In silicoADME-Tox modeling: progress and prospects

3D computer modeling of inhibitors targeting the MCF-7 breast cancer cell line

This study focused on developing new inhibitors for the MCF-7 cell line to contribute to our understanding of breast cancer biology and various experimental techniques. 3D QSAR modeling was used to design new tetrahydrobenzo[4, 5]thieno[2, 3-d]pyrimidine derivatives with good characteristics. Two robust 3D-QSAR models were developed, and their predictive capacities were confirmed through high correlations [CoMFA (Q2 = 0.62, R 2 = 0.90) and CoMSIA (Q2 = 0.71, R 2 = 0.88)] via external validations (R2 ext = 0.90 and R2 ext = 0.91, respectively). These successful evaluations confirm the potential of the models to provide reliable predictions. Six candidate inhibitors were discovered, and two new inhibitors were developed in silico using computational methods. The ADME-Tox properties and pharmacokinetic characteristics of the new derivatives were evaluated carefully. The interactions between the new tetrahydrobenzo[4, 5]thieno[2, 3-d]pyrimidine derivatives and the protein ERα (PDB code: 4XO6) were highlighted by molecular docking. Additionally, MM/GBSA calculations and molecular dynamics simulations provided interesting information on the binding stabilities between the complexes. The pharmaceutical characteristics, interactions with protein, and stabilities of the inhibitors were examined using various methods, including molecular docking and molecular dynamics simulations over 100 ns, binding free energy calculations, and ADME-Tox predictions, and compared with the FDA-approved drug capivasertib. The findings indicate that the inhibitors exhibit significant binding affinities, robust stabilities, and desirable pharmaceutical characteristics. These newly developed compounds, which act as inhibitors to mitigate breast cancer, therefore possess considerable potential as prospective drug candidates.

Sustainable methods for the carboxymethylation and methylation of ursolic acid with dimethyl carbonate under mild and acidic conditions

Ursolic acid is a triterpene plant extract that exhibits significant potential as an anti-cancer, anti-tumour, and anti-inflammatory agent. Its direct use in the pharmaceutical industry is hampered by poor uptake of ursolic acid in the human body coupled with rapid metabolism causing a decrease in bioactivity. Modification of ursolic acid can overcome such issues, however, use of toxic reagents, unsustainable synthetic routes and poor reaction metrics have limited its potential. Herein, we demonstrate the first reported carboxymethylation and/or methylation of ursolic acid with dimethyl carbonate (DMC) as a green solvent and sustainable reagent under acidic conditions. The reaction of DMC with ursolic acid, in the presence of PTSA, ZnCl2, or H2SO4-SiO2 yielded the carboxymethylation product 3β-[[methoxy]carbonyl]oxyurs-12-en-28-oic acid, the methylation product 3β-methoxyurs-12-en-28-oic acid and the dehydration product urs-2,12-dien-28-oic acid. PTSA demonstrated high conversion and selectivity towards the previously unreported carboxymethylation of ursolic acid, while the application of formic acid in the system led to formylation of ursolic acid (3β-formylurs-12-en-28-oic acid) in quantitative yields via esterification, with DMC acting solely as a solvent. Meanwhile, the methylation product of ursolic acid, 3β-methoxyurs-12-en-28-oic acid, was successfully synthesised with FeCl3, demonstrating exceptional conversion and selectivity, >99% and 99%, respectively. Confirmed with the use of qualitative and quantitative green metrics, this result represents a significant improvement in conversion, selectivity, safety, and sustainability over previously reported methods of ursolic acid modification. It was demonstrated that these methods could be applied to other triterpenoids, including corosolic acid. The study also explored the potential pharmaceutical applications of ursolic acid, corosolic acid, and their derivatives, particularly in anti-inflammatory, anti-cancer, and anti-tumour treatments, using molecular ADMET and docking methods. The methods developed in this work have led to the synthesis of novel molecules, thus creating opportunities for the future investigation of biological activity and the modification of a wide range of triterpenoids applying acidic DMC systems to deliver novel active pharmaceutical intermediates.

Prediction of physicochemical and pharmacokinetic properties of botanical constituents by computational models

Botanicals contain complex mixtures of chemicals most of which lack pharmacokinetic data in humans. Since physicochemical and pharmacokinetic properties dictate the in vivo exposure of botanical constituents, these parameters greatly impact the pharmacological and toxicological effects of botanicals in consumer products. This study sought to use computational (i.e., in silico) models, including quantitative structure-activity relationships (QSAR) and physiologically based pharmacokinetic (PBPK) modeling, to predict properties of botanical constituents. One hundred and three major constituents (e.g., withanolides, mitragynine, and yohimbine) in 13 botanicals (e.g., ashwagandha, kratom, and yohimbe) were investigated. The predicted properties included biopharmaceutical classification system (BCS) classes based on aqueous solubility and permeability, oral absorption, liver microsomal clearance, oral bioavailability, and others. Over half of these constituents fell into BCS classes I and II at dose levels no greater than 100 mg per day, indicating high permeability and absorption (%Fa > 75%) in the gastrointestinal tract. However, some constituents such as glycosides in ashwagandha and Asian ginseng showed low bioavailability after oral administration due to poor absorption (BCS classes III and IV, %Fa < 40%). These in silico results fill data gaps for botanical constituents and could guide future safety studies. For example, the predicted human plasma concentrations may help select concentrations for in vitro toxicity testing. Additionally, the in silico data could be used in tiered or batteries of assays to assess the safety of botanical products. For example, highly absorbed botanical constituents indicate potential high exposure in the body, which could lead to toxic effects.

Gigantol, a promising natural drug for inflammation: a literature review and computational based study

Gigantol, a bibenzyl compound extracted from various medicinal plants, has shown a number of biological activities, making it an attractive candidate for potential medical applications. This systematic review aims to shed light on gigantol's promising role in inflammation treatment and its underlying mechanisms. Gigantol exhibits potential anti-inflammatory properties in pre-clinical pharmacological test systems. It effectively reduced the levels of pro-inflammatory markers and arachidonic acid metabolites through various pathways, such as NF-κB, AKT, PI3K, and JNK/cPLA2/12-LOX. The in-silico investigations demonstrated that the MMP-13 enzyme served as the most promising target for gigantol with highest binding affinity (docking score = -8.8 kcal/mol). Encouragingly, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis of gigantol confirmed its compatibility with the necessary physiochemical, pharmacokinetic, and toxicity properties, bolstering its potential as a drug candidate. Gigantol, with its well-documented anti-inflammatory properties, could be a promising agent for treating inflammation in the near future.

