Purpose: During biopharmaceutical drug manufacturing, storage, and distribution, proteins in both liquid and solid dosage forms go through various processes that could lead to protein aggregation. The extent of aggregation in the sub-micron range can be measured by analyzing a liquid or post-reconstituted powder sample using Micro-Flow Imaging (MFI) technique. MFI is widely used in biopharmaceutical industries due to its high sensitivity in detecting and analyzing particle size distribution. However, the MFI's sensitivity to various factors makes accurate measurement challenging. Therefore, in light of the inherent variability of the method, this work aims to explore the capabilities of an adopted coupled sensitivity analysis and machine learning algorithm to quantify the influencing factors on the formed sub-visible particles and method variability.
Methods: The proposed algorithm consists of two interconnected components, namely a surrogate model with a neural network and a sensitivity analyzer. A machine learning tool based on artificial neural networks (ANN) is constructed with MFI data. The best fit with an optimized configuration is found. Sensitivity and uncertainty analysis is performed using this network as the surrogate model to understand the impacts of input parameters on MFI data.
Results: Results reveal the most impactful reconstitution preparation factors and others that are masked by the instrument variabilities. It is shown that instrument inaccuracy is a function of size category, with higher variabilities associated with larger size ranges.
Conclusion: Utilizing this tool while assessing the sensitivity of outputs to various parameters, measurement variabilities for analytical characterization tests can be quantified.
Keywords: artificial neural network; machine learning; measurement variability; micro-flow imaging; protein formulation; sensitivity analysis; sub-visible particle.
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.