Absorption, Distribution, Metabolism, Excretion And Toxicity Properties Research Articles

4D-QSAR, ADMET properties, and molecular dynamics simulations for designing N-substituted urea/thioureas as human glutaminyl cyclase inhibitors

Human glutaminyl cyclase (hQC) inhibitors have great potential to be used as anti- Alzheimer's disease (AD) agents by reducing the toxic pyroform of β-amyloid in the brains of AD patients. The four-dimensional quantitative structure activity relationship (4D-QSAR) model of N-substituted urea/thioureas was established with satisfying predictive ability and statistical reliability (Q2 = 0.521, R2 = 0.933, R2prep = 0.619). By utilizing the developed 4D-QSAR model, a set of new N-substituted urea/thioureas was designed and evaluated for their Absorption Distribution Metabolism Excretion and Toxicity (ADMET) properties. The results of molecular dynamics (MD) simulations, Principal component analysis (PCA), free energy landscape (FEL), dynamic cross-correlation matrix (DCCM) and molecular mechanics generalized Born Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, revealed that the designed compounds were remained stable in protein binding pocket and compounds b ∼ f (−35.1 to −44.55 kcal/mol) showed higher binding free energy than that of compound 14 (−33.51 kcal/mol). The findings of this work will be a theoretical foundation for further research and experimental validation of urea/thiourea derivatives as hQC inhibitors.

Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks.

Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect is the characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, which require early assessment in the drug discovery and development process. This study aims to present an innovative approach for predicting ADMET properties using attention-based graph neural networks (GNNs). The model utilizes a graph-based representation of molecules directly derived from Simplified Molecular Input Line Entry System (SMILE) notation. Information is processed sequentially, from substructures to the whole molecule, employing a bottom-up approach. The developed GNN is tested and compared with existing approaches using six benchmark datasets and by encompassing regression (lipophilicity and aqueous solubility) and classification (CYP2C9, CYP2C19, CYP2D6 and CYP3A4 inhibition) tasks. Results show the effectiveness of our model, which bypasses the computationally expensive retrieval and selection of molecular descriptors. This approach provides a valuable tool for high-throughput screening, facilitating early assessment of ADMET properties and enhancing the likelihood of drug success in the development pipeline.

Discovery of putative inhibitors of human Pkd1 enzyme: Molecular docking, dynamics and simulation, QSAR, and MM/GBSA

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme.

AdmetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370000 high-quality experimental ADMET data for 104652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

In-silico Approach to Investigate the Phytochemicals of Terminalia arjuna as Multitarget Inhibitors of Proteins Involved with Lung Cancer

Background: Existing medications for treating cancer are reported to exhibit severe side effects, therefore, there is an urgent need to address these unprecedented health risks. With the advancements in ethnobotanical studies and research on phytochemicals, information on several medicinal plants is being revisited nowadays. Terminalia arjuna is a widely used medicinal plant in ayurvedic and Unani medicine for curing several diseases. Although the bioactives from this plant are reported to possess anticarcinogenic, antiproliferative and antioxidant activities, information on the potentials of its specific phytoconstituents on the inhibition of receptor molecules associated with lung cancer is scarce. Objectives: The primary goal of this study is to virtually screen the phytochemicals of Terminalia arjuna as potential drug candidate molecules for lung cancer. Considering all major reported receptor molecules that inevitably take part in lung cancer, it highlights the phytochemicals as novel multitargeted inhibitors of proteins responsible for lung cancer. Methods: A thorough literature review was done to select twenty-seven receptor molecules associated with lung cancer cases. The molecular docking study using PyRx predicts protein-ligand interactions and identifies potential drug targets. Evaluating the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties of the phytochemicals present in Terminalia arjuna, this study takes into account thirty-four bioactive compounds as the chosen ligands in molecular docking. The binding affinity, inhibition constant (Ki), and the interacting residues of these phytochemicals with the receptors are compared with the docking results of twelve selected standard anticancer drugs. The study finally categorizes the phytochemicals that can potentially act as multitargeted inhibitors of proteins associated with lung cancer. Results: The results from PyRx highlighted the phytoconstituents having a higher binding affinity with inhibition constant comparable to the standard drugs. Among the standard anticancer drugs, alectinib, pralsetinib, and ibrutinib are marked as potent inhibitors of several lung cancer receptors. The phytochemicals of Terminalia arjuna proved to be the potential candidates against ALK2, ALK5, DDR2, BRAF, KRAS, Tankyrase, vasopressin V2, VEGFR1 and VEGFR2 mediated lung cancer but the effectiveness (in comparison to the standard drugs) is limited against Bcl-2, IL22R1, NCAM, RET, MET and ROS1 receptors. Conclusions: The findings indicate that phytochemicals namely luteolin, friedelin, oleanolic acid, and 14,16-dianhydrogitoxigenin bind strongly to multiple receptors under consideration with high affinity and hence could be investigated as effective alternatives for treating lung cancer with minimal side effects.

