New Advances in the Bioprocess Pipeline

Listicle 1 New Advances in the Bioprocess Pipeline Aron Gyorgypal, PhD The bioprocess landscape is an ever-advancing industry looking to optimize the manufacturing of biologics. Of key interest is addressing the challenges of high costs, time-consuming assays and processing complexities. These challenges are tackled with technological innovation meant to streamline and optimize the production pipeline. Here we delve into groundbreaking advancements throughout the bioprocess pipeline that are poised to advance bioprocessing. Figure 1: Bioprocess scheme showing the overall bioprocess scheme of the upstream and downstream unit operations, including early clone clean selection and the use of model and control along the product pipeline. Credit: Gyorgypal, A. doi: 10.7282/t3-1c02-k359. Clone selection Fed-batch reactor Medium/feed Harvest Product capture Filtration Product polishing Biologic Process model and control NEW ADVANCES IN THE BIOPROCESS PIPELINE 2 Listicle High-throughput upstream process screening technology Early biologic production is known to come with high costs as it takes time to evaluate an optimized process, including the medium, feed, reactor conditions and purification methodologies. A critical need is to speed up analytical times while scaling down cell culture systems, to help better assess cell lines for key attributes while decreasing the costs of goods manufactured. This will ultimately lead to accelerated production of innovators and biosimilars. Both liquid handlers and microfluidics coupled with analytical equipment such as high-performance liquid chromatography (HPLC) and mass spectrometry (MS) have been showcased as effective for achieving this goal. A successful cell line has high productivity. Cell clones that produce high titers allow for the production of drugs in a higher quantity but in less time. While new technologies enable the engineering of high titer cell clones, there is still a need to screen these clones quickly.1 The current paradigm for titer analysis depends on chromatographic approaches which, even with the newest ultra-high-performance liquid chromatography (UHPLC) models, will take minutes to analyze one cell line. However, a recent innovation by D’Amico et al. used microfluidics coupled with electrospray ionization MS to improve analysis time tremendously from 3 minutes to 25 seconds per sample.2 This is achieved using a multiplate Protein A-based cleaning coupled with microdroplet entrainment and subsequent analysis by native MS. The authors also imply that this technology can measure key product quality attributes. Unsurprisingly, microfluidics has recently gained prevalence as a technology to aid in high throughput automation, with both MS and HPLC techniques as an avenue towards process automation and away from the need of manual in-process testing.3 A common product attribute that needs to be monitored in biopharmaceutical manufacturing is protein aggregation, which is known to influence a product's safety and efficacy. During early-stage development, identifying aggregation in scale-down cell culture systems can be tedious, as the small sample volumes make aggregate detection challenging. Scientists at the University of Sheffield looked to overcome this hurdle by developing an automated small-scale analytical platform workflow, combining purification and aggregation analysis of protein biopharmaceuticals expressed in 96 deep well plate cell cultures.4 Their workflow utilized a liquid handler equipped with a protein-A PhyTip column for purification and a size-exclusion chromatography (SEC) column for aggregate monitoring. The data was then analyzed, and cultures were ranked by their aggregation levels. This high-throughput method, allowing up to 384 clones to be screened in 32 hours, enables high aggregation phenotypes to be eliminated from clone selection early on in cell line screening and can be used for both monoclonal as well as bispecific antibodies alike. Technology such as this one showcases the utility of liquid handlers for high-throughput automated screening. Downstream purification – New ligands for multimodal chromatography Downstream product processing accounts for over half of the production cost for monoclonal antibodies, and significant research is devoted to developing compact and affordable purification processes. Scientists are looking to innovate new chromatographic technologies using novel ligands capable of improved separation for higher-throughput operation. Within separation science, attention is put on multimodal ligands that combine multiple techniques, such as hydrophobic, hydrogen binding and electrostatic integrations, that work orthogonally or complementary to affinity or conventional hydrophobic and ion exchange chromatography.5 The use of multimodal chromatography effectively removes process-related impurities, such as host cell protein (HCP), DNA, aggregates and protein fragments. Scientists at the North Carolina State University developed a multimodal salt tolerant cation exchange (CEX) membrane, which is the first of its kind based on nonwoven fabrics to be published and used for product capture and polishing of biologics.6 NEW ADVANCES IN THE BIOPROCESS PIPELINE 3 Listicle This membrane was produced by coupling 2-mercaptopyridine-3-carboxylic acid (MPCA) to a polybutylene terephthalate (PBT) nonwoven fabric, modified by UV grafting of glycidyl methacrylate (polyGMA). The membrane achieved higher biologic binding and capacity versus commercial CEX multimodal resins and may be an effective alternative to current commercial resin columns. Future efforts by the team look to expand on the success of this technology to bring more purification applications, such as a low-cost Protein- A alternative and peptide-based resins for HCP depletion. Ultimately, technology such as this looks to disrupt the current downstream process purification paradigm to allow for low-cost, high-throughput purification of biologics. Establishing multi-attribute monitoring The complexity of biologics processing comes from the multitude of heterogenous post-translational modifications that can alter protein-based therapeutics' efficacy and safety. Using multi-attribute monitoring (MAM), a liquid chromatography-mass spectrometry (LC-MS)-based method, allows for direct characterization and monitoring of numerous product quality attributes at the amino acid level. Simultaneously monitoring multiple attributes on the peptide level makes MAM a more informative and streamlined approach than typical LC and MS methodologies. A recent publication by Millán-Martín et al. described a comprehensive MAM workflow for biologic characterization and current good manufacturing practices (cGMP).7 This work aims to standardize the protocols used for MAM to help with implementation and is the first protocol to establish the MAM methodology, which has not been available in the literature prior. The overview of the workflow explains the three major parts of setting up a MAM protocol: 1. Sample digest: A proteolytic digest of the protein to produce peptides. 2. High-resolution accurate MS (HRAM-MS) of the peptide: Discovery phase (phase 1) to enable confident identification of peaks to produce a peptide library for the biologic. Monitoring phase (phase 2), where full MS acquisition is carried out for the predefined product quality attributes (PQAs) and critical quality attributes (CQAs) based on the phase 1 peptide library. 3. Data processing and analysis: Data processing for phase 1 looks to evaluate the sequence coverage and assess the PQAs/CQAs present for the biologic. In phase 2, during targeted monitoring, the predicted PQAs and CQAs are monitored, and detection of new peaks are screened/investigated for the presence of either product or process-related impurities that may be present during bioprocessing. This MAM method relies on a bottom-up approach. Other technologies using MAM seek to do native MS to allow for PQA/CQA analysis on the intact protein level, which can give a different level of information. Work by Bhattacharya et al. explains the use of native-MS MAM to characterize antibody titer, size, charge and glycoform heterogeneity in cell culture supernatant.8 The workflow was achieved by 2D-LC-MS in which the first dimension coupled Protein A in series with size exclusion chromatography, and the second dimension was cation exchange chromatography. The glycosylation was then characterized with quadrupole time of flight mass spectrometry (qTOF-MS). Academic research has seen the quick adoption of the use of MAM workflows, which will help speed up workflows in industry applications once validated for use. Forecasting multistep-ahead profiles using data-driven models Process models and simulations are increasingly used to better understand, optimize and predict different aspects of a bioprocess. More dense datasets are now available for bioprocesses with new advancements in process analytical technology (PAT) and artificial intelligence (AI). By integrating AI-based data-driven models with an upstream bioprocess, the outcome can be forecasted with better prediction. NEW ADVANCES IN THE BIOPROCESS