Synthesis, biosimulation and pharmacological evaluation of benzimidazole derivatives with antihypertensive multitarget effect

In this study, we synthesized a series of seven benzimidazole derivatives incorporating the structural acidic framework of angiotensin II (Ang II) type 1 receptor (AT1R) antagonists (ARA-II) employing a three-step reaction sequence. The chemical structures were confirmed by 1H NMR, 13C NMR and mass spectral data. Through biosimulation, compounds 1-7 were identified as computational safe hits, thus, best candidates underwent ex vivo testing against two distinct mechanisms implicated in hypertension: antagonism of the Ang II type 1 receptor and the blockade of calcium channel. Molecular docking studies helped to understand at the molecular level the dual vasorelaxant effects with the recognition sites of the AT1R and the L-type calcium channel. In an in vivo spontaneously hypertensive rat model (SHR), intraperitoneally administration of compound 1 at 20 mg/kg resulted in a 25 % reduction in systolic blood pressure, demonstrating both ex vivo vasorelaxant action and in vivo antihypertensive multitarget efficacy. ©2024 Elsevier.

AC-ModNet: Molecular Reverse Design Network Based on Attribute Classification

Deep generative models are becoming a tool of choice for exploring the molecular space. One important application area of deep generative models is the reverse design of drug compounds for given attributes (solubility, ease of synthesis, etc.). Although there are many generative models, these models cannot generate specific intervals of attributes. This paper proposes a AC-ModNet model that effectively combines VAE with AC-GAN to generate molecular structures in specific attribute intervals. The AC-ModNet is trained and evaluated using the open 250K ZINC dataset. In comparison with related models, our method performs best in the FCD and Frag model evaluation indicators. Moreover, we prove the AC-ModNet created molecules have potential application value in drug design by comparing and analyzing them with medical records in the PubChem database. The results of this paper will provide a new method for machine learning drug reverse design.

Pharmacophore modelling based virtual screening and molecular dynamics identified the novel inhibitors and drug targets against Waddlia chondrophila

Waddlia chondrophila is a possible cause of fetal death in humans. This Chlamydia-related bacterium is an emergent pathogen that causes human miscarriages and ruminant abortions, which results in financial losses. Despite the years of efforts, the underlying mechanism behind the pathogenesis of W. chondrophila is little known which hindered the development of novel treatment options. In the framework of current study, computational approaches were used to identify novel inhibitors (phytocompounds) and drug targets against W. chondrophila. At first, RNA polymerase sigma factor SigA and 3-deoxy-D-manno-octulosonic acid transferase were identified through subtractive proteomics pipeline. Afterwards, extensive docking and simulation analyses were conducted to optimize potentially novel phytocompounds by assessing their binding affinity to target proteins. A 100ns molecular dynamics simulation well complimented the compound's binding affinity and indicated strong stability of predicted compounds at the docked site. The calculation of binding free energies with MMGBSA corroborated the significant binding affinity between phytocompounds and target protein binding sites. The proposed phytocompounds may be a viable treatment option for patients infected with W. chondrophila; however, further research is required to ensure their safety.

Network pharmacology-based screening of active constituents of Avicennia marina and their clinical biochemistry related mechanism against breast cancer

Breast cancer is the second major cause of cancer death in women globally. Avicennia marina is a medicinal plant that belongs to the family Acanthaceae and is known as grey or white mangrove. It has antioxidant, antiviral, anticancer, anti-inflammatory, and antibacterial activity in the treatment of various diseases including cancer. The goal of the study is to use a network pharmacology method to identify the potential phenomena of bioactive compounds of A. marina in the treatment of breast cancer and explore clinical biochemistry related aspects. A total of 74 active compounds of A. marina were retrieved from various databases as well as a literature review and collectively 429 targets of these compounds were identified by STITCH and Swiss Target Prediction databases. Breast cancer related 15606 potential targets were retrieved from the GeneCards database. A Venn diagram was drawn to find common key targets. To check the biological functions, the GO enrichment and KEGG pathways analysis of 171 key targets were performed through the DAVID database. To understand the interactions among key targets, Protein-protein interaction (PPI) studies were completed using the STRING database, and the Protein-Protein Interaction (PPI) network, as well as the compound-target-pathway network, was constructed using Cytoscape 3.9.0. Finally, molecular docking analysis of 5 hub genes named tumor protein 53 (TP53), catenin beta 1 (CTNNB1), interleukin 6 (IL6), tumor necrosis factor (TNF), and RAC-alpha serine/threonine protein kinases 1 (AKT1) with the active constituent of A. marina against breast cancer were performed. Additionally, a molecular docking study demonstrates that active drugs have a higher affinity for the target that may be used to decrease breast cancer. The molecular dynamic simulation analysis predicted the very stable behavior of docked complexes with no global structure deviations seen. The MMGBSA further supported strong intermolecular interactions with net energy values as; AKT1_Betulinic_acid (-20.97 kcal/mol), AKT1_Stigmasterol (-44.56 kcal/mol), TNF_Betulinic_acid (-28.68 kcal/mol) and TNF_Stigmastero (-29.47 kcal/mol).Communicated by Ramaswamy H. Sarma.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Isolation of quinic acid from dropped Citrus reticulata Blanco fruits: its derivatization, antibacterial potential, docking studies, and ADMET profiling

Citrus reticulata dropped fruits are generally discarded as waste, causing environmental pollution and losses to farmers. In the present study, column chromatography has been used to isolate quinic acid (1,3,4,5-tetrahydroxycyclohexane-1-carboxylic acid) from the ethyl acetate fraction of a methanol extract of citrus fruits dropped in April. Quinic acid is a ubiquitous plant metabolite found in various plants and microorganisms. It is an important precursor in the biosynthesis of aromatic natural compounds. It was further derivatized into 3,4-o-isopropylidenequinic acid 1,5-lactone (QA1), 1,3,4,5-tetraacetoxycyclohexylaceticanhydride (QA2), and cyclohexane-1,2,3,5-tetraone (QA3). These compounds were further tested for their antibacterial potential against the foodborne pathogens Staphylococcus aureus, Bacillus spp., Yersinia enterocolitica, and Escherichia coli. QA1 exhibited maximum antibacterial potential (minimum inhibitory concentration; 80-120 μg/mL). QA1 revealed synergistic behavior with streptomycin against all the tested bacterial strains having a fractional inhibitory concentration index ranging from 0.29 to 0.37. It also caused a significant increase in cell constituent release in all the tested bacteria compared to the control, along with prominent biofilm reduction. The results obtained were further checked with computational studies that revealed the best docking score of QA1 (-6.30 kcal/mol, -5.8 kcal/mol, and -4.70 kcal/mol) against β-lactamase, DNA gyrase, and transpeptidase, respectively. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis revealed that the drug-like properties of QA1 had an ideal toxicity profile, making it a suitable candidate for the development of antimicrobial drugs.

Crystal Polymorph Search in the NPT Ensemble via a Deposition/Sublimation Alchemical Path

The formulation of active pharmaceutical ingredients involves discovering stable crystal packing arrangements or polymorphs, each of which has distinct pharmaceutically relevant properties. Traditional experimental screening techniques utilizing various conditions are commonly supplemented with in silico crystal structure prediction (CSP) to inform the crystallization process and mitigate risk. Predictions are often based on advanced classical force fields or quantum mechanical calculations that model the crystal potential energy landscape but do not fully incorporate temperature, pressure, or solution conditions during the search procedure. This study proposes an innovative alchemical path that utilizes an advanced polarizable atomic multipole force field to predict crystal structures based on direct sampling of the NPT ensemble. The use of alchemical (i.e., nonphysical) intermediates, a novel Monte Carlo barostat, and an orthogonal space tempering bias combine to enhance the sampling efficiency of the deposition/sublimation phase transition. The proposed algorithm was applied to 2-((4-(2-(3,4-dichlorophenyl)ethyl)phenyl)amino)benzoic acid (Cambridge Crystallography Database Centre ID: XAFPAY) as a case study to showcase the algorithm. Each experimentally determined polymorph with one molecule in the asymmetric unit was successfully reproduced via approximately 1000 short 1 ns simulations per space group where each simulation was initiated from random rigid body coordinates and unit cell parameters. Utilizing two threads of a recent Intel CPU (a Xeon Gold 6330 CPU at 2.00 GHz), 1 ns of sampling using the polarizable AMOEBA force field can be acquired in 4 h (equating to more than 300 ns/day using all 112 threads/56 cores of a dual CPU node) within the Force Field X software (https://ffx.biochem.uiowa.edu). These results demonstrate a step forward in the rigorous use of the NPT ensemble during the CSP search process and open the door to future algorithms that incorporate solution conditions using continuum solvation methods.

