The application of in silico drug-likeness predictions in pharmaceutical research

Discovery of Novel Androgen Receptor Ligands by Structure-based Virtual Screening and Bioassays

Androgen receptor (AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of many severe diseases such as prostate cancer, muscle atrophy, and osteoporosis. Binding of ligands to AR triggers the conformational changes in AR that may affect the recruitment of coactivators and downstream response of AR signaling pathway. Therefore, AR ligands have great potential to treat these diseases. In this study, we searched for novel AR ligands by performing a docking-based virtual screening (VS) on the basis of the crystal structure of the AR ligand binding domain (LBD) in complex with its agonist. A total of 58 structurally diverse compounds were selected and subjected to LBD affinity assay, with five of them (HBP1-3, HBP1-17, HBP1-38, HBP1-51, and HBP1-58) exhibiting strong binding to AR-LBD. The IC₅₀ values of HBP1-51 and HBP1-58 are 3.96 µM and 4.92 µM, respectively, which are even lower than that of enzalutamide (Enz, IC₅₀ = 13.87 µM), a marketed second-generation AR antagonist. Further bioactivity assays suggest that HBP1-51 is an AR agonist, whereas HBP1-58 is an AR antagonist. In addition, molecular dynamics (MD) simulations and principal components analysis (PCA) were carried out to reveal the binding principle of the newly-identified AR ligands toward AR. Our modeling results indicate that the conformational changes of helix 12 induced by the bindings of antagonist and agonist are visibly different. In summary, the current study provides a highly efficient way to discover novel AR ligands, which could serve as the starting point for development of new therapeutics for AR-related diseases.

Molecular insight into the therapeutic potential of phytoconstituents targeting protein conformation and their expression

BACKGROUND

Native protein conformation is essential for the functional activity of the proteins and enzymes. Defects in conformation or alterations in expression of the proteins have been reported in various diseases.

PURPOSE

The aim of this study is to review the molecular insight into the therapeutic potential of phytoconstituents targeting protein conformations or expressions.

METHODS

Published literatures were searched in PubMed, Scopus, Web of Science; Article published till Dec 2017 were extracted. The literature was assessed from the Central University of Rajasthan, India. Present study evaluate article based on the role of active plant constituents on the conformation and expression of the different proteins.

RESULTS

Plant components play their role either at the molecular level or cellular level and exhibit antibacterial, antiviral, anti-neurodegenerative and other activities. Plant active compounds isolated from different plants may either stabilize or destabilize the conformation of proteins or alter expression level of the protein involved in these diseases, therefore, can play a significant role in preventing diseases caused by the alteration in these proteins.

CONCLUSION

In the present article, we have reviewed the molecular mechanism of plant active compounds, their target proteins, methods of extraction and identification, and their biological significances. Therefore, a proper understanding of the effect of these herbal molecules on the concerned proteins may help to develop new herbal-based therapeutics for various diseases.

ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness

Chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET), play key roles in drug discovery and development. A high-quality drug candidate should not only have sufficient efficacy against the therapeutic target, but also show appropriate ADMET properties at a therapeutic dose. A lot of in silico models are hence developed for prediction of chemical ADMET properties. However, it is still not easy to evaluate the drug-likeness of compounds in terms of so many ADMET properties. In this study, we proposed a scoring function named the ADMET-score to evaluate drug-likeness of a compound. The scoring function was defined on the basis of 18 ADMET properties predicted via our web server admetSAR. The weight of each property in the ADMET-score was determined by three parameters: the accuracy rate of the model, the importance of the endpoint in the process of pharmacokinetics, and the usefulness index. The FDA-approved drugs from DrugBank, the small molecules from ChEMBL and the old drugs withdrawn from the market due to safety concerns were used to evaluate the performance of the ADMET-score. The indices of the arithmetic mean and p-value showed that the ADMET-score among the three data sets differed significantly. Furthermore, we learned that there was no obvious linear correlation between the ADMET-score and QED (quantitative estimate of drug-likeness). These results suggested that the ADMET-score would be a comprehensive index to evaluate chemical drug-likeness, and might be helpful for users to select appropriate drug candidates for further development.

Docking with SwissDock

Protein-ligand docking simulation is central in drug design and development. Therefore, the development of web servers intended to docking simulations is of pivotal importance. SwissDock is a web server dedicated to carrying out protein-ligand docking simulation intuitively and elegantly. SwissDock is based on the protein-ligand docking program EADock DSS and has a simple and integrated interface. The SwissDock allows the user to upload structure files for a protein and a ligand, and returns the results by e-mail. To facilitate the upload of the protein and ligand files, we can prepare these input files using the program UCSF Chimera. In this chapter, we describe how to use UCSF Chimera and SwissDock to perform protein-ligand docking simulations. To illustrate the process, we describe the molecular docking of the competitive inhibitor roscovitine against the structure of human cyclin-dependent kinase 2.

