QSAR methods for the discovery of new inflammatory bowel disease drugs

García-Domènech, Ramón; Gálvez-Llompart, María; Zanni, Riccardo; Recio, M. C.; Gálvez, Jorge

doi:10.1517/17460441.2013.800043

Cited by 8 publications

(5 citation statements)

References 67 publications

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“…102 Other web-based tools are available for the prediction of pharmacodynamics/pharmacokinetic and toxicity profiles of compounds based on structural input data. 103,104 Recently, several QSAR-based methods have been used in the discovery of novel agents for the treatment of IBD, 105 uncovering compounds capable of inhibiting NF-κB 106 and TNF-α converting enzyme, 107 two molecules implicated in disease pathogenesis.…”

Section: Big Data-driven Techniques For Drug Discoverymentioning

confidence: 99%

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

et al. 2017

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

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Section: Big Data-driven Techniques For Drug Discoverymentioning

confidence: 99%

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

et al. 2017

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“…From drug identification to pharmacological role discovery and from preclinical testing to drug marketing, computers have played a fundamental role in the entire drug discovery and development process[ 15 ]. In addition, CADD opens the door to the possibility of drug repurposing, which is the systematic identification of new potential uses from drugs that have already been approved for other indications[ 79 ].…”

Section: Role Of Computational Methods In Ibd Drug Discoverymentioning

confidence: 99%

“…To achieve the desired results of immune homeostasis restoration and better symptom control, additional progress is still required. Emerging treatment options for IBD include cell therapy, intestinal microecology, apheresis therapy, exosome therapy, and small molecules[ 15 ]. Low-molecular-mass compounds with excellent oral bioavailability and the capacity to cross the cell membrane to modulate the functions of parts of the inflammatory signaling pathway are now regarded to be promising therapeutic alternatives for the treatment of IBD.…”

Section: Introductionmentioning

confidence: 99%

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Johnson¹,

Akinsanmi²,

Ejembi³

et al. 2023

World J Gastroenterol

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Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided ( in silico ) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

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“…QSAR, ADME and docking have become inexorably embedded as essential tools in the pharmaceutical industry, from lead discovery, optimization to the lead development and computer-aided drug designing [26]. A growing trend is to use these modern techniques in early stage of drug discovery process as a screening and enrichment tool to eliminate the compounds lacking drug like properties from further development process [26,27]. The structurally related properties of a compound can be determined by computational mean much more efficiently in quicker and easier mode than its biological activity (LD 50 /IC 50 /ED 50 ) using in vivo or in vitro approaches.…”

Section: Introductionmentioning

confidence: 99%

QSAR, ADME and docking guided semi-synthesis and in vitro evaluation of 4-hydroxy-α-tetralone analogs for anti-inflammatory activity

et al. 2020

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The compound 4-hydroxy-α-tetralone (1) is major bioactive secondary metabolite of genus Ammannia (Family-Lythraceae). The compound 1 and its various derivatives are reported to possess anti-tubercular, anti-diabetic, anti-leishmanial and bioenhancing activities. A quantitative structure activity relationship (QSAR) model for predicting anti-inflammatory activity of 4-hydroxy-α-tetralone derivatives against the tumour necrosis factor (TNF)-α, a pro-inflammatory cytokine was developed by non-linear model using artificial neural network (ANN). The regression coefficient (r 2) and the leave-oneout cross-validation regression coefficient (LOO rCV 2) of the QSAR model were 0.6976 and 0.4016, respectively, while the regression coefficient (r 2) for the external set of experimental compounds was 0.835. The 4-hydroxy-α-tetralone virtual derivatives, which showed significant inhibition of TNF-α were subjected to docking and in-silico absorption, distribution, metabolism and excretion (ADME) studies and the results showed similar binding affinity and bioavailability in compliance with the standard drug, Diclofenac. Finally, in order to validate the developed QSAR model, the most and least active virtual derivatives were semi-synthesized, characterized on the basis of their 1 H and 13 C NMR spectroscopic data and in vitro tested for concentration dependent inhibition of TNF-α. The experimental results obtained, agreed well with the predicted values. Keywords QSAR • Docking • Anti-inflammatory • TNF-α • Ammannia baccifera • In vitro studies Harish C. Upadhyay and Monika Singh contributed equally to this research.

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QSAR methods for the discovery of new inflammatory bowel disease drugs

Cited by 8 publications

References 67 publications

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

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

Modern drug discovery for inflammatory bowel disease: The role of computational methods

QSAR, ADME and docking guided semi-synthesis and in vitro evaluation of 4-hydroxy-α-tetralone analogs for anti-inflammatory activity

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