Characterization of Plant-Derived Natural Inhibitors of Dipeptidyl Peptidase-4 as Potential Antidiabetic Agents: A Computational Study

Diabetes, characterized by elevated blood sugar levels, poses significant health and economic risks, correlating with complications like cardiovascular disease, kidney failure, and blindness. Dipeptidyl peptidase-4 (DPP-4), also referred to as T-cell activation antigen CD26 (EC 3.4.14.5.), plays a crucial role in glucose metabolism and immune function. Inhibiting DPP-4 was anticipated as a potential new therapy for diabetes. Therefore, identification of plant-based natural inhibitors of DPP-4 would help in eradicating diabetes worldwide. Here, for the identification of the potential natural inhibitors of DPP-4, we developed a phytochemicals library consisting of over 6000 phytochemicals detected in 81 medicinal plants that exhibited anti-diabetic potency. The library has been docked against the target proteins, where isorhamnetin, Benzyl 5-Amino-5-deoxy-2,3-O-isopropyl-alpha-D-mannofuranoside (DTXSID90724586), and 5-Oxo-7-[4-(trifluoromethyl) phenyl]-4H,6H,7H-[1,2]thiazolo[4,5-b]pyridine 3-carboxylic acid (CHEMBL3446108) showed binding affinities of -8.5, -8.3, and -8.3 kcal/mol, respectively. These compounds exhibiting strong interactions with DPP-4 active sites (Glu205, Glu206, Tyr547, Trp629, Ser630, Tyr662, His740) were identified. ADME/T and bioactivity predictions affirmed their pharmacological safety. Density functional theory calculations assessed stability and reactivity, while molecular dynamics simulations demonstrated persistent stability. Analyzing parameters like RMSD, RG, RMSF, SASA, H-bonds, MM-PBSA, and FEL confirmed stable protein-ligand compound formation. Principal component analysis provided structural variation insights. Our findings suggest that those compounds might be possible candidates for developing novel inhibitors targeting DPP-4 for treating diabetes.

Chemical profiling of volatile compounds of the essential oil of grey-leaved rockrose (Cistus albidus L.) and its antioxidant, anti-inflammatory, antibacterial, antifungal, and anticancer activity in vitro and in silico

Cistus albidus: L., also known as Grey-leaved rockrose and locally addressed as šṭab or tûzzâla lbîḍa, is a plant species with a well-established reputation for its health-promoting properties and traditional use for the treatment of various diseases. This research delves into exploring the essential oil extracted from the aerial components of Cistus albidus (referred to as CAEO), aiming to comprehend its properties concerning antioxidation, anti-inflammation, antimicrobial efficacy, and cytotoxicity. Firstly, a comprehensive analysis of CAEO's chemical composition was performed through Gas Chromatography-Mass Spectrometry (GC-MS). Subsequently, four complementary assays were conducted to assess its antioxidant potential, including DPPH scavenging, β-carotene bleaching, ABTS scavenging, and total antioxidant capacity assays. The investigation delved into the anti-inflammatory properties via the 5-lipoxygenase assay and the antimicrobial effects of CAEO against various bacterial and fungal strains. Additionally, the research investigated the cytotoxic effects of CAEO on two human breast cancer subtypes, namely, MCF-7 and MDA-MB-231. Chemical analysis revealed camphene as the major compound, comprising 39.21% of the composition, followed by α-pinene (19.01%), bornyl acetate (18.32%), tricyclene (6.86%), and melonal (5.44%). Notably, CAEO exhibited robust antioxidant activity, as demonstrated by the low IC50 values in DPPH (153.92 ± 4.30 μg/mL) and β-carotene (95.25 ± 3.75 μg/mL) assays, indicating its ability to counteract oxidative damage. The ABTS assay and the total antioxidant capacity assay also confirmed the potent antioxidant potential with IC50 values of 120.51 ± 3.33 TE μmol/mL and 458.25 ± 3.67 µg AAE/mg, respectively. In terms of anti-inflammatory activity, CAEO displayed a substantial lipoxygenase inhibition at 0.5 mg/mL. Its antimicrobial properties were broad-spectrum, although some resistance was observed in the case of Escherichia coli and Staphylococcus aureus. CAEO exhibited significant dose-dependent inhibitory effects on tumor cell lines in vitro. Additionally, computational analyses were carried out to appraise the physicochemical characteristics, drug-likeness, and pharmacokinetic properties of CAEO's constituent molecules, while the toxicity was assessed using the Protox II web server.

Mechanism of herb pair containing Astragali Radix and Spatholobi Caulis in the treatment of myelosuppression based on network pharmacology and experimental investigation

ETHNOPHARMACOLOGICAL RELEVANCE

The Astragali Radix and Spatholobi Caulis herb pair (ARSC) is one of the most commonly used herbal combinations for bone marrow suppression. According to traditional Chinese medicine, Astragali Radix strengthens the spleen and replenishes qi, while Spatholobi Caulis is a hematinic agent that promotes blood circulation and enrichment. The compatibility of the two helps the body to tonify the spleen and kidneys and compensate for visceral deficiencies. However, the multi-target mechanism of ARSC in bone marrow suppression has remained largely unknown.

AIM OF THE STUDY

The aim of this study is to explore the key targets and signaling pathways of the traditional Chinese herbal pair ARSC for the treatment of bone marrow suppression.

MATERIALS AND METHODS

The active components of ARSC and targets for myelosuppression were screened using network databases. Cytoscape 3.8.0 was used to construct compound-target, compound-disease-target and protein-protein interaction (PPI) networks. Go-function and pathway enrichment analyses were performed to explore the potential mechanism. In vivo animal experiments were conducted to verify the molecular mechanisms.

RESULTS

The 36 active compounds were identified from the ARSC, and a total of 108 genes involved in myelosuppression were screened. VEGFA, IL6, TNF, JUN, STAT3, PTGS2, CASP3 and MMP9 genes were identified as potential drug targets in the PPI network analyzed by CytoHubba. Enrichment analysis indicated that ARSC may treat myelosuppression through various biological processes, such as apoptosis, TNF-α signaling pathway via NF-κB, PI3K/AKT/mTOR signaling pathway, IL6/JAK/STAT3 signaling pathway, P53 signaling pathway and G2/M checkpoint signaling pathway. The results of the experiment showed that the aqueous extract of ARSC significantly alleviated myelosuppression, reduced the apoptosis rate of bone marrow cells, upregulated the mRNA expression levels of TNF-α, IL-6 and VEGF, and promoted NF-κB phosphorylation in myelosuppressed mice.

CONCLUSIONS

This study identified the active components and relevant mechanisms of ARSC in the treatment of myelosuppression. Our findings predicted that ARSC could treat bone marrow suppression through multiple components, multiple targets and multiple pathways. Pharmacological experiments showed that ARSC alleviated fluorouracil-induced myelosuppression by reducing the apoptosis rate of bone marrow cells and regulating the TNF-α/NF-κB signaling pathway.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Elucidating the role of phytocompounds from Brassica oleracea var. italic (Broccoli) on hyperthyroidism: an in-silico approach

Thyroid hormone (TH) plays a crucial role in regulating the metabolism in every cell and all organs in of the human body. TH also control the rate of calorie burning, body weight, and function of the heartbeat. Therefore, the aim of the present study is to investigate the role of phytocompounds from Brassica oleracea var. italic (Broccoli) against irregularities of TH biosynthesis (hyperthyroidism) through in silico molecular modelling. Initially, the genetic network was built with graph theoretical network analysis to find the right target to control excessive TH production. Based on the network analysis, the three-dimensional crystal structure of the mammalian enzyme lactoperoxidase (PDB id: 5ff1) was retrieved from the protein data bank (PDB), and the active site was predicted using BIOVIA Discovery studio. Sixty-three phytocompounds were selected from the IMPPAT database and other literature. Selected sixty-six phytocompounds were docked against lactoperoxidase enzyme and compared with the standard drug methimazole. Based on the docking scores and binding energies, the top three compounds, namely brassicoside (- 10.00 kcal × mol-1), 24-methylene-25-methylcholesterol (- 9.50 kcal × mol-1), 5-dehydroavenasterol (- 9.40 kcal × mol-1) along with standard drug methimazole (- 4.10 kcal × mol-1) were selected for further ADMET and molecular dynamics simulation analysis. The top-scored compounds were for their properties such as ADMET, physicochemical and drug-likeness. The molecular dynamics simulation analyses proved the stability of lactoperoxidase-ligand complexes. The intermolecular interaction assessed by the dynamic conditions paved the way to discover the bioactive compounds brassicoside, 24-methylene-25-methylcholesterol, and 5-dehydroavenasterol prevent the excessive production of thyroid hormones.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00180-2.

