ADME-Space: a new tool for medicinal chemists to explore ADME properties

Mechanism of modified danggui buxue decoction in glucocorticoid-induced osteoporosis: A discussion based on network pharmacology and molecular docking

Objective

Glucocorticoid-induced osteoporosis (GIOP) represents a major complication arising from the long-term use of glucocorticoids, which are widely prescribed for various inflammatory and autoimmune conditions. Despite its prevalence, the current therapeutic options for GIOP are limited in terms of efficacy, safety profiles, and patient compliance. The Modified Danggui Buxue Decoction (DGBXD), a traditional Chinese herbal formulation, has shown promise in preliminary studies for its potential osteoprotective effects. The present study aimed to explore the mechanistic underpinnings of DGBXD's action on GIOP using network pharmacology and molecular docking approaches, bridging traditional medicine with modern pharmacological insights.

Method

Network pharmacology is applied to screen drug-active compounds and potential core target proteins for disease treatment and to explore the drugs' therapeutic mechanisms.

Result

Altogether, 78 DGBXD active compounds and 223 DGBXD-related, 146 component-disease common, and 2168 GIOP-associated target genes were obtained. The PPI network had 43 nodes and 462 edges, and a total of 10 core target genes, including TP53, JUN and MAPK3, were identified. The results of the GO enrichment analysis implied that DGBXD might participate in biological activities, including responses to oxidative stress and nutrient levels. The outcomes of the KEGG pathway enrichment analysis showed that DGBXD may treat GIOP through TNF, IL-17, and phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathways. Based on to the molecular docking results, biologically active compounds (beta-carotene, formononetin, luteolin, and isorhamnetin) exhibited good binding to AKT1 and ESR1.

Conclusion

DGBXD may aid in GIOP treatment by modulating multiple therapeutic targets and signaling pathways.

Novel Multiplexed High Throughput Screening of Selective Inhibitors for Drug-Metabolizing Enzymes Using Human Hepatocytes

Selective chemical inhibitors are critical for reaction phenotyping to identify drug-metabolizing enzymes that are involved in the elimination of drug candidates. Although relatively selective inhibitors are available for the major cytochrome P450 enzymes (CYP), they are quite limited for the less common CYPs and non-CYPs. To address this gap, we developed a multiplexed high throughput screening (HTS) assay using 20 substrate reactions of multiple enzymes to simultaneously monitor the inhibition of enzymes in a 384-well format. Four 384-well assay plates can be run at the same time to maximize throughput. This is the first multiplexed HTS assay for drug-metabolizing enzymes reported. The HTS assay is technologically enabled with state-of-the-art robotic systems and highly sensitive modern LC-MS/MS instrumentation. Virtual screening is utilized to identify inhibitors for HTS based on known inhibitors and enzyme structures. Screening of ~4600 compounds generated many hits for many drug-metabolizing enzymes including the two time-dependent and selective aldehyde oxidase inhibitors, erlotinib and dibenzothiophene. The hit rate is much higher than that for the traditional HTS for biological targets due to the promiscuous nature of the drug-metabolizing enzymes and the biased compound selection process. Future efforts will focus on using this method to identify selective inhibitors for enzymes that do not currently have quality hits and thoroughly characterizing the newly identified selective inhibitors from our screen. We encourage colleagues from other organizations to explore their proprietary libraries using a similar approach to identify better inhibitors that can be used across the industry.

Rational Design of Benzobisheterocycle Metallo-β-Lactamase Inhibitors: A Tricyclic Scaffold Enhances Potency against Target Enzymes

Antimicrobial resistance is a global public health threat. Metallo-β-lactamases (MBLs) inactivate β-lactam antibiotics, including carbapenems, are disseminating among Gram-negative bacteria, and lack clinically useful inhibitors. The evolving bisthiazolidine (BTZ) scaffold inhibits all three MBL subclasses (B1-B3). We report design, synthesis, and evaluation of BTZ analogues. Structure-activity relationships identified the BTZ thiol as essential, while carboxylate is replaceable, with its removal enhancing potency by facilitating hydrophobic interactions within the MBL active site. While the introduction of a flexible aromatic ring is neutral or detrimental for inhibition, a rigid (fused) ring generated nM benzobisheterocycle (BBH) inhibitors that potentiated carbapenems against MBL-producing strains. Crystallography of BBH:MBL complexes identified hydrophobic interactions as the basis of potency toward B1 MBLs. These data underscore BTZs as versatile, potent broad-spectrum MBL inhibitors (with activity extending to enzymes refractory to other inhibitors) and provide a rational approach to further improve the tricyclic BBH scaffold.

Synthesis, in silico modelling, and in vitro biological evaluation of substituted pyrazole derivatives as potential anti-skin cancer, anti-tyrosinase, and antioxidant agents

Twenty-five azole compounds (P1-P25) were synthesised using regioselective base-metal catalysed and microwave-assisted approaches, fully characterised by high-resolution mass spectrometry (HRMS), nuclear magnetic resonance (NMR), and infrared spectra (IR) analyses, and evaluated for anticancer, anti-tyrosinase, and anti-oxidant activities in silico and in vitro. P25 exhibited potent anticancer activity against cells of four skin cancer (SC) lines, with selectivity for melanoma (A375, SK-Mel-28) or non-melanoma (A431, SCC-12) SC cells over non-cancerous HaCaT-keratinocytes. Clonogenic, scratch-wound, and immunoblotting assay data were consistent with anti-proliferative results, expression profiling therewith implicating intrinsic and extrinsic apoptosis activation. In a mushroom tyrosinase inhibition assay, P14 was most potent among the compounds (half-maximal inhibitory concentration where 50% of cells are dead, IC50 15.9 μM), with activity greater than arbutin and kojic acid. Also, P6 exhibited noteworthy free radical-scavenging activity. Furthermore, in silico docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) simulations predicted prominent-phenotypic actives to engage diverse cancer/hyperpigmentation-related targets with relatively high affinities. Altogether, promising early-stage hits were identified - some with multiple activities - warranting further hit-to-lead optimisation chemistry with further biological evaluations, towards identifying new skin-cancer and skin-pigmentation renormalising agents.

