Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL)

Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure-activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform's graphical user interface (GUI).

Mechanistic advances in osteoporosis and anti-osteoporosis therapies

Osteoporosis is a type of bone loss disease characterized by a reduction in bone mass and microarchitectural deterioration of bone tissue. With the intensification of global aging, this disease is now regarded as one of the major public health problems that often leads to unbearable pain, risk of bone fractures, and even death, causing an enormous burden at both the human and socioeconomic layers. Classic anti-osteoporosis pharmacological options include anti-resorptive and anabolic agents, whose ability to improve bone mineral density and resist bone fracture is being gradually confirmed. However, long-term or high-frequency use of these drugs may bring some side effects and adverse reactions. Therefore, an increasing number of studies are devoted to finding new pathogenesis or potential therapeutic targets of osteoporosis, and it is of great importance to comprehensively recognize osteoporosis and develop viable and efficient therapeutic approaches. In this study, we systematically reviewed literatures and clinical evidences to both mechanistically and clinically demonstrate the state-of-art advances in osteoporosis. This work will endow readers with the mechanistical advances and clinical knowledge of osteoporosis and furthermore present the most updated anti-osteoporosis therapies.

TNF-α Inhibitors from Natural Compounds: An Overview, CADD Approaches, and their Exploration for Anti-inflammatory Agents

Inflammation is a natural process that occurs in the organism in response to harmful external agents. Despite being considered beneficial, exaggerated cases can cause severe problems for the body. The main inflammatory manifestations are pain, increased temperature, edema, decreased mobility, and quality of life for affected individuals. Diseases such as arthritis, cancer, allergies, infections, arteriosclerosis, neurodegenerative diseases, and metabolic problems are mainly characterized by an exaggerated inflammatory response. Inflammation is related to two categories of substances: pro- and anti-inflammatory mediators. Among the pro-inflammatory mediators is Tumor Necrosis Factor-α (TNF-α). It is associated with immune diseases, cancer, and psychiatric disorders which increase its excretion. Thus, it becomes a target widely used in discovering new antiinflammatory drugs. In this context, secondary metabolites biosynthesized by plants have been used for thousands of years and continue to be one of the primary sources of new drug scaffolds against inflammatory diseases. To decrease costs related to the drug discovery process, Computer-Aided Drug Design (CADD) techniques are broadly explored to increase the chances of success. In this review, the main natural compounds derived from alkaloids, flavonoids, terpene, and polyphenols as promising TNF-α inhibitors will be discussed. Finally, we applied a molecular modeling protocol involving all compounds described here, suggesting that their interactions with Tyr⁵⁹, Tyr¹¹⁹, Tyr¹⁵¹, Leu⁵⁷, and Gly¹²¹ residues are essential for the activity. Such findings can be useful for research groups worldwide to design new anti-inflammatory TNF-α inhibitors.

An orally active, small-molecule TNF inhibitor that disrupts the homotrimerization interface improves inflammatory arthritis in mice

Excessive signaling by the proinflammatory cytokine TNF is involved in several autoimmune diseases, including rheumatoid arthritis (RA). However, unlike the approved biologics currently used to treat this and other conditions, commercially available small-molecule inhibitors of TNF trimerization are cytotoxic or exhibit low potency. Here, we report a TNF-inhibitory molecule (TIM) that reduced TNF signaling in vitro and was an effective treatment in a mouse model of RA. The initial lead compound, TIM1, attenuated TNF-induced apoptosis of human and mouse cells by delaying the induction of proinflammatory NF-κB and MAPK signaling and caspase 3– and caspase 8–dependent apoptosis. TIM1 inhibited the secretion of the proinflammatory cytokines IL-6 and IL-8 by disrupting TNF homotrimerization, thereby preventing its association with the TNF receptor. In a mouse model of collagen-induced polyarthritis, the more potent TIM1 analog TIM1c was orally bioavailable and reduced paw swelling, histological indicators of knee joint pathology, inflammatory infiltration of the joint, and the overall arthritis index. Orally delivered TIM1c showed immunological effects similar to those elicited by intraperitoneal injection of the FDA-approved TNF receptor decoy etanercept. Thus, TIM1c is a promising lead compound for the development of small-molecule therapies for the treatment of RA and other TNF-dependent systemic inflammation disorders.

Review of potential medical treatments for middle ear cholesteatoma

Middle ear cholesteatoma (MEC), is a destructive, and locally invasive lesion in the middle ear driven by inflammation with an annual incidence of 10 per 100,000. Surgical extraction/excision remains the only treatment strategy available and recurrence is high (up to 40%), therefore developing the first pharmaceutical treatments for MEC is desperately required. This review was targeted at connecting the dysregulated inflammatory network of MEC to pathogenesis and identification of pharmaceutical targets. We summarized the numerous basic research endeavors undertaken over the last 30+ years to identify the key targets in the dysregulated inflammatory pathways and judged the level of evidence for a given target if it was generated by in vitro, in vivo or clinical experiments. MEC pathogenesis was found to be connected to cytokines characteristic for Th1, Th17 and M1 cells. In addition, we found that the inflammation created damage associated molecular patterns (DAMPs), which further promoted inflammation. Similar positive feedback loops have already been described for other Th1/Th17 driven inflammatory diseases (arthritis, Crohn’s disease or multiple sclerosis). A wide-ranging search for molecular targeted therapies (MTT) led to the discovery of over a hundred clinically approved drugs already applied in precision medicine. Based on exclusion criteria designed to enable fast translation as well as efficacy, we condensed the numerous MTTs down to 13 top drugs. The review should serve as groundwork for the primary goal, which is to provide potential pharmaceutical therapies to MEC patients for the first time in history.

