclear.

AIM OF THE STUDY

The aim of this study was to further investigate the therapeutic effects of BSTSF on AD using an integrated strategy of network pharmacology and serum metabolomics.

MATERIALS AND METHODS

The rat models of AD were established using Aβ 1-42 injection, and morris water maze test was used to evaluate the efficacy of BSTSF on AD. Next, network pharmacology analysis was applied to identify the active compounds and target genes, which might be responsible for the effect of BSTSF. Then, a metabolomics strategy has been developed to find the possible significant serum metabolites and metabolic pathway induced by BSTSF. Additionally, two parts of the results were integrated to confirm each other.

RESULTS

The results of the network pharmacology analysis showed 37 compounds and 64 potential target genes related to the treatment of AD with BSTSF. The functional enrichment analysis indicated that the potential mechanism was mainly associated with the tumor necrosis factor signaling pathway and phosphatidylinositol 3 kinase/protein kinase B signaling pathway. Based on metabolomics, 78 differential endogenous metabolites were identified as potential biomarkers related to the BSTSF for treating AD. These metabolites were mainly involved in the relevant pathways of linoleic acid metabolism, α-linolenic acid metabolism, glycerophospholipid metabolism, tryptophan metabolism, and arginine and proline metabolism. These findings were partly consistent with the findings of the network pharmacology analysis.

CONCLUSIONS

In conclusion, our results solidly supported and enhanced out current understanding of the therapeutic effects of BSTSF on AD. Meanwhile, our work revealed that the proposed network pharmacology-integrated metabolomics strategy was a powerful means for identifying active components and mechanisms contributing to the pharmacological effects of traditional Chinese medicine.

Identification of Novel 1-O-Substituted Aporphine Analogues as Potent 5-HT2C Receptor Agonists

The 5-HT_2C receptor has emerged as a promising target in the treatment of a variety of central nervous system disorders. We have first identified aporphines as a new class of 5-HT_2C receptor agonists. Structure-activity relationship results indicate that the aporphine core may be required for 5-HT_2C receptor activity, and substitutions at its C1 position are important for 5-HT_2C receptor activity. Our efforts to optimize our hit 15781 lead to the identification of the highly potent and selective 5-HT_2C agonist 18b (MQ02-439) with an EC₅₀ value of 104 nM and weak antagonism at the 5-HT_2A and 5-HT_2B receptors. The findings may serve as good starting points for the development of more potent and selective 5-HT_2C agonists as valuable pharmacological tools or potential drug candidates.

Discovery of novel and potent P2Y14R antagonists via structure-based virtual screening for the treatment of acute gouty arthritis

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A reliable Glide docking-based virtual screening (VS) pipeline for P2Y₁₄R was developed.

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Several potent P2Y₁₄R antagonists with novel scaffolds were identified utilizing the VS strategy.

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P2Y₁₄R inhibitory effect was evaluated by testing cAMP levels in HEK293 cells.

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Anti-gout activity of screened compound was detected in MSU-treated THP-1 cells.

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The mechanism of test compound in treating acute gouty arthritis was elucidated.

P2Y₁₄ nucleotide receptor is a Gi protein-coupled receptor, which is widely involved in physiological and pathologic events. Although several P2Y₁₄R antagonists have been developed thus far, few have successfully been developed into a therapeutic drug. In this study, on the basis of two P2Y₁₄R homology models, Glide docking-based virtual screening (VS) strategy was employed for finding potent P2Y₁₄R antagonists with novel chemical architectures. A total of 19 structurally diverse compounds identified by VS and drug-like properties testing were set to experimental testing. 10 of them showed good inhibitory effects against the P2Y₁₄R (IC₅₀ < 50 nM), including four compounds (compounds 8, 10, 18 and 19) with IC₅₀ value below 10 nM. The best VS hit, compound 8 exhibited the best antagonistic activity, with IC₅₀ value of 2.46 nM. More importantly, compound 8 restrained monosodium uric acid (MSU)-induced pyroptosis of THP-1 cells through blocking the activation of Nod-like receptor 3 (NLRP3) inflammasome, which was attributed to its inhibitory effects on P2Y₁₄R-cAMP pathways. The key favorable residues uncovered using MM/GBSA binding free energy calculations/decompositions were detected and discussed. These findings suggest that the compound 8 can be used as a good lead compound for further optimization to obtain more promising P2Y₁₄R antagonists for the treatment of acute gouty arthritis.

Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery

BACKGROUND

In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases is crucial. To the best of our knowledge, this is the first systematic review on this issue.

OBJECTIVE

The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review.

RESULTS

Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced.

CONCLUSION

In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure.

ADMET profiling of geographically diverse phytochemical using chemoinformatic tools

Aim: Phytocompounds are important due to their uniqueness, however, only few reach the development phase due to their poor pharmacokinetics. Therefore, preassessing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties is essential in drug discovery. Methodology: Biologically diverse databases (Phytochemica, SerpentinaDB, SANCDB and NuBBE_DB) covering the region of India, Brazil and South Africa were considered to predict the ADMET using chemoinformatic tools (Qikprop, pkCSM and DataWarrior). Results: Screening through each of pharmacokinetic criteria resulted in identification of 24 compounds that adhere to all the ADMET properties. Furthermore, assessment revealed that five have potent anticancer biological activity against cancer cell lines. Conclusion: We have established an open-access database (ADMET-BIS) to enable identification of promising molecules that follow ADMET properties and can be considered for drug development.

Recent Advances in Developing K-Ras Plasma Membrane Localization Inhibitors

The Ras proteins play an important role in cell growth, differentiation, proliferation and survival by regulating diverse signaling pathways. Oncogenic mutant K-Ras is the most frequently mutated class of Ras superfamily that is highly prevalent in many human cancers. Despite intensive efforts to combat various K-Ras-mutant-driven cancers, no effective K-Ras-specific inhibitors have yet been approved for clinical use to date. Since K-Ras proteins must be associated to the plasma membrane for their function, targeting K-Ras plasma membrane localization represents a logical and potentially tractable therapeutic approach. Here, we summarize the recent advances in the development of K-Ras plasma membrane localization inhibitors including natural product-based inhibitors achieved from high throughput screening, fragment-based drug design, virtual screening, and drug repurposing as well as hit-to-lead optimizations.

Synthesis, computational studies, tyrosinase inhibitory kinetics and antimelanogenic activity of hydroxy substituted 2-[(4-acetylphenyl)amino]-2-oxoethyl derivatives

The over expression of melanogenic enzymes like tyrosinase caused many hyperpigmentaion disorders. The present work describes the synthesis of hydroxy substituted 2-[(4-acetylphenyl)amino]-2-oxoethyl derivatives 3a-e and 5a-e as antimelanogenic agents. The tyrosinase inhibitory activity of synthesized derivatives 3a-e and 5a-e was determined and it was found that derivative 5c possesses excellent activity with IC50 = 0.0089 µM compared to standard kojic acid (IC50 = 16.69 µM). The presence of hydroxyl groups at the ortho and the para position of cinnamic acid phenyl ring in compound 5c plays a vital role in tyrosinase inhibitory activity. The compound 5d also exhibited good activity (IC50 = 8.26 µM) compared to standard kojic acid. The enzyme inhibitory kinetics results showed that compound 5c is a competitive inhibitor while 5d is a mixed-type inhibitor. The mode of binding for compounds 5c and 5d with tyrosinase enzyme was also assessed and it was found that both derivatives irreversibly bind with target enzyme. The molecular docking and molecular dynamic simulation studies were also performed to find the position of attachment of synthesized compounds at tyrosinase enzyme (PDB ID 2Y9X). The results showed that all of the synthesized compounds bind well with the active binding sites and most potent derivative 5c formed stable complex with target protein. The cytotoxicity results showed that compound 5c is safe at a dose of 12 µg/mL against murine melanoma (B16F10) cells. The same dose of 5c was selected to determine antimelanogenic activity; the results showed that it produced antimelenogenic effects in murine melanoma (B16F10) cells. Based on our investigations, it was proposed that compound 5c may serve as a lead structure to design more potent antimelanogenic agents.

