Design, synthesis, and molecular docking of novel 3,5-disubstituted-1,3,4-oxadiazole derivatives as iNOS inhibitors.

To obtain new anti-inflammatory agents, recent studies have aimed to replace the carboxylate functionality of nonsteroidal anti-inflammatory drugs with less acidic heterocyclic bioisosteres like 1,3,4-oxadiazole to protect the gastric mucosa from free carboxylate moieties. In view of these observations, we designed and synthesized a series of 3,5-disubstituted-1,3,4-oxadiazole derivatives as inhibitors of prostaglandin E2 (PGE2 ) and NO production with an improved activity profile. As initial screening, and to examine the anti-inflammatory activities of the compounds, the inhibitions of the productions of lipopolysaccharide-induced NO and PGE2 in RAW 264.7 macrophages were evaluated. The biological assays showed that, compared with indomethacin, compounds 5a, 5g, and 5h significantly inhibited NO production with 12.61 ± 1.16, 12.61 ± 1.16, and 18.95 ± 3.57 µM, respectively. Consequently, the three compounds were evaluated for their in vivo anti-inflammatory activities. Compounds 5a, 5g, and 5h showed a potent anti-inflammatory activity profile almost equivalent to indomethacin at the same dose in the carrageenan-induced paw edema test. Moreover, the treatment with 40 mg/kg of 5h produced significant anti-inflammatory activity data. Furthermore, docking studies were performed to reveal possible interactions with the inducible nitric oxide synthase enzyme. Docking results were able to rationalize the biological activity data of the studied inhibitors. In summary, our data suggest that compound 5h is identified as a promising candidate for further anti-inflammatory drug development with an extended safety profile.

Comparing benefits from many possible computed tomography lung cancer screening programs: extrapolating from the National Lung Screening Trial using comparative modeling.

BACKGROUND: The National Lung Screening Trial (NLST) demonstrated that in current and former smokers aged 55 to 74 years, with at least 30 pack-years of cigarette smoking history and who had quit smoking no more than 15 years ago, 3 annual computed tomography (CT) screens reduced lung cancer-specific mortality by 20% relative to 3 annual chest X-ray screens. We compared the benefits achievable with 576 lung cancer screening programs that varied CT screen number and frequency, ages of screening, and eligibility based on smoking. METHODS AND FINDINGS: We used five independent microsimulation models with lung cancer natural history parameters previously calibrated to the NLST to simulate life histories of the US cohort born in 1950 under all 576 programs. 'Efficient' (within model) programs prevented the greatest number of lung cancer deaths, compared to no screening, for a given number of CT screens. Among 120 'consensus efficient' (identified as efficient across models) programs, the average starting age was 55 years, the stopping age was 80 or 85 years, the average minimum pack-years was 27, and the maximum years since quitting was 20. Among consensus efficient programs, 11% to 40% of the cohort was screened, and 153 to 846 lung cancer deaths were averted per 100,000 people. In all models, annual screening based on age and smoking eligibility in NLST was not efficient; continuing screening to age 80 or 85 years was more efficient. CONCLUSIONS: Consensus results from five models identified a set of efficient screening programs that include annual CT lung cancer screening using criteria like NLST eligibility but extended to older ages. Guidelines for screening should also consider harms of screening and individual patient characteristics.

Comparative analysis of 5 lung cancer natural history and screening models that reproduce outcomes of the NLST and PLCO trials.

BACKGROUND: The National Lung Screening Trial (NLST) demonstrated that low-dose computed tomography screening is an effective way of reducing lung cancer (LC) mortality. However, optimal screening strategies have not been determined to date and it is uncertain whether lighter smokers than those examined in the NLST may also benefit from screening. To address these questions, it is necessary to first develop LC natural history models that can reproduce NLST outcomes and simulate screening programs at the population level. METHODS: Five independent LC screening models were developed using common inputs and calibration targets derived from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Imputation of missing information regarding smoking, histology, and stage of disease for a small percentage of individuals and diagnosed LCs in both trials was performed. Models were calibrated to LC incidence, mortality, or both outcomes simultaneously. RESULTS: Initially, all models were calibrated to the NLST and validated against PLCO. Models were found to validate well against individuals in PLCO who would have been eligible for the NLST. However, all models required further calibration to PLCO to adequately capture LC outcomes in PLCO never-smokers and light smokers. Final versions of all models produced incidence and mortality outcomes in the presence and absence of screening that were consistent with both trials. CONCLUSIONS: The authors developed 5 distinct LC screening simulation models based on the evidence in the NLST and PLCO. The results of their analyses demonstrated that the NLST and PLCO have produced consistent results. The resulting models can be important tools to generate additional evidence to determine the effectiveness of lung cancer screening strategies using low-dose computed tomography.