A Quadrupole Dalton-based multi-attribute method for product characterization,process development,and quality control of therapeutic proteins

During manufacturing and storage process, therapeutic proteins are subject to various post-translational modifications (PTMs), such as isomerization, deamidation, oxidation, disulfide bond modifications and glycosylation. Certain PTMs may affect bioactivity, stability or pharmacokinetics and pharmacodynamics profile and are therefore classified as potential critical quality attributes (pCQAs). Identifying, monitoring and controlling these PTMs are usually key elements of the Quality by Design (QbD) approach. Traditionally, multiple analytical methods are utilized for these purposes, which is time consuming and costly. In recent years, multi-attribute monitoring methods have been developed in the biopharmaceutical industry. However, these methods combine high-end mass spectrometry with complicated data analysis software, which could pose difficulty when implementing in a quality control (QC) environment. Here we report a multi-attribute method (MAM) using a Quadrupole Dalton (QDa) mass detector to selectively monitor and quantitate PTMs in a therapeutic monoclonal antibody. The result output from the QDa-based MAM is straightforward and automatic. Evaluation results indicate this method provides comparable results to the traditional assays. To ensure future application in the QC environment, this method was qualified according to the International Conference on Harmonization (ICH) guideline and applied in the characterization of drug substance and stability samples. The QDa-based MAM is shown to be an extremely useful tool for product and process characterization studies that facilitates facile understanding of process impact on multiple quality attributes, while being QC friendly and cost-effective.     

Quality attributes of recombinant therapeutic proteins: an assessment of impact on safety and efficacy as part of a quality by design development approach

Quality by Design (QbD) is a new approach to the development of recombinant therapeutic protein products that promotes a better understanding of the product and its manufacturing process. The first step in the QbD approach consists in identifying the critical quality attributes (CQA), i.e., those quality attributes of the product that have an impact on its clinical efficacy or safety. CQAs are identified through a science-based risk assessment taking into consideration a combination of clinical and nonclinical data obtained with the molecule or other similar molecules or platform products, as well as the published literature. The purpose of this article is to perform a comprehensive review of the published literature, supporting an assessment of the impact on safety and efficacy of the quality attributes commonly encountered in recombinant therapeutic proteins, more specifically those produced in mammalian cell expression systems. Quality attributes generally observed in biopharmaceutical proteins including product-related impurities and substances, process-related impurities, product attributes, and contaminants are evaluated one by one for their impact on biological activity, pharmacokinetics and pharmacodynamics, immunogenicity, and overall safety/toxicity.     

High-throughput process development for biopharmaceutical drug substances

Quality by Design (QbD) is gaining industry acceptance as an approach towards development and commercialization of biotechnology therapeutic products that are expressed via microbial or mammalian cell lines. In QbD, the process is designed and controlled to deliver specified quality attributes consistently. To acquire the enhanced understanding that is necessary to achieve the above, however, requires more extensive experimentation to establish the design space for the process and the product. With biotechnology companies operating under ever-increasing pressure towards lowering the cost of manufacturing, the use of high-throughput tools has emerged as a necessary enabler of QbD in a time- and resource-constrained environment. We review this topic for those in academia and industry that are engaged in drug substance process development.     

Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach

The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799–812, 2016     

Application of quality by design principles to the development and technology transfer of a major process improvement for the manufacture of a recombinant protein

This study describes the application of quality by design (QbD) principles to the development and implementation of a major manufacturing process improvement for a commercially distributed therapeutic protein produced in Chinese hamster ovary cell culture. The intent of this article is to focus on QbD concepts, and provide guidance and understanding on how the various components combine together to deliver a robust process in keeping with the principles of QbD. A fed-batch production culture and a virus inactivation step are described as representative examples of upstream and downstream unit operations that were characterized. A systematic approach incorporating QbD principles was applied to both unit operations, involving risk assessment of potential process failure points, small-scale model qualification, design and execution of experiments, definition of operating parameter ranges and process validation acceptance criteria followed by manufacturing-scale implementation and process validation. Statistical experimental designs were applied to the execution of process characterization studies evaluating the impact of operating parameters on product quality attributes and process performance parameters. Data from process characterization experiments were used to define the proven acceptable range and classification of operating parameters for each unit operation. Analysis of variance and Monte Carlo simulation methods were used to assess the appropriateness of process design spaces. Successful implementation and validation of the process in the manufacturing facility and the subsequent manufacture of hundreds of batches of this therapeutic protein verifies the approaches taken as a suitable model for the development, scale-up and operation of any biopharmaceutical manufacturing process.     

