Non-steroidal anti-inflammatory drugs (NSAIDs) and organ damage: A current perspective.

Owing to the efficacy in reducing pain and inflammation, non-steroidal anti-inflammatory drugs (NSAIDs) are amongst the most popularly used medicines confirming their position in the WHO's Model List of Essential Medicines. With escalating musculoskeletal complications, as evident from 2016 Global Burden of Disease data, NSAID usage is evidently unavoidable. Apart from analgesic, anti-inflammatory and antipyretic efficacies, NSAIDs are further documented to offer protection against diverse critical disorders including cancer and heart attacks. However, data from multiple placebo-controlled trials and meta-analyses studies alarmingly signify the adverse effects of NSAIDs in gastrointestinal, cardiovascular, hepatic, renal, cerebral and pulmonary complications. Although extensive research has elucidated the mechanisms underlying the clinical hazards of NSAIDs, no review has extensively collated the outcomes on various multiorgan toxicities of these drugs together. In this regard, the present review provides a comprehensive insight of the existing knowledge and recent developments on NSAID-induced organ damage. It precisely encompasses the current understanding of structure, classification and mode of action of NSAIDs while reiterating on the emerging instances of NSAID drug repurposing along with pharmacophore modification aimed at safer usage of NSAIDs where toxic effects are tamed without compromising the clinical benefits. The review does not intend to vilify these 'wonder drugs'; rather provides a careful understanding of their side-effects which would be beneficial in evaluating the risk-benefit threshold while rationally using NSAIDs at safer dose and duration.

Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods.

This work reports the classification study conducted on the biggest COX-2 inhibitor data set so far. Using 2925 diverse COX-2 inhibitors collected from 168 pieces of literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF), to develop 12 classification models. The best SVM and RF models resulted in MCC values of 0.73 and 0.72, respectively. The 2925 COX-2 inhibitors were reduced to a data set of 1630 molecules by removing intermediately active inhibitors, and 12 new classification models were constructed, yielding MCC values above 0.72. The best MCC value of the external test set was predicted to be 0.68 by the RF model using ECFP_4 fingerprints. Moreover, the 2925 COX-2 inhibitors were clustered into eight subsets, and the structural features of each subset were investigated. We identified substructures important for activity including halogen, carboxyl, sulfonamide, and methanesulfonyl groups, as well as the aromatic nitrogen atoms. The models developed in this study could serve as useful tools for compound screening prior to lab tests.

Ibuprofen: from invention to an OTC therapeutic mainstay.

The discovery of ibuprofen's anti-inflammatory activity by Dr (now Professor) Stewart Adams and colleagues (Boots Pure Chemical Company Ltd, Nottingham, UK) 50 years ago represented a milestone in the development of anti-inflammatory analgesics. Subsequent clinical studies were the basis for ibuprofen being widely accepted for treating painful conditions at high anti-rheumatic doses (≤ 2400 mg/d), with lower doses (≤ 1200 mg/d for ≤ 10 days) for mild-moderate acute pain (e.g. dental pain, headache, dysmenorrhoea, respiratory symptoms and acute injury). The early observations have since been verified in studies comparing ibuprofen with newer cyclo-oxygenase-2 selective inhibitors ('coxibs'), paracetamol and other non-steroidal anti-inflammatory drugs (NSAIDs). The use of the low-dose, non-prescription, over-the-counter (OTC) drug was based on marketing approval in 1983 (UK) and 1984 (USA); and it is now available in over 80 countries. The relative safety of OTC ibuprofen has been supported by large-scale controlled studies. It has the same low gastro-intestinal (GI) effects as paracetamol (acetaminophen) and fewer GI effects than aspirin. Ibuprofen is a racemate. Its physicochemical properties and the short plasma-elimination half-life of the R(-) isomer, together with its limited ability to inhibit cyclo-oxygenase-1 (COX-1) and thus prostaglandin (PG) synthesis, compared with that of S(+)-ibuprofen, are responsible for the relatively low GI toxicity. The R(-) isomer is then converted in the body to the S(+) isomer after absorption in the GI tract. Ex vivo inhibition of COX-1 (thromboxane A(2)) and COX-2 (PGE(2)) at the plasma concentrations of S(+)-ibuprofen corresponding to those found in the plasma following ingestion of 400 mg ibuprofen in dental and other inflammatory pain models provides evidence of the anti-inflammatory mechanism at OTC dosages. R(-)-ibuprofen has effects on leucocytes, suggesting that ibuprofen has anti-leucocyte effects, which underlie its anti-inflammatory actions. Future developments include novel gastro-tolerant forms for 'at risk' patients, and uses in the prevention of neuro-inflammatory states and cancers.