Synthesis, characterization, in vitro antimycobacterial and cytotoxicity evaluation, DFT calculations, molecular docking and ADME studies of new isomeric benzimidazole-1,2,3-triazole-quinoline hybrid mixtures

New benzimidazole-1,2,3-triazole-quinoline hybrids and their intermediates, differing in substitutions at the C-2 and/or C6 positions of the benzimidazole ring, were successfully synthesized in 55---80 % yields, with the C6-substituted ones forming as inseparable tautomeric mixtures. The synthesized compounds were fully characterised by FT-IR, 1D- and 2D-NMR, and HRMS. In-depth NMR analysis and DFT molecular calculations showed that the tautomeric mixtures formed in a ratio of almost 1:1 ratio (cis and trans), except for 5 g, where the ratio is 1:2. In vitro antimycobacterial activity evaluation against the H37Rv strain of Mycobacterial tuberculosis was undertaken on all synthesized compounds, and a selected number were further screened for their cytotoxicity on TZM-bl cell lines. Hybrid compounds showed excellent MIC90 activities ranging from 1.07 to 8.66 μM and were all more efficacious than the first-line reference drug, ethambutol (MIC90 = 9.54 μM). In particular, hybrid compounds 5b (MIC90 = 1.54 μM, CC50 = 58.89 μM and % cell viability = 14.07), 5d (MIC90 = 2.08 μM, CC50 = 0.27 μM, and % cell viability = 149.50 %) and 5 g (MIC90 = 1.49 μM, CC50 = 4.62 μM and % cell viability = 44.03) were the most promising. Significantly, 5b and 5 g were over six times more efficacious than ethambutol but exhibited cytotoxicity towards TZM-bl cell-lines compared to 5d, which was over four times more active than ethambutol. The physical combination (mimicking combination therapy) of individual pharmacophoric components making up 5 g were less active, indicating the synergistic effect of hybridization. In addition, more than 60 % of all the synthesized hybrids showed better activity than their respective pharmacophoric components. In silico ADME studies of the hybrids revealed favourable physico-chemical properties, while molecular modeling studies suggested binding interactions with Val 61, Gly 62, Glu 65, Ala 66, and Phe 69 amino acid in a reported similar manner to bedaquiline, an approved quinoline-based anti-TB drug.

Unravelling the Molecular Mechanisms of a Quercetin Nanocrystal for Treating Potential Parkinson's Disease in a Rotenone Model: Supporting Evidence of Network Pharmacology and In Silico Data Analysis

The prevalence of Parkinson's disease places a significant burden on society; therefore, there is an urgent need to develop more effective drugs. However, the development of these drugs is both expensive and risky. Quercetin (QUE) has potent pharmacological effects on neurodegenerative diseases, but its low solubility in water and poor bioavailability limit its use in pharmaceutical applications. In this study, Quercetin nanocrystals (QNC) were synthesized and compared to standard QUE. A network-pharmacology-based methodology was applied, including target prediction, network construction, a gene ontology (GO) analysis, a KEGG pathway enrichment analysis, and molecular docking. This study aimed to identify the targets of QUE relevant to the treatment of Parkinson's disease and investigate the associated pharmacological mechanisms. Most of the predicted targets are involved in dopamine uptake during synaptic transmission. QUE regulates the key targets DRD2 and DRD4, which significantly affect dopaminergic synapses. The molecular docking results showed that QUE had a better binding affinity than the standard drug l-Dopa. From these experiments, it can be concluded that QNC effectively reduced the adverse effects caused by rotenone-induced oxidative stress in biochemical, neurochemical, and histopathological alterations. Therefore, QNC can potentially treat Parkinson's disease, and its effectiveness should be assessed in future clinical trials.

Multivariate QSAR, similarity search and ADMET studies based in a set of methylamine derivatives described as dopamine transporter inhibitors

The dopamine transporter (DAT), responsible for the regulation of dopaminergic neurotransmission, is implicated in the etiology of several neuropsychiatric disorders which, in turn, have contributed to high rates of disability and numerous deaths in recent years, significantly impacting the global health system. Although the research for new drugs for the treatment of neuropsychiatric disorders has evolved in recent years, the availability of DAT-selective drugs that do not generate the same psychostimulant effects observed in drugs of abuse remains scarce. Therefore, we performed a QSAR study based on a dataset of 36 methylamine derivatives described as DAT inhibitors. The model was obtained based only in descriptors derived from 2D structures, and it was validated and generated satisfactory results considering the metrics used for internal and external validation. Subsequently, a virtual screening step also based on 2D similarity was performed, where it was possible to identify a total of 1157 compounds. After a series of reductions of the set using toxicity filters, applicability domain evaluation, and pharmacokinetic properties in silico assessment, seven hit compounds were selected as the most promising to be used, in future studies, as new scaffolds for the development of new DAT inhibitors.