Utilizing network pharmacology and experimental validation to investigate the underlying mechanism of Denglao Qingguan decoction against HCoV-229E

Background

Denglao Qingguan decoction (DLQGD) has been extensively utilized for the treatment of colds, demonstrating significant therapeutic efficacy. Human Coronavirus 229E (HCoV-229E) is considered a crucial etiological agent of influenza. However, the specific impact and underlying mechanisms of DLQGD on HCoV-229E remain poorly understood.

Methods

Active ingredients and targets information of DLQGD were collected from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), literature search, and Swiss ADEM database. The Genecard database was used to collect HCoV-229E related targets. We built an "ingredient-target network" through Cytoscape. Protein - Protein interaction (PPI) networks were mapped using the String database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were enriched using the DAVID database. Then, we used molecular docking techniques to verify the binding activity between the core compounds and the core gene targets. Finally, in vitro experiments were conducted to validate DLQGD's antiviral activity against HCoV-229E and assess its anti-inflammatory effects.

Results

In total, we identified 227 active components in DLQGD. 18 key targets involved in its activity against HCoV-229E. Notably, the core active ingredients including quercetin, luteolin, kaempferol, β-sitosterol, and apigenin, and the core therapeutic targets were CXCL8, RELA, MAPK14, NFKB1, and CXCL10, all associated with HCoV-229E. KEGG enrichment results included IL-17 signaling pathway, Toll-like receptor signaling pathway, RIG-I-like receptor signaling pathway and so on. The core active ingredients and the core therapeutic targets and Human Aminopeptidase N (ANPEP) all showed good binding activity by molecular docking verification. In vitro, DLQGD exhibited anti-HCoV-229E activity and anti-inflammatory effects.

Conclusion

Our study suggests that DLQGD has both effects of anti-HCoV-229E and anti-inflammatory. The core active ingredients (quercetin, luteolin, kaempferol, β-sitosterol, apigenin) and the core therapeutic targets (CXCL8, RELA, MAPK14, NFKB1, CXCL10) may play key roles in the pharmacological action of DLQGD against HCoV-229E.

Design, Synthesis, and In Vivo Evaluation of a New Series of Indole-Chalcone Hybrids as Analgesic and Anti-Inflammatory Agents

Indole-chalcone hybrids have burst into prominence as potent weapons in the battle against pain and inflammation due to their unique features, allowing these ligands to form pivotal interactions with biological targets. In this context, the base-catalyzed Claisen-Schmidt condensation of 3',4'-(methylenedioxy)acetophenone with heteroaromatic aldehydes carrying an indole scaffold yielded new chalcones (1-7). The central and peripheral antinociceptive activities of all chalcones (compounds 1-7) at the dose of 10 mg/kg (i.p.) were evaluated by hot plate (supraspinal response), tail immersion (spinal response), and acetic acid-induced writhing tests in mice. The anti-inflammatory activities of compounds 1-7 were also investigated by means of a carrageenan-induced mouse paw edema model. The results revealed that compounds 1-7 extended the latency of response to thermal stimulus significantly in a hot-plate test similar to dipyrone (300 mg/kg; i.p.), the positive control drug. However, only compounds 2-7 were found to be significantly effective in the tail-immersion test. Compounds 1-7 also significantly showed analgesic effect by reducing the number of writhes and anti-inflammatory activity by inhibiting edema formation at different time intervals and levels. 1-(1,3-Benzodioxol-5-yl)-3-(1-methyl-1H-indol-2-yl)prop-2-en-1-one (4) drew attention by providing the highest efficacy results in both acute analgesia and inflammation models. Based on the in silico data acquired from the QikProp module, compound 4 was predicted to possess favorable oral bioavailability and drug-like properties. Taken together, it can be concluded that chalcones (1-7), especially compound 4, are outstanding candidates for further research to investigate their potential use in the management of pain and inflammation.

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

N-Substituted Phenylhydrazones Kill the Ring Stage of Plasmodium falciparum

Antimalarial resistance has hampered the effective treatment of malaria, a parasitic disease caused by Plasmodium species. As part of our campaign on phenotypic screening of phenylhydrazones, a library of six phenylhydrazones was reconstructed and evaluated for their in vitro antimalarial and in silico receptor binding and pharmacokinetic properties. The structures of the phenylhydrazone hybrids were largely confirmed using nuclear magnetic resonance techniques. We identified two compounds which exhibited significant antimalarial potential against the ring stage (trophozoite) of 3D7 chloroquine-sensitive (CS) strain and DD2 chloroquine-resistant (CR) strains of Plasmodium falciparum with monosubstituted analogs bearing meta or para electron-donating groups showing significant activity in the single-digit micromolar range. Structure activity relationship is presented showing that electron-donating groups on the substituent hydrophobic pharmacophore are required for antimalarial activity. Compounds PHN6 and PHN3 were found to be the most potent with pIC50s (calculated form in vitro IC50s) of 5.37 and 5.18 against 3D7 CS and DD2 CR strains, respectively. Our selected ligands (PHN3 and PHN6) performed better when compared to chloroquine regarding binding affinity and molecular stability with the regulatory proteins of Plasmodium falciparum, hence predicted to be largely responsible for their in vitro activity. Pharmacokinetic prediction demonstrated that the phenylhydrazones may not cross the blood-brain barrier and are not P-glycoprotein (P-gp) substrates, a good absorption of 62% to 69%, and classified as a category IV compound based on toxicity grading.

First Description of Marinoquinoline Derivatives' Activity against Toxoplasma gondii

Toxoplasmosis is a globally prevalent zoonotic disease with significant clinical implications, including neurotoxoplasmosis, a leading cause of cerebral lesions in AIDS patients. The current pharmacological treatments for toxoplasmosis face clinical limitations, necessitating the urgent development of new therapeutics. Natural sources have yielded diverse bioactive compounds, serving as the foundation for clinically used derivatives. The exploration of marine bacteria-derived natural products has led to marinoquinolines, which feature a pyrroloquinoline core and demonstrate in vitro and in vivo anti-Plasmodium activity. This study investigates the in vitro anti-Toxoplasma gondii potential of six marinoquinoline derivatives. Additionally, it conducts absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions, and evaluates the in vivo efficacy of one selected compound. The compounds displayed half-maximal effective concentration (EC50) values between 1.31 and 3.78 µM and half-maximal cytotoxic concentration (CC50) values ranging from 4.16 to 30.51 µM, resulting in selectivity indices (SI) from 3.18 to 20.85. MQ-1 exhibiting the highest in vitro SI, significantly reduced tachyzoite numbers in the peritoneum of RH-infected Swiss mice when it was orally administered at 12.5 mg/kg/day for eight consecutive days. Also, MQ-1 significantly reduced the cerebral parasite burden in chronically ME49 infected C57BL/6 mice when it was orally administered at 25 mg/kg/day for 10 consecutive days. These findings underscore the promising anti-T. gondii activity of marinoquinolines and their potential as novel therapeutic agents against this disease.

