Repository of Research and Investigative Information

Repository of Research and Investigative Information

Dezful University of Medical Sciences

Proposed Multi-linear Regression Model to Identify Cyclooxygenase-2 Selective Active Pharmaceutical Ingredients

(UNSPECIFIED) Proposed Multi-linear Regression Model to Identify Cyclooxygenase-2 Selective Active Pharmaceutical Ingredients. Journal of Pharmaceutical Innovation. p. 7. ISSN 1872-5120

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Purpose Anti-inflammatory drugs are in the spotlight of pharmaceutical investigations due to the involvement of this condition in different cancers and diseases. Non-steroidal anti-inflammatory drugs (NSAIDs) are considered to be the most widely used anti-inflammatory drugs. However, the inability of these molecules to distinguish between COX-1 and COX-2 results in concomitant side effects. In the present study, a new algorithmic procedure is applied to build a multi-linear regression model capable of circumventing this problem. Methods In this regard, the structures of FDA-approved NSAIDs and COX-1 and COX-2 molecules were prepared. The top 10 COX-2 specific molecules were selected based on their interaction energies and exploited for similarity searches. The resulting 2000 molecules were subjected to various screening processes. Several dependable bioactivities of these compounds along with partial coefficients of all possible affecting descriptors such as molecular weight, H-acceptor/donor, and polar surface area were calculated. The best multi-linear regression approach was used to analyze the descriptors with the highest impact. Results Ultimately, a highly reliable model was designed based on 17 screened molecules with higher than 90 anti-inflammatory activity. The attained model was endowed with higher than 84 accuracy according to 5 descriptors, including Log P, Log D, molar refractivity, polarity number, and aromaticity ratio. Our results demonstrated that the bipolarity of drugs is more important than the number of hydrogen bonds to achieve the better anti-inflammatory activity. Moreover, in contrast to prior reports, it is assumable that some Lipinski elements could play a less critical role in drug discovery and improvement efforts. Conclusions The quantitative structure and activity relationship (QSAR) model formulated in this study demonstrates an accurate prediction of anti-inflammatory activity of NSAID-like structures. Newly suggested structures are highly resembled to third-generation NSAIDs. This property endorses the trustworthiness and reliability of the obtained equation to design a new generation of NSAIDs.

Item Type: Article
Keywords: NSAID Cyclooxygenase-2 Molecular descriptors Multi-linear regression model QSAR Docking gastrointestinal toxicity prediction expression proteins cox-2 Pharmacology & Pharmacy
Page Range: p. 7
Journal or Publication Title: Journal of Pharmaceutical Innovation
Journal Index: ISI
Identification Number:
ISSN: 1872-5120
Depositing User: مهندس مهدی شریفی

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