Recent advances in upstream process development for production of recombinant adeno-associated virus.

Recombinant adeno-associated virus (rAAV) is rapidly emerging as the preferred delivery vehicle for gene therapies, with promising advantages in safety and efficacy. Key challenges in systemic in-vivo rAAV gene therapy applications are the gap in production capabilities versus potential market demand and complex production process. This review summarizes current available information on rAAV upstream manufacturing processes and proposed optimizations for production. The advancements in rAAV production media were reviewed with proposals to speed up the cell culture process development. Furthermore, major methods for genetic element delivery to host cells were summarized with their advantages, limitations, and future directions for optimization. In addition, culture vessel selection criteria were listed based on production cell system, scale, and development stage. Process control at the production step was also outlined with an in-depth understanding of production kinetics and quality control.

Productivity improvement and charge variant modulation for intensified cell culture processes by adding a carboxypeptidase B (CpB) treatment step.

The goal of cell culture process intensification is to improve productivity while maintaining acceptable quality attributes. In this report, four processes, namely a conventional manufacturing Process A, and processes intensified by enriched N-1 seed (Process B), by perfusion N-1 seed (Process C), and by perfusion production (Process D) were developed for the production of a monoclonal antibody. The three intensified processes substantially improved productivity, however, the product either failed to meet the specification for charge variant species (main peak) for Process D or the production process required early harvest to meet the specification for charge variant species, Day 10 or Day 6 for Processes B and C, respectively. The lower main peak for the intensified processes was due to higher basic species resulting from higher C-terminal lysine. To resolve this product quality issue, we developed an enzyme treatment method by introducing carboxypeptidase B (CpB) to clip the C-terminal lysine, leading to significantly increased main peak and an acceptable and more homogenous product quality for all the intensified processes. Additionally, Processes B and C with CpB treatment extended bioreactor durations to Day 14 increasing titer by 38% and 108%, respectively. This simple yet effective enzyme treatment strategy could be applicable to other processes that have similar product quality issues.

Online monitoring and control of upstream cell culture process using 1D and 2D-LC with SegFlow interface.

The biopharmaceutical industry is transitioning from currently deployed batch-mode bioprocessing to a highly efficient and agile next-generation bioprocessing with the adaptation of continuous bioprocessing, which reduces capital investment and operational costs. Continuous bioprocessing, aligned with FDA's quality-by-design platform, is designed to develop robust processes to deliver safe and effective drugs. With the deployment of knowledge-based operations, product quality can be built into the process to achieve desired critical quality attributes (CQAs) with reduced variability. To facilitate next-generation continuous bioprocessing, it is essential to embrace a fundamental shift-in-paradigm from "quality-by-testing" to "quality-by-design," which requires the deployment of process analytical technologies (PAT). With the adaptation of PAT, a systematic approach of process and product understanding and timely process control are feasible. Deployment of PAT tools for real-time monitoring of CQAs and feedback control is critical for continuous bioprocessing. Given the current deficiency in PAT tools to support continuous bioprocessing, we have integrated Infinity 2D-LC with a post-flow-splitter in conjunction with the SegFlow autosampler to the bioreactors. With this integrated system, we have established a platform for online measurements of titer and CQAs of monoclonal antibodies as well as amino acid analysis of bioreactor cell culture.

A CFD model for predicting protein aggregation in low-pH virial inactivation for mAb production.

Significant amounts of soluble product aggregates were observed in the low-pH viral inactivation (VI) operation during an initial scale-up run for an immunoglobulin-G 4 (IgG4) monoclonal antibody (mAb IgG4-N1). Being earlier in development, a scale-down model did not exist, nor was it practical to use costly Protein A eluate (PAE) for testing the VI process at scale, thus, a computational fluid dynamics (CFD)-based high-molecular weight (HMW) prediction model was developed for troubleshooting and risk mitigation. It was previously reported that the IgG4-N1 molecules upon exposure to low pH tend to change into transient and partially unfolded monomers during VI acidification (i.e., VIA) and form aggregates after neutralization (i.e., VIN). Therefore, the CFD model reported here focuses on the VIA step. The model mimics the continuous addition of acid to PAE and tracks acid distribution during VIA. Based on the simulated low-pH zone (≤pH 3.3) profiles and PAE properties, the integrated low-pH zone (ILPZ) value was obtained to predict HMW level at the VI step. The simulations were performed to examine the operating parameters, such as agitation speed, acid addition rate, and protein concentration of PAE, of the pilot scale (50-200 L) runs. The conditions with predictions of no product aggregation risk were recommended to the real scale-up runs, resulted in 100% success rate of the consecutive 12 pilot-scale runs. This study demonstrated that the CFD-based HMW prediction model could be used as a tool to facilitate the scale up of the low-pH VI process directly from bench to pilot/production scale.

