Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models

Boppana, Kiran; Dubey, P. K.; Jagarlapudi, Sarma A.R.P.; Vadivelan, S.; Rambabu, G.

doi:10.1016/j.ejmech.2009.02.031

Cited by 39 publications

(26 citation statements)

References 24 publications

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“…This screening paradigm involves the use of complex laboratory automation but assumes no prior knowledge of the nature of the chemotype likely to have activity at the target protein. Focused or knowledge‐based screening involves selecting from the chemical library smaller subsets of molecules that are likely to have activity at the target protein based on knowledge of the target protein and literature or patent precedents for the chemical classes likely to have activity at the drug target (Boppana et al ., 2009). This type of knowledge has given rise, more recently, to early discovery paradigms using pharmacophores and molecular modelling to conduct virtual screens of compound databases (McInnes, 2007).…”

Section: The Hit Discovery Processmentioning

confidence: 99%

Principles of early drug discovery

Jp¹,

Rees

Kalindjian

et al. 2011

British J Pharmacology

2,142

1,309

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Developing a new drug from original idea to the launch of a finished product is a complex process which can take 12–15 years and cost in excess of $1 billion. The idea for a target can come from a variety of sources including academic and clinical research and from the commercial sector. It may take many years to build up a body of supporting evidence before selecting a target for a costly drug discovery programme. Once a target has been chosen, the pharmaceutical industry and more recently some academic centres have streamlined a number of early processes to identify molecules which possess suitable characteristics to make acceptable drugs. This review will look at key preclinical stages of the drug discovery process, from initial target identification and validation, through assay development, high throughput screening, hit identification, lead optimization and finally the selection of a candidate molecule for clinical development.

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Section: The Hit Discovery Processmentioning

confidence: 99%

Principles of early drug discovery

Jp¹,

Rees

Kalindjian

et al. 2011

British J Pharmacology

2,142

1,309

View full text Add to dashboard Cite

show abstract

“…1A) is a natural polyphenol that belongs to the flavonoid family and is produced in the leaves of Lindera aggregata (Sims) Kosterm. QI has a wide range of pharmacological activities, including antioxidant (9), antiviral (10), antidepressant (11), diabetic resistant (12), liver protectant (13) and cardiovascular protectant activities (14). It has been reported that QI reduces the apoptosis of endothelial progenitor cells caused by oxidized low-density lipoprotein, and promotes autophagy via extracellular signal-regulated kinase activation (15).…”

Section: Introductionmentioning

confidence: 99%

Quercetin‑3‑O‑α‑L‑rhamnopyranoside derived from the leaves of Lindera aggregata (Sims) Kosterm. evokes the autophagy‑induced nuclear factor erythroid 2‑related factor 2 antioxidant pathway in human umbilical vein endothelial cells

Han

Amin

et al. 2018

Int J Mol Med

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Quercetin-3-O-α-L-rhamnopyranoside (QI) is derived from the leaves of Lindera aggregata (Sims) Kosterm. And exhibits multiple biological activities, including an antioxidant activity. However, the detailed molecular mechanism of its antioxidant activity remains unknown. The aim of the present study was to investigate the antioxidant activity of QI and the underlying molecular mechanism in human umbilical vein endothelial cells (HUVEcs). An oxidative stress model was established in HUVEcs using H 2 O 2 , and cells were then treated with different concentrations of QI. The results revealed that the exposure of HUVEcs to QI protected these cells from H 2 O 2-induced damage. QI treatment also increased the activities of the antioxidant enzymes superoxide dismutase (SOd) and glutathione (GSH) in the cell culture medium. In addition, QI inhibited H 2 O 2-induced apoptosis by decreasing the expression levels of cleaved caspase-9 and poly(AdP-ribose) polymerase. QI also inhibited the production of dNA fragments and reactive oxygen species induced by H 2 O 2. Furthermore, QI decreased the oxidative stress by promoting the nuclear transfer of nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 by activating autophagy, and inhibited the competition of Bach1 from Nrf2. Finally, QI significantly improved the activities of T-SOD and GSH, and decreased the content of malondialdehyde in the serum and heart tissue of aging rats. These data support the use of QI as a health supplement to alleviate oxidative stress or further development of this compound as an antioxidant drug.

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“…We used three methods to validate the quality of the PhSIA: (i) Fischer’s cross-validation test [23]–[25] (Fischer’s test) was used to assess the confidence of the training set selection; (ii) the partial least squares (PLS) validation method [26]–[29], [39], [40] was used to assess PhSIA prediction quality and accuracy; and (iii) the GH test method [35], [41]–[44] was used to determine the confidence of statistical significance when screening compound databases.…”

Section: Methodsmentioning

confidence: 99%

Development of a Human Dihydroorotate Dehydrogenase (hDHODH) Pharma-Similarity Index Approach with Scaffold-Hopping Strategy for the Design of Novel Potential Inhibitors

et al. 2014

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Human dihydroorotate dehydrogenase (hDHODH) is a class-2 dihydroorotate dehydrogenase. Because it is extensively used by proliferating cells, its inhibition in autoimmune and inflammatory diseases, cancers, and multiple sclerosis is of substantial clinical importance. In this study, we had two aims. The first was to develop an hDHODH pharma-similarity index approach (PhSIA) using integrated molecular dynamics calculations, pharmacophore hypothesis, and comparative molecular similarity index analysis (CoMSIA) contour information techniques. The approach, for the discovery and design of novel inhibitors, was based on 25 diverse known hDHODH inhibitors. Three statistical methods were used to verify the performance of hDHODH PhSIA. Fischer’s cross-validation test provided a 98% confidence level and the goodness of hit (GH) test score was 0.61. The q2, r2, and predictive r2 values were 0.55, 0.97, and 0.92, respectively, for a partial least squares validation method. In our approach, each diverse inhibitor structure could easily be aligned with contour information, and common substructures were unnecessary. For our second aim, we used the proposed approach to design 13 novel hDHODH inhibitors using a scaffold-hopping strategy. Chemical features of the approach were divided into two groups, and the Vitas-M Laboratory fragment was used to create de novo inhibitors. This approach provides a useful tool for the discovery and design of potential inhibitors of hDHODH, and does not require docking analysis; thus, our method can assist medicinal chemists in their efforts to identify novel inhibitors.

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Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models

Cited by 39 publications

References 24 publications

Principles of early drug discovery

Principles of early drug discovery

Quercetin‑3‑O‑α‑L‑rhamnopyranoside derived from the leaves of Lindera aggregata (Sims) Kosterm. evokes the autophagy‑induced nuclear factor erythroid 2‑related factor 2 antioxidant pathway in human umbilical vein endothelial cells

Development of a Human Dihydroorotate Dehydrogenase (hDHODH) Pharma-Similarity Index Approach with Scaffold-Hopping Strategy for the Design of Novel Potential Inhibitors

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