Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand

Ligand-based analysis of the antifungal potential of phytosterols and triterpenes isolated from Cryptostegia grandiflora against Candida auris FKBP12

Candida auris, a pathogenic fungus, has posed significant challenges to conventional medical treatments due to its increasing resistance to antifungal agents. Consequently, due to their promising pharmacological properties, there is a compelling interest in exploring novel bioactive compounds, such as phytosterols and triterpenes. This study aimed to conduct virtual screening utilizing computational methods, including ADMET, molecular docking, and molecular dynamics, to assess the activity and feasibility of phytosterols extracted from Cryptostegia grandiflora as potential therapeutic agents. Computational predictions suggest that compounds bearing structural similarities to Fsp3-rich molecules hold promise for inhibiting enzymes and G protein-coupled receptor (GPCR) modulators, with particular emphasis on ursolic acid, which, in its conjugated form, exhibits high oral bioavailability and metabolic stability, rendering it a compelling drug candidate. Molecular docking calculations identified ursolic acid and stigmasterol as promising ligands. While stigmasterol displayed superior affinity during molecular dynamics simulations, it exhibited instability, contrasting with ursolic acid's slightly lower affinity yet sustained stability throughout the dynamic assessments. This suggests that ursolic acid is a robust candidate for inhibiting the FKBP12 isomerase in C. auris. Moreover, further investigations could focus on experimentally validating the molecular docking predictions and evaluating the efficacy of ursolic acid as an FKBP12 isomerase inhibitor in models of C. auris infection.

Reporting the anti-neuroinflammatory potential of selected spondias mombin flavonoids through network pharmacology and molecular dynamics simulations

Neuroinflammation plays a pivotal role in the development and progression of neurodegenerative diseases, with a complex interplay between immune responses and brain activity. Understanding this interaction is crucial for identifying therapeutic targets and developing effective treatments. This study aimed to explore the neuroprotective properties of flavonoid compounds from Spondias mombin via the modulation of neuroinflammatory pathway using a comprehensive in-silico approach, including network pharmacology, molecular docking, and dynamic simulations. Active flavonoid ingredients from S. mombin were identified, and their potential protein targets were predicted through Network Pharmacology. Molecular docking was conducted to determine the binding affinities of these compounds against targets obtained from network pharmacology, prioritizing docking scores ≥ - 8.0 kcal/mol. Molecular dynamic simulations (MDS) assessed the stability and interaction profiles of these ligand-protein complexes. The docking study highlighted ≥ - 8.0 kcal/mol for the ligands (catechin and epicatechin) against FYN kinase as a significant target. However, these compounds failed the blood-brain barrier (BBB) permeability test. MDS confirmed the stability of catechin and the reference ligand at the FYN kinase active site, with notable interactions involving hydrogen bonds, hydrophobic contacts, and water bridges. GLU54 emerged as a key residue in the catechin-FYN complex stability due to its prolonged hydrogen bond interaction. The findings underscore the potential of S. mombin flavonoids as therapeutic agents against neuroinflammation, though optimization and nanotechnology-based delivery methods are suggested to enhance drug efficacy and overcome BBB limitations.

Graphical abstract

Rational Approach toward COVID-19's Main Protease Inhibitors: A Hierarchical Biochemoinformatics Analysis

This study investigated the potential of selected compounds as inhibitors of SARS-CoV-2 Mpro through pharmacokinetic and toxicological analyses, molecular docking, and molecular dynamics simulations. In silico molecular docking simulations revealed promising ligands with favorable binding affinities for Mpro, ranging from -6.2 to -9.5 kcal/mol. Moreover, molecular dynamics simulations demonstrated the stability of protein-ligand complexes over 200 ns, maintaining protein secondary structures. MM-PBSA analysis revealed favorable interactions between ligands and Mpro, with negative binding energy values. Hydrogen bond formation capacity during molecular dynamics was confirmed, indicating consistent interactions with Mpro catalytic residues. Based on these findings, selected ligands show promise for future studies in developing COVID-19 treatments.

Phytochemical analysis, physicochemical, pharmacokinetic properties and molecular docking studies of bioactive compounds in Ottelia alismoides (L.) pers. Against breast cancer proteins

Plants provide compounds that can be used to treat diseases, and in silico methods help to expedite drug discovery while reducing costs. This study explored the phytochemical profile of methanol extract of O. alismoides using GC-MS to identify potential bioactive compounds. Autodock 4.2.6. was employed for molecular docking evaluation of the efficacy of these identified compounds against Estrogen Receptor Alpha (ERα), Human Epidermal Growth Factor Receptor 2 (HER2), and Epidermal Growth Factor Receptor (EGFR), proteins. Additionally, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties of the compounds were predicted using the SwissADME online tool. The preliminary phytochemical analysis revealed the presence of alkaloids, carbohydrates, glycosides, and steroids. During the GC-MS analysis, seven compounds were identified, and drug-likeness prediction of these compounds showed good pharmacokinetic properties having high gastrointestinal absorption, and orally bioavailable. The molecular docking studies exhibited promising binding affinities of bioactive compounds against all target proteins. Specifically, the compounds Tricyclo[5.2.1.0(2,6)]decan-10-ol and 2,2,6-Trichloro-7-oxabicyclo[4.1.0]heptane-1-carboxamide demonstrated the highest binding affinities with the ERα (-6.3 and - 6.0 k/cal), HER2 (-5.6 and - 6.1 k/cal), and EGFR (-5.4 and - 5.4 k/cal), respectively. These findings suggest the potential of O. alismoides as a source for developing new cancer therapeutics. The study highlights the effectiveness of in silico approaches for accelerating drug discovery from natural sources and paves the way for further exploration of these promising compounds.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00227-y.

