Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

Effects of fenofibrate on cardiovascular events in patients with diabetes, with and without prior cardiovascular disease: The Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study.

BACKGROUND: In the FIELD study, comparison of the effect of fenofibrate on cardiovascular disease (CVD) between those with prior CVD and without was a prespecified subgroup analysis. METHODS: The effects of fenofibrate on total CVD events and its components in patients who did (n = 2,131) and did not (n = 7,664) have a history of CVD were computed by Cox proportional hazards modeling and compared by testing for treatment-by-subgroup interaction. The analyses were adjusted for commencement of statins, use of other CVD medications, and baseline covariates. Effects on other CVD end points were explored. RESULTS: Patients with prior CVD were more likely than those without to be male, to be older (by 3.3 years), to have had a history of diabetes for 2 years longer at baseline, and to have diabetic complications, hypertension, and higher rates of use of insulin and CVD medications. Discontinuation of fenofibrate was similar between the subgroups, but more patients with prior CVD than without, and also more placebo than fenofibrate-assigned patients, commenced statin therapy. The borderline difference in the effects of fenofibrate between those who did (hazard ratio [HR] 1.02, 95% CI 0.86-1.20) and did not have prior CVD (HR 0.81, 95% CI 0.70-0.94; heterogeneity P = .045) became nonsignificant after adjustment for baseline covariates and other CVD medications (HR 0.96, 95% CI 0.81-1.14 vs HR 0.78, 95% CI 0.67-0.90) (heterogeneity P = .06). CONCLUSIONS: Our findings do not support treating patients with fenofibrate differently based on any history of CVD, in line with evidence from other trials

Improving the prediction of the brain disposition for orally administered drugs using BDDCS

In modeling blood–brain barrier (BBB) passage, in silico models have yielded ~80% prediction accuracy, and are currently used in early drug discovery. Being derived from molecular structural information only, these models do not take into account the biological factors responsible for the in vivo outcome. Passive permeability and P-glycoprotein (Pgp, ABCB1) efflux have been successfully recognized to impact xenobiotic extrusion from the brain, as Pgp is known to play a role in limiting the BBB penetration of oral drugs in humans. However, these two properties alone fail to explain the BBB penetration for a significant number of marketed central nervous system (CNS) agents. The Biopharmaceutics Drug Disposition Classification System (BDDCS) has proved useful in predicting drug disposition in the human body, particularly in the liver and intestine. Here we discuss the value of using BDDCS to improve BBB predictions of oral drugs. BDDCS class membership was integrated with in vitro Pgp efflux and in silico permeability data to create a simple 3-step classification tree that accurately predicted CNS disposition for more than 90% of 153 drugs in our data set. About 98% of BDDCS class 1 drugs were found to markedly distribute throughout the brain; this includes a number of BDDCS class 1 drugs shown to be Pgp substrates. This new perspective provides a further interpretation of how Pgp influences the sedative effects of H1-histamine receptor antagonists