Technology outlook for real-time quality attribute and process parameter monitoring in biopharmaceutical development-A review.

Real-time monitoring of bioprocesses by the integration of analytics at critical unit operations is one of the paramount necessities for quality by design manufacturing and real-time release (RTR) of biopharmaceuticals. A well-defined process analytical technology (PAT) roadmap enables the monitoring of critical process parameters and quality attributes at appropriate unit operations to develop an analytical paradigm that is capable of providing real-time data. We believe a comprehensive PAT roadmap should entail not only integration of analytical tools into the bioprocess but also should address automated-data piping, analysis, aggregation, visualization, and smart utility of data for advanced-data analytics such as machine and deep learning for holistic process understanding. In this review, we discuss a broad spectrum of PAT technologies spanning from vibrational spectroscopy, multivariate data analysis, multiattribute chromatography, mass spectrometry, sensors, and automated-sampling technologies. We also provide insights, based on our experience in clinical and commercial manufacturing, into data automation, data visualization, and smart utility of data for advanced-analytics in PAT. This review is catered for a broad audience, including those new to the field to those well versed in applying these technologies. The article is also intended to give some insight into the strategies we have undertaken to implement PAT tools in biologics process development with the vision of realizing RTR testing in biomanufacturing and to meet regulatory expectations.

Metabolic understanding of disulfide reduction during monoclonal antibody production.

The disulfide reduction of intact monoclonal antibodies (mAbs) and subsequent formation of low molecular weight (LMW) species pose a direct risk to product stability, potency, and patient safety. Although enzymatic mechanisms of reduction are well established, an understanding of the cellular mechanisms during the bioreactor process leading to increased risk of disulfide reduction after harvest remains elusive. In this study, we examined bench, pilot, and manufacturing-scale batches of two mAbs expressed in Chinese hamster ovary (CHO) cells, where harvested cell culture fluid (HCCF) occasionally demonstrated disulfide reduction. Comparative proteomics highlighted a significant elevation in glyceraldehyde-3-phosphate dehydrogenase (GAPDH) levels in a highly reducing batch of HCCF, compared to a non-reducing batch. Analysis during production cell culture showed that increased GAPDH gene and protein expression correlated to disulfide reduction risk in HCCF in every case examined. Additionally, glucose 6-phosphate dehydrogenase (G6PD) activity and an increased (≥ 300%) lactate/pyruvate molar ratio (lac/pyr) during production cell culture correlated to disulfide reduction risk, suggesting a metabolic shift to the pentose phosphate pathway (PPP). In all, these results suggest that metabolic alterations during cell culture lead to changes in protein expression and enzyme activity that in turn increase the risk of disulfide reduction in HCCF. KEY POINTS: • Bioreactor conditions resulted in reduction susceptible harvest material. • GAPDH expression, G6PD activity, and lac/pyr ratio correlated with mAb reduction. • Demonstrated role for cell metabolic changes in post-harvest mAb reduction. Graphical abstract.

Industrial bioprocessing perspectives on managing therapeutic protein charge variant profiles.

Controlling the charge profile of therapeutic protein is a critical challenge in the current quality-by-design (QbD) paradigm, throughout all phases of biologics process development (PD): cell line development, upstream cell culture, recovery process, downstream purification, and analytical characterization. Charge variant profiles may influence efficacy and/or lead to unintended side-effects. Thus, maintaining a consistent charge profile is of tremendous importance, and increasingly, researchers have focused efforts toward developing strategies to mitigate variability during cell culture and to improve separation and detection of charge variants. Current understanding of factors affecting charge variant formation during manufacturing remains inadequate, and sometimes, even substantial commitment of resources may still not fully achieve the desired or consistent profiles. As such, this review attempts to provide a comprehensive resource for the biologics community by summarizing the impact of charge variants and CQA management, analytical methods for charge variant detection, as well as strategies in downstream and upstream PD for controlling charge variant profiles.

Hypoxia and transforming growth factor-beta1 pathway activation promote Chinese Hamster Ovary cell aggregation.