Atom Type Independent Modeling of the Conformational Energy of Benzylic, Allylic, and Other Bonds Adjacent to Conjugated Systems

Applications of computational methods to predict binding affinities for protein/drug complexes are routinely used in structure-based drug discovery. Applications of these methods often rely on empirical force fields (FFs) and their associated parameter sets and atom types. However, it is widely accepted that FFs cannot accurately cover the entire chemical space of drug-like molecules, due to the restrictive cost of parametrization and the poor transferability of existing parameters. To address these limitations, initiatives have been carried out to develop more transferable methods, in order to allow for more rigorous descriptions of any drug-like molecule. We have previously reported H-TEQ, a method which does not rely on atom types and incorporates well established chemical principles to assign parameters to organic molecules. The previous implementation of H-TEQ (a torsional barrier prediction method) only covered saturated and lone pair containing molecules; here, we report our efforts to incorporate conjugated systems into our model. The next step was the evaluation of the introduction of unsaturations. The developed model (H-TEQ3.0) has been validated on a wide variety of molecules containing heteroaromatic groups, alkyls, and fused ring systems. Our method performs on par with one of the most commonly used FFs (GAFF2), without relying on atom types or any prior parametrization.

A drug-likeness toolbox facilitates ADMET study in drug discovery

Undesirable pharmacokinetic (PK) properties or unacceptable toxicity are the main causes of the failure of drug candidates at the clinical trial stage. Since the concept of drug-likeness was first proposed, it has become an important consideration in the selection of compounds with desirable bioavailability during the early phases of drug discovery. Over the past decade, online resources have effectively facilitated drug-likeness studies in an economical and time-efficient manner. Here, we provide a comprehensive summary and comparison of current accessible online resources, in terms of their key features, application fields, and performance for in silico drug-likeness studies. We hope that the assembled toolbox will provide useful guidance to facilitate future in silico drug-likeness research.

Combining the wisdoms of traditional medicine with cutting-edge science and technology at the forefront of medical sciences

BACKGROUND

A central topic is to bring traditional medicine to a new horizon by integrating the latest advances in genomic, metabolomic, and system biological approaches, in order to re-examine the wisdom and knowledge of traditional Chinese medicine (TCM) and other traditional medicines.

PURPOSE

A new consortium has been formed at a conference of the Harvard Medical School, Boston, on October 29-30, 2018. The main goal was to build a collaborative platform for the scientific investigation of traditional medicine with cutting edge sciences and technologies at the forefront of biomedicine.

RESULTS

Traditional medicines are largely experience-based, but the scientific basis is largely non-satisfactory. Therefore, the transformation from experience-based to evidence-based medicine would be an important step forward. The consortium covers three main fields: TCM diagnostics, acupuncture and TCM pharmacology. Diseases occur because of regulatory imbalances of holistic physiological display and genetic information/expression related to systems biology and energy consumption/release (e.g. cold and hot) within body. As organs are interconnected by meridians, affecting the meridians by acupuncture and medicinal herbs restores healthy organ function and body balance. There are two concepts in herbal medicine: The traditional way is based on complex herbal mixtures. The second concept is related to Western pharmacological drug development including the isolation of bioactive phytochemicals, which are subjected to preclinical and clinical investigations.

CONCLUSION

Development of collaborative scientific project to integrate the best of both worlds - Western and Eastern medicine into a "One World Integrative Medicine" for the sake of patients worldwide.

