In silico ADME/T modelling for rational drug design

Analysis of bioactive compounds of Olea europaea as potential inhibitors of SARS-CoV-2 main protease: a pharmacokinetics, molecular docking and molecular dynamics simulation studies

COVID-19 is a highly infectious disease caused by a new type of extremely contagious coronavirus called SARS-CoV-2. The virus's main protease enzyme, SARS-CoV-2 Mpro, is essential for its replication and transcription processes. Targeting this enzyme presents a promising avenue for antiviral drug development. Researchers have explored the intricate three-dimensional configurations of the enzyme, analyzing its interactions with various inhibitors. These findings provide a foundation for designing specific and powerful inhibitors targeting SARS-CoV-2 Mpro. Certain plants possess medicinal attributes due to the presence of bioactive compounds that inhibit pathogens. The olive tree (Olea europaea) has served as a source of food and medicine, containing bioactive compounds in its leaves that hinder the proliferation of various pathogens including viruses. This study explores the potential of bioactive compounds from olive leaf extract (OLE) to inhibit SARS-CoV-2 Mpro. In-silico study was conducted to predict the pharmacokinetic and toxicity profiles of these compounds. Molecular docking was utilized to assess their binding affinity to SARS-CoV-2 Mpro and their potential interference with its function. The top three compounds, apigenin (Api), luteolin-7-O-glucoside (Lut) and rutin (Rut), were chosen based on their favorable drug-like properties and strong binding affinities to Mpro. Detailed molecular dynamics simulations demonstrated the stability of SARS-CoV-2 Mpro in conjunction with these compounds, showing minimal structural alterations over the simulation period. Particularly, Lut and Rut formed bonds with critical amino acid residues His41 and Cys145 of Mpro, suggesting their potential inhibitory effect. These findings suggest that these compounds hold promise as natural drug candidates for combating COVID-19.Communicated by Ramaswamy H. Sarma.

Molecular modeling, synthesis and biological evaluation of caffeic acid based Dihydrofolate reductase inhibitors

Dihydrofolate reductase (DHFR) is an enzyme that plays a crucial role in folate metabolism, which is essential for cell growth and division. DHFR has been identified as a molecular target for numerous diseases due to its significance in various biological processes. DHFR inhibitors can disrupt folate metabolism by inhibiting DHFR, leading to the inhibition of cell growth. So, a series of caffeic acid derivatives were designed, synthesized, characterized and evaluated for their in vitro ability to inhibit DHFR, as well as their antimicrobial and anticancer properties. Among all synthesized compounds, compound CE11 exhibited the highest DHFR inhibitory activity, with an IC50 value of 0.048 µM, which is approximately four times more potent than methotrexate. Compound CE11 exhibited similar docking performance to methotrexate, binding to the same site and engaging key residues such as Glh30, Phe31, Phe34, and Ser59. It also fit snugly in the hydrophobic pocket of modeled protein. Moreover, substantial hydrophobic interactions were noted between the ligand and the hydrophobic amino acid residues of DHFR. This effectively secured the derivative within the restricted substrate cavity. Furthermore, compound CE11 demonstrated a significant anticancer activity against MCF-7 breast cancer cell line, with an IC50 value of 5.37 ± 0.16 µM. Compounds CE3 and CE15 displayed better antibacterial activity compared to trimethoprim and comparable to ampicillin against the gram-positive bacteria S. aureus. Moreover, compounds CE3 and CE15 have shown better antibacterial activity than standard trimethoprim, ampicillin and tetracycline against the gram-negative bacteria, particularly P. aeruginosa and E. coli. Molecular docking analysis of CE3 revealed that it firmly entrapped into the active site of enzyme through hydrophobic interaction with hydrophobic residues. Additionally, it forms hydrogen bonds with important amino acid residues Ala7, Asn18, and Thr121 with excellent docking score and binding energy (-9.9, -71.77 kcal/mol). These interactions might be contributed to the significant DHFR inhibition and antimicrobial activity. The generated model holds potential value in facilitating the development of a novel category of DHFR inhibitors as anticancer and antimicrobial agents.

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine

Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identification, lead optimization, de novo drug design, and drug repurposing. Frameworks such as deep neural networks (DNNs), convolutional neural networks (CNNs), and deep reinforcement learning (DRL) models have shown promise in identifying drug targets, optimizing delivery systems, and accelerating drug repurposing. Generative adversarial networks (GANs) and variational autoencoders (VAEs) aid de novo drug design by creating novel drug-like compounds with desired properties. Case studies, such as DDR1 kinase inhibitors designed using generative models and CDK20 inhibitors developed via structure-based methods, highlight AI's ability to produce highly specific therapeutics. Models like SNF-CVAE and DeepDR further advance drug repurposing by uncovering new therapeutic applications for existing drugs. Advanced ML algorithms enhance precision in predicting drug efficacy, toxicity, and ADME-Tox properties, reducing development costs and improving drug-target interactions. AI also supports polypharmacology by optimizing multi-target drug interactions and enhances combination therapy through predictions of drug synergies and antagonisms. In nanomedicine, AI models like CURATE.AI and the Hartung algorithm optimize personalized treatments by predicting toxicological risks and real-time dosing adjustments with high accuracy. Despite its potential, challenges like data quality, model interpretability, and ethical concerns must be addressed. High-quality datasets, transparent models, and unbiased algorithms are essential for reliable AI applications. As AI continues to evolve, it is poised to revolutionize drug discovery and personalized medicine, advancing therapeutic development and patient care.

Identification of AChE targeted therapeutic compounds for Alzheimer's disease: an in-silico study with DFT integration

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by cognitive deterioration and changes in behavior. Acetylcholinesterase (AChE), which hydrolyzes acetylcholine, is a key drug target for treating AD. This research aimed to identify new AChE inhibitors using the IMPPAT database. We used known drugs as a basis to search for similar chemicals in the IMPPAT database and created a library of 127 plant-based compounds. Initial screening of these compounds was performed using molecular docking, followed by an analysis of their drug-likeness and ADMET properties. Compounds with favorable properties underwent density functional theory (DFT) calculations to assess their electronic properties such as HOMO-LUMO gap, electron density, and molecular orbital distribution. These descriptors provided insights into each compound's reactivity, stability, and binding potential with AChE. Promising candidates were further evaluated through molecular dynamics (MD) simulations over 100 ns and MMPBSA analysis for the last 30 ns. Two compounds, Biflavanone (IMPHY013027) with a binding free energy of - 130.394 kcal/mol and Calomelanol J (IMPHY007737) with - 107.908 kcal/mol, demonstrated strong binding affinities compared to the reference molecule HOR, which has a binding free energy of - 105.132 kcal/mol. These compounds exhibited promising drug-ability profiles in both molecular docking and MD simulations, indicating their potential as novel AChE inhibitors for AD treatment. However, further experimental validation is necessary to verify their effectiveness and safety.