Advanced network pharmacology study reveals multi-pathway and multi-gene regulatory molecular mechanism of Bacopa monnieri in liver cancer based on data mining, molecular modeling, and microarray data analysis

Liver cancer is a malignant tumor that grows on the surface or inside the liver. The leading cause is a viral infection with hepatitis B or C virus. Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer. A list of studies evidences the therapeutic efficacy of Bacopa monnieri against liver cancer, but the precise molecular mechanism is yet to be discovered. This study combines data mining, network pharmacology, and molecular docking analysis to potentially revolutionize liver cancer treatment by identifying effective phytochemicals. Initially, the information on active constituents of B. monnieri and target genes of both liver cancer and B. monnieri were retrieved from literature as well as from publicly available databases. Based on the matching results between B. monnieri potential targets and liver cancer targets, the protein-protein interaction (PPI) network was constructed using the STRING database and imported into Cytoscape for screening of hub genes based on their degree of connectivity. Later, the interactions network between compounds and overlapping genes was constructed using Cytoscape software to analyze the network pharmacological prospective effects of B. monnieri on liver cancer. Gene Ontology (GO) and KEGG pathway analysis of hub genes revealed that these genes are involved in the cancer-related pathway. Lastly, the expression level of core targets was analyzed using microarray data (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790). Further, the GEPIA server and PyRx software were used for survival and molecular docking analysis, respectively. In summary, we proposed that quercetin, luteolin, apigenin, catechin, epicatechin, stigmasterol, beta-sitosterol, celastrol, and betulic acid inhibit tumor growth by affecting tumor protein 53 (TP53), interleukin 6 (IL6), RAC-alpha serine/threonine protein kinases 1 (AKT1), caspase-3 (CASP3), tumor necrosis factor (TNF), jun proto-oncogene (JUN), heat shot protein 90 AA1 (HSP90AA1), vascular endothelial growth factor A (VEGFA), epidermal growth factor receptor (EGFR), and SRC proto-oncogene (SRC). Through, microarray data analysis, the expression level of JUN and IL6 were found to be upregulated while the expression level of HSP90AA1 was found to be downregulated. Kaplan-Meier survival analysis indicated that HSP90AA1 and JUN are promising candidate genes that can serve as diagnostic and prognostic biomarkers for liver cancer. Moreover, the molecular docking and molecular dynamic simulation of 60ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between the compound and binding pockets of HSP90AA1 and JUN. Despite that, in vivo and in vitro studies are mandatory to unveil pharmacokinetics and biosafety profiles to completely track the candidature status of B. monnieri in liver cancer.

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.

The integrative bioinformatics approaches to predict the xanthohumol as anti-breast cancer molecule: Targeting cancer cells signaling PI3K and AKT kinase pathway

Background

Breast cancer is the most common type of cancer in women, and vast research is being conducted throughout the world for the treatment of this malignancy by natural products using various computational approaches. Xanthohumol, a prenylated flavonoid, is known for its anticancer activity; however, the mechanism behind its action is still in the preliminary stage.

Methods

The current study aimed to analyze the efficacy of xanthohumol compared to the currently available anticancer drugs targeting phosphoinositide-3-kinase (PI3K), serine/threonine kinase (AKT) receptors, and human epidermal growth factor receptor 2 (HER2) for breast cancer treatment through in silico analysis.

Results

The result revealed that the target compound showed significant binding affinity to targets within the PI3K, AKT, and HER2 signaling pathways with a binding energy of -7.5, -7.9, and -7.9 kcal/mol, respectively. Further prediction studies were then made concerning this compound's absorption, distribution, metabolism, and excretion (ADME) as well as drug-likeness properties, resulting in its oral bioavailability with only a single violation of Lipinski's rule of five.

Conclusions

The finding revealed the ability of xanthohumol to bind with multiple cancer cell signaling molecules including PI3K, AKT kinase, and HER2. The current novel study opened the door to advancing research into the management and treatment of breast cancer.

Uncovering the molecular mechanism of Gynostemma pentaphyllum (Thunb.) Makino against breast cancer using network pharmacology and molecular docking

Because of their strong anti-cancer efficacy with fewer side effects, traditional Chinese medicines (TCM) have attracted considerable attention for their potential application in treating breast cancer (BC). However, knowledge about the underlying systematic mechanisms is scarce. Gynostemma pentaphyllum (Thunb.) Makino (GP), a creeping herb, has been regularly used as a TCM to prevent and treat tumors including BC. Again, mechanisms underlying its anti-BC properties have remained elusive. We used network pharmacology and molecular docking to explore the mechanistic details of GP against BC. The TCM systems pharmacology database and analysis platform and PharmMapper Server database were used to retrieve the chemical constituents and potential targets in GP. In addition, targets related to BC were identified using DrugBank and Therapeutic Target Database. Protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of crucial targets were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins and database for annotation, visualization, and integrated discovery databases, whereas the network visualization analysis was performed using Cytoscape 3.8.2. In addition, the molecular docking technique was used to validate network pharmacology-based predictions. A comparison of the predicted targets of GP with those of BC-related drugs revealed 26 potential key targets related to the treatment of BC, among which ALB, EGFR, ESR1, AR, PGR, and HSP90AA1 were considered the major potential targets. Finally, network pharmacology-based prediction results were preliminarily verified by molecular docking experiments. In addition, chemical constituents and potential target proteins were scored, followed by a comparison with the ligands of the protein. We provide a network of pharmacology-based molecular mechanistic insights on the therapeutic action of GP against BC. We believe that our data will serve as a basis to conduct future studies and promote the clinical applications of GP.