Computational exploration of microsomal cytochrome P450 3A1 enzyme modulation by phytochemicals of Cichorium intybus L.: Insights into drug metabolism

Drug metabolism plays a crucial role in drug fate, including therapeutic inactivation or activation, as well as the formation of toxic compounds. This underscores the importance of understanding drug metabolism in drug discovery and development. Considering the substantial costs associated with traditional drug development methods, computational approaches have emerged as valuable tools for predicting the metabolic fate of drug candidates. With this in mind, the present study aimed to investigate the potential mechanisms underlying the modulation of microsomal cytochrome P450 3A1 (CYP3A1) enzyme activity by various phytochemicals found in Cichorium intybus L., commonly known as chicory. To achieve this goal, several in silico methods, including molecular docking and molecular dynamics (MD) simulation, were employed to explore computationally the microsomal CYP3A1 enzyme. Schrodinger software was utilized for the molecular docking study, which involved the interaction analysis between CYP3A1 and 28 phytoconstituents of Cichorium intybus. Virtual screening of 28 compounds from chicory led to the identification of the top five ranked compounds. These compounds were evaluated for drug-likeness properties, pharmacokinetic profiles, and predicted binding affinities to CYP3A1. Caffeoylshikimic acid and cichoric acid emerged as promising candidates due to their favorable characteristics, including good oral bioavailability and high binding affinities to CYP3A1. Molecular dynamics simulations were conducted to assess the stability of caffeoylshikimic acid within the CYP3A1 binding pocket. The results demonstrated that caffeoylshikimic acid maintained stable interactions with the enzyme throughout the simulation, suggesting its potential as an effective modulator of CYP3A1 activity. The findings of this study have the potential to provide valuable insights into the complex molecular mechanisms by which Cichorium intybus L. acts on hepatocytes and modulates CYP3A1 enzyme expression or activity. By elucidating the impact of these phytochemicals on drug metabolism, this research contributes to our understanding of how chicory may interact with drugs and influence their efficacy and safety profiles.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Oxidation-derived anticancer potential of sumanene-ferrocene conjugates

An effective synthetic protocol towards the oxidation of sumanene-ferrocene conjugates bearing one to four ferrocene moieties has been established. The oxidation protocol was based on the transformation of FeII from ferrocene to FeIII-containing ferrocenium cations by means of the treatment of the title organometallic buckybowls with a mild oxidant. Successful isolation of these ferrocenium-tethered sumanene derivatives 5-7 gave rise to the biological evaluation of the first, buckybowl-based anticancer agents, as elucidated by in vitro assays with human breast adenocarcinoma cells (MDA-MB-231) and embryotoxicity trials in zebrafish embryos supported with in silico toxicology studies. The designed ferrocenium-tethered sumanene derivatives featured attractive properties in terms of their use in cancer treatments in humans. The tetra-ferrocenium sumanene derivative 7 featured especially beneficial biological features, elucidated by low (<40% for 10 μM) viabilities of MDA-MB-231 cancer cells together with a 1.4-1.7-fold higher viability of normal cells (human mammary fibroblasts, HMF) for respective concentrations. Compound 7 featured significant cytotoxicity against cancer cells thanks to the presence of sumanene and ferrocenium moieties; the latter motif also provided the selectivity of anticancer action. The biological properties of 7 were also improved in comparison with those of native building blocks, which suggested the effects of the presence of the sumanene skeleton towards the anticancer action of this molecule. Ferrocenium-tethered sumanene derivatives exhibited potential towards the generation of reactive oxygen species (ROS), responsible for biological damage to the cancer cells, with the most efficient generation of the tetra-ferrocenium sumanene derivative 7. Derivative 7 also did not show any embryotoxicity in zebrafish embryos at the tested concentrations, which supports its potential as an effective and cancer-specific anticancer agent. In silico computational analysis also showed no chromosomal aberrations and no mutation with AMES tests for the compound 7 tested with and without microsomal rat liver fractions, which supports its further use as a potent drug candidate in detailed anticancer studies.

Virtual Screening and ADMET Prediction to Uncover the Potency of Flavonoids from Genus Erythrina as Antibacterial Agent through Inhibition of Bacterial ATPase DNA Gyrase B

The emergence of antimicrobial resistance due to the widespread and inappropriate use of antibiotics has now become the global health challenge. Flavonoids have long been reported to be a potent antimicrobial agent against a wide range of pathogenic microorganisms in vitro. Therefore, new antibiotics development based on flavonoid structures could be a potential strategy to fight against antibiotic-resistant infections. This research aims to screen the potency of flavonoids of the genus Erythrina as an inhibitor of bacterial ATPase DNA gyrase B. From the 378 flavonoids being screened, 49 flavonoids show potential as an inhibitor of ATPase DNA gyrase B due to their lower binding affinity compared to the inhibitor and ATP. Further screening for their toxicity, we identified 6 flavonoids from these 49 flavonoids, which are predicted to have low toxicity. Among these flavonoids, erystagallin B (334) is predicted to have the best pharmacokinetic properties, and therefore, could be further developed as new antibacterial agent.

Pharmacophore based virtual screening for identification of effective inhibitors to combat HPV 16 E6 driven cervical cancer

Targeting HPV16 E6 has emerged as an effective drug target for the treatment/management of cervical cancer. We utilized pharmacophore-based virtual screening, molecular docking, absorption, distribution, metabolism and excretion (ADME) prediction, and molecular dynamics simulation approach for identifying potential inhibitors of HPV16 E6. Initially, we generated a ligand-based pharmacophore model based on the features of four known HPV16 E6 inhibitors (CA24, CA25, CA26, and CA27) via the PHASE module implanted in the Schrödinger suite. We constructed four-point pharmacophore features viz., three hydrogen bond acceptors (A) and one aromatic ring (R). The common pharmacophore feature further employed as a query for virtual screening against the ASINEX database via Schrödinger suite. The pharmacophore-based virtual screening filtered out top 2000 hits, based on the fitness score. We then applied the high throughput virtual screening (HTVS), standard precision (SP) and extra precision (XP). 1000 compounds were obtained from HTVS docking. Based on the glide score, they were further filtered to 500 hits by employing docking in standard precision mode. Finally, the best four hits and a negative molecule were identified using docking in XP mode. The four lead compounds and a negative molecule were then further subjected to ADME profile prediction by engaging Qikprop module. The ADME properties of the four lead molecules indicate good pharmacokinetic (PK) properties rather than the negative molecule. The binding stability of the HPV16 E6-hit complexes were investigated at a different time scale (100 ns) by using the desmond package and the results were examined using Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) and it revealed the stability of the protein-ligand complex throughout the simulation. Key residues, CYS 51 and GLN 107, also play a crucial role in enhancing the stability of the protein-ligand complex during the simulation. Furthermore, the binding free energy of the HPV16 E6-leads complexes was analyzed by prime which revealed that the ΔGbind coulomb and ΔGbind vdW interactions are crucially contributes to the binding affinity. In order to validate the computational findings, the efficacy of benzoimidazole and benzotriazole were ascertained for regulating ME180 cervical cancer cell survival, migration and ability to release MMP-2.