OptADMET: a web-based tool for substructure modifications to improve ADMET properties of lead compounds.

Lead optimization is a crucial step in the drug discovery process, which aims to design potential drug candidates from biologically active hits. During lead optimization, active hits undergo modifications to improve their absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles. Medicinal chemists face key questions regarding which compound(s) should be synthesized next and how to balance multiple ADMET properties. Reliable transformation rules from multiple experimental analyses are critical to improve this decision-making process. We developed OptADMET ( https://cadd.nscc-tj.cn/deploy/optadmet/ ), an integrated web-based platform that provides chemical transformation rules for 32 ADMET properties and leverages prior experimental data for lead optimization. The multiproperty transformation rule database contains a total of 41,779 validated transformation rules generated from the analysis of 177,191 reliable experimental datasets. Additionally, 146,450 rules were generated by analyzing 239,194 molecular data predictions. OptADMET provides the ADMET profiles of all optimized molecules from the queried molecule and enables the prediction of desirable substructure transformations and subsequent validation of drug candidates. OptADMET is based on matched molecular pairs analysis derived from synthetic chemistry, thus providing improved practicality over other methods. OptADMET is designed for use by both experimental and computational scientists.

A facile synthesis, characterization, DFT, ADMET and in-silico molecular docking analysis of novel 4-ethyl acridine-1,3,9 (2,4,10H)-trione

A novel synthetic reaction was employed to produce an acridine derivative of 4-ethyl acridine-1,3,9 (2,4,10H)-trione. The structural characterizations of the synthesized compound were confirmed through FT-IR, NMR 1H,13C, and GC-Mass spectral data. Furthermore, the DFT approaches were performed to the vibrational wavenumbers obtained through simulation with experimental wave numbers. The Vibrational Energy Distribution Analysis (VEDA) software is employed in FT-IR wave number for conducting potential energy decomposition (PED) analysis. DFT calculations were also applied to optimize the structural parameters and investigate Frontier molecular orbitals (FMOs), molecular electrostatic potentials, RDG studies, and global chemical reactivity descriptors, Natural bond orbital analysis and Non-linear optical properties were obtained for the title compound using the DFT method. Molecular docking studies were conducted to explore the binding mode mechanism of title compound and were evaluated for cancer-related proteins, specifically 4y72. Also, the synthesised compound was evaluated for Drug-likeness, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties indicating favourable pharmacokinetic profiles with no observed signs of toxicity.

New Perspective for Using Antimicrobial and Cell-Penetrating Peptides to Increase Efficacy of Antineoplastic 5-FU in Cancer Cells.

This study explores the effectiveness of the antineoplastic agent 5-FU in cancer cells by leveraging the unique properties of cationic antimicrobial peptides (CAMPs) and cell-penetrating peptides (CPPs). Traditional anticancer therapies face substantial limitations, including unfavorable pharmacokinetic profiles and inadequate specificity for tumor sites. These drawbacks often necessitate higher therapeutic agent doses, leading to severe toxicity in normal cells and adverse side effects. Peptides have emerged as promising carriers for targeted drug delivery, with their ability to selectively deliver therapeutics to cells expressing specific receptors. This enhances intracellular drug delivery, minimizes drug resistance, and reduces toxicity. In this research, we comprehensively evaluate the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of various AMPs and CPPs to gain insights into their potential as anticancer agents. The peptide synthesis involved a solid-phase synthesis using a Liberty Microwave Peptide Synthesizer. The peptide purity was confirmed via LC-MS and HPLC methods. For the ADMET screening, computational tools were employed, assessing parameters like absorption, distribution, metabolism, excretion, and toxicity. The cell lines A549 and UM-UC-5 were cultured and treated with 5-FU, CAMPs, and CPPs. The cell viability was measured using the MTT assay. The physicochemical properties analysis revealed favorable drug-likeness attributes. The peptides exhibited potential inhibitory activity against CYP3A4. The ADMET predictions indicated variable absorption and distribution characteristics. Furthermore, we assessed the effectiveness of these peptides alone and in combination with 5-FU, a widely used antineoplastic agent, in two distinct cancer cell lines, UM-UC-5 and A549. Our findings indicate that CAMPs can significantly reduce the cell viability in A549 cells, while CPPs exhibit promising results in UM-UC-5 cells. Understanding these multifaceted effects could open new avenues for antiviral and anticancer research. Further, experimental validation is necessary to confirm the mechanism of action of these peptides, especially in combination with 5-FU.

