Aspirin and non-aspirin non-steroidal anti-inflammatory drug use and risk of lung cancer

Saw Swee Hock School of Public Health, National University of Singapore, MD3, 16 Medical Drive, Singapore 117597, Singapore. wei-yen
Lung cancer (Amsterdam, Netherlands) (Impact Factor: 3.96). 04/2012; 77(2):246-51. DOI: 10.1016/j.lungcan.2012.03.005
Source: PubMed


There is evidence that aspirin and non-aspirin non-steroidal anti-inflammatory drug (NSAID) have anti-carcinogenic properties, but their effect on lung cancer, in particular in never-smokers, is unclear. Information on past or current use of anti-inflammatory medication was obtained in 398 Chinese female primary lung cancer cases and 814 controls in a hospital-based study in Singapore. 65% of cases and 88% of controls were never-smokers. Controls were excluded if they had been admitted for conditions associated with aspirin or NSAID use (n=174). Regular aspirin use (twice a week or more, for a month or more) was associated with a reduced risk of lung cancer (adjusted odds ratio [OR] 0.50, 95% confidence intervals [95%CI] 0.31-0.81 in non-smokers; OR 0.38, 95%CI 0.16-0.93 in smokers). Regular use of non-aspirin NSAID, paracetamol, steroid creams and steroid pills was uncommon and no association with lung cancer was detected. Our results suggest that aspirin consumption may reduce lung cancer risk in Asian women and are consistent with current understanding of the role of cyclooxygenase in lung carcinogenesis.

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    • "In addition to its chemopreventive effects on colorectal cancers, aspirin has also been shown to have a beneficial role in reducing lung cancer incidence and mortality [8]. A pooled analysis conducted by the International Lung Cancer Consortium (ILCCO) suggests that aspirin use was significantly associated with reduced risk of lung cancer [16] regardless of the smoking status of the individual [17]. Of further interest, a study published within the last year suggests that aspirin use may also reduce metastasis of lung cancer cells to the regional lymph nodes, and thus has the potential to extend the lives of lung cancer patients [18]. "
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    ABSTRACT: Nonsteroidal anti-inflammatory drugs (NSAIDs) are a group of agents important for their analgesic, anti-inflammatory, and antipyretic properties. This study presents several approaches to predict and elucidate new molecular structures of NSAIDs based on 36 known and proven anti-inflammatory compounds. Based on 36 known NSAIDs the mean value of Log P is found to be 3.338 (standard deviation= 1.237), mean value of polar surface area is 63.176 Angstroms2 (standard deviation = 20.951 A2), and the mean value of molecular weight is 292.665 (standard deviation = 55.627). Nine molecular properties are determined for these 36 NSAID agents, including Log P, number of -OH and -NHn, violations of Rule of 5, number of rotatable bonds, and number of oxygens and nitrogens. Statistical analysis of these nine molecular properties provides numerical parameters to conform to in the design of novel NSAID drug candidates. Multiple regression analysis is accomplished using these properties of 36 agents followed with examples of predicted molecular weight based on minimum and maximum property values. Hierarchical cluster analysis indicated that licofelone, tolfenamic acid, meclofenamic acid, droxicam, and aspirin are substantially distinct from all remaining NSAIDs. Analysis of similarity (ANOSIM) produced R = 0.4947, which indicates low to moderate level of dissimilarity between these 36 NSAIDs. Non-hierarchical K-means cluster analysis separated the 36 NSAIDs into four groups having members of greatest similarity. Likewise, discriminant analysis divided the 36 agents into two groups indicating the greatest level of distinction (discrimination) based on nine properties. These two multivariate methods together provide investigators a means to compare and elucidate novel drug designs to 36 proven compounds and ascertain to which of those are most analogous in pharmacodynamics. In addition, artificial neural network modeling is demonstrated as an approach to predict numerous molecular properties of new drug designs that is based on neural training from 36 proven NSAIDs. Comprehensive and effective approaches are presented in this study for the design of new NSAID type agents which are so very important for inhibition of COX-2 and COX-1 isoenzymes.
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