Analysis of metabolites in human gut: illuminating the design of gut-targeted drugs

Gut-targeted drugs provide a new drug modality besides that of oral, systemic molecules, that could tap into the growing knowledge of gut metabolites of bacterial or host origin and their involvement in biological processes and health through their interaction with gut targets (bacterial or host, too). Understanding the properties of gut metabolites can provide guidance for the design of gut-targeted drugs. In the present work we analyze a large set of gut metabolites, both shared with serum or present only in gut, and compare them with oral systemic drugs. We find patterns specific for these two subsets of metabolites that could be used to design drugs targeting the gut. In addition, we develop and openly share a Super Learner model to predict gut permanence, in order to aid in the design of molecules with appropriate profiles to remain in the gut, resulting in molecules with putatively reduced secondary effects and better pharmacokinetics.

Pharmacoinformatics and Breed-Based De Novo Hybridization Studies to Develop New Neuraminidase Inhibitors as Potential Anti-Influenza Agents

Influenza represents a profoundly transmissible viral ailment primarily afflicting the respiratory system. Neuraminidase inhibitors constitute a class of antiviral therapeutics employed in the management of influenza. These inhibitors impede the liberation of the viral neuraminidase protein, thereby impeding viral dissemination from the infected cell to host cells. As such, neuraminidase has emerged as a pivotal target for mitigating influenza and its associated complications. Here, we apply a de novo hybridization approach based on a breed-centric methodology to elucidate novel neuraminidase inhibitors. The breed technique amalgamates established ligand frameworks with the shared target, neuraminidase, resulting in innovative inhibitor constructs. Molecular docking analysis revealed that the seven synthesized breed molecules (designated Breeds 1-7) formed more robust complexes with the neuraminidase receptor than conventional clinical neuraminidase inhibitors such as zanamivir, oseltamivir, and peramivir. Pharmacokinetic evaluations of the seven breed molecules (Breeds 1-7) demonstrated favorable bioavailability and optimal permeability, all falling within the specified parameters for human application. Molecular dynamics simulations spanning 100 nanoseconds corroborated the stability of these breed molecules within the active site of neuraminidase, shedding light on their structural dynamics. Binding energy assessments, which were conducted through MM-PBSA analysis, substantiated the enduring complexes formed by the seven types of molecules and the neuraminidase receptor. Last, the investigation employed a reaction-based enumeration technique to ascertain the synthetic pathways for the synthesis of the seven breed molecules.

Investigations of Electronic, Structural, and In Silico Anticancer Potential of Persuasive Phytoestrogenic Isoflavene-Based Mannich Bases

Isoflavenes have received the greatest research attention among the many groups of phytoestrogens. In this study, various isoflavene-based Mannich bases were selected for their theoretical studies. The purpose of this research was to discover the binding potential of all the designated Mannich bases acting as inhibitors against cancerous proteins EGFR, cMet, hTrkA, and HER2 (PDB codes: 5GTY, 3RHK, 6PL2, and 7JXH, respectively). For their virtual screening, DFT calculations and molecular docking studies were undertaken using in silico software. Docking studies predicted that ligands 5 and 15 exhibited the highest docking score by forming hydrogen bonds within the active pocket of protein 6PL2, ligands 1 and 15 both with protein 3RHK, and 7JXH, 12, and 17 with protein 5GTY. Rendering to the trends in polarizability and dipole moment, the energy gap values (0.2175 eV, 0.2106 eV) for the firm conformers of Mannich bases (1 and 4) replicate the increase in bioactivity and chemical reactivity. The energy gap values (0.2214 eV and 0.2172 eV) of benzoxazine-substituted isoflavene-based Mannich bases (9 and 10) reflect the increase in chemical potential due to the most stable conformational arrangements. The energy gap values (0.2188 eV and 0.2181 eV) of isoflavenes with tertiary amine-based Mannich bases (14 and 17) reflect the increase in chemical reactivity and bioactivity due to the most stable conformational arrangements. ADME was also employed to explore the pharmacokinetic properties of targeted moieties. This study revealed that these ligands have a strong potential to be used as drugs for cancer treatment.

Computer-Aided Drug Design of Novel Derivatives of 2-Amino-7,9-dihydro-8H-purin-8-one as Potent Pan-Janus JAK3 Inhibitors

Rheumatoid arthritis (RA) remains one of the most prevalent autoimmune diseases worldwide. Janus kinase 3 (JAK3) is an essential enzyme for treating autoimmune diseases, including RA. Molecular modeling techniques play a crucial role in the search for new drugs by reducing time delays. In this study, the 3D-QSAR approach is employed to predict new JAK3 inhibitors. Two robust models, both field-based with R2 = 0.93, R = 0.96, and Q2 = 87, and atom-based with R2 = 0.94, R = 0.97, and Q2 = 86, yielded good results by identifying groups that may readily direct their interaction. A reliable pharmacophore model, DHRRR1, was provided in this work to enable the clear characterization of chemical features, leading to the design of 13 inhibitors with their pIC50 values. The DHRRR1 model yielded a validation result with a ROC value of 0.87. Five promising inhibitors were selected for further study based on an ADMET analysis of their pharmacokinetic properties and covalent docking (CovDock). Compared to the FDA-approved drug tofacitinib, the pharmaceutical features, binding affinity and stability of the inhibitors were analyzed through CovDock, 300 ns molecular dynamics simulations, free energy binding calculations and ADMET predictions. The results show that the inhibitors have strong binding affinity, stability and favorable pharmaceutical properties. The newly predicted molecules, as JAK3 inhibitors for the treatment of RA, are promising candidates for use as drugs.