Tunable Stability of Imidazotetrazines Leads to a Potent Compound for Glioblastoma

Even in the era of personalized medicine and immunotherapy, temozolomide (TMZ), a small molecule DNA alkylating agent, remains the standard-of-care for glioblastoma (GBM). TMZ has an unusual mode-of-action, spontaneously converting to its active component via hydrolysis in vivo. While TMZ has been FDA approved for two decades, it provides little benefit to patients whose tumors express the resistance enzyme MGMT and gives rise to systemic toxicity through myelosuppression. TMZ was first synthesized in 1984, but certain key derivatives have been inaccessible due to the chemical sensitivity of TMZ, precluding broad exploration of the link between imidazotetrazine structure and biological activity. Here, we sought to discern the relationship between the hydrolytic stability and anticancer activity of imidazotetrazines, with the objectives of identifying optimal timing for prodrug activation and developing suitable compounds with enhanced efficacy via increased blood-brain barrier penetrance. This work necessitated the development of new synthetic methods to provide access to previously unexplored functionality (such as aliphatic, ketone, halogen, and aryl groups) at the C8 position of imidazotetrazines. Through synthesis and evaluation of a suite of compounds with a range of aqueous stabilities (from 0.5 to 40 h), we derive a predictive model for imidazotetrazine hydrolytic stability based on the Hammett constant of the C8 substituent. Promising compounds were identified that possess activity against a panel of GBM cell lines, appropriate hydrolytic and metabolic stability, and brain-to-serum ratios dramatically elevated relative to TMZ, leading to lower hematological toxicity profiles and superior activity to TMZ in a mouse model of GBM. This work points a clear path forward for the development of novel and effective anticancer imidazotetrazines.

Synthesis of Jacaranone-Derived Nitrogenous Cyclohexadienones and Their Antiproliferative and Antiprotozoal Activities

The cytotoxic and antiprotozoal activities of the phytoquinoide, jacaranone, and related compounds have been an ongoing topic in recent drug discovery. Starting from the natural product-derived cyclohexadienone scaffold, a series of nitrogen-containing derivatives were synthesized and subsequently evaluated for their antiproliferative and antiprotozoal activity. Anticancer potency was analyzed using different types of cancer cell lines: MDA-MB-231 breast cancer, CCRF-CEM leukemia, HCT-116 colon cancer, U251 glioblastoma, and, in addition, non-tumorigenic MRC-5 lung fibroblasts. Antiproliferative activities at micromolar concentrations could be shown. Antiprotozoal activity was assessed against Plasmodium falciparum NF54 and Trypanosoma brucei rhodesiense STIB900. For all compounds, selectivity indices (SI) were calculated based on assessed cytotoxicity towards L6 cells. In addition, the structure-activity-relationships and physicochemical parameters of these compounds are discussed.

Pharmacokinetic profiling of anticancer phytocompounds using computational approach

INTRODUCTION

Natural products exhibit diverse scaffolds and are considered as suitable candidates for development of leads. However, poor pharmacokinetics often acts as a hindrance during the drug discovery process.

OBJECTIVE

With a view of exploring the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of plant-based anticancer compounds, open-access databases (NPACT, CancerHSP and TaxKB) were analysed to identify molecules having properties favourable for drug ability.

METHODOLOGY

Our workflow involved identification of molecules capable of passing each of the ADMET barriers based on physicochemical properties of molecules, and physiological barriers and factors.

RESULTS

The results revealed that out of 5086 phytomolecules, 63% were orally absorbable and 52% distributable. Also, an appreciable proportion of these compounds (45%) could be metabolised and excreted. Furthermore, 28% were found to be non-toxic for cardio toxicity and central nervous system (CNS) activity. Additionally, comparison against known anticancer drugs (reference dataset) revealed that the three libraries exhibit similar trends, thus providing additional confidence to the predictions. Overall, 28% of the molecular dataset was found to have suitable pharmacokinetic properties. We have also discussed a few natural products which exhibit favourable ADMET as well as low nano-micromolar in vitro anticancer activity.

CONCLUSION

We have created an interactive database (ADMETCan), which provides access to predicted ADMET of these anticancer phytomolecules. The ease of availability of this dataset is expected to minimise failure rate of these compounds and thus is expected to be beneficial to the scientific community involved in anticancer identification and development.

A Systems-Level Analysis of Mechanisms of Platycodon grandiflorum Based on A Network Pharmacological Approach

Platycodon grandiflorum (PG) is widely used in Asia for its various beneficial effects. Although many studies were conducted to understand the molecular mechanisms of PG, it is still unclear how the combinations of multiple ingredients work together to exert its therapeutic effects. The aim of the present study was to provide a comprehensive review of the systems-level mechanisms of PG by adopting network pharmacological analysis. We constructed a compound⁻target⁻disease network for PG using experimentally validated and machine-leaning-based prediction results. Each target of the network was analyzed based on previously known pharmacological activities of PG. Gene ontology analysis revealed that the majority of targets were related to cellular and metabolic processes, responses to stimuli, and biological regulation. In pathway enrichment analyses of targets, the terms related to cancer showed the most significant enrichment and formed distinct clusters. Degree matrix analysis for target⁻disease associations of PG suggested the therapeutic potential of PG in various cancers including hepatocellular carcinoma, gastric cancer, prostate cancer, small-cell lung cancer, and renal cell carcinoma. We expect that network pharmacological approaches will provide an understanding of the systems-level mechanisms of medicinal herbs and further develop their therapeutic potentials.