Design and Synthesis of Novel 1,3-Thiazole and 2-Hydrazinyl-1,3-Thiazole Derivatives as Anti-Candida Agents: In Vitro Antifungal Screening, Molecular Docking Study, and Spectroscopic Investigation of their Binding Interaction with Bovine Serum Albumin

In the context of there being a limited number of clinically approved drugs for the treatment of Candida sp.-based infections, along with the rapid development of resistance to the existing antifungals, two novel series of 4-phenyl-1,3-thiazole and 2-hydrazinyl-4-phenyl-1,3-thiazole derivatives were synthesized and tested in vitro for their anti-Candida potential. Two compounds (7a and 7e) showed promising inhibitory activity against the pathogenic C. albicans strain, exhibiting substantially lower MIC values (7.81 μg/mL and 3.9 μg/mL, respectively) as compared with the reference drug fluconazole (15.62 μg/mL). Their anti-Candida activity is also supported by molecular docking studies, using the fungal lanosterol C14α-demethylase as the target enzyme. The interaction of the most biologically active synthesized compound 7e with bovine serum albumin was investigated through fluorescence spectroscopy, and the obtained data suggested that this molecule might efficiently bind carrier proteins in vivo in order to reach the target site.

Designing of promising medicinal scaffolds for Alzheimer's disease through enzyme inhibition, lead optimization, molecular docking and dynamic simulation approaches

In the designed research work, a series of 2-furoyl piperazine based sulfonamide derivatives were synthesized as therapeutic agents to target the Alzheimer's disease. The structures of the newly synthesized compounds were characterized through spectral analysis and their inhibitory potential was evaluated against butyrylcholinesterase (BChE). The cytotoxicity of these sulfonamides was also ascertained through hemolysis of bovine red blood cells. Furthermore, compounds were inspected by Lipinki Rule and their binding profiles against BChE were discerned by molecular docking. The protein fluctuations in docking complexes were recognized by dynamic simulation. From our in vitro and in silico results 5c, 5j and 5k were identified as promising lead compounds for the treatment of targeted disease.

4-Aminocoumarin based Aroylthioureas as Potential Jack Bean Urease Inhibitors; Synthesis, Enzyme Inhibitory Kinetics and Docking Studies

BACKGROUND

Urease enzyme catalyzes the hydrolysis of urea into ammonia and CO2, excess ammonia causes global warming and crop reduction. Ureases are also responsible for certain human diseases such as stomach cancer, peptic ulceration, pyelonephritis, and kidney stones. New urease inhibitors are developed to get rid of such problems.

OBJECTIVE

This article describes the synthesis of a series of novel 1-aroyl-3-(2-oxo-2H-chromen-4- yl)thiourea derivatives (5a-j) as Jack bean urease inhibitors.

METHODS

Freshly prepared aryl isothiocyanates were reacted with 4-aminocoumarin in the same pot in an anhydrous medium of acetone. The structures of the title thioureas (5a-j) were ascertained by their spectroscopic data. The inhibitory effects against jack bean urease were determined.

RESULTS

It was found that compounds 5i and 5j showed excellent activity with IC50 values 0.0065 and 0.0293, µM respectively. Compound 5i bearing 4-methyl substituted phenyl ring plays a vital role in enzyme inhibitory activity. The kinetic mechanism analyzed by Lineweavere-Burk plots revealed that compound 5i inhibits the enzyme non-competitively. The Michaelis-Menten constant Km and inhibition constants Ki calculated from Lineweavere-Burk plots for compound 5i are 4.155mM and 0.00032µM, respectively. The antioxidant activity results displayed that compound 5j showed excellent radical scavenging activity. The cytotoxic effects determined against brine shrimp assay showed that all of the synthesized compounds are non-toxic to shrimp larvae. Molecular docking studies were performed against target protein (PDBID 4H9M) and it was determined that most of the synthesized compounds exhibited good binding affinity with the target protein. Molecular dynamics simulation (MDS) results revealed that compound 5i forms a stable complex with target protein showing little fluctuation.

CONCLUSIONS

Based upon our investigations, it is proposed that 5i derivative may serve as a lead structure for devising more potent urease inhibitors.

Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription

Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet been elucidated. The purpose of this study was to develop neuroprotective models for identifying neuroprotective compounds in XXMD against hypoxia-induced and H2O2-induced brain cell damage. In this study, a phenotype-based classification method was designed by machine learning to identify neuroprotective compounds and to clarify the compatibility of XXMD components. Four different single classifiers (AB, kNN, CT, and RF) and molecular fingerprint descriptors were used to construct stacked naïve Bayesian models. Among them, the RF algorithm had a better performance with an average MCC value of 0.725±0.014 and 0.774±0.042 from 5-fold cross-validation and test set, respectively. The probability values calculated by four models were then integrated into a stacked Bayesian model. In total, two optimal models, s-NB-1-LPFP6 and s-NB-2-LPFP6, were obtained. The two validated optimal models revealed Matthews correlation coefficients (MCC) of 0.968 and 0.993 for 5-fold cross-validation and of 0.874 and 0.959 for the test set, respectively. Furthermore, the two models were used for virtual screening experiments to identify neuroprotective compounds in XXMD. Ten representative compounds with potential therapeutic effects against the two phenotypes were selected for further cell-based assays. Among the selected compounds, two compounds significantly inhibited H2O2-induced and Na2S2O4-induced neurotoxicity simultaneously. Together, our findings suggested that machine learning algorithms such as combination Bayesian models were feasible to predict neuroprotective compounds and to preliminarily demonstrate the pharmacological mechanisms of TCM.

In Silico Prediction of Hemolytic Toxicity on the Human Erythrocytes for Small Molecules by Machine-Learning and Genetic Algorithm

Hemolytic toxicity of small molecules, as one of the important ADMET end points, can cause the lysis of erythrocytes membrane and leaking of hemoglobin into the blood plasma, which leads to various side effects. Thus, it is very crucial to assess the hemolytic potential of small molecules during the early stage of drug development process. However, so far there is no computational model to predict the human hemolytic toxicity of small molecules. To this end, we manually curate the hemolytic toxicity data set for the small molecules experimentally evaluated on the human erythrocytes, develop the first machine-learning (ML) based models to predict the human hemolytic toxicity of small molecules, harness the genetic algorithm (GA) and ML based model to optimize human hemolytic toxicity based on the molecular fingerprint to derive "optimal virtual fingerprints (OVFs)" with the desired hemolytic/nonhemolytic property, and finally implement a free software for the users to predict/optimize the human hemolytic toxicity with ML and GA in the automatic manner.

Cheminformatics techniques in antimalarial drug discovery and development from natural products 1: basic concepts

A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.

Quantitative Structure-Activity Relationships for Structurally Diverse Chemotypes Having Anti-Trypanosoma cruzi Activity

Small-molecule compounds that have promising activity against macromolecular targets from Trypanosoma cruzi occasionally fail when tested in whole-cell phenotypic assays. This outcome can be attributed to many factors, including inadequate physicochemical and pharmacokinetic properties. Unsuitable physicochemical profiles usually result in molecules with a poor ability to cross cell membranes. Quantitative structure-activity relationship (QSAR) analysis is a valuable approach to the investigation of how physicochemical characteristics affect biological activity. In this study, artificial neural networks (ANNs) and kernel-based partial least squares regression (KPLS) were developed using anti-T. cruzi activity data for broadly diverse chemotypes. The models exhibited a good predictive ability for the test set compounds, yielding q² values of 0.81 and 0.84 for the ANN and KPLS models, respectively. The results of this investigation highlighted privileged molecular scaffolds and the optimum physicochemical space associated with high anti-T. cruzi activity, which provided important guidelines for the design of novel trypanocidal agents having drug-like properties.

Dexibuprofen amide derivatives as potential anticancer agents: synthesis, in silico docking, bioevaluation, and molecular dynamic simulation

BACKGROUND

The amide derivatives of nonsteroidal anti-inflammatory drugs have been reported to possess antitumor activity. The present work describes the synthesis of dexibuprofen amide analogues (4a-j) as potential anticancer agents.

METHODS

The title amides (4a-j) were obtained by simple nucleophilic substitution reaction of dexibuprofen acid chloride with substituted amines in good yield and chemical structures were confirmed by FTIR, 1H NMR, 13C NMR and mass spectral data.

