Sensitivity and Uncertainty Analysis of Micro-Flow Imaging for Sub-Visible Particle Measurements Using Artificial Neural Network

• Published in: Pharmaceutical research Vol. 40; no. 3; pp. 721 - 733
• Main Authors: Poozesh, Sadegh, Cannavò, Flavio, Manikwar, Prakash
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2023; Springer; Springer Nature B.V
• Subjects: Algorithms; Biochemistry; Biological Products; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biopharmaceuticals; Data mining; Diagnostic Imaging; Learning algorithms; Machine learning; Measurement; Measuring instruments; Medical Law; Neural networks; Neural Networks, Computer; Original Research Article; Particle Size; Pharmaceutical industry; Pharmacology/Toxicology; Pharmacy; Protein interaction; Proteins; Sensitivity analysis; Size distribution; Uncertainty; measurement variability; protein formulation; sub-visible particle; artificial neural network; sensitivity analysis; machine learning; micro-flow imaging
• ISSN: 0724-8741; 1573-904X
• DOI: 10.1007/s11095-023-03474-4

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.