Chemical Characterization, DNA-Damage Protection, Antiproliferative Activity and in Silico Studies of the Essential Oils from Perralderia coronopifolia Coss.

In this study, for the first time, we analyzed the chemical composition of essential oils (EOs) steam-distilled from the flowers and leaves of Perralderia coronopifolia by GC-FID/MS. The objective was to explore new anticancer and antioxidant bioactive substances and understand their mechanisms of action through the use of plant-derived natural products. The major chemical components characterizing the EOs were cis-chrysanthenyl acetate 1, 6-oxocyclonerolidol 2, cis-8-acetoxychrysanthenyl acetate 3, and 6α-hydroxycyclonerolidol 4, respectively. Furthermore, the EOs inhibited cell proliferation in HeLa (human cervix carcinoma) and PC3 (human prostate cancer) cells and protected plasmid DNA from oxidative damage caused by UV-photolyzed H2 O2 . Employing a molecular docking study, we elucidated the main compounds' inhibition mechanisms. Consequently, the antitumor activity could be related to the inhibitory property of compound 3 against CDC25B phosphatase. The evaluation of ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and the density functional theory (DFT) calculations of the major compounds, especially compound 3, offer potential insights for designing and developing new cancer drug candidates. In conclusion, our study provides a framework for future research and development in the field by establishing a scientific foundation for the use of Perralderia coronopifolia essential oils as a prospective source of antioxidant and anticancer agents.

Molecular docking, drug-likeness and DFT study of some modified tetrahydrocurcumins as potential anticancer agents

The present study utilized molecular docking and density functional theory (DFT) approaches, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties to investigate the binding interactions, reactivity, stability, and drug-likeness of curcumin (1), tetrahydrocurcumin (2), and tetrahydrocurcumin derivatives (3–6) as potential anti-cancer agents. MGL (Molecular Graphic Laboratory) and Discovery Studio Visualizer (DSV) software employed for docking studies. Pharmacokinetic and pharmacodynamic (ADME-Tox) analyses were conducted using SwissADME and pKCSM web servers. Total Electron Density (TED) measurements identified molecular adsorption sites, considering various factors, including quantum chemical characteristics, to assess compound effectiveness using DFT method implanted in the Gaussian software. The binding energy (Eb) from docking simulations was used to evaluate inhibitory potential. ADMET analysis suggested favorable oral bioavailability and pharmacokinetics for all studied substances, excluding compound 4. DFT and docking investigations highlighted compounds 1, 2, and 6 as optimal scaffolds for drug design based on in silico screening tests.

Dihydrobenzothiazole coupled N-piperazinyl acetamides as antimicrobial agents: Design, synthesis, biological evaluation and molecular docking studies.

Substituted saturated N-heterocycles have gained momentum as effective scaffolds for the development of new drugs. In this study, we coupled partly saturated benzothiazoles with substituted piperazines and evaluated their antimicrobial activity. Following a three-step reaction sequence from commercially available cyclic 1,3-diones, a series of novel 2-[4-substituted-1-piperazinyl]-N-(7-oxo-4,5,6,7-tetrahydrobenzo[d]thiazol-2-yl)acetamides (7a-af) were synthesised. 2-Amino-5,6-dihydro-benzo[d]thiazol-7(4H)-ones, obtained through the condensation of cyclohexane-1,3-diones with thiourea, were acetylated with chloroacetic chloride and then reacted with N-substituted piperazines 6a-p to give the desired products 7a-af in excellent yields. All 32 new compounds were fully characterised by their 1 H-nuclear magnetic resonance (NMR), 13 C-NMR and high-resolution mass spectrometry spectra. The synthetic compounds 7a-af were tested in vitro for their efficacy as antimicrobials against pathogenic strains of Gram-positive and Gram-negative bacteria, Streptococcus mutans and Salmonella typhi, respectively, as well as against fungal strains, including Candida albicans 3018 and C. albicans 4748. Ciprofloxacin and fluconazole served as the reference drugs. While compounds 7c and 7l showed inhibition against fungal strains with zones of inhibition of 11 and 1 mm, respectively, four analogues (7d, 7l, 7n, and 7r) demonstrated strong antibacterial action (zone of inhibition in the range of 10-15 mm). Three compounds (7j, 7l, and 7w) also exhibited moderate antitubercular activity (MIC: 6.25 µg/mL) against Mycobacterium tuberculosis H37Rv. Molecular docking investigations and the predicted physicochemical and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties for the potent compounds made this scaffold useful as a pharmacologically active framework for the development of potential antimicrobial hits.