Quality by Design and Process Analytical Technology for Sterile Products—Where Are We Now?

Quality by design (QbD) and process analytical technology (PAT) have become priorities for the Center for Drug Evaluation and Research (CDER) at the Food and Drug Administration (FDA). Numerous recent initiatives within CDER and FDA have had the objective of encouraging the pharmaceutical industry to utilize QbD and PAT in their product development and manufacturing processes. Although sterile products may be a minority compared to non-sterile dosage forms (e.g., solid orals), their absolute requirement for sterility make design and control of the manufacturing processes extremely critical. This emphasis on the manufacturing process makes the sterile drug product an obvious target for QbD and PAT. Although the FDA encourages QbD submissions, the utilization of QbD and PAT for sterile products so far is still limited. This paper will examine the present state of QbD and PAT for sterile products and review some examples currently in use. Additional potential applications of QbD and PAT for sterile product development and manufacturing will also be discussed.     

Implementation of quality by design toward processing of food products

Quality by design (QbD) is a systematic approach that begins with predefined objectives and emphasizes product and process understanding and process control. It is an approach based on principles of sound science and quality risk management. As the food processing industry continues to embrace the idea of in-line, online, and/or at-line sensors and real-time characterization for process monitoring and control, the existing gaps with regard to our ability to monitor multiple parameters/variables associated with the manufacturing process will be alleviated over time. Investments made for development of tools and approaches that facilitate high-throughput analytical and process development, process analytical technology, design of experiments, risk analysis, knowledge management, and enhancement of process/product understanding would pave way for operational and economic benefits later in the commercialization process and across other product pipelines. This article aims to achieve two major objectives. First, to review the progress that has been made in the recent years on the topic of QbD implementation in processing of food products and second, present a case study that illustrates benefits of such QbD implementation.     

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.     

Towards the implementation of quality by design to the production of therapeutic monoclonal antibodies with desired glycosylation patterns

Quality by design (QbD) is a scheme for the development, manufacture, and approval of pharmaceutical products. The end goal of QbD is to ensure product quality by building it into the manufacturing process. The main regulatory bodies are encouraging its implementation to the manufacture of all new pharmaceuticals including biological products. Monoclonal antibodies (mAbs) are currently the leading products of the biopharmaceutical industry. It has been widely reported that glycosylation directly influences the therapeutic mechanisms by which mAbs function in vivo. In addition, glycosylation has been identified as one of the main sources of monoclonal antibody heterogeneity, and thus, a critical parameter to follow during mAb manufacture. This article reviews the research on glycosylation of mAbs over the past 2 decades under the QbD scope. The categories presented under this scope are: (a) definition of the desired clinical effects of mAbs, (b) definition of the glycosylation‐associated critical quality attributes (glycCQAs) of mAbs, (c) assessment of process parameters that pose a risk for mAb glycCQAs, and (d) methods for accurately quantifying glycCQAs of mAbs. The information available in all four areas leads us to conclude that implementation of QbD to the manufacture of mAbs with specific glycosylation patterns will be a reality in the near future. We also foresee that the implementation of QbD will lead to the development of more robust and efficient manufacturing processes and to a new generation of mAbs with increased clinical efficacy. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.     

Real‐time product attribute control to manufacture antibodies with defined N‐linked glycan levels

Pressures for cost‐effective new therapies and an increased emphasis on emerging markets require technological advancements and a flexible future manufacturing network for the production of biologic medicines. The safety and efficacy of a product is crucial, and consistent product quality is an essential feature of any therapeutic manufacturing process. The active control of product quality in a typical biologic process is challenging because of measurement lags and nonlinearities present in the system. The current study uses nonlinear model predictive control to maintain a critical product quality attribute at a predetermined value during pilot scale manufacturing operations. This approach to product quality control ensures a more consistent product for patients, enables greater manufacturing efficiency, and eliminates the need for extensive process characterization by providing direct measures of critical product quality attributes for real time release of drug product. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1433–1441, 2015     

Single‐step affinity purification of enzyme biotherapeutics: A platform methodology for accelerated process development