A turbulent decade for NSAIDs: update on current concepts of classification, epidemiology, comparative efficacy, and toxicity.

Non-steroidal anti-inflammatory drugs (NSAIDs) represent a diverse class of drugs and are among the most commonly used analgesics for arthritic pain worldwide, though long-term use is associated with a spectrum of adverse effects. The introduction of cyclooxygenase-2-selective NSAIDs early in the last decade offered an alternative to traditional NSAIDs with similar efficacy and improved gastrointestinal tolerability; however, emerging concerns about cardiovascular safety resulted in the withdrawal of two agents (rofecoxib and valdecoxib) in the mid-2000s and, subsequently, in an overall reduction in NSAID use. It is now understood that all NSAIDs are associated with some varying degree of gastrointestinal and cardiovascular risk. Guidelines still recommend their use, but little is known of how patients use these agents. While strategies and guidelines aimed at reducing NSAID-associated complications exist, there is a need for evidence-based algorithms combining cardiovascular and gastrointestinal factors that can be used to aid treatment decisions at an individual patient level.

Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method.

BACKGROUND: A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. RESULTS: We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC50 values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naïve Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. CONCLUSION: Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.

Risk of congestive heart failure with nonsteroidal antiinflammatory drugs and selective Cyclooxygenase 2 inhibitors: a class effect?

OBJECTIVE: Nonsteroidal antiinflammatory drugs (NSAIDs) as a class have been shown to increase the risk of congestive heart failure (CHF) compared with celecoxib. The magnitude of the risk for individual NSAIDs is not known. METHODS: Using administrative databases, we performed a nested case-control study in a population-based cohort of patients ages >or=66 years admitted for CHF between January 1998 and March 2003. Cases were patients readmitted for CHF after cohort entry (index date). Four controls were matched to each case on date of cohort entry and time between cohort entry and index date. Exposure was the current use of an NSAID or a coxib in the 7 days prior to CHF readmission. Using conditional logistic regression, we calculated the odds of readmission for CHF in patients exposed to naproxen, diclofenac, ibuprofen, indomethacin, or rofecoxib compared with celecoxib, after adjusting for possible confounding variables. RESULTS: We identified 8,512 cases and 34,048 controls. The baseline characteristics between the groups were similar in general. The odds of being readmitted for CHF were higher in patients currently exposed to indomethacin (odds ratio [OR] 2.04, 95% confidence interval [95% CI] 1.16-3.58) or rofecoxib (OR 1.58, 95% CI 1.19-2.11) compared with celecoxib. There was no difference between naproxen, diclofenac, and ibuprofen compared with celecoxib, although the numbers of exposed cases and controls were small. CONCLUSION: In elderly patients with known CHF, indomethacin and rofecoxib are associated with a greater risk of recurrent CHF compared with celecoxib. Alternatives should be considered for patients with CHF who require antiinflammatory drugs.

Extraction and visualization of potential pharmacophore points using support vector machines: application to ligand-based virtual screening for COX-2 inhibitors.

Support vector machines (SVM) were trained to predict cyclooxygenase 2 (COX-2) and thrombin inhibitors. The classifiers were obtained using sets of known COX-2 and thrombin inhibitors as "positive examples" and a large collection of screening compounds as "negative examples". Molecules were encoded by topological pharmacophore-point triangles. In retrospective virtual screening, 50-90% of the known active compounds were listed within the first 0.1% of the ranked database. To check the validity of the constructed classifiers, we developed a method for feature extraction and visualization using SVM. As a result, potential pharmacophore points were weighted according to their importance for COX-2 and thrombin inhibition. Known thrombin and COX-2 pharmacophore points were correctly recognized by the machine learning system. In a prospective virtual screening study, several potential COX-2 inhibitors were predicted and tested in a cellular activity assay. A benzimidazole derivative exhibited significant inhibitory activity with an IC(50) of 0.2 microM, which is better than Celecoxib in our assay. It was demonstrated that the SVM machine-learning method can be used in virtual screening and be analyzed in a human-interpretable way that results in a set of rules for designing novel molecules.