Modeling natural anti-inflammatory compounds by molecular topology

Natural products as IL-6 inhibitors for inflammatory diseases: Synthetic and SAR perspective

Interleukin-6 (IL-6), a pleiotropic cytokine, plays a pivotal role in the pathophysiology of various diseases including diabetes, atherosclerosis, Alzheimer's disease, multiple myeloma, rheumatoid arthritis, and prostate cancer. The signaling pathways associated with IL-6 offer promising targets for therapeutic interventions in inflammatory diseases and IL-6-dependent tumors. Although certain anti-IL-6 monoclonal antibodies are currently employed clinically, their usage is hampered by drawbacks such as high cost and potential immunogenicity, limiting their application. Thus, the imperative arises to develop novel small non-peptide molecules acting as IL-6 inhibitors. Various natural products derived from diverse sources have been investigated for their potential to inhibit IL-6 activity. Nevertheless, these natural products remain inadequately explored in terms of their structure-activity relationships. In response, our review aims to provide syntheses and structure activity perspective of natural IL-6 inhibitors. The comprehensive amalgamation of information presented in this review holds the potential to serve as a foundation for forthcoming research endeavors by medicinal chemists, facilitating the design of innovative IL-6 inhibitors to address the complexities of inflammatory diseases.

Artifical intelligence: a virtual chemist for natural product drug discovery

Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.

Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

Molecular Topology for the Discovery of New Broad-Spectrum Antibacterial Drugs

In this study, molecular topology was used to develop several discriminant equations capable of classifying compounds according to their antibacterial activity. Topological indices were used as structural descriptors and their relation to antibacterial activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four equations were constructed, named DF1, DF2, DF3, and DF4, all with good statistical parameters such as Fisher-Snedecor's F (over 25 in all cases), Wilk's lambda (below 0.36 in all cases) and percentage of correct classification (over 80% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. From the four discriminant functions, it can be extracted that the presence of sp3 carbons, ramifications, and secondary amine groups in a molecule enhance antibacterial activity, whereas the presence of 5-member rings, sp2 carbons, and sp2 oxygens hinder it. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of antibacterial activity.

Potential Therapeutic Applications of P2 Receptor Antagonists: From Bench to Clinical Trials

BACKGROUND

Extracellular purines and pyrimidines have important physiological functions in mammals. Purines and pyrimidines act on P1 and P2 purinergic receptors, which are widely expressed in the plasma membrane in various cell types. P2 receptors act as important therapeutic targets and are associated with several disorders, such as pain, neurodegeneration, cancer, inflammation, and thrombosis. However, the use of antagonists for P2 receptors in clinical therapy, with the exception of P2Y12, is a great challenge. Currently, many research groups and pharmaceutical companies are working on the development of specific antagonist molecules for each receptor subtype that could be used as new medicines to treat their respective disorders.

OBJECTIVE

The present review compiles some interesting findings on the application of P2 receptor antagonists in different in vitro and in vivo experimental models as well as the progress of advanced clinical trials with these compounds.

CONCLUSION

Despite all of the exciting results obtained on the bench, few antagonists of P2 receptors advanced to the clinical trials, and once they reach this stage, the effectiveness of the therapy is not guaranteed, as in the example of P2X7 antagonists. Despite this, P2Y12 receptor antagonists have a history of success and have been used in therapy for at least two decades to prevent thrombosis in patients at risk for myocardial infarctions. This breakthrough is the motivation for scientists to develop new drugs with antagonistic activity for the other P2 receptors; thus, in a matter of years, we will have an evolution in the field of purinergic therapy.

A panoramic review of IL-6: Structure, pathophysiological roles and inhibitors

Interleukin-6 (IL-6) is a pleiotropic pro-inflammatory cytokine. Its deregulation is associated with chronic inflammation, and multifactorial auto-immune disorders. It mediates its biological roles through a hexameric complex composed of IL-6 itself, its receptor IL-6R, and glycoprotein 130 (IL-6/IL-6R/gp130). This complex, in turn, activates different signaling mechanisms (classical and trans-signaling) to execute various biochemical functions. The trans-signaling mechanism activates various pathological routes, like JAK/STAT3, Ras/MAPK, PI3K-PKB/Akt, and regulation of CD4+ T cells and VEGF levels, which cause cancer, multiple sclerosis, rheumatoid arthritis, anemia, inflammatory bowel disease, Crohn's disease, and Alzheimer's disease. Involvement of IL-6 in pathophysiology of these complex diseases makes it an important target for the treatment of these diseases. Though some anti-IL-6 monoclonal antibodies are being used clinically, but their high cost, only parenteral administration, and possibility of immunogenicity have limited their use, and warranted the development of novel small non-peptide molecules as IL-6 inhibitors. In the present report, all molecules reported in literature as IL-6 inhibitors have been classified as IL-6 production, IL-6R, and IL-6 signaling inhibitors. Reports available till date are critically studied to identify important and salient structural features common in these molecules. These analyses would assist medicinal chemists to design novel and potent IL-6 production and signaling inhibitors, through knowledge- and/or computer-based approaches, for the treatment of complex multifactorial diseases.