Development of a new drug candidate for the inhibition of Lassa virus glycoprotein and nucleoprotein by modification of evodiamine as promising therapeutic agents

The Lassa virus (LASV), an RNA virus prevalent in West and Central Africa, causes severe hemorrhagic fever with a high fatality rate. However, no FDA-approved treatments or vaccines exist. Two crucial proteins, LASV glycoprotein and nucleoprotein, play vital roles in pathogenesis and are potential therapeutic targets. As effective treatments for many emerging infections remain elusive, cutting-edge drug development approaches are essential, such as identifying molecular targets, screening lead molecules, and repurposing existing drugs. Bioinformatics and computational biology expedite drug discovery pipelines, using data science to identify targets, predict structures, and model interactions. These techniques also facilitate screening leads with optimal drug-like properties, reducing time, cost, and complexities associated with traditional drug development. Researchers have employed advanced computational drug design methods such as molecular docking, pharmacokinetics, drug-likeness, and molecular dynamics simulation to investigate evodiamine derivatives as potential LASV inhibitors. The results revealed remarkable binding affinities, with many outperforming standard compounds. Additionally, molecular active simulation data suggest stability when bound to target receptors. These promising findings indicate that evodiamine derivatives may offer superior pharmacokinetics and drug-likeness properties, serving as a valuable resource for professionals developing synthetic drugs to combat the Lassa virus.

MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.

Improving on in-silico prediction of oral drug bioavailability

INTRODUCTION

Although significant development has been made in high-throughput screening of oral drug absorption and oral bioavailability, in silico prediction continues to play an important role in prediction of oral bioavailability and assisting in the proper selection of potential drug candidates.

AREAS COVERED

This review describes the improvements and latest modeling methods and algorithms available for the prediction of this important parameter. We performed a PubMed database search with a focus on the literature published in the last 15 years.

EXPERT OPINION

A tremendous efforts have been done in the past several years to develop reliable prediction tools that can provide accurate prediction for oral bioavailability. Several studies demonstrated new methodologies and techniques to develop either web-based in silico predictive tools or integrated PBPK models to predict oral bioavailability for new molecules. Improvements in the databases and the computational power will enhance the in silico prediction accuracy and reliability. Finally, introducing artificial intelligence to the drug development process will help improve the prediction tools.

Design and Identification of Inhibitors for the Spike-ACE2 Target of SARS-CoV-2

When an epidemic started in the Chinese city of Wuhan in December 2019, coronavirus was identified as the cause. Infection by the virus occurs through the interaction of viral S protein with the hosts' angiotensin-converting enzyme 2. By leveraging resources such as the DrugBank database and bioinformatics techniques, ligands with potential activity against the SARS-CoV-2 spike protein were designed and identified in this investigation. The FTMap server and the Molegro software were used to determine the active site of the Spike-ACE2 protein's crystal structure. Virtual screening was performed using a pharmacophore model obtained from antiparasitic drugs, obtaining 2000 molecules from molport^®. The ADME/Tox profiles were used to identify the most promising compounds with desirable drug characteristics. The binding affinity investigation was then conducted with selected candidates. A molecular docking study showed five structures with better binding affinity than hydroxychloroquine. Ligand_003 showed a binding affinity of -8.645 kcal·mol^-1, which was considered an optimal value for the study. The values presented by ligand_033, ligand_013, ligand_044, and ligand_080 meet the profile of novel drugs. To choose compounds with favorable potential for synthesis, synthetic accessibility studies and similarity analyses were carried out. Molecular dynamics and theoretical IC₅₀ values (ranging from 0.459 to 2.371 µM) demonstrate that these candidates are promising for further tests. Chemical descriptors showed that the candidates had strong molecule stability. Theoretical analyses here show that these molecules have potential as SARS-CoV-2 antivirals and therefore warrant further investigation.

Experimental and computational models to investigate intestinal drug permeability and metabolism

Oral administration is the preferred route for drug administration that leads to better therapy compliance. The intestine plays a key role in the absorption and metabolism of oral drugs, therefore, new intestinal models are being continuously proposed, which contribute to the study of intestinal physiology, drug screening, drug side effects, and drug-drug interactions.Advances in pharmaceutical processes have produced more drug formulations, causing challenges for intestinal models. To adapt to the rapid evolution of pharmaceuticals, more intestinal models have been created. However, because of the complexity of the intestine, few models can take all aspects of the intestine into account, and some functions must be sacrificed to investigate other areas. Therefore, investigators need to choose appropriate models according to the experimental stage and other requirements to obtain the desired results.To help researchers achieve this goal, this review summarised the advantages and disadvantages of current commonly used intestinal models and discusses possible future directions, providing a better understanding of intestinal models.

In Vitro Drug Repurposing: Focus on Vasodilators

Drug repurposing aims to identify new therapeutic uses for drugs that have already been approved for other conditions. This approach can save time and resources compared to traditional drug development, as the safety and efficacy of the repurposed drug have already been established. In the context of cancer, drug repurposing can lead to the discovery of new treatments that can target specific cancer cell lines and improve patient outcomes. Vasodilators are a class of drugs that have been shown to have the potential to influence various types of cancer. These medications work by relaxing the smooth muscle of blood vessels, increasing blood flow to tumors, and improving the delivery of chemotherapy drugs. Additionally, vasodilators have been found to have antiproliferative and proapoptotic effects on cancer cells, making them a promising target for drug repurposing. Research on vasodilators for cancer treatment has already shown promising results in preclinical and clinical studies. However, additionally research is needed to fully understand the mechanisms of action of vasodilators in cancer and determine the optimal dosing and combination therapy for patients. In this review, we aim to explore the molecular mechanisms of action of vasodilators in cancer cell lines and the current state of research on their repurposing as a treatment option. With the goal of minimizing the effort and resources required for traditional drug development, we hope to shed light on the potential of vasodilators as a viable therapeutic strategy for cancer patients.

Galantamine Based Novel Acetylcholinesterase Enzyme Inhibitors: A Molecular Modeling Design Approach

Acetylcholinesterase (AChE) enzymes play an essential role in the development of Alzheimer's disease (AD). Its excessive activity causes several neuronal problems, particularly psychopathies and neuronal cell death. A bioactive pose on the hAChE B site of the human acetylcholinesterase (hAChE) enzyme employed in this investigation, which was obtained from the Protein Data Bank (PDB ID 4EY6), allowed for the prediction of the binding affinity and free binding energy between the protein and the ligand. Virtual screening was performed to obtain structures similar to Galantamine (GNT) with potential hAChE activity. The top 200 hit compounds were prioritized through the use of filters in ZincPharmer, with special features related to the pharmacophore. Critical analyses were carried out, such as hierarchical clustering analysis (HCA), ADME/Tox predictions, molecular docking, molecular simulation studies, synthetic accessibility (SA), lipophilicity, water solubility, and hot spots to confirm the stable binding of the two promising molecules (ZINC16951574-LMQC2, and ZINC08342556-LMQC5). The metabolism prediction, with metabolites M3-2, which is formed by Glutathionation reaction (Phase II), M1-2, and M2-2 formed from the reaction of S-oxidation and Aliphatic hydroxylation (Phase I), were both reactive but with no side effects. Theoretical synthetic routes and prediction of synthetic accessibility for the most promising compounds are also proposed. In conclusion, this study shows that in silico modeling can be used to create new drug candidate inhibitors for hAChE. The compounds ZINC16951574-LMQC2, and ZINC08342556-LMQC5 are particularly promising for oral administration because they have a favorable drug-likeness profile, excellent lipid solubility, high bioavailability, and adequate pharmacokinetics.