Development of an Orally Active Small-Molecule Inhibitor of Receptor Activator of Nuclear Factor-κB Ligand

Receptor activator of nuclear factor-κB (RANK) and its ligand, RANKL, play pivotal roles in bone remodeling. The monoclonal antibody denosumab successfully inhibited the maturation of osteoclasts (OCs) by binding to RANKL in the clinic. We continued our efforts to develop small-molecule inhibitors of RANKL. In this work, 41 β-carboline derivatives were synthesized based on previously synthesized compound Y1599 to improve its drug-like properties. Compound Y1693 was identified as a potent RANKL inhibitor that improved absorption-distribution-metabolism-excretion properties and effectively prevented RANKL-induced osteoclastogenesis and bone resorption. Furthermore, Y1693 also suppressed the expression of OC marker genes. Moreover, Y1693 demonstrated good tolerability and efficacy in an orally administered mouse model of osteoporosis as well as the ability to rescue alveolar bone loss in vivo caused by periodontal disease. Collectively, the above findings may provide a valuable direction for the development of novel antiresorptive therapies that target RANKL.

Bioactive Bibenzyl Enantiomers From the Tubers of Bletilla striata

Six new bibenzyls (three pairs of enantiomers), bletstrins D-F (1-3), were isolated from the ethyl acetate-soluble (EtOAc) extract of tubers of Bletilla striata (Thunb.) Rchb f. Their structures, including absolute configurations, were determined by 1D/2D NMR spectroscopy, optical rotation value, and experimental electronic circular dichroism (ECD) data analyses, respectively. Compounds 1-3 possess a hydroxyl-substituted chiral center on the aliphatic bibenzyl bridge, which represented the first examples of natural bibenzyl enantiomers from the genus of Bletilla. The antibacterial, antitumor necrosis factor (anti-TNF-α), and neuroprotective effects of the isolates have been evaluated. Compounds 3a and 3b were effective against three Gram-positive bacteria with minimum inhibitory concentrations (MICs) of 52-105 μg/ml. Compounds 2a and 2b exhibited significant inhibitory effects on TNF-α-mediated cytotoxicity in L929 cells with IC₅₀ values of 25.7 ± 2.3 μM and 21.7 ± 1.7 μM, respectively. Subsequently, the possible anti-TNF-α mechanism of 2 was investigated by molecular docking simulation. Furthermore, the neuroprotective activities were tested on the H₂O₂-induced PC12 cell injury model, and compounds 2b, 3a, and 3b (10 μM) could obviously protect the cells with the cell viabilities of 57.86 ± 2.08%, 64.82 ± 2.84%, and 64.11 ± 2.52%, respectively.

Reducing health & environmental impacts of chemical warfare agents: Computational chemistry contributions

This review article summarizes advances in computational chemistry and cheminformatics methods and techniques that are used or have potential for use in reducing health and environmental impacts of Chemical Warfare Agents (CWA). These methods, include, but are not limited to, predictive modeling, data mining and virtual screening, similarity searching, molecular docking and dynamics and are briefly presented here. Applications of these in silico approaches, specifically for the protection of personnel and civilians against CWA, but also beyond, are discussed. CWA include toxic chemicals that can cause death, injury, or temporary incapacitation through their chemical action. CWA impose a significant worldwide threat and as such, destruction, remediation as well as protection measurements need to be carefully designed. Towards this goal computational chemistry and cheminformatics can play a key role specifically as far as decontamination, risk assessment and risk management are concerned. Among the wide range of in silico techniques applied for CWA, specific previously published paradigms are presented, including toxicity and property prediction, CWA simulant identification and CWA detoxification. Beyond CWA research, other applications with military interest are briefly presented and emerging trends of potential relevance noted.

In Silico Identification and Evaluation of Natural Products as Potential Tumor Necrosis Factor Function Inhibitors Using Advanced Enalos Asclepios KNIME Nodes

Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients' progressive immunodeficiency and loss of response, high cost, and intravenous administration. In order to find new potential anti-TNF small molecule inhibitors, we employed an in silico approach, aiming to find natural products, analogs of Ampelopsin H, a compound that blocks the formation of TNF active trimer. Two out of nine commercially available compounds tested, Nepalensinol B and Miyabenol A, efficiently reduced TNF-induced cytotoxicity in L929 cells and production of chemokines in mice joints' synovial fibroblasts, while Nepalensinol B also abolished TNF-TNFR1 binding in non-toxic concentrations. The binding mode of the compounds was further investigated by molecular dynamics and free energy calculation studies, using and advancing the Enalos Asclepios pipeline. Conclusively, we propose that Nepalensinol B, characterized by the lowest free energy of binding and by a higher number of hydrogen bonds with TNF, qualifies as a potential lead compound for TNF inhibitors' drug development. Finally, the upgraded Enalos Asclepios pipeline can be used for improved identification of new therapeutics against TNF-mediated chronic inflammatory diseases, providing state-of-the-art insight on their binding mode.