Suspension cultivation is the preferred mode of operation for the large-scale production of many biologics. Chinese Hamster Ovary (CHO) cells are anchorage-dependent in origin, but they have been widely adapted to suspension culture. In suspension culture, formation of CHO cell aggregates is a common phenomenon and compromises cell culture performance in multiple ways. To better understand the underlying mechanisms that regulate cell aggregation, we utilized CHO-specific transcriptome profiling as a screening tool and demonstrated that many genes encoding extracellular matrix (ECM) proteins were upregulated in the cultures with increased cell aggregation. Significantly, hypoxia was identified to be a cause for promoting CHO cell aggregation, and transforming growth factor beta1 (TGFß1) pathway activation served as an intermediate step mediating this biological cascade. These transcriptomics findings were confirmed by cell culture experiments, and it was further demonstrated that adding recombinant TGFß1 to the culture significantly increased ECM protein fibronectin expression and cell aggregation. The results of this study emphasize the importance of adequate mixing and oxygen supply for suspension cultures from a new angle, and regulating the TGFß1 pathway is proposed as a new strategy for mitigating cell aggregation to improve cell culture performance.

Vitamin B12 association with mAbs: Mechanism and potential mitigation strategies.

Process control for manufacturing biologics is critical for ensuring product quality, safety, and lot to lot consistency of therapeutic proteins. In this study, we investigated the root cause of the pink coloration observed for various in-process pools and drug substances in the antibody manufacturing process. Vitamin B12 is covalently bound to mAbs via a cobalt-sulfur coordinate bond via the cysteine residues. The vitamin B12 was identified to attach to an IgG4 molecule at cysteine residues on light chain (Cys-214), and heavy chain (Cys-134, Cys-321, Cys-367, and Cys-425). Prior to attachment to mAbs, the vitamin B12 needs to be in its active form of hydroxocobalamin. During culture media preparation, storage and cell culture processing, cyanocobalamin, the chemical form of vitamin B12 added to media, is converted to hydroxocobalamin by white fluorescence light (about 50% degradation in 11-14 days at room temperature and with room light intensity about 500-1,000 lux) and by short-wavelength visible light (400-550 nm). However, cyanocobalamin is stable under red light (wavelength >600 nm) exposure and does not convert to hydroxocobalamin. Our findings suggests that the intensity of pink color depends on concentrations of both free sulfhydryl groups on reduced mAb and hydroxocobalamin, the active form of vitamin B12 . Both reactants are necessary and neither one of them is sufficient to generate pink color, therefore process control strategy can consider limiting either one or both factors. A process control strategy to install red light (wavelength >600 nm) in culture media preparation, storage and culture processing areas is proposed to provide safe light for biologics and to prevent light-induced color variations in final products.

Early identification of unusually clustered mutations and root causes in therapeutic antibody development.

This study reports findings of an unusual cluster of mutations spanning 22 bp (base pairs) in a monoclonal antibody expression vector. It was identified by two orthogonal methods: mass spectrometry on expressed protein and next-generation sequencing (NGS) on the plasmid DNA. While the initial NGS analysis confirmed the designed sequence modification, intact mass analysis detected an additional mass of the antibody molecule expressed in CHO cells. The extra mass was eventually found to be associated with unmatched nucleotides in a distal region by checking full-length sequence alignment plots. Interestingly, the complementary sequence of the mutated sequence was a reverse sequence of the original sequence and flanked by two 10-bp reverse-complementary sequences, leading to an undesirable DNA recombination. The finding highlights the necessity of rigorous examination of expression vector design and early monitoring of molecule integrity at both DNA and protein levels to prevent clones from having sequence variants during cell line development.

Advances in analytical methodologies to guide bioprocess engineering for bio-therapeutics.