In-silico molecular interactions among the secondary metabolites of Caulerpa spp. and colorectal cancer targets

Caulerpa spp. secrete more than thirty different bioactive chemicals which have already been used in cancer treatment research since they play a pivotal role in cancer metabolism. Colorectal cancer is one of the most common cancer types, thus using novel and effective chemicals for colorectal cancer treatment is crucial. In the cheminformatics pipeline of this study, ADME-Tox and drug-likeness tests were performed for filtering the secondary metabolites of Caulerpa spp. The ligands which were selected from the ADME test were used for in silico molecular docking studies against the enzymes of the oxidative branch of the pentose phosphate pathway (glucose-6-phosphate dehydrogenase and 6-phosphoglutarate dehydrogenase), which is of great importance for colorectal cancer, by using AutoDock Vina. Pharmacophore modeling was carried out to align the molecules. Molecular dynamic simulations were performed for each target to validate the molecular docking studies and binding free energies were calculated. According to the ADME test results, 13 different secondary metabolites were selected as potential ligands. Molecular docking studies revealed that vina scores of caulerpin and monomethyl caulerpinate for G6PDH were found as -10.6 kcal mol-1, -10.5 kcal mol-1, respectively. Also, the vina score of caulersin for 6PGD was found as -10.7 kcal mol-1. The highest and the lowest binding free energies were calculated for monomethyl caulerpinate and caulersin, respectively. This in silico study showed that caulerpin, monomethyl caulerpinate, and caulersin could be evaluated as promising marine phytochemicals against pentose phosphate pathway enzymes and further studies are recommended to investigate the detailed activity of these secondary metabolites on these targets.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Network Pharmacology and Bioinformatics Approach Reveals the Multi-Target Pharmacological Mechanism of Fumaria indica in the Treatment of Liver Cancer

Liver cancer (LC), a frequently occurring cancer, has become the fourth leading cause of cancer mortality. The small number of reported data and diverse spectra of pathophysiological mechanisms of liver cancer make it a challenging task and a serious economic burden in health care management. Fumaria indica is a herbaceous annual plant used in various regions of Asia to treat a variety of ailments, including liver cancer. Several in vitro investigations have revealed the effectiveness of F. indica in the treatment of liver cancer; however, the exact molecular mechanism is still unrevealed. In this study, the network pharmacology technique was utilized to characterize the mechanism of F. indica on liver cancer. Furthermore, we analyzed the active ingredient-target-pathway network and uncovered that Fumaridine, Lastourvilline, N-feruloyl tyramine, and Cryptopine conclusively contributed to the development of liver cancer by affecting the MTOR, MAPK3, PIK3R1, and EGFR gene. Afterward, molecular docking was used to verify the effective activity of the active ingredients against the prospective targets. The results of molecular docking predicted that several key targets of liver cancer (along with MTOR, EGFR, MAPK3, and PIK3R1) bind stably with the corresponding active ingredient of F. indica. We concluded through network pharmacology methods that multiple biological processes and signaling pathways involved in F. indica exerted a preventing effect in the treatment of liver cancer. The molecular docking results also provide us with sound direction for further experiments. In the framework of this study, network pharmacology integrated with docking analysis revealed that F. indica exerted a promising preventive effect on liver cancer by acting on liver cancer-associated signaling pathways. This enables us to understand the biological mechanism of the anti liver cancer activity of F. indica.

New Quinoline-Urea-Benzothiazole Hybrids as Promising Antitubercular Agents: Synthesis, In Vitro Antitubercular Activity, Cytotoxicity Studies, and In Silico ADME Profiling

A series of 25 new benzothiazole−urea−quinoline hybrid compounds were synthesized successfully via a three-step synthetic sequence involving an amidation coupling reaction as a critical step. The structures of the synthesized compounds were confirmed by routine spectroscopic tools (1H and 13C NMR and IR) and by mass spectrometry (HRMS). In vitro evaluation of these hybrid compounds for their antitubercular inhibitory activity against the Mycobacterium tuberculosis H37Rv pMSp12::GPF bioreporter strain was undertaken. Of the 25 tested compounds, 17 exhibited promising anti-TB activities of less than 62.5 µM (MIC90). Specifically, 13 compounds (6b, 6g, 6i−j, 6l, 6o−p, 6r−t, and 6x−y) showed promising activity with MIC90 values in the range of 1−10 µM, while compound 6u, being the most active, exhibited sub-micromolar activity (0.968 µM) in the CAS assay. In addition, minimal cytotoxicity against the HepG2 cell line (cell viability above 75%) in 11 of the 17 compounds, at their respective MIC90 concentrations, was observed, with 6u exhibiting 100% cell viability. The hybridization of the quinoline, urea, and benzothiazole scaffolds demonstrated a synergistic relationship because the activities of resultant hybrids were vastly improved compared to the individual entities. In silico ADME predictions showed that the majority of these compounds have drug-like properties and are less likely to potentially cause cardiotoxicity (QPlogHERG > −5). The results obtained in this study indicate that the majority of the synthesized compounds could serve as valuable starting points for future optimizations as new antimycobacterial agents.