Synthesis of novel N-(1,3-thiazol-2-yl)benzamide clubbed oxadiazole scaffolds: Urease inhibition, Lipinski rule and molecular docking analyses

Present work aimed to synthesize some unique bi-heterocyclic benzamides as lead compounds for the in vitro inhibition of urease enzyme, followed by in silico studies. These targeted benzamides were synthesized in good yields through a multi-step protocol and their structures were confirmed by IR, ¹H NMR, ¹³C NMR, EI-MS and elemental analysis. The in vitro screening results showed that most of the ligands exhibited good inhibitory potentials against the urease. Chemo-informatics analysis envisaged that all these compounds obeyed the Lipinski's rule. Molecular docking results showed that 7h exhibited good binding energy value (-8.40 kcal/mol) and was bound within the active region of urease enzyme. From the present investigation, it was inferred that some of these potent urease inhibitors might serve as novel templates in drug designing.

Exploration of synthetic multifunctional amides as new therapeutic agents for Alzheimer's disease through enzyme inhibition, chemoinformatic properties, molecular docking and dynamic simulation insights

A new series of multifunctional amides has been synthesized having moderate enzyme inhibitory potentials and mild cytotoxicity. 2-Furyl(1-piperazinyl)methanone (1) was coupled with 3,5-dichloro-2-hydroxybenzenesulfonyl chloride (2) to form {4-[(3,5-dichloro-2-hydroxyphenyl)sulfonyl]-1-piperazinyl}(2-furyl)methanone (3). Different elecrophiles were synthesized by the reaction of various un/substituted anilines (4a-o) with 2-bromoacetylbromide (5), 2‑bromo‑N-(un/substituted-phenyl)acetamides (6a-o). Further, equimolar ratios of 3 and 6a-o were allowed to react in the presence of K₂CO₃ in acetonitrile to form desired multifunctional amides (7a-o). The structural confirmation of all the synthesized compounds was carried out by their EI-MS, IR, ¹H NMR and ¹³C NMR spectral data. Enzyme inhibition activity was performed against acetyl and butyrylcholinestrase enzymes, whereby 7e showed very good activity having IC₅₀ value of 5.54 ± 0.03 and 9.15 ± 0.01 μM, respectively, relative to eserine, a reference standard. Hemolytic activity of the molecules was checked to asertain their cytotoxicity towards red blood cell membrance and it was observed that most of the compounds were not toxic up to certain range. Moreover, chemoinformatic protepties and docking simulation results also showed the significance of 7e as compared to other compounds. Based on in vitro and in silico analysis 7e could be used as a template for the development of new drugs against Alzheimer's disease.

2-Furoic piperazide derivatives as promising drug candidates of type 2 diabetes and Alzheimer's diseases: In vitro and in silico studies

The heterocyclic compounds have been extensively reported for their bioactivity potential. The current research work reports the synthesis of some new multi-functional derivatives of 2-furoic piperazide (1; 1-(2-furoyl)piperazine). The synthesis was initiated by reacting the starting compound 1 with 3,5-dichloro-2-hydroxybenzenesulfonyl chloride (2) in a basic, polar and protic medium to obtain the parent sulfonamide 3 which was then treated with different electrophiles, 4a-g, in a polar and aprotic medium to acquire the designed molecules, 5a-g. These convergent derivatives were evaluated for their inhibitory potential against α-glucosidase, acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes. Acarbose was used as a reference standard for α-glucosidase inhibition while eserine for AChE and BChE inhibition. Some of the synthesized compounds were identified as promising inhibitors of these three enzymes and their bioactivity potentials were also supported by molecular docking study. The most active compounds among the synthetic analogues might be helpful in drug discovery and development for the treatment of type 2 diabetes and Alzhiemer's diseases.

Multi-objective de novo drug design with conditional graph generative model

Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although available, current graph generative models are are often too general and computationally expensive. In this work, a new de novo molecular design framework is proposed based on a type of sequential graph generators that do not use atom level recurrent units. Compared with previous graph generative models, the proposed method is much more tuned for molecule generation and has been scaled up to cover significantly larger molecules in the ChEMBL database. It is shown that the graph-based model outperforms SMILES based models in a variety of metrics, especially in the rate of valid outputs. For the application of drug design tasks, conditional graph generative model is employed. This method offers highe flexibility and is suitable for generation based on multiple objectives. The results have demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold, compounds with specific drug-likeness and synthetic accessibility requirements, as well as dual inhibitors against JNK3 and GSK-3β.

Synthesis, enzyme inhibitory kinetics mechanism and computational study of N-(4-methoxyphenethyl)-N-(substituted)-4-methylbenzenesulfonamides as novel therapeutic agents for Alzheimer's disease