In-silico identification and exploration of small molecule coumarin-1,2,3-triazole hybrids as potential EGFR inhibitors for targeting lung cancer

Globally, lung cancer is a significant public health concern due to its role as the leading cause of cancer-related mortalities. The promising target of EGFR for lung cancer treatment has been identified, providing a potential avenue for more effective therapies. The purpose of the study was to design a library of 1843 coumarin-1,2,3-triazole hybrids and screen them based on a designed pharmacophore to identify potential inhibitors targeting EGFR in lung cancer with minimum or no side effects. Pharmacophore-based screening was carried out and 60 hits were obtained. To gain a better understanding of the binding interactions between the compounds and the targeted receptor, molecular docking was conducted on the 60 screened compounds. In-silico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results indicated that coumarin-1,2,3-triazole hybrids COUM-0849, COUM-0935, COUM-0414, COUM-1335, COUM-0276, and COUM-0484 exhibit dock score of - 10.2, - 10.2, - 10.1, - 10.1, - 10, - 10 while reference molecule - 7.9 kcal/mol for EGFR (PDB ID: 4HJO) respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, we performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of coumarin-1,2,3-triazole hybrids as promising EGFR inhibitors for the management of lung cancer.

Potent ROS inhibitors from Zanthoxylum armatumDC of Nepali origin

A bioassay-guided isolation on the plant Zanthoxylum armatum DC yielded compounds tambulin (1), and prudomestin (2), from ethyl acetate fraction which showed the highest ROS inhibiting activity (IC50 = 17.8 ± 1.1 µg/mL). Structure elucidation of pure compounds was done using mass and NMR spectroscopic techniques. Compounds 1 and 2 revealed potent ROS inhibition with IC50 = 7.5 ± 0.3 and 1.5 ± 0.3 µg/mL, respectively, as compared to standard ibuprofen (IC50 = 11.2 ± 1.9 µg/mL). Likewise, both compounds 1 and 2 showed potent antioxidant activity with IC50 = 32.65 ± 0.31 and 26.96 ± 0.19 µg/mL, respectively. In vitro studies were supported by molecular docking and drug-likeliness properties. In silico studies of 1 and 2 with cyclooxygenase-2 (COX-2) showed perfect binding affinity with binding energies of -8.4 and -8.6 kcal/mol, respectively, comparable to standard ibuprofen (-7.7 kcal/mol). Drug likeness and ADMET showed higher gastrointestinal absorption of 1 and 2 and no toxic impact.

In-silico design, pharmacophore-based screening, and molecular docking studies reveal that benzimidazole-1,2,3-triazole hybrids as novel EGFR inhibitors targeting lung cancer

Lung cancer is a complex and heterogeneous disease, which has been associated with various molecular alterations, including the overexpression and mutations of the epidermal growth factor receptor (EGFR). In this study, designed a library of 1843 benzimidazole-1,2,3-triazole hybrids and carried out pharmacophore-based screening to identify potential EGFR inhibitors. The 164 compounds were further evaluated using molecular docking and molecular dynamics simulations to understand the binding interactions between the compounds and the receptor. In-si-lico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results of this study indicate that benzimidazole-1,2,3-triazole hybrids BENZI-0660, BENZI-0125, BENZI-0279, BENZI-0415, BENZI-0437, and BENZI-1110 exhibit dock scores of -9.7, -9.6, -9.6, -9.6, -9.6, -9.6 while referencing molecule -7.9 kcal/mol for EGFR (PDB ID: 4HJO), respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of benzimidazole-1,2,3-triazole hybrids as promising EGFR inhibitors for the treatment of lung cancer. This research opens up a new avenue for the discovery and development of potent and selective EGFR inhibitors for the treatment of lung cancer.Communicated by Ramaswamy H. Sarma.

Design, synthesis and biological evaluation of a new series of imidazothiazole-hydrazone hybrids as dual EGFR and Akt inhibitors for NSCLC therapy

In search of small molecules for targeted therapy of non-small cell lung carcinoma (NSCLC), an efficient four-step synthetic route was followed for the synthesis of new imidazothiazole-hydrazone hybrids, which were assessed for their cytotoxic effects on human lung adenocarcinoma (A549) and human lung fibroblast (CCD-19Lu) cells. Among them, compounds 4, 6, 13, 16, 17 and 21 exhibited selective cytotoxic activity against A549 cell line. In vitro mechanistic studies were performed to assess their effects on apoptosis, caspase-3, cell cycle, EGFR and Akt in A549 cells. Compounds 6, 16, 17 and 21 promoted apoptotic cell death more than erlotinib. According to the in vitro data, it is quite clear that compound 6 promotes apoptosis through caspase-3 activation and arrests the cell cycle at the G0/G1 phase in A549 cells. Compounds 16 and 17 arrested the cell cycle at the S phase, whereas compounds 4, 13 and 21 caused the cell cycle arrest at the G2/M phase. The most effective EGFR inhibitor in this series was found as compound 13, followed by compounds 17 and 16. Furthermore, Akt inhibitory effects of compounds 16 and 17 in A549 cells were close to that of GSK690693. In particular, it can be concluded that the cytotoxic and apoptotic effects of compounds 16 and 17 are associated with their inhibitory effects on both EGFR and Akt. Molecular docking studies suggest that compounds 16 and 17 interact with crucial amino acid residues in the binding sites of human EGFR (PDB ID: 1M17) and Akt2 (PDB ID: 3D0E). Based on the in silico data, both compounds are predicted to possess favorable oral bioavailability and drug-likeness. Further studies are required to benefit from these compounds as anticancer agents for targeted therapy of NSCLC.

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

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

Protective Effects of Anethole in Foeniculum vulgare Mill. Seed Ethanol Extract on Hypoxia/Reoxygenation Injury in H9C2 Heart Myoblast Cells

BACKGROUND

Active compounds from plants and herbs are increasingly incorporated into modern medical systems to address cardiovascular diseases (CVDs). Foeniculum vulgare Mill., commonly known as fennel, is an aromatic medicinal plant and culinary herb that is popular worldwide.

METHODS

Protective effects against cellular damage were assessed in the H9C2 cardiomyocyte hypoxia/reoxygenation (H/R) experimental model. The identities of phytochemicals in FVSE were determined by GC-MS analysis. The phytochemical's potential for nutrients and pharmacokinetic properties was assessed by ADMET analysis.

RESULTS

GC-MS analysis of the ethanol extracts of F. vulgare identified 41 bioactive compounds, with four prominent ones: anethole, 1-(4-methoxyphenyl)-2-propanone, ethoxydimethylphenylsilane, and para-anisaldehyde diethyl acetal. Among these, anethole stands out due to its potential for nutrients and pharmacokinetic properties assessed by ADMET analysis, such as bioavailability, lipophilicity, flexibility, and compliance with Lipinski's Rule of Five. In the H/R injury model of H9C2 heart myoblast cells, FVSE and anethole suppressed H/R-induced reactive oxygen species (ROS) generation, DNA double-strand break damage, nuclear condensation, and the dissipation of mitochondrial membrane potential (ΔΨm).

CONCLUSIONS

These findings highlight the therapeutic potential of FVSE and its prominent component, anethole, in the treatment of CVDs, particularly those associated with hypoxia-induced damage.

The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

PharmaBench: Enhancing ADMET benchmarks with large language models

Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects.