New contributions to the drug profile of TNFα inhibitor SPD304: Affinity, selectivity and ADMET considerations

Tumor necrosis factor alpha (TNFα) is a relevant clinical target for the treatment of chronic inflammatory diseases. Currently, only few small molecules are known as direct inhibitors of TNFα. To date, none of these molecules has shown both an efficient activity and a low toxicity to be considered for clinical trials. The SPD304 is considered as a reference of direct inhibitors of TNFα because of its well demonstrated mechanism (He et al., 2005). Herein, we provide new insights regarding the drug profile, selectivity and absorption, distribution, metabolism, excretion and toxicity (ADMET) considerations of SPD304 to evaluate its potential as a hit for the structure-based design of novel TNFα inhibitors. ELISA experiments confirmed the inhibition of TNFα/TNF receptor 1 binding (IC₅₀ = 12 μM). Cellular-based assays highlighted the cytotoxicity of SPD304, as well as its ability to inhibit TNFα signaling pathways at non-cytotoxic concentrations. A surface acoustic wave (SAW) experiment highlighted only one binding site with a dissociation constant of 6.1 ± 4.7 nM. SPD304 inhibited the binding of the cytokines like interleukins (IL)-4 and IL-13 to their receptors and showed no direct inhibition on proteins involved in the TNFα pathway. Finally, the thermodynamic solubility and Caco-2 cells permeability of SPD304 were experimentally evaluated and ADMET in silico predictions are also discussed. The physicochemical, pharmacological and ADMET studies of SPD304 have shown that is not an ideal hit for a drug optimization program based on its chemical structure.

Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning

Protein–protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593 small-to-medium-sized newly synthesized molecules. This study validates a promising combined method using PPI-oriented library and ligand-based virtual screening (LBVS) to discover novel PPI inhibitory compounds for Kelch-like ECH-associated protein 1 (Keap1) and nuclear factor erythroid 2-related factor 2 (Nrf2). We performed LBVS with two random forest models against our PPI library and the following time-resolved fluorescence resonance energy transfer (TR-FRET) assays of 620 compounds identified 15 specific hit compounds. The high hit rates for the entire PPI library (estimated 0.56–1.3%) and the LBVS (maximum 5.4%) compared to a conventional screening library showed the utility of the library and the efficiency of LBVS. All the hit compounds possessed novel structures with Tanimoto similarity ≤ 0.26 to known Keap1/Nrf2 inhibitors and aqueous solubility (AlogP < 5). Reasonable binding modes were predicted using 3D alignment of five hit compounds and a Keap1/Nrf2 peptide crystal structure. Our results represent a new, efficient method combining the PPI library and LBVS to identify novel PPI inhibitory ligands with expanded chemical space.

Computational Investigation of Zerumbone as an Inhibitor of TNF-alpha Using Molecular Dynamics and Molecular Docking Methods

Background:

There are several reports on the anti-cancer property of zerumbone, such as in breast, cervical and ovarian cancer. But the investigation of the actual protein target has been the least concerned and there are few reports on the inhibitory effect of zerumbone against specific cancer-causing proteins and enzymes. Therefore, further investigation is required at a much deeper molecular level.

Objective:

This study aimed to determine the anti-proliferative activity of Zerumbone against cervical cancer cell and assess its TNF-α enzyme inhibitory action.

Methods:

The investigation emphasized the anti-cancer activity of zerumbone against HeLa cells and its subsequent TNF-α assay. Furthermore, computational studies on Zerumbone as an inhibitor of TNF-α were carried out using computational techniques such as docking and MD simulations.

Results and Discussion:

From the molecular docking analysis, it was observed and substantiated that the α,β-Unsaturated carbonyl scaffold is the main driving force for anti-cancer activity of zerumbone and inhibition of TNF-α.

Conclusion:

Zerumbone might be a potent anti-cancer agent targeting the HeLa cancer cell lines and inhibiting the TNF-α enzyme.

Rheumatoid Arthritis: Applicability of Ready-to-Use Human Cartilaginous Cells for Screening of Compounds with TNF-Alpha Inhibitory Activity

In the context of modern drug discovery, there is an obvious advantage to designing phenotypic bioassays based on human disease-relevant cells that express disease-relevant markers. The specific aim of the study was to develop a convenient and reliable method for screening compounds with Tumor Necrosis Factor-alpha (TNF-α) inhibitory activity. This assay was developed using cryopreserved ready-to-use cartilage-derived cells isolated from juvenile donors diagnosed with polydactyly. It has been demonstrated that all donor (10 donors) cells were able to respond to TNF-α treatment by increased secretion of pro-inflammatory cytokine IL-6 into subcultural medium. Inhibition of TNF-α using commercially available TNF-α inhibitor etanercept resulted in a dose-dependent decrease in IL-6 production which was measured by Enzyme-Linked Immunosorbent Assay (ELISA). TNF-α dependent IL-6 production was detected in the cells after both their prolonged cultivation in vitro (≥20 passages) and cryopreservation. This phenotypic bioassay based on ready-to-use primary human cells was developed for detection of novel TNF-α inhibitory compounds and profiling of biosimilar drugs.