The present study comprises the synthesis of a new series of sulfonamides derived from 4-methoxyphenethylamine (1). The synthesis was initiated by the reaction of 1 with 4-methylbenzenesulfonyl chloride (2) in aqueous sodium carbonate solution at pH 9 to yield N-(4-methoxyphenethyl)-4-methylbenzensulfonamide (3).This parent molecule 3 was subsequently treated with various alkyl/aralkyl halides, (4a–j), using N,N-dimethylformamide (DMF) as solvent and LiH as activator to produce a series of new N-(4-methoxyphenethyl)-N-(substituted)-4-methylbenzenesulfonamides (5a–j). The structural characterization of these derivatives was carried out by spectroscopic techniques like IR, ¹H-NMR, and ¹³C-NMR. The elemental analysis data was also coherent with spectral data of these molecules. The inhibitory effects on acetylcholinesterase and DPPH were evaluated and it was observed that N-(4-Methoxyphenethyl)-4-methyl-N-(2-propyl)benzensulfonamide (5c) showed acetylcholinesterase inhibitory activity 0.075 ± 0.001 (IC₅₀ 0.075 ± 0.001 µM) comparable to Neostigmine methylsulfate (IC₅₀ 2.038 ± 0.039 µM).The docking studies of synthesized ligands 5a–j were also carried out against acetylcholinesterase (PDBID 4PQE) to compare the binding affinities with IC₅₀ values. The kinetic mechanism analyzed by Lineweaver-Burk plots demonstrated that compound (5c) inhibits the acetylcholinesterase competitively to form an enzyme inhibitor complex. The inhibition constants Ki calculated from Dixon plots for compound (5c) is 2.5 µM. It was also found from kinetic analysis that derivative 5c irreversible enzyme inhibitor complex. It is proposed on the basis of our investigation that title compound 5c may serve as lead structure for the design of more potent acetylcholinesterase inhibitors.

Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products

This is a new golden age for drug discovery based on natural products derived from both marine and terrestrial sources. Herein, a straightforward but important question is "what are the major structural differences between marine natural products (MNPs) and terrestrial natural products (TNPs)?" To answer this question, we analyzed the important physicochemical properties, structural features, and drug-likeness of the two types of natural products and discussed their differences from the perspective of evolution. In general, MNPs have lower solubility and are often larger than TNPs. On average, particularly from the perspective of unique fragments and scaffolds, MNPs usually possess more long chains and large rings, especially 8- to 10-membered rings. MNPs also have more nitrogen atoms and halogens, notably bromines, and fewer oxygen atoms, suggesting that MNPs may be synthesized by more diverse biosynthetic pathways than TNPs. Analysis of the frequently occurring Murcko frameworks in MNPs and TNPS also reveals a striking difference between MNPs and TNPs. The scaffolds of the former tend to be longer and often contain ester bonds connected to 10-membered rings, while the scaffolds of the latter tend to be shorter and often bear more stable ring systems and bond types. Besides, the prediction from the naïve Bayesian drug-likeness classification model suggests that most compounds in MNPs and TNPs are drug-like, although MNPs are slightly more drug-like than TNPs. We believe that MNPs and TNPs with novel drug-like scaffolds have great potential to be drug leads or drug candidates in drug discovery campaigns.

Synthesis of sulfadiazinyl acyl/aryl thiourea derivatives as calf intestinal alkaline phosphatase inhibitors, pharmacokinetic properties, lead optimization, Lineweaver-Burk plot evaluation and binding analysis

To seek the new medicinal potential of sulfadiazine drug, the free amino group of sulfadiazine was exploited to obtain acyl/aryl thioureas using simple and straightforward protocol. Acyl/aryl thioureas are well recognized bioactive pharmacophore containing moieties. A new series (4a-4j) of sulfadiazine derived acyl/aryl thioureas was synthesized and characterized through spectroscopic and elemental analysis. The synthesized derivatives 4a-4j were subjected to calf intestinal alkaline phosphatase (CIAP) activity. The derivative 4a-4j showed better inhibition potential compared to standard monopotassium phosphate (MKP). The compound 4c exhibited higher potential in the series with IC₅₀ 0.251 ± 0.012 µM (standard KH₂PO₄ 4.317 ± 0.201 µM). Lineweaver-Burk plots revealed that most potent derivative 4c inhibition CIAP via mixed type pathway. Pharmacological investigations showed that synthesized compounds 4a-4j obey Lipinsk's rule. ADMET parameters evaluation predicted that these molecule show significant lead like properties with minimum possible toxicity and can serve as templates in drug designing. The synthetic compounds show none mutagenic and irritant behavior. Molecular docking analysis showed that compound 4c interacts with Asp273, His317 and Arg166 amino acid residues.

Molecular Docking and Dynamic Simulation of AZD3293 and Solanezumab Effects Against BACE1 to Treat Alzheimer's Disease

The design of novel inhibitors to target BACE1 with reduced cytotoxicity effects is a promising approach to treat Alzheimer's disease (AD). Multiple clinical drugs and antibodies such as AZD3293 and Solanezumab are being tested to investigate their therapeutical potential against AD. The current study explores the binding pattern of AZD3293 and Solanezumab against their target proteins such as β-secretase (BACE1) and mid-region amyloid-beta (Aβ) (PDBIDs: 2ZHV & 4XXD), respectively using molecular docking and dynamic simulation (MD) approaches. The molecular docking results show that AZD3293 binds within the active region of BACE1 by forming hydrogen bonds against Asp32 and Lys107 with distances 2.95 and 2.68 Å, respectively. However, the heavy chain of Solanezumab interacts with Lys16 and Asp23 of amyloid beta having bond length 2.82, 2.78, and 3.00 Å, respectively. The dynamic cross correlations and normal mode analyses show that BACE1 depicted good residual correlated motions and fluctuations, as compared to Solanezumab. Using MD, the Root Mean Square Deviation and Fluctuation (RMSD/F) graphs show that AZD3293 residual fluctuations and RMSD value (0.2 nm) was much better compared to Solanezumab (0.7 nm). Moreover, the radius of gyration (Rg) results also depicts the significance of AZD3293 docked complex compared to Solanezumab through residual compactness. Our comparative results show that AZD3293 is a better therapeutic agent for treating AD than Solanezumab.