This study was performed to monitor the glycoform distribution of a recombinant antibody fusion protein expressed in CHO cells over the course of fed-batch bioreactor runs using high-throughput methods to accurately determine the glycosylation status of the cell culture and its product. Three different bioreactors running similar conditions were analysed at the same five time-points using the advanced methods described here. N-glycans from cell and secreted glycoproteins from CHO cells were analysed by HILIC-UPLC and MS, and the total glycosylation (both N- and O-linked glycans) secreted from the CHO cells were analysed by lectin microarrays. Cell glycoproteins contained mostly high mannose type N-linked glycans with some complex glycans; sialic acid was α-(2,3)-linked, galactose ß-(1,4)-linked, with core fucose. Glycans attached to secreted glycoproteins were mostly complex with sialic acid α-(2,3)-linked, galactose ß-(1,4)-linked, with mostly core fucose. There were no significant differences noted among the bioreactors in either the cell pellets or supernatants using the HILIC-UPLC method and only minor differences at the early time-points of days 1 and 3 by the lectin microarray method. In comparing different time-points, significant decreases in sialylation and branching with time were observed for glycans attached to both cell and secreted glycoproteins. Additionally, there was a significant decrease over time in high mannose type N-glycans from the cell glycoproteins. A combination of the complementary methods HILIC-UPLC and lectin microarrays could provide a powerful and rapid HTP profiling tool capable of yielding qualitative and quantitative data for a defined biopharmaceutical process, which would allow valuable near 'real-time' monitoring of the biopharmaceutical product.

A carbon dioxide stripping model for mammalian cell culture in manufacturing scale bioreactors.

Control of carbon dioxide within the optimum range is important in mammalian bioprocesses at the manufacturing scale in order to ensure robust cell growth, high protein yields, and consistent quality attributes. The majority of bioprocess development work is done in laboratory bioreactors, in which carbon dioxide levels are more easily controlled. Some challenges in carbon dioxide control can present themselves when cell culture processes are scaled up, because carbon dioxide accumulation is a common feature due to longer gas-residence time of mammalian cell culture in large scale bioreactors. A carbon dioxide stripping model can be used to better understand and optimize parameters that are critical to cell culture processes at the manufacturing scale. The prevailing carbon dioxide stripping models in literature depend on mass transfer coefficients and were applicable to cell culture processes with low cell density or at stationary/cell death phase. However, it was reported that gas bubbles are saturated with carbon dioxide before leaving the culture, which makes carbon dioxide stripping no longer depend on a mass transfer coefficient in the new generation cell culture processes characterized by longer exponential growth phase, higher peak viable cell densities, and higher specific production rate. Here, we present a new carbon dioxide stripping model for manufacturing scale bioreactors, which is independent of carbon dioxide mass transfer coefficient, but takes into account the gas-residence time and gas CO2 saturation time. The model was verified by CHO cell culture processes with different peak viable cell densities (7 to 12 × 106 cells mL-1 ) for two products in 5,000-L and 25,000-L bioreactors. The model was also applied to a next generation cell culture process to optimize cell culture conditions and reduce carbon dioxide levels at manufacturing scale. The model provides a useful tool to understand and better control cell culture carbon dioxide profiles for process development, scale up, and characterization. Biotechnol. Bioeng. 2017;114: 1184-1194. © 2016 Wiley Periodicals, Inc.

Ultrafiltration behavior of monoclonal antibodies and Fc-fusion proteins: Effects of physical properties.

Ultrafiltration (UF) is used for the final concentration and formulation of essentially all antibody-based therapeutics including both monoclonal antibodies (mAbs) and Fc-fusion proteins. The objective of this study was to quantitatively compare the filtrate flux behavior for two highly purified mAbs and an Fc-fusion protein under identical flow and buffer conditions. Filtrate flux data were obtained using a Pellicon 3 tangential flow filtration cassette over a wide range of transmembrane pressures and bulk protein concentrations. Independent experimental measurements were performed to evaluate the protein osmotic pressure and solution viscosity. The maximum achievable protein concentration was directly correlated with the solution viscosity, which controls the pressure drop and extent of back-filtration in the cassette. The filtrate flux data were analyzed using a recently developed model that accounts for the effects of intermolecular interactions and transmembrane pressure gradients on the extent of concentration polarization. These results provide important insights into the factors controlling the filtrate flux during the UF of concentrated protein solutions and an effective framework for the design/analysis of UF processes for the formulation of antibody-based therapeutics. Biotechnol. Bioeng. 2017;114: 2057-2065. © 2017 Wiley Periodicals, Inc.

The use of 'Omics technology to rationally improve industrial mammalian cell line performance.