Ligand-based pharmacophore modeling of TNF-α to design novel inhibitors using virtual screening and molecular dynamics

Tumor necrosis factor-α (TNF-α) is one of the promising targets for treating inflammatory (Crohn disease, psoriasis, psoriatic arthritis, rheumatoid arthritis) and various other diseases. Commercially available TNF-α inhibitors are associated with several risks and limitations. In the present study, we have identified small TNF-α inhibitors using in silico approaches, namely pharmacophore modeling, virtual screening, molecular docking, molecular dynamics simulation and free binding energy calculations. The study yielded better and potent hits that bind to TNF-α with significant affinity. The best pharmacophore model generated using LigandScout has an efficient hit rate and Area Under the operating Curve. High throughput virtual screening of SPECS database molecules against crystal structure of TNF-α protein, coupled with physicochemical filtration, PAINS test. Virtual hit compounds used for molecular docking enabled the identification of 20 compounds with better binding energies when compared with previously known TNF-α inhibitors. MD simulation analysis on 20 virtual identified hits showed that ligand binding with TNF-α protein is stable and protein-ligand conformation remains unchanged. Further, 16 compounds passed ADMET analysis suggesting these identified hit compounds are suitable for designing a future class of potent TNF-α inhibitors.Communicated by Ramaswamy H. Sarma.

Discovery of Small-Molecule Inhibitors of Receptor Activator of Nuclear Factor-κB Ligand with a Superior Therapeutic Index

Receptor activator of nuclear factor-κB ligand (RANKL) constitutes the master mediator of osteoclastogenesis, while its pharmaceutical inhibition by a monoclonal antibody has been approved for the treatment of postmenopausal osteoporosis. To date, the pursuit of pharmacologically more favorable approaches using low-molecular-weight inhibitors has been hampered by low specificity and high toxicity issues. This study aimed to discover small-molecule inhibitors targeting RANKL trimer formation. Through a systematic screening of 39 analogues of SPD-304, a dual inhibitor of tumor necrosis factor (TNF) and RANKL trimerization, we identified four compounds (1b, 3b, 4a, and 4c) that selectively inhibited RANKL-induced osteoclastogenesis in a dose-dependent manner, without affecting TNF activity or osteoblast differentiation. Based on structure-activity observations extracted from the most potent and less toxic inhibitors of RANKL-induced osteoclastogenesis, we synthesized a focused set of compounds that revealed three potent inhibitors (19a, 19b, and 20a) with remarkably low cell-toxicity and improved therapeutic indexes as shown by the LC₅₀ to IC₅₀ ratio. These RANKL-selective inhibitors are an excellent starting point for the development of small-molecule therapeutics against osteolytic diseases.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Therapeutic potential of TNFα inhibitors in chronic inflammatory disorders: Past and future

In the past 20 years, patients with rheumatoid arthritis (RA), Crohn's disease (CD), and other immune diseases have witnessed the impact of a great treatment advance with the availability of biological TNFα inhibitors. With 5 approved anti-TNFα biologics on the market and soon available biosimilars, patients have more treatment options and have benefited from understanding the biology of TNFα. Nevertheless, many unmet needs remain for people living with TNFα-related diseases, namely some side effects and tolerance of current anti-TNFα biologics and resistance to therapies. Furthermore, common diseases such as osteoarthritis and back/neck pain may respond to anti-TNFα therapies at early onset of symptoms. Development of new TNFα inhibitors focusing on TNFR1 specific inhibitors, preferably small molecules that can be delivered orally, is much needed.

Conlecs: A novel procedure for deriving the concentration limits of chemicals outside the criteria of human drinking water using existing criteria and species sensitivity distribution based on quantitative structure-activity relationship prediction

Water quality criteria (WQC) for an increasing number of emerging chemicals need to be developed to protect human health and biological safety. Existing species sensitivity distribution (SSD) methods can only be used to help establish WQC for ecological protection, and cannot be extended to the protection of human beings from various hazards. In this study, a novel procedure called Conlecs is proposed to derive the concentration limits (ConLs) of pesticides outside the criteria for human drinking water (CHDW) using the existing criteria of pesticides and SSD integrated with the toxicity prediction achieved through robust QSAR models. Optimal SSD models of four pesticides (within the CHDW) and two pesticides (outside the CHDW) on 12 species were first constructed, and the existing ConLs of four pesticides within the CHDW were then utilized to select the most suitable species for the optimal proportions to avoid human hazards (PHH), allowing the ConLs of two pesticides outside the CHDW to be derived.

Structure-Based Identification and Design of Angiotensin Converting Enzyme-Inhibitory Peptides from Whey Proteins

Besides their nutritional value, whey protein (WP) peptides are food components retaining important pharmacological properties for controlling hypertension. We herein report how the use of complementary experimental and theoretical investigations allowed the identification of novel angiotensin converting enzyme inhibitory (ACEI) peptides obtained from a WP hydrolysate and addressed the rational design of even shorter sequences based on molecular pruning. Thus, after bromelain digestion followed by a 5 kDa cutoff ultrafiltration, WP hydrolysate with ACEI activity was fractioned by RP-HPLC; 2 out of 23 collected fractions retained ACEI activity and were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). In the face of 128 identified peptides, molecular docking was carried out to prioritize peptides and to rationally guide the design of novel shorter and bioactive sequences. Therefore, 11 peptides, consisting of 3-6 amino acids and with molecular weights in the range from 399 to 674 Da, were rationally designed and then purchased to determine the IC₅₀ value. This approach allowed the identification of two novel peptides: MHI and IAEK with IC₅₀ ACEI values equal to 11.59 and 25.08 μM, respectively. Interestingly, we also confirmed the well-known ACEI IPAVF with an IC₅₀ equal to 9.09 μM. In light of these results, this integrated approach could pave the way for high-throughput screening and identification of new peptides in dairy products. In addition, the herein proposed ACEI peptides could be exploited for novel applications both for food production and pharmaceuticals.

Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique

Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal descriptors to be used as a tool to build up predictive models for anti-cancer activity is examined from practical point of view. Various perspectives of application of optimal descriptors are reviewed. Stochastic nature of phenomena which are related to carcinogenic potential of various substances can be successfully detected and interpreted by the Monte Carlo technique. Hypothesises related to practical strategy and tactics of the searching for new anticancer agents are suggested.

Virtual Prediction of the Delphinidin-3-O-glucoside and Peonidin-3-O-glucoside as Anti-inflammatory of TNF-α Signaling

INTRODUCTION

Anthocyanin is the bioactive compound in black rice, which promotes some health benefits for human body. Present study revealed that black rice anthocyanins improve the biomarker of the metabolic syndrome, such as tumor necrosis factor alpha (TNF-α). However, the mechanism of anthocyanin in preventing metabolic syndrome has not been elucidated.

AIM

This study was performed to identify the interaction of six types of black rice anthocyanin towards TNF-α protein and TNF-α receptor through in silico studies, to assess the molecular properties and bioactivity of black rice anthocyanin.

METHODS

We retrieved the black rice anthocyanin compounds from the PubChem database and the proteins (TNF-α protein and TNF-α receptor) from Protein Data Bank (PDB) database. Protein and ligands were docked using Hex 8.0 software and visualized by Discovery Studio 4.1 program.

RESULTS

This study found the possibility that black rice anthocyanins interacted with TNF-α have no influence into TNF-α and TNF-α receptor interaction. The binding of delphinidin-3-O-glucoside & peonidin-3-O-glucoside to TNF-α receptor inhibited the TNF-α and TNF-α receptor signaling. The black rice anthocyanins had low activity as a drug. Interestingly, black rice anthocyanins had a potency as an antioxidant due to the hydrogen donor or acceptor in their structure, as protein kinase inhibitor, nuclear receptor ligand, and enzyme kinase inhibitor.

CONCLUSION

This study suggests that delphinidin-3-O-glucoside and peonidin-3-O-glucoside might have function as anti-inflammatory factor related with TNF-α signaling.

In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method

Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug-induced liver injury prediction.

ImmtorLig_DB: repertoire of virtually screened small molecules against immune receptors to bolster host immunity

Host directed therapies to boost immunity against infection are gaining considerable impetus following the observation that use of antibiotics has become a continuous source for the emergence of drug resistant strains of pathogens. Receptors expressed by the cells of immune system play a cardinal role in initiating sequence of events necessary to ameliorate many morbid conditions. Although, ligands for the immune receptors are available; but their use is limited due to complex structure, synthesis and cost-effectiveness. Virtual screening (VS) is an integral part of chemoinformatics and computer-aided drug design (CADD) and aims to streamline the process of drug discovery. ImmtorLig_DB is a repertoire of 5000 novel small molecules, screened from ZINC database and ranked using structure based virtual screening (SBVS) against 25 immune receptors which play a pivotal role in defending and initiating the activation of immune system. Consequently, in the current study, small molecules were screened by docking on the essential domains present on the receptors expressed by cells of immune system. The screened molecules exhibited efficacious binding to immune receptors, and indicated a possibility of discovering novel small molecules. Other features of ImmtorLig_DB include information about availability, clustering analysis, and estimation of absorption, distribution, metabolism, and excretion (ADME) properties of the screened small molecules. Structural comparisons indicate that predicted small molecules may be considered novel. Further, this repertoire is available via a searchable graphical user interface (GUI) through http://bioinfo.imtech.res.in/bvs/immtor/ .

3D Modeling of Tumor Necrosis Factor Receptor and Tumor Necrosis Factor-bound Receptor Systems

The interactions between the tumor necrosis factor (TNF) and its receptor molecule are responsible for various signaling networks that are central to the functioning of human immune homeostasis. The present work is a computational study of certain structural aspects of this cell-signaling protein, specifically focusing on the molecular level analyses of the TNF receptor (TNF-R), guided by its crystallographic structure. We also examine the possible binding sites of the TNF onto TNF-R, and the associated interactions. The structural and conformational variations in the TNF-R and TNF bound TNF-R systems are examined in this context using molecular dynamics (MD) simulations. The time dependent variations of the dimeric TNF-R structures are compared with, and shown to be steadier than their isolated monomers. This dimeric stability is favored under acidic conditions. The results are used to further illustrate how 3D modeling and computer simulations can aid the structure-based approach to probing a ligand-receptor system.