In silico design of novel proton-pump inhibitors with reduced adverse effects

The development of new proton-pump inhibitors (PPIs) with less adverse effects by lowering the pKa values of nitrogen atoms in pyrimidine rings has been previously suggested by our group. In this work, we proposed that new PPIs should have the following features: (1) number of ring II = number of ring I + 1; (2) preferably five, six, or seven-membered heteroatomic ring for stability; and (3) 1 < pKa1 < 4. Six molecular scaffolds based on the aforementioned criteria were constructed, and R groups were extracted from compounds in extensive data sources. A virtual molecule dataset was established, and the pKa values of specific atoms on the molecules in the dataset were calculated to select the molecules with required pKa values. Drug-likeness screening was further conducted to obtain the candidates that significantly reduced the adverse effects of long-term PPI use. This study provided insights and tools for designing targeted molecules in silico that are suitable for practical applications.

Uncovering the action mechanism of polydatin via network pharmacological target prediction

Polydatin (PD), a small natural compound originally extracted from Polygonum cuspidatum, exerts distinct biological functions in a variety of diseases. However, the action mechanism of PD has yet to be systematically explored. In this study, we firstly corroborated the druggability of PD by evaluating the medicinal properties of PD using a TCMSP server. We then conducted in silico target-prediction for PD using PharmMapper and ChemMapper, which led to the identification of 15 potential targets overlapping in both approaches. These 15 targets were subsequently evaluated by GeneMANIA, GO biological process and KEGG pathway analysis, which finally contribute to the construction of a drug-target-pathway network for PD. The network analysis revealed that these targets were mainly associated with cancer, cell growth and apoptosis, hormones and other physiological processes, outlining the pharmacological influences of PD on multiple integrated pathways involved in a particular network.

Chemical probes and drug leads from advances in synthetic planning and methodology

Screening of small-molecule libraries is a productive method for identifying both chemical probes of disease-related targets and potential starting points for drug discovery. In this article, we focus on strategies such as diversity-oriented synthesis that aim to explore novel areas of chemical space efficiently by populating small-molecule libraries with compounds containing structural features that are typically under-represented in commercially available screening collections. Drawing from more than a decade's worth of examples, we highlight how the design and synthesis of such libraries have been enabled by modern synthetic chemistry, and we illustrate the impact of the resultant chemical probes and drug leads in a wide range of diseases.

Antitumor Mechanisms of Curcumae Rhizoma Based on Network Pharmacology

Curcumae Rhizoma, a traditional Chinese medication, is commonly used in both traditional treatment and modern clinical care. Its anticancer effects have attracted a great deal of attention, but the mechanisms of action remain obscure. In this study, we screened for the active compounds of Curcumae Rhizoma using a drug-likeness approach. Candidate protein targets with functions related to cancer were predicted by reverse docking and then checked by manual search of the PubMed database. Potential target genes were uploaded to the GeneMANIA server and DAVID 6.8 database for analysis. Finally, compound-target, target-pathway, and compound-target-pathway networks were constructed using Cytoscape 3.3. The results revealed that the anticancer activity of Curcumae Rhizoma potentially involves 13 active compounds, 33 potential targets, and 31 signaling pathways, thus constituting a “multiple compounds, multiple targets, and multiple pathways” network corresponding to the concept of systematic actions in TCM. These findings provide an overview of the anticancer action of Curcumae Rhizoma from a network perspective, as well as setting an example for future studies of other materials used in TCM.

Iridoids and Other Monoterpenes in the Alzheimer's Brain: Recent Development and Future Prospects

Iridoids are a class of monoterpenoid compounds constructed from 10-carbon skeleton of isoprene building units. These compounds in their aglycones and glycosylated forms exist in nature to contribute to mechanisms related to plant defenses and diverse plant-animal interactions. Recent studies have also shown that iridoids and other structurally related monoterpenes display a vast array of pharmacological effects that make them potential modulators of the Alzheimer’s disease (AD). This review critically evaluates the therapeutic potential of these natural products by assessing key in vitro and in vivo data published in the scientific literature. Mechanistic approach of scrutiny addressing their effects in the Alzheimer’s brain including the τ-protein phosphorylation signaling, amyloid beta (Aβ) formation, aggregation, toxicity and clearance along with various effects from antioxidant to antiinflammatory mechanisms are discussed. The drug likeness of these compounds and future prospects to consider in their development as potential leads are addressed.

Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity

BACKGROUND

The merits of ethnomedicine-led approach to identify and prioritize anticancer medicinal plants have been challenged as cancer is more likely to be poorly understood in traditional medicine practices. Nonetheless, it is also believed that useful data can be generated by combining ethnobotanical findings with available scientific studies. Thus, this study combined an ethnobtanical study with ligand based in silico screening to identify relevant medical plants and predict their anticancer potential based on their phytoconstiutents reported in scientific literatures.