Biologics represent an increasingly important class of therapeutics, with 7 of the 10 top selling drugs from 2013 being in this class. Furthermore, health authority approval of biologics in the immuno-oncology space is expected to transform treatment of patients with debilitating and deadly diseases. The growing importance of biologics in the healthcare field has also resulted in the recent approvals of several biosimilars. These recent developments, combined with pressure to provide treatments at lower costs to payers, are resulting in increasing need for the industry to quickly and efficiently develop high yielding, robust processes for the manufacture of biologics with the ability to control quality attributes within narrow distributions. Achieving this level of manufacturing efficiency and the ability to design processes capable of regulating growth, death and other cellular pathways through manipulation of media, feeding strategies, and other process parameters will undoubtedly be facilitated through systems biology tools generated in academic and public research communities. Here we discuss the intersection of systems biology, 'Omics technologies, and mammalian bioprocess sciences. Specifically, we address how these methods in conjunction with traditional monitoring techniques represent a unique opportunity to better characterize and understand host cell culture state, shift from an empirical to rational approach to process development and optimization of bioreactor cultivation processes. We summarize the following six key areas: (i) research applied to parental, non-recombinant cell lines; (ii) systems level datasets generated with recombinant cell lines; (iii) datasets linking phenotypic traits to relevant biomarkers; (iv) data depositories and bioinformatics tools; (v) in silico model development, and (vi) examples where these approaches have been used to rationally improve cellular processes. We critically assess relevant and state of the art research being conducted in academic, government and industrial laboratories. Furthermore, we apply our expertise in bioprocess to define a potential model for integration of these systems biology approaches into biologics development.

Clarification technologies for monoclonal antibody manufacturing processes: Current state and future perspectives.

Considerable progress has been made increasing productivity of cell cultures to meet the rapidly growing demand for antibody biopharmaceuticals through increased cell densities and longer culture times. This in turn has dramatically increased the burden of process and product related impurities on the purification processes. In addition, current trends in the biopharmaceutical industry point toward both increased productivity and targeting smaller patient populations for new indications. Taken together, these developments are driving the industry to explore alternative separation technologies as a future manufacturing strategy. Clarification technologies well established in other industries, such as flocculation and precipitation are increasingly considered as a viable solution to address this bottleneck in antibody processes. However, several technical issues need to be fully addressed including suitability as a platform application, robustness, process cost, toxicity, and clearance. This review will focus on recent efforts to incorporate new generation clarification technologies for mammalian cell cultures producing monoclonal antibodies as well as challenges to their implementation supported by a case study.

Development of robust antibody purification by optimizing protein-A chromatography in combination with precipitation methodologies.

To be administered to patients, therapeutic monoclonal antibodies must have very high purity, with process related impurities like host-cell proteins (HCPs) and DNA reduced to <100 ppm and <10 ppb, respectively, relative to desired product. Traditionally, Protein-A chromatography as a capture step has been the work horse for clearing a large proportion of these impurities. However, remaining levels of process and product related impurities still present significant challenges on the development of polishing steps further downstream. In this study, we have incorporated high throughput screening to evaluate three areas of separation: (i) Harvest treatment; (ii) Protein-A Chromatography; and (iii) Low pH Viral Inactivation. Precipitation with low pH treatment of cell culture harvest resulted in selective removal of impurities while manipulating the pH of wash buffers used in Protein-A chromatography and incorporating wash additives that disrupt various modes of protein-protein interaction resulted in further and more pronounced reduction in impurity levels. In addition, our study also demonstrate that optimizing the neutralization pH post Protein-A elution can result in selective removal of impurities. When applied over multiple mAbs, this optimization method proved to be very robust and the strategy provides a new and improved purification process that reduces process related impurities like HCPs and DNA to drug substance specifications with just one chromatography column and open avenues for significant decrease in operating costs in monoclonal antibody purification.

Fed-batch performance profiles for mAb production using different intensified N - 1 seed strategies are CHO cell-line dependent.

Recent optimizations of cell culture processes have focused on the final seed scale-up step (N - 1 stage) used to inoculate the production bioreactor (N-stage bioreactor) to enable higher inoculation cell densities (2-20 × 106 cells/mL), which could shorten the production culture duration and/or increase the volumetric productivity. N - 1 seed process intensification can be achieved by either non-perfusion (enriched-batch or fed-batch) or perfusion culture to reach those higher final N - 1 viable cell densities (VCD). In this study, we evaluated how different N - 1 intensification strategies, specifically enriched-batch (EB) N - 1 versus perfusion N - 1, affect cell growth profiles and monoclonal antibody (mAb) productivity in the final N-stage production bioreactor operated in fed-batch mode. Three representative Chinese Hamster Ovary (CHO) cell lines producing different mAbs were cultured using either EB or perfusion N - 1 seeds and found that the N-stage cell growth and mAb productivities were comparable between EB N - 1 and perfusion N - 1 conditions for two of the cell lines but were very different for the third. In addition, within the two similar cell growth cell lines, differences in cell-specific productivity were observed. This suggests that the impact of the N - 1 intensification process on production was cell-line dependent. This study revealed that the N - 1 intensification strategy and the state of seeds from the different N - 1 conditions may affect the outcome of the N production stage, and thus, the choice of N - 1 intensification strategy could be a new target for future upstream optimization of mAb production.