Novel tumor necrosis factor-α (TNF-α) inhibitors from small molecule library screening for their therapeutic activity profiles against rheumatoid arthritis using target-driven approaches and binary QSAR models

Tumor necrosis factor alpha (TNF-α) is a multifunctional cytokine that acts as a central biological mediator for critical immune functions, including inflammation, infection, and antitumor responses. It plays pivotal role in autoimmune diseases like rheumatoid arthritis (RA). The synthetic antibodies etanercept, infliximab, and adalimumab are approved drugs for the treatment of inflammatory diseases bind to TNF-α directly, preventing its association with the tumor necrosis factor receptor (TNFR). These biologics causes serious side effects such as triggering an autoimmune anti-antibody response or the weakening of the body's immune defenses. Therefore, alternative small-molecule based therapies for TNF-α inhibition is a hot topic both in academia and industry. Most of small-molecule inhibitors reported in the literature target TNF-α, indirectly. In this study, combined in silico approaches have been applied to better understand the important direct interactions between TNF-α and small inhibitors. Our effort executed with the extensive literature review to select the compounds that inhibit TNF-α. High-throughput structure-based and ligand-based virtual screening methods are applied to identify TNF-α inhibitors from 3 different small molecule databases (∼256.000 molecules from Otava drug-like green chemical collection, ∼ 500.000 molecules from Otava Tangible database, ∼2.500.000 Enamine small molecule database) and ∼240.000 molecules from ZINC natural products libraries. Moreover, therapeutic activity prediction, as well as pharmacokinetic and toxicity profiles are also investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism and toxicity information, uses binary QSAR models. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular Dynamics (MD) simulations were also performed for selected hits to investigate their detailed structural and dynamical analysis beyond docking studies. As a result, at least one hit from each database were identified as novel TNF-α inhibitors after comprehensive virtual screening, multiple docking, e-Pharmacophore modeling (structure-based pharmacophore modeling), MD simulations, and MetaCore/MetaDrug analysis. Identified hits show predicted promising anti-arthritic activity and no toxicity. Communicated by Ramaswamy H. Sarma.

Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR)

INTRODUCTION

Quantitative structure-activity/property relationships (QSAR/QSPR) are statistical models which quantitatively correlate quantitative chemical structure information (described as molecular descriptors) to the response end points (biological activity, property, toxicity, etc.). Important strategies for QSAR model development and validation include dataset curation, variable selection, and dataset division, selection of modeling algorithms and appropriate measures of model validation. Areas covered: Different feature selection methods and various linear and nonlinear learning algorithms are employed to address the complexity of data sets for selection of appropriate features important for the responses being modeled, to reduce overfitting of the models, and to derive interpretable models. This review provides an overview of various feature selection methods as well as different statistical learning algorithms for QSAR modeling at an elementary level for nonexpert readers. Expert opinion: Novel sets of descriptors are being continuously introduced to this field; therefore, to handle this issue, there is a need to improve new tools for feature selection, which can lead to development of statistically meaningful models, usable by nonexperts in the fields. While handling data sets of limited size, special techniques like double cross-validation and consensus modeling might be more meaningful in order to remove the possibility of bias in descriptor selection.

Dysregulated Up-Frameshift Protein 1 Promotes Ulcerative Colitis Pathogenesis Through the TNFR1-NF-κB/MAPKs Pathway

BACKGROUND

Ulcerative colitis (UC) is an idiopathic colonic mucosal disease, and its pathogenesis has not been fully understood. Up-frameshift protein 1 (UPF1) is a potential molecule for UC predicted by a computational approach.

AIM

The present study aimed to validate the underlying mechanism of UPF1 in UC.

METHODS

UPF1 expression was detected by qRT-PCR, western blotting, and immunohistochemistry in dextran sulfate sodium-induced colitis in mice. To simulate the intestinal inflammation microenvironment, NCM460 human colonic epithelial cells were exposed to a mixture of inflammatory mediators. The potential mechanism involving TNFR1-NF-κB/MAPKs pathway activation was addressed by western blotting, reporter gene assays, and siRNA (siUPF1) or UPF1-expressing plasmid pENTER-transfected cells.

RESULTS

UPF1 was downregulated in colonic epithelial cells of colitic mice, and in vitro, contrary to the mRNA levels of the associated cytokines enhanced in the UPF1 dysregulation group within stimulatory factors, most relevant cytokines were significantly decreased in UPF1 overexpression group. Mechanistically, the increased expression of tumor necrosis factor receptor 1 (TNFR1) was found in NCM460 cells pre-treated with siUPF1, with the activation of IKK/NF-κB and MAPKs pathways, including JNK/AP-1 and P38, but not the ERK1/2 pathway. Moreover, the repression of TNFR1 required the interaction of UPF1 with the promoter.

CONCLUSION

UPF1, which negatively regulated the transcription of TNFR1, is a novel factor regulating intestinal inflammation. The downregulation of UPF1 activated the TNFR1-dependent NF-κB/MAPKs pathway, and promoting inflammatory responses in colon might act as a causal role in UC.