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski's rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

Structure-activity relationship of a peptide permeation enhancer

The pentapeptide L-R5 has previously been shown to transiently increase the permeability of nasal epithelial cell layers in vitro, allowing paracellular transport of molecules of up to 4 kDa. Protein kinase C zeta (PKC ζ), a member of a family of serine/threonine kinases was shown to be involved in tight junction modulation induced by L-R5. We show here that the ability of L-R5 to modulate tight junctions is comparable to other permeability enhancers such as bilobalide, latrunculin A or C₁₀. Interaction of the peptide with the target protein occurs via electrostatic interaction, with the presence of positive charges being essential for its functionality. L-R5 is myristoylated to allow quick cell entry and onset of activity. While no epithelial cytotoxicity was detected, the hydrophobic myristoyl rest was shown to cause haemolysis. Taken together, these data show that a structural optimization of L-R5 may be possible, both from a toxicological and an efficacy point of view.

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug-drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.

Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations

Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypic effects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs. In this study, first, we constructed quantitative associations of substructure-domain, domain-ADR, and domain-ATC (Anatomical Therapeutic Chemical Classification System code) through L1LOG and L1SVM machine learning models. These associations represent relationships between phenotypes (ADRs and ATCs) and local structures of drugs and proteins. Then, based on these established associations, substructure-phenotype relationships were constructed which were utilized to quantify drug-phenotype relationships. Thus, this approach could achieve high-throughput and effective evaluations of the druggability of NDCs by referring to the established substructure-phenotype relationships and structural information of NDCs without additional prior knowledge. Using this computational pipeline, 83,205 drug-ATC relationships (including 1,479 drugs and 178 ATCs) and 306,421 drug-ADR relationships (including 1,752 drugs and 454 ADRs) were predicted in total. The prediction results were validated at four levels: five-fold cross validation, public databases, literature, and molecular docking. Furthermore, three case studies demonstrated the feasibility of our method. 79 ATCs and 269 ADRs were predicted to be related to Maraviroc, an approved drug, including the existing antiviral effect in clinical use. Additionally, we also found risk substructures of severe ADRs, for example, SUB215 (>= 1, saturated or only aromatic carbon ring size 7) can result in shock. And we analyzed the mechanism of action (MOA) of interested drugs based on the established drug-substructure-domain-protein associations. In a word, this approach through establishing drug-substructure-phenotype relationships can achieve quantitative prediction of phenotypes for a given NDC or drug without any prior knowledge except its structure information. Using that way, we can directly obtain the relationships between substructure and phenotype of a compound, which is more convenient to analyze the phenotypic mechanism of drugs and accelerate the process of rational drug design.

Identification and structural studies of natural inhibitors against SARS-CoV-2 viral RNA methyltransferase (NSP16)

Pathogenic RNA viruses are emerging as one of the major threats and posing challenges to human community. RNA viruses have an exceptionally shorter generation time and easy to adapt in host cells. The recent emergence of SARS-CoV-2, a long RNA virus, has shown us how difficult it is to overcome this kind of pandemic without understanding the viral infection and replication mechanisms. It is essential to comprehend replications of the viral genome, including RNA polymerization and the final capping process. The mRNAs of SARS-CoV-2 coronaviruses are protected at their 5'-ends by cap structure. The cap-like system plays a significant role in viral translational process, viral RNA stability, and scatting in detecting innate immune recognition in host cells. Two coronavirus enzymes, Nsp14 and Nsp16, critically help in the formation of capping and are considered as potential drug targets for antiviral therapy. Natural and herbal medicines have a past record of treating various acute respiratory diseases. In this work, we have exploited 56000 natural compounds to screen potential inhibitors against NSP16. In silico virtual screening, docking and Molecular Dynamics (MD) simulation studies were performed to understand how these potential inhibitors are bound to NSP16. We observed that the most highly screened compound binds to protein molecules with a high dock score, primarily through hydrophobic interactions and hydrogen bonding, as previously reported for NSP16. Compound-13 (2-hydroxy-N-({1-[2-hydroxy-1-(hydroxymethyl)ethyl]piperidin-3-yl}methyl)-5-methylbenzamide) and compound-51 (N-(2-isobutoxybenzyl)-N,2-dimethyl-2,8-diazaspiro[4.5]decane-3-carboxamide) occupied in active site along with good pharmokinetices properties. In conclusion, the selected compounds could be used as a novel therapeutic against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Pharmacophore-Oriented Identification of Potential Leads as CCR5 Inhibitors to Block HIV Cellular Entry

Cysteine-cysteine chemokine receptor 5 (CCR5) has been discovered as a co-receptor for cellular entry of human immunodeficiency virus (HIV). Moreover, the role of CCR5 in a variety of cancers and various inflammatory responses was also discovered. Despite the fact that several CCR5 antagonists have been investigated in clinical trials, only Maraviroc has been licensed for use in the treatment of HIV patients. This indicates that there is a need for novel CCR5 antagonists. Keeping this in mind, the present study was designed. The active CCR5 inhibitors with known IC₅₀ value were selected from the literature and utilized to develop a ligand-based common feature pharmacophore model. The validated pharmacophore model was further used for virtual screening of drug-like databases obtained from the Asinex, Specs, InterBioScreen, and Eximed chemical libraries. Utilizing computational methods such as molecular docking studies, molecular dynamics simulations, and binding free energy calculation, the binding mechanism of selected inhibitors was established. The identified Hits not only showed better binding energy when compared to Maraviroc, but also formed stable interactions with the key residues and showed stable behavior throughout the 100 ns MD simulation. Our findings suggest that Hit1 and Hit2 may be potential candidates for CCR5 inhibition, and, therefore, can be considered for further CCR5 inhibition programs.

