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Published byArnold Hall Modified over 6 years ago
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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 Cosmetics Food suplements e-CTD Tools APIs (cep‘s&DMF) Regulatory Training Risk Analysis Project Management Quality Audits Quality Assurance systems Computer System Validation Equipment,Services, Process Validation Business Development Business Development Operational excellence 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.
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