Structure and Ligand Based Virtual Screening and MPO Topological Analysis of Triazolo Thiadiazepine-fused Coumarin Derivatives as Anti-Parkinson Drug Candidates

Parkinson's disease (PD) is a debilitating condition that can cause locomotor problems in affected patients, such as tremors and body rigidity. PD therapy often includes the use of monoamine oxidase B (MAOB) inhibitors, particularly phenylhalogen compounds and coumarin-based semi-synthetic compounds. The objective of this study was to analyze the structural, pharmacokinetic, and pharmacodynamic profile of a series of Triazolo Thiadiazepine-fused Coumarin Derivatives (TDCDs) against MAOB, in comparison with the inhibitor safinamide. To achieve this goal, we utilized structure-based virtual screening techniques, including target prediction and absorption, distribution, metabolism, and excretion (ADME) prediction based on multi-parameter optimization (MPO) topological analysis, as well as ligand-based virtual screening techniques, such as docking and molecular dynamics. The findings indicate that the TDCDs exhibit structural similarity to other bioactive compounds containing coumarin and MAOB-binding azoles, which are present in the ChEMBL database. The topological analyses suggest that TDCD3 has the best ADME profile, particularly due to the alignment between low lipophilicity and high polarity. The coumarin and triazole portions make a strong contribution to this profile, resulting in a permeability with Papp estimated at 2.15 × 10-5 cm/s, indicating high cell viability. The substance is predicted to be metabolically stable. It is important to note that this is an objective evaluation based on the available data. Molecular docking simulations showed that the ligand has an affinity energy of - 8.075 kcal/mol with MAOB and interacts with biological substrate residues such as Pro102 and Phe103. The results suggest that the compound has a safe profile in relation to the MAOB model, making it a promising active ingredient for the treatment of PD.

Structure-based virtual screening of mangiferin derivatives with antidiabetic action: a molecular docking and dynamics study and MPO-based drug-likeness approach

Extracts from Mangifera indica leaves and its main component, mangiferin, have proven antidiabetic activity. In this study, mangiferin and its natural derivatives Homomangiferin (HMF), Isomangiferin (IMF), Neomangiferin (NMF), Glucomangiferin (GMF), Mangiferin 6'-gallate (MFG), and Norathyriol (NRT) were compared regarding their action on Diabetes mellitus (DM), employing docking and molecular dynamics (MD) simulations to analyze interactions with the aldose reductase enzyme, the precursor to the conversion of glucose into sorbitol. Notably, HMF showed significant affinity to residues in the active site of the enzyme, including Trp 79, His 110, Trp 111, Phe 122, and Phe 300, with an energy of - 7.2 kcal/mol, observed in the molecular docking simulations. MD reinforced the formation of stable complexes for HMF and MFG with the aldose reductase, with interaction potential energies (IPE) in the order of - 300.812 ± 52 kJ/mol and - 304.812 ± 52 kJ/mol, respectively. The drug-likeness assessment, by multiparameter optimization (MPO), highlighted that HMF and IMF have similarities with polyphenols and glycosidic flavonoids recently patented as antidiabetics, revealing that high polarity (TPSA > 180 Å2) is a favorable property for subcutaneous administration, especially because of the gradual passive cell permeability values in biological tissues, with Papp values estimated at < 10 × 10-6 cm/s. These compounds are metabolically stable against metabolic enzymes, resulting in a low toxic incidence by metabolic activation, corroborating with a lethal dose (LD50) greater than 2000 mg/kg. In this way, HMF showed a systematic alignment between predicted pharmacokinetics and pharmacodynamics, characterizing it as the most favorable substance for inhibiting aldose reductase.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03978-9.

