Potential Hits Research Articles

Exploring target selectivity in designing and identifying PI3Kα inhibitors for triple negative breast cancer with fragment-based and bioisosteric replacement approach

Triple-negative breast cancer (TNBC) is one of the most fatal malignancies in the world, accounting for 42% of all deaths due to metastasis. The significant development is hindered by the multi-drug resistance and poor patient compliance. PIK3CA gene mutation is one of the important causes of TNBC, which causes dysregulation of the cell cycle and cell proliferation. PI3Kα selective inhibition can decrease the TNBC by a significant level with minimal off-target effects. Novel compounds with high selectivity towards PI3Kα are crucial for treating TNBC. After extensive literature analysis, it was observed that fragment-based drug discovery, combined with structure-based virtual screening and bioisosteric replacement strategy, could provide a novel way for hit-to-lead optimization. The present study focussed on the fragment-based direct linking of 11269 moieties of the ChemDiv fragment library, - to generate novel moieties and further screened them using molecular docking, MMGBSA, and target selectivity analysis. Further, the top 2 moieties – Djh1 and Djh2 were selected after MMGBSA analysis and target selectivity prediction towards kinase. Further induced fit docking (IFD) analysis, DFT analysis, and MD simulation were employed to establish that – Djh1 and Djh2 could act as potential hit molecules for selective inhibition of PI3Kα. Further bioisosteric replacement, docking analysis, and target selectivity analysis were performed with the bioisosteres. The top two bioisosteres of Djh1 – Compound 10, Compound 06 represented excellent efficacy and selectivity towards PI3Kα in the treatment of TNBC after analysis of ADMET analysis. Further, in vitro and in vivo analysis might prove the effectiveness of the hit compounds.

Computational screening and molecular dynamics of natural compounds targeting the SH2 domain of STAT3: a multitarget approach using network pharmacology.

SH2 (Src Homology 2) domains play a crucial role in phosphotyrosine-mediated signaling and have emerged as promising drug targets, particularly in cancer therapy. STAT3 (Signal Transducer and Activator of Transcription 3), which contains an SH2 domain, plays a pivotal role in cancer progression and immune evasion because it facilitates the dimerization of STAT3, which is essential for their activation and subsequent nuclear translocation. SH2 domain-mediated STAT3 inhibition disrupts this binding, reduces phosphorylation of STAT3, and impairs dimerization. This study employed an in silico approach to screen potential natural compounds that could target the SH2 domain of STAT3 and inhibit its function. The phytomolecules (182455) were retrieved from the ZINC 15 database and were docked using various modes like HTVS, SP, and XP. The phytomolecules exhibiting higher binding affinity were selected. MM-GBSA was performed to determine binding free energy, and the QikProp tool was utilized to assess the pharmacokinetic properties of potential hit compounds, narrowing down the list of candidates. Molecular dynamics simulations, thermal MM-GBSA, and WaterMap analysis were performed on compounds that exhibited favorable binding affinities and pharmacokinetic characteristics. Based on docking scores and binding interactions, ZINC255200449, ZINC299817570, ZINC31167114, and ZINC67910988 were identified as potential STAT3 inhibitors. ZINC67910988 demonstrated superior stability in molecular dynamics simulation and WaterMap analysis. Furthermore, DFT was performed to determine energetic and electronic properties, and HOMO and LUMO sites were predicted for electronic structure calculation. Additionally, network pharmacology was performed to map the compounds' interactions within biological networks, highlighting their multitarget potential. Compound-target networks elucidate the relationships between compounds and multiple targets, along with their associated pathways and help to minimize off-target effects. The identified lead compound showed strong potential as a STAT3 inhibitor, warranting further validation through in vitro and in vivo studies.

Discovery of INCB159020, an Orally Bioavailable KRAS G12D Inhibitor.

The inhibition of mutant KRAS proteins has emerged as a promising approach for treating KRAS-driven cancers, as evidenced by the clinical success of KRAS G12C inhibitors. KRAS G12D, the most common mutant, promises significant expansion of the addressable patient population; however, the reduced nucleophilicity of aspartate compared to cysteine poses significant challenges in balancing sufficient potency with ADME properties to support oral exposure. Herein, we describe the discovery of KRAS G12D inhibitor 23 (INCB159020), which achieves oral exposure in nonhuman primate (NHP). Starting from a weakly potent hit, structure-based drug design was utilized to drive significant potency. Focus on molecular rigidity and balanced polarity then allowed for successful optimization of properties required for oral exposure.