In-silico drug-likeness analysis, ADME properties, and molecular docking studies of cyanidin-3-arabinoside, pelargonidin-3-glucoside, and peonidin-3-arabinoside as natural anticancer compounds against acting receptor-like kinase 5 receptor

BACKGROUND

The aim of the study was in-silico drug-likeness analysis, absorption, distribution, metabolism, and excretion (ADME) properties, and molecular docking studies of anthocyanins as natural anticancer compounds against acting receptor-like kinase 5 (ALK5) receptor. Transforming growth factor-β (TGF-β) plays an essential role in various cellular processes. Increased expression of TGF-β and its receptor TGFβR-I (i.e. ALK5) have been associated with poor prognosis in cancer patients.

METHODS

The drug-likeness activity of anthocyanins was performed using SwissADME tool. Molecular docking studies were carried out by using the Autodock Vina 1.5.6 tool.

RESULTS

The results revealed that cyanidin-3-arabinoside (C3A), pelargonidin-3-glucoside (P3G), and peonidin-3-arabinoside (P3A) were able to use both Lipinski's rule of five and Ghose variations. The binding energies of C3A, P3G, and P3A against ALK5 were found as -8.0, -8.3, and -8.4 kcal mol-1, respectively.

CONCLUSION

These selected anthocyanins have shown higher binding energies than known inhibitors to the ALK5 receptor. Further in-vitro and in-vivo studies were strongly recommended to clarify the whole mechanism.

A Brief Review of Machine Learning-Based Bioactive Compound Research

Bioactive compounds are often used as initial substances for many therapeutic agents. In recent years, both theoretical and practical innovations in hardware-assisted and fast-evolving machine learning (ML) have made it possible to identify desired bioactive compounds in chemical spaces, such as those in natural products (NPs). This review introduces how machine learning approaches can be used for the identification and evaluation of bioactive compounds. It also provides an overview of recent research trends in machine learning-based prediction and the evaluation of bioactive compounds by listing real-world examples along with various input data. In addition, several ML-based approaches to identify specific bioactive compounds for cardiovascular and metabolic diseases are described. Overall, these approaches are important for the discovery of novel bioactive compounds and provide new insights into the machine learning basis for various traditional applications of bioactive compound-related research.

In Silico Tools and Software to Predict ADMET of New Drug Candidates

Implication of computational techniques and in silico tools promote not only reduction of animal experimentations but also save time and money followed by rational designing of drugs as well as controlled synthesis of those "Hits" which show drug-likeness and possess suitable absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile. With globalization of diseases, resistance of drugs over the time and modification of viruses and microorganisms, computational tools, and artificial intelligence are the future of drug design and one of the important areas where the principles of sustainability and green chemistry (GC) perfectly fit. Most of the new drug entities fail in the clinical trials over the issue of drug-associated human toxicity. Although ecotoxicity related to new drugs is rarely considered, but this is the high time when ecotoxicity prediction should get equal importance along with human-associated drug toxicity. Thus, the present book chapter discusses the available in silico tools and software for the fast and preliminary prediction of a series of human-associated toxicity and ecotoxicity of new drug entities to screen possibly safer drugs before going into preclinical and clinical trials.

Implementation of System Pharmacology and Molecular Docking Approaches to Explore Active Compounds and Mechanism of Ocimum Sanctum against Tuberculosis

Worldwide, Tuberculosis (TB) is caused by Mycobacterium tuberculosis bacteria. Ocimum sanctum, commonly known as holy basil (Tulsi), is an herbaceous perennial that belongs to the family Lamiaceae and is considered one of the most important sources of medicine and drugs for the treatment of various diseases. The presented study aims to discover the potential phenomenon of Ocimum sanctum in the medicament of tuberculosis using a network pharmacology approach. Active ingredients of Ocimum sanctum were fetched through two different databases and from literature review and then targets of these compounds were harvested by SwissTargetPrediction. Potential targets of TB were downloaded from GeneCards and DisGNet databases. After screening of mutual targets, enrichment analysis through DAVID was performed. Protein–protein interaction was performed using the String database and visualized by Cytoscape. Then the target-compound-pathway network was constructed with Cytoscape. In the end, molecular docking was performed to get the potential active ingredients against tuberculosis. Eight active ingredients with 776 potential therapeutic targets were obtained from O. sanctum, 632 intersected targets from two databases were found in TB, 72 common potential targets were found from TB and O. sanctum. The topological analysis exposes those ten targets that formed the core PPI network. Furthermore, molecular docking analysis reveals that active compounds have the greater binding ability with the potential target to suppress TB.

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications

Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly desirable for in silico drug discovery and medicinal chemistry applications. In this work, we revised a well‐established molecular mechanics' force field and applied a hybrid quantum mechanics and machine learning approach to parametrize the hydrogen‐bonding (HB) potentials of small molecules, improving this aspect of the molecular description. Approximately 66,000 molecules were chosen from available drug databases and subjected to density functional theory calculations (DFT). For each atom, the molecular electrostatic potential (EP) was extracted and used to derive new HB energy contributions; this was subsequently combined with a fingerprint‐based description of the structural environment via partial least squares modeling, enabling the new potentials to be used for molecules outside of the training set. We demonstrate that parameter prediction for molecules outside of the training set correlates with their DFT‐derived EP, and that there is correlation of the new potentials with hydrogen‐bond acidity and basicity scales. We show the newly derived MIFs vary in strength for various ring substitution in accordance with chemical intuition. Finally, we report that this derived parameter, when extended to non‐HB atoms, can also be used to estimate sites of reaction.