A Network Pharmacology and Molecular-Docking-Based Approach to Identify the Probable Targets of Short-Chain Fatty-Acid-Producing Microbial Metabolites against Kidney Cancer and Inflammation.

(1) Background: A large and diverse microbial population exists in the human intestinal tract, which supports gut homeostasis and the health of the host. Short-chain fatty acid (SCFA)-secreting microbes also generate several metabolites with favorable regulatory effects on various malignancies and immunological inflammations. The involvement of intestinal SCFAs in kidney diseases, such as various kidney malignancies and inflammations, has emerged as a fascinating area of study in recent years. However, the mechanisms of SCFAs and other metabolites produced by SCFA-producing bacteria against kidney cancer and inflammation have not yet been investigated. (2) Methods: We considered 177 different SCFA-producing microbial species and 114 metabolites from the gutMgene database. Further, we used different online-based database platforms to predict 1890 gene targets associated with metabolites. Moreover, DisGeNET, OMIM, and Genecard databases were used to consider 13,104 disease-related gene targets. We used a Venn diagram and various protein-protein interactions (PPIs), KEGG pathways, and GO analyses for the functional analysis of gene targets. Moreover, the subnetwork of protein-protein interactions (through string and cytoscape platforms) was used to select the top 20% of gene targets through degree centrality, betweenness centrality, and closeness centrality. To screen the possible candidate compounds, we performed an analysis of the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of metabolites and then found the best binding affinity using molecular docking simulation. (3) Results: Finally, we found the key gene targets that interact with suitable compounds and function against kidney cancer and inflammation, such as MTOR (with glycocholic acid), PIK3CA (with 11-methoxycurvularin, glycocholic acid, and isoquercitrin), IL6 (with isoquercitrin), PTGS2 (with isoquercitrin), and IGF1R (with 2-amino-1-methyl-6-phenylimidazo[4,5-b] pyridine, isoquercitrin), showed a lower binding affinity. (4) Conclusions: This study provides evidence to support the positive effects of SCFA-producing microbial metabolites that function against kidney cancer and inflammation and makes integrative research proposals that may be used to guide future studies.

Machine learning study of the extended drug-target interaction network informed by pain related voltage-gated sodium channels.

Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein-protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug-target interaction network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor data sets from a pool of more than 1000 targets in the PPI network. We employ 3 distinct machine learning algorithms combined with advanced natural language processing (NLP)-based embeddings, specifically pretrained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of more than 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. In addition, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.

Determination of the antioxidant and enzyme-inhibiting activities and evaluation of selected terpenes' ADMET properties: in vitro and in silico approaches

