The US Food and Drug Administration's 'quality by design' approach is likely to transform the manufacture of biologics.

Quality by design for biopharmaceuticals

Roadmap for implementation of quality by design (QbD) for biotechnology products

The “Pharmaceutical Current Good Manufacturing Practices (CGMPs) for the 21st Century—A Risk Based Approach” initiative announced by the FDA in August 2002 to improve and modernize pharmaceutical manufacturing facilitated adoption of process analytical technology (PAT) by the pharmaceutical industry. The potential for improved operational control and compliance resulting from continuous real-time quality assurance was highlighted as a likely benefit that would result from PAT implementation. A considerable amount of work has been done on this topic by academic and industrial contributors in the last decade. In this paper, we will start with a brief overview of evolution of PAT concepts and a review of their application in the wider pharmaceutical industry. The rest of the paper focuses on PAT applications for biotech processes with emphasis on developments in the last five years. It is our observation that while significant advances have been accomplished with regard to our ability to analyze/monitor key process and quality attributes in the biotech industry, much more needs to be done with regard to utilizing the collected data for subsequent control of the process, to achieve optimum yield and product quality. The latter is necessary to achieve the benefits that will result from PAT implementation.

Process analytical technology (PAT) for biopharmaceutical products.

Therapeutic antibodies: market considerations, disease targets and bioprocessing.

Implementing real-time product quality control meets one or both of the key goals outlined in FDA's PAT guidance: "variability is managed by the process" and "product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions." The first part of the paper presented an overview of PAT concepts and applications in the areas of upstream and downstream processing. In this second part, we present principles and case studies to illustrate implementation of PAT for drug product manufacturing, rapid microbiology, and chemometrics. We further present our thoughts on how PAT will be applied to biotech processes going forward. The role of PAT as an enabling component of the Quality by Design framework is highlighted. Integration of PAT with the principles stated in the ICH Q8, Q9, and Q10 guidance documents is also discussed.

Process analytical technology (PAT) for biopharmaceutical products: Part II. Concepts and applications.

This paper examines the feasibility of using multivariate data analysis (MVDA) for supporting some of the key activities that are required for successful manufacturing of biopharmaceutical products. These activities include scale-up, process comparability, process characterization, and fault diagnosis. Multivariate data analysis and modeling were performed using representative data from small-scale (2 L) and large-scale (2000 L) batches of a cell-culture process. Several input parameters (pCO2, pO2, glucose, pH, lactate, ammonium ions) and output parameters (purity, viable cell density, viability, osmolality) were evaluated in this analysis. Score plots, loadings plots, and VIP plots were utilized for assessing scale-up and comparability of the cell-culture process. Batch control charts were found to be useful for fault diagnosis during routine manufacturing. Finally, observations made from reviewing VIP plots were found to be in agreement with conclusions from process characterization studies demonstrating the effectiveness of MVDA as a tool for extracting process knowledge.

Application of Multivariate Analysis toward Biotech Processes: Case Study of a Cell‐Culture Unit Operation

Multivariate Data Analysis (MVDA) can be used for supporting key activities required for successful bioprocessing These activities include process characterization, process scale-up, process monitoring, fault diagnosis and root cause analysis This paper examines an application of MVDA towards root cause analysis for identifying scale-up differences and parameter interactions that adversely impact cell culture process performance Multivariate data analysis and modeling were performed using data from small-scale (2 L), pilot-scale (2,000 L) and commercial-scale (15,000 L) batches The input parameters examined included bioreactor pCO2, glucose, lactate, ammonium, raw materials and seed inocula The output parameters included product attributes, product titer, viable cell density, cell viability and osmolality Time course performance variables (daily, initial, peak and end point) were also evaluated Application of MVDA as a diagnostic tool was successful in identifying the root cause and designing experimental conditions to demonstrate and correct it Process parameters and their interactions that adversely impact cell culture performance and product attributes were successfully identified MVDA was successfully used as an effective tool for collating process knowledge and increasing process understanding

https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1021/bp0704384

Application of Multivariate Data Analysis for Identification and Successful Resolution of a Root Cause for a Bioprocessing Application

Control of raw materials based on an understanding of their impact on product attributes has been identified as a key aspect of developing a control strategy in the Quality by Design (QbD) paradigm. This article presents a case study involving use of a combined approach of Near-infrared (NIR) spectroscopy and Multivariate Data Analysis (MVDA) for screening of lots of basal medium powders based on their impact on process performance and product attributes. These lots had identical composition as per the supplier and were manufactured at different scales using an identical process. The NIR/MVDA analysis, combined with further investigation at the supplier site, concluded that grouping of medium components during the milling and blending process varied with the scale of production and media type. As a result, uniformity of blending, impurity levels, chemical compatibility, and/or heat sensitivity during the milling process for batches of large-scale media powder were deemed to be the source of variation as detected by NIR spectra. This variability in the raw materials was enough to cause unacceptably large variability in the performance of the cell culture step and impact the attributes of the resulting product. A combined NIR/MVDA approach made it possible to finger print the raw materials and distinguish between good and poor performing media lots.

Application of near-infrared (NIR) spectroscopy for screening of raw materials used in the cell culture medium for the production of a recombinant therapeutic protein.

Multivariate data analysis (MVDA) is quickly gaining popularity both in basic research and applied scientific fields as a statistical method of choice for examining variable interactions that were previously undefined. Multivariate data analysis by means of projection and regression methods overcomes challenges associated with applications such as multidimensionality of the data set, missing data, and variation introduced by disturbing factors such as experimental error and noise. This article presents how four of the major biotech companies, Amgen, Genentech, Wyeth Biotech, and Medlmmune, are using multivariate analysis to solve problems encountered in biotech processing.

Alime Ozlem Kirdar

Papers

Application of Multivariate Analysis toward Biotech Processes: Case Study of a Cell‐Culture Unit Operation

Application of Multivariate Data Analysis for Identification and Successful Resolution of a Root Cause for a Bioprocessing Application

Application of near-infrared (NIR) spectroscopy for screening of raw materials used in the cell culture medium for the production of a recombinant therapeutic protein.

Applications of multivariate data analysis in biotech processing