A Combination of Pharmacophore Generation, Ligand-based Virtual Screening, Atom-based 3D-QSAR, and Molecular Docking Studies on Febuxostat-based Amides Analogues as Anti-inflammatory Agents.

A defence mechanism of the body includes inflammation. It is a process through which the immune system identifies, rejects, and starts to repair foreign and damaging stimuli. In the world, chronic inflammatory disorders are the leading cause of death. To obtain optimized pharmacophore, previously reported febuxostat- based anti-inflammatory amide derivatives series were subjected to pharmacophore hypothesis, ligand-based virtual screening, and 3D-QSAR studies in the present work using Schrodinger suite 2022-4. QuikProp module of Schrodinger was used for ADMET prediction, and HTVS, SP, and XP protocols of GLIDE modules were used for molecular docking on target protein (PDB ID:3LN1). Utilising 29 compounds, a five-point model of common pharmacophore hypotheses was created, having pIC50 ranging between 5.34 and 4.871. The top pharmacophore hypothesis AHHRR_ 1 model consists of one hydrogen bond acceptor, two hydrophobic groups and two ring substitution features. The hypothesis model AHHRR_1 underwent ligand-based virtual screening using the molecules from Asinex. Additionally, a 3D-QSAR study based on individual atoms was performed to assess their contributions to model development. The top QSAR model was chosen based on the values of R2 (0.9531) and Q2 (0.9424). Finally, four potential hits were obtained by molecular docking based on virtual screening. The virtual screen compounds have shown similar docking interaction with amino acid residues as shown by standard diclofenac sodium drugs. Therefore, the findings in the present study can be explored in the development of potent anti-inflammatory agents.

Synthesis and biological characterization of a 17β hydroxysteroid dehydrogenase type 10 (17β-HSD10) inhibitor.

Alzheimer's disease (AD) is estimated to affect over 55 million people across the world. Small molecule treatment options are limited to symptom management with no impact on disease progression. The need for new protein targets and small molecule hit compounds is unmet and urgent. Hydroxysteroid 17-β dehydrogenase type 10 (17β-HSD10) is a mitochondrial enzyme known to bind amyloid beta, a hallmark of AD, and potentiate its toxicity to neurons. Identification of small molecules capable of interacting with 17β-HSD10 may drive drug discovery efforts for AD. The screening compound BCC0100281 (1), was previously identified as an inhibitor of 17β-HSD10. Herein we report the first synthetic access to the hit compound following a convergent pathway starting from simple heterocyclic building blocks. The compound was found to be toxic to 'neuron-like' cells, specifically those of neuroblastoma origin, providing a potential hit compound for cancer drug discovery, wherein the protein is known to be overexpressed. However, assay of synthetic intermediates identified novel scaffolds with effect to rescue amyloid beta-induced cytotoxicity, showcasing the power of organic synthesis and medicinal chemistry to optimize hit compounds.

Targeting LIMK1 in Alzheimer's Disease: A Multifaceted Computational Investigation Involving ADMET, Virtual Screening, Molecular Docking, and Molecular Dynamics

LIM domain kinases (LIMKs), which include LIMK1 and LIMK2, are key proteins in actin dynamics. On this basis, the inhibition of LIMK1 enhances dendritic spine density and size in dementia, reducing Alzheimer's disease (AD) effects. Therefore, several small molecules were discovered as potential therapeutic targets for AD. Herein, a pharmacophore-based virtual screening was employed to identify novel potential LIMK1 inhibitors. The pharmacophore model derived from the co-crystallized receptor structure of PubChem-329823760: LIMK1 (PDB ID: 5NXC) was then used for virtual screening. After applying Lipinski's rules and pharmacophore filters, 29 potential hits were identified. Molecular docking simulations were performed to determine the binding affinities of these candidates against LIMK1, with results ranging from -5.20 to -10.60 kcal/mol. Notably, PubChem-136621040 showed the highest binding affinity against the target protein, with a docking score of -10.60 kcal/mol, slightly surpassing the native ligand, PubChem-329823760, possessing a lower docking score of -9.80 kcal/mol. The drug-likeness and toxicity properties of target compounds were assessed through ADMET evaluations. A series of 75 nanosecond molecular dynamics (MD) simulations were conducted on the complexes generated by the best-docked molecule and the native ligand. RMSD, RMSF, SASA, and Rg calculations of their trajectories were also calculated. PubChem-136621040 possessed an average RMSD value of 0.23 nm, lower than the native ligand's 0.31 nm, indicating a greater binding stability. The RMSF results also revealed that the best-docked compound had a lower value (0.10 nm), while the native ligand possessed a value of 0.12 nm. The SASA values for both the native ligand and the best-docked compound were nearly identical, at 150.20 nm2 and 150.80 nm2, respectively. The Rg results demonstrated that both complexes maintained their rigidity throughout the simulation, with similar average values of 2.04 nm for the native ligand and 2.06 nm for the best-docked compound.

