Topological virtual screening: a way to find new compounds active in ulcerative colitis by inhibiting NF-κB

Ligand-based design of anticancer MMP2 inhibitors: a review

MMP2, a Zn²⁺-dependent metalloproteinase, is related to cancer and angiogenesis. Inhibition of this enzyme might result in a potential antimetastatic drug to leverage the anticancer drug armory. In silico or computer-aided ligand-based drug design is a method of rational drug design that takes multiple chemometrics (i.e., multi-quantitative structure-activity relationship methods) into account for virtually selecting or developing a series of probable selective MMP2 inhibitors. Though existing matrix metalloproteinase inhibitors have shown plausible pan-matrix metalloproteinase (MMP) activity, they have resulted in various adverse effects leading to their being rescinded in later phases of clinical trials. Therefore a review of the ligand-based designing methods of MMP2 inhibitors would result in an explicit route map toward successfully designing and synthesizing novel and selective MMP2 inhibitors.

A robust classification-dependent multi-molecular modelling study on some biphenyl sulphonamide based MMP-8 inhibitors

Matrix metalloproteinases (MMPs) are a group of zinc and calcium-dependent endopeptidases, which contribute to different physiological and biological activities via extracellular matrix (ECM) degradation. Matrix metalloproteinase-8 (MMP-8) belongs to type-II collagenases of the MMP family that has contribution in several physiological disorders such as cardiovascular diseases, joint, renal, digestive and respiratory disorders as well as in cancer. In clinical study, MMP-8 is found to be associated with periodontal disease condition. Therefore, MMP-8 specific inhibitors should be developed to target these disorders. The biphenyl sulphonamide (BPS) moiety is one of the crucial structural characteristics found in several MMP-8 inhibitors. Here, different classification-based molecular modelling methods were used to explore the structural features that lead to the activity variation of a series of MMP-8 inhibitors possessing a BPS moiety. Our current classification-based structural analysis of these BPS-derived MMP-8 inhibitors was able to identify the importance of several structural features such as the tetrahydroisoquinoline and N-Boc pyridyl groups, which have positive influences on MMP-8 inhibition. This study was also reflected the importance of the zinc-binding groups (ZBGs) like the hydroxamate and phosphonate for potent and sub-nanomolar range MMP-8 inhibition, which may benefit the development of highly potent MMP-8 inhibitors.

Prediction of critical properties of sulfur-containing compounds: New QSPR models

In this study, new models have been proposed for the prediction of different critical properties (critical temperature (T_C), critical pressure (P_C), critical volume (V_C), and acentric factor (ω)) of the sulfur-containing compounds based on quantitative structure-property relationship (QSPR). An extensive data set containing experimental data of over 130 different sulfur-containing compounds was employed. Enhanced Replacement Method (ERM) was applied for subset variable selection. Based on ERM selected descriptors, two different models, including linear model and genetic programming (GP) based non-linear model have been proposed for each critical property. The predicted values of each target were in good agreement with the experimental data. For GP-based models, the values of the coefficient of determination (R²) were 0.936, 0.976, 0.990, and 0.917 for T_C, P_C, V_C, and ω, respectively. After revisiting the available QSPR models, it was found that the domain of applicability of new models has been expanded.

KGF Is Delivered to Inflammatory and Induces the Epithelial Hyperplasia in Trinitrobenzene Sulfonic Acid-Induced Ulcerative Colitis Rats

Introduction

KGF-modified MSCs can promote the repair of spinal cord injury and pulmonary fibrosis injury in rats. However, the effect of KGF-modified MSCs on UC rats is unclear. We aimed to explore the therapeutic effect and possible mechanism of KGF gene-modified MSCs on trinitrobenzene sulfonic acid (TNBS)-induced UC rats.

Methods

The lentivirus-mediated KGF gene was introduced into bone marrow MSCs of male rats. Female SD rats were induced to establish a UC model by TNBS. Untreated MSCs, MSCs carrying empty vectors (MSCs-vec) or MSCs carrying KGF gene (MSCs-KGF) were transplanted into UC rats by tail vein injection.