Identification of potential inhibitors of Mtb InhA: a pharmacoinformatics approach

The emergence of superbugs of multi-drug resistant (MDR/RR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis (Mtb) strains at a faster rate is posing a serious threat to Tuberculosis (TB) control worldwide. Mtb enoyl-acyl carrier protein reductase (InhA) is a well-established target of the front-line anti-TB prodrug Isoniazid (INH), which requires activation by Catalase-peroxidase enzyme (KatG) in order to inhibit InhA enzyme, that is crucial for the biosynthesis of the mycobacterial cell wall. Currently, due to widespread resistance to this drug, it is necessary to identify new clinical candidates that directly inhibit InhA enzyme and do not require activation by KatG, thereby circumventing most of the resistance mechanisms. In the present study, high-throughput virtual screening of ASINEX database was carried out to identify potential direct inhibitors of Mtb InhA. Best twenty compounds with good binding energies ranging between -12.36 and -9.27 kcal/mol were selected as promising virtual screening hits. These molecules were subjected to ADME study followed by toxicity prediction. Finally, four top-ranked molecules which are structurally diverse and possess best binding affinity than the co-crystalized ligand have been chosen for MD simulation studies followed by MM-GBSA analysis to validate and ensure the stability of hits in the active site of the enzyme. Based on the 100 ns MD simulation studies and binding free energy estimates, three hit molecules B244, B369, and B310 could be considered as potential inhibitors for Mtb InhA, which are likely to be potent against INH-resistant Mtb strains after successful experimental validation.Communicated by Ramaswamy H. Sarma.

Synthetic Naphthoquinone Inhibits Herpes Simplex Virus Type-1 Replication Targeting Na+, K+ ATPase

Since 1970 acyclovir (ACV) has been the reference drug in treating herpes simplex virus (HSV) infections. However, resistant herpes simplex virus type 1 (HSV-1) strains have emerged, narrowing the treatment efficacy. The antiviral activity of classical Na+, K+ ATPase enzyme (NKA) inhibitors linked the viral replication to the NKA's activity. Herein, we evaluated the anti-HSV-1 activity of synthetic naphthoquinones, correlating their antiviral activity with NKA inhibition. We tested seven synthetic naphthoquinones initially at 50 μM on HSV-1-infected African green monkey kidney cells (VERO cells). Only one compound, 2-hydroxy-3-(2-thienyl)-1,4-naphthoquinone (AN-06), exhibited higher antiviral activity with a low cytotoxicity. AN-06 reduced the viral titer of 9 (log10) to 1.32 (log10) and decreased the steps of attachment and penetration. The addition of AN-06 up to 20 h postinfection (hpi) interfered with the viral cycle. The viral infection alone increases NKA activity 3 h postinfection (hpi), scaling up to 6 hpi. The addition of AN-06 in a culture infected with HSV-1 decreased NKA activity, suggesting that its antiviral action is linked to NKA inhibition. Also, docking results showed that this compound binds at the same site of NKA in which adenosine triphosphate (ATP) binds. AN-06 exhibited promising pharmacokinetic and toxicology properties. Thus, we postulate that AN-06 may be a good candidate for antiviral compounds with a mechanism of action targeting NKA activity.

In vitro and in silico studies of alpha glucosidase inhibition and antifungal activity of coffea canephora husk

The coffea canephora husk, a protected agricultural crop, is abundant in Vietnam. Examining the effects of C. canephora husk compounds on α-glucosidase and antifungal drug activity was the primary objective of this research. A cholestane-type steroid, coffeacanol A (1), was extracted from the ethyl acetate extract. Three cholestane-type derivatives (2-4) and three additional known compounds (5-7) were separated, and we used a variety of chromatographic techniques to identify a total of six substances. We used NMR to determine the chemical structures of these substances. Extensive HR-MS-ESI analysis and NMR experimental data were used to confirm the structure of the novel metabolite (1). The cholestane-type steroid was initially discovered in the Coffea canephora husk, marking the first instance in the coffee plant family to reveal chemical structures (1-7). The inhibition of α-glucosidase was found to be significantly higher in all compounds tested, with the exception of compounds (2) and (5). In vitro, the positive control showed the lowest inhibition, and the range of IC50 values was calculated to be 27.4 to 96.5 μM, which is lower than the IC50 value of 214.50 μM for the acarbose control. With an IC50 value of 27.4 μM, compound (7) showed the greatest capacity to inhibit α-glucosidase among the test compounds. The 3TOP and 2VF5 enzyme crystal structures were used for in silico docking investigations and validations of compounds (1-7). In silico calculations to explain how compound (7) shows high activity in vitro via the enzyme inhibition mechanism by residual amino acids, like Gly 1102 (B chain) and Glu 1095 (B chain), and their relative interaction with compounds (7) and acarbose. Compound (7) exhibited the best antifungal activity against Candida albicans fungus among three fungi, namely Candida albicans, Trichophyton mentagrophytes, and Trichophyton rubrum, with a MIC value of 25 μM. Compound (7) and fluconazole combined to form similar interactions in the contact ligand model, including the functional group, capping group, and linker part, which interacted fully with the 2VF5 enzyme, leading to effective in vitro inhibition.

Exploring Bryophyllum pinnatum compounds as potential inhibitors for Vespula vulgaris allergen proteins: A systematic computational approach

Vespula vulgaris (V. vulgaris), commonly known as the common wasp, poses a significant health threat due to its venom-induced allergic reactions. This research focused on the exploration of bioactive compounds from Bryophyllum pinnatum as potential inhibitors for V. vulgaris allergen proteins, including Phospholipase A1 (Ves V1), Hyaluronoglucosaminidase (Ves V2), and Antigen 5 (Ves V5). Through a multidisciplinary approach involving literature reviews, molecular docking analyses, ADMET assessments and Molecular Dynamics Simulations (MDS) of 100ns we identified two promising drug candidates from four bioactive compounds- Bryophyllin A, Bryophyllin B, Bryotoxin A, and Bryotoxin B of Bryophyllum pinnatum. Molecular docking results revealed strong binding interactions, with Bryophyllin B and Bryotoxin A consistently exhibiting the highest affinity (-9.6 kcal/mol and -10.0 kcal/mol) across the allergen proteins. ADMET analyses highlighted Bryophyllin B as a favorable candidate, showing high absorption (HIA: 92.1 %), minimal metabolic interactions (CYP1A2: No), and a low toxicity profile (LD50 (rat): 2.431). MDS analysis revealed Bryophyllin B and Bryotoxin A as promising drug inhibitors, exhibiting the highest binding stability with the allergen proteins of V. vulgaris, as indicated by the lowest Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (RG) values and highest protein-ligand contacts. Our study provides valuable insights into the therapeutic potential of Bryophyllum pinnatum compounds as inhibitors for V. vulgaris allergen proteins having two promising candidates- Bryophyllin B and Bryotoxin A.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach

BACKGROUND

Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth.

OBJECTIVES/AIMS

This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model.

METHODS

This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP.

RESULTS

The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis.