Downstream sample purification for quality attribute analysis is a significant bottleneck in process development for non‐antibody biologics. Multi‐step chromatography process train purifications are typically required prior to many critical analytical tests. This prerequisite leads to limited throughput, long lead times to obtain purified product, and significant resource requirements. In this work, immunoaffinity purification technology has been leveraged to achieve single‐step affinity purification of two different enzyme biotherapeutics (Fabrazyme® [agalsidase beta] and Enzyme 2) with polyclonal and monoclonal antibodies, respectively, as ligands. Target molecules were rapidly isolated from cell culture harvest in sufficient purity to enable analysis of critical quality attributes (CQAs). Most importantly, this is the first study that demonstrates the application of predictive analytics techniques to predict critical quality attributes of a commercial biologic. The data obtained using the affinity columns were used to generate appropriate models to predict quality attributes that would be obtained after traditional multi‐step purification trains. These models empower process development decision‐making with drug substance‐equivalent product quality information without generation of actual drug substance. Optimization was performed to ensure maximum target recovery and minimal target protein degradation. The methodologies developed for Fabrazyme were successfully reapplied for Enzyme 2, indicating platform opportunities. The impact of the technology is significant, including reductions in time and personnel requirements, rapid product purification, and substantially increased throughput. Applications are discussed, including upstream and downstream process development support to achieve the principles of Quality by Design (QbD) as well as integration with bioprocesses as a process analytical technology (PAT). © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:708–717, 2014     

In silico process characterization for biopharmaceutical development following the quality by design concept

With the quality by design (QbD) initiative, regulatory authorities demand a consistent drug quality originating from a well-understood manufacturing process. This study demonstrates the application of a previously published mechanistic chromatography model to the in silico process characterization (PCS) of a monoclonal antibody polishing step. The proposed modeling workflow covered the main tasks of traditional PCS studies following the QbD principles, including criticality assessment of 11 process parameters and establishment of their proven acceptable ranges of operation. Analyzing effects of multi-variate sampling of process parameters on the purification outcome allowed identification of the edge-of-failure. Experimental validation of in silico results demanded approximately 75% less experiments compared to a purely wet-lab based PCS study. Stochastic simulation, considering the measured variances of process parameters and loading material composition, was used to estimate the capability of the process to meet the acceptance criteria for critical quality attributes and key performance indicators. The proposed workflow enables the implementation of digital process twins as QbD tool for improved development of biopharmaceutical manufacturing processes.     

De-risking Pharmaceutical Tablet Manufacture Through Process Understanding,Latent Variable Modeling,and Optimization Technologies

In pharmaceutical tablet manufacturing processes, a major source of disturbance affecting drug product quality is the (lot-to-lot) variability of the incoming raw materials. A novel modeling and process optimization strategy that compensates for raw material variability is presented. The approach involves building partial least squares models that combine raw material attributes and tablet process parameters and relate these to final tablet attributes. The resulting models are used in an optimization framework to then find optimal process parameters which can satisfy all the desired requirements for the final tablet attributes, subject to the incoming raw material lots. In order to de-risk the potential (lot-to-lot) variability of raw materials on the drug product quality, the effect of raw material lot variability on the final tablet attributes was investigated using a raw material database containing a large number of lots. In this way, the raw material variability, optimal process parameter space and tablet attributes are correlated with each other and offer the opportunity of simulating a variety of changes in silico without actually performing experiments. The connectivity obtained between the three sources of variability (materials, parameters, attributes) can be considered a design space consistent with Quality by Design principles, which is defined by the ICH-Q8 guidance (USDA 2006). The effectiveness of the methodologies is illustrated through a common industrial tablet manufacturing case study.     

Integrating Artificial and Human Intelligence into Tablet Production Process

We developed a new machine learning-based method in order to facilitate the manufacturing processes of pharmaceutical products, such as tablets, in accordance with the Process Analytical Technology (PAT) and Quality by Design (QbD) initiatives. Our approach combines the data, available from prior production runs, with machine learning algorithms that are assisted by a human operator with expert knowledge of the production process. The process parameters encompass those that relate to the attributes of the precursor raw materials and those that relate to the manufacturing process itself. During manufacturing, our method allows production operator to inspect the impacts of various settings of process parameters within their proven acceptable range with the purpose of choosing the most promising values in advance of the actual batch manufacture. The interaction between the human operator and the artificial intelligence system provides improved performance and quality. We successfully implemented the method on data provided by a pharmaceutical company for a particular product, a tablet, under development. We tested the accuracy of the method in comparison with some other machine learning approaches. The method is especially suitable for analyzing manufacturing processes characterized by a limited amount of data.KEY WORDS: artificial intelligence, machine learning, process analytical technology, process optimization, tablet manufacture     

Quantitation and pharmacokinetic modeling of therapeutic antibody quality attributes in human studies