The in vitro antioxidant capacity of eugenol, eucalyptol, linalool, and carvacrol was performed using six test systems: phosphomolybdenum, DPPH (2,2′-diphenyl-1-picrylhydrazyl radical), ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonate), CUPRAC (cupric ion reducing antioxidant capacity), FRAP (Ferric reducing ability of plasma), and metal chelating effect. Their inhibitory activities on cholinesterases, α-amylase, α-glucosidase, and tyrosinase were assessed as well. Molecular docking helped to explain the obtained outcomes while in silico ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction served for assessing the physicochemical and pharmacokinetic profiles. Our findings showed that the examined terpenes have promising antioxidative activity with eugenol and carvacrol displaying the highest efficiency. Linalool presented the highest anti-AChE and anti-tyrosinase activities (7.931 ± 0.034 mg GALAE/g and 58.707 ± 5.120 mg KAE/g, respectively). Eucalyptol exhibited the strongest anti-BChE activity (2.704 ± 0.025 mg GALAE/g), while carvacrol exhibited the highest rates for α-glucosidase and α-amylase inhibition (1.414 ± 0.042 and 1.023 ± 0.054 mM ACAE/g, respectively). Molecular docking revealed that all compounds interacted in varied ways with the five enzymes’ active sites. Excellent binding profiles and greatest inhibitory activity to AChE (−7.4 kcal/mol), α-glucosidase (−6.5 kcal/mol), and α-amylase (−6.5 kcal/mol) was attributed to carvacrol. As regards to tyrosinase, eugenol (−6.2 kcal/mol) was the potent one whereas carvacrol and eugenol (−6.5 kcal/mol for both) strongly interacted with BChE. Furthermore, in silico ADMET-related physicochemical and pharmacokinetic properties revealed good profiles. To sum up, here we demonstrate the powerful antioxidant potency and enzymatic inhibitory effects of the four terpenes which may enable the discovery of lead compounds that are extremely potent antioxidants and enzyme inhibitors.

Discovery of MAO-A Inhibitors as Antidepressant Based on Virtual Screening

Aim: Major depression and anxiety have increased significantly worldwide since the 2019 outbreak of COVID-19. The development of highly effective antidepressants with low side effects is attracting researchers. Methods: Monoamine oxidase A (MAO-A) is a key enzyme that catalyzes the metabolism of norepinephrine (NE), dopamine (DA), and serotonin (5-HT), etc. Elevated level of MAO-A would lead to increased metabolism of its substrates, thereby causing a decrease in the levels of these neurotransmitter monoamines in the brain leading to depression. Consequently, inhibition of MAO-A was thought to be an effective strategy, as this would treat the root cause of depression. Results and Discussion: Based on the crystal structure of MAO-A, 4 star-hits, as potential MAO-A inhibitors was screened from the compound libraries with central nervous system (CNS) activity by using various computational techniques. Molecular dynamics simulation was used to verify the stability of the ligand- receptor complexes. Conclusion: Furthermore, the ADMET (absorption, distribution, metabolism, excretion and toxicity properties) of the virtual hits were predicted in order to evaluate their lead-like properties and safety. This work provides ideas for the drugs discovery of antidepressant.

Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.

Opioid use disorder (OUD) has emerged as a significant global public health issue, necessitating the discovery of new medications. In this study, we propose a deep generative model that combines a stochastic differential equation (SDE)-based diffusion model with a pretrained autoencoder. The molecular generator enables efficient generation of molecules that target multiple opioid receptors, including mu, kappa, and delta. Additionally, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the generated molecules to identify druglike compounds. We develop a molecular optimization approach to enhance the pharmacokinetic properties of some lead compounds. Advanced binding affinity predictors were built using molecular fingerprints, including autoencoder embeddings, transformer embeddings, and topological Laplacians. Our process yields druglike molecules that can be used in highly focused experimental studies to further evaluate their pharmacological effects. Our machine learning platform serves as a valuable tool for designing effective molecules to address OUD.

In vitro and in silico studies of antioxidant peptides from tarwi (Lupinus mutabilis) as inhibitors of angiotensin‐converting enzyme and dipeptidyl peptidase IV enzyme

SummaryThe present research aimed to evaluate three peptides synthesised from tarwi: AVPFWM, YSGWLGL and AHAGFGMLY in their antioxidant, antihypertensive and hypoglycemic properties, using in vitro and in silico studies, this last one considering the molecular docking evaluation and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties. The in vitro results found ABTS and ORAC antioxidant capacity values between 2.12 and 3.94 and 1.64 and 3.10 μmol TE/μmol of peptide, respectively, while the IC50 values for angiotensin‐converting enzyme (ACE) and dipeptidyl peptidase IV (DPP‐IV) inhibition ranged between 0.14 and 6.38 μM and 16.4 and 1140.0 μM, respectively. The molecular docking study performed between peptides and ACE and DPP‐IV enzymes, showed affinity towards both enzymes establishing molecular interactions at the level of the active site of both enzymes through hydrogen bonds and hydrophobic interactions. This study together with the in vitro study highlight the YSGWLGL peptide for the favourable results found. Finally, the predicted ADMET properties of the three peptides suggest that they have the potential for absorption, appropriate distribution, no participation in the metabolism of P450 enzymes and no toxicity concerns. It is concluded that tarwi peptides may be a valuable source for developing nutraceuticals, functional ingredients or supplements.