Results

Significantly high expression of KGF was observed in the intestinal tissues of the MSCs-KGF group. Compared with the challenged control group, the DAI score, CMDI score and TDI score of the MSCs group, MSCs-vec group and MSCs-KGF group were markedly lower. Treatment with MSCs obviously promoted the expression of claudin-1 and PCNA in intestinal tissues of UC rats. Simultaneously, compared with the challenged control group, the levels of TNF-α, IL-6 and IL-8 in the intestinal tissues of the MSCs groups were significantly decreased, while the levels of IL-10 were significantly increased. Most importantly, we found that MSCs-KGF significantly improved colonic morphology and tissue damage and inflammation in UC rats compared with MSCs and MSCs-vec. Further analysis showed that MSCs-KGF clearly promoted phosphorylation of PI3K and Akt and inhibited nuclear translocation of NF-κB in intestinal tissues of UC rats.

Discussion

MSCs, especially KGF-modified MSCs, can improve colonic tissue damage in UC rats by promoting intestinal epithelial cell proliferation and reducing colonic inflammatory response, which may be related to activation of PI3K/Akt pathway and inhibition of NF-κB activation.

Quantitative structure-activity relationship models for compounds with anticonvulsant activity

Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.

First Report on the Validated Classification-Based Chemometric Modeling of Human Rhinovirus 3C Protease (HRV 3Cpro) Inhibitors

Human rhinoviruses (HRVs), a major cause of common cold and upper respiratory infections, may trigger severe respiratory complications like asthma and COPD. To date, no drugs are available in the market which are designed as novel HRV inhibitors despite the involvement of some pharmaceutical companies' due to economical and clinical constraints. HRV 3C protease may be a potential target for drug design as it plays crucial role in viral RNA replication and virion assembly process. Therefore, designing novel HRV 3Cpro inhibitors is necessary and demanding in the field of antiviral drug design. In this article, statistically significant and validated classification-based QSARs of a series of HRV 3Cpro inhibitors were performed for the first time as per the authors' knowledge. Results suggest that oxopyrrolidine and piperidinone rings are favored whereas carboxybenzyl and unsubstituted benzyl functions may be unfavorable. Moreover, this group, along with cyclic alkyl or aryl ring structures may favor HRV 3Cpro inhibition. These observations may be utilized for the design of a higher active anti-HRV agent in future.

Multiple molecular modelling studies on some derivatives and analogues of glutamic acid as matrix metalloproteinase-2 inhibitors

Matrix metalloproteinase-2 (MMP-2) is a potential target in anticancer drug discovery due to its association with angiogenesis, metastasis and tumour progression. In this study, 67 glutamic acid derivatives, synthesized and evaluated as MMP-2 inhibitors, were taken into account for multi-QSAR modelling study (regression-based 2D-QSAR, classification-based LDA-QSAR, Bayesian classification QSAR, HQSAR, 3D-QSAR CoMFA and CoMSIA as well as Open3DQSAR). All these QSAR studies were statistically validated individually. Regarding the 3D-QSAR analysis, the Open3DQSAR results were better than CoMFA and CoMSIA, although all these 3D-QSAR models supported each other. The importance of biphenylsulphonyl moiety over phenylacetyl/naphthylacetyl moieties was established due to its association with favourable steric and hydrophobic characters. HQSAR, LDA-QSAR and Bayesian classification QSAR studies also suggested that the biphenylsulphonamido group was better than the phenylacetylcarboxamido function. Additionally, glutamines were proven to be far better inhibitors than isoglutamines. Observations obtained from the current study were revalidated and supported by the earlier reported molecular modelling studies. Depending on these observations, newer glutamic acid-based compounds may be designed further in future for potent MMP-2 inhibitory activity.

The application of molecular topology for ulcerative colitis drug discovery

INTRODUCTION

Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.