CONCLUSION

Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

An in-silico receptor-pharmacophore based multistep molecular docking and simulation study to evaluate the inhibitory potentials against NS1 of DENV-2

DENV-2 strain is the most fatal and infectious of the five dengue virus serotypes. The non-structural protein NS1 encoded by its genome is the most significant protein required for viral pathogenesis and replication inside the host body. Thus, targeting the NS1 protein and designing an inhibitor to limit its stability and secretion is a propitious attempt in our fight against dengue. Four novel inhibitors are designed to target the conserved cysteine residues (C55, C313, C316, and C329) and glycosylation sites (N130 and N207) of the NS1 protein in an attempt to halt the spread of the dengue infection in the host body altogether. Numerous computer-aided drug designing techniques including molecular docking, molecular dynamics simulation, virtual screening, principal component analysis, and dynamic cross-correlation matrix were employed to determine the structural and functional activity of the NS1-inhibitor complexes. From our analysis, it was evident that the extent of structural and atomic level fluctuations of the ligand-bound protein exhibited a declining trend in contrast to unbound protein which was prominently noticeable through the RMSD, RMSF, Rg, and SASA graphs. The ADMET analysis of the four ligands revealed a promising pharmacokinetics and pharmacodynamic profile, along with good bioavailability and toxicity properties. The proposed drugs when bound to the targeted cavities resulted in stable conformations in comparison to their unbound state, implying they have good affinity promising effective drug action. Thus, they can be tested in vitro and used as potential anti-dengue drugs.Communicated by Ramaswamy H. Sarma.

Design, Synthesis, and Biological Evaluations of Novel Thiazolo[4,5-d]pyrimidine Corticotropin Releasing Factor (CRF) Receptor Antagonists as Potential Treatments for Stress Related Disorders and Congenital Adrenal Hyperplasia (CAH)

Corticotropin-releasing factor (CRF) is a key neuropeptide hormone that is secreted from the hypothalamus. It is the master hormone of the HPA axis, which orchestrates the physiological and behavioral responses to stress. Many disorders, including anxiety, depression, addiction relapse, and others, are related to over-activation of this system. Thus, new molecules that may interfere with CRF receptor binding may be of value to treat neuropsychiatric stress-related disorders. Also, CRF1R antagonists have recently emerged as potential treatment options for congenital adrenal hyperplasia. Previously, several series of CRF1 receptor antagonists were developed by our group. In continuation of our efforts in this direction, herein we report the synthesis and biological evaluation of a new series of CRF1R antagonists. Representative compounds were evaluated for their binding affinities compared to antalarmin. Four compounds (2, 5, 20, and 21) showed log IC50 values of -8.22, -7.95, -8.04, and -7.88, respectively, compared to -7.78 for antalarmin. This result indicates that these four compounds are superior to antalarmin by 2.5, 1.4, 1.7, and 1.25 times, respectively. It is worth mentioning that compound 2, in terms of IC50, is among the best CRF1R antagonists ever developed in the last 40 years. The in silico physicochemical properties of the lead compounds showed good drug-like properties. Thus, further research in this direction may lead to better and safer CRF receptor antagonists that may have clinical applications, particularly for stress-related disorders and the treatment of congenital adrenal hyperplasia.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

Computational Design of Novel Tau-Tubulin Kinase 1 Inhibitors for Neurodegenerative Diseases

The tau-tubulin kinase 1 (TTBK1) protein is a casein kinase 1 superfamily member located at chromosome 6p21.1. It is expressed explicitly in the brain, particularly in the cytoplasm of cortical and hippocampal neurons. TTBK1 has been implicated in the phosphorylation and aggregation of tau in Alzheimer's disease (AD). Considering its significance in AD, TTBK1 has emerged as a promising target for AD treatment. In the present study, we identified novel TTBK1 inhibitors using various computational techniques. We performed a virtual screening-based docking study followed by E-pharmacophore modeling, cavity-based pharmacophore, and ligand design techniques and found ZINC000095101333, LD7, LD55, and LD75 to be potential novel TTBK1 lead inhibitors. The docking results were complemented by Molecular Mechanics/Generalized Born Surface Area (MMGBSA) calculations. The molecular dynamics (MD) simulation studies at a 500 ns scale were carried out to monitor the behavior of the protein toward the identified ligands. Pharmacological and ADME/T studies were carried out to check the drug-likeness of the compounds. In summary, we identified a new series of compounds that could effectively bind the TTBK1 receptor. The newly designed compounds are promising candidates for developing therapeutics targeting TTBK1 for AD.

Reducing overconfident errors in molecular property classification using Posterior Network

Deep-learning-based classification models are increasingly used for predicting molecular properties in drug development. However, traditional classification models using the Softmax function often give overconfident mispredictions for out-of-distribution samples, highlighting a critical lack of accurate uncertainty estimation. Such limitations can result in substantial costs and should be avoided during drug development. Inspired by advances in evidential deep learning and Posterior Network, we replaced the Softmax function with a normalizing flow to enhance the uncertainty estimation ability of the model in molecular property classification. The proposed strategy was evaluated across diverse scenarios, including simulated experiments based on a synthetic dataset, ADMET predictions, and ligand-based virtual screening. The results demonstrate that compared with the vanilla model, the proposed strategy effectively alleviates the problem of giving overconfident but incorrect predictions. Our findings support the promising application of evidential deep learning in drug development and offer a valuable framework for further research.

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

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

Supplementary Information

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

A structure-based pharmacophore modelling approach to identify and design new neprilysin (NEP) inhibitors: An in silico-based investigation

Neutral endopeptidase or neprilysin (NEP) cleaves the natriuretic peptides, bradykinin, endothelin, angiotensin II, amyloid β protein, substance P, etc., thus modulating their effects on heart, kidney, and other organs. NEP has a proven role in hypertension, heart disease, renal disease, Alzheimer's, diabetes, and some cancers. NEP inhibitor development has been in focus since the US FDA approved a combination therapy of angiotensin II type 1 receptor inhibitor (valsartan) and NEP inhibitor (sacubitril) for use in heart failure. Considering the importance of NEP inhibitors the present work focuses on the designing of a potential lead for NEP inhibition. A structure-based pharmacophore modelling approach was employed to identify NEP inhibitors from the pool of 1140 chemical entities obtained from the ZINC database. Based on the docking score and pivotal interactions, ten molecules were selected and subjected to binding free energy calculations and ADMET predictions. The top two compounds were studied further by molecular dynamics simulations to determine the stability of the ligand-receptor complex. ZINC0000004684268, a phenylalanine derivative, showed affinity and complex stability comparable to sacubitril. However, in silico studies indicated that it may have poor pharmacokinetic parameters. Therefore, the molecule was optimized using bioisosteric replacements, keeping the phenylalanine moiety intact, to obtain five potential lead molecules with an acceptable pharmacokinetic profile. The works thus open up the scope to further corroborate the present in silico findings with the biological analysis.

A review of natural products and small-molecule therapeutics acting on central nervous system malignancies: Approaches for drug development, targeting pathways, clinical trials, and challenges

In 2021, the World Health Organization released the fifth edition of the central nervous system (CNS) tumor classification. This classification uses histopathology and molecular pathogenesis to group tumors into more biologically and molecularly defined entities. The prognosis of brain cancer, particularly malignant tumors, has remained poor worldwide, approximately 308,102 new cases of brain and other CNS tumors were diagnosed in the year 2020, with an estimated 251,329 deaths. The cost and time-consuming nature of studies to find new anticancer agents makes it necessary to have well-designed studies. In the present study, the pathways that can be targeted for drug development are discussed in detail. Some of the important cellular origins, signaling, and pathways involved in the efficacy of bioactive molecules against CNS tumorigenesis or progression, as well as prognosis and common approaches for treatment of different types of brain tumors, are reviewed. Moreover, different study tools, including cell lines, in vitro, in vivo, and clinical trial challenges, are discussed. In addition, in this article, natural products as one of the most important sources for finding new chemotherapeutics were reviewed and over 700 reported molecules with efficacy against CNS cancer cells are gathered and classified according to their structure. Based on the clinical trials that have been registered, very few of these natural or semi-synthetic derivatives have been studied in humans. The review can help researchers understand the involved mechanisms and design new goal-oriented studies for drug development against CNS malignancies.