A thorough understanding of drug metabolism and disposition can aid in the assessment of efficacy and safety. However, analytical methods used in pharmacokinetics (PK) studies of protein therapeutics are usually based on ELISA, and therefore can provide a limited perspective on the quality of the drug in concentration measurements. Individual post-translational modifications (PTMs) of protein therapeutics are rarely considered for PK analysis, partly because it is technically difficult to recover and quantify individual protein variants from biological fluids. Meanwhile, PTMs may be directly linked to variations in drug efficacy and safety, and therefore understanding of clearance and metabolism of biopharmaceutical protein variants during clinical studies is an important consideration. To address such challenges, we developed an affinity-purification procedure followed by peptide mapping with mass spectrometric detection, which can profile multiple quality attributes of therapeutic antibodies recovered from patient sera. The obtained data enable quantitative modeling, which allows for simulation of the PK of different individual PTMs or attribute levels in vivo and thus facilitate the assessment of quality attributes impact in vivo. Such information can contribute to the product quality attribute risk assessment during manufacturing process development and inform appropriate process control strategy.     

Characterization of a Monoclonal Antibody Cell Culture Production Process Using a Quality by Design Approach

The goal of quality by design (QbD) in cell culture manufacturing is to develop manufacturing processes which deliver products with consistent critical quality attributes (CQAs). QbD approaches can lead to better process understanding through the use of process parameter risk ranking and statistical design of experiments (DOE). The QbD process starts with an analysis of process parameter risk with respect to CQAs and key performance indicators (KPIs). Initial DOE study designs and their factor test ranges are based on the outcomes of the process parameter risk ranking exercises. Initial DOE studies screen factors for significant influences on CQAs as well as characterize responses for process KPIs. In the case study provided here, multifactor process characterization studies using a scale-down model resulted in significant variation in charge heterogeneity of a monoclonal antibody (MAb) as measured by ion-exchange chromatography (IEC). Iterative DOE studies, using both screening and response surface designs, were used to narrow the operating parameter ranges so that charge heterogeneity could be controlled to an acceptable level. The data from the DOE studies were used to predict worst-case conditions, which were then verified by testing at those conditions. Using the approach described here, multivariate process parameter ranges were identified that yield acceptable CQA levels and that still provide operational flexibility for manufacturing.     

Application of Quality by Design to the Process Development of Botanical Drug Products: A Case Study

This paper was designed to assess the value of quality by design (QbD) to improve the manufacturing process understanding of botanical drug products. Ethanol precipitation, a widely used unit operation in the manufacture of botanical drug products was employed to illustrate the use of QbD, taking the process of danshen (the dry root of Salvia miltiorrhiza Bunge) as an example. The recovery of four active pharmaceutical ingredients (APIs) and the removal of saccharides were used to represent the performance of ethanol precipitation. Potentially critical variables, including density of concentrate, ethanol consumption, and settling temperature were identified through risk assessment methods. Design of experiments (DOE) was used to evaluate the effects of the potentially critical factors on the performance of ethanol precipitation. It was observed that higher density of concentrate leads to higher removal of saccharides, but results in lower recovery of APIs. With the rise of ethanol consumption, the recovery of different APIs behaves in different ways. A potential design space of ethanol precipitation operation was established through DOE studies. The results in this work facilitate the enhanced understanding of the relationships between multiple factors (material attributes and process parameters) and the performance of ethanol precipitation. This case study demonstrated that QbD is a powerful tool to develop manufacturing process of botanical drug products.     

Using enhanced development tools offered by analytical Quality by Design to support switching of a quality control method

Quality by Design (QbD) principles play an increasingly important role in the pharmaceutical industry. Here, we used an analytical QbD (AQbD) approach to develop a capillary electrophoresis sodium dodecyl sulfate under reducing conditions (rCE-SDS), with the aim of replacing SDS-polyacrylamide gel electrophoresis (SDS-PAGE) as release and stability test method for a commercialized monoclonal antibody product. Method development started with defining analytical method performance requirements as part of an analytical target profile, followed by a systematic risk assessment of method input parameters and their relation to defined method outputs. Based on this, design of experiments studies were performed to identify a method operable design region (MODR). The MODR could be leveraged to improve method robustness. In a bridging study, it was demonstrated that the rCE-SDS method is more sensitive than the legacy SDS-PAGE method, and a conversion factor could be established to compensate for an off-set due to the higher sensitivity, without losing the correlation to the historical data acquired with the former method. Overall, systematic application of analytical Quality by Design principles for designing and developing a new analytical method helped to elucidate the complex dependency of method outputs on its input parameters. The link of the method to product quality attributes and the definition of method performance requirements were found to be most relevant for derisking the analytical method switch, regarding impact on the control strategy.