Juvenile neuronal ceroid lipofuscinosis (CLN3) is a rare neurodegenerative disorder lacking effective therapies. This study aimed at developing a drug repurposing approach to identify potential therapeutic candidates for CLN3 using its protein expression profile (CPEP) constructed from proteomics data. Differentially expressed proteins were identified and applied to query the iLINCS database, resulting in 60 FDA-approved drugs with reversal effects on CPEP. These candidates were further prioritized based on regulation strength, coverage, and blood-brain barrier permeability. Top candidates include Vorinostat and Cyclosporine, which have shown promise due to their significant regulation scores and blood-brain barrier permeation probability. These results provide opportunities for further investigation on novel therapies for CLN3.

The purpose of drug repurposing is to identify alternative uses of FDA approved drugs, which significantly accelerates the drug development process. Meanwhile, clinical data illustrate the patterns and clinical outcomes of drug use, so they have been increasingly applied to support drug development, particularly for drug repurposing. The NIH Biomedical Translational Research Information System (BTRIS) is a resource which compiles deidentified patient data from clinical research done across NIH Institutes and Centers. In this study, we analyzed clinical data available from BTRIS to identify drug repurposing candidates, i.e., identifying drugs that were correlated with an increased survival rate for glioblastoma (GBM) patients. Specifically, we extracted all the administered drugs on GBM patients and fitted them to elastic-net penalized Cox proportional hazards (CPH) models, a regression model for investigating the association between the survival rate of patients and covariates (administered drugs in this study). We were able to identify several potential drug candidates for GBM to be further evaluated with other data types and by performing biological experiments.

MOTIVATION: High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. RESULTS: In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. AVAILABILITY AND IMPLEMENTATION: The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.