1. Novel approaches for parenteral protein formulations
Lisa Uhlmann1,2, Ulrich Rössl1,2, Bernd Nidetzsky2, Andreas Zimmer3, Stefan Leitgeb1
1 Research Center Pharmaceutical Engineering GmbH, Graz, Austria, 2 Institute of Biotechnology and Biochemical Engineering, Graz University of Technology, Graz, Austria, 3 Institute of Pharmaceutical Sciences, Department of Pharmaceutical Technology, Karl-Franzens-University, Graz, Austria -
Introduction
Finding the optimal protein formulation condition is still dependent on a large amount of experiments. In this study we focused - in the context on QbD – on rational formulation development in medium-to-high-throughput to decrease time to market by increasing product understanding. Accelerated stability studies were employed to test the integrity of IgG1 mAb under harsh conditions for a short period of time with the aim to use the results for qualitative real-time stability prediction. Therefore different kinds of stress - freeze-thaw cycles, thermal stress and agitation - were employed whereby different optimal formulations exist for the different stressing conditions. Differential scanning fluoremetry (DSF) and Micro-Flow Imaging (MFI) were used for initial screening experiments and to track changes in melting temperature, particle size and concentration of pre-stressed and native formulations. We developed a weighted desirability function that allows the integration of several optimised design spaces targeting at a distinct stress condition into a global optimum.
Quality by Design (QbD)
Design of Experiments (DoE)
GLP / GCP / QbD / PAT
EMA / FDA / ROW
…is applied to gain maximum output of a minimal amount of experiments
allows the use of various experimental designs for the identification not only of the influence of single parameters but also of interactions
Software for DoE and optimization MODDE (Umetrics)
Software tool that helps with the DoE setup, analysis of results, optimization and presentation of results and possible solutions
Design and handling of multiple variable factor types (qualitative, quantitative)
Generation of design space
Data management, analysis and interpretation
Pharmaceutical Development Q8 & Q11
Preclinical & clinical advice
Quality systems
Development
laboratory
Medical&Biotech Products
Medical Devices
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Food suplements
e-CTD Tools
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Process Reengineering QbD
Industrial
LEAN / SIX-SIGMA / QbD / ICENCING IN&OUT
GMP / ISO22716/
ISO13485 / ICH Q9 / ICH Q10
Formulation development of IgG1 mAb
Basic Formulation Parameters
Stress conditions
High-throughput methods
Model Protein: IgG1 mAb
Surfactants: Polysorbate 80
Polysorbate 20
Cremophor EL
Pluronic F68 Polyoxyethanyl-α- tocopheryl sebacate
Excipients: NaCl
Sorbitol
Buffer: Citrate-Phosphate Buffer
Differential Scanning Fluorometry (DSF)
Temperature stress
Agitation stress
Freeze / Thaw stress
Set up Screening plan using MODDE
Evaluation using MODDE
Set up optimization of each stressing condition using MODDE
Micro-Flow Imaging (MFI)
Optimal formulation for IgG1 mAb
[°C]
Bundle the outcome of the different independent stress experiments in one multivariate model
[Particle/mL]
Conclusion
The data generated, based on the Design of Experiments, were the basis for a multivariate data analysis. Based of the results of the initial screening experiments the conditions were refined using an optimization DoE and integrated in the initial model. The expected output was then a generic formulation optimum for the target protein.