Kinase-Bench: Comprehensive Benchmarking Tools and Guidance for Achieving Selectivity in Kinase Drug Discovery.

Developing selective kinase inhibitors remains a formidable challenge in drug discovery because of the highly conserved structural information on adenosine triphosphate (ATP) binding sites across the kinase family. Tailoring docking protocols to identify promising kinase inhibitor candidates for optimization has long been a substantial obstacle to drug discovery. Therefore, we introduced "Kinase-Bench," a pioneering benchmark suite designed for an advanced virtual screen, to improve the selectivity and efficacy of kinase inhibitors. Our comprehensive data set includes 6875 selective ligands and 422,799 decoys for 75 kinases, using extensive bioactivity and structural data from the ChEMBL database and decoys generated by the Directory of Useful Decoys-Enhanced version. Our benchmarking sets and retrospective case studies were designed to provide useful guidance in discovering selective kinase inhibitors. We employed a Glide High-Throughput Virtual Screen and Standard Precision complemented by three scoring functions and customized protein-ligand interaction filters that target specific kinase residue interactions. These innovations were successfully implemented in our virtual screen efforts targeting JAK1 inhibitors, achieving selectivity against its family member, TYK2. Consequently, we identified novel potential hits: Compound 2 (JAK1 IC50: 980.5 nM, TYK2 IC50: 4.5 μM) and the approved pan-AKT inhibitor Capivasertib (JAK1 IC50: 275.9 nM, TYK2 IC50: 10.9 μM). Using the Kinase-Bench protocol, both compounds demonstrated substantial JAK1 selectivity, making them strong candidates for further investigation. Our pharmaceutical results underscore the utility of tailored virtual screen protocols in identifying selective kinase inhibitors with substantial implications for rational drug design. Kinase-Bench offers a robust toolset for selective kinase drug discovery with the potential to effectively guide future therapeutic strategies effectively.

High throughput application of the NanoBiT Biochemical Assay for the discovery of selective inhibitors of the interaction of PI3K-p110α with KRAS

The NanoBiT Biochemical Assay (NBBA) was designed as a biochemical format of the NanoBiT cellular assay, aiming to screen weak protein-protein interactions (PPIs) in mammalian cell lysates. Here we present a High Throughput Screening (HTS) application of the NBBA to screen small molecule and fragment libraries to identify compounds that block the interaction of KRAS-G12D with phosphatidylinositol 3-kinase (PI3K) p110α. This interaction promotes PI3K activity, resulting in the promotion of cell growth, proliferation and survival, and is required for tumour initiation and growth in mouse lung cancer models, whilst having little effect on the health of normal adult mice, establishing the significance of the p110α/KRAS interaction as an oncology drug target. Despite the weak binding affinity of the p110α/KRAS interaction (KD = 3 μM), the NBBA proved to be robust and displayed excellent Z’-factor statistics during the HTS primary screening of 726,000 compounds, which led to the identification of 8,000 active compounds. A concentration response screen comparing KRAS/p110α with two closely related PI3K isoforms, p110δ and p110γ, identified selective p110α-specific compounds and enabled derivation of an IC50 for these hits. We identified around 30 compounds showing greater than 20-fold selectivity towards p110α versus p110δ and p110γ with IC50 < 2 μM. By using Differential Scanning Fluorimetry (DSF) we confirmed several compounds that bind directly to purified p110α. The most potent hits will be followed up by co-crystallization with p110α to aid further elucidation of the nature of the interaction and extended optimisation of these compounds.

KAHA ligation as a platform for the rapid discovery of Protein Tyrosine phosphatase 1B (PTP1B) inhibitors.

We have successfully designed and assembled a 66-member library of protein tyrosine phosphatases (PTP) inhibitor candidates using α-ketoacid-hydroxylamine (KAHA) ligation. Subsequent in situ enzymatic screening revealed a potent hit (IC50=1.67μM) against PTP1B, which displayed 6.8- to 50-fold selectivity over other phosphatases.

Experimental and computational biophysics to identify vasodilator drugs targeted at TRPV2 using agonists based on the probenecid scaffold

TRP channels are important pharmacological targets in physiopathology. TRPV2 plays distinct roles in cardiac and neuromuscular function, immunity, and metabolism, and is associated with pathologies like muscular dystrophy and cancer. However, TRPV2 pharmacology is unspecific and scarce at best. Using in silico similarity-based chemoinformatics we obtained a set of 270 potential hits for TRPV2 categorized into families based on chemical nature and similarity. Docking the compounds on available rat TRPV2 structures allowed the clustering of drug families in specific ligand binding sites. Starting from a probenecid docking pose in the piperlongumine binding site and using a Gaussian accelerated molecular dynamics approach we have assigned a putative probenecid binding site. In parallel, we measured the EC50 of 7 probenecid derivatives on TRPV2 expressed in Pichia pastoris using a novel medium-throughput Ca2+ influx assay in yeast membranes together with an unbiased and unsupervised data analysis method. We found that 4-(piperidine-1-sulfonyl)-benzoic acid had a better EC50 than probenecid, which is one of the most specific TRPV2 agonists to date. Exploring the TRPV2-dependent anti-hypertensive potential in vivo, we found that 4-(piperidine-1-sulfonyl)-benzoic acid shows a sex-biased vasodilator effect producing larger vascular relaxations in female mice. Overall, this study expands the pharmacological toolbox for TRPV2, a widely expressed membrane protein and orphan drug target.

The two-component system ArlRS is essential for wall teichoic acid glycoswitching in Staphylococcus aureus.

Staphylococcus aureus is among the leading causes of hospital-acquired infections. Critical to S. aureus biology and pathogenesis are the cell wall-anchored glycopolymers wall teichoic acids (WTA). Approximately one-third of S. aureus isolates decorates WTA with a mixture of α1,4- and β1,4-N-acetylglucosamine (GlcNAc), which requires the dedicated glycosyltransferases TarM and TarS, respectively. Environmental conditions, such as high salt concentrations, affect the abundance and ratio of α1,4- and β1,4-GlcNAc WTA decorations, thereby impacting biological properties such as antibody binding and phage infection. To identify regulatory mechanisms underlying WTA glycoswitching, we screened 1,920 S. aureus mutants (Nebraska Transposon Mutant Library) by immunoblotting for differential expression of WTA-linked α1,4- or β1,4-GlcNAc using specific monoclonal antibody Fab fragments. Three two-component systems (TCS), GraRS, ArlRS, and AgrCA, were among the 230 potential hits. Using isogenic TCS mutants, we demonstrated that ArlRS is essential for WTA β1,4-GlcNAc decoration. ArlRS repressed tarM expression through the transcriptional regulator MgrA. In bacteria lacking arlRS, the increased expression of tarM correlated with the absence of WTA β1,4-GlcNAc, likely by outcompeting TarS enzymatic activity. ArlRS was responsive to Mg2+, but not Na+, revealing its role in the previously reported salt-induced WTA glycoswitch from α1,4-GlcNAc to β1,4-GlcNAc. Importantly, ArlRS-mediated regulation of WTA glycosylation affected S. aureus interaction with the innate receptor langerin and lysis by β1,4-GlcNAc-dependent phages. Since WTA represents a promising target for future immune-based treatments and vaccines, our findings provide important insight to align strategies targeting S. aureus WTA glycosylation patterns during infection.IMPORTANCEStaphylococcus aureus is a common colonizer but can also cause severe infections in humans. The development of antibiotic resistance complicates the treatment of S. aureus infections, increasing the need for antibiotic alternatives such as vaccines and therapies with bacterial viruses also known as phages. Wall teichoic acids (WTA) are abundant glycosylated structures of the S. aureus cell wall that have gained attention as a promising target for new treatments. Importantly, WTA glycosylation patterns show variation depending on environmental conditions, thereby impacting phage binding and interaction with host factors, such as antibodies and innate pattern-recognition receptors. Here, we show that the two-component system ArlRS is involved in the regulation of WTA glycosylation by responding to environmental changes in Mg2+ concentration. These findings may support the design of new treatment strategies that target WTA glycosylation patterns of S. aureus during infection.