RESULTS

The brine shrimp lethality assay results showed that all of the synthesized compounds are non-toxic to shrimp larvae. The inhibitory effects on tumor growth were evaluated and it was observed that N-(2,5-dichlorophenyl)-2-(4-isobutylphenyl) propionamide (4e) and N-(2-chlorophenyl)-2-(4-isobutylphenyl) propionamide (4g) exhibited excellent antitumor activity compared to all other derivatives. The compound 4e bearing 2,5-dichloro substituted phenyl ring and 4g possesses 2-chloro substituted phenyl ring exhibited 100% inhibition of the tumor growth. The anticancer activity was evaluated against breast carcinoma cell line (MCF-7) and it was observed that derivative 4e exhibited excellent growth inhibition of cancer cells with IC50 value of 0.01±0.002 µm, which is better than the standard drugs. The docking studies against breast cancer type 1 susceptibility protein BRCA1 (PDBID 3K0H) exhibited good binding affinities, which are in good agreement with the wet lab results. The compounds 4e and 4g showed the binding energy values of -6.39 and -6.34 Kcal/mol, respectively. The molecular dynamic (MD) simulation was also carried out to evaluate the residual flexibility of the best docking complexes of compounds 4e and 4g. The MD simulation analysis assured that the 4e formed a more stable complex with the target protein than the 4g. The synthesized amide derivatives exhibited were devoid of gastrointestinal side effects and no cytotoxic effects against human normal epithelial breast cell line (MCF-12A) were found.

CONCLUSION

Based upon our wet lab and dry lab findings we propose that dexibuprofen analogue 4e may serve as a lead structure for the design of more potent anticancer drugs.

Novel C-2 Symmetric Molecules as α-Glucosidase and α-Amylase Inhibitors: Design, Synthesis, Kinetic Evaluation, Molecular Docking and Pharmacokinetics

A series of symmetrical salicylaldehyde-bishydrazine azo molecules, 5a–5h, have been synthesized, characterized by ¹H-NMR and ¹³C-NMR, and evaluated for their in vitro α-glucosidase and α-amylase inhibitory activities. All the synthesized compounds efficiently inhibited both enzymes. Compound 5g was the most potent derivative in the series, and powerfully inhibited both α-glucosidase and α-amylase. The IC₅₀ of 5g against α-glucosidase was 0.35917 ± 0.0189 µM (standard acarbose IC₅₀ = 6.109 ± 0.329 µM), and the IC₅₀ value of 5g against α-amylase was 0.4379 ± 0.0423 µM (standard acarbose IC₅₀ = 33.178 ± 2.392 µM). The Lineweaver-Burk plot indicated that compound 5g is a competitive inhibitor of α-glucosidase. The binding interactions of the most active analogues were confirmed through molecular docking studies. Docking studies showed that 5g interacts with the residues Trp690, Asp548, Arg425, and Glu426, which form hydrogen bonds to 5g with distances of 2.05, 2.20, 2.10 and 2.18 Å, respectively. All compounds showed high mutagenic and tumorigenic behaviors, and only 5e showed irritant properties. In addition, all the derivatives showed good antioxidant activities. The pharmacokinetic evaluation also revealed promising results

Structural optimization on a virtual screening hit of smoothened receptor

The Hedgehog (Hh) pathway plays a critical role during embryonic development by controlling cell patterning, growth and migration. In adults, the function of Hh pathway is curtailed to tissue repair and maintenance. Aberrant reactivation of Hh signaling has been linked to tumorigenesis in various cancers, such as basal cell carcinoma (BCC) and medulloblastoma. The Smoothened (Smo) receptor, a key component of the Hh pathway which is central to the signaling transduction, has emerged as an attractive therapeutic target for the treatment of human cancers. Taking advantage of the availability of several crystal structures of Smo in complex with different antagonists, we have previously conducted a molecular docking-based virtual screening to identify several compounds which exhibited significant inhibitory activity against the Hh pathway activation (IC₅₀ < 10 μM) in a Gli-responsive element (GRE) reporter gene assay. The most potent compound (ChemDiv ID C794-1677: 47 nM) showed comparable Hh signaling inhibition to the marketed drug vismodegib (46 nM). Herein, we report our structural optimization based on the virtual screening hit C794-1677. Our efforts are aimed to improve potency, decrease cLogP, and remove potentially metabolic labile/toxic pyrrole and aniline functionalities presented in C794-1677. The optimization led to the identification of numerous potent compounds exemplified by 25 (7.1 nM), which was 7 folds more potent compared with vismodegib. In addition, 25 was much less lipophilic compared with C794-1677 and devoid of the potentially metabolic labile/toxic pyrrole and aniline functional groups. Furthermore, 25 exhibited promising efficacy in inhibiting Gli1 mRNA expression in NIH3T3 cells with either wildtype Smo or D473H Smo mutant. These results represented significant improvement over the virtual screening hit C794-1677 and suggested that compound 25 can be used as a good starting point to support lead optimization.