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition

Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate kobs < 0.01 min-1), weak positive (0.01 ≤ kobs ≤ 0.025 min-1), or positive (kobs > 0.025 min-1) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information.

Investigation on the mechanisms by which the herbal remedies induce anti-prostate cancer activity: uncovering the most practical natural compound

Prostate cancer (PCa) is one of the most reported cancers among men worldwide. Targeting the essential proteins associated with PCa could be a promising method for cancer treatment. Traditional and herbal remedies (HRs) are the most practical approaches for PCa treatment. Here, the proteins and enzymes associated with PCa were determined based on the information obtained from the DisGeNET database. The proteins with a gene-disease association (GDA) score greater than 0.7 and the genes that have a disease specificity index (DSI) = 1 were selected as the target proteins. 28 HRs with anti-PCa activity as a traditional treatment for PCa were chosen as potential bioactive compounds. More than 500 compound-protein complexes were screened to find the top-ranked bioactives. The results were further evaluated using the molecular dynamics (MD) simulation and binding free energy calculations. The outcomes revealed that procyanidin B2 3,3'-di-O-gallate (B2G2), the most active ingredient of grape seed extract (GSE), can act as an agonist for PTEN. PTEN has a key role in suppressing PCa cells by applying phosphatase activity and inhibiting cell proliferation. B2G2 exhibited a considerable binding affinity to PTEN (11.643 kcal/mol). The MD results indicated that B2G2 could stabilize the key residues of the phosphatase domain of PTEN and increase its activity. Based on the obtained results, the active ingredient of GSE, B2G2, could play an agonist role and effectively increase the phosphatase activity of PTEN. The grape seed extract is a useful nutrition that can be used in men's diets to inhibit PCa in their bodies.Communicated by Ramaswamy H. Sarma.

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.

Identification of bioactive compounds of Zanthoxylum armatum as potential inhibitor of pyruvate kinase M2 (PKM2): Computational and virtual screening approaches

PKM2 (Pyruvate kinase M2) is the isoform of pyruvate kinase which is known to catalyse the last step of glycolysis that is responsible for energy production. This specific isoform is known to be highly expressed in certain cancerous conditions. Considering the role of this protein in various cancer conditions, we used PKM2 as a target protein to identify the potential compounds against this target. In this study, we have examined 96 compounds of Zanthoxylum armatum using an array of computational and in silico tools. The compounds were assessed for toxicity then their anticancer potential was predicted. The virtual screening was done with molecular docking followed by a detailed examination using molecular dynamics simulation. The majority of the compounds showed a higher probability of being antineoplastic. Based on toxicity, predicted anticancer potential, binding affinity, and binding site, three compounds (nevadensin, asarinin, and kaempferol) were selected as hit compounds. The binding energy of these compounds with PKM2 ranged from -7.7 to -8.3 kcal/mol and all hit compounds interact at the active site of the protein. The selected hit compounds formed a stable complex with PKM2 when simulated under physiological conditions. The dynamic analysis showed that these compounds remained attached to the active site till the completion of molecular simulation. MM-PBSA analysis showed that nevadensin exhibited a higher affinity towards PKM2 compared to asarinin and kaempferol. These compounds need to be assessed properties in vivo and in vitro to validate their efficacy.

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.

Design, synthesis and evaluation of aminothiazole derivatives as potential anti-Alzheimer's candidates

Aim: The objective of the present study was to design, synthesize and evaluate diverse Schiff bases and thiazolidin-4-one derivatives of aminothiazole as key pharmacophores possessing acetylcholinesterase inhibitory activity. Materials & methods: Two series of compounds (13 each) were synthesized and evaluated for their acetylcholinesterase inhibition and antioxidant activity. Molecular docking of all compounds was performed to provide an insight into their binding interactions. Results: Compounds 2j (IC50 = 0.03 μM) and 3e (IC50 = 1.58 μM) were found to be the best acetylcholinesterase inhibitors among compounds of their respective series. Molecular docking analysis supported the results of in vitro activity by displaying good docking scores with the binding pocket of human acetylcholinesterase (Protein Data Bank ID: 4EY7). Conclusion: Compound 2j emerged as a potential lead compound with excellent acetylcholinesterase inhibition, antioxidant and chelation activity.

Computational resources and chemoinformatics for translational health research

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.

Structure-Based Drug Design for Targeting IRE1: An in Silico Approach for Treatment of Cancer

BACKGROUND

Endoplasmic Reticulum (ER) stress and Unfolded Protein Response (UPR) play a key role in cancer progression. The aggregation of incorrectly folded proteins in the ER generates ER stress, which in turn activates the UPR as an adaptive mechanism to fix ER proteostasis. Inositol-requiring enzyme 1 (IRE1) is the most evolutionary conserved ER stress sensor, which plays a pro-tumoral role in various cancers. Targeting its' active sites is one of the most practical approaches for the treatment of cancers.

OBJECTIVE

In this study, we aimed to use the structure of 4μ8C as a template to produce newly designed compounds as IRE1 inhibitors.

METHODS

Various functional groups were added to the 4μ8C, and their binding affinity to the target sites was assessed by conducting a covalent molecular docking study. The potential of the designed compound for further in vitro and in vivo studies was evaluated using ADMET analysis.

RESULTS

Based on the obtained results, the addition of hydroxyl groups to 4μ8C enhanced the binding affinity of the designed compound to the target efficiently. Compound 17, which was constructed by the addition of one hydroxyl group to the structure of 4μ8C, can construct a strong covalent bond with Lys907. The outcomes of ADMET analysis indicated that compound 17 could be considered a drug-like molecule.

CONCLUSION

Our results revealed that designed compound 17 could inhibit IRE1 activity. Therefore, this designed compound is a remarkable inhibitor of IRE1 and introduces a promising therapeutic strategy for cancer treatment.

An in silico approach to investigate the theranostic potential of coumarin-derived self-immolative luminescent probes

Till date the challenge exists in the treatments of cancer for various reasons. Most importantly, the available diagnostics are expensive with research gap for enhancing the cancer detection sensitivity. Herein, a series of coumarin-derived fluorescent theranostic probes are reported that can serve as potent anticancer agents as well as in the detection of cancer cells. The potential of these probes to efficiently block one of the well-known cancer drug targets NADPH quinone oxidoreductase-1 (NQO1) is evaluated through various pharmacokinetic methods including absorption, distribution, metabolism and excretion (ADME) properties evaluation, PASS (prediction of activity spectra for substance) algorithm along with molecular docking and dynamic simulations. Further the luminescent properties of these molecules were evaluated by investigating their electronic properties in the ground and excited states with the help of density functional theory methods. Results indicate that the proposed molecules can potentially block the NADPH (reduced form of nicotinamide adenine dinucleotide) binding site of NQO1, thereby inhibiting the activity of the enzyme to ultimately disrupt the metabolism of cancer cells.

