Ligand-based Pharmacophore Research Articles

Ligand-based pharmacophore modeling targeting the fluoroquinolone antibiotics to identify potential antimicrobial compounds

Antibiotic resistance presents a serious challenge to global healthcare, making the discovery of new therapeutic agents crucial. In this study, we used pharmacophore modeling and virtual screening to identify potential lead compounds against drug-resistant bacteria. We developed a shared feature pharmacophore (SFP) map using four antibiotics—Ciprofloxacin, Delafloxacin, Levofloxacin, and Ofloxacin—and generated a drug library of 160,000 compounds from ZINCPharmer based on these features, which included hydrophobic areas, hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), and aromatic moieties (Ar). Through virtual screening, we identified 25 potential drug compounds as hits, with fit scores ranging from 97.85 to 116 and RMSD values from 0.28 to 0.63. These hits were then subjected to molecular docking against the DNA gyrase subunit A protein (PDB ID: 4DDQ), with ciprofloxacin as a control. The top five compounds—ZINC09133461, ZINC03791737, ZINC21983587, ZINC26469742, and ZINC26740199—achieved the highest docking scores, ranging from − 7.3 to − 7.4 kcal/mol and ciprofloxacin − 7.3 kcal/mol. After evaluating the drug-likeness of these compounds using Lipinski’s rule and analyzing their physicochemical properties, ZINC26740199 emerged as the most promising lead, offering hope in the fight against antibiotic resistance. Furthermore, molecular scaffold analysis revealed key similarities between ZINC26740199 and Ciprofloxacin, particularly in aromatic rings, hydrophobic regions, and hydrogen bond acceptors. These features suggest its potential as an effective antimicrobial agent, though further laboratory studies are needed to confirm its efficacy.

In Silico Screening of 1,3,4-Thiadiazole Derivatives as Inhibitors of Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2).

Angiogenesis plays a pivotal role in the growth, survival, and metastasis of solid tumors, with Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) being overexpressed in many human solid tumors, making it an appealing target for anti-cancer therapies. This study aimed to identify potential lead compounds with azole moiety exhibiting VEGFR-2 inhibitory effects. A ligand-based pharmacophore model was constructed using the X-ray crystallographic structure of VEGFR-2 complexed with tivozanib (PDB ID: 4ASE) to screen the ZINC15 database. Following virtual screening, six compounds demonstrated promising docking scores and drug-likeness comparable to tivozanib. These hits underwent detailed pharmacokinetic analysis to assess their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Furthermore, Density Functional Theory (DFT) analysis was employed to investigate the molecular orbital properties of the top hits from molecular docking. Molecular dynamics (MD) simulations were conducted to evaluate the conformational stability of the complexes over a 100 ns run. Results indicated that the compounds (ZINC8914312, ZINC8739578, ZINC8927502, and ZINC17138581) exhibited the most promising lead requirements for inhibiting VEGFR-2 and suppressing angiogenesis in cancer therapy. This integrated approach, combining pharmacophore modeling, molecular docking, ADMET studies, DFT analysis, and MD simulations, provides valuable insights into the identification of potential anti-cancer agents targeting VEGFR-2.

Exploring novel natural compound-based therapies for Duchenne muscular dystrophy management: insights from network pharmacology, QSAR modeling, molecular dynamics, and free energy calculations.

Muscular dystrophies encompass a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration and weakness. Among these, Duchenne muscular dystrophy (DMD) stands out as one of the most severe forms. The present study employs an integrative approach combining network pharmacology, quantitative structure-activity relationship (QSAR) modeling, molecular dynamics (MD) simulations, and free energy calculations to identify potential therapeutic targets and natural compounds for DMD. Upon analyzing the GSE38417 dataset, it was found that individuals with DMD exhibited 290 upregulated differentially expressed genes (DEGs) compared to healthy controls. By utilizing gene ontology (GO) and protein-protein interaction (PPI) network analysis, this study provides insights into the functional roles of the identified DEGs, identifying ten hub genes that play a critical role in the pathology of DMD. These key genes include DMD, TTN, PLEC, DTNA, PKP2, SLC24A, FBXO32, SNTA1, SMAD3, and NOS1. Furthermore, through the use of ligand-based pharmacophore modeling and virtual screening, three natural compounds were identified as potential inhibitors. Among these, compounds 3874518 and 12314417 have demonstrated significant promise as an inhibitor of the SMAD3 protein, a crucial factor in the fibrotic and inflammatory mechanisms associated with DMD. The therapeutic potential of the compounds was further supported by molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis. These findings suggest that the compounds are viable candidates for experimental validation against DMD.

Ligand-based pharmacophore modeling, virtual screening, and molecular dynamics simulations of Pfhsp90 fingerprints in Plasmodium malaria treatment

The World Health Organization (WHO) documents malaria as one of the leading causes of high morbidity and mortality worldwide. The disease affects millions and kills thousands of people annually. Efforts to reduce the global burden of malaria have prompted the WHO to recommend prevention strategies such as using antimalarial drugs. However, these strategies have been ineffective because of antimalarial drug resistance. The only efficacious malaria treatment is Artemisinin-based Combination Therapy (ACT). However, the extended ACT clearance times, linked to the emergence of artemisinin monotherapy resistance recorded most recently in Africa and the Great Mekong region, pose a danger to its efficacy. Therefore, better efficacious antimalarial drugs are required. Since P. falciparum heat shock protein 90 (PfHsp90) is a well-characterized malaria drug target, this study uses it to discover more efficacious antimalarial drugs. An in silico approach was used to discover PfHsp90 inhibitors with pharmacological properties against Plasmodium malaria using a molecular dynamics simulation (MDS) and hierarchical virtual screening. Geldanamycin (GDM), a well-known anti-PfHsp90 compound, was used to identify PfHsp90 inhibitors with pharmacological properties against Plasmodium malaria by screening it against the ZINC20 database via the ZINCPHARMER web server. This virtual screening process resulted in 17 hits. These ZINCPHARMER hits were subjected to drug-likeness and pharmacokinetics properties analysis in the SwissADME web server, and nine of them satisfied the requirements. The nine ZINC20 compounds were docked with PfHsp90 using the PyRx software version 0.8 to understand their interactions. From the molecular docking results, ZINC09060002 (−8.2 kcal/mol), ZINC72133064 (−7.8 kcal/mol), ZINC72163401 (−7.7 kcal/mol), ZINC72358537 (−8.1 kcal/mol), and ZINC72358557 (−7.6 kcal/mol) had better binding affinities to PfHsp90 than GDM (−7.5 kcal/mol). The stability of these molecularly docked protein–inhibitor complexes was assessed through MDS using GROMACS 2022. ZINC72163401, ZINC72358537, and ZINC72358557 formed stable complexes with PfHsp90. The lead compounds were subjected to in vitro validation for their inhibitory capability. They showed promising inhibition of parasite growth with IC50 values ranging between 200 and 400 ng/mL. In this regard, the three PfHsp90 inhibitors can be further developed as potential antimalarial drugs. However, further structural optimization studies and clinical (in vivo) tests are necessary to ascertain the antimalarial activity of these compounds in humans.

Natural volatiles preventing mosquito biting: an integrated screening platform for accelerated discovery of ORco antagonists

Insect olfactory receptors are heteromeric ligand-gated cation channels composed of an obligatory receptor subunit, ORco, and one of many variable subunits, ORx, in as yet undefined molar ratios. When expressed alone ex vivo, ORco forms homotetrameric channels gated by ORco-specific ligands acting as channel agonists. Using an insect cell-based system as a functional platform for expressing mosquito odorant receptors ex vivo, we identified small molecules of natural origin acting as specific ORco channel antagonists, orthosteric or allosteric relative to a postulated ORco agonist binding site, which cause severe inhibition of olfactory function in mosquitoes. In the present communication, we have compiled common structural features of such orthosteric antagonists and developed a ligand-based pharmacophore whose properties are deemed necessary for binding to the agonist binding site and causing inhibition of ORco's biological function. In silico screening of an available collection of natural volatile compounds with the pharmacophore resulted in identification of several ORco antagonist hits. Cell-based functional screening of the same compound collection resulted in the identification of several compounds acting as orthosteric and allosteric antagonists of ORco channel function ex vivo and inducing anosmic behaviors to Aedes albopictus mosquitoes in vivo. Comparison of the in silico screening results with those of the functional assays revealed that the pharmacophore predicted correctly 7 out of the 8 confirmed orthosteric antagonists and none of the allosteric ones. Because the pharmacophore screen produced additional hits that did not cause inhibition of the ORco channel function, we also generated a Support Vector Machine (SVM) model based on two descriptors of all pharmacophore hits. Training of the SVM on the ex vivo validated compound collection resulted in the selection of the confirmed orthosteric antagonists with a very low cross-validation out-of-sample misclassification rate. Employment of the combined pharmacophore-SVM platform for in silico screening of a larger collection of olfaction-relevant volatiles produced several new hits. Functional validation of randomly selected hits and rejected compounds from this screen confirmed the power of this virtual screening platform as a convenient tool for accelerating the pace of discovery of novel vector control agents. To the best of our knowledge, this study is the first one that combines a pharmacophore with a SVM model for identification of AgamORco antagonists and specifically orthosteric ones.

Structural insight into the lead identification of a dual inhibitor of PDE1B and PDE10A: Integrating pharmacophore-based virtual screening, molecular docking, and structure-activity-relationship approaches

Schizophrenia is a chronic neuropsychiatric disorder affecting more than 1% of the world's population. Current antipsychotic treatments show inadequacy in mitigating the negative and cognitive symptoms of schizophrenia. In addition, these medications cause undesirable extrapyramidal side effects. According to the studies, inhibition of phosphodiesterase (PDE) 1B and PDE10A simultaneously can alleviate positive, negative, and cognitive symptoms of schizophrenia. Thus, this study aims to identify new dual inhibitors of PDE1B and PDE10A using ligand-based pharmacophore modelling, virtual screening, and molecular docking studies. Accordingly, the generated pharmacophore models of PDE1B and PDE10A comprised hydrogen bond acceptor, aromatic ring, and hydrophobic features. These features were essential for retrieving the active hits from the Universal Natural Product Database in the virtual screening. Additional filters were subsequently employed to identify potential hits that could be developed into central nervous system-active compounds. Hits meeting all the screening criteria were subjected to docking studies with PDE1B and PDE10A. Among these hits, UNPD167314 exhibited significant binding affinities for the target receptors. It occupied the P-clamp and displayed hydrophobic, aromatic, and hydrogen bond interactions with the active site residues of both receptors, thus selected as a lead compound for the design of potent and selective dual inhibitors. The structural modifications of UNPD167314 resulted in the design of 35 novel inhibitors. Out of 35, four compounds exhibited high and comparable binding affinities for both PDE1B and PDE10A, making them promising candidates for further evaluation and optimisation.

Integrating computational 3D-QSAR and pharmacophore modelling in diverse natural phytochemicals library for targeted therapy against breast cancer estrogen receptors

Breast cancer stands as a pervasive global health challenge, profoundly impacting the physical and emotional well-being of individuals. The relentless growth of malignant cells within breast tissues necessitates comprehensive research into the disease's origin, early detection, and treatment strategies. This study focused on constructing pharmacophores and 3D-QSAR models to dissect the crucial structure attributes essential for inhibiting Estrogen receptors. By employing ligand-based pharmacophore modeling and Atom-based-3D-QSAR techniques within the Schrödinger Phase module, we analyzed the inhibitory activity of 202 natural compounds and Tamoxifen as the control drug. To identify promising candidates, five molecules (Genistein, Coumestrol, Apigenin, Emodin, and Daidzein) were strategically chosen based on prediction from the 3D-QSAR pharmacophore model. These molecules are further assessed through model validation and virtual screening. The generated pharmacophore model (ADHR.10) reveals key features like hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), and hydrophobic interactions. The resulting 3D-QSAR model predicted and stability was validated through the Xternal validation plus tool. The ADHR.10 model guided the systematic screening of natural compounds, resulting in the identification of the top five hits with promising pharmacological activity. Molecular docking scores demonstrated the compound's binding affinity with ESR1 (PDB ID: 6PSJ) and ESR2 (PDB ID: 1QKM). One standout compound, Coumestrol, displayed an exceptional binding affinity with ESR2 outperforming Tamoxifen. The top compounds exhibited both strong binding and stability, making them a promising candidate. Furthermore, Molecular Dynamics Simulation (MDS) demonstrated Coumestrol's superior stability compared to the control drug against the ESR1 and ERS2 proteins. Coumestrol exhibits lower average fluctuation in RMSD and residue-specific dynamics (RMSF) indicating stability than control. Specifically, designed compounds, rooted in natural sources, demonstrated excellent potential as breast cancer treatment candidates. These findings highlight them as promising lead candidates for the development of targeted breast cancer therapies and emphasize the potential of computational approaches in discovering new therapeutic avenues.

Discovery of novel chemotype inhibitors targeting Anaplastic Lymphoma Kinase receptor through ligand-based pharmacophore modelling

ABSTRACT Anaplastic Lymphoma Kinase (ALK) is a receptor tyrosine kinase within the insulin receptor superfamily. Alterations in ALK, such as rearrangements, mutations, or amplifications, have been detected in various tumours, including lymphoma, neuroblastoma, and non-small cell lung cancer. In this study, we outline a computational workflow designed to uncover new inhibitors of ALK. This process starts with a ligand-based exploration of the pharmacophoric space using 13 diverse sets of ALK inhibitors. Subsequently, quantitative structure-activity relationship (QSAR) modelling is employed in combination with a genetic function algorithm to identify the optimal combination of pharmacophores and molecular descriptors capable of elucidating variations in anti-ALK bioactivities within a compiled list of inhibitors. The successful QSAR model revealed three pharmacophores, two of which share three similar features, prompting their merger into a single pharmacophore model. The merged pharmacophore was used as a 3D search query to mine the National Cancer Institute (NCI) database for novel anti-ALK leads. Subsequent in vitro bioassay of the top 40 hits identified two compounds with low micromolar IC50 values. Remarkably, one of the identified leads possesses a novel chemotype compared to known ALK inhibitors.

Potent Small Molecules Inhibitors Discovery through Ligand-based Modelling for Effective Treatment of Parkinson’s Disease

Background: Parkinson’s disease (PD) is a chronic neurodegenerative disease affecting mostly aged people. The disease's symptoms develop gradually over time and include tremors, bradykinesia, rigidity, and postural instability. Current treatment options for PD are only symptom-targeted. Prolyl oligopeptidase (POP) is a serine protease enzymes implicated in PD pathogenesis via an increase in the aggregation of α-synuclein protein in the brain. Aim: This study aims to identify potent anti-PD ligands with inhibitory potential against POP Methods: Ligand-based pharmacophore modeling, Glide extra precision (XP) docking, and post-simulation analysis methods were used. Results: The adopted ligand-based (LB) modeling generated pharmacophoric features, including 1 hydrophobic group, 1 positive ionizable group, 2 aromatic rings, and 2 hydrogen bond acceptors. A total of 23 hits with a Gunner-Henry score of 0.7 and an enrichment factor of 30.24 were obtained as validation protocols, making it an ideal model. The LB model retrieved 177 hit compounds from the 69,543 natural screening ligands available in the Interbioscreen database. Interestingly, ligands 1, 2, 3, 4, and 5 orderly demonstrated higher binding affinities with Glide XP docking of -9.0, -8.8, -8.7, -8.7, -8.7 kcal/mol compared to reference drugs, GSK552 and ZPP with -8.2, and -6.8 kcal/mol respectively. Similarly, their MM/GBSA values were recorded as -54.4, -51.3, -58.4, -49.3, - 33.5, & -32.5 kJ/mol respectively. Further, MD analysis indicated that ligands had higher favorable binding and stability to the receptor. Conclusion: Overall, the study paves the way for developing potential anti-PD therapeutics. The ligands are recommended as adjuvant/single candidate as anti-PD candidates upon further experiment.

Identification of novel PHGDH inhibitors based on computational investigation: an all-in-one combination strategy to develop potential anti-cancer candidates.

Biological studies have elucidated that phosphoglycerate dehydrogenase (PHGDH) is the rate-limiting enzyme in the serine synthesis pathway in humans that is abnormally expressed in numerous cancers. Inhibition of the PHGDH activity is thought to be an attractive approach for novel anti-cancer therapy. The development of structurally diverse novel PHGDH inhibitors with high efficiency and low toxicity is a promising drug discovery strategy. A ligand-based 3D-QSAR pharmacophore model was developed using the HypoGen algorithm methodology of Discovery Studio. The selected pharmacophore model was further validated by test set validation, cost analysis, and Fischer randomization validation and was then used as a 3D query to screen compound libraries with various chemical scaffolds. The estimated activity, drug-likeness, molecular docking, growing scaffold, and molecular dynamics simulation processes were applied in combination to reduce the number of virtual hits. The potential candidates against PHGDH were screened based on estimated activity, docking scores, predictive absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties, and molecular dynamics simulation. Finally, an all-in-one combination was employed successfully to design and develop three potential anti-cancer candidates.

Lead Identification Through In Silico Studies: Targeting Acetylcholinesterase Enzyme Against Alzheimer's Disease.

In this work, we aimed to acquire the best potential small molecule for Alzheimer's disease (AD) treatment using different models in Biovia Discovery Studio to identify new potential inhibitors of acetylcholinesterase (AChE) via in silico studies. The prevalence of cognitive impairment-related neurodegenerative disorders, such as AD, has been observed to escalate rapidly. However, we still know little about the underlying functions, outcome predictors, or intervention targets causing AD. The objective of the study was to optimize and identify the lead compound to target AChE against Alzheimer's disease. Different in silico studies were employed, including the pharmacophore model, virtual screening, molecular docking, de novo evolution model, and molecular dynamics. The pharmacophoric features of AChE inhibitors were determined by ligand-based pharmacophore models and 3D QSAR pharmacophore generation. Further validation of the best pharmacophore model was done using the cost analysis method, Fischer's randomization method, and test set. The molecules that harmonized the best pharmacophore model with the estimated activity < 1 nM and ADMET parameters were filtered, and 12 molecules were subjected to molecular docking studies to obtain binding energy. 3vsp_EK8_1 secured the highest binding energy of 65.60 kcal/mol. Further optimization led to a 3v_Evo_4 molecule with a better binding energy of 70.17 kcal/mol. The molecule 3v_evo_4 was subjected to 100 ns molecular simulation compared to donepezil, which showed better stability at the binding site. A lead compound, 3v_Evo_4 molecule, was identified to inhibit AChE, and it could be further studied to develop as a drug with better efficacy than the existing available drugs for treating AD.

Molecular Regulator Driving Endometriosis Towards Endometrial Cancer: A Multi-Scale Computational Investigation to Repurpose Anti-Cancer drugs.

Endometriosis is a gynecological disorder among reproductive-aged women. Recent epidemiological investigations suggest endometriosis increases the risk of endometrial cancer. However, the molecular entity leading to endometriosis-to-endometrial cancer is largely unknown. This study aimed to combine a variety of computational approaches to identify the key therapeutic target promoting endometriosis-to-endometrial cancer and screen potential inhibitors against target to prevent cancer development. Our systematic investigations, includes transcriptomic profiling, protein network, pharmacophore modeling, docking, binding free energy calculation, dynamics simulation, and quantum mechanics. The gene expression analysis on endometriosis and endometrial cancer was performed and showed 108 shared upregulated genes in both conditions. Further construction of interaction network with 108 genes showed intercellular adhesion molecule 1 (ICAM1) to be a crucial molecule with a high degree of connectivity that influences vital mechanisms related to cancer pathways. We then generated ligand-based pharmacophore models using established ICAM1 inhibitors. Among the models, the ADRRR_8 pharmacophore exhibited a robust area under curve (AUC = 0.83), was employed to screen 1739 anti-cancer drugs. On screening, 421 anti-cancer drugs displayed ICAM1-inhibiting pharmacophore features. Further, the docking of 421 drugs with ICAM1 showed lanreotide (-7.80 kcal/mol) with better affinity than the reference ICAM1 inhibitor (-3.59 kcal/mol). Further validation though binding free energy and dynamics simulation of the lanreotide-ICAM1 complex showed a high binding affinity of -55.90 kcal/mol and contributed stable confirmation. According to quantum chemical calculations, lanreotide's electronic properties favour ICAM1 binding with highest occupied molecular orbital was -6.91 eV and lowest unoccupied molecular orbital was -3.93 eV. Our study supports using lanreotide to treat endometriosis, which could delay or prevent endometrial cancer. These predictions need to be confirmed and examined to determine the use of lanreotide in endometriosis treatment.

Hybrid Virtual Screening Approach to Predict Novel Natural Compounds against HIV-1 CCR5.

Human immunodeficiency virus (HIV) infection continues to pose a major global health challenge. HIV entry into host cells via membrane fusion mediated by the viral envelope glycoprotein gp120/gp41 is a key step in the HIV life cycle. CCR5, expressed on CD4+ T cells and macrophages, acts as a coreceptor facilitating HIV-1 entry. The CCR5 antagonist maraviroc is used to treat HIV infection. However, it can cause adverse effects and has limitations such as only inhibiting CCR5-tropic viruses. There remains a need to develop alternative CCR5 inhibitors with improved safety profiles. Natural products may offer advantages over synthetic inhibitors including higher bioavailability, binding affinity, effectiveness, lower toxicity, and molecular diversity. However, screening the vast chemical space of natural compounds to identify novel CCR5 inhibitors presents challenges. This study aimed to address this gap through a hybrid ligand-based pharmacophore modeling and molecular docking approach to virtually screen large natural product databases. A reliable pharmacophore model was developed based on 311 known CCR5 antagonists and validated against an external data set. Five natural product databases containing over 306,000 compounds were filtered based on drug-likeness rules. The validated pharmacophore model screened the databases to identify 611 hits. Key residues of the CCR5 receptor crystal structure were identified for docking. The top hits were docked, and interactions were analyzed. Molecular dynamics simulations were conducted to examine complex stability. Computational prediction evaluated pharmacokinetic properties. Three compounds exhibited similar interactions and binding energies to maraviroc. MD simulations demonstrated complex stability comparable to maraviroc. One compound showed optimal predicted absorption, minimal metabolism, and a lower likelihood of interactions than maraviroc. This computational screening workflow identified three natural compounds with promising CCR5 inhibition and favorable pharmacokinetic profiles. One compound emerged as a lead based on bioavailability potential and minimal interaction risk. These findings present opportunities for developing alternative CCR5 antagonists and warrant further experimental investigation. Overall, the hybrid virtual screening approach proved effective for mining large natural product spaces to discover novel molecular entities with drug-like properties.

A Computational Investigation on Chitosan Derivatives using Pharmacophore- based Screening, Molecular Docking, and Molecular Dynamics Simulations against Kaposi Sarcoma.

Cancer is one of the most dangerous illnesses to the human body due to its severity and progressive nature. Kaposi's Sarcoma (KS) tumor can appear as painless purple spots on the legs, foot, or face. This cancer develops in the lining of lymph arteries and blood vessels. Along with the enlargement of lymph nodes, the vaginal region and the mouth portion are the additional target areas of KS. DNA-binding proteins known as Sox proteins are found in all mammals and belong to the HMG box superfamily. They controlled a wide range of developmental procedures, such as the formation of the germ layer, the growth of organs, and the selection of the cell type. Human developmental abnormalities and congenital illnesses are frequently caused by the deletion or mutation of the Sox protein. The purpose of this study is to determine the promising Kaposi's sarcoma inhibitors through computational studies. In this present study computational approaches were used to evaluate the anti- carcinogenic efficacy against Kaposi's sarcoma. Ligand-based pharmacophore screening was performed utilising four different chemical libraries (Asinex, Chembridge, Specs, and NCI Natural products (NSC)) depending on the top hypothesis. The top hits were examined using molecular docking, absorption, distribution, metabolism and excretion. Highest occupied molecular orbital and lowest unoccupied molecular orbital were analysed to determine the lead compounds' biological and pharmacological efficacy. The results of the study indicated that the leading candidates were possible SOX protein inhibitors. A pharmacophore model to inhibit the production of SOX protein in Kaposi Sarcoma was generated in this computational experiment using a set of 19 Chitosan compounds. The results revealed that the top hits responded to all of the pharmacological druglikening criteria and had the best interaction residues, fitness scores, and docking scores. The resulting leads might be potential Kaposi's Sarcoma alternative treatments.

Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulation

GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibitors. In this study, multiclass classification-based QSAR models were developed using diverse machine learning algorithms. These models were built from curated compounds with experimentally derived GRK5 inhibitory activity. Additionally, a pharmacophore model was constructed using active compounds from the dataset. Among the models, the SVM-based approach proved most effective and was initially used to screen DrugBank compounds within the applicability domain. Compounds showing significant GRK5 inhibitory potential underwent evaluation for key pharmacophoric features. Prospective compounds were subjected to molecular docking to assess binding affinity towards GRK5's key active site amino acid residues. Stability at the binding site was analyzed through 200 ns molecular dynamics simulations. MM-GBSA analysis quantified individual free energy components contributing to the total binding energy with respect to binding site residues. Metadynamics analysis, including PCA, FEL, and PDF, provided crucial insights into conformational changes of both apo and holo forms of GRK5 at defined energy states. The study identifies DB02844 (S-Adenosyl-1,8-Diamino-3-Thiooctane) and DB13155 (Esculin) as promising GRK5 inhibitors, warranting further in vitro and in vivo validation studies.

Discovery of a SUCNR1 antagonist for potential treatment of diabetic nephropathy: In silico and in vitro studies

Diabetic nephropathy (DN) is one of the most severe complications of diabetes mellitus. Succinate Receptor 1 (SUCNR1), a member of the G-protein-coupled receptor (GPCR) family, represents a potential target for treatment of DN. Here, utilizing multi-strategy in silico virtual screening methods containing AlphaFold2 modelling, molecular dynamics (MD) simulation, ligand-based pharmacophore screening, molecular docking and machine learning-based similarity clustering, we successfully identified a novel antagonist of SUCNR1, AK-968/12117473 (Cpd3). Through extensive in vitro experiments, including dual-luciferase reporter assay, cellular thermal shift assay, immunofluorescence, and western blotting, we substantiated that Cpd3 could specifically target SUCNR1, inhibit the activation of NF-κB pathway, and ameliorate epithelial-mesenchymal transition (EMT) and extracellular matrix (ECM) deposition in renal tubular epithelial cells (NRK-52E) under high glucose conditions. Further in silico simulations revealed the molecular basis of the SUCNR1-Cpd3 interaction, and the in vitro metabolic stability assay indicated favorable drug-like pharmacokinetic properties of Cpd3. This work not only successfully pinpointed Cpd3 as a specific antagonist of SUCNR1 to serve as a promising candidate in the realm of therapeutic interventions for DN, but also provides a paradigm of dry-wet combined discovery strategies for GPCR-based therapeutics.

Exploring a repurposed candidate with dual hIDO1/hTDO2 inhibitory potential for anticancer efficacy identified through pharmacophore-based virtual screening and in vitro evaluation

