Inhibition of Monoamine Oxidase by Pirlindole Analogues:  3D-QSAR and CoMFA Analysis

Медведев, А. Е.; Veselovsky, A. V.; Shvedov, V. I.; Tikhonova, Olga V.; Moskvitina, T. A.; Fedotova, O. A.; Axenova, L. N.; Kamyshanskaya, N. S.; Kirkel, A. Z.; Иванов, А. С.

doi:10.1021/ci9802068

Cited by 37 publications

(29 citation statements)

References 14 publications

(37 reference statements)

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“…[39][40][41] In a similar way compound 14 was obtained from 2-methoxyresorcinol (13) 42 (Scheme 3). Compound 16 43 was prepared from 2,4-dihydroxy-3-methoxybenzaldehyde (15) and was N-acetylated and treated with chloroacetone under Williamson conditions to give compound 18 in 60% overall yield. Treatment of 18 in a strong alkaline medium led to the furan ring, and subsequent hydrolysis of the amide group in an acidic medium afforded furocoumarin 20 in 63% overall yield (Scheme 4).…”

Section: Resultsmentioning

confidence: 99%

“…15 In this sense, the development of QSARs using simple molecular indices appears to be a promising alternative or complementary technique to drug-protein docking, highthroughput screening, and combinatorial chemistry techniques. Almost all QSAR techniques are based on the use of molecular descriptors, which are numerical series that codify useful chemical information and enable correlations between statistical and biological properties.…”

Section: Introductionmentioning

confidence: 99%

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A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins

et al. 2005

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This work explores the potential of the MARCH-INSIDE methodology to seek a QSAR for MAO-A inhibitors from a heterogeneous series of compounds. A Markov model was used to quickly calculate the molecular electron delocalization, polarizability, refractivity, and n-octanol/water partition coefficients for a series of 1406 active/nonactive compounds. LDA was subsequently used to fit a classification function. The model showed 92.8% and 91.8% global accuracy and predictability in training and validation studies. This QSAR model was validated through a virtual screening of a series of coumarin derivatives. The 15 selected compounds were prepared and evaluated as in vitro MAO-A inhibitors. The theoretical prediction was compared with the experimental results and the model correctly predicted 13 compounds with only two mistakes on compounds with activities very close to the cutoff point established for the model. Consequently, this method represents a useful tool for the "in silico" screening of MAO-A inhibitors.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins

et al. 2005

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“…We used these studies to confirm the relevance of some of our biophores. For example, biophore #2 was found to be an important factor enhancing the interaction between the inhibitor and the protein core of the MAO enzyme [23,25,27]. …”

Section: Resultsmentioning

confidence: 99%

An MCASE approach to the search of a cure for Parkinson's Disease.

Klopman

Sedykh

2002

BMC Pharmacol

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Background: Parkinson's disease is caused by a dopamine deficiency state in the fore brain area. Dopamine receptor agonists, MAO-B inhibitors, and N-Methyl-D-Aspartate (NMDA) receptor antagonists are known to have antiparkinson effect. Levodopa, a dopamine structural analog, is the best currently available medication for the treatment of Parkinsons disease. Unfortunately, it also induces side effects upon long administration time. Thus, multidrug therapy is often used, in which various adjuvants alleviate side effects of levodopa and enhance its antiparkinsonian action.

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“…Pirlindole analogues-Inhibitory activity (IC 50 ) for pirlindole analogues with different substituents at C 8 was studied by Medvedev et al [122] through CoMFA analysis (see Fig. 4, structure 6).…”

Section: Indole and Isatin Analogues-a Series Of Indole And Isatin Anmentioning

confidence: 99%

Predicting Monoamine Oxidase Inhibitory Activity Through Ligand-Based Models

Vilar¹,

Ferino²,

Quezada³

et al. 2013

CTMC

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The evolution of bio-and cheminformatics associated with the development of specialized software and increasing computer power has produced a great interest in theoretical in silico methods applied in drug rational design. These techniques apply the concept that "similar molecules have similar biological properties" that has been exploited in Medicinal Chemistry for years to design new molecules with desirable pharmacological profiles. Ligand-based methods are not dependent on receptor structural data and take into account two and three-dimensional molecular properties to assess similarity of new compounds in regards to the set of molecules with the biological property under study. Depending on the complexity of the calculation, there are different types of ligand-based methods, such as QSAR (Quantitative Structure-Activity Relationship) with 2D and 3D descriptors, CoMFA (Comparative Molecular Field Analysis) or pharmacophoric approaches. This work provides a description of a series of ligand-based models applied in the prediction of the inhibitory activity of monoamine oxidase (MAO) enzymes. The controlled regulation of the enzymes' function through the use of MAO inhibitors is used as a treatment in many psychiatric and neurological disorders, such as depression, anxiety, Alzheimer's and Parkinson's disease. For this reason, multiple scaffolds, such as substituted coumarins, indolylmethylamine or pyridazine derivatives were synthesized and assayed toward MAO-A and MAO-B inhibition. Our intention is to focus on the description of ligand-based models to provide new insights in the relationship between the MAO inhibitory activity and the molecular structure of the different inhibitors, and further study enzyme selectivity and possible mechanisms of action.

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Inhibition of Monoamine Oxidase by Pirlindole Analogues: 3D-QSAR and CoMFA Analysis

Cited by 37 publications

References 14 publications

A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins

A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins

An MCASE approach to the search of a cure for Parkinson's Disease.

Predicting Monoamine Oxidase Inhibitory Activity Through Ligand-Based Models

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