Structure-based virtual screening of anti-breast cancer compounds from Artemisia absinthium-insights through molecular docking, pharmacokinetics, and molecular dynamic simulations

Breast cancer is the world's second most frequent malignancy, with a significant mortality and morbidity rate. Nowadays, natural breast cancer medicine has piqued attention as disease-curing agent with low side effects. Herein, the leaf powder of Artemisia absinthium was extracted with ethanol, and GC-MS and LC-MS methods were employed to identify the phytocompounds. Using commercial software SeeSAR-9.2 and StarDrop, identified phytocompounds were docked with estrogen and progesterone breast cancer receptors as they promote breast cancer growth to find the binding affinity of the ligands, drugability, and toxicity. Hormone-mediated breast cancer accounts for about 80% of all cases of breast cancer. Cancer cells proliferate when estrogen and progesterone hormones are attached to these receptors. The molecular docking results demonstrated that 3',4',5,7-Tetrahydroxyisoflavanone (THIF) has stronger binding efficacy than standard drugs and other phytocompounds with -28.71 (3 hydrogen bonds) and -24.18 kcal/mol (6 hydrogen bonds) binding energies for estrogen and progesterone receptors, respectively. Pharmacokinetics and toxicity analysis were done to predict the drug-likeness of THIF which results in good drugability and less toxicity. The best fit THIF was subjected to a molecular dynamics simulation analysis by using Gromacs to analyze the conformational changes that occurred during protein-ligand interaction and found that, the structural changes were observed. The results from MD simulation and pharmacokinetic studies suggested that THIF can be expected that in vitro and in vivo research on this compound may lead to the development of a potent anti-breast cancer drug in the future.Communicated by Ramaswamy H. Sarma.

In Silico Study in MPO and Molecular Docking of the Synthetic Drynaran Analogues Against the Chronic Tinnitus: Modulation of the M1 Muscarinic Acetylcholine Receptor

Tinnitus is a syndrome that affects the human auditory system and is characterized by a perception of sounds in the absence of acoustic stimuli, or in total silence. Research indicates that muscarinic acetylcholine receptors (mAChRs), especially the M1 type, have a fundamental role in the alterations of auditory perceptions of tinnitus. Here, a series of computer-aided tools were used, from molecular surface analysis software to services available on the web for estimating pharmacokinetics and pharmacodynamics. The results infer that the low lipophilicity ligands, that is, the 1a-d alkyl furans, present the best pharmacokinetic profile, as compounds with an optimal alignment between permeability and clearance. However, only ligands 1a and 1b have properties that are safe for the central nervous system, the site of cholinergic modulation. These ligands showed similarity with compounds deposited in the European Molecular Biology Laboratory chemical (ChEMBL) database acting on the mAChRs M1 type, the target selected for the molecular docking test. The simulations suggest that the 1 g ligand can form the ligand-receptor complex with the best affinity energy order and that, together with the 1b ligand, they are competitive agonists in relation to the antagonist Tiotropium, in addition to acting in synergism with the drug Bromazepam in the treatment of chronic tinnitus.

Synthesis and anxiolytic effect of europium metallic complex containing lapachol [Eu(DBM)3. LAP] in adult zebrafish through serotonergic neurotransmission: in vivo and in silico approach

Anxiety-related mental health problems are estimated at 3.6% globally, benzodiazepines (BZDs) are the class of drugs indicated for the treatment of anxiety, including lorazepam and diazepam. However, concerns have been raised about the short- and long-term risks associated with BZDs. Therefore, despite anxiolytic and antidepressant drugs, there is a need to develop more effective pharmacotherapies with fewer side effects than existing drugs. The present work reported the synthesis, anxiolytic activity, mechanism of action in Adult Zebrafish (Danio rerio) and in silico study of a europium metallic complex with Lapachol, [Eu(DBM)3. LAP]. Each animal (n = 6/group) was treated intraperitoneally (i.p.; 20 µL) with the synthesized complex (4, 20 and 40 mg/Kg) and with the vehicle (DMSO 3%; 20 µL), being submitted to the tests of locomotor activity and 96h acute toxicity. The light/dark test was also performed, and the serotonergic mechanism (5-HT) was evaluated through the antagonists of the 5-HTR1, 5-HTR2A/2C and 5-HTR3A/3B receptors. The complex was characterized using spectrometric techniques, and the anxiolytic effect of complex may be involved the neuromodulation of receptors 5-HT3A/3B, since the pre-treatment with pizotifen and cyproheptadine did not block the anxiolytic effect of [Eu(DBM)3. LAP], unlike fluoxetine had its anxiolytic effect reversed. In addition, molecular docking showed interaction between the [Eu(DBM)3. LAP] and 5HT3A receptor with binding energy -7.8 kcal/mol and the ADMET study showed that complex has low toxic risk. It is expected that the beginning of this study will allow the application of the new anxiolytic drugs, given the pharmacological potential of the lapachol complex.Communicated by Ramaswamy H. Sarma.

