Implementation of chemometrics, design of experiments, and neural network analysis for prior process knowledge assessment, failure modes and effect analysis, scale‐down model development, and process characterization for a chromatographic purification of Teriparatide. (7th April 2022)

• Record Type: Journal Article
• Authors: Pathak, Mili; Pokhriyal, Prashant; Gandhi, Irshad; Khambhampaty, Sridevi
• Is Part Of: Biotechnology progress. Volume 38:Number 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-07
• Subjects: chemometrics -- continuous process verification -- neural network analysis -- prior process knowledge assessment -- process characterization; Biotechnology -- Periodicals; Food industry and trade -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/btpr.3252
• Languages: English
• ISSNs: 8756-7938

Abstract

Abstract: Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modeling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach toward implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behavior of a mixed‐mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e., potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non‐ key process parameters, set points, operating, process acceptance and characterized ranges. The scale‐down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built‐in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks.