Hit Molecules Research Articles

Extension of impurity profiling on Eltrombopag olamine to in-silico Predictions: An Effort to Exploit correlated forced degradation products and known Drug-Related substances in drug discovery

The recent pandemic has highlighted the impact of diseases on global health and the economy. The rapid discovery of new hit molecules remains a tough challenge. Pharmaceutical impurity profiling can be linked to drug discovery through the identification of new hits from compounds identified during the analytical profiling. The present study demonstrates this linkage through the extension of the impurity (forced degradation) profiling of eltrombopag (ELT) olamine, a thrombopoietin (TPO) receptor agonist. The drug was exposed to standard degradation and the degradation products were primarily resolved and identified by UPLC-ESI-MS. This led to the identification of five forced degradation products (FDP). Thirty-three other known related substances (RS) of ELT, identified in the literature, were also considered. Molecular similarity checks were performed using Tanimoto similarity searches. A set of structurally and topologically similar molecules, including ELT and 15 RS, was established and subjected to in-silico toxicity-, absorption-, distribution-, metabolism-, and elimination (ADME) predictions. The RS, predicted with similar or lower toxicity than ELT and a comparable ADME profile, were subjected to molecular docking to trace changes in TPO receptor affinity. The results indicated that five RS had a high Jaccard’s similarity with ELT and higher or comparable docking scores. These compounds, along with few other impurities were predicted to have lower toxicity, better or comparable absorption, distribution, metabolism, and a better excretion profile than ELT. This justifies their entry as potential novel TPO receptor agonists in drug discovery.

AI-driven transcriptome profile-guided hit molecule generation

Denovo generation of bioactive and drug-like hit molecules is a pivotal goal in computer-aided drug discovery. While artificial intelligence (AI) has proven adept at generating molecules with desired chemical properties, previous studies often overlook the influence of disease-specific cellular environments. This study introduces GxVAEs, a novel AI-driven deep generative model designed to produce hit molecules from transcriptome profiles using dual variational autoencoders (VAEs). The first VAE, ProfileVAE, extracts latent features from transcriptome profiles to guide the second VAE, MolVAE, in generating hit molecules. GxVAEs aim to bridge the gap between molecule generation and the biological context of disease, producing molecules that are biologically relevant within specific cellular environments or pathological conditions. Experimental results and case studies focused on hit molecule generation demonstrate that GxVAEs surpass current state-of-the-art methods, in terms of reproducibility of known ligands. This approach is expected to effectively find potential molecular structures with bioactivities across diverse disease contexts.

Pharmacophore‐Based Modeling, Synthesis, and Biological Evaluation of Novel Quinazoline/Quinoline Derivatives: Discovery of EGFR Inhibitors with Low Nanomolar Activity

AbstractThe main aim of this study is to obtain novel molecules that are more selective on cancer cells compared to healthy cells. For this purpose, four hit molecules are identified using 11 new pharmacophore hypotheses followed by scanning the in‐house database. Then, based on those hit molecules, the synthesis and analysis of four different series (three quinazolines and one quinoline series) are carried out, and their anticancer activity is investigated. Finally, by using molecular docking and dynamics simulation methods, binding mode and structure–activity relationship are examined. Among the quinazolin‐4(3H)‐one derivatives, those containing halogen atom are found to be potentially effective, while the best epidermal growth factor receptor (EGFR) inhibition and apoptosis induction are displayed by compounds containing 4‐amino‐1,2,4‐triazole moiety. Notably, four compounds (4h, 8d, 8l, and 8m) show EGFR inhibition activity at 5.298 ± 0.164, 5.46 ± 0.221, 2.670 ± 0.124, and 2.191 ± 0.908 × 10−9 m, their inhibitory activity is similar to or stronger than gefitinib (IC50: 4.169 ± 0.156 × 10−9 m). In addition, EGFR inhibitor concentration of 4g, 8e, and 8o is determined as 27588 ± 6.945, 52.41 ± 2.312, and 33657 ± 8.512 × 10−9 m. These findings indicate that generated pharmacophore hypotheses successfully determine new EGFR inhibitors. In conclusion, four novel compounds, more active than gefitinib with fewer side effects, are reached, and the structure–activity relationships are clarified.

Phytochemicals of Swertia chirayita Roxb. ex Fleming against malarial dihydroorotate dehydrogenase: an in silico study

The global economic burden of malaria is rising and lack of effective drugs against it have imposed an insurmountable challenge in designing it that is both affordable and resistanceless. The plants with ethnobotanical values are relatively safer and its use is a viable option as therapeutics for various demographic locations. In this regard, the chemical components of a plant, Swertia chirayita Roxb. ex Fleming (traditionally used as an antimalarial agent) were explored by multiple computational tools against dihydroorotate dehydrogenase (DHODH) protein of Plasmodium falciparum (PDB ID: 6GJG), a causative agent of the disease. The docking scores were calculated to be − 10.220, − 10.131, − 10.071, − 10.024, and − 9.905 kcal/mol for the top five molecules (decussatin, swerchirin, 1-hydroxy-3,5,8-trimethoxyxanthen-9-one, swertinin, and chiratol), respectively with the native ligand possessing a higher value of − 9.873 kcal/mol. The adducts were geometrically and thermodynamically stable as revealed by several parameters extracted from 200 ns molecular dynamics simulations (MDS) and verified by a single run of 600 ns. The smooth RMSD profiles from a cumulative MDS of 2.0 μs, pointed towards a sturdy nature of the receptor geometry with limited translational or rotational motion of the ligands. The involvement of LEU11, CYS14, LEU15, CYS23, PHE27, ILE102, ARG104, TYR337, LEU340, and VAL341 amino acid residues with mainly hydrophobic interactions at the orthosteric site were observed for most of the adducts. The binding free energy changes (up to − 30.18 ± 4.38 kcal/mol) hinted at the feasibility and sustained thermodynamical spontaneity of the forward reaction of the complex formation. The hit molecules could possibly disrupt or inhibit the normal functioning of the DHODH receptor leading to the likely prevention and cure of the disease and are recommended for experimental trials.

Identification of novel brain penetrant GSK-3β inhibitors toward Alzheimer’s disease therapy by virtual screening, molecular docking, dynamic simulation, and MMPBSA analysis

One of the significant therapeutic targets for Alzheimer’s disease (AD) is Glycogen Synthase Kinase-3β (GSK-3β). Inhibition of GSK-3β can prevent hyperphosphorylation of tau, and thus prevent formation and accumulation of neurofibrillary tangles and neuropil threads that block intracellular transport, trigger unfolded protein response, and increase oxidative stress, cumulatively leading to neurodegeneration. In this study, we have performed structure-based virtual screening of two small-molecule libraries from ChemDiv CNS databases using AutoDock Vina to identify hit molecules based on their binding affinities compared to that of an established GSK-3β inhibitor, indirubin-3’-monoxime (IMO). Pharmacoinformatic screening on SwissADME and pkCSM servers enabled identification of lead molecules with favorable pharmacoinformatic properties for drug likeliness, including blood brain barrier (BBB) permeability. Further, molecular dynamic simulations identified six candidate lead molecules that show stable complex formation with GSK-3β in dynamic state under physiological conditions. Principal component analysis of the dynamic state was used to plot Free Energy Landscapes (FELs) of GSK-3β-ligand complexes. STRIDE secondary structure analysis of the lowest energy conformations identified from FEL plots, and binding free energy calculations by Molecular Mechanics Poisson-Boltzmann Surface Area ((ΔGbind )MM-PBSA) of the simulation trajectories led to the identification of two ligands as potential lead molecules having favorable free energy landscape profiles as well as significantly lower (ΔGbind )MM-PBSA in dynamic state compared to that of reference inhibitor IMO. Hence, this study identifies two novel brain penetrant GSK-3β inhibitors that are likely to have therapeutic potential against Alzheimer’s disease.

Design and Synthesis of Benzimidazole Carboxamide Cysteine Protease Inhibitors as Promising Anti-leishmanial Agents.

More than 20 protozoan species of Leishmania are responsible for causing Leishmaniasis, an infection spread by blood-feeding phlebotomine sandflies. A narrow pool of drugs is currently available rendering the current drug stratagem to treat this infection . Development of novel, less toxic, and more effective regimens is thus a need of the hour. Design and synthesis of benzo[d]imidazole carboxamides as agents to combat Leishmaniasis are also required. 14 benzo[d]imidazole carboxamides were synthesized and gauged against L. donovani promastigotes and intramacrophage amastigote forms. All of the tested compounds exhibited significant anti-promastigote properties with IC50 well below 10 uM. Compounds 4a, 4b, and 4d, showing the highest anti-parasitic activity against promastigote forms (IC50 0.91- 1.33 μM), were also found to be associated with better anti-leishmanial potential (IC50 0.78- 1.67 μM) against the intramacrophage amastigotes comparable to Amphotericin-B (0.13 μM), a drug used for Leishmaniasis. Compound (4a), namely N-(2-(trifluoromethyl)-1Hbenzo[ d]imidazol-5-yl)benzo[d][1,3]-5-carboxamide-dioxole, was found to be most potent against L. donovani amastigotes among all the tested compounds, and demonstrated better antileishmanial properties (IC50 0.78 μM) when compared to the standard. Compound 4a was also assessed for its toxicity profile against THP-1 human monocytic cells. To establish the molecular target(s) in silico, molecular docking studies were performed against cysteine protease, a putative virulence factor of Leishmania parasites, and nucleoside diphosphate kinase, an enzyme with a critical role in nucleotide recycling, also associated with resistance in Leishmania strains. Compound 4a showed better binding affinity than the standard to these targets; furthermore, the molecular dynamic simulation studies further affirmed the stability of compound 4a, within the active site of the targets. In vitro, cysteine protease inhibitory activity (IC50 8.53 μM) using Bz-Arg-AMC hydrochloride fluorogenic peptide substrate established the promising potential of 4a as a cysteine protease inhibitor. Computational ADMET analysis indicated appropriate pharmacokinetic profile and physicochemical characteristics for all members of the synthesized library. Both in vitro and in silico studies indicate that the synthesized imidazole carboxamides can act as potent hits and that N-(2-(trifluoromethyl)-1H-benzo[d]imidazol-5- yl)benzo[d][1,3]-5-carboxamide-dioxole 4a can be an effective hit molecule which can be further developed into potent lead molecule (s) to fight Leishmania donovani.

Identification of Potent CHK2 Inhibitors‐Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment‐Based Drug Design Approach

AbstractThe CHK2 protein regulates the cell division cycle and responds to DNA damage. Additionally, it facilitates the repair of DNA damage and maintains the integrity of its biological processes. Dysregulation of the CHK2 protein is associated with a predisposition to harmful diseases. The current research protocol was designed to identify novel hit molecules as CHK2 inhibitors and disrupt the normal biological function of the CHK2 protein via a fragment‐based drug discovery approach. The protocol involved generating fragments using the MacFrag tool, followed by a chemical similarity search utilizing RDKit to identify fragment molecules analogous to previously established CHK2 inhibitor scaffolds. The bioactive molecules were constructed using the Fragmenstein tool, followed by molecular docking simulations to investigate their binding affinity. In addition, pharmacokinetic properties were analyzed, and a molecular dynamics simulation study was conducted to assess the stability of selected compounds with CHK2 protein. Finally, five novel compounds were identified as excellent CHK2 inhibitors through the FBDD and show good binding interactions at active sites of CHK2 with beneficial ADMET properties. This research work presents novel CHK2 inhibitor molecules that have the potential to be utilized in drug discovery, serving as key leads for future advancements in healthcare industries and sectors.

Designing potential lead compounds targeting aminoglycoside N (6′)-acetyltransferase in Serratia marcescens: A drug discovery strategy

Serratia marcescens is an opportunistic human pathogen that causes urinary tract infections, ocular lens infections, and respiratory tract infections. S. marcescens employs various defense mechanisms to evade antibiotics, one of which is mediated by aminoglycoside N-acetyltransferase (AAC). In this mechanism, the enzyme AAC facilitates the transfer and linkage of the acetyl moiety from the donor substrate acetyl-coenzyme A to specific positions on antibiotics. This modification alters the antibiotic's structure, leading to the inactivation of aminoglycoside antibiotics. In the current scenario, antibiotic resistance has become a global threat, and targeting the enzymes that mediate resistance is considered crucial to combat this issue. The study aimed to address the increasing global threat of antibiotic resistance in Serratia marcescens by targeting the aminoglycoside N-acetyltransferase (AAC (6′)) enzyme, which inactivates aminoglycoside antibiotics through acetylation. Due to the absence of experimental structure, we constructed a homology model of aminoglycoside N (6′)-acetyltransferase (AAC (6′)) of S. marcescens using the atomic structure of aminoglycoside N-acetyltransferase AAC (6′)-Ib (PDB ID: 1V0C) as a template. The stable architecture and integrity of the modelled AAC (6′) structure were analyzed through a 100 ns simulation. Structure-guided high-throughput screening of four small molecule databases (Binding, Life Chemicals, Zinc, and Toslab) resulted in the identification of potent inhibitors against AAC (6′). The hits obtained from screening were manually clustered, and the five hit molecules were shortlisted based on the docking score, which are observed in the range of −17.09 kcal/mol to −11.95 kcal/mol. These selected five molecules displayed acceptable pharmacological properties in ADME predictions. The binding free energy calculations, and molecular dynamics simulations of ligand bound AAC (6′) complexes represented higher affinity and stable binding. The selected molecules demonstrated stable binding with AAC (6′), indicating their strong potential to hamper the binding of aminoglycoside in the respective site. and thereby inhibit. This process mitigates enzyme mediated AAC (6′) activity on aminoglycosides and reverse the bactericidal function of aminoglycosides, and also this method could serve as a platform for the development of potential antimicrobials.

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition.

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

Discovery of Potential Inhibitors of CDK1 by Integrating Pharmacophore-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation Studies, and Evaluation of Their Inhibitory Activity.

The ability of CDK1 to compensate for the absence of other cell cycle CDKs poses a great challenge to treat cancers that overexpress these proteins. Despite several studies focusing on the area, there are no FDA-approved drugs selectively targeting CDK1. Here, the study aimed to develop potential CDK1 selective inhibitors through drug repurposing and leveraging the structural insights provided by the hit molecules generated. Approximately 280,000 compounds from DrugBank, Selleckchem, Otava and an in-house library were screened initially based on fit values using 3D QSAR pharmacophores built for CDK1 and subsequently through Lipinski, ADMET, and TOPKAT filters. 10,310 hits were investigated for docking into the binding site of CDK1 determined using the crystal structure of human CDK1 in complex with NU6102. The best 55 hits with better docking scores were further analyzed, and 12 hits were selected for 100 ns MD simulations followed by binding energy calculations using the MM-PBSA method. Finally, 10 hit molecules were tested in an in vitro CDK1 Kinase inhibition assay. Out of these, 3 hits showed significant CDK1 inhibitory potential with IC50 < 5 μM. These results indicate these compounds can be used to develop subtype-selective CDK1 inhibitors with better efficacy and reduced toxicities in the future.

Harnessing marine natural products to inhibit PAD4 triple mutant: A structure-based virtual screening approach for rheumatoid arthritis therapy

Peptidylarginine deiminase type4 (PAD4) is a pivotal pro-inflammatory protein within the human immune system, intricately involved in both inflammatory processes and immune responses. Its role extends to the generation of diverse immune cell types, including T cells, B cells, natural killer cells, and dendritic cells. PAD4 has recently garnered attention due to its association with a spectrum of inflammatory and autoimmune disorders, notably rheumatoid arthritis (RA). Mutations in the PAD4 gene, leading to the conversion of arginine to citrulline, have emerged as significant factors in the pathogenesis of RA and related conditions. As a calcium-dependent enzyme, PAD4 is central to the citrullination process, a crucial post-translational modification implicated in disease pathophysiology. Its critical role in autoimmune disorders and inflammation makes PAD4 a prime candidate for therapeutic intervention in RA. Inhibiting PAD4 presents a promising avenue for mitigating inflammatory responses and curtailing joint degradation and impairment. To explore its therapeutic potential, a structure-based virtual screening (SBVS) approach was employed, harnessing an array of marine natural products (MNPs) sourced from databases such as CMNPD, MNPD, and Seaweed. Notably, MNPD10752, CMNPD12680, and CMNPD2751 emerged as potential hit molecules, exhibiting adherence to essential pharmacokinetic properties and favorable toxicity profiles. Quantum mechanics studies using density functional theory (DFT) calculations revealed the inhibitory potential of these identified natural products. Further structural elucidation through molecular dynamics simulations (MDS) and principal component-based free energy landscape (FEL) analysis shed light on the stability of MNP-bound PAD4 complexes. In conclusion, this computational study serves as a stepping stone for further experimental evaluation, aiming to explore the potential of MNPs in addressing PAD4-related human pathologies.

Development and Evaluation of Bis-benzothiazoles as a New Class of Benzothiazoles Targeting DprE1 as Antitubercular Agents.

Benzothiazole-bearing compounds have emerged as potential noncovalent DprE1 (decaprenylphosphoryl-β-d-ribose-2'-epimerase) inhibitors active against Mycobacterium tuberculosis. Based on structure-based virtual screening (PDB ID: 4KW5), a focused library of thirty-one skeletally diverse benzothiazole amides was prepared, and the compounds were assessed for their antitubercular activity against M.tb H37Ra. Most potent compounds 3b and 3n were further evaluated against the M.tb H37Rv strain by the microdilution assay method. Among the compounds evaluated, bis-benzothiazole amide 3n emerged as a hit molecule and demonstrated promising antitubercular activity with minimum inhibitory concentration (MIC) values of 0.45 μg/mL and 8.0 μg/mL against H37Ra and H37Rv, respectively. Based on the preliminary hit molecule (3n), a focused library of 12 more bis-benzothiazole amide derivatives was further prepared by varying the substituents on either side to obtain new leads and generate a structure-activity relationship (SAR). Among these compounds, 6a, 6c, and 6d demonstrated remarkable antitubercular activity with MIC values of 0.5 μg/mL against H37Ra and 1.0, 2.0, and 8.0 μg/mL against H37Rv, respectively. The most active compound, 6a, also displayed significant efficacy against four drug-resistant tuberculosis strains. Compound 6a was assessed for in vitro cytotoxicity against the HepG2 cell line, and it displayed insignificant cytotoxicity. Furthermore, time-kill kinetic studies demonstrated time- and dose-dependent bactericidal activity of this compound. The GFP release assay revealed that compound 6a targets the inhibition of a cell wall component. SNPs in dprE-1 gene assessment revealed that compound 6a binds to tyrosine at position 314 of DprE1 and replaces it with histidine, causing resistance similar to that of standard TCA1. In silico docking studies further suggest that the strong noncovalent interactions of these compounds may lead to the development of potent noncovalent DprE1 inhibitors.

Pharmacophore-based approach for the identification of potent inhibitors against LpxC Enzyme from Salmonella Typhi

Antimicrobial resistance (AMR) is currently a global health concern, mostly caused by microorganisms like bacteria, viruses, parasites, and fungi that acquire resistance to antimicrobial drugs. Salmonella is responsible for a variety of diseases but mainly cause typhoid. The primary concern is the rise in resistance in both non-typhoid and typhoid strains of this species. To address this issue, it is necessary to identify novel targets and strategies for the development of new antibacterial drugs. Lipid A, a strong bacterial endotoxin that modulates the immune system in human, is a key component of the virulence factor generated during the salmonella infection. Lipid A is synthesized in case of Gram-negative bacteria by cascade of nine enzyme pathway. The second step in case of Lipid A biosynthesis, catalysed by LpxC, a Zn+ dependent metallo-amidase considered as rate limiting step. In this manuscript we have used protein-ligand interaction fingerprint (PLIF)–derived pharmacophore models to screen small molecules (natural products library from Zinc database, Asinex database, Thiophene analogues) against Salmonella typhi LpxC (StLpxC). Further top hit molecules were subjected to MD-simulation and ADMET studies. We identified three optimal compounds, s1_dl_mseq2, s1_ll_mseq2, and s2_ll_mseq8, that exhibit strong binding affinity towards the LpxC active site.

Microwave synthesis, density functional theory study and antiproliferative activity of the novel spiropyrazole derivatives

Different spiro-pyrazole-oxy-indoline substituted compounds I-IV have been synthesized. In Jaguar, Hybrid DFT using Becke’s three-parameter transfer possibility also,Lee-Yang-Parr correlated practical (B3LYP) viewwith a 6-31 G** basis set were utilized to improve the geometrical characteristics of the hit molecules and reference compound. The MTT test exhibited whether the prepared substance has an inhibitory impact on cell development or not. This was successfully represented that in hepatocellular carcinoma (HEPG-2), colorectal carcinoma (HCT-116), and mammary gland breast cancer cell lines (MCF-7) colorimetric test, the mitochondrial succinate dehydrogenase in live cells transform yellow tetrazolium bromide (MTT) to a purple formazan product. Compound IV was found to be a better cytotoxic action against to the HEPG-2 hepatocellular carcinoma cell line, and it has been seen a strong activity against the colorectal carcinoma cell lines HCT-116 and MCF-7 mammary gland breast cancer cell lines. While the other three compounds I, II, and III showed a moderate cytotoxic action to high action against the same cell lines.

In silico approach for identification of potential tetracyclic triterpenoids from mushroom as HMG-CoA reductase inhibitor

Cardiovascular disease is estimated to be responsible for one-third of all global deaths annually. It occurs mostly due to hyperlipidemia, a condition where excessive cholesterol deposits in blood vessels. A favorable target for treating hyperlipidemia involves the crucial role of inhibition of a specific enzyme known as 3-Hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoA reductase). The primary goal of this present study is to identify potential HMG-CoA reductase inhibitors containing tetracyclic triterpene nucleus derived from mushrooms. A library of 86 myco-constituents bearing a tetracyclic triterpene scaffold was prepared and screened to identify potential HMG-CoA reductase inhibitors targeting proteins 1HW8 and 1HW9. For this purpose, molecular docking, ADME prediction, and molecular dynamics (MD) simulation studies were performed on this in-house prepared database. The virtual screening results exhibited M_02(c) as the best hit with promising SP Glide scores compared to standard statin drugs. In order to assess the stability and interactions, a 100 ns MD simulation was performed. Further, M_02(c) was also analysed for MMGBSA binding energy to access and validate the thermodynamic stability of the protein-ligand complex. The results of this study revealed that M_02(c) is a promising hit molecule and may emerge as a potent HMG-CoA reductase inhibitor in preventing and treating hyperlipidemia.

Kaurane Diterpenoids from Xylopia aethiopica Inhibits Alpha-Amylase and Alpha-Glucosidase: A Combined in Silico and in Vitro Approach

Background/Objectives Diabetes mellitus is among the leading cause of death worldwide. This study evaluated the alpha amylase and alpha glucosidase inhibitory properties of kaurane diterpenoids (kauran-13-ol (D1), xylopic acid (D2) and kauran-16α-ol (D3)) from the fruit of X. aethiopica. Methodology In vitro alpha amylase and alpha glucosidase inhibitory assays were performed on D1, D2 and D3 (0.03125, 0.0625, 0.125, 0.25, 0.5, 1.0 and 2.0 mg/mL) and acarbose (0.003125, 0.00625, 0.0125, 0.025, 0.05 and 0.1 mg/mL) followed by molecular docking studies, molecular dynamics simulation and MMPBSA to examine their mode of interaction, binding affinity, binding mode and binding energy established with the enzymes. Also, ADMET properties of the studied diterpenes were examined to explain their druggability potential. Result The in vitro alpha-amylase and alpha-glucosidase studies identified D3 as the most promising inhibitor among the kaurane diterpenes with lower IC50 values of 0.65 and 0.17 mg/mL. The molecular docking analysis showed that D1, D2 and D3 established vital hydrogen bond, hydrophobic and pi-interaction with the receptors, while the molecular dynamics simulation showed the kaurane diterpenes exhibited good stability at the enzymes’ binding pocket. The MMPBSA binding energy values showed the diterpenes had good binding energies that corroborated that of molecular docking. The ADMET properties identified the compounds as promising drug candidate. Conclusion The study demonstrated that the kaurane diterpenoids have good inhibitory effect on the diabetes enzymes. Further comprehensive investigation into the antidiabetic properties of the diterpenes may lead to the identification of new hit molecule.

The Implication of Fragment-centric Mapping Strategy to Explore the First Selective Inhibitor Against Target Protein

Introduction: Identification of the first selective inhibitor, also called “hit molecules, " is crucial for developing drugs against a protein target. However, the crystal structures of protein-ligand complexes are usually not resolved in time due to the process's time-consuming and costly nature. However, it does not prevent scientists from understanding the binding modes’ urgent advantages and drawbacks of protein-ligand interaction to guide the optimization of hit molecules. Methods: Here, we have developed a pocket-centric computational strategy to facilitate a comprehensive understanding of the hit molecules against the protein target. Results: The results show that the pocket-centered mapping method not only allows for accurate prediction of the native docking pose and in-depth analysis of the binding mode but also has the potential of rapidly identifying partially unoccupied, unutilized, but targetable pockets to afford optimized hit molecules. We tested the strategy on the first selective inhibitor, epigallocatechin gallate (EGCG), against human arylacetamide deacetylase (AADAC). Molecular dynamics simulation and MM/PBSA binding energy calculation are used to verify the efficacy of the strategy. Conclusion: The results show that the pocket-centered mapping method not only allows for accurate prediction of the native docking pose and in-depth analysis of the binding mode but also has the potential of rapidly identifying partially unoccupied, unutilized, but targetable pockets to afford optimized hit molecules.

Gene network analysis combined with preclinical studies to identify and elucidate the mechanism of action of novel irreversible Keap1 inhibitor for Parkinson's disease.

The cysteine residues of Keap1 such as C151, C273, and C288 are critical for its repressor activity on Nrf2. However, to date, no molecules have been identified to covalently modify all three cysteine residues for Nrf2 activation. Hence, in this study, our goal is to discover new Keap1 covalent inhibitors that can undergo a Michael addition with all three cysteine residues. The Keap1's intervening region was modeled using Modeller v10.4. Covalent docking and binding free energy were calculated using CovDock. Molecular dynamics (MD) was performed using Desmond. Various in-vitro assays were carried out to confirm the neuroprotective effects of the hit molecule in 6-OHDA-treated SH-SY5Y cells. Further, the best hit was evaluated in vivo for its ability to improve rotenone-induced postural instability and cognitive impairment in male rats. Finally, network pharmacology was used to summarize the complete molecular mechanism of the hit molecule. Chalcone and plumbagin were found to form the necessary covalent bonds with all three cysteine residues. However, MD analysis indicated that the binding of plumbagin is more stable than chalcone. Plumbagin displayed neuroprotective effects in 6-OHDA-treated SH-SY5Y cells at concentrations 0.01 and 0.1 μM. Plumbagin at 0.1 µM had positive effects on reactive oxygen species formation and glutathione levels. Plumbagin also improved postural instability and cognitive impairment in rotenone-treated male rats. Our network analysis indicated that plumbagin could also improve dopamine signaling. Additionally, plumbagin could exhibit anti-oxidant and anti-inflammatory activity through the activation of Nrf2. Cumulatively, our study suggests that plumbagin is a novel Keap1 covalent inhibitor for Nrf2-mediated neuroprotection in PD.

A Structure-Based Design Strategy with Pyrazole-Pyridine Derivatives Targeting TNFα as Anti-Inflammatory Agents: E-Pharmacophore, Dynamic Simulation, Synthesis and In Vitro Evaluation.

Any pathogenic attack, infection, or disease can initiate inflammation. It results in significant adverse consequences like inflammatory bowel disease, rheumatoid arthritis, etc. TNFα is one of the major pro-inflammatory cytokines for the progression of inflammation-the present study designed a series of hybrid compounds consisting of the pyrazole-pyridine moiety. Virtual screening was performed utilizing the e-pharmacophore hypothesis with the co-ligand of TNFα, screening, docking, and ADMET study. Induced fit docking, DFT analysis, and molecular dynamic simulation showed that the four best molecules - Dh1- Dh4-showed crucial interaction with Tyrosine, higher dock scores, and better stability than Diclofenac. Following the synthesis of hit molecules, an in vitro albumin denaturation IC50 of Dh1 was found to be 118.01 μM. Further in-depth in vitro and in vivo analyses of these pyrazole-pyridine small compounds may serve as potential space for creating new anti-inflammatory leads.

E-pharmacophore and deep learning based high throughput virtual screening for identification of CDPK1 inhibitors of Cryptosporidium parvum

Cryptosporidiosis, a prevalent gastrointestinal illness worldwide, is caused by the protozoan parasite Cryptosporidium parvum. Calcium-dependent protein kinase 1 (CpCDPK1), crucial for the parasite's life cycle, serves as a promising drug target due to its role in regulating invasion and egress from host cells. While potent Pyrazolopyrimidine analogs have been identified as candidate hit molecules, they exhibit limitations in inhibiting Cryptosporidium growth in cell culture, prompting exploration of alternative scaffolds. Leveraging the most potent compound, RM-1–95, co-crystallized with CpCDPK1, an E-pharmacophore model was generated and validated alongside a deep learning model trained on known CpCDPK1 compounds. These models facilitated screening Enamine's 2 million HTS compound library for novel CpCDPK1 inhibitors. Subsequent hierarchical docking prioritized hits, with final selections subjected to Quantum polarized docking for accurate ranking. Results from docking studies and MD simulations highlighted similarities in interactions between the cocrystallized ligand RM-1–95 and identified hit molecules, indicating comparable inhibitory potential against CpCDPK1. Furthermore, assessing metabolic stability through Cytochrome 450 site of metabolism prediction offered crucial insights for drug design, optimization, and regulatory approval processes.