Investigation of the cytotoxicity, genotoxicity and antioxidant prospects of JM-20 on human blood cells: A multi-target compound with potential therapeutic applications

JM-20 is a 1,5-benzodiazepine compound fused to a dihydropyridine fraction with different pharmacological properties. However, its potential toxic effects on blood cells have not yet been reported. Thus, the present study aimed to investigate, for the first time, the possible cytotoxicity of JM-20 through cell viability, cell cycle, morphology changes, reactive species (RS) to DCFH-DA, and lipid peroxidation in human leukocytes, its hemolytic effect on human erythrocytes, and its potential DNA genotoxicity using plasmid DNA in vitro. Furthermore, the compound's ability to reduce the DPPH radical was also measured. Human blood was obtained from healthy volunteers (30 ± 10 years old), and the leukocytes or erythrocytes were immediately isolated and treated with different concentrations of JM-20. A cytoprotective effect was exhibited by 10 μM JM-20 against 1 mM tert-butyl hydroperoxide (t-but-OOH) in the leukocytes. However, the highest tested concentrations of the compound (20 and 50 μM) changed the morphology and caused a significant decrease in the cell viability of leukocytes (p < 0.05, in comparison with Control). All tested concentrations of JM-20 also resulted in a significant increase in intracellular RS as measured by DCFH-DA in these cells (p < 0.05, in comparison with Control). On the other hand, the results point out a potent antioxidant effect of JM-20, which was similar to the classical antioxidant α-tocopherol. The IC50 value of JM-20 against the lipid peroxidation induced by (FeII) was 1.051 μM ± 0.21, while the IC50 value of α-tocopherol in this parameter was 1.065 μM ± 0.34. Additionally, 50 and 100 μM JM-20 reduced the DPPH radical in a statistically similar way to the 100 μM α-tocopherol (p < 0.05, in comparison with the control). No significant hemolysis in erythrocytes, no cell cycle changes in leukocytes, and no genotoxic effects in plasmid DNA were induced by JM-20 at any tested concentration. The in silico pharmacokinetic and toxicological properties of JM-20, derivatives, and nifedipine were also studied. Here, our findings demonstrate that JM-20 and its putative metabolites exhibit similar characteristics to nifedipine, and the in vitro and in silico data support the low toxicity of JM-20 to mammals.

AI-Driven Enhancements in Drug Screening and Optimization

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.

The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence

The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzyme in mediating replication and viral transcription. This work presented the construction of new bioactive compounds for possible inhibition. The De novo molecular design of drugs method in the incremental construction of a ligant model within a receptor model was used, producing new structures with the help of artificial intelligence. The research algorithm and the scoring function responsible for predicting orientation and affinity in the molecular target at the time of coupling showed, as a result of the simulation, the compound with the highest bioaffinity value, Hit 998, with the energy of -17.62 kcal/mol, and synthetic viability close to 50%. While hit 1103 presented better synthetic viability (80%), its affinity energy of -10.28 kcal/mol. Both were compared with the reference linker N3, with a binding affinity of -7.5 kcal/mol. ADMET tests demonstrated that simulated compounds have a low risk of metabolic activation and do not exert effective distribution in the CNS, suggesting a pharmacokinetic mechanism based on local action, even with high topological polarity, which resulted in low oral bioavailability. In conclusion, MMGBSA, H-bonds, RMSD, SASA, and RMSF values were also obtained through molecular dynamics to verify the stability of the receptor-ligant complex within the active protein site to seek new therapeutic propositions in the fight against the pandemic.Communicated by Ramaswamy H. Sarma.

Synthesis and in silico study of chenodeoxycholic acid and its analogues as an alternative inhibitor of spike glycoprotein of SARS-CoV-2

COVID-19, caused by SARS-CoV-2, is a viral infection that has generated one of the most significant health problems in the world. Spike glycoprotein is a crucial enzyme in viral replication and transcription mediation. There are reports in the literature on using bile acid in the fight against this virus through in vitro tests. This work presents the synthesis of nine chenodeoxycholic acid derivatives (1-9), which were prepared by oxidation, acetylation, formylation, and esterification reactions, and the analogs 6-9 have not yet been reported in the literature and the possibility of conducting an in silico study of bile acid derivatives as a therapeutic alternative to combat the virus using glycoprotein as a macromolecular target. As a result, five compounds (1, 6-9) possessed favorable competitive interactions with the lowest energies compared to the native ligand (BLA), and the highlighted compound 9 got the best scores. At the same time, analog 1 presented the best ADME filter result. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. Physical and spectroscopic data have fully characterized all the compounds.Communicated by Ramaswamy H. Sarma.

Antichagasic evaluation, molecular docking and ADMET properties of the chalcone (2E)-3-(2-fluorophenyl)-1-(2-hydroxy- 3,4,6-trimethoxyphenyl)prop-2-en-1-one against Trypanosoma cruzi

Characterized as a neglected disease, Chagas disease is an infection that, in the current scenario, affects about 8 million people per year, with a higher incidence in underdeveloped countries, Chagas is responsible for physiological disabilities that result in impacts that are slightly reflected in world socioeconomic stability. Although treatments are based on drugs such as Benznidazole, the pathology lacks a continuous treatment method with low toxicological incidence. The present study estimates the anti-chagasic activity of the synthetic chalcone CPN2F based on the alignment between in vitro tests and structural classification in silico studies, molecular docking and ADMET studies. The in vitro tests showed a reduction in the protozoan metabolism in host cells (LLC-MK2). At the same time, the molecular docking models evaluate this growth inhibition through the synergistic effect associated with Benznida- zole against validated therapeutic target key stages (Cruzaine TcGAPDH and Trypanothione reductase) of the Trypanosoma cruzi development cycle. The in silico prediction results reveal an alignment between pharmacokinetic attributes, such as renal absorption and release, which allow the preparation of CPN2F as an antichagasic drug with a low incidence of organic toxicity.Communicated by Ramaswamy H. Sarma.

