Pharmaceutical Regulatory Affairs: Open Access

The pathogenic fungus Candida albicans causes disseminated candidiasis with a poor prognosis in immunocompromised hosts. Secreted aspartyl protease (Sap) from the microorganism acts as a hydrolase to facilitate invasion into host tissues. Inhibition of Candida Sap activity could be a new treatment strategy for candidiasis. In the present study, we screened compounds from an FDA-approved drug library, Screen-Well, for their ability to inhibit Candida Sap activity. Sixteen compounds (piroxicam, carbidopa, nisoldipine, cerivastatin, fluvastatin, mycophenolic acid, rapamycin, bleomycin, bortezomib, 5-fluorouracil, floxuridine, fumagillin, pentamidine, albendazole, fenbendazole, and amprenavir) inhibited Sap activity in a dose-dependent manner in vitro, although strain differences in the activity of the compounds were observed. Our study shows that existing drug compounds have the potential to inhibit Sap activity.

The standard primary analysis in a clinical trial is the change from baseline analysis, failing to use all information and multiple measurements collected for each individual at various timepoints. Change from baseline analysis fails to observe the trend of the outcome, however, most decision-making in regulatory science is based on the single p-value from the change from baseline analysis. There are many possible longitudinal analysis models, utilizing repeated measurements, with the random effects model, otherwise known as the Laird-Ware model, being the most powerful and efficient model under certain assumptions. A semi-parametric approach is the Generalized Estimating Equation2. Although the longitudinal models may result in more appropriate p-values for decision-making, the complexity of the models can result in false results thus it is key to appropriately understand and apply the models.