Novel potential agents for ulcerative colitis by molecular topology: suppression of IL-6 production in Caco-2 and RAW 264.7 cell lines. Mol Divers. 2013;17:573-593. [PMID: 23793777 DOI: 10.1007/s11030-013-9458-6]

Gallic acid mitigates intestinal inflammation and loss of tight junction protein expression using a 2D-Caco-2 and RAW 264.7 co-culture model

A 2D-intestinal epithelial Caco-2/RAW 264.7 macrophage co-culture model was developed to demonstrate the relative efficacy of different phenolic acids to mitigate changes in Caco-2 epithelial cell redox state initiated both directly by autoxidation products, H2O2, and indirectly through cell communication events originating from cytokine stimulated macrophage. An inducer cocktail (lipopolysaccharide + interferon gamma) was used to activate RAW 264.7 cells in the 2D- Caco-2/RAW co-culture and intracellular changes in Caco-2 cell redox signaling occurred in response to positive changes (p < 0.05) in inflammatory biomarkers derived in macrophage that included IL-6, TNF-α, nitric oxide and peroxynitrite, respectively. Phenolic acids varied in relative capacity to reduce NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) in cocktail inflamed induced macrophage. This response in addition to the relative predisposition of gallic acid (GA) to undergo autoxidation to generate H2O2 activity (p < 0.05), culminated in downstream cell signaling in Caco-2 nuclear factor erythroid 2-related factor (Nrf2) activity (increase 26.9 %), altered monolayer integrity (increase 33.7 %), and release of interleukin 8 (IL-8) (decrease 80.5 %) (p < 0.05). It can be concluded that the co-culture model described herein was useful to assess the importance of communication between cytokine stimulated macrophage and intestinal cells. Moreover, the relative unique efficacy of GA, compared to other phenolic acids tested to protect against activated macrophage induced changes related to intestinal dysfunction were particularly relevant to epithelial redox signaling, intestinal permeability and regulation of tight junction proteins. This study concludes that phenolic acids are not equal in the capacity to protect against intestinal cell dysfunction despite some indication of biological activity.

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.

A Brief Review of Machine Learning-Based Bioactive Compound Research

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

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.

Direct On-Chip Differentiation of Intestinal Tubules from Induced Pluripotent Stem Cells

Intestinal organoids have emerged as the new paradigm for modelling the healthy and diseased intestine with patient-relevant properties. In this study, we show directed differentiation of induced pluripotent stem cells towards intestinal-like phenotype within a microfluidic device. iPSCs are cultured against a gel in microfluidic chips of the OrganoPlate, in which they undergo stepwise differentiation. Cells form a tubular structure, lose their stem cell markers and start expressing mature intestinal markers, including markers for Paneth cells, enterocytes and neuroendocrine cells. Tubes develop barrier properties as confirmed by transepithelial electrical resistance (TEER). Lastly, we show that tubules respond to pro-inflammatory cytokine triggers. The whole procedure for differentiation lasts 14 days, making it an efficient process to make patient-specific organoid tubules. We anticipate the usage of the platform for disease modelling and drug candidate screening.

4-(3-Nitrophenyl)thiazol-2-ylhydrazone derivatives as antioxidants and selective hMAO-B inhibitors: synthesis, biological activity and computational analysis

A new series of 4-(3-nitrophenyl)thiazol-2-ylhydrazone derivatives were designed, synthesised, and evaluated to assess their inhibitory effect on the human monoamine oxidase (hMAO) A and B isoforms. Different (un)substituted (hetero)aromatic substituents were linked to N1 of the hydrazone in order to establish robust structure–activity relationships. The results of the biological testing demonstrated that the presence of the hydrazothiazole nucleus bearing at C4 a phenyl ring functionalised at the meta position with a nitro group represents an important pharmacophoric feature to obtain selective and reversible human MAO-B inhibition for the treatment of neurodegenerative disorders. In addition, the most potent and selective MAO-B inhibitors were evaluated in silico as potential cholinesterase (AChE/BuChE) inhibitors and in vitro for antioxidant activities. The results obtained from molecular modelling studies provided insight into the multiple interactions and structural requirements for the reported MAO inhibitory properties.