Validation of a CFD model for cell culture bioreactors at large scale and its application in scale-up.

Among all the operating parameters that control the cell culture environment inside bioreactors, appropriate mixing and aeration are crucial to ensure sufficient oxygen supply, homogeneous mixing, and CO2 stripping. A model-based manufacturing facility fit approach was applied to define agitation and bottom air flow rates during the process scale-up from laboratory to manufacturing, of which computational fluid dynamics (CFD) was the core modeling tool. The realizable k-ε turbulent dispersed Eulerian gas-liquid flow model was established and validated using experimental values for the volumetric oxygen transfer coefficient (kLa). Model validation defined the process operating parameter ranges for application of the model, identified mixing issues (e.g., impeller flooding, dissolved oxygen gradients, etc.) and the impact of antifoam on kLa. Using the CFD simulation results as inputs to the models for oxygen demand, gas entrance velocity, and CO2 stripping aided in the design of the agitation and bottom air flow rates needed to meet cellular oxygen demand, control CO2 levels, mitigate risks for cell damage due to shear, foaming, as well as fire hazards due to high O2 levels in the bioreactor gas outlet. The recommended operating conditions led to the completion of five manufacturing runs with a 100% success rate. This model-based approach achieved a seamless scale-up and reduced the required number of at-scale development batches, resulting in cost and time savings of a cell culture commercialization process.

Rapid total sialic acid monitoring during cell culture process using a machine learning model based soft sensor.

Total sialic acid content (TSA) in biotherapeutic proteins is often a critical quality attribute as it impacts the drug efficacy. Traditional wet chemical assays to quantify TSA in biotherapeutic proteins during cell culture typically takes several hours or longer due to the complexity of the assay which involves isolation of sialic acid from the protein of interest, followed by sample preparation and chromatographic based separation for analysis. Here, we developed a machine learning model-based technology to rapidly predict TSA during cell culture by using typically measured process parameters. The technology features a user interface, where the users only have to upload cell culture process parameters as input variables and TSA values are instantly displayed on a dashboard platform based on the model predictions. In this study, multiple machine learning algorithms were assessed on our dataset, with the Random Forest model being identified as the most promising model. Feature importance analysis from the Random Forest model revealed that attributes like viable cell density (VCD), glutamate, ammonium, phosphate, and basal medium type are critical for predictions. Notably, while the model demonstrated strong predictability by Day 14 of observation, challenges remain in forecasting TSA values at the edges of the calibration range. This research not only emphasizes the transformative power of machine learning and soft sensors in bioprocessing but also introduces a rapid and efficient tool for sialic acid prediction, signaling significant advancements in bioprocessing. Future endeavors may focus on data augmentation to further enhance model precision and exploration of process control capabilities.

An innovative hybrid modeling approach for simultaneous prediction of cell culture process dynamics and product quality.

The use of hybrid models is extensively described in the literature to predict the process evolution in cell cultures. These models combine mechanistic and machine learning methods, allowing the prediction of complex process behavior, in the presence of many process variables, without the need to collect a large amount of data. Hybrid models cannot be directly used to predict final product critical quality attributes, or CQAs, because they are usually measured only at the end of the process, and more mechanistic knowledge is needed for many classes of CQAs. The historical models can instead predict the CQAs better; however, they cannot directly relate manipulated process parameters to final CQAs, as they require knowledge of the process evolution. In this work, we propose an innovative modeling approach based on combining a hybrid propagation model with a historical data-driven model, that is, the combined hybrid model, for simultaneous prediction of full process dynamics and CQAs. The performance of the combined hybrid model was evaluated on an industrial dataset and compared to classical black-box models, which directly relate manipulated process parameters to CQAs. The proposed combined hybrid model outperforms the black-box model by 33% on average in predicting the CQAs while requiring only around half of the data for model training to match performance. Thus, in terms of model accuracy and experimental costs, the combined hybrid model in this study provides a promising platform for process optimization applications.