SARS-CoV-2 Mpro binding profile and drug-likeness of two novel thiazole derivatives: structural elucidation, DFT studies, ADME-T and molecular docking simulations

Two novel thiazole derivatives, ethyl 5-((4-fluorophenyl)carbamoyl)-thiazole-4-carboxylate (2b) and ethyl 5-(p-tolylcarbamoyl)thiazole-4-carboxylate (6b) have been synthesized, and their crystal structures determined by X-ray diffraction. To rationalize their structure, reactivity and druggability, we have performed a series of separate, but complementary studies. Hirshfeld surface and 2D-fingerprint plots were first scrutinized to qualitatively unveil all the intermolecular interactions that ensure their crystal packing. Moreover, topological electron density parameters established from the quantum theory of atoms-in-molecules (QTAIM) and Reduced Density Gradient (RDG) were later relied on to characterize the chemical bonding of these species, in terms of the nature and magnitude of noncovalent interactions developed within their monomeric and dimeric forms. In both structures, C-H…O hydrogen bonds are found to be stronger than other noncovalent interactions. Furthermore, H…H bonding contacts and non-conventional C-H…O hydrogen bonds both exhibit a closed shell nature, and play in crucial role in the stability of the novel thiazoles. The isosurfaces in the intermolecular region furnished by NCI molecular diagram signifies the existence of weak noncovalent interactions. Finally, the potential inhibitory activity of the titled compounds and their drug-likeness are demonstrated by molecular docking and ADME-T calculations respectively. Both compounds adhere to the Lipinski's rule of five and present encouraging pharmacokinetic properties and safety profiles.Communicated by Ramaswamy H. Sarma.

Novel pseudonucleosides and sulfamoyl-oxazolidinone β-D-glucosamine derivative as anti-COVID-19: design, synthesis, and in silico study

New pseudonucleosides containing cyclic sulfamide moiety and sulfamoyl β-D-glucosamine derivative are described. These pseudonucleosides are synthesized in good yields starting from chlorosulfonyl isocyanate and β-D-glucosamine hydrochloride in five steps; (protection, acetylation, removal of the Boc group, sulfamoylation, and cyclization). Further, novel glycosylated sulfamoyloxazolidin-2-one is prepared in three steps; carbamoylation, sulfamoylation, and intramolecular cyclization. The structures of the synthesized compounds were confirmed by usual spectroscopic and spectrometric methods NMR, IR, MS, and EA. Interesting molecular docking of the prepared pseudonucleosides and (Beclabuvir, Remdesivir) drugs with SARS-CoV-2/Mpro (PDB:5R80) was conducted using the same parameters for a fair comparison. A low binding affinity of the synthesized compounds compared to the Beclabuvir and other analysis showed that pseudonucleosides have the ability to inhibit SARS-CoV-2. After the motivating results of molecular docking study, the complex between the SARS-CoV-2 Mpro and compound 7 was subjected to 100 ns molecular dynamics (MD) simulation using Desmond module of Schrodinger suite, during which the receptor-ligand complex showed substantial stability after 10 ns of MD simulation. Also, we studied the prediction of absorption, distribution, properties of metabolism, excretion, and toxicity (ADMET) of the synthesized compounds.Communicated by Ramaswamy H. Sarma.

Network Pharmacology Integrated Molecular Docking and Dynamics to Elucidate Saffron Compounds Targeting Human COX-2 Protein

Background and Objectives: Cyclooxygenase-2 (COX-2) is mostly linked to inflammation and has been validated as a molecular target for treating inflammatory diseases. The present study aimed to identify novel compounds that could inhibit COX-2, which is associated with various diseases including inflammation, and in such a scenario, plant-derived biomolecules have been considered as attractive candidates. Materials and Methods: In the present study, physiochemical properties and toxicity of natural compounds/drugs were determined by SWISSADME and ProTox-II. In the present study, the molecular docking binding features of saffron derivatives (crocetin, picrocrocin, quercetin, safranal, crocin, rutin, and dimethylcrocetin) against human COX-2 protein were assessed. Moreover, protein-protein interactions, topographic properties, gene enrichment analysis and molecular dynamics simulation were also determined. Results: The present study revealed that picrocrocin showed the highest binding affinity of -8.1 kcal/mol when docked against the COX-2 protein. PROCHECK analysis revealed that 90.3% of the protein residues were found in the most favored region. Compartmentalized Protein-Protein Interaction identified 90 interactions with an average interaction score of 0.62, and the highest localization score of 0.99 found in secretory pathways. The Computed Atlas of Surface Topography of Proteins was used to identify binding pockets and important residues that could serve as drug targets. Use of WEBnmα revealed protein dynamics by using normal mode analysis. Ligand and Receptor Dynamics used the Molecular Generalized Born Surface Area approach to determine the binding free energy of the protein. Gene enrichment analysis revealed that ovarian steroidogenesis, was the most significant enrichment pathway. Molecular dynamic simulations were executed for the best docked (COX-2-picrocrocin) complex, and the results displayed conformational alterations with more pronounced surface residue fluctuations in COX-2 with loss of the intra-protein hydrogen bonding network. The direct interaction of picrocrocin with various crucial amino-acid residues like GLN203, TYR385, HIS386 and 388, ASN382, and TRP387 causes modifications in these residues, which ultimately attenuates the activity of COX-2 protein. Conclusions: The present study revealed that picrocrocin was the most effective biomolecule and could be repurposed via computational approaches. However, various in vivo and in vitro observations are still needed.

ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection

It is a critical step in lead optimization to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. Classical single-task learning (STL) has effectively predicted individual ADMET endpoints with abundant labels. Conversely, multi-task learning (MTL) can predict multiple ADMET endpoints with fewer labels, but ensuring task synergy and highlighting key molecular substructures remain challenges. To tackle these issues, this work elaborates a multi-task graph learning framework for predicting multiple ADMET properties of drug-like small molecules (MTGL-ADMET) by holding a new paradigm of MTL, "one primary, multiple auxiliaries." It first adeptly combines status theory with maximum flow for auxiliary task selection. The subsequent phase introduces a primary-task-centric MTL model with integrated modules. MTGL-ADMET not only outstrips existing STL and MTL methods but also offers a transparent lens into crucial molecular substructures. It is anticipated that this work can promote lead compound finding and optimization in drug discovery.

Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery

In the drug discovery paradigm, the evaluation of absorption, distribution, metabolism, and excretion (ADME) and toxicity properties of new chemical entities is one of the most critical issues, which is a time-consuming process, immensely expensive, and poses formidable challenges in pharmaceutical R&D. In recent years, emerging technologies like artificial intelligence (AI), big data, and cloud technologies have garnered great attention to predict the ADME and toxicity of molecules. Currently, the blend of quantum computation and machine learning has attracted considerable attention in almost every field ranging from chemistry to biomedicine and several engineering disciplines as well. Quantum computers have the potential to bring advances in high-throughput experimental techniques and in screening billions of molecules by reducing development costs and time associated with the drug discovery process. Motivated by the efficiency of quantum kernel methods, we proposed a quantum machine learning (QML) framework consisting of a classical support vector classifier algorithm with a kernel-based quantum classifier. To demonstrate the feasibility of the proposed QML framework, the simplified molecular input line entry system (SMILES) notation-based string kernel, combined with a quantum support vector classifier, is used for the evaluation of chemical/drug ADME-Tox properties. The proposed quantum machine learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the quantum model achieved the best performance as compared to classical counterparts in terms of the area under the curve of the receiver operating characteristic curve (AUC ROC; 0.80-0.95) for predicting outcomes on ADME-Tox data sets for small molecules, with a different number of features. The deployment of the proposed framework in the pharmaceutical industry would be extremely valuable in making the best decisions possible.

