Aggregation challenges in the formulation development of multi-dose peptide products

The formulation development of parenteral peptide therapeutics frequently encounters aggregation challenges. In-depth biophysical understanding of the molecule and formulation are required to achieve formulation robustness. Further, unique considerations need to be given for peptide products that require multi-dose as the use of preservatives can promote aggregation while preservative effectiveness can also be impacted by its interaction with the peptide. This presentation will focus on the reversible and irreversible fibril aggregates in peptide formulations. Biophysical characterization of aggregation and formulation will be discussed in detail. Formation of reversible aggregates and the impact of excipients especially preservatives will be discussed. For the development of fibril-prone peptides, analytical challenges, formulation strategies, as well as predictive test for kinetics will also be discussed. In particular, studies on the temperature-dependent fibril nucleation kinetics and its impact on formulation development will be presented. Please click Additional Files below to see the full abstract

Intracellular Release of 17-β Estradiol from Cationic Polyamidoamine Dendrimer Surface-Modified Poly (Lactic-co-Glycolic Acid) Microparticles Improves Osteogenic Differentiation of Human Mesenchymal Stromal Cells

Human bone marrow mesenchymal stromal cells (MSCs) are considered a potential cell source for MSC-based bone regeneration, but improvements in the proliferation and differentiation capacity of MSCs are necessary for practical applications. Estrogen effectively improves MSC capabilities and has strong potential as a regulator of MSCs. The aim of this study was to develop a delivery system that provides intracellular release of estrogen and test its ability to improve osteogenic differentiation of MSCs. Biodegradable poly (lactic-co-glycolic acid) (PLGA) microparticles were developed that entrap 17-β estradiol (E2) and provide intracellular release of E2. The results show that we can prepare PLGA particles with efficient loading of E2 and maintain release of E2 up to 7 days. Surface modifying E2-loaded PLGA particles with cationic polyamidoamine dendrimers enabled increased uptake by human MSCs. Human MSC uptake of the E2-loaded PLGA particles significantly upregulates osteogenic differentiation markers of alkaline phosphatase and osteocalcin. In conclusion, cationic-modified PLGA particles can serve as a tool for intracellular delivery of estrogen to effectively execute estrogen regulation of MSCs. This approach has the potential to improve the osteogenic capabilities of MSCs and to develop appropriate environments of implantation for MSC-based bone tissue engineering

Learning relationships between chemical and physical stability for drug development

Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. For peptide products, it is common to measure physical stability via aggregation or fibrillation using the fluorescent reporter Thioflavin T. Executing stability studies is a lengthy process and requires extensive resources. To reduce the resources and shorten the process for stability studies during the development of a product, we introduce a machine learning based model for predicting the chemical stability over time using both the formulation conditions as well as the aggregation curve. In this work, we explore the relationships between the formulation, stability time point, and the measurements of chemical stability and achieve a coefficient of determination on a random test set of 0.945 and a mean absolute error (MAE) of 0.421 when using a multilayer perceptron (MLP) neural network for total degradation. Similarly, we achieve a coefficient of determination of 0.908 and an MAE of 1.435 when predicting the potency using a random forest model. When measurements of physical stability are included into the model, the MAE in the prediction of TD decreases to 0.148 for the MLP model. Using a similar strategy for the prediction of potency, the MAE decreases to 0.705 for the random forest model. Therefore, we can conclude two important points: first, chemical stability can be modeled using machine learning techniques and second there is a relationship between the physical stability of a peptide and its chemical stability