Homoisoflavonoids as potential antiangiogenic agents for retinal neovascularization

A number of people worldwide have been suffering from ocular neovascularization that may be treated by a variety of drugs but these may possess adverse effects. Therefore, small antiangiogenic molecules with higher potency and lower toxic effects are intended. However, homoisoflavonoids of natural origin show the potential antiangiogenic effect in ocular neovascularization. These homoisoflavonoids are judged quantitatively in terms of statistical validation through multi-chemometric modeling approaches for the betterment and refinement of their structures required for higher antiangiogenic activity targeted to ocular neovascularization. These approaches may be utilized to design better antiangiogenic homoisoflavonoids.

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery.

Molecular topology: a strategy to identify novel compounds against ulcerative colitis

In the present paper, a strategy to identify novel compounds against ulcerative colitis (UC) by molecular topology (MT) is presented. Several quantitative structure-activity relationship (QSAR) models based on molecular topology have been developed to predict inducible nitric oxide synthase (iNOS) and tumor necrosis factor alpha ([Formula: see text]) mediated anti-ulcerative colitis (UC) activity and protective activity against a dextran sulfate sodium (DSS)-induced UC model. Each one has been used for the screening of four previously selected compounds as potential therapeutic agents for UC: alizarin-3-methyliminodiacetic acid (AMA), Calcein, (+)-dibenzyl-L-tartrate, and Ro 41-0960. These four compounds were then tested in vitro and in vivo and confirmed AMA and Ro 41-0960 as the best lead candidates for further development against UC.

Novel cancer chemotherapy hits by molecular topology: dual Akt and Beta-catenin inhibitors

Background and Purpose

Colorectal and prostate cancers are two of the most common types and cause of a high rate of deaths worldwide. Therefore, any strategy to stop or at least slacken the development and progression of malignant cells is an important therapeutic choice. The aim of the present work is the identification of novel cancer chemotherapy agents. Nowadays, many different drug discovery approaches are available, but this paper focuses on Molecular Topology, which has already demonstrated its extraordinary efficacy in this field, particularly in the identification of new hit and lead compounds against cancer. This methodology uses the graph theoretical formalism to numerically characterize molecular structures through the so called topological indices. Once obtained a specific framework, it allows the construction of complex mathematical models that can be used to predict physical, chemical or biological properties of compounds. In addition, Molecular Topology is highly efficient in selecting and designing new hit and lead drugs. According to the aforementioned, Molecular Topology has been applied here for the construction of specific Akt/mTOR and β-catenin inhibition mathematical models in order to identify and select novel antitumor agents.

Experimental Approach

Based on the results obtained by the selected mathematical models, six novel potential inhibitors of the Akt/mTOR and β-catenin pathways were identified. These compounds were then tested in vitro to confirm their biological activity.

Conclusion and Implications

Five of the selected compounds, CAS n° 256378-54-8 (Inhibitor n°1), 663203-38-1 (Inhibitor n°2), 247079-73-8 (Inhibitor n°3), 689769-86-6 (Inhibitor n°4) and 431925-096 (Inhibitor n°6) gave positive responses and resulted to be active for Akt/mTOR and/or β-catenin inhibition. This study confirms once again the Molecular Topology’s reliability and efficacy to find out novel drugs in the field of cancer.

Molecular topology applied to the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a non-ligand-binding-pocket antiandrogen

We report the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a novel non-ligand binding pocket (non-LBP) antagonist of the androgen receptor (AR) through the application of molecular topology techniques. This compound, validated through time-resolved fluorescence resonance energy transfer and fluorescence polarization biological assays, provides the basis for lead optimization and structure-activity relationship analysis of a new series of non-LBP AR antagonists. Induced-fit docking and molecular dynamics studies have been performed to establish a consistent hypothesis for the interaction of the new active molecule on the AR surface.

The rm2 metrics and regression through origin approach: reliable and useful validation tools for predictive QSAR models (Commentary on 'Is regression through origin useful in external validation of QSAR models?')