Discovering effective anti-cancer agents poses a formidable challenge given the limited efficacy of current therapeutic modalities against various cancer types due to intrinsic resistance mechanisms. Cancer immunochemotherapy is an alternative strategy for breast cancer treatment and overcoming cancer resistance. Human Indoleamine 2,3-dioxygenase (hIDO1) and human Tryptophan 2,3-dioxygenase 2 (hTDO2) play pivotal roles in tryptophan metabolism, leading to the generation of kynurenine and other bioactive metabolites. This process facilitates the de novo synthesis of Nicotinamide Dinucleotide (NAD), promoting cancer resistance. This study identified a new dual hIDO1/hTDO2 inhibitor using a drug repurposing strategy of FDA-approved drugs. Herein, we delineate the development of a ligand-based pharmacophore model based on a training set of 12 compounds with reported hIDO1/hTDO2 inhibitory activity. We conducted a pharmacophore search followed by high-throughput virtual screening of 2568 FDA-approved drugs against both enzymes, resulting in ten hits, four of them with high potential of dual inhibitory activity. For further in silico and in vitro biological investigation, the anti-hypercholesterolemic drug Pitavastatin deemed the drug of choice in this study. Molecular dynamics (MD) simulations demonstrated that Pitavastatin forms stable complexes with both hIDO1 and hTDO2 receptors, providing a structural basis for its potential therapeutic efficacy. At nanomolar (nM) concentration, it exhibited remarkable in vitro enzyme inhibitory activity against both examined enzymes. Additionally, Pitavastatin demonstrated potent cytotoxic activity against BT-549, MCF-7, and HepG2 cell lines (IC50 = 16.82, 9.52, and 1.84 µM, respectively). Its anticancer activity was primarily due to the induction of G1/S phase arrest as discovered through cell cycle analysis of HepG2 cancer cells. Ultimately, treating HepG2 cancer cells with Pitavastatin affected significant activation of caspase-3 accompanied by down-regulation of cellular apoptotic biomarkers such as IDO, TDO, STAT3, P21, P27, IL-6, and AhR.

Expanding the Chemical Space of Transforming Growth Factor-β (TGFβ) Receptor Type II Degraders with 3,4-Disubstituted Indole Derivatives.

The TGFβ type II receptor (TβRII) is a central player in all TGFβ signaling downstream events, has been linked to cancer progression, and thus, has emerged as an auspicious anti-TGFβ strategy. Especially its targeted degradation presents an excellent goal for effective TGFβ pathway inhibition. Here, cellular structure-activity relationship (SAR) data from the TβRII degrader chemotype 1 was successfully transformed into predictive ligand-based pharmacophore models that allowed scaffold hopping. Two distinct 3,4-disubstituted indoles were identified from virtual screening: tetrahydro-4-oxo-indole 2 and indole-3-acetate 3. Design, synthesis, and screening of focused amide libraries confirmed 2r and 3n as potent TGFβ inhibitors. They were validated to fully recapitulate the ability of 1 to selectively degrade TβRII, without affecting TβRI. Consequently, 2r and 3n efficiently blocked endothelial-to-mesenchymal transition and cell migration in different cancer cell lines while not perturbing the microtubule network. Hence, 2 and 3 present novel TβRII degrader chemotypes that will (1) aid target deconvolution efforts and (2) accelerate proof-of-concept studies for small-molecule-driven TβRII degradation in vivo.

Proline-derived quinoline formamide compounds as human urate transporter 1 inhibitors with potent uric acid-lowering activities

We report the design and synthesis of a series of proline-derived quinoline formamide compounds as human urate transporter 1 (URAT1) inhibitors via a ligand-based pharmacophore approach. Structure−activity relationship studies reveal that the replacement of the carboxyl group on the polar fragment with trifluoromethanesulfonamide and substituent modification at the 6-position of the quinoline ring greatly improve URAT1 inhibitory activity compared with lesinurad. Compounds 21c, 21e, 24b, 24c, and 23a exhibit potent activities against URAT1 with IC50 values ranging from 0.052 to 0.56 μM. Furthermore, compound 23a displays improved selectivity towards organic anion transporter 1 (OAT1), good microsomal stability, low potential for genotoxicity and no inhibition of the hERG K+ channel. Compounds 21c and 23a, which have superior pharmacokinetic properties, also demonstrate significant uric acid-lowering activities in a mouse model of hyperuricemia. Notably, 21c also exhibits moderate anti-inflammatory activity related to the gout inflammatory pathway. Compounds 21c and 23a with superior druggability are potential candidates for the treatment of hyperuricemia and gout.

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes.

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.