Multi-targeting of virulence factors of P. aeruginosa by β-lactam antibiotics to combat antimicrobial resistance

Antimicrobial resistance poses a significant challenge to public health, especially in developing countries, due to a substantial rise in bacterial resistance. This situation has become so concerning that we are now at risk of losing the effectiveness of antibiotics altogether. Recent research has firmly established that bacteria engage in a process called quorum sensing (QS). QS regulates various functions, including nutrient scavenging, immune response suppression, increased virulence, biofilm formation and mobility. Pseudomonas aeruginosa, an opportunistic bacterial pathogen, plays a significant role in various medical conditions such as chronic wounds, corneal infections, burn wounds and cystic fibrosis. While antibiotics are effective in killing bacteria, only a few antibiotics, particularly those from the β-lactam group, have been studied for their impact on the quorum sensing of P. aeruginosa. Given the lack of concentrated efforts in this area, we have investigated the role of β-lactam antibiotics on various potential targets of P. aeruginosa. Based on their toxicological profiles and the average binding energy obtained through molecular docking, azlocillin and moxalactam have emerged as lead antibiotics. The binding energy for the docking of azlocillin and moxalactam with LasA was determined to be -8.2 and -8.6 kcal/mol, respectively. Molecular simulation analysis has confirmed the stable interaction of both these ligands with all three target proteins (LasI, LasA and PqsR) under physiological conditions. The results of this research underscore the effectiveness of azlocillin and moxalactam. These two antibiotics may be repurposed to target the quorum sensing of P. aeruginosa.Communicated by Ramaswamy H. Sarma.

Insights into molecular docking and dynamics to reveal therapeutic potential of natural compounds against P53 protein

P53 is eminent tumour suppressor protein that plays a prominent role in cell cycle arrest, DNA repair, senescence, differentiation and initiation of apoptosis. P53 is an attractive drug target and the high toxicity of some cancer chemotherapy drugs increase the demand for new anti-cancer drugs from natural products. In this current scenario, identification of promising anticancer compounds from natural sources by repurposing approach is still relevant for the early prevention and effective management of cancer. In present study, we docked natural compounds like podophyllotoxin, quercetin and rutin along standard drugs (MG-132 and Bay 61-3606) against p53 protein. ADME/T analysis predicted toxicity of phytochemicals and drugs. In silico docking analysis of podophyllotoxin, quercetin and rutin gave HDOCK docking scores of -187.87, -148. 97 and -143.85, whereas control drugs MG-132 and Bay 61-3606 showed docking scores of -159.59 and -140.71 against p53 respectively. AutoDock analysis of rutin and MG-132 showed highest binding affinity scores of -7.3 and -6.8 kcal/mol against p53. Molecular dynamic simulation for p53 protein displayed stable conformation and convergence. In this study, P53-rutin complex showed free binding energy score of 11.84 kcal/mol and P53-MG-132 complex reported free energy score of 16.3 kcal/mol. Protein contacts atlas gives non-covalent contacts framework by exploring interfaces of individual subunits and protein-ligand interactions. STRING tool predicts physical and functional interactions between proteins. The results of this study revealed that rutin and MG-132 could be promising inhibitors against targeted p53 protein and this could prove detrimental for molecular therapeutics and drug-designing strategies.Communicated by Ramaswamy H. Sarma.

Antitumor activity against human promyelocytic leukemia and in silico studies of some benzoxazines

Cancer is one of the deadliest diseases in the world today, and the incidence of cancer is increasing. Leukemia is a type of blood cancer defined as the uncontrolled proliferation of abnormal leukocytes in the blood and bone marrow. The HL-60 (human promyelocytic leukemia) cell line, derived from a single patient with acute promyelocytic leukemia, provides a unique in vitro model system for studying the cellular and molecular events involved in the proliferation and differentiation of leukemic cells. In this study, antitumor activities on the HL-60 of some of the resynthesized benzoxazine derivatives (BXN-01 and BXN-02) were investigated. The results of in vitro studies obtained were compared a standard drug, etoposide. In vitro results showed that BXN-01 and BXN-02 were found to be extremely effective compared to etoposide (IC50 value: 10 µM) with IC50 values of 5 nM and 25 nM, respectively. Furthermore, molecular docking studies were carried out for preliminary prediction of possible interaction modes between compounds and the active site of the target macromolecules, hTopo IIα, HDAC2, and RXRA. Then, in silico ADME/Tox studies were performed to predict drug-likeness and pharmacokinetic properties of BXN-01 and BXN-02.Communicated by Ramaswamy H. Sarma.

In-silico identification of small molecule benzofuran-1,2,3-triazole hybrids as potential inhibitors targeting EGFR in lung cancer via ligand-based pharmacophore modeling and molecular docking studies

Lung cancer is one of the most common and deadly types of cancer worldwide, and the epidermal growth factor receptor (EGFR) has emerged as a promising therapeutic target for the treatment of this disease. In this study, we designed a library of 1840 benzofuran-1,2,3-triazole hybrids and conducted pharmacophore-based screening to identify potential EGFR inhibitors. The 20 identified compounds were further evaluated using molecular docking and molecular dynamics simulations to understand their binding interactions with the EGFR receptor. In-silico ADME and toxicity studies were also performed to assess their drug-likeness and safety profiles. The results of this study showed the benzofuran-1,2,3-triazole hybrids BENZ-0454, BENZ-0143, BENZ-1292, BENZ-0335, BENZ-0332, and BENZ-1070 dock score of - 10.2, - 10, - 9.9, - 9.8, - 9.7, - 9.6, while reference molecule - 7.9 kcal/mol for EGFR (PDB ID: 4HJO) respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of the receptor, indicating their potential as inhibitors. The in-silico ADME and toxicity studies suggested that the compounds had good pharmacokinetic and safety profiles, further supporting their potential as therapeutic agents. Finally, performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of benzofuran-1,2,3-triazole hybrids as promising EGFR inhibitors for the treatment of lung cancer. Overall, this study provides a valuable starting point for the development of novel EGFR inhibitors with improved efficacy and safety profiles.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00157-1.

Evaluation of the Usefulness of Topological Indices for Predicting Selected Physicochemical Properties of Bioactive Substances with Anti-Androgenic and Hypouricemic Activity

Due to the observed increase in the importance of computational methods in determining selected physicochemical parameters of biologically active compounds that are key to understanding their ADME/T profile, such as lipophilicity, there is a great need to work on accurate and precise in silico models based on some structural descriptors, such as topological indices for predicting lipophilicity of certain anti-androgenic and hypouricemic agents and their derivatives, for which the experimental lipophilicity parameter is not accurately described in the available literature, e.g., febuxostat, oxypurinol, ailanthone, abiraterone and teriflunomide. Therefore, the following topological indices were accurately calculated in this paper: Gutman (M, Mν), Randić (0χ, 1χ, 0χν, 1χν), Wiener (W), Rouvray-Crafford (R) and Pyka (A, 0B, 1B) for the selected anti-androgenic drugs (abiraterone, bicalutamide, flutamide, nilutamide, leflunomide, teriflunomide, ailanthone) and some hypouricemic compounds (allopurinol, oxypurinol, febuxostat). Linear regression analysis was used to create simple linear correlations between the newly calculated topological indices and some physicochemical parameters, including lipophilicity descriptors of the tested compounds (previously obtained by TLC and theoretical methods). Our studies confirmed the usefulness of the obtained linear regression equations based on topological indices to predict ADME/T important parameters, such as lipophilicity descriptors of tested compounds with anti-androgenic and hypouricemic effects. The proposed calculation method based on topological indices is fast, easy to use and avoids valuable and lengthy laboratory experiments required in the case of experimental ADME/T studies.