Quantitative structure-activity relationship (QSAR) is an in silico technique which can be used in drug discovery, environmental fate modeling, property and toxicity prediction of chemical entities and regulatory toxicology. The predictive potential of a QSAR model is judged from various validation metrics in order to evaluate how well it is capable to predict endpoint values of new untested compounds. The rm2 group of metrics is one of the stringent validation metrics currently used by the QSAR fraternity in different reports. We scrutinized a recently published paper which raised an issue that the constructed criteria based on regression through origin (RTO) are not optimal and there is a significant difference in the rm2 metrics values computed from different statistical software packages. According to our point of view, the conclusion drawn in this paper appears to be misleading. Any inconsistency in the software algorithms has nothing to do with the calculation of rm2 metrics, as such computation is not limited by the use of any specific software, rather it depends only on fundamental mathematical formulae that are well established. However, it is a concern to the QSAR users that Excel and SPSS can return different results for the metrics using the RTO method. Thus, a proper validation of the software tool is required before its use for computation of any validation metric.

Predictive modeling of chemical toxicity towards Pseudokirchneriella subcapitata using regression and classification based approaches

Biodiversity nurturing may be a valuable pathway in controlling chemical stress on the ecosystem. In the present work, in silico studies have been performed to develop regression based quantitative structure toxicity relationship (QSTR) models using a data set containing 105 organic chemicals for the prediction of 48-h chemical toxicity towards Pseudokirchneriella subcapitata. Classification based linear discriminant analysis (LDA) was also performed to distinguish chemicals into toxic and nontoxic groups using the same data set. The developed models were found to possess good predictive quality in terms of internal, external and overall validation parameters. The regression based QSTR model suggests that second order molecular connectivity index (molecular size and lipophilicity), density (aromaticity), relative shape of molecules (cyclicity/aromaticity), and specific molecular fragments of the chemicals are important properties of chemicals to exert their toxicity on P. subcapitata. The classification based LDA QSTR model suggested that fused ring aromatic systems, secondary carbon atom fragments, second order valence molecular connectivity indices (molecular size and branching) and molecular weight are the distinguishing features to differentiate chemicals into toxic and nontoxic groups.

Novel potential agents for ulcerative colitis by molecular topology: suppression of IL-6 production in Caco-2 and RAW 264.7 cell lines

Ulcerative colitis (UC) is an immune-mediated chronic and relapsing intestinal inflammatory disease. Interleukin (IL)-6, a pro-inflammatory cytokine, plays a key role in the uncontrolled intestinal inflammatory process, which is a main characteristic of UC. In this work, a quantitative structure-activity relationship model based on molecular topology (MT) has been built up to predict the IL-6 mediated anti-UC activity. After an external validation of the model, a virtual screening of the MicroSource Pure Natural Products Collection and Sigma-Aldrich databases was carried out looking for potential new active compounds. From the entire set of compounds labeled as active by the model, four of them, namely alizarin-3-methylimino-N,N-diacetic acid (AMA), Calcein, (+)-dibenzyl-L-tartrate (DLT), and Ro 41-0960, were tested in vitro by determination of IL-6 production in two cell lines (RAW 264.7 and Caco-2). The results demonstrate that three of them were able to significantly reduce IL-6 levels in both cell lines and particularly one, namely Ro 41-0960. These results confirm MT's efficacy as a tool for the selection of compounds potentially active in UC.

QSAR methods for the discovery of new inflammatory bowel disease drugs

INTRODUCTION

Inflammatory bowel disease (IBD) represents an important class of chronic gastrointestinal tract disease. And although there are already several useful treatments to reduce and control the symptoms, there is still no cure. One drug discovery technique used is the computer-aided (in silico) discovery approach which has largely demonstrated efficacy. Computational techniques, when used in combination with traditional drug discovery methodology, greatly increase the chance of drug discovery in a sustainable and economical fashion.