Synthesis, characterization, in vitro cholinesterase and hRBCs hemolysis assay and computational evaluation of novel 2,3,4,5-tetrahydrobenzothiazepine appended α-aminophosphonates

A series of novel 2,3,4,5-tetrahydrobenzothiazepine appended α-aminophosphonate derivatives were synthesized by subjecting 2,3-dihydrobenzothiazepine to Pudovik reaction using diethyl phosphite. Tested derivatives exhibited better AChE inhibition (0.86-12.85 µM) when compared to BuChE (3.13-19.36 µM). Derivative 5f (IC₅₀ = 0.86 ± 0.08 µM), 5g (IC₅₀ = 1.05 ± 0.06 µM) and 5d (IC₅₀ = 1.64 ± 0.06 µM) exhibited higher AChE inhibitory activity as compared to standard drug galantamine (IC₅₀ = 2.15 ± 0.05 µM). Similarly, derivative 5e (IC₅₀ = 3.13 ± 0.11 µM) and 5f (IC₅₀ = 3.64 ± 0.06 µM) demonstrated comparable BuChE inhibitory activity to reference drug galantamine (IC₅₀ = 3.86 ± 0.03 µM). Further, enzyme kinetic studies were carried out for the most active molecule i.e. derivative 5f (for AChE) and derivative 5e (for BuChE) and the results imply that derivatives 5f and 5e show mixed-type inhibition with K_i values of 1.779 µM and 3.851 µM respectively. Enzyme reversibility inhibition studies demonstrated that all the tested derivatives possess reversible inhibitor characteristics. In addition, % hemolysis studies were carried out using human red blood cells (hRBCs) and the results demonstrated that the synthesized derivatives were biocompatible in nature as they impart very less cytotoxicity to hRBCs (CC₅₀ > 1000 μg/mL). Also, cell viability studies for tested derivatives revealed no cytotoxicity in N2a cells. Moreover, molecular docking studies revealed that derivative 5e and 5f bind to the PAS and CAS of the AChE. ADME predictions suggested that synthesized derivatives have high possibility of being drug-like.

Structural optimization, synthesis and in vitro synergistic anticancer activities of combinations of new N3-substituted dihydropyrimidine calcium channel blockers with cisplatin and etoposide

T-type calcium channels are considered potential drug targets to combat cancer. Combining T-type calcium channel blockers with conventional chemotherapy drugs represents a promising strategy towards successful cancer treatment. From this perspective, we report in this study the design and synthesis of a novel series of N3-sustituted dihydropyrimidines (DHPMs) as anticancer adjuvants to cisplatin (Cis) and etoposide (Eto). Full spectral characterization of the new compounds was done using FT-IR, ¹H NMR, ¹³C NMR, and HRMS. Structure elucidation was confirmed by 2D NMR ¹H-H COSY, HSQC and NOESY experiments. Novel derivatives were tested for their Ca²⁺ channel blocking activity by employing the whole cell patch-clamp technique. Results demonstrated that most compounds were potential T-type calcium channel blockers with the triazole-based C12 and C13 being the most selective agents against Ca_V3.2 channel. Further electrophysiological studies demonstrated that C12 and C13 inhibited Ca_V3.2 currents with respective affinity of 2.26 and 1.27 µM, and induced 5 mV hyperpolarizing shifts in the half-inactivation potential. Subsequently, C12 and C13 were evaluated for their anticancer activities alone and in combination with Cis and Eto against A549 and MDA-MB 231 cancer cells. Interestingly, both compounds exhibited potential anticancer effects with IC₅₀ values < 5 µM. Combination studies revealed that both compounds had synergistic effects (combination index CI < 1) on Cis and Eto through induction of apoptosis (p53 activation and up-regulation of BAX and p21 gene expression). Importantly, in silico physicochemical and ADMET assessment of both compounds revealed their potential drug-like properties with decreased risk of cardiac toxicity. Hence, C12 and C13 are promising anticancer adjuvants through inhibition of Ca_V3.2 T-type calcium channels, thereby serving as eminent leads for further modification.

Comparison of beta-ligands used in cattle production: structures, safety, and biological effects

Technologies that increase the efficiency and sustainability of food animal production to provide meat for a growing population are necessary and must be used in a manner consistent with good veterinary practices, approved labeled use, and environmental stewardship. Compounds that bind to beta-adrenergic receptors (β-AR), termed beta-adrenergic receptor ligands (β-ligands), are one such technology and have been in use globally for many years. Though all β-ligands share some similarities in structure and function, the significance of their structural and pharmacological differences is sometimes overlooked. Structural variations in these molecules can affect absorption, distribution, metabolism, and excretion as well as cause substantial differences in biological and metabolic effects. Several β-ligands are available for use specifically in cattle production. Ractopamine and zilpaterol are beta-adrenergic agonists approved to increase weight gain, feed efficiency, and carcass leanness in cattle. They both bind to and activate β1- and β2-AR. Lubabegron is a newly developed selective beta-adrenergic modulator with unique structural and functional features. Lubabegron displays antagonistic behavior at the β1- and β2-AR but agonistic behavior at the β3-AR. Lubabegron is approved for use in cattle to reduce ammonia emissions per unit of live or carcass weight. Additionally, lubabegron can withstand prolonged use as the β3-AR lacks structural features needed for desensitization. Due to these unique features of lubabegron, this new β-ligand provides an additional option in cattle production. The individual properties of each β-ligand should be considered when making risk management decisions, as unique properties result in varying human food safety profiles that can determine appropriate safe β-ligand use.