In Silico ADMET and Molecular Interaction Profiles of Phytochemicals from Medicinal Plants in Dakshina Kannada

AbstractThe success or failure of a potential drug depends on its absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics, and these features are usually rate-limiting in the drug development process. Hence, it is essential to know about the predicted ADMET properties of the most promising leads to avoid the risk of late-stage attrition. This project focuses on in silico screening of ADMET properties of phytochemicals found in Dakshina Kannada's medicinal plants, which include Tinospora cordifolia, Azadirachta indica, Ocimum sanctum, and Plectranthus amboinicus, mainly known for their antimicrobial properties.The physicochemical properties, bioactivity scores, ADMET, and molecular interactions of the selected phytoconstituents were determined by QikProp, Molinspiration, ADMETlab 2.0, ProTox-II, and GLIDE. In addition, molecular docking checked for their binding interactions with target proteins 1JIJ and 4 HOE of Staphylococcus aureus and Candida albicans, respectively, as they were well known for their antimicrobial properties. In this studies, rosmarinic acid was well interacted phytochemical with both target proteins and has highest docking score.The physicochemical properties showed that all compounds fell under the recommended molecular weight, volume, and polar surface area range. Xanosporic acid violated two rules of Lipinski's Rule of Five, indicating that it may have problems with oral bioavailability. The ADME properties for most of the phytocompounds were within the recommended ranges; hence, they are promising candidates for drug development. Most phytoconstituents showed good bioactivity scores, indicating they have good druglikeness properties. On the analysis of the toxicity, most of the phytoconstituents were found to be noncarcinogenic and nonmutagenic. Therefore, this data can further be utilized as primary tools for determining the biological actions of these plants.Xanosporic acid was found to violate two out of three rules of Lipinski. Similarly, ursolic acid and oleanolic acid also showed a few undesirable properties. All other compounds otherwise showed desirable properties and hence are promising candidates for drug development. This data can be further utilized as primary tool for determining the biological actions of the plants.

New pyrimidinothiophene derivatives: Synthesis, spectroscopic analysis, X-ray, DFT calculation, biological activity studies and ADMET prediction

Pyrimidine has been found in numerous essential components of the organism and plays a significant role in the human body. Therefore, the derivatives have potent antibacterial and anticancer properties. Based on the broadly activity of pyrimidine, a series of pyrimidinothiophene were designed and synthesized. The structure of final compounds was exposed to 1HNMR , 13CNMR , HRMS. Moreover, compound 3e was characterized by single crystal X-ray diffraction and FT-IR. The intermolecular interactions and contacts arising from supramolecular stacking from compound 3e were quantified through Hirshfeld surface and 2D fingerprinting analysis. Density functional theory (DFT) calculation of the crystal compound 3e was accomplished on the monoclinic crystals. At last, these compounds were examined for anticancer activity against U87-MG cell lines and predicted ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties through ADMETlab 2.0.

Identification of Potential Inhibitors of PDE5 based on Structure-based Virtual Screening Approaches.

Phosphodiesterase type 5 (PDE5), exclusively specific for cyclic guanidine monophosphate (cGMP), a potential target for the therapy of various diseases, and PDE5 inhibitors could be used as a treatment for erectile dysfunction (ED) or chronic pulmonary hypertension. In the present study, we carried out an integrated computer-aided virtual screening technique against the natural products in the ZINC database to discover potential inhibitors of PDE5. Pharmacophore, molecular docking and ADMET (Absorption, distribution, metabolism, excretion and toxicity) properties filtration were used to select the PDE5 inhibitors with the best binding affinities and drug-like properties. The binding modes of PDE5 inhibitors were investigated, and these complexes' stabilities were explored by molecular dynamic simulations and MM/GBSA free energy calculations. Two natural compounds (Z171 and Z283) were identified and may be used as a critical starting point for the development of novel PDE5 inhibitors. The MM/GBSA free energy decomposition analysis quantitatively analyzed the importance of hydrophobic interaction in PDE5- ligands binding. In this study, we identified two novel natural compounds from the ZINC database to effectively inhibit PDE5 through virtual screening. The novel scaffolds of these compounds can be used as the starting templates in the drug design of PDE5 inhibitors with good pharmacokinetic profiles. These results may promote the de novo design of new compounds against PDE5.