Machine learning-aided identification of natural compounds targeting Marburg virus VP40 with potential antiviral activity

Marburg virus infection poses a significant threat to humans due to its high fatality rate. The application of in-silico drug design to target the essential protein target of the virus has been proven to be a fundamental technique to inhibit viral growth. Here, VP40 (a matrix protein) was used as an essential protein target of Marburg, and 2569 natural compounds were screened using the molecular docking and neural network-based DeepPurpose architecture. The top 138 compounds that exhibited a binding score of −8 kcal/mol in molecular docking were used in the best DeepPurpose model to predict the IC50. The best model in DeepPurpose was composed of Morgan and CNN-based encoding for protein and ligand, respectively. The top three compounds, NPL130 (CHEMBL2087156), NPL313 (CHEMBL76073), and NPL371 (CHEMBL54440), were selected from the machine learning model, and molecular dynamics simulation was performed for their best complex along with the control compound complex, Nilotinib (CHEMBL255863). Protein showed less than 0.3 nm of deviation in the complex formed with control and NPL130 compounds. Control had shown the best average MM/GBSA binding energy of −36.97 kcal/mol with the lowest standard deviation of 1.86. A stable complex was indicated by the negative binding free energies of −20 to −25 kcal/mol for all three hits. The free energy landscape showed that, along with the control compound, NPL130 had the biggest conformational space with the lowest free energy. Overall, this study proposed NPL130 as a potential hit compound to target Marburg viral growth.

From nature’s pharmacy: harnessing bioactive phytoconstituents as fibroblast growth factor receptor 3 inhibitors for anti-cancer therapeutics

Fibroblast growth factor receptor 3 (FGFR3) is a key protein involved in regulating cell growth and development. Aberrant activation of FGFR3 has been linked to several diseases, including cancer and skeletal disorders. Therefore, identifying potential inhibitors of FGFR3 is of great interest in developing targeted therapies. In this study, we employed a combined docking and molecular dynamics simulation (MDS) approach to investigate the inhibitory potential of plant-based compounds against FGFR3. Here, we utilized structure-based virtual screening to identify potential phytoconstituents from the IMPPAT library with the ability to inhibit FGFR3 activity. The initial screening process involved molecular docking to assess the binding potential of the compounds towards FGFR3. Afterward, we employed various filters to determine physicochemical properties, ADMET, and PASS evaluation to identify potential hits against FGFR3. This process discovered two phytoconstituents, Cycloartobiloxanthone and Desoxylimonin, as promising candidates against FGFR3. These compounds exhibited favorable binding affinity, efficiency, and specific interaction towards the FGFR3 binding pocket. They preferred binding to the active site of FGFR3 and possessed desirable drug-like properties. To gain a deeper understanding of their interaction mechanism, conformational dynamics, and stability, we conducted all-atom MDS lasting 200 nanoseconds (ns) on the FGFR3-Cycloartobiloxanthone and FGFR3-Desoxylimonin complexes. The MDS consistently demonstrated the formation of stable protein-ligand complexes between FGFR3 and Cycloartobiloxanthone/Desoxylimonin throughout the trajectory. Based on these results, it can be inferred that Cycloartobiloxanthone and Desoxylimonin can potentially serve as valuable scaffolds in developing drugs targeting FGFR3 for cancer therapeutic after required validation.

A Collection of Useful Nuisance Compounds (CONS) for Interrogation of Bioassay Integrity.

High-throughput screening (HTS) is a crucial technique for identifying potential hits to fuel drug discovery pipelines. However, this process naturally concentrates nuisance compounds that are not optimizable yet signal positively in a convincing manner. To be able to understand what types of nuisance compounds a particular assay is sensitive to, would be of great utility in being able to prioritize progressable over nonprogressable screening hits. In this study, we present a carefully compiled set of over 100 nuisance compounds that are known to interfere with assay readouts in either phenotypic or target-based screenings. Readily accessible in an assay-ready screening plate, we believe this nuisance compound set will be of great interest to the research community, helping to establish high-quality HTS assays and identify promising, optimizable hits.

Genomic rare variant mechanisms for congenital cardiac laterality defect: A digenic model approach.