Comparative in silico prediction of P-glycoprotein-mediated transport for 2010-2020 US FDA-approved drugs using six Web-tools

P-glycoprotein (P-gp) is an efflux pump implicated in pharmacokinetics and drug-drug interactions. The identification of its substrates is consequently an important issue, notably for drugs under development. For such a purpose, various in silico methods have been developed, but their relevance remains to be fully established. The present study was designed to get insight about this point, through determining the performance values of six freely accessible Web-tools (ADMETlab, AdmetSAR2.0, PgpRules, pkCSM, SwissADME and vNN-ADMET), computationally predicting P-gp-mediated transport. Using an external test set of 231 marketed drugs, approved over the 2010-2020 period by the US Food and Drug Administration and fully in vitro characterized for their P-gp substrate status, various performance parameters (including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the receiver operating characteristics curve) were determined. They were found to rather poorly meet criteria commonly required for acceptable prediction, whatever the Web-tools were used alone or in combination. Predictions of being P-gp substrate or non-substrate by these online in silico methods may therefore be considered with caution.

Docking and antibacterial activity of novel nontoxic 5-arylidenepyrimidine-triones as inhibitors of NDM-1 and MetAP-1

Background: Antibiotic resistance, which occurs through the action of metallo-β-lactamases (NDM-1), is a serious problem in the treatment of infectious diseases. Therefore, the discovery of new NDM-1 inhibitors and promising antibacterial agents as inhibitors of alternative targets (MetAP-1) is important. Method & results: In this study, a virtual library of 5-arylidene barbituric acids was created and molecular docking was performed for identification of novel possible inhibitors of NDM-1 and MetAP-1. Antibacterial activity (agar well-diffusion assay) and cytotoxicity (alamarBlue assay) of perspective compounds were evaluated. Pharmacokinetic profiles and molecular properties were predicted. Conclusion: We have identified possible novel inhibitors of NDM-1 and MetAP-1 with bacteriostatic activity, most of which are not cytotoxic and have potential excellent drug-likeness properties.

Synthesis, inverse docking-assisted identification and in vitro biological characterization of Flavonol-based analogs of fisetin as c-Kit, CDK2 and mTOR inhibitors against melanoma and non-melanoma skin cancers

Due to hurdles, including resistance, adverse effects, and poor bioavailability, among others linked with existing therapies, there is an urgent unmet need to devise new, safe, and more effective treatment modalities for skin cancers. Herein, a series of flavonol-based derivatives of fisetin, a plant-based flavonoid identified as an anti-tumorigenic agent targeting the mammalian targets of rapamycin (mTOR)-regulated pathways, were synthesized and fully characterized. New potential inhibitors of receptor tyrosine kinases (c-KITs), cyclin-dependent kinase-2 (CDK2), and mTOR, representing attractive therapeutic targets for melanoma and non-melanoma skin cancers (NMSCs) treatment, were identified using inverse-docking, in vitro kinase activity and various cell-based anticancer screening assays. Eleven compounds exhibited significant inhibitory activities greater than the parent molecule against four human skin cancer cell lines, including melanoma (A375 and SK-Mel-28) and NMSCs (A431 and UWBCC1), with IC50 values ranging from 0.12 to < 15 μM. Seven compounds were identified as potentially potent single, dual or multi-kinase c-KITs, CDK2, and mTOR kinase inhibitors after inverse-docking and screening against twelve known cancer targets, followed by kinase activity profiling. Moreover, the potent compound F20, and the multi-kinase F9 and F17 targeted compounds, markedly decreased scratch wound closure, colony formation, and heightened expression levels of key cancer-promoting pathway molecular targets c-Kit, CDK2, and mTOR. In addition, these compounds downregulated Bcl-2 levels and upregulated Bax and cleaved caspase-3/7/8 and PARP levels, thus inducing apoptosis of A375 and A431 cells in a dose-dependent manner. Overall, compounds F20, F9 and F17, were identified as promising c-Kit, CDK2 and mTOR inhibitors, worthy of further investigation as therapeutics, or as adjuvants to standard therapies for the control of melanoma and NMSCs.

Petasis adducts of tryptanthrin – synthesis, biological activity evaluation and druglikeness assessment

The first example of a tryptanthrin-based Petasis multicomponent reaction is reported, with one of the new derivatives showing moderate fungicidal activity.

The application of isatin-based multicomponent-reactions in the quest for new bioactive and druglike molecules

Oxindole derivatives are known for their great interest in the field of Medicinal Chemistry, as they display vast biological activities. Recent efforts concerning the preparation of oxindole derivatives using isatin-based multicomponent reactions (MCRs) constitute a great advance in generating druglike libraries fast and with wide scaffold diversity. In this review, we address those recent developments, exploring the synthetic pathways and biological activities described for these compounds, namely antitumor, antibacterial, antifungal, antiparasitic, antiviral, antioxidant, anti-inflammatory and central nervous system (CNS) pathologies. To add new depth to this work, we used a well-established web-based free tool (SwissADME) to evaluate the most promising scaffolds in what concerns their druglike properties, namely by evaluating their compliance with some of the most valuable rules applied by medicinal chemists in both academia and industrial settings (Lipinski, Ghose, Veber, Egan, Muegge). The aim of this review is to endorse isatin-based MCRs as a valuable synthetic approach to attain new hit compounds bearing the oxindole privileged structure, while critically exploring these scaffolds' druglike properties.

