Considerations for the Terminal Sterilization of Oligonucleotide Drug Products.

A primary function of the parenteral drug product manufacturing process is to ensure sterility of the final product. The two most common methods for sterilizing parenteral drug products are terminal sterilization (TS), whereby the drug product is sterilized in the final container following filling and finish, and membrane sterilization, whereby the product stream is sterilized by membrane filtration and filled into presterilized containers in an aseptic processing environment. Although TS provides greater sterility assurance than membrane sterilization and aseptic processing, not all drug products are amenable to TS processes, which typically involve heat treatment or exposure to ionizing radiation. Oligonucleotides represent an emerging class of therapeutics with great potential for treating a broad range of indications, including previously undruggable targets. Owing to their size, structural complexity, and relative lack of governing regulations, several challenges in drug development are unique to oligonucleotides. This exceptionality justifies a focused assessment of traditional chemistry, manufacturing, and control strategies before their adoption. In this article, we review the current state of sterile oligonucleotide drug product processing, highlight the key aspects to consider when assessing options for product sterilization, and provide recommendations to aid in the successful evaluation and development of TS processes. We also explore current regulatory expectations and provide our interpretation as it pertains to oligonucleotide drug products.

Bagging with CTD--a novel signature for the hierarchical prediction of secreted protein trafficking in eukaryotes.

Protein trafficking or protein sorting in eukaryotes is a complicated process and is carried out based on the information contained in the protein. Many methods reported prediction of the subcellular location of proteins from sequence information. However, most of these prediction methods use a flat structure or parallel architecture to perform prediction. In this work, we introduce ensemble classifiers with features that are extracted directly from full length protein sequences to predict locations in the protein-sorting pathway hierarchically. Sequence driven features, sequence mapped features and sequence autocorrelation features were tested with ensemble learners and their performances were compared. When evaluated by independent data testing, ensemble based-bagging algorithms with sequence feature composition, transition and distribution (CTD) successfully classified two datasets with accuracies greater than 90%. We compared our results with similar published methods, and our method equally performed with the others at two levels in the secreted pathway. This study shows that the feature CTD extracted from protein sequences is effective in capturing biological features among compartments in secreted pathways.