Rapid synthesis and immunogenicity of mycobacterial (1→5)-α-d-arabinofuranan

A rapid synthesis of the α(1→5) arabinofuranan polysaccharides, found on the outer surface of Mycobacterium tuberculosis (Mtb), is achieved by a regio- and stereocontrolled ring opening polymerization of β-d-arabinofuranose-1,2,5-orthobenzoate. The robust polymerization reaction allows the incorporation of an amine linker, which was used to conjugate with protein tetanus toxoid (TT) to further investigate its adjuvant activities. The synthetic arabinan, which is the glycan on the non-reducing end of Mtb lipoarabinomannan (LAM), was evaluated for its immunological properties in vitro and in vivo. Systemic inflammation and the promotion of innate immune response were observed in macrophages treated with the synthetic arabinan as an adjuvant through an increase in the production of TNF-α and IL-12. In vivo evaluation of IFN-γ, IL-2, and TNF-α productions in mice pre-immunized with the synthetic arabinan conjugated TT indicated great enhancements of the immunological responses when compared to that of TT alone.

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.

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.

Computational fishing of new DNA methyltransferase inhibitors from natural products

DNA methyltransferase inhibitors (DNMTis) have become an alternative for cancer therapies. However, only two DNMTis have been approved as anticancer drugs, although with some restrictions. Natural products (NPs) are a promising source of drugs. In order to find NPs with novel chemotypes as DNMTis, 47 compounds with known activity against these enzymes were used to build a LDA-based QSAR model for active/inactive molecules (93% accuracy) based on molecular descriptors. This classifier was employed to identify potential DNMTis on 800 NPs from NatProd Collection. 447 selected compounds were docked on two human DNA methyltransferase (DNMT) structures (PDB codes: 3SWR and 2QRV) using AutoDock Vina and Surflex-Dock, prioritizing according to their score values, contact patterns at 4 Å and molecular diversity. Six consensus NPs were identified as virtual hits against DNMTs, including 9,10-dihydro-12-hydroxygambogic, phloridzin, 2',4'-dihydroxychalcone 4'-glucoside, daunorubicin, pyrromycin and centaurein. This method is an innovative computational strategy for identifying DNMTis, useful in the identification of potent and selective anticancer drugs.

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.

Bone marrow mesenchymal stem cells repair colonic vascular endothelium in rats with ulcerative colitis

AIM: To investigate whether bone marrow mesenchymal stem cells (MSCs) can repair colonic vascular endothelium in rats with ulcerative colitis (UC).

METHODS: Monocytes were purified from bone marrow, amplified and identified as MSCs in vitro. Thirty female Wistar rats were randomly and equally divided into three groups: a normal control, a model group and a MSC group. Colitis was induced with trinitro-benzene-sulfonic acid in rats of the model and MSC groups. Rats of the MSC group were injected with 1 mL of MSC suspension via the tail vein, while the rats of the normal control group and model group were injected with equal volume of normal saline. After two weeks, colon tissue samples were analyzed for histopathology, and serial sections of the colon tissue were made for determining the distribution of Y chromosome and CD34 double positive cells. The protein expression of CD34 was detected by immunohistochemisty. The mRNA expression of CD34 was determined by RT-PCR. The contents of interleukin-6 (IL-6) and interleukin-10 (IL-10) in colon tissue were determined by ELISA.

RESULTS: Y chromosome and CD34 double positive cells could be seen in colon tissue of rats injected with MSCs. Compared to the normal control group, the expression of CD34 and IL-6 was increased (1.629 ± 0.067 vs 1.000 ± 0.113, P < 0.05; 238.304 pg/mL ± 0.019 pg/mL vs 81.439 pg/mL ± 0.120 pg/mL, P < 0.01) and the expression of IL-10 was decreased (87.531 pg/mL ± 0.101 pg/mL vs 289.413 pg/mL ± 0.039 pg/mL, P < 0.01) in the model group. Compared to the model group, the expression of CD34 and IL-10 was increased (2.502 ± 0.189 vs 1.629 ± 0.067, P < 0.05; 158.185 pg/mL ± 0.033 pg/mL vs 87.531 pg/mL ± 0.115 pg/mL, P < 0.01) and the expression of IL-6 was decreased (160.95 pg/mL ± 0.116 pg/mL vs 238.304 pg/mL ± 0.109 pg/mL, P < 0.01) in the MSC group.

CONCLUSION: MSCs may exert a therapeutic effect on colitis by differentiating into vascular cell endothelial cells and inhibiting inflammation.