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

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

Network pharmacology based virtual screening of active constituents of Prunella vulgaris L. and the molecular mechanism against breast cancer

Prunella vulgaris L, a perennial herb widely used in Asia in the treatment of various diseases including cancer. In vitro studies have demonstrated the therapeutic effect of Prunella vulgaris L. against breast cancer through multiple pathways. However, the nature of the biological mechanisms remains unclear. In this study, a Network pharmacology based approach was used to explore active constituents and potential molecular mechanisms of Prunella vulgaris L. for the treatment of breast cancer. The methods adopted included active constituents prescreening, target prediction, GO and KEGG pathway enrichment analysis. Molecular docking experiments were used to further validate network pharmacology results. The predicted results showed that there were 19 active ingredients in Prunella vulgaris L. and 31 potential gene targets including AKT1, EGFR, MYC, and VEGFA. Further, analysis of the potential biological mechanisms of Prunella vulgaris L. against breast cancer was performed by investigating the relationship between the active constituents, target genes and pathways. Network analysis showed that Prunella vulgaris L. exerted a promising preventive effect on breast cancer by acting on tumor-associated signaling pathways. This provides a basis to understand the mechanism of the anti-breast cancer activity of Prunella vulgaris L.

In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM

Natural products continue to be major sources of bioactive compounds and drug candidates not only because of their unique chemical structures but also because of their overall favorable metabolism and pharmacokinetic properties. The number of publicly accessible natural product databases has increased significantly in the past few years. However, the systematic ADME/Tox profile has been reported on a limited basis. For instance, BIOFACQUIM was recently published as a public database of natural products from Mexico, a country with a rich source of biomolecules. However, its ADME/Tox profile has not been reported. Herein, we discuss the results of an in-depth in silico ADME/Tox profile of natural products in BIOFACQUIM and other large public collections of natural products. It was concluded that the absorption and distribution profiles of compounds in BIOFACQUIM are similar to those of approved drugs, while the metabolism profile is comparable to that in the other natural product databases. The excretion profile of compounds in BIOFACQUIM is different from that of the approved drugs, but their predicted toxicity profile is comparable. This work further contributes to the deeper characterization of natural product collections as major sources of bioactive compounds with therapeutic potential.

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs

The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.

Exploring the mechanism of action Xianlingubao Prescription in the treatment of osteoporosis by network pharmacology

In this study, the network pharmacology analysis method was used to explore the bioactive components and targets of Xianlinggubao (XLGB) and further elucidate its potential biological mechanisms of action in the treatment of osteoporosis (OP). The bioactive compounds and predictive targets of XLGB were collected from the traditional Chinese medicine systems pharmacology databases and analysis platform(TCMSP), the Encyclopeida of traditional Chinese medicine (ETCM), traditional Chinese medicine Databse@Taiwan, ChEMBL, STITCH, and SymMap database. The targets corresponding to OP were obtained by using Online Mendelian Inheritance in Man® (OMIM), GeneCards, the National Center for Biotechnology Information-Gene database. The XLGB-OP targets were obtained by intersecting with the targets of XLGB and OP. Protien-Protien interaciton (PPI) network was constructed using STRING online database and analyzed using Cytoscape 3.7.0 software to screen out hub genes. Gene ontology (GO) and KEGG enrichment analysis of the target in the PPI network was conducted using the ClusterProfiler package in R with adjusted p-value<0.05. A total of 65 XLGB bioactive compounds were screened corresponding to 776 XLGB targets and 2556 OP targets. The GO analysis and KEGG enrichment analyses suggested XLGB played a therapeutic roles in OP treatment via the interleukin-17 signaling pathway, hypoxia-inducible factor-1 signaling pathway, insulin resistance, Th-17 signaling pathway, etc. Five hub genes (AKT1, MAPK1, MAPK8, TP53, and STAT3) were screened using the degree algorithm, and molecular docking stimulation results showed that most bioactive compounds of XLGB had strong binding efficiency with hub genes. Overall, this study laid the foundation for further in vivo and in vitro experimental research and expanded the clinical applications of XLGB.

New insights in the in vitro characterisation and molecular modelling of the P-glycoprotein inhibitory promiscuity

The presence of several binding sites for both substrates and inhibitors is yet a poorly explored thematic concerning the assessment of the drug-drug interactions risk due to interactions of multiple drugs with the human transport protein P-glycoprotein (P-gp or MDR1, gene ABCB1). In this study we measured the inhibitory behaviour of a set of known drugs towards P-gp by using three different probe substrates (digoxin, Hoechst 33,342 and rhodamine 123). A structure-based model was built to unravel the different substrates binding sites and to rationalize the cases where drugs were not inhibiting all the substrates. A separate set of experiments was used to validate the model and confirmed its suitability to either detect the substrate-dependent P-gp inhibition and to anticipate proper substrates for in vitro experiments case by case. The modelling strategy described can be applied for either design safer drugs (P-gp as antitarget) or to target specific sub-site inhibitors towards other drugs (P-gp as target).