METHODS

First, relevant medicinal plants were identified through an ethnobotanical survey. A list of phytochemicals was prepared based on literature review of articles which reported on the natural products of identified medicinal plants. Then, their phytochemicals were subjected to in silico evaluation, which included a hybrid score similarity measure, rule of five, Ghose-Viswanadhan-Wendoloski (GVW)-indices and structural features criteria, to predict their anticancer activity and drugability.

RESULTS

A total of 18 medicinal plants and 265 phytoconstituents were identified. The natural product pool constituted 109(41.13%) terpenoids, 67(25.28%) phenolics, 29(10.94%) simple and functionalized hydrocarbons, 26(9.81%) alkaloids, 25(9.43%) glycosides and 9(3.40%) compounds belonging to different phytochemical classes. The similarity measure using CDRUG identified 34(12.73%) phytochemicals with high (p-Value < 0.05) and 35(13.21%) with moderate possibility (p-Value < 0.1) of anticancer activity. In fact, three of the predicted compounds had the same structure with known anticancer compounds (HSCORE=1). The 80% GVW-indices based antineoplastic drugabilityranges were all mate by 25 of the predicted compounds. Predicted compounds were also shown to have ring structures and functional groups deemed important for anticancer activity.

CONCLUSIONS

Given the findings, there is a promising anticancer activity by the traditionally used medicinal plants and a potential for the predicted phytochemicals to be pursued as possible hits or me-too drugs.

VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization

BACKGROUND

Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance.

METHOD

We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions.

RESULTS

VB-MK-LMF achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods, which can be traced back to multiple factors. The systematic evaluation of the effect of multiple kernels confirm their benefits, but also highlights the limitations of linear kernel combinations, already recognized in other fields. The analysis of the effect of prior kernels using varying sample sizes sheds light on the balance of data and knowledge in DTI tasks and on the rate at which the effect of priors vanishes. This also shows the existence of "small sample size" regions where using side information offers significant gains. Alongside favorable predictive performance, a notable property of MF methods is that they provide a unified space for drugs and targets using latent representations. Compared to earlier studies, the dimensionality of this space proved to be surprisingly low, which makes the latent representations constructed by VB-ML-LMF especially well-suited for visual analytics. The probabilistic nature of the predictions allows the calculation of the expected values of hits in functionally relevant sets, which we demonstrate by predicting drug promiscuity. The variational Bayesian approximation is also implemented for general purpose graphics processing units yielding significantly improved computational time.

CONCLUSION

In standard benchmarks, VB-MK-LMF shows significantly improved predictive performance in a wide range of settings. Beyond these benchmarks, another contribution of our work is highlighting and providing estimates for further pharmaceutically relevant quantities, such as promiscuity, druggability and total number of interactions.

Sulfonamide-Linked Ciprofloxacin, Sulfadiazine and Amantadine Derivatives as a Novel Class of Inhibitors of Jack Bean Urease; Synthesis, Kinetic Mechanism and Molecular Docking

Sulfonamide derivatives serve as an important building blocks in the drug design discovery and development (4D) process. Ciprofloxacin-, sulfadiazine- and amantadine-based sulfonamides were synthesized as potent inhibitors of jack bean urease and free radical scavengers. Molecular diversity was explored and electronic factors were also examined. All 24 synthesized compounds exhibited excellent potential against urease enzyme. Compound 3e (IC₅₀ = 0.081 ± 0.003 µM), 6a (IC₅₀ = 0.0022 ± 0.0002 µM), 9e (IC₅₀ = 0.0250 ± 0.0007 µM) and 12d (IC₅₀ = 0.0266 ± 0.0021 µM) were found to be the lead compounds compared to standard (thiourea, IC₅₀ = 17.814 ± 0.096 µM). Molecular docking studies were performed to delineate the binding affinity of the molecules and a kinetic mechanism of enzyme inhibition was propounded. Compounds 3e, 6a and 12d exhibited a mixed type of inhibition, while derivative 9e revealed a non-competitive mode of inhibition. Compounds 12a, 12b, 12d, 12e and 12f showed excellent radical scavenging potency in comparison to the reference drug vitamin C.

Development of highly potent melanogenesis inhibitor by in vitro, in vivo and computational studies

The present work describes the synthesis of few hydroxylated amide derivatives as melanogenesis inhibitors. In vitro, in vivo and computational studies proved that compound 6d is a highly potent melanogenesis inhibitor compared to standard kojic acid. The title amides 4a–e and 6a–e were synthesized following simple reaction routes with excellent yields. Most of the synthesized compounds exhibited good mushroom tyrosinase inhibitory activity, but compound 6d showed excellent activity (IC₅₀ 0.15 µM) compared to standard kojic acid (IC₅₀ 16.69 µM). Lineweaver–Burk plots were used for the determination of kinetic mechanism, and it was found that compounds 4c and 6d showed non-competitive inhibition while 6a and 6b showed mixed-type inhibition. The kinetic mechanism further revealed that compound 6d formed irreversible complex with the target enzyme tyrosinase. The Ki values determined for compounds 4c, 6a, 6b and 6d are 0.188, 0.84, 2.20 and 0.217 µM respectively. Results of human tyrosinase inhibitory activity in A375 human melanoma cells showed that compound 6d exhibited 91.9% inhibi-tory activity at a concentration of 50 µg/mL. In vivo cytotoxicity evaluation of compound 6d in zebrafish embryos showed that it is non-toxic to zebrafish. Melanin depigmentation assay performed in zebrafish indicated that compound 6d possessed greater potential in decreasing melanin contents compared to kojic acid at the same concentration. Computational studies also supported the wet lab findings as compound 6d showed a highest binding affinity with the target protein (PDBID: 2Y9X) with a binding energy value of −7.90 kcal/mol. Molecular dynamic simulation studies also proved that amide 6d formed the most stable complex with tyrosinase. Based upon our in vitro, in vivo and computational studies, we propose that compound 6d is a promising candidate for the development of safe cosmetic agent.

ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity

As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theoretical models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse intraperitoneal respiratory toxicity characterized by multiple symptoms, a number of quantitative and qualitative predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 molecular descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant analysis (LDA). Among all of the models, the SVM regression model shows the most accurate quantitative predictions for the test set (q²_ext = 0.707), and the XGBoost classification model achieves the most accurate qualitative predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools for respiratory toxicity.

In silico estimation of basic activity-relevant parameters for a set of drug absorption promoters

Finding a balance between a desired drug's potency and its physicochemical properties that are important for its molecule pharmacokinetic or pharmacodynamics profile is still a challenging issue in rational drug discovery. Quantitative assessment of the lipophilic characteristics of potential drug molecules is indispensable for efficient development of Absorption, Distribution, Metabolism, Excretion, Toxicity-tailored structure-activity models; therefore reliable procedures for deriving log P from molecular structure are desirable. In the current work a range of various software log P predictors for estimation of the numerical lipophilic values for a set of cholic acid derivatives were employed and subsequently cross-compared with the experimental parameters. Thus, the empirical lipophilicity (R_M) was compared with the corresponding log P characteristics calculated using alternative methods for deducing the lipophilic features. The mean values of the selected molecular descriptors that were averaged over the chosen calculation methods (consensus clog P) were subsequently correlated with the R_M parameter. As an additional experiment, the iterative variable elimination partial least squares (IVE-PLS) methodology for an ensemble of descriptors retrieved from Dragon 6.0 software was applied for a set of drug transporters. To investigate the variations within the ensemble of cholic acid derivatives principal component analysis (PCA) and self-organizing neural network (SOM) procedures were used to visualize the major differences in the performance of drug promoters with respect to their lipophilic profile.

Discovery of Novel and Selective Adenosine A2A Receptor Antagonists for Treating Parkinson's Disease through Comparative Structure-Based Virtual Screening

Among non-dopaminergic strategies for combating Parkinson's disease (PD), antagonism of the A_2A adenosine receptor (AR) has emerged to show great potential. In this study, on the basis of two crystal structures of the A_2A AR with the best capability to distinguish known antagonists from decoys, docking-based virtual screening (VS) was conducted to identify novel A_2A AR antagonists. A total of 63 structurally diverse compounds identified by VS were submitted to experimental testing, and 11 of them exhibited substantial activity against the A_2A AR (K_i < 10 μM), including two compounds with K_i below 1 μM (compound 43, 0.42 μM; compound 51, 0.27 μM) and good A_2A/A₁ selectivity (fold < 0.1). Compounds 43 and 51 demonstrated antagonistic activity according to the results of cAMP measurements (cAMP IC₅₀ = 1.67 and 1.80 μM, respectively) and showed good efficacy in the haloperidol-induced catalepsy (HIC) rat model for PD at doses of up to 30 mg/kg. Further lead optimization based on a substructure searching strategy led to the discovery of compound 84 as an excellent A_2A AR antagonist (A_2A K_i = 54 nM, A_2A/A₁ fold < 0.1, cAMP IC₅₀ = 0.3 μM) that exhibited significant improvement in anti-PD efficacy in the HIC rat model.

Comparative analyses of structural features and scaffold diversity for purchasable compound libraries

Large purchasable screening libraries of small molecules afforded by commercial vendors are indispensable sources for virtual screening (VS). Selecting an optimal screening library for a specific VS campaign is quite important to improve the success rates and avoid wasting resources in later experimental phases. Analysis of the structural features and molecular diversity for different screening libraries can provide valuable information to the decision making process when selecting screening libraries for VS. In this study, the structural features and scaffold diversity of eleven purchasable screening libraries and Traditional Chinese Medicine Compound Database (TCMCD) were analyzed and compared. Their scaffold diversity represented by the Murcko frameworks and Level 1 scaffolds was characterized by the scaffold counts and cumulative scaffold frequency plots, and visualized by Tree Maps and SAR Maps. The analysis demonstrates that, based on the standardized subsets with similar molecular weight distributions, Chembridge, ChemicalBlock, Mucle, TCMCD and VitasM are more structurally diverse than the others. Compared with all purchasable screening libraries, TCMCD has the highest structural complexity indeed but more conservative molecular scaffolds. Moreover, we found that some representative scaffolds were important components of drug candidates against different drug targets, such as kinases and guanosine-binding protein coupled receptors, and therefore the molecules containing pharmacologically important scaffolds found in screening libraries might be potential inhibitors against the relevant targets. This study may provide valuable perspective on which purchasable compound libraries are better for you to screen.Graphical abstract

Systematic Understanding of the Mechanism of Salvianolic Acid A via Computational Target Fishing

Salvianolic acid A (SAA) is one of the most abundant water-soluble and potent anti-oxidative compounds isolated from Danshen, a traditional Chinese medicine. A systematic overview of its mechanism of action is yet to be performed. In the present study, the druggability of SAA was measured using the TCMSP server, and potential targets of SAA were identified by PharmMapper and DRAR-CPI. Intersecting targets were then assessed by GeneMANIA and GO pathway analysis, and drug-target-pathway networks were constructed to give a visual view. The results showed that SAA has good druggability, and 13 putative protein targets were identified. Network analysis showed that these targets were associated with cancer, metabolism and other physiological processes. In summary, SAA is predicted to target multiple proteins and pathways to form a network that exerts systematic pharmacological effects.