Advances in the Applications of Bioinformatics and Chemoinformatics

Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as "in silico techniques", in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer's disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity-all the previously mentioned-were presented in the current review.

Selection of potential natural compounds for poly-ADP-ribose polymerase (PARP) inhibition in glioblastoma therapy by in silico screening methods

Glioblastoma (GBM), the most prevalent brain tumor, is one of the least treatable malignancies due to its propensity for intracranial spread, high proliferative potential, and innate resistance to radiation and chemotherapy. Current GBM therapy is limited due to unfavorable, non-specific therapeutic effects in healthy cells and the difficulty of small molecules to penetrate the blood brain barrier (BBB) and reach the tumor microenvironment. Adding PARP-1 inhibitors inhibit DNA repair enzymes thereby increasing the cytotoxicity of anticancer agents. Hence, we aimed to discover potential naturally occurring PARP-1 inhibitors that can be utilized in the treatment of glioma by using multiple in silico tools like molecular docking, absorption, distribution, metabolism, and excretion (ADME) profile, pharmacophore modeling, and molecular dynamic (MD) simulations. Among 43 phytocompounds we screened, two of them (Ellagic acid and Naringin) were discovered to be bound to the catalytic site of PARP-1 with an affinity more remarkable than commercially available PARP-1 inhibitors (Talazoparib, Niraparib, and Rucaparib) except Olaparib. The molecular interactions were analyzed, and data shows that bound entity attained a conserved domain via hydrogen bond interactions, polar interactions, and π-π stacking. Pharmacophore modeling studies showed electronic and steric features of ligands responsible for supramolecular interaction with PARP-1. ADME properties were studied, to assess drug-likeness, hydrophilic nature, hydrophobicity, brain permeability, and oral bioavailability of the natural PARP-1 inhibitors. The simulation study demonstrated the development of a stable complex between Naringin, Ellagic acid, and PARP-1 protein. Moreover, cell culture studies and animal investigations are essential to determine pharmacokinetics and pharmacodynamics.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M^pro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the M^pro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

MasitinibL shows promise as a drug-like analog of masitinib that elicits comparable SARS-Cov-2 3CLpro inhibition with low kinase preference

SARS-CoV-2 infection has led to several million deaths worldwide and ravaged the economies of many countries. Hence, developing therapeutics against SARS-CoV-2 remains a core priority in the fight against COVID-19. Most of the drugs that have received emergency use authorization for treating SARS-CoV-2 infection exhibit a number of limitations, including side effects and questionable efficacy. This challenge is further compounded by reinfection after vaccination and the high likelihood of mutations, as well as the emergence of viral escape mutants that render SARS-CoV-2 spike glycoprotein-targeting vaccines ineffective. Employing de novo drug synthesis or repurposing to discover broad-spectrum antivirals that target highly conserved pathways within the viral machinery is a focus of current research. In a recent drug repurposing study, masitinib, a clinically safe drug against the human coronavirus OC43 (HCoV-OC43), was identified as an antiviral agent with effective inhibitory activity against the SARS-CoV-2 3CLpro. Masitinib is currently under clinical trial in combination with isoquercetin in hospitalized patients (NCT04622865). Nevertheless, masitinib has kinase-related side effects; hence, the development of masitinib analogs with lower anti-tyrosine kinase activity becomes necessary. In this study, in an attempt to address this limitation, we executed a comprehensive virtual workflow in silico to discover drug-like compounds matching selected pharmacophore features in the SARS-CoV-2 3CLpro-bound state of masitinib. We identified a novel lead compound, "masitinibL", a drug-like analog of masitinib that demonstrated strong inhibitory properties against the SARS-CoV-2 3CLpro. In addition, masitinibL further displayed low selectivity for tyrosine kinases, which strongly suggests that masitinibL is a highly promising therapeutic that is preferable to masitinib.

Prediction of Compound Plasma Concentration-Time Profiles in Mice Using Random Forest

Pharmacokinetic (PK) parameters such as clearance (CL) and volume of distribution (Vd) have been the subject of previous in silico predictive models. However, having information of the concentration over time profile explicitly can provide additional value like time above MIC or AUC, etc., to understand both the efficacy and safety-related aspects of a compound. In this work, we developed machine learning models for plasma concentration-time profiles after both i.v. and p.o. dosing for a series of 17 in-house projects. For explanatory variables, MACCS Keys chemical descriptors as well as in silico and experimental in vitro PK parameters were used. The predictive accuracy of random forest (RF), message passing neural network, 2-compartment models using estimated CL and Vdss, and an average model (as a control experiment) was investigated using 5-fold cross-validation (5-fold CV) and leave-one-project-out validation (LOPO-V). The predictive accuracy of RF in 5-fold CV for i.v. and p.o. plasma concentration-time profiles was the best among the models studied, with an RMSE for i.v. dosing at 0.08, 1, and 8 h of 0.245, 0.474, and 0.462, respectively, and an RMSE for p.o. dosing at 0.25, 1, and 8 h of 0.500, 0.612, and 0.509, respectively. Furthermore, by investigating the importance of the in vitro PK parameters using the Gini index, we observed that the general prior knowledge in ADME research was reflected well in the respective feature importance of in vitro parameters such as predicted human Vd (hVd) for the initial distribution, mouse intrinsic CL and unbound fraction of mouse plasma for the elimination process, and Caco2 permeability for the absorption process. Also, this model is the first model that can predict twin peaks in the concentration-time profile much better than a baseline compartment model. Because of its combination of sufficient accuracy and speed of prediction, we found the model to be fit-for-purpose for practical lead optimization.

Repurposing FDA-approved drugs as FXR agonists: a structure based in silico pharmacological study

Farnesoid X receptor (FXR) modulates the expression of genes involved in lipid and carbohydrate homeostasis and inflammatory processes. This nuclear receptor is likely a tumor suppressor in several cancers, but its molecular mechanism of suppression is still under study. Several studies reported that FXR agonism increases the survival of colorectal, biliary tract, and liver cancer patients. In addition, FXR expression was shown to be down-regulated in many diseases such as obesity, irritable bowel syndrome, glomerular inflammation, diabetes, proteinuria, and ulcerative colitis. Therefore, development of novel FXR agonists may have significant potential in the prevention and treatment of these diseases. In this scenario, computer-aided drug design procedures can be resourcefully applied for the rapid identification of promising drug candidates. In the present study, we applied the molecular docking method in conjunction with molecular dynamics (MD) simulations to find out potential agonists for FXR based on structural similarity with the drug that is currently used as FXR agonist, obeticholic acid. Our results showed that alvimopan and montelukast could be used as potent FXR activators and outperform the binding affinity of obeticholic acid by forming stable conformation with the protein in silico. However, further investigational studies and validations of the selected drugs are essential to figure out their suitability for preclinical and clinical trials.