Metal-Ligand Recognition Index Determination by NMR Proton Relaxation Study

In this study, we developed and validated a new proposed parameter quantifying the interaction strength between natural and/or synthetic molecules with paramagnetic metal ions. The Metal ion Recognition Index, Miri, is a quantitative parameter to describe the proton environment and to define their involvement in the inner and/or outer sphere of the paramagnetic metal ion. The method is based on the analysis of NMR proton spin-lattice relaxation rates of a specific ligand in both the diamagnetic and paramagnetic conditions. The proposed procedure is also useful to calculate the ligand proton spin-lattice relaxation rate in the paramagnetic bound conditions, which is typically very difficult to determine experimentally. Miri was used to compare the ligand proton involvement toward different paramagnetic species, in particular the Copper(II)-Piroxicam system. Copper(II)-Piroxicam complex is one of the most active anti-inflammatory and anti-arthritic species. Miri provides an opportunity to improve our knowledge of metal-ligand complexes that play a fundamental role in bioinorganic interactions.

New Insights in Purinergic Therapy: Novel Antagonists for Uridine 5'-Triphosphate-Activated P2Y Receptors from Brazilian Flora

P2Y2 and P2Y4 receptors are physiologically activated by uridine 5'-triphosphate (UTP) and are widely expressed in many cell types in humans. P2Y2 plays an important role in inflammation and proliferation of tumor cells, which could be attenuated with the use of antagonists. However, little is known about the physiological functions related to P2Y4, due to the lack of selective ligands for these receptors. This can be solved through the search for novel compounds with antagonistic activity. The aim of this study was to discover new potential antagonist candidates for P2Y2 and P2Y4 receptors from natural products. We applied a calcium measurement methodology to identify new antagonist candidates for these receptors. First, we established optimal conditions for the calcium assay using J774.G8, a murine macrophage cell line, which expresses functional P2Y2 and P2Y4 receptors and then, we performed the screening of plant extracts at a cutoff concentration of 50 μg/mL. ATP and ionomycin, known intracellular calcium inductors, were used to stimulate cells. The calculated EC₅₀ were 11 μM and 103 nM, respectively. These cells also responded to the UTP stimulation with an EC₅₀ of 1.021 μM. Screening assays were performed and a total of 100 extracts from Brazilian plants were tested. Joannesia princeps Vell. (stem) and Peixotoa A. Juss (flower and leaf) extracts stood out due to their ability to inhibit UTP-induced responses without causing cytotoxicity, and presented an IC₅₀ of 32.32, 14.99, and 12.98 μg/mL, respectively. Collectively, our results point to the discovery of potential antagonist candidates from Brazilian flora for UTP-activated receptors.

Environmental impact of switching from the synthetic glucocorticoid prednisolone to the natural alkaloid berberine

Low amounts of human pharmaceuticals in the aquatic environment can affect bacteria, animals and ultimately humans. Here, the environmental consequences of a shift in prescription behavior from prednisolone to berberine was modeled using an environmental decision support system based on four consecutive steps: emission, fate, exposure and effect. This model estimates the relative aquatic and human health impacts of alternative pharmaceutical prescriptions throughout Europe. Since a Defined Daily Dose (DDD) of berberine has yet to be formulated, the environmental impacts of berberine and prednisolone were compared under the assumption of equal DDDs. Subsequently, the relative impact ratio indicates the extent to which the actual DDD of berberine might be higher to still be environmentally preferable over prednisolone. In fact, berberine can be administered at a six times higher dose throughout Europe before its impact on the aquatic environment exceeds that of one prescription of prednisolone. On average, the results for impacts on human health are similar, with the median impact ratio ranging between 5.87 and 22.8 depending on the level of drinking water purification. However, for some regions in Spain, Austria, Baltic States and Finland, berberine can only be considered an environmentally better alternative if it is administered at a lower dose than prednisolone. We conclude that for most regions in Europe it is, up until a certain dose of berberine, beneficial for the aquatic environment and therefore human health to prefer prescription of berberine over prednisolone.

Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus

Molecular topology was used to develop a mathematical model capable of classifying compounds according to antimicrobial activity against methicillin resistant Staphylococcus aureus (MRSA). Topological indices were used as structural descriptors and their relation to antimicrobial activity was determined by using linear discriminant analysis. This topological model establishes new structure activity relationships which show that the presence of cyclopropyl, chlorine and ramification pairs at a distance of two bonds favor this activity, while the presence of tertiary amines decreases it. This model was applied to a combinatorial library of a thousand and one 6-fluoroquinolones, from which 117 theoretical active molecules were obtained. The compound 10 and five new quinolones were tested against MRSA. They all showed some activity against MRSA, although compounds 6, 8 and 9 showed anti-MRSA activity similar to ciprofloxacin. This model was also applied to 263 theoretical antibacterial agents described by us in a previous work, from which 34 were predicted as theoretically active. Anti-MRSA activity was found bibliographically in 9 of them (ensuring at least 26% of success), and from the rest, 3 compounds were randomly chosen and tested, finding mitomycin C to be more active than ciprofloxacin. The results demonstrate the utility of the molecular topology approaches for identifying new drugs active against MRSA.

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.

Docosahexaenoic acid ester of phloridzin inhibit lipopolysaccharide-induced inflammation in THP-1 differentiated macrophages

Phloridzin or phlorizin (PZ) is a predominant phenolic compound found in apple and also used in various natural health products. Phloridzin shows poor absorption and cellular uptake due to its hydrophilic nature. The aim was to investigate and compare the effect of docosahexaenoic acid (DHA) ester of PZ (PZ-DHA) and its parent compounds (phloridzin and DHA), phloretin (the aglycone of PZ) and cyclooxygenase inhibitory drugs (diclofenac and nimesulide) on production of pro-inflammatory biomarkers in inflammation-induced macrophages by lipopolysaccharide (LPS)-stimulation. Human THP-1 monocytes were seeded in 24-well plates (5×10(5)/well) and treated with phorbol 12-myristate 13-acetate (PMA, 0.1μg/mL) for 48h to induce macrophage differentiation. After 48h, the differentiated macrophages were washed with Hank's buffer and treated with various concentrations of test compounds for 4h, followed by the LPS-stimulation (18h). Pre-exposure of PZ-DHA ester was more effective in reducing tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and cyclooxygenase-2 (COX-2) protein levels compared to DHA and nimesulide. However, diclofenac was the most effective in reducing prostaglandin (PGE2) level by depicting a dose-dependent response. However, PZ-DHA ester and DHA were the most effective in inhibiting the activation of nuclear factor-kappa B (NF-κB) among other test compounds. Our results suggest that PZ-DHA ester might possess potential therapeutic activity to treat inflammation related disorders such as type 2 diabetes, asthma, atherosclerosis and inflammatory bowel disease.

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.

Molecular topology - dissimilar similarities

The present paper depicts the role of molecular topology in the study of similarity-dissimilarity between molecular structures. It proves that molecular topology represents a very useful tool for getting common patterns of pharmacological activity and hence an efficient approach for the search of novel lead drugs.

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.

Natural Products as a Source for New Anti-Inflammatory and Analgesic Compounds through the Inhibition of Purinergic P2X Receptors

Natural products have reemerged in traditional medicine as a potential source of new molecules or phytomedicines to help with health disorders. It has been established that members of the P2X subfamily, ATP-gated ion channels, are crucial to the inflammatory process and pain signalization. As such, several preclinical studies have demonstrated that P2X2R, P2X3R, P2X4R and P2X7R are promising pharmacological targets to control inflammatory and pain disorders. Several studies have indicated that natural products could be a good source of the new specific molecules needed for the treatment of diseases linked to inflammation and pain disorders through the regulation of these receptors. Herein, we discuss and give an overview of the applicability of natural products as a source to obtain P2X receptors (P2XR) selective antagonists for use in clinical treatment, which require further investigation.

Predicting Dyspnea Inducers by Molecular Topology

QSAR based on molecular topology (MT) is an excellent methodology used in predicting physicochemical and biological properties of compounds. This approach is applied here for the development of a mathematical model capable to recognize drugs showing dyspnea as a side effect. Using linear discriminant analysis, it was found a four-variable regression equations enabling a predictive rate of about 81% and 73% in the training and test sets of compounds, respectively. These results demonstrate that QSAR-MT is an efficient tool to predict the appearance of dyspnea associated with drug consumption.