A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints

The increasing use of nanoparticles (NPs) in a wide range of consumer and industrial applications has necessitated significant effort to address the challenge of characterizing and quantifying the underlying nanostructure - biological response relationships to ensure that these novel materials can be exploited responsibly and safely. Such efforts demand reliable experimental data not only in terms of the biological dose-response, but also regarding the physicochemical properties of the NPs and their interaction with the biological environment. The latter has not been extensively studied, as a large surface to bind biological macromolecules is a unique feature of NPs that is not relevant for chemicals or pharmaceuticals, and thus only limited data have been reported in the literature quantifying the protein corona formed when NPs interact with a biological medium and linking this with NP cellular association/uptake. In this work we report the development of a predictive model for the assessment of the biological response (cellular association, which can include both internalized NPs and those attached to the cell surface) of surface-modified gold NPs, based on their physicochemical properties and protein corona fingerprints, utilizing a dataset of 105 unique NPs. Cellular association was chosen as the end-point for the original experimental study due to its relevance to inflammatory responses, biodistribution, and toxicity in vivo. The validated predictive model is freely available online through the Enalos Cloud Platform ( http://enalos.insilicotox.com/NanoProteinCorona/ ) to be used as part of a regulatory or NP safe-by-design decision support system. This online tool will allow the virtual screening of NPs, based on a list of the significant NP descriptors, identifying those NPs that would warrant further toxicity testing on the basis of predicted NP cellular association.

In Silico Discovery of Plant-Origin Natural Product Inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL)

An in silico drug discovery pipeline for the virtual screening of plant-origin natural products (NPs) was developed to explore new direct inhibitors of TNF and its close relative receptor activator of nuclear factor kappa-B ligand (RANKL), both representing attractive therapeutic targets for many chronic inflammatory conditions. Direct TNF inhibition through identification of potent small molecules is a highly desired goal; however, it is often hampered by severe limitations. Our approach yielded a priority list of 15 NPs as potential direct TNF inhibitors that were subsequently tested in vitro against TNF and RANKL. We thus identified two potent direct inhibitors of TNF function with low micromolar IC₅₀ values and minimal toxicity even at high concentrations. Most importantly, one of them (A11) was proved to be a dual inhibitor of both TNF and RANKL. Extended molecular dynamics simulations with the fully automated EnalosMD suite rationalized the mode of action of the compounds at the molecular level. To our knowledge, these compounds constitute the first NP TNF inhibitors, one of which being the first NP small-molecule dual inhibitor of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.

Assessing the Effects of Alloxydim Phototransformation Products by QSAR Models and a Phytotoxicity Study

Once applied, an herbicide first makes contact with leaves and soil. It is known that photolysis can be one of the most important processes of dissipation of herbicides in the field. However, degradation does not guarantee detoxification and can give rise to byproducts that could be more toxic and/or persistent than the active substance. In this work, the photodegradation of alloxydim herbicide in soil and leaf cuticle surrogates was studied and a detailed study on the phytotoxicity of the main byproduct on sugar beet, tomato, and rotational crops was performed. Quantitative structure⁻activity relationship (QSAR) models were used to obtain a first approximation of the possible ecotoxicological and environmental implications of the alloxydim and its degradation product. The results show that alloxydim is rapidly degraded on carnauba and sandy loam soil surfaces, two difficult matrices to analyze and not previously studied with alloxydim. Two transformation products that formed in both matrices were identified: alloxydim Z-isomer and imine derivative (mixture of two tautomers). The phytotoxicity of alloxydim and the major byproduct shows that tomato possesses high sensitivity to the imine byproduct, while wheat crops are inhibited by the parent compound. This paper demonstrates the need to further investigate the behavior of herbicide degradation products on target and nontarget species to determine the adequate use of herbicidal products to maximize productivity in the context of sustainable agriculture.

Using Peptidomimetics and Constrained Peptides as Valuable Tools for Inhibiting Protein⁻Protein Interactions

Protein–protein interactions (PPIs) are tremendously important for the function of many biological processes. However, because of the structure of many protein–protein interfaces (flat, featureless and relatively large), they have largely been overlooked as potential drug targets. In this review, we highlight the current tools used to study the molecular recognition of PPIs through the use of different peptidomimetics, from small molecules and scaffolds to peptides. Then, we focus on constrained peptides, and in particular, ways to constrain α-helices through stapling using both one- and two-component techniques.

Consensus Predictive Model for Human K562 Cell Growth Inhibition through Enalos Cloud Platform

β-Thalassemia is an inherited hematologic disorder caused by various mutations of the β-globin gene, thus resulting in a significant decrease in adult hemoglobin (HbA) production. An increase in fetal hemoglobin (HbF) levels by drug molecules is considered of great potential in β-thalassemia treatment and is expected to counterbalance the impaired production of HbA. In this work, based on a set of 129 experimentally tested biological inhibitors, we developed and validated a computational model for the prediction of K562 functional inhibition, possibly associated with HbF induction. To facilitate future advancements in the field, we incorporated our model into Enalos Cloud Platform, which enabled online access to our computational scheme (http://enalos.insilicotox.com/K562) through a user-friendly interface. This web service is offered to the wider community to promote in silico drug discovery through fast and reliable predictions.

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC₅₀ and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.