Laterality defects are defined by perturbations in the usual left-right asymmetry of organs. Due to low known genetic etiology of congenital heart disease (CHD) cases (less than 40%), we used a digenic model approach for the identification of contributing variants in known laterality defect genes (N = 115) in the exome/genome sequencing (ES/GS) data from individuals with clinically diagnosed laterality defects. The unsolved ES/GS data were analyzed from three CHD cohorts: Baylor College of Medicine-Genomics Research to Elucidate the Genetics of Rare Diseases (BCM-GREGoR; N = 247 proband ES), Gabriella Miller Kids First Pediatric Research program (Kids First; N = 158 trio GS), and Pediatric Cardiac Genomics Consortium (PCGC; N = 163 trio ES), and trans-heterozygous digenic variants were identified in 2.8% (inherited digenic variants in 0.4%), 8.2%, and 13.5% cases respectively, which was significantly higher as compared to 602 control trios provided by the 1000 Genomes Project (p = 0.001, 1.4e-07, and 8.9e-13, respectively). Trans-heterozygous digenic variants were also identified in 0.4%, and 1.4% cases with non-laterality CHD in Kids First and PCGC datasets, respectively, which was not statistically significant as compared to control trios ( p = 1, and 0.059, respectively). Altogether, in laterality cohorts, 23% of digenic pairs were in the same structural complex of motile cilia. Out of 39 unique digenic pairs in laterality CHD, 29 are more likely to be potential digenic hits as predicted by DiGePred tool. These findings provide further evidence that digenic epistatic interaction can contribute to the complex genetics of laterality defects.

New PKS/NRPS Tenuazamines A-H from the Endophytic Fungus Alternaria alternata FL7 Isolated from Huperzia serrata.

In this paper, we present a novel class of hybrid polyketides, tenuazamines A-H (1-8), which exhibit a unique tautomeric equilibrium from Alternaria alternata FL7. The elucidation of the structures was achieved through a diverse combination of NMR, HR-ESIMS, and ECD methods, with a focus on extensive spectroscopic data analysis. Notably, compounds 1, 4, 8-9 exhibited potent toxic effects on the growth of Arabidopsis thaliana. This research expands the structural diversity of tenuazonic acid compounds derived from endophytic fungi and provides potential hit compounds for the development of herbicides.

Harnessing computational and experimental approaches to identify potent hits against Leishmania donovani sterol C-24 methyltransferase from ChemBridge library

Leishmaniasis is a neglected tropical disease and is one of the major causes of mortality in poverty-stricken areas. A limited chemotherapeutics arsenal is available to tackle this deadly infection. Thus, identifying novel potent scaffolds using innovative strategies is the need of the hour. High-throughput screening (HTS) is a critical technique that can accelerate the process of drug discovery by evaluating millions of drug-like molecules using various automation tools and biological assays. In the present study, we have employed the HTS strategy to identify potent hits against Leishmania donovani sterol C-24 methyltransferase (LdSMT) from the in-house ChemBridge library. Firstly, a robust dataset was prepared with previously reported sterol C-24 methyltransferase inhibitors, belonging to diverse structural classes. Then, ligand-based virtual screening using similarity search was performed to screen the ChemBridge library having ∼20,000 molecules. This computational approach yielded 81 candidate compounds, which were selected for further molecular docking and biological evaluation. Anti-leishmanial assays revealed that out of 81 molecules, seven showed potential parasitic killing. Three molecules namely IIIM-CB-14, IIIM-CB-29, and IIIM-CB-45 were the most potent ones with 50 % inhibitory concentration (IC50) of 5.76, 8.08, and 10.64 µg/mL, respectively. SEM analyses suggest that these potent hits cause considerable morphological alterations. ADME studies of the potent hit molecules indicate that all the hits have considerable drug-likeness properties. Further, molecular dynamics studies were also performed to check the stable confirmation of LdSMT protein with the top two hits (IIIM-CB-14 and IIIM-CB-45). Thus, the present study harnesses computational and experimental approaches to unravel potent anti-leishmanial scaffolds.

Synthesis of arylated tetrahydrobenzo[H]quinoline-3-carbonitrile derivatives as potential hits for treatment of diabetes.