In silico and in vitro ADME-Tox analysis and in vivo pharmacokinetic study of representative pan-PDE inhibitors from the group of 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione

Phosphodiesterase (PDE) inhibitors represent a wide class of chemically different compounds that have been extensively studied in recent years. Their anti-inflammatory and anti-fibrotic effects are particularly desirable in the treatment of chronic respiratory diseases, including asthma and chronic obstructive pulmonary disease (COPD). Due to diversified expression of individual PDEs within cells and/or tissues as well as PDE signaling compartmentalization, pan-PDE inhibitors (compounds capable of simultaneously blocking various PDE subtypes) are of particular interest. Recently, a large group of 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione (theophylline) was designed and synthesized. These compounds were characterized as potent pan-PDE inhibitors and their prominent anti-inflammatory and anti-fibrotic activity in vitro has been proved. Herein, we investigated a general in vitro safety profile and pharmacokinetic characteristics of two leading compounds from this group: a representative compound with N'-benzylidenebutanehydrazide moiety (38) and a representative derivative containing N-phenylbutanamide fragment (145). Both tested pan-PDE inhibitors revealed no cytotoxic, mutagenic, and genotoxic activity in vitro, showed moderate metabolic stability in mouse and human liver microsomes, as well as fell into the low or medium permeation category. Additionally, 38 and 145 revealed a lack of interaction with adenosine receptors, including A1, A2A, and A2B. Pharmacokinetic analysis revealed that both tested 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione were effectively absorbed from the peritoneal cavity. Simultaneously, they were extensively distributed to mouse lungs and after intraperitoneal (i.p.) administration were detected in bronchoalveolar lavage fluid. These findings provide evidence that investigated compounds represent a new drug candidates with a favorable in vitro safety profile and satisfactory pharmacokinetic properties after a single i.p. administration. As the next step, further pharmacokinetic studies after multiple i.p. and p.o. doses will be conducted to ensure effective 38 and 145 serum and lung concentrations for a longer period of time. In summary, 7,8-disubstituted derivatives of 1,3-dimethyl-7H-purine-2,6-dione represent a promising compounds worth testing in animal models of chronic respiratory diseases, the etiology of which involves various PDE subtypes.

A Review: Meridianins and Meridianins Derivatives

Meridianins are a family of indole alkaloids derived from Antarctic tunicates with extensive pharmacological activities. A series of meridianin derivatives had been synthesized by drug researchers. This article reviews the extraction and purification methods, biological activities and pharmacological applications, pharmacokinetic characters and chemical synthesis of meridianins and their derivatives. And prospects on discovering new bioactivities of meridianins and optimizing their structure for the improvement of the ADMET properties are provided.

Evaluation of the Antifungal, Antioxidant, and Anti-Diabetic Potential of the Essential Oil of Curcuma longa Leaves from the North-Western Himalayas by In Vitro and In Silico Analysis

Essential oils (EOs) have gained immense popularity due to considerable interest in the health, food, and pharmaceutical industries. The present study aimed to evaluate the antimicrobial and antioxidant activity and the anti-diabetic potential of Curcuma longa leaf (CLO) essential oil. Further, major phytocompounds of CLO were analyzed for their in-silico interactions with antifungal, antioxidant, and anti-diabetic proteins. CLO was found to have a strong antifungal activity against the tested Candida species with zone of inhibition (ZOI)-11.5 ± 0.71 mm to 13 ± 1.41 mm and minimum inhibitory concentration (MIC) was 0.63%. CLO also showed antioxidant activity, with IC₅₀ values of 5.85 ± 1.61 µg/mL using 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging assay and 32.92 ± 0.64 µM using ferric reducing antioxidant power (FRAP) assay. CLO also showed anti-diabetic activity with an IC₅₀ of 43.06 ± 1.24 µg/mL as compared to metformin (half maximal inhibitory concentration, IC₅₀-16.503 ± 0.66 µg/mL). Gas chromatography-mass spectrometry (GC-MS) analysis of CLO showed the presence of (-)-zingiberene (17.84%); 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-(15.31%); cyclohexene, 4-methyl-3-(1-methylethylidene) (12.47%); and (+)-4-Carene (11.89%) as major phytocompounds. Molecular docking of these compounds with antifungal proteins (cytochrome P450 14 alpha-sterol demethylase, PDB ID: 1EA1, and N-myristoyl transferase, PDB ID: 1IYL), antioxidant (human peroxiredoxin 5, PDB ID: 1HD2), and anti-diabetic proteins (human pancreatic alpha-amylase, PDB ID: 1HNY) showed strong binding of 3,7-cyclodecadien-1-one with all the selected protein targets. Furthermore, molecular dynamics (MD) simulations for a 100 ns time scale revealed that most of the key contacts of target proteins were retained throughout the simulation trajectories. Binding free energy calculations using molecular mechanics generalized born surface area (MM/GBSA), and drug-likeness and toxicity analysis also proved the potential for 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) to replace toxic synthetic drugs and act as natural antioxidants.

In Vitro Cytotoxicity Evaluation of Plastoquinone Analogues against Colorectal and Breast Cancers along with In Silico Insights

Colorectal cancer (CRC) and breast cancer are leading causes of death globally, due to significant challenges in detection and management. The late-stage diagnosis and treatment failures require the discovery of potential anticancer agents to achieve a satisfactory therapeutic effect. We have previously reported a series of plastoquinone analogues to understand their cytotoxic profile. Among these derivatives, three of them (AQ-11, AQ-12, and AQ-15) were selected by the National Cancer Institute (NCI) to evaluate their in vitro antiproliferative activity against a panel of 60 human tumor cell lines. AQ-12 exhibited significant antiproliferative activity against HCT-116 CRC and MCF-7 breast cancer cells at a single dose and further five doses. MTT assay was also performed for AQ-12 at different concentrations against these two cells, implying that AQ-12 exerted notable cytotoxicity toward HCT-116 (IC₅₀ = 5.11 ± 2.14 μM) and MCF-7 (IC₅₀ = 6.06 ± 3.09 μM) cells in comparison with cisplatin (IC₅₀ = 23.68 ± 6.81 μM and 19.67 ± 5.94 μM, respectively). This compound also augmented apoptosis in HCT-116 (62.30%) and MCF-7 (64.60%) cells comparable to cisplatin (67.30% and 78.80%, respectively). Molecular docking studies showed that AQ-12 bound to DNA, forming hydrogen bonding through the quinone scaffold. In silico pharmacokinetic determinants indicated that AQ-12 demonstrated drug-likeness with a remarkable pharmacokinetic profile for future mechanistic anti-CRC and anti-breast cancer activity studies.