Quantum molecular modeling of hepatitis C virus inhibition through non-structural protein 5B polymerase receptor binding of C5-arylidene rhodanines

We have carried out high-level quantum chemical computations followed by molecular docking studies on a set of 17C₅-arylidene rhodanine isomers to provide insights into the binding modes with different reported binding pockets of the nonstructural protein 5B (NS5B) polymerase that contribute to the hepatitis C virus (HCV) inhibition. We optimized the multi-target profile of the selected rhodanine analogs to investigate potential non-nucleotide inhibitors (NNIs) by quantum chemical optimization of the 18 isomers followed by docking with quantum chemically optimized structures of each isomer with NS5B polymerase at multiple binding pockets. The binding affinities of the PP-I, PP-II and TP-II pockets of NS5B polymerase were analyzed for all the 17 isomers of 2-[(5Z)-5-(2,4-dichlorobenzylidene)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]-3-phenylpropanoic acid. On the basis of binding propensity at the different pockets and inhibitor constants, we ranked these isomers as potential candidates for the HCV inhibition. We have identified four isomers as promising NNIs of NS5B polymerase with comparable binding and inhibition to the standard (1,3) dichloro substituted isomer that exhibits in vitro activity and several other isomers as candidates in a "multi-targeted drug" approach.

Enalos+ KNIME Nodes: New Cheminformatics Tools for Drug Discovery

In this chapter we present and discuss Enalos+ nodes designed and developed by NovaMechanics Ltd. for the open-source KNIME platform, as a useful aid when dealing with cheminformatics and nanoinformatics problems or medicinal applications. Enalos+ nodes facilitate tasks performed in molecular modeling and allow access, data mining, and manipulation for multiple chemical databases through the KNIME interface. Enalos+ nodes automate common procedures that greatly facilitate the rapid workflow prototyping within KNIME. Μethods and techniques that are included in Enalos+ nodes are presented in order to offer a deeper understanding of the theoretical background of the incorporated functionalities. An emphasis is given to demonstrate the usefulness of Enalos+ nodes in different cheminformatics applications by presenting four indicative case studies. Specifically, we present case studies that underline the value and the effectiveness of the nodes for molecular descriptors calculation and QSAR predictive model development. In addition, case studies are also presented demonstrating the benefits of the use of Enalos+ nodes for database exploitation within a drug discovery project.

Enalos Suite: New Cheminformatics Platform for Drug Discovery and Computational Toxicology

In this chapter we present and discuss, with the aid of several representative case studies from drug discovery and computational toxicology, a new cheminformatics platform, Enalos Suite, that was developed with open source and freely available software. Enalos Suite ( http://enalossuite.novamechanics.com/ ) was designed and developed as a useful tool to address a variety of cheminformatics problems, given that it expedites tasks performed in predictive modeling and allows access, data mining and manipulation for multiple chemical databases (PubChem, UniChem, etc.). Enalos Suite was carefully designed to permit its extension and adjustment to the special field of interest of each user, including, for instance, nanoinformatics, biomedical, and other applications. To demonstrate the functionalities of Enalos Suite that are useful in different cheminformatics applications, we present indicative case studies that include the exploitation of chemical databases within a drug discovery project, the calculation of molecular descriptors, and finally the development of a predictive QSAR model validated according to OECD principles. We aspire that at the end of this chapter, the reader will capture the effectiveness of different functionalities included in the Enalos Suite that could be of significant value in a multitude of cheminformatics applications.

Mimicking Intermolecular Interactions of Tight Protein-Protein Complexes for Small-Molecule Antagonists

Tight protein-protein interactions (K_d <100 nm) that occur over a large binding interface (>1000 Ų ) are highly challenging to disrupt with small molecules. Historically, the design of small molecules to inhibit protein-protein interactions has focused on mimicking the position of interface protein ligand side chains. Here, we explore mimicry of the pairwise intermolecular interactions of the native protein ligand with residues of the protein receptor to enrich commercial libraries for small-molecule inhibitors of tight protein-protein interactions. We use the high-affinity interaction (K_d =1 nm) between the urokinase receptor (uPAR) and its ligand urokinase (uPA) to test our methods. We introduce three methods for rank-ordering small molecules docked to uPAR: 1) a new fingerprint approach that represents uPA's pairwise interaction energies with uPAR residues; 2) a pharmacophore approach to identify small molecules that mimic the position of uPA interface residues; and 3) a combined fingerprint and pharmacophore approach. Our work led to small molecules with novel chemotypes that inhibited a tight uPAR⋅uPA protein-protein interaction with single-digit micromolar IC₅₀ values. We also report the extensive work that identified several of the hits as either lacking stability, thiol reactive, or redox active. This work suggests that mimicking the binding profile of the native ligand and the position of interface residues can be an effective strategy to enrich commercial libraries for small-molecule inhibitors of tight protein-protein interactions.

MouseTox: An online toxicity assessment tool for small molecules through Enalos Cloud platform

Advances in the drug discovery research substantially depend on in silico methods and techniques that capitalize on experimental data to enable the accurate property/activity assessment by employing a variety of computational techniques. These in silico tools can significantly reduce expensive and time consuming experimental procedures required and are strongly recommended to avoid animal testing, especially as far as toxicity evaluation and risk assessment is concerned. In this context, in the present work we aim to develop a predictive model for the cytotoxic effects of a wide range of compounds based solely on calculated molecular descriptors that account for their topological, geometric and structural characteristics. The developed model was fully validated and was released online via Enalos Cloud platform accessible through http://enalos.insilicotox.com/MouseTox/. This ready-to-use web service offers, through a user-friendly interface, free access to the model results and therefore can act as a toxicity prediction tool for the risk assessment of novel compounds, without any special requirements or prior programming skills.