De novo design of bioactive phenol and chromone derivatives for inhibitors of Spike glycoprotein of SARS-CoV-2 in silico

This work presents the synthesis of 12 phenol and chromone derivatives, prepared by the analogs, and the possibility of conducting an in silico study of its derivatives as a therapeutic alternative to combat the SARS-CoV-2, pathogen responsible for COVID-19 pandemic, using its S-glycoprotein as a macromolecular target. After the initial screening for the ranking of the products, it was chosen which structure presented the best energy bond with the target. As a result, derivative 4 was submitted to a molecular growth study using artificial intelligence, where 8436 initial structures were obtained that passed through the interaction filters and similarity to the active glycoprotein pocket through the MolAICal computational package. Thus, 557 Hits with active configuration were generated, which is very promising compared to the BLA reference link for inhibiting the biological target. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. The Hit 48 and 250 are the most active compounds against SARS-CoV-2. In summary, the results show that the Hit 250 would be more active than the natural compound, which could be further developed for further testing against SARS-CoV-2. The study employs the de novo approach to design new drugs, combining artificial intelligence and molecular dynamics simulations to create efficient molecular structures. This research aims to contribute to the development of effective therapeutic strategies against the pandemic.

In silico docking based screening of constituents from Persian shallot as modulators of human glucokinase

Purpose

Small molecule glucokinase (GK) modulators not only decrease fasting and basal plasma sugar contents but also progress glucose tolerance. The hydro-ethanolic extract of the Persian shallot (Allium hirtifolium Boiss.) decreased blood glucose, improved plasma insulin and amplified GK action. The present study was proposed to screen phytoconstituents from Persian shallot as human GK activators using in silico docking studies.

Methods

A total of 91 phytoconstituents reported in Persian shallot (A. hirtifolium Boiss.) were assessed in silico for the prediction of drug-like properties and molecular docking investigations were carried out with human GK using AutoDock vina with the aim of exploring the binding interactions between the phytoconstituents and GK enzyme followed by in silico prediction of toxicity.

Results

Almost all the phytoconstituents tested showed good pharmacokinetic parameters for oral bioavailability and drug-likeness. In the docking analysis, cinnamic acid, methyl 3,4,5-trimethoxy benzoate, quercetin, kaempferol, kaempferol 3-O-β-D-glucopyranosyl-(1- > 4)-glucopyranoside, 5-hydroxy-methyl furfural, ethyl N-(O-anisyl) formimidate, 2-pyridinethione and ascorbic acid showed appreciable hydrogen bond and hydrophobic type interactions with the allosteric site residues of the GK enzyme.

Conclusion

These screened phytoconstituents may serve as promising hit molecules for further development of clinically beneficial and safe allosteric activators of the human GK enzyme.

Using Graph-Based Signatures to Guide Rational Antibody Engineering

Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.

In silico and in vitro evaluation of efflux pumps inhibition of α,β-amyrin

The use of the bacterial efflux pump mechanism to reduce the concentrations of antibiotics in the intracellular to the extracellular region is one of the main mechanisms by which bacteria acquire resistance to antibiotics. The present study aims to evaluate the antibacterial activity of the α,β-amyrin mixture isolated from Protium heptaphyllum against the multidrug-resistant strains of Escherichia coli 06 and Staphylococcus aureus 10, and to verify the inhibition of the efflux resistance mechanisms against the strains of S. aureus 1199B and K2068, carrying the NorA and MepA efflux pumps, respectively. The α,β-amyrin did not show clinically relevant direct bacterial activity. However, the α,β-amyrin when associated with the gentamicin antibiotic presented synergistic effect against the multidrug-resistant bacterial strain of S. aureus 10. In strains with efflux pumps, α,β-amyrin was able to inhibit the action of the efflux protein NorA against Ethidium Bromide. However, this inhibitory effect was not observed in the MepA efflux pump. In addition, when evaluating the effect of standard efflux pump inhibitors, clorptomazine and CCCP, α,β-amyrin showed a decrease in MIC, demonstrating the presence of the efflux mechanism through synergism. Docking studies indicate that α, β-amyrin have a higher affinity energy to MepA, and NorA than ciprofloxacin and norfloxacin. Also, α, β-amyrin bind to the same region of the binding site as these antibiotics. It was concluded that the α, β-amyrin has the potential to increase antibacterial activity with the association of antibiotics, together with the ability to be a strong candidate for an efflux pump inhibitor.Communicated by Ramaswamy H. Sarma.

HIF1 inhibition of the biflavonoids against pancreas cancer: drug-likeness, bioavailability, ADMET, PASS, molecular docking, molecular dynamics, and MM/GBSA calculations

Pancreatic cancer is an aggressive disease with a high death rate and is difficult to treat. This disease, in the most cases, is asymptomatic until it progresses to an advanced stage. Therefore, the search for bioactive molecules is urgent to combat pancreatic cancer. Then, this work analyzed the interaction potential of agathisflavone and amentoflavone molecules against the HIF1 target using the ADMET, molecular docking, and molecular dynamics simulations. More recent drug-likeness filters that combine physicochemical and physiological parameters have shown that high polar surface area (TPSA > 75 Ų) drives biflavonoids out of the toxic drug space of Pfizer dataset. Regarding the pharmacokinetic descriptors, it was possible to notice that Amentoflavone showed a better order of passive cell permeability (P_app = 8 × 10^-6cm/s) and better metabolic stability, biotransformed by aromatic hydroxylation reactions by the CYP3A4 isoenzyme on the human liver, that favor its hepatic clearance. The molecular docking and molecular dynamics simulations indicated the high interaction potential and stability between the ligands analyzed (highlighted the amentoflavone molecule), respectively. The MM/GBSA calculations showed that the amentoflavone ligand registered the highest ΔG binding value of -32.6957 kcal/mol with the HIF1 target. Then, this molecule may be used as a potential inhibitor of pancreatic cancer. In this perspective, the present work represents an initial step in the virtual bioprospecting a pharmacological tool for treating of pancreatic cancer.Communicated by Ramaswamy H. Sarma.

toxCSM: comprehensive prediction of small molecule toxicity profiles

Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high attrition rates in later development stages. Toxicity has been one of the main causes of failure during clinical trials, increasing drug development time and costs. To facilitate early identification and optimisation of toxicity profiles, several computational tools emerged aiming at improving success rates by timely pre-screening drug candidates. Despite these efforts, there is an increasing demand for platforms capable of assessing both environmental as well as human-based toxicity properties at large scale. Here, we present toxCSM, a comprehensive computational platform for the study and optimisation of toxicity profiles of small molecules. toxCSM leverages on the well-established concepts of graph-based signatures, molecular descriptors and similarity scores to develop 36 models for predicting a range of toxicity properties, which can assist in developing safer drugs and agrochemicals. toxCSM achieved an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of up to 0.99 and Pearson's correlation coefficients of up to 0.94 on 10-fold cross-validation, with comparable performance on blind test sets, outperforming all alternative methods. toxCSM is freely available as a user-friendly web server and API at http://biosig.lab.uq.edu.au/toxcsm.

Combined study of docking and molecular dynamics against DNV-3 SN1 protein by bixinoids

Dengue (DENV), Zica virus (ZIKV), and Chikungunya fever (CHIK) are tropical diseases that have caused a lot of problems in general worldwide. Transmitted by mosquitoes of the species Aedes aegypti and albopictus, they have not been completely eradicated in the country, and their proliferation has only increased in the Northeast region. Within the structure of the virus, it is possible to verify the presence of glycoprotein SN1, which is responsible for its replication. If this macromolecule is inhibited using a specific or complex linker, it can interrupt its replication activity. An alternative to this problem has been using structures derived from natural products that have pharmacological properties. A dynamic and molecular docking combined study used computational simulation in the four isomeric forms of bixin against the SN1 protein. The Z,E-bixin and E,E-bixin isomers, both with affinity energy -6.7 and -6.5 Kcal/mol, presented the best results. Thus, bixin and its isomers, found in annatto seeds, maybe an initial proposal in the search for prototype compounds to study to fight this lethal virus in the future.Communicated by Ramaswamy H. Sarma.

pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors

Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other drug-like molecules, mimicking the properties of interacting interfaces. Here, we propose a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions, pdCSM-PPI. This approach was applied to 21 different PPI targets. We developed interaction-specific models that were able to accurately identify active compounds achieving MCC and F1 scores up to 1, and Pearson's correlations up to 0.87, outperforming previous approaches. Using insights from these individual models, we developed a generic protein-protein interaction modulator predictive model, which accurately predicted IC50 with a Pearson's correlation of 0.64 on a low redundancy blind test. Importantly, we were able to accurately identify active from inactive compounds, achieving an AUC of 0.77 and sensitivity and specificity of 76% and 78%, respectively. We believe pdCSM-PPI will be an important tool to help guide more efficient screening of new PPI inhibitors; it is freely available as an easy-to-use web server and API at http://biosig.unimelb.edu.au/pdcsm_ppi.

GC-MS Analysis, Molecular Docking and Pharmacokinetic Properties of Phytocompounds from Solanum torvum Unripe Fruits and Its Effect on Breast Cancer Target Protein

This study was designed to identify phytocompounds from the aqueous extract of Solanum torvum unripe fruits using GC–MS analysis against breast cancer. For this, the identified phytocompounds were subjected to perform molecular docking studies to find the effects on breast cancer target protein. Pharmacokinetic properties were also tested for the identified phytocompounds to evaluate the ADMET properties. Molecular docking studies were done using docking software PyRx, and pharmacokinetic properties of phytocompounds were evaluated using SwissADME. From the results, ten best compounds were identified from GC–MS analysis against breast cancer target protein. Of which, three compounds showed very good binding affinity with breast cancer target protein. They are ergost-25-ene-3,6-dione,5,12-dihydroxy-,(5.alpha.,12.beta.) (− 7.3 kcal/mol), aspidospermidin-17-ol,1-acetyl-16-methoxy (− 6.7 kcal/mol) and 2-(3,4-dichlorophenyl)-4-[[2-[1-methyl-2-pyrrolidinyl]ethyl amino]-6-[trichloromethyl]-s-triazine (− 6.7 kcal/mol). Further, docking study was performed for the synthetic drug doxorubicin to compare the efficiency of phytocompounds. The binding affinity of ergost-25-ene-3,6-dione,5,12-dihydroxy-,(5.alpha.,12.beta.) is higher than the synthetic drug doxorubicin (− 7.2 kcal/mol), and the binding affinity of other compounds is also very near to the drug. Hence, the present study concludes that the phytocompounds from the aqueous extract of Solanum torvum unripe fruits have the potential ability to treat breast cancer.

The synthesis and the antioxidant activity of 1-phenoxymethyl-4-aryl-5,6,7,8-tetrahydro-2a,4a,8a-triazacyclopenta[cd]azulene-3-carboxylic (or carbothionic) acid derivatives

New 1-phenoxymethyl-4-aryl-5,6,7,8-tetrahydro-2а,4a,8a-triazacyclopenta[cd]azulene-3-carboxylic (or carbothionic) acid derivatives have been designed, synthesized and evaluated for their in vitro antioxidant activity under conditions of the artificial oxidative stress using ionol, ascorbic acid and α-tocopherol as the reference drugs. It has been found that 1-phenoxymethyl-4-aryl-5,6,7,8-tetrahydro-2а,4a,8a-triazacyclopenta[cd]azulene-3-carbothionic acid derivatives 9b, 9c, 9d, 9e, 9f, 9i and 1-phenoxymethyl-4-(41-chlorophenyl)-5,6,7,8-tetrahydro-2,2a,8-triazacyclopenta[cd]azulene-3-carboxylic acid phenylamide 10 reveal a high antioxidant activity and a good in silico pharmacokinetic profile. The data obtained allowed us to select the most promising objects from the substances synthesized for further pharmacological screening for the presence of the antioxidant activity in vivo.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

The Impact of Polymer Size and Cleavability on the Intravenous Pharmacokinetics of PEG-Based Hyperbranched Polymers in Rats

A better understanding of the impact of molecular size and linkers is important for PEG-based hyperbranched polymers (HBPs) intended as tailored drug delivery vehicles. This study aimed to evaluate the effects of crosslinker chemistry (cleavable disulphide versus non-cleavable ethylene glycol methacrylate (EGDMA) linkers) and molecular weight within the expected size range for efficient renal elimination (22 vs. 48 kDa) on the intravenous pharmacokinetic and biodistribution properties of 89Zr-labelled HBPs in rats. All HBPs showed similar plasma pharmacokinetics over 72 h, despite differences in linker chemistry and size. A larger proportion of HBP with the cleavable linker was eliminated via the urine and faeces compared to a similar-sized HBP with the non-cleavable linker, while size had no impact on the proportion of the dose excreted. The higher molecular weight HBPs accumulated in organs of the mononuclear phagocyte system (liver and spleen) more avidly than the smaller HBP. These results suggest that HBPs within the 22 to 48 kDa size range show no differences in plasma pharmacokinetics, but distinct patterns of organ biodistribution and elimination are evident.

Pharmacoinformatic Investigation of Medicinal Plants from East Africa

Medicinal plants have widely been used in the traditional treatment of ailments and have been proven effective. Their contribution still holds an important place in modern drug discovery due to their chemical, and biological diversities. However, the poor documentation of traditional medicine, in developing African countries for instance, can lead to the loss of knowledge related to such practices. In this study, we present the Eastern Africa Natural Products Database (EANPDB) containing the structural and bioactivity information of 1870 unique molecules isolated from about 300 source species from the Eastern African region. This represents the largest collection of natural products (NPs) from this geographical region, covering literature data of the period from 1962 to 2019. The computed physicochemical properties and toxicity profiles of each compound have been included. A comparative analysis of some physico-chemical properties like molecular weight, H-bond donor/acceptor, logPo/w , etc. as well scaffold diversity analysis has been carried out with other published NP databases. EANPDB was combined with the previously published Northern African Natural Products Database (NANPDB), to form a merger African Natural Products Database (ANPDB), containing ∼6500 unique molecules isolated from about 1000 source species (freely available at http://african-compounds.org). As a case study, latrunculins A and B isolated from the sponge Negombata magnifica (Podospongiidae) with previously reported antitumour activities, were identified via substructure searching as molecules to be explored as putative binders of histone deacetylases (HDACs).

Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase

Recently emerged SARS-CoV-2 caused a major outbreak of coronavirus disease 2019 (COVID-19) and instigated a widespread fear, threatening global health safety. To date, no licensed antiviral drugs or vaccines are available against COVID-19 although several clinical trials are under way to test possible therapies. During this urgent situation, computational drug discovery methods provide an alternative to tiresome high-throughput screening, particularly in the hit-to-lead-optimization stage. Identification of small molecules that specifically target viral replication apparatus has indicated the highest potential towards antiviral drug discovery. In this work, we present potential compounds that specifically target SARS-CoV-2 vital proteins, including the main protease, Nsp12 RNA polymerase and Nsp13 helicase. An integrative virtual screening and molecular dynamics simulations approach has facilitated the identification of potential binding modes and favourable molecular interaction profile of corresponding compounds. Moreover, the identification of structurally important binding site residues in conserved motifs located inside the active site highlights relative importance of ligand binding based on residual energy decomposition analysis. Although the current study lacks experimental validation, the structural information obtained from this computational study has paved way for the design of targeted inhibitors to combat COVID-19 outbreak.

A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods

High-throughput computational techniques have become invaluable tools to help increase the overall success, process efficiency, and associated costs of drug development. By designing ligands tailored to specific protein structures in a disease of interest, an understanding of molecular interactions and ways to optimize them can be achieved prior to chemical synthesis. This understanding can help direct crucial chemical and biological experiments by maximizing available resources on higher quality leads. Moreover, predicting molecular binding affinity within specific biological contexts, as well as ligand pharmacokinetics and toxicities, can aid in filtering out redundant leads early on within the process. We describe a set of computational tools which can aid in drug discovery at different stages, from hit identification (EasyVS) to lead optimization and candidate selection (CSM-lig, mCSM-lig, Arpeggio, pkCSM). Incorporating these tools along the drug development process can help ensure that candidate leads are chemically and biologically feasible to become successful and tractable drugs.

Integrated Protocol to Design Potential Inhibitors of Dipeptidyl Peptidase- 4 (DPP-4)

BACKGROUND

A strategy for the treatment of type II diabetes mellitus is the inhibition of the enzyme known as dipeptidyl peptidase-4 (DPP-4).

AIMS

This study aims to investigate the main interactions between DPP-4 and a set of inhibitors, as well as proposing potential candidates to inhibit this enzyme.

METHODS

We performed molecular docking studies followed by the construction and validation of CoMFA and CoMSIA models. The information provided from these models was used to aid in the search for new candidates to inhibit DPP-4 and the design of new bioactive ligands from structural modifications in the most active molecule of the studied series.

RESULTS

We were able to propose a set of analogues with biological activity predicted by the CoMFA and CoMSIA models, suggesting that our protocol can be used to guide the design of new DPP-4 inhibitors as drug candidates to treat diabetes.

CONCLUSION

Once the integration of the techniques mentioned in this article was effective, our strategy can be applied to design possible new DPP-4 inhibitors as candidates to treat diabetes.

dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction

Nanomedicine development currently suffers from a lack of efficient tools to predict pharmacokinetic behavior without relying upon testing in large numbers of animals, impacting success rates and development costs. This work presents dendPoint, the first in silico model to predict the intravenous pharmacokinetics of dendrimers, a commonly explored drug vector, based on physicochemical properties. We have manually curated the largest relational database of dendrimer pharmacokinetic parameters and their structural/physicochemical properties. This was used to develop a machine learning-based model capable of accurately predicting pharmacokinetic parameters, including half-life, clearance, volume of distribution and dose recovered in the liver and urine. dendPoint successfully predicts dendrimer pharmacokinetic properties, achieving correlations of up to r = 0.83 and Q² up to 0.68. dendPoint is freely available as a user-friendly web-service and database at http://biosig.unimelb.edu.au/dendpoint. This platform is ultimately expected to be used to guide dendrimer construct design and refinement prior to embarking on more time consuming and expensive in vivo testing.

Ellagic Acid, Kaempferol, and Quercetin from Acacia nilotica: Promising Combined Drug With Multiple Mechanisms of Action

The pharmacological activity of Acacia nilotica's phytochemical constituents was confirmed with evidence-based studies, but the determination of exact targets that they bind and the mechanism of action were not done; consequently, we aim to identify the exact targets that are responsible for the pharmacological activity via the computational methods. Furthermore, we aim to predict the pharmacokinetics (ADME) properties and the safety profile in order to identify the best drug candidates. To achieve those goals, various computational methods were used including the ligand-based virtual screening and molecular docking. Moreover, pkCSM and SwissADME web servers were used for the prediction of pharmacokinetics and safety. The total number of the investigated compounds and targets was 25 and 61, respectively. According to the results, the pharmacological activity was attributed to the interaction with essential targets. Ellagic acid, Kaempferol, and Quercetin were the best A. nilotica's phytochemical constituents that contribute to the therapeutic activities, were non-toxic as well as non-carcinogen. The administration of Ellagic acid, Kaempferol, and Quercetin as combined drug via the novel drug delivery systems will be a valuable therapeutic choice for the treatment of recent diseases attacking the public health including cancer, multidrug-resistant bacterial infections, diabetes mellitus, and chronic inflammatory systemic disease.