Polymyxin B Activity Rescue by (−)-Camphene-Based Thiosemicarbazide Against Carbapenem-Resistant Enterobacterales

Due to the significant shortage of therapeutic options for carbapenem-resistant Enterobacterales (CRE) infections, new drugs or therapeutic combinations are urgently required. We show in this study that (-)-camphene-based thiosemicarbazide (TSC) may act synergistically with polymyxin B (PMB) against CRE, rescuing the activity of this antimicrobial. With the specific aim of a better molecular understanding of this effect caused by the presence of TSC, theoretical calculations were also performed in this study. Based on these findings, it is concluded that the presence of TSC moieties contributes to significant changes in the hydrogen atom charge of PMB structure, which trend more positives for the PMB/TSC system studied. This could lead to the formation of stronger hydrogen bonds in the Enterobacterales active site and, thus contribute to a molecular understanding of the PMB rescue of activity promoted by the presence of TSC moiety. As such, the clinical potential of these drug combinations requires further evaluation.

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.

Synthesis, Antimicrobial Activity, and Molecular Modeling Studies of Some Benzoxazole Derivatives

Background:

The need to develop novel antimicrobial agents is apparent as infectious diseases are increasing and resistance is rapidly developing against the drugs used in the treatment.

Objective:

This study aimed at the synthesis, antimicrobial susceptibility testing, and computational elucidation of the mechanism of action of benzoxazole derivatives. It also aimed to compare the results obtained in this study with the previous studies by our group. This would pave the way for designing novel molecules with better antimicrobial activity. The other goal was pharmacophore analysis and in silico ADMET analysis of them.

Methods:

In this study, synthesis, antimicrobial susceptibility testing, molecular docking, pharmacophore analysis, and ADMET prediction were carried out.

Results:

The antimicrobial activity studies demonstrated that the synthesized compounds were active against standard strains and clinical isolates at high concentrations. Then, the antimicrobial testing results were compared to similar benzoxazoles tested by our group previously. Benzoxazole derivatives without a methylene bridge between oxazole and phenyl ring were found to be more active than those with the methylene bridge. This was also confirmed by molecular modeling undertaken in this study. The computational results indicated that the antibacterial activity could be achieved by DNA gyrase inhibition. Pharmacophore analysis showed that hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), and hydrophobicity features would contribute to the inhibition. In addition, in silico ADMET property investigation of the compounds exhibited that they had the desired pharmacokinetics.

Conclusion:

Although antibacterial activity by inhibiting DNA gyrase is selective, the synthesized compounds were active at much higher concentrations than the standards. Therefore, in prospective antimicrobial studies, it is better to focus on benzoxazole derivatives without the methylene bridge. Since the compounds had suitable in silico ADMET properties, screening them against the other pharmacologic activities should be carried out. It is recommended to support the molecular modeling results with in vitro or in vivo studies.

Pluripotent Stem Cell Assays: Modalities and Applications For Predictive Developmental Toxicity

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A systematic scoping review of the literature evaluated the embryonic stem cell test (EST).

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1533 publications included 18 publications testing 10 or more compounds in human or mouse EST.

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Selected case examples included 5-fluorouracil, thalidomide, and caffeine.

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Applicability, limitations, and recommendations for further work are discussed.

This manuscript provides a review focused on embryonic stem cell-based models and their place within the landscape of alternative developmental toxicity assays. Against the background of the principles of developmental toxicology, the wide diversity of alternative methods using pluripotent stem cells developed in this area over the past half century is reviewed. In order to provide an overview of available models, a systematic scoping review was conducted following a published protocol with inclusion criteria, which were applied to select the assays. Critical aspects including biological domain, readout endpoint, availability of standardized protocols, chemical domain, reproducibility and predictive power of each assay are described in detail, in order to review the applicability and limitations of the platform in general and progress moving forward to implementation. The horizon of innovative routes of promoting regulatory implementation of alternative methods is scanned, and recommendations for further work are given.

Deep learning tools for advancing drug discovery and development

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

Virtual Therapy with the NMDA Antagonist Memantine in Hippocampal Models of Moderate to Severe Alzheimer’s Disease, in Silico Trials

The variability in clinical trial results on memantine treatment of Alzheimer’s disease remains incompletely explained. The aim of this in silico study is a virtual memantine therapy for Alzheimer’s disease that provides a different perspective on clinical trials; An in silico randomised trial using virtual hippocampi to treat moderate to severe Alzheimer’s disease with doses of memantine 3–30 µM compared to placebo. The primary endpoint was the number of impulses (spikes). Secondary endpoints included interspike interval and frequency; The number of virtual moderate-AD hippocampal spikes was significantly lower, at 1648.7 (95% CI, 1344.5–1952.9), versus those treated with the 3 µM dose, 2324.7 (95% CI, 2045.9–2603.5), and the 10 µM dose, 3607.0 (95% CI, 3137.6–4076.4). In contrast, the number of virtual spikes (spikes) of severe AD of the hippocampus was significantly lower, at 1461.8 (95% CI, 1196.2–1727.4), versus those treated with the 10 µM dose, at 2734.5 (95% CI, 2369.8–3099.2), and the 30 µM dose, at 3748.9 (95% CI, 3219.8–4278.0). The results of the analysis of secondary endpoints, interspike intervals and frequencies changed statistically significantly relative to the placebo; The results of the in silico study confirm that memantine monotherapy is effective in the treatment of moderate to severe Alzheimer’s disease, as assessed by various neuronal parameters.

In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions

The notion of a contribution of a specific group in an organic molecule’s property and/or activity is both common in our thinking and is still not strictly correct due to the inherent non-additivity of free energy with respect to molecular fragments composing a molecule. The fragment- based drug discovery (FBDD) approach has proven to be fruitful in addressing the above notions. The main difficulty of the FBDD, however, is in its reliance on the low throughput and expensive experimental means of determining the fragment-sized molecules binding. In this article we propose a way to enhance the throughput and availability of the FBDD methods by judiciously using an in silico means of assessing the contribution to ligand-receptor binding energy of fragments of a molecule under question using a previously developed in silico Reverse Fragment Based Drug Discovery (R-FBDD) approach. It has been shown that the proposed structure-based drug discovery (SBDD) type of approach fills in the vacant niche among the existing in silico approaches, which mainly stem from the ligand-based drug discovery (LBDD) counterparts. In order to illustrate the applicability of the approach, our work retrospectively repeats the findings of the use case of an FBDD hit-to-lead project devoted to the experimentally based determination of additive group efficiency (GE)—an analog of ligand efficiency (LE) for a group in the molecule—using the Free-Wilson (FW) decomposition. It is shown that in using our in silico approach to evaluate fragment contributions of a ligand and to estimate GE one can arrive at similar decisions as those made using the experimentally determined activity-based FW decomposition. It is also shown that the approach is rather robust to the choice of the scoring function, provided the latter demonstrates a decent scoring power. We argue that the proposed approach of in silico assessment of GE has a wider applicability domain and expect that it will be widely applicable to enhance the net throughput of drug discovery based on the FBDD paradigm.