An Overview of Drug Substance Manufacturing Processes.

To develop a comprehensive understanding of pharmaceutical drug substance manufacturing (DSM) processes, we conducted a data mining study to examine 50 new drug applications (NDAs) approved in 2010-2016. We analyzed the prevalence of several frequently deployed in-process control (IPC) techniques and postreaction workup procedures, as well as the operational conditions specified for reactions and workups. Our findings show that crystallization and high-performance liquid chromatography (HPLC) were the most commonly used workup steps and in-process controls, respectively, in drug substance manufacturing. On average, each NDA implemented 12.6 in-process controls and 11.3 workups. Operation time for reactions and workup procedures varied from a few minutes to multiple days, though 61% of these were between 1 and 10 h.

Quantifying stability in gene list ranking across microarray derived clinical biomarkers.

BACKGROUND: Identifying stable gene lists for diagnosis, prognosis prediction, and treatment guidance of tumors remains a major challenge in cancer research. Microarrays measuring differential gene expression are widely used and should be versatile predictors of disease and other phenotypic data. However, gene expression profile studies and predictive biomarkers are often of low power, requiring numerous samples for a sound statistic, or vary between studies. Given the inconsistency of results across similar studies, methods that identify robust biomarkers from microarray data are needed to relay true biological information. Here we present a method to demonstrate that gene list stability and predictive power depends not only on the size of studies, but also on the clinical phenotype. RESULTS: Our method projects genomic tumor expression data to a lower dimensional space representing the main variation in the data. Some information regarding the phenotype resides in this low dimensional space, while some information resides in the residuum. We then introduce an information ratio (IR) as a metric defined by the partition between projected and residual space. Upon grouping phenotypes such as tumor tissue, histological grades, relapse, or aging, we show that higher IR values correlated with phenotypes that yield less robust biomarkers whereas lower IR values showed higher transferability across studies. Our results indicate that the IR is correlated with predictive accuracy. When tested across different published datasets, the IR can identify information-rich data characterizing clinical phenotypes and stable biomarkers. CONCLUSIONS: The IR presents a quantitative metric to estimate the information content of gene expression data with respect to particular phenotypes.