In vitro, in vivo and in silico anti-hyperglycemic inhibition by sinigrin

OBJECTIVE

To evaluate the anti-hyperglycemic potential of sinigrin using in vitro, in silico and in vivo streptozotocin (STZ) induced hyperglycemic zebrafish model.

METHODS

The in vitro enzyme inhibition assay was carried out to determine the IC₅₀ value against α-glucosidase and α-amylase, in silico molecular docking was performed against both enzymes with PyRx tool and simulations were performed using GROMACS tool. Hyperglycemia was induced in zebrafishes using three intraperitoneal injections on alternating days for 1 week at 350 mg/kg of STZ. Hyperglycemic fishes were treated intraperitoneally with 50, 100 and 150 mg of sinigrin/kg of body weight for 24 h and glucose levels were measured.

RESULTS

The sinigrin showed very strong inhibition against α-glucosidase and α-amylase with 0.248 and 0.00124 μM while reference drug acarbose showed IC₅₀ value of 73.0700 and 0.0017 μM against α-glucosidase and α-amylase, respectively. Kinetic analysis revealed that sinigrin has the mixed type mode of inhibition against α-glucosidase. Molecular docking results revealed its strong binding affinity with α-glucosidase (-10.00 kcal/mol) and α-amylase (-8.10 kcal/mol). Simulations graphs confirmed its stability against both enzymes. Furthermore, in hyperglycemic zebrafishes most significant (P < 0.001) reduction of glucose was occurred at 150 mg/kg, moderate significant reduction of glucose was observed at 100 mg/kg and no any significant reduction of glucose was measured at 50 mg/kg.

CONCLUSIONS

It can be evident from the present results that sinigrin has potent anti-hyperglycemic activity and it may prove to be effective treatment for the hyperglycemia.

The Application of Modeling and Prediction to the Formation and Stability of Amorphous Solid Dispersions

Amorphous solid dispersion (ASD) formulation development is frequently difficult owing to the inherent physical instability of the amorphous form, and limited understanding of the physical and chemical interactions that translate to initial dispersion formation and long-term physical stability. Formulation development for ASDs has been historically accomplished through trial and error or experience with extant systems; however, rational selection of appropriate excipients is preferred to reduce time to market and decrease costs associated with development. Current efforts to develop thermodynamic and computational models attempt to rationally direct formulation and show promise. This review compiles and evaluates important methods used to predict ASD formation and physical stability. Recent literature in which these methods are applied is also reviewed, and limitations of each method are discussed.

Pharmacoinformatics exploration of polyphenol oxidases leading to novel inhibitors by virtual screening and molecular dynamic simulation study

Polyphenol oxidases (PPOs)/tyrosinases are metal-dependent enzymes and known as important targets for melanogenesis. Although considerable attempts have been conducted to control the melanin-associated diseases by using various inhibitors. However, the exploration of the best anti-melanin inhibitor without side effect still remains a challenge in drug discovery. In present study, protein structure prediction, ligand-based pharmacophore modeling, virtual screening, molecular docking and dynamic simulation study were used to screen the strong novel inhibitor to cure melanogenesis. The 3D structures of PPO1 and PPO2 were built through homology modeling, while the 3D crystal structures of PPO3 and PPO4 were retrieved from PDB. Pharmacophore modeling was performed using LigandScout 3.1 software and top five models were selected to screen the libraries (2601 of Aurora and 727, 842 of ZINC). Top 10 hit compounds (C1-10) were short-listed having strong binding affinities for PPO1-4. Drug and synthetic accessibility (SA) scores along with absorption, distribution, metabolism, excretion and toxicity (ADMET) assessment were employed to scrutinize the best lead hit. C4 (name) hit showed the best predicted SA score (5.75), ADMET properties and drug-likeness behavior among the short-listed compounds. Furthermore, docking simulations were performed to check the binding affinity of C1-C10 compounds against target proteins (PPOs). The binding affinity values of complex between C4 and PPOs were higher than those of other complexes (-11.70, -12.1, -9.90 and -11.20kcal/mol with PPO1, PPO2, PPO3, or PPO4, respectively). From comparative docking energy and binding analyses, PPO2 may be considered as better target for melanogenesis than others. The potential binding modes of C4, C8 and C10 against PPO2 were explored using molecular dynamics simulations. The root mean square deviation and fluctuation (RMSD/RMSF) graphs results depict the significance of C4 over the other compounds. Overall, bioactivity and ligand efficiency profiles suggested that the proposed hit may be more effective inhibitors for melanogenesis.

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.