Aim: Quinoline scaffolds are serving as the core structure for numerous antifungal, analgesic, antipyretic, anti-inflammatory drugs as well as have also been investigated for their potential antidiabetic properties. Though further exploration is required in this area as the current antidiabetic agents, such as acarbose, miglitoland voglibose, are associated with several adverse side effects. In this context, arylated tetrahydrobenzo[H]quinoline-3-carbonitrile derivatives were designed and evaluated as potential antidiabetic agents.Materials & methods: A one-pot multicomponent reaction of 6-methoxy-1-tetralone with ethyl cyanoacetate, ammonium acetateand varying aldehydes yielded a range of new arylated tetrahydrobenzo[h]quinoline-3-carbonitrile molecules 1-36.Results: Compounds 2-5, 12, 13, 19 and 32-34 showed excellent inhibition against α-amylase (IC50=3.42-15.14μM) and α-glucosidase (IC50=0.65-9.23μM) enzymes in comparison to the standard acarbose (IC50=14.35μM). In addition, all compounds revealed significant to moderate DPPH radical scavenging activity (SC50=21.30-138.30μM) compared with BHT (SC50=64.40μM). Kinetic studies confirmed competitive inhibition mode, while molecular docking studies comprehend ligands'interaction with enzyme's active sitesand absorption, distribution, metabolism, and excretion analysis confirms that all synthetic derivatives are nontoxic.Conclusion: This research offers a range of lead candidates to become antidiabetic agents after further advanced study.

Pharmacophore-guided in-silico discovery of SIRT1 inhibitors for targeted cancer therapy

Epigenetic modifier, Sirtuin (SIRTs) is a family of seven isoforms (SIRT1‐7) and nicotinamide adenine dinucleotide (NAD+) dependent class III histone deacetylase (HDACs) protein. SIRT1 in association with the p53 protein can regulate crucial cell processes such as glucose metabolism, lipid metabolism, mitochondrial biogenesis, DNA repair, oxidative stress, apoptosis, and inflammation through the process of deacetylation. When SIRT1 deacetylates p53, it loses its tumor suppression property. To promote apoptosis and decrease cell proliferation by inhibiting SIRT1 protein and ultimately raising the acetylation of p53 to regain its tumor suppressor function. Though we have many SIRT1 protein inhibitors, they exhibited off-target effects and inefficiency at the clinical trial stage. This study has been executed to identify more potentially effective and reliable SIRT1 inhibitors that can perform better than the existing options. To do so, pharmacophore-based screening of compound libraries followed by virtual screening, pharmacokinetic, drug-likeness, and toxicity studies were conducted which gave 42 compounds to evaluate further. Subsequently, exhaustive molecular docking and molecular dynamics simulation predicted four potential hits to inhibit the SIRT1 protein better than the reference compound. Further studies such as principal components analysis, free energy landscape, and estimation of binding free energy were done which concluded Hit4 (PubChem ID: 55753455) to be a novel and potent SIRT1 small molecule inhibitor among the others. The total binding free energy for Hit4 was found to be −44.68 kcal/mol much better than the reference complex i.e., −29.38 kcal/mol.

DSDPFlex: Flexible-Receptor Docking with GPU Acceleration.

Molecular docking is an essential tool in structure-based drug discovery, widely utilized to model ligand-protein interactions and enrich potential hits. Among the different docking strategies, semiflexible docking (rigid-receptor and flexible-ligand model) is the most popular, benefiting from its balance of docking accuracy and speed. However, this approach ignores the conformational changes of proteins and hence demands suitable protein conformations as input. When the binding interaction adheres to an induced-fit model, flexible methods such as molecular dynamics simulation can be utilized, but they are computationally demanding. To balance between speed and accuracy, the flexible docking approach is an effective choice, as exemplified by AutoDock Vina and AutoDockFR, which treat selected protein side chains as flexible parts. However, the efficiency of flexible docking methods is yet to be improved for virtual screening usage. In this article, we introduce DSDPFlex, an improved flexible-receptor docking method accelerated by GPU parallelization. Beyond acceleration, optimizations with respect to sampling, scoring, and search space are implemented in DSDPFlex to further improve its capability in flexible tasks. In cross-docking evaluation, DSDPFlex demonstrates superior accuracy compared to AutoDock Vina and is 100 times faster than Vina in flexible-receptor tasks. We also show the advantage of flexible-receptor methods on suboptimal pockets and validate the advantage of DSDPFlex in screening on apo and AlphaFold2-predicted structures. With improvements in both efficiency and accuracy, DSDPFlex is expected to hold potential in future docking-based studies.