Drug-1,3,4-Thiadiazole Conjugates as Novel Mixed-Type Inhibitors of Acetylcholinesterase: Synthesis, Molecular Docking, Pharmacokinetics, and ADMET Evaluation

A small library of new drug-1,3,4-thiazidazole hybrid compounds (3a⁻3i) was synthesized, characterized, and assessed for their acetyl cholinesterase enzyme (AChE) inhibitory and free radical scavenging activities. The newly synthesized derivatives showed promising activities against AChE, especially compound 3b (IC₅₀ 18.1 ± 0.9 nM), which was the most promising molecule in the series, and was substantially more active than the reference drug (neostigmine methyl sulfate; IC₅₀ 2186.5 ± 98.0 nM). Kinetic studies were performed to elucidate the mode of inhibition of the enzyme, and the compounds showed mixed-type mechanisms for inhibiting AChE. The Ki of 3b (0.0031 µM) indicates that it can be very effective, even at low concentrations. Compounds 3a⁻3i all complied with Lipinski's Rule of Five, and showed high drug-likeness scores. The pharmacokinetic parameters revealed notable lead-like properties with insignificant liver and skin-penetrating effects. The structure⁻activity relationship (SAR) analysis indicated π⁻π interactions with key amino acid residues related to Tyr124, Trp286, and Tyr341.

Synthesis of sulfonamide, amide and amine hybrid pharmacophore, an entry of new class of carbonic anhydrase II inhibitors and evaluation of chemo-informatics and binding analysis

Selective inhibition of carbonic anhydrase (CA) enzyme is an active area of research for medicinal chemists. In the current account, a hybrid pharmacophore approach was employed to design sulfonamide, amide and amine containing new series of potent carbonic anhydrase II inhibitors. The aromatic fragment associated with pharmacophore was altered suitably in order to find effective inhibitors of CA-II. All the derivatives 4a-4m showed better inhibition compared to the standard acetazolamide. In particular, compound 4l exhibited significant inhibition with IC₅₀ value of 0.01796 ± 0.00036 µM. The chemo-informatics analysis justified that all the designed compounds possess <10 HBA and <5 HBD. The ligands-protein binding analyses showed that 4l confined in the active binding pocket with three hydrogen bonds observed with His63, Asn66 and Thr197 residues.

Investigation on the effect of alkyl chain linked mono-thioureas as Jack bean urease inhibitors, SAR, pharmacokinetics ADMET parameters and molecular docking studies

The increasing resistance of pathogens to common antibiotics, as well as the need to control urease activity to improve the yield of soil nitrogen fertilization in agricultural applications, has stimulated the development of novel classes of molecules that target urease as an enzyme. In this context, the newly developed compounds on the basis of 1-heptanoyl-3-arylthiourea family were evaluated for Jack bean urease enzyme inhibition activity to validate their role as potent inhibitors of this enzyme. 1-Heptanoyl-3-arylthioureas were obtained in excellent yield and characterized through spectral and elemental analysis. All the compounds displayed remarkable potency against urease inhibition as compared to thiourea standard. It was found that novel compounds fulfill the criteria of drug-likeness by obeying Lipinski's rule of five. Particularly compound 4a and 4c can serve as lead molecules in 4D (drug designing discovery and development). Kinetic mechanism and molecular docking studies also carried out to delineate the mode of inhibition and binding affinity of the molecules.