AREAS COVERED

This review aims to provide the most recent and important advances of in silico IBD drug discovery. While this review is mainly focused on QSAR methods, especially those based on molecular topology (MT), additional topics, such as docking or comparative field analysis are also addressed.

EXPERT OPINION

IBD is a worldwide growing health concern that can only be currently treated in symptomatic and palliative way; thus, the search for new drugs is imperative. Computer-aided methods, which focus on the drug-receptor interaction, are essential tool in this regard. It is noted, however that a major problem is that although there are many known receptors associated with IBD, none of these have yet been found essential. The use of other approaches, including QSAR methodology, is certainly a complementary and attractive alternative; especially QSAR methods based on MT, which has proven successful in other drug discovery.

Development of classification and regression based QSAR models to predict rodent carcinogenic potency using oral slope factor

Carcinogenicity is among the toxicological endpoints posing the highest concern for human health. Oral slope factors (OSFs) are used to estimate quantitatively the carcinogenic potency or the risk associated with exposure to the chemical by oral route. Regulatory agencies in food and drug administration and environmental protection are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. In this background, we have developed quantitative structure-carcinogenicity regression models for rodents based on the carcinogenic potential of 70 chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the Organization for Economic Cooperation and Development (OECD) principles for the validation of QSAR models. We have also attempted to develop a carcinogenicity classification model based on Linear Discriminant Analysis (LDA). Developed regression and LDA models are rigorously validated internally as well as externally. Our in silico studies make it possible to obtain a quantitative interpretation of the structural information of carcinogenicity along with identification of the discriminant functions between lower and higher carcinogenic compounds by LDA. Pharmacological distribution diagrams (PDDs) are used as a visualizing technique for the identification and selection of chemicals with lower carcinogenicity. Constructive, informative and comparable interpretations have been observed in both cases of classification and regression based modeling.

Molecular topology as a novel approach for drug discovery

INTRODUCTION

Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. One key part of MT is that, in the process of drug design/discovery, there is no need for an explicit knowledge of a drug's mechanism of action unlike other drug discovery methods.

AREAS COVERED

In this review, the authors introduce the topic by explaining briefly the most common methodology used today in drug design/discovery and address the most important concepts of MT and the methodology followed (QSAR equations, LDA, etc.). Furthermore, the significant results achieved, from this approach, are outlined and discussed.

EXPERT OPINION

The results outlined herein can be explained by considering that MT represents a new paradigm in the field of drug design. This means that it is not only an alternative method to the conventional methods, but it is also independent, that is, it represents a pathway to connect directly molecular structure with the experimental properties of the compounds (particularly drugs). Moreover, the process can be realized also in the reverse pathway, that is, designing new molecules from their topological pattern, what opens almost limitless expectations in new drugs development, given that the virtual universe of molecules is much greater than that of the existing ones.

Proanthocyanidins from Grape Seeds Modulate the NF-κB Signal Transduction Pathways in Rats with TNBS-Induced Ulcerative Colitis

Abstract To elucidate the molecular mechanisms involved in the therapeutic effects of proanthocyanidins from grape seeds (GSPE), we explore whether GSPE regulates the inflammatory response of TNBS-induced colitis in rats at the levels of NF-κB signal transduction pathway. Rats were intragastrically administered of different doses of GSPE (100, 200 and 400 mg·kg⁻¹) per day for seven days after ulcerative colitis (UC) was induced by intracolonic injection of 2,4,6-trinitrobenzenesulfonic acid (TNBS) dissolved in 50% ethanol. Sulfasalazine (SASP) at 400 mg/kg was used as a positive control drug. The expression of nuclear factor-kappa B (NF-κB), phospho-I kappaB-alpha (pIκBα), inhibitor kappa B kinase (IκK) in the colon tissues were all measured by enzyme-linked immunosorbent assay (ELISA) methods. Treatment with GSPE reduced the expression of NF-κB, pIκBα and IκK in the colon. The results of this study show that GSPE exerts beneficial effects in inflammatory bowel disease by inhibition of NF-κB signal transduction pathways.