Inhibitory effect of thymoquinone from Nigella sativa against SARS-CoV-2 main protease. An in-silico study

Abstract Nigella sativa is known for the safety profile, containing a wealth of useful antiviral compounds. The main protease (Mpro, 3CLpro) of severe acute respiratory syndrome 2 (SARS-CoV-2) is being considered as one of the most attractive viral target, processing the polyproteins during viral pathogenesis and replication. In the current investigation we analyzed the potency of active component, thymoquinone (TQ) of Nigella sativa against SARS-CoV-2 Mpro. The structures of TQ and Mpro was retrieved from PubChem (CID10281) and Protein Data Bank (PDB ID 6MO3) respectively. The Mpro and TQ were docked and the complex was subjected to molecular dynamic (MD) simulations for a period 50ns. Protein folding effect was analyzed using radius of gyration (Rg) while stability and flexibility was measured, using root means square deviations (RMSD) and root means square fluctuation (RMSF) respectively. The simulation results shows that TQ is exhibiting good binding activity against SARS-CoV-2 Mpro, interacting many residues, present in the active site (His41, Cys145) and also the Glu166, facilitating the pocket shape. Further, experimental approaches are needed to validate the role of TQ against virus infection. The TQ is interfering with pocket maintaining residues as well as active site of virus Mpro which may be used as a potential inhibitor against SARS-CoV-2 for better management of COVID-19.

In silico Molecular Docking Analysis of Three Molecules Isolated from Litsea guatemalensis Mez on Anti-inflammatory Receptors

BACKGROUND

The Litsea genus has four native species from Mesoamerica. Litsea guatemalensis Mez. is a native tree, traditionally used as a condiment and herbal medicine in the region. It has demonstrated antimicrobial, aromatic, anti-inflammatory and antioxidant activity. Bioactive fractionation attributed the anti-inflammatory and anti-hyperalgesic activities to pinocembrin, scopoletin, and 5,7,3´4´-tetrahydroxy-isoflavone. In silico analysis, these molecules were analyzed on receptors involved in the anti-inflammatory process to determine which pathways they interact.

OBJECTIVE

To analyze and evaluate 5,7,3',4'-tetrahydroxyisoflavone, pinocembrin, and scopoletin using the in silico analysis against selected receptors involved in the inflammatory pathway.

METHODS

Known receptors involved in the anti-inflammatory process found as protein-ligand complex in the Protein Data Bank (PDB) were used as references for each receptor and compared with the molecules of interest. The GOLD-ChemScore function, provided by the software, was used to rank the complexes and visually inspect the overlap between the reference ligand and the poses of the studied metabolites.

RESULTS

53 proteins were evaluated, each one in five conformations minimized by molecular dynamics. The scores obtained for dihydroorotate dehydrogenase were greater than 80 for the three molecules of interest, scores for cyclooxygenase 1 and glucocorticoid receptor were greater than 50, and identified residues with interaction in binding sites overlap with the reference ligands in these receptors.

CONCLUSION

The three molecules involved in the anti-inflammatory process of L. guatemalensis show in silico high affinity to the enzyme dihydroorotate dehydrogenase, glucocorticoid receptors and cyclooxygenase-1.

Antiproliferative Activities and SwissADME Predictions of Physicochemical Properties of Carbonyl Group-Modified Rotenone Analogues

Rotenone is a naturally occurring compound shown to exhibit antiproliferative activity against various cancer cell lines, indicating its potential as a lead anticancer agent. However, its toxicity against normal cells has prompted further investigation and chemical modifications. In this study, a library of carbonyl group-modified rotenone derivatives was synthesized and evaluated for their antiproliferative activities against MCF-7 breast cancer cells, A549 human lung carcinoma cells, and HCT116 human colorectal cancer cells using 3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide (MTT) assay. The results showed several promising compounds that inhibited cell proliferation. Specifically, the oxime and alcohol rotenone derivatives exhibited antiproliferative activities against all 3 cancer cell lines, while the ethoxy, carbamate, and alkene derivatives are selective against MCF-7 (IC50 =5.72 μM), HCT116 (IC50 =8.86 μM), and A549 (IC50 =0.11 μM), respectively. SwissADME analysis showed that the physicochemical properties and drug-likeness of the synthesized rotenone derivatives were within the set limits, suggesting the favorable characteristics of these compounds for drug development. The findings obtained in this work highlight the potential of rotenone derivatives as promising chemotherapeutic candidates.

Design, synthesis, and antitumor screening of new thiazole, thiazolopyrimidine, and thiazolotriazine derivatives as potent inhibitors of VEGFR-2

New thiazole, thiazolopyrimidine, and thiazolotriazine derivatives 3-12 and 14a-f were synthesized. The newly synthesized analogs were tested for in vitro antitumor activity against HepG2, HCT-116, MCF-7, HeP-2, and Hela cancer cells. Results indicated that compound 5 displayed the highest potency toward the tested cancer cells. Compound 11b possessed enhanced effectiveness over MCF-7, HepG2, HCT-116, and Hela cancer cells. In addition, compounds 4 and 6 showed promising activity toward HCT-116, MCF-7, and Hela cancer cells and eminent activity against HepG2 and HeP-2 cells. Moreover, compounds 3-6 and 11b were tested for their capability to inhibit vascular endothelial growth factor receptor-2 (VEGFR-2) activity. The obtained results showed that compound 5 displayed significant inhibitory activity against VEGFR-2 (half-maximal inhibitory concentration [IC50 ] = 0.044 μM) comparable to sunitinib (IC50  = 0.100 μM). Also, the synthesized compounds 3-6 and 11b were subjected to in vitro cytotoxicity tests over WI38 and WISH normal cells. It was found that the five tested compounds displayed significantly lower cytotoxicity than doxorubicin toward normal cell lines. Cell cycle analysis proved that compound 5 induces cell cycle arrest in the S phase for HCT-116 and Hela cancer cell lines and in the G2/M phase for the MCF-7 cancer cell line. Moreover, compound 5 induced cancer cell death through apoptosis accompanied by a high ratio of BAX/BCL-2 in the screened cancer cells. Furthermore, docking results revealed that compound 5 showed the essential interaction bonds with VEGFR-2, which agreed with in vitro enzyme assay results. In silico studies showed that most of the analyzed compounds complied with the requirements of good oral bioavailability with minimal toxicity threats in humans.

A computational study of potent series of selective estrogen receptor degraders for breast cancer therapy

A detailed multistep framework combining quantitative structure-activity relationship, global reactivity, absorption, distribution, metabolism and elimination properties, molecular docking and molecular dynamics simulation (MD) on a series of Selective Estrogen Receptor Down-Regulators (SERDs) interacting with Estrogen Receptor α (ERα) has been performed. The partial least squares regression method derived an empirical model with better predictive capability. The results of global reactivity descriptors revealed that all the compounds are considered strong electrophiles, allowing them to participate in polar reactions more easily. The Brain Or IntestinaL EstimateD permeation diagram revealed that compounds 49 and 31 were predicted to be well absorbed by the human gastrointestinal tract and would not enter the brain. The elucidation of the binding mode between the most active compounds that comply with Lipinski's and Veber's rules from the dataset and ERα targets was explored by molecular docking. The MD simulations were performed for 100 ns on the best compounds, which indicated their stability state under dynamics simulations. These findings are expected to help predict the anticancer activities of the studied SERD compounds and better understand their binding mechanism with ERα targets.Communicated by Ramaswamy H. Sarma.

Therapeutic Potential of the Genus Zanthoxylum Phytochemicals: A Theoretical ADME/Tox Analysis

Natural products (NPs) are essential in the search for new drugs to treat a wide range of diseases, including infectious and malignant disorders. However, despite the discovery of many bioactive NPs, they often do not make it to market as drugs due to toxicity and other challenges. The development of NPs into drugs is a long and expensive process, and many promising compounds are abandoned along the way. These molecules require in silico ADMET profiling in order to speed up their development into drugs lower costs, and the high attrition rate. The objective of this work was to produce thorough ADMET profiles of secondary metabolites from several classes that were isolated from Zanthoxylum species. The genus has a long history of therapeutic use, including treating tumours, hypertension, gonorrhoea, coughs, bilharzia, chest pains, and toothaches. The study used a dataset of 406 compounds from the genus for theoretical ADMET analysis. The findings revealed that 81% of the compounds met Lipinski's rule of five, indicating good oral bioavailability. The drug-likeness criteria were taken into account, with percentages ranging from 66.2 to 88.1 percent. Additionally, 9.2% of the compounds were predicted to be lead-like, demonstrating their potential as promising drug development candidates. Interestingly, none of the compounds inhibited hERG I, while 33% inhibited hERG II, potentially having cardiac implications. Additionally, 30% of the compounds exhibited AMES toxicity inhibition, while 23.6% were identified as hepatotoxic and 22.2% would cause skin sensitivity. Moreover, 81.8% of the compounds demonstrated high intestinal absorption, making them desirable for oral drugs. In conclusion, these findings highlight the diverse properties of the investigated compounds and their potential for drug development.

Absorption matters: A closer look at popular oral bioavailability rules for drug approvals

This study examines how two popular drug-likeness concepts used in early development, Lipinski Rule of Five (Ro5) and Veber's Rules, possibly affected drug profiles of FDA approved drugs since 1997. Our findings suggest that when all criteria are applied, relevant compounds may be excluded, addressing the harmfulness of blindly employing these rules. Of all oral drugs in the period used for this analysis, around 66 % conform to the RO5 and 85 % to Veber's Rules. Molecular Weight and calculated LogP showed low consistent values over time, apart from being the two least followed rules, challenging their relevance. On the other hand, hydrogen bond related rules and the number of rotatable bonds are amongst the most followed criteria and show exceptional consistency over time. Furthermore, our analysis indicates that topological polar surface area and total count of hydrogen bonds cannot be used as interchangeable parameters, contrary to the original proposal. This research enhances the comprehension of drug profiles that were FDA approved in the post-Lipinski period. Medicinal chemists could utilize these heuristics as a limited guide to direct their exploration of the oral bioavailability chemical space, but they must also steer the wheel to break these rules and explore different regions when necessary.

Antiemetic effects of sclareol, possibly through 5-HT3 and D2 receptor interaction pathways: In-vivo and in-silico studies

BACKGROUND

Emesis is a complex physiological phenomenon that serves as a defense against numerous toxins, stressful situations, adverse medication responses, chemotherapy, and movement. Nevertheless, preventing emesis during chemotherapy or other situations is a significant issue for researchers. Hence, the majority view contends that successfully combining therapy is the best course of action. In-vivo analysis offers a more comprehensive grasp of how compounds behave within a complex biological environment, whereas in-silico evaluation refers to the use of computational models to forecast biological interactions.

OBJECTIVES

The objectives of the present study were to evaluate the effects of Sclareol (SCL) on copper sulphate-induced emetic chicks and to investigate the combined effects of these compounds using a conventional co-treatment approach and in-silico study.

METHODS

SCL (5, 10, and 15 mg/kg) administered orally with or without pre-treatment with anti-emetic drugs (Ondansetron (ODN): 24 mg/kg, Domperidone (DOM): 80 mg/kg, Hyoscine butylbromide (HYS): 100 mg/kg, and Promethazine hydrochloride (PRO): 100 mg/kg) to illustrate the effects and the potential involvement with 5HT3, D2, M3/AChM, H1, or NK1 receptors by SCL. Furthermore, an in-silico analysis was conducted to forecast the role of these receptors in the emetic process.

RESULTS

The results suggest that SCL exerted a dose-dependent anti-emetic effect on the chicks. Pretreatment with SCL-10 significantly minimized the number of retches and lengthened the emesis tendency of the experimental animals. SCL-10 significantly increased the anti-emetic effects of ODN and DOM. However, compared to the ODN-treated group, (SCL-10 + ODN) group considerably (p < 0.0001) extended the latency duration (109.40 ± 1.03 s) and significantly (p < 0.01) decreased the number of retches (20.00 ± 0.70), indicating an anti-emetic effect on the test animals. In in-silico analysis, SCL exhibited promising binding affinities with suggesting receptors.

CONCLUSION

SCL-10 exerted an inhibitory-like effect on emetic chicks, probably through the interaction of the 5HT3 and D2 receptors. Further studies are highly appreciated to validate this study and determine the precise mechanism(s) behind the anti-emetic effects of SCL. We expect that SCL-10 may be utilized as an antiemetic treatment in a single dosage form or that it may function as a synergist with other traditional medicines.

Transcript-Level In Silico Analysis of Alzheimer's Disease-Related Gene Biomarkers and Their Evaluation with Bioactive Flavonoids to Explore Therapeutic Interactions

Alzheimer's disease (AD) is a progressive brain disorder that can significantly affect the quality of life. We used a variety of in silico tools to investigate the transcript-level mutational impact of exonic missense rare variations (single nucleotide polymorphisms, SNPs) on protein function and to identify potential druggable protein cavities that correspond to potential therapeutic targets for the management of AD. According to the NIA-AA (National Institute on Aging-Alzheimer's Association) framework, we selected three AD biomarker genes (APP, NEFL, and MAPT). We systematically screened transcript-level exonic rare SNPs from these genes with a minor allele frequency of 1% in 1KGD (1000 Genomes Project Database) and gnomAD (Genome Aggregation Database). With downstream functional effect predictions, a single variation (rs182024939: K > N) of the MAPT gene with nine transcript SNPs was identified as the most pathogenic variation from the large dataset of mutations. The machine learning consensus classifier predictor categorized these transcript-level SNPs as the most deleterious variations, resulting in a large decrease in protein structural stability (ΔΔG kcal/mol). The bioactive flavonoid library was screened for drug-likeness and toxicity risk. Virtual screening of eligible flavonoids was performed using the MAPT protein. Identification of druggable protein-binding cavities showed VAL305, GLU655, and LYS657 as consensus-interacting residues present in the MAPT-docked top-ranked flavonoid compounds. The MM/PB(GB)SA analysis indicated hesperetin (-5.64 kcal/mol), eriodictyol (-5.63 kcal/mol), and sakuranetin (-5.60 kcal/mol) as the best docked flavonoids with the near-native binding pose. The findings of this study provide important insights into the potential of hesperetin as a promising flavonoid that can be utilized for further rational drug design and lead optimization to open new gateways in the field of AD therapeutics.

Luteolin is a potential inhibitor of COVID-19: An in silico analysis

The severe respiratory syndrome 2019 novel coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread explosively, raising global health concerns. Luteolin shows antiviral properties, but its effect on SARS-CoV-2 and the associated mechanisms are not elucidated. We used network pharmacology, molecular docking and molecular dynamics to provide potential molecular support of luteolin (3,4,5,7-tetrahydroxyflavone) (LUT) against COVID-19. We employed network pharmacology, molecular docking, and molecular dynamics techniques to investigate how LUT affected COVID-19. Several databases were queried to determine potential target proteins related to LUT and COVID-19. Protein-protein interaction network was constructed, and core targets were filtered by degree value. Following that, functional enrichment was conducted. Molecular docking was utilized to ensure LUT was compatible with core target proteins. Finally, molecular dynamics was used to analyze the effects of the LUT on the optimal hub target. A total of 64 potential target genes for treating COVID-19 were identified, of which albumin, RAC-alpha serine/threonine-protein kinase, caspase-3, epidermal growth factor receptor, heat shock protein HSP 90-alpha, and mitogen-activated protein kinase 1 might be the most promising. In addition, molecular docking results showed that LUT could interact with SARS-CoV-2 major protease 3CL. LUT can bind to the active sites of 3CL protease and mitogen-activated protein kinase 1, showing an anti-SARS-CoV-2 potential.

In silico studies of alkaloids and their derivatives against N-acetyltransferase EIS protein from Mycobacterium tuberculosis

Antibiotic resistance against Mycobacterium tuberculosis (M.tb.) has been a significant cause of death worldwide. The Enhanced intracellular survival (EIS) protein of the bacteria is an acetyltransferase that multiacetylates aminoglycoside antibiotics, preventing them from binding to the bacterial ribosome. To overcome the EIS-mediated antibiotics resistance of M.tb., we compiled 888 alkaloids and derivatives from five different databases and virtually screened them against the EIS receptor. The compound library was filtered down to 87 compounds, which underwent additional analysis and filtration. Moreover, the top 15 most prominent phytocompounds were obtained after the drug-likeness prediction and ADMET screening. Out of 15, nine compounds confirmed the maximum number of hydrogen bond interactions and reliable binding energies during molecular docking. Additionally, the Molecular dynamics (MD) simulation of nine compounds showed the three most stable complexes, further verified by re-docking with mutated protein. The density functional theory (DFT) calculation was performed to identify the HOMO-LUMO energy gaps of the selected three potential compounds. Finally, our selected top lead compounds i.e., Alkaloid AQC2 (PubChem85634496), Nobilisitine A (ChEbi68116), and N-methylcheilanthifoline (ChEbi140673) demonstrated more favourable outcomes when compared with reference compounds (i.e., 39b and 2i) in all parameters used in this study. Therefore, we anticipate that our findings will help to explore and develop natural compound therapy against multi and extensively drug-resistant strains of M.tb.Communicated by Ramaswamy H. Sarma.

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives

Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)-as a known target of toxins in fathead minnows and Daphnia magna, causing the inhibition of AChE-was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure-activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions.

Natural polymers as potential P-glycoprotein inhibitors: Pre-ADMET profile and computational analysis as a proof of concept to fight multidrug resistance in cancer

P-glycoprotein (P-gp) is known as the "multidrug resistance protein" because it contributes to tumor resistance to several different classes of anticancer drugs. The effectiveness of such polymers in treating cancer and delivering drugs has been shown in a wide range of in vitro and in vivo experiments. The primary objective of the present study was to investigate the inhibitory effects of several naturally occurring polymers on P-gp efflux, as it is known that P-gp inhibition can impede the elimination of medications. The objective of our study is to identify polymers that possess the potential to inhibit P-gp, a protein involved in drug resistance, with the aim of enhancing the effectiveness of anticancer drug formulations. The ADMET profile of all the selected polymers (Agarose, Alginate, Carrageenan, Cyclodextrin, Dextran, Hyaluronic acid, and Polysialic acid) has been studied, and binding affinities were investigated through a computational approach using the recently released crystal structure of P-gp with PDB ID: 7O9W. The advanced computational study was also done with the help of molecular dynamics simulation. The aim of the present study is to overcome MDR resulting from the activity of P-gp by using such polymers that can inhibit P-gp when used in formulations. The docking scores of native ligand, Agarose, Alginate, Carrageenan, Chitosan, Cyclodextrin, Dextran, Hyaluronic acid, and Polysialic acid were found to be -10.7, -8.5, -6.6, -8.7, -8.6, -24.5, -6.7, -8.3, and -7.9, respectively. It was observed that, Cyclodextrin possess multiple properties in drug delivery science and here also demonstrated excellent binding affinity. We propose that drug efflux-related MDR may be prevented by the use of Agarose, Carregeenan, Chitosan, Cyclodextrin, Hyaluronic acid, and/or Polysialic acid in the administration of anticancer drugs.

Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humans

BACKGROUND

Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients' life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials.

METHODS

Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects. To evaluate the discrepancy to predict drug approval (1404 approved and 1070 unapproved drugs), we analysed CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects on cells and humans, respectively. To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems.

FINDINGS

Drug approvals in clinical trials were correlated with the cells/humans discrepancy in gene perturbation effects. Genes tolerant to perturbation effects on cells but intolerant to those on humans were associated with failed drug targets. Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects. Motivated by previous studies assessing drug safety through chemical properties, we improved drug approval prediction by integrating chemical information with the cells/humans discrepancy.

INTERPRETATION

The cells/humans discrepancy in gene perturbation effects facilitates drug approval prediction and explains drug safety failures in clinical trials.

FUNDING

S.K. received grants from the Korean National Research Foundation (2021R1A2B5B01001903 and 2020R1A6A1A03047902) and IITP (2019-0-01906, Artificial Intelligence Graduate School Program, POSTECH).

Impact of Applicability Domains to Generative Artificial Intelligence

Molecular generative artificial intelligence is drawing significant attention in the drug design community, with several experimentally validated proof of concepts already published. Nevertheless, generative models are known for sometimes generating unrealistic, unstable, unsynthesizable, or uninteresting structures. This calls for methods to constrain those algorithms to generate structures in drug-like portions of the chemical space. While the concept of applicability domains for predictive models is well studied, its counterpart for generative models is not yet well-defined. In this work, we empirically examine various possibilities and propose applicability domains suited for generative models. Using both public and internal data sets, we use generative methods to generate novel structures that are predicted to be actives by a corresponding quantitative structure-activity relationships model while constraining the generative model to stay within a given applicability domain. Our work looks at several applicability domain definitions, combining various criteria, such as structural similarity to the training set, similarity of physicochemical properties, unwanted substructures, and quantitative estimate of drug-likeness. We assess the structures generated from both qualitative and quantitative points of view and find that the applicability domain definitions have a strong influence on the drug-likeness of generated molecules. An extensive analysis of our results allows us to identify applicability domain definitions that are best suited for generating drug-like molecules with generative models. We anticipate that this work will help foster the adoption of generative models in an industrial context.

Feronia elephantum reverses insulin resistance in fructose-induced hyper-insulinemic rats; an in-silico, in-vitro, and in-vivo approach

ETHNOPHARMACOLOGICAL RELEVANCE

Feronia elephantum corr. (synonym: Feronia limonia, Murraya odorata, Schinus Limonia, or Limonia acidissima; common names: Bela, Kath, Billin, and Kavitha), belonging to the family Rutaceae has been known for clinical conditions such as pruritus, diarrhea, impotence, dysentery, heart diseases, and is also used as a liver tonic. However, the effect of the fruit pulp of F. elephantum on insulin resistance has yet not been reported.

AIM OF THE STUDY

The present study aimed to assess the effect of hydroalcoholic extract/fraction of F. elephantum fruit pulp on fasting blood glucose, oral glucose tolerance test, and glucose uptake in fructose-induced insulin-resistant rats and predict the gene-set enrichment of lead hits of F. elephantum with targets related to insulin resistance.

MATERIAL AND METHODS

System biology tools were used to predict the best category of fraction and propose a possible mechanism. Docking was carried out with adiponectin and its receptor (hub gene). Further, fructose supplementation was used for the induction of insulin resistance. Later, three doses of extract (400, 200, and 100 mg/kg) and a flavonoid-rich fraction (63 mg/kg) were used for treatment along with metformin as standard. The physical parameters like body weight, food intake, and water intake were measured along with oral glucose tolerance test, insulin tolerance test, glycogen content in skeletal muscles and liver, glucose uptake by rat hemidiaphragm, lipid profiles, anti-oxidant biomarkers, and histology of the liver and adipose tissue.

RESULTS

Network pharmacology reflected the potency of F. elephantum to regulate adiponectin (ADIPOQ) which may promote the reversal of insulin resistance and inhibit α-amylase and α-glucosidase. Vitexin was predicted to modulate the most genes associated with diabetes mellitus. Further, F. elephantum ameliorated the exogenous glucose clearance, promoted insulin sensitivity, reduced oxidative stress, and improved glucose and lipid metabolism. HPLC profiling revealed the presence of apigenin and quercetin in the extract for the first time.

CONCLUSION

The fruit pulp of F. elephantum reverses insulin resistance by an increase in glucose uptake and a decrease in gluconeogenesis which may be due to the regulation of multiple proteins via multiple bio-actives.

A New and an Eco-Friendly Approach for the Construction of Multi-Functionalized Benzenes with Computational Studies

The new path chosen is more appropriate in the context of green chemistry. This research aims to construct 5,6,7,8-tetrahydronaphthalene-1,3-dicarbonitrile (THNDC) and 1,2,3,4-tetrahydroisoquinoline-6,8-dicarbonitrile (THIDC) derivatives via the cyclization of three easily obtainable reactants under an environmentally benign mortar and pestle grinding technique. Notably, the robust route offers an esteemed opportunity for the introduction of multi-substituted benzenes and ensures the good compatibility of bioactive molecules. Furthermore, the synthesized compounds are investigated using docking simulations with two representative drugs (6c and 6e) for target validation. The physicochemical, pharmacokinetic, drug-like properties (ADMET), and therapeutic friendliness characteristics of these synthesized compounds are computed.

Design and Computational Analysis of an MMP9 Inhibitor in Hypoxia-Induced Glioblastoma Multiforme

The main therapeutic difficulties in treating hypoxia-induced glioblastoma multiforme (GBM) are toxicity of current treatments and the resistance brought on by the microenvironment. More effective therapeutic alternatives are urgently needed to reduce tumor lethality. Hence, we screened plant-based natural product panels intending to identify novel drugs without elevating drug resistance. We explored GEO for the hypoxia GBM model and compared hypoxic genes to non-neoplastic brain cells. A total of 2429 differentially expressed genes expressed exclusively in hypoxia were identified. The functional enrichment analysis demonstrated genes associated with GBM, further PPI network was constructed, and biological pathways associated with them were explored. Seven webtools, including GEPIA2.0, TIMER2.0, TCGA-GBM, and GlioVis, were used to validate 32 hub genes discovered using Cytoscape tool in GBM patient samples. Four GBM-specific hypoxic hub genes, LYN, MMP9, PSMB9, and TIMP1, were connected to the tumor microenvironment using TIMER analysis. 11 promising hits demonstrated positive drug-likeness with nontoxic characteristics and successfully crossed blood-brain barrier and ADMET analyses. Top-ranking hits have stable intermolecular interactions with the MMP9 protein according to molecular docking, MD simulation, MM-PBSA, PCA, and DCCM analyses. Herein, we have reported flavonoids, 7,4'-dihydroxyflavan, (3R)-3-(4-hydroxybenzyl)-6-hydroxy-8-methoxy-3,4-dihydro-2H-1-benzopyran, and 4'-hydroxy-7-methoxyflavan, to inhibit MMP9, a novel hypoxia gene signature that could serve as a promising predictor in various clinical applications, including GBM diagnosis, prognosis, and targeted therapy.

In silico Structure Prediction, Molecular Docking, and Dynamic Simulation of Plasmodium falciparum AP2-I Transcription Factor

Plasmodium falciparum Apicomplexan Apetala 2 Invasion (PfAP2-I) transcription factor (TF) is a protein that regulates the expression of a subset of gene families involved in P. falciparum red blood cell (RBC) invasion. Inhibiting PfAP2-I TF with small molecules represents a potential new antimalarial therapeutic target to combat drug resistance, which this study aims to achieve. The 3D model structure of PfAP2-I was predicted ab initio using ROBETTA prediction tool and was validated using Save server 6.0 and MolProbity. Computed Atlas of Surface Topography of proteins (CASTp) 3.0 was used to predict the active sites of the PfAP2-I modeled structure. Pharmacophore modeling of the control ligand and PfAP2-I modeled structure was carried out using the Pharmit server to obtain several compounds used for molecular docking analysis. Molecular docking and postdocking studies were conducted using AutoDock vina and Discovery studio. The designed ligands' toxicity predictions and in silico drug-likeness were performed using the SwissADME predictor and OSIRIS Property Explorer. The modeled protein structure from the ROBETTA showed a validation result of 96.827 for ERRAT, 90.2% of the amino acid residues in the most favored region for the Ramachandran plot, and MolProbity score of 1.30 in the 98th percentile. Five (5) best hit compounds from molecular docking analysis were selected based on their binding affinity (between -8.9 and -11.7 Kcal/mol) to the active site of PfAP2-I and were considered for postdocking studies. For the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, compound MCULE-7146940834 had the highest drug score (0.63) and drug-likeness (6.76). MCULE-7146940834 maintained a stable conformation within the flexible protein's active site during simulation. The good, estimated binding energies, drug-likeness, drug score, and molecular dynamics simulation interaction observed for MCULE-7146940834 against PfAP2-I show that MCULE-7146940834 can be considered a lead candidate for PfAP2-I inhibition. Experimental validations should be carried out to ascertain the efficacy of these predicted best hit compounds.

Prophylactic and therapeutic potential of selected immunomodulatory agents from Ayurveda against coronaviruses amidst the current formidable scenario: an in silico analysis

There is currently a dearth of specific therapies to treat respiratory infections caused by the three related species of coronaviruses viz. SARS-CoV-2, SARS-CoV and MERS-CoV. Prevention from disease is currently the safest and most convenient alternative available. The present study aimed to evaluate the preventive and therapeutic effect of fifteen phytoconstituents from medicinal plants of Ayurveda against coronaviruses by in silico screening. All the phytoconstituents exhibited rapid GI absorption and bioavailability and most of them had no toxicity versus reference drug chloroquine. BAS analyses revealed that most of the phytocomponents had favorable bioactivity scores towards biological target proteins. Principal component analysis revealed that most of the phytoconstituents fell close to chloroquine in 3D projection of chemical space. Affinity of phytoconstituents towards SARS-CoV-2 spike protein-human ACE2 complex decreased as isomeldenin > tinosporaside > EGCG whereas in case of unbound ACE2, the strength of binding followed the order isomeldenin > tinosporaside > ellagic acid. Towards SARS-CoV-2 main and papain-like proteases, the affinity decreased as isomeldenin > EGCG > tinosporaside and EGCG > tinosporaside > isomeldenin, respectively. Most phytoconstituents displayed significant binding kinetics to the selected protein targets than chloroquine. SAR analysis revealed that isomeldenin, tinosporaside, EGCG and ellagic acid bind to viral spike glycoproteins via H-bond, Pi-Pi, Pi-sigma and Pi-alkyl type interactions. Molecular dynamics simulation of isomeldenin and EGCG with SARS-CoV and SARS-CoV-2 spike glycoproteins exhibited low deviations throughout the 100 ns simulation indicating good stability and compactness of the protein-ligand complexes. Thus, the above four phytoconstituents have the potential to emerge as prophylactic and therapeutic agents against coronaviruses if investigated further in vitro and in vivo.

The combination of molecular docking and network pharmacology reveals the molecular mechanism of Danggui Niantong decoction in treating gout

BACKGROUND

Due to unhealthy diet and living habits, the incidence of gout is on the rise and has become a common disease with a high incidence. Danggui Niantong decoction (DGNTD), as a classic formula composed of 15 common herbs, has been widely used in clinical practice since ancient times to prevent and treat gout. However, the pharmacological mechanism and target of DGNTD are not clear.

METHODS

The potential active compounds and targets of DGNTD were obtained by traditional Chinese medicine systems pharmacology (TCMSP) database, and the differential genes of gout patients and controls were analyzed in gene expression omnibus (GEO) database. GSEA analysis of differential genes with GSEA 4.1.0 software and then the differential genes were intersected with the gout-related disease targets searched by GeneCard, CTD and OMIM disease database to obtain the final disease target. The "Traditional Chinese medicine-Active compounds-Targets" network was constructed by Cytoscape3.7.2 software. The R packet is used for enrichment analysis. The molecular docking between the active compound of DGNTD and the core target was verified by AutoDockTools software.

RESULTS

Two hundred eighty six and 244 targets of DGNTD-related active components and 652 targets of gout were obtained, of which 13 targets were potential targets of DGNTD in the treatment of gout. GSEA analysis showed that the differential genes were mainly involved in apoptosis, inflammatory reaction, and receptor metabolism and so on. Gene ontology (GO) functional enrichment analysis shows that DGNTD regulates many biological processes, such as the response to purine-containing compound and response to lipopolysaccharide, positive regulation of acute inflammatory response and other cellular components. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis shows that DGNTD treatment of gout is mainly related to interleukin-17 (IL-17), Toll-like receptor, rheumatoid arthritis, tumor necrosis factor (TNF) and so on. The results of molecular docking showed that the five active compounds in DGNTD had strong binding activity to core protein receptors.

CONCLUSIONS

The active compounds of DGNTD may achieve the purpose of treating gout by acting on the core target (CASP8, CXCL8, FOS, IL1B, IL6, JUN, PTGS2, STAT1, MMP1, TNF) to regulate cell metabolism, proliferation and apoptosis, and improve inflammatory response, which is the result of multi-component, multi-target and multi-pathway interaction. It provides an idea for the development of new combined drugs for gout.

Flavonol morin targets host ACE2, IMP-α, PARP-1 and viral proteins of SARS-CoV-2, SARS-CoV and MERS-CoV critical for infection and survival: a computational analysis

A sudden outbreak of a novel coronavirus SARS-CoV-2 in 2019 has now emerged as a pandemic threatening to efface the existence of mankind. In absence of any valid and appropriate vaccines to combat this newly evolved agent, there is need of novel resource molecules for treatment and prophylaxis. To this effect, flavonol morin which is found in fruits, vegetables and various medicinal herbs has been evaluated for its antiviral potential in the present study. PASS analysis of morin versus reference antiviral drugs baricitinib, remdesivir and hydroxychloroquine revealed that morin displayed no violations of Lipinski's rule of five and other druglikeness filters. Morin also displayed no tumorigenic, reproductive or irritant effects and exhibited good absorption and permeation through GI (clogP <5). In principal component analysis, morin appeared closest to baricitinib in 3D space. Morin displayed potent binding to spike glycoprotein, main protease 3CLPro and papain-like protease PLPro of SARS-CoV-2, SARS-CoV and MERS-CoV using molecular docking and significant binding to three viral-specific host proteins viz. human ACE2, importin-α and poly (ADP-ribose) polymerase (PARP)-1, further lending support to its antiviral efficacy. Additionally, morin displayed potent binding to pro-inflammatory cytokines IL-6, 8 and 10 also supporting its anti-inflammatory activity. MD simulation of morin with SARS-CoV-2 3CLPro and PLPro displayed strong stability at 300 K. Both complexes exhibited constant RMSDs of protein side chains and Cα atoms throughout the simulation run time. In conclusion, morin might hold considerable therapeutic potential for the treatment and management of not only COVID-19, but also SARS and MERS if studied further. Communicated by Ramaswamy H. Sarma.

Network pharmacology and molecular docking approach to elucidate the mechanisms of Liuwei Dihuang pill in diabetic osteoporosis

Background

Diabetic osteoporosis (DOP) is one of the chronic complications of diabetes mellitus, but without a standardized treatment plan till now. Liuwei Dihuang pill (LDP) has gradually exerted a remarkable effect on DOP in recent years; its specific mechanism is not clear yet.

Methods

We adopted network pharmacology approaches, including multi-database search, pharmacokinetic screening, network construction analysis, gene ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and molecular docking to elaborate the active components, signaling pathways and potential mechanisms of LDP in the treatment of DOP.

Results

Twenty-seven active ingredients and 55 related disease targets have been found through integrated network pharmacology. Functional enrichment analysis shows that five key active ingredients, including beta-sitosterol, stigmasterol, diosgenin, tetrahydroalstonine, and kadsurenone, may give full scope to insulin secretion estrogen-level raising and angiogenesis in biological process through the pivotal targets. In addition, the underlying effect of PI3K/AKT/FOXO and VEGF pathways is also suggested in the treatment.

Conclusion

Based on systematic network pharmacology methods, we predicted the basic pharmacological effects and potential mechanisms of LDP in the treatment of DOP, revealing that LDP may treat DOP through multiple targets and multiple signaling pathways, which provide evidence for the further study of pharmacological mechanism and broader clinical thinking.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-022-03194-2.

Aphrodisiac Performance of Bioactive Compounds from Mimosa pudica Linn.: In Silico Molecular Docking and Dynamics Simulation Approach

Plants and their derived molecules have been traditionally used to manage numerous pathological complications, including male erectile dysfunction (ED). Mimosa pudica Linn. commonly referred to as the touch-me-not plant, and its extract are important sources of new lead molecules in drug discovery research. The main goal of this study was to predict highly effective molecules from M. pudica Linn. for reaching and maintaining penile erection before and during sexual intercourse through in silico molecular docking and dynamics simulation tools. A total of 28 bioactive molecules were identified from this target plant through public repositories, and their chemical structures were drawn using Chemsketch software. Graph theoretical network principles were applied to identify the ideal target (phosphodiesterase type 5) and rebuild the network to visualize the responsible signaling genes, proteins, and enzymes. The 28 identified bioactive molecules were docked against the phosphodiesterase type 5 (PDE5) enzyme and compared with the standard PDE5 inhibitor (sildenafil). Pharmacokinetics (ADME), toxicity, and several physicochemical properties of bioactive molecules were assessed to confirm their drug-likeness property. Molecular dynamics (MD) simulation modeling was performed to investigate the stability of PDE5–ligand complexes. Four bioactive molecules (Bufadienolide (−12.30 kcal mol⁻¹), Stigmasterol (−11.40 kcal mol⁻¹), Isovitexin (−11.20 kcal mol⁻¹), and Apigetrin (−11.20 kcal mol⁻¹)) showed the top binding affinities with the PDE5 enzyme, much more powerful than the standard PDE5 inhibitor (−9.80 kcal mol⁻¹). The four top binding bioactive molecules were further validated for a stable binding affinity with the PDE5 enzyme and conformation during the MD simulation period as compared to the apoprotein and standard PDE5 inhibitor complexes. Further, the four top binding bioactive molecules demonstrated significant drug-likeness characteristics with lower toxicity profiles. According to the findings, the four top binding molecules may be used as potent and safe PDE5 inhibitors and could potentially be used in the treatment of ED.

Design and Synthesis of Novel Dual Cholinesterase Inhibitors: In Vitro Inhibition Studies Supported with Molecular Docking

The major cholinesterase enzymes, acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), are important in the therapy of Alzheimer's disease (AD) based on the cholinergic hypothesis. As a result, in recent years, the investigation of dual cholinesterase inhibition methods has become important among scientists. In this study, novel N-(4-chlorobenzyl)-3,4-dimethoxy-N-(m-substituted)benzamide derivatives were synthesized. Then, inhibitory properties of these derivatives were examined in human AChE and BuChE in vitro and possible interactions were determined by molecular docking studies. All benzamide derivatives were exhibited dual inhibitory character and high BBB permeability. The most effective inhibitor was found as N7 for both AChE and BuChE with IC₅₀ values of 1.57 and 2.85 μM, respectively. Besides the most potent inhibitor was predicted as N7 in terms of binding energies with -12.18 kcal/mol and -9.92 kcal/mol, respectively. The reason for these results is that bromine (N7) is the bulkiest molecule among the other substituted groups. These derivatives could be exploited to develop new medications for the treatment of central nervous system-related diseases as AD by acting as dual inhibitors of AChE and BChE.

Molecular Dynamics Simulations of Essential Oil Ingredients Associated with Hyperbranched Polymer Drug Carriers

Our work concerns the study of four candidate drug compounds of the terpenoid family, found as essential oil ingredients in species of the Greek endemic flora, namely carvacrol, p-cymene, γ-terpinene, and thymol, via the simulation method of molecular dynamics. Aquatic solutions of each compound, as well as a solution of all four together in realistic (experimental) proportions, are simulated at atmospheric pressure and 37 °C using an OPLS force field combined with TIP3P water. As verified, all four compounds exhibit a strong tendency to phase-separate, thereby calling for the use of carrier molecules as aids for the drug to circulate in the blood and enter the cells. Systems of two such carrier molecules, the hyperbranched poly(ethylene imine) (HBPEI) polyelectrolyte and hyperbranched polyglycerol (HPG), are examined in mixtures with carvacrol, the most abundant among the four compounds, at a range of concentrations, as well as with all four compounds present in natural proportions. Although a tendency of the terpenoids to cluster separately persists at high concentrations, promising association effects are observed for all drug–polymer ratios. HBPEI systems tend to form diffuse structures comprising small mixed clusters as well as freely floating polymer and essential oil molecules, a finding attributed to the polymer–polymer electrostatic repulsions, which here are only partially screened by the counterions. On the other hand, the electrically neutral HPG molecules cluster together with essential oil species to form a single nanodroplet. Currently, terpenoid–polymer clusters near lipid bilayer membranes are being studied to determine the propensity of the formed complexes to enter cell membranes.

How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases

•

Five proteins related to diabetic disease were selected from Protein Data Bank.

•

Binding scores were calculated for five proteins with 169 fullerene derivatives.

•

Correlation between drug-like descriptors and binding scores activity was examined.

•

The contribution of descriptors to protein-ligand binding was demonstrated.

•

The QSARs models for prediction of binding scores activity were built.

Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein–ligand binding in order to identify the key binding characteristics of FDs.

Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction.

The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles.

All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity.

phytochemdb: a platform for virtual screening and computer-aided drug designing

The phytochemicals of medicinal plants are regarded as a rich source of diverse chemical spaces that have been used as supplements and alternative medicines in the millennium. Even in this era of combinatorial chemical drugs, phytomedicines account for a large share of the statistics of newly approved drugs. In the field of computational aided and rational drug design, there is an urgent need to develop and build a useful phytochemical database management system with a user-friendly interface that allows proper data storage, retrieval and management. We showed ‘phytochemdb’, a manually managed database that compiles 525 plants and their corresponding 8093 phytochemicals, aiming to incorporate the activities of phytochemicals from medicinal plants. The database collects molecular formula, three-dimensional/two-dimensional structure, canonical SMILES, molecular weight, no. of heavy atoms, no. of aromatic heavy atoms, fraction Csp3, no. of rotatable bonds, no. of H-bond acceptors, no. of H-bond donors, molar refractivity, topological polar surface area, gastrointestinal absorption, Blood–Brain Barrier (BBB) permeant, P-gp substrate, CYP1A2 inhibitor, CYP2C19 inhibitor, CYP2C9 inhibitor, CYP2D6 inhibitor, CYP3A4 inhibitor, Log Kp, Ghose, Veber, Egan, Muegge, bioavailability scores, pan-assay interference compounds, Brenk, Leadlikeness, synthetic accessibility, iLOGP and Lipinski rule of five with the number of violations for each compound. It provides open contribution functions for the researchers who screen phytochemicals in the laboratory and have released their data. ‘phytochemdb’ is a comprehensive database that gathers most of the information about medicinal plants in one platform, which is considered to be very beneficial to the work of researchers on medicinal plants. ‘phytochemdb’ is available for free at https://phytochemdb.com/.

In silico ADME/Pharmacokinetic and Target Prediction Studies of Ethambutol as Drug Molecule

BACKGROUND

The conventional approach for the development of any pharmaceutically active molecule is a time-consuming and costly process because the synthesis is followed by laboratory tests which are then followed by long clinical trials. Hence a faster approach is desired. This article discusses Ethambutol, a frontline anti-tubercular drug that has its properties predicted by the SwissADME tool and the results would be compared with the findings published in the literature.

OBJECTIVE

The main objective is to study the predicted and experimental ADME properties, compare them. As well as study the predicted targets and understand the use of SwissADME for designing other drug molecules.

METHOD

SwissADME, an online tool for ADME prediction was used along with Swiss Target Prediction to understand the targets of the drug. Further, experimental data was obtained from the available scientific literature.

RESULTS

We found certain similarities between the predicted and experimental data. However, there were some variations, depending on the testing conditions. The results are interpreted ahead in the article.

CONCLUSION

Ethambutol's predicted ADME properties are discussed and as per findings from results, it can be concluded that other drug molecules can be similarly predicted using these tools. Also, based on predicted data we can reformulate and prepare some different preparations of the drug.

Optimization of physicochemical properties is a strategy to improve drug-likeness associated with activity: novel active and selective compounds against Trypanosoma cruzi

Trypanosoma cruzi is the causing agent of Chagas disease, a parasitic infection without efficient treatment for chronic patients. Despite the efforts, no new drugs have been approved for this disease in the last 60 years. Molecular modifications based on a natural product led to the development of a series of compounds (LINS03 series) with promising antitrypanosomal activity, however previous chemometric analysis revealed a significant impact of excessive lipophilicity and low aqueous solubility on potency of amine and amide derivatives. Therefore, this work reports different modifications in the core structure to achieve adequate balance of the physicochemical properties along with biological activity. A set of 34 analogues were designed considering predicted properties related to lipophilicity/hydrosolubility and synthesized to assess their activity and selective toxicity towards the parasite. Results showed that this strategy contributed to improve the drug-likeness of the series while considerable impacts on potency were observed. The rational analysis of the obtained data led to the identification of seven active piperazine amides (28-34, IC₅₀ 8.7 to 35.3 µM against intracellular amastigotes), devoid of significant cytotoxicity to mammalian cells. The addition of water-solubilizing groups and privileged substructures such as piperazines improved the physicochemical properties and overall drug-likeness of these compounds, increased potency and maintained selectivity towards the parasite. The obtained results brought important structure-activity relationship (SAR) data and new lead structures for further modifications were identified to achieve improved antitrypanosoma compounds.

Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction

The improvement in the ability of the pharmaceutical industry to predict human pharmacokinetic behavior are attributable to major technological shifts from 1990 to the present day. The opportunity for the application of AI/ML based approaches in the pharmaceutical industry is driven by the abundance of data sets that exist within individual pharmaceutical and biotech companies and the availability, within these environments, of abundant computing power. This chapter seeks to describe opportunities for artificial intelligence to contribute to the assessment and evaluation of the dug metabolism and pharmacokinetic (DMPK) properties of novel compounds across the drug discovery and development continuum. Many initiatives are already underway with respect to the application of AI/ML in predicting pharmacokinetic profiles so the question is not whether AI will influence pharmacokinetic prediction but rather how to best utilize and incorporate this and how to evaluate the value added from these applications. Since our understanding of the underlying biology of the in vitro and in vivo systems with respect to ADME, one of the key challenges to AI-based methods will be the ability to adapt to data sets that change in quality over time.

Gene ontology enrichment analysis of PPAR-γ modulators from Cassia glauca in diabetes mellitus

BACKGROUND

PPAR-γ has an integrative role in the management of insulin resistance; ligands of this receptor have emerged as potent insulin sensitizers and may modulate proteins involved in the pathogenesis of diabetes mellitus. Hence the present study is aimed to identify PPAR-γ modulators from the plant Cassia glauca and predict the ontology enrichment analysis utilizing various in-silico tools.

METHODS

ChEBI database was used to mine the phytoconstituents present in the plant C. glauca, SwissTargetPrediction database was used to identify the targets, and scrutinizing of phytoconstituents modulating PPAR-γ was performed. Autodock4.0 was used to dock phytoconstituent ligands with the target PPAR-γ. Multiple open-source databases and in-silico tools were utilized to predict the drug-likeness characters and predict side effects of the phytoconstituents modulating PPAR-γ and STRING database was used to construct a network between the modulated genes.

RESULTS

Twenty-four phytoconstituents were identified from the plant Cassia glauca from which four were found to modulate PPAR-γ, sennoside was predicted to have the greatest drug-likeness score and a significantly less side effect whereas diphenyl sulfone was predicted to show hepatotoxicity with the greatest pharmacological activity of 0.815. [epicatechin-(4beta- > 8)]5-epicatechin showed the lowest binding affinity with target PPAR-γ i.e. -8.6 kcal/mol and possessing a positive drug-likeness score with no side effect data.

CONCLUSION

Bioctives were found free from probable side effects leaving out diphenyl sulfone having a prediction of hepatotoxicity, the anti-diabetic property of the plant may be due to the presence of [epicatechin-(4beta- > 8)]5-epicatechin which needs further validation by in-vitro and in-vivo protocols.

An Evaluation of the Potential of Essential Oils against SARS-CoV-2 from In Silico Studies through the Systematic Review Using a Chemometric Approach

Essential oils (EOs) and their compounds have attracted particular attention for their reported beneficial properties, especially their antiviral potential. However, data regarding their anti-SARS-CoV-2 potential are scarce in the literature. Thus, this study aimed to identify the most promising EO compounds against SARS-CoV-2 based on their physicochemical, pharmacokinetic, and toxicity properties. A systematic literature search retrieved 1669 articles; 40 met the eligibility criteria, and 35 were eligible for analysis. These studies resulted in 465 EO compounds evaluated against 11 human and/or SARS-CoV-2 target proteins. Ninety-four EO compounds and seven reference drugs were clustered by the highest predicted binding affinity. Furthermore, 41 EO compounds showed suitable drug-likeness and bioactivity score indices (≥0.67). Among these EO compounds, 15 were considered the most promising against SARS-CoV-2 with the ADME/T index ranging from 0.86 to 0.81. Some plant species were identified as EO potential sources with anti-SARS-CoV-2 activity, such as Melissa officinalis Arcang, Zataria multiflora Boiss, Eugenia brasiliensis Cambess, Zingiber zerumbet Triboun & K.Larsen, Cedrus libani A.Rich, and Vetiveria zizanoides Nash. Our work can help fill the gap in the literature and guide further in vitro and in vivo studies, intending to optimize the finding of effective EOs against COVID-19.

Analysis of the uncharted, druglike property space by self-organizing maps

Physicochemical properties are fundamental to predict the pharmacokinetic and pharmacodynamic behavior of drug candidates. Easily calculated descriptors such as molecular weight and logP have been found to correlate with the success rate of clinical trials. These properties have been previously shown to highlight a sweet-spot in the chemical space associated with favorable pharmacokinetics, which is superior against other regions during hit identification and optimization. In this study, we applied self-organizing maps (SOMs) trained on sixteen calculated properties of a subset of known drugs for the analysis of commercially available compound databases, as well as public biological and chemical databases frequently used for drug discovery. Interestingly, several regions of the property space have been identified that are highly overrepresented by commercially available chemical libraries, while we found almost completely unoccupied regions of the maps (commercially neglected chemical space resembling the properties of known drugs). Moreover, these underrepresented portions of the chemical space are compatible with most rigorous property filters applied by the pharma industry in medicinal chemistry optimization programs. Our results suggest that SOMs may be directly utilized in the strategy of library design for drug discovery to sample previously unexplored parts of the chemical space to aim at yet-undruggable targets.

Size Doesn't Matter: Predicting Physico- or Biochemical Properties Based on Dozens of Molecules

The use of machine learning in chemistry has become a common practice. At the same time, despite the success of modern machine learning methods, the lack of data limits their use. Using a transfer learning methodology can help solve this problem. This methodology assumes that a model built on a sufficient amount of data captures general features of the chemical compound structure on which it was trained and that the further reuse of these features on a data set with a lack of data will greatly improve the quality of the new model. In this paper, we develop this approach for small organic molecules, implementing transfer learning with graph convolutional neural networks. The paper shows a significant improvement in the performance of the models for target properties with a lack of data. The effects of the data set composition on the model's quality and the applicability domain of the resulting models are also considered.

In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis

We have performed theoretical calculations with 70 drugs that have been considered in 231 clinical trials as possible candidates to repurpose drugs for schizophrenia based on their interactions with the dopaminergic system. A hypothesis of shared pharmacophore features was formulated to support our calculations. To do so, we have used the crystal structure of the D2-like dopamine receptor in complex with risperidone, eticlopride, and nemonapride. Linagliptin, citalopram, flunarizine, sildenafil, minocycline, and duloxetine were the drugs that best fit with our model. Molecular docking calculations, molecular dynamics outcomes, blood-brain barrier penetration, and human intestinal absorption were studied and compared with the results. From the six drugs selected in the shared pharmacophore features input, flunarizine showed the best docking score with D2, D3, and D4 dopamine receptors and had high stability during molecular dynamics simulations. Flunarizine is a frequently used medication to treat migraines and vertigo. However, its antipsychotic properties have been previously hypothesized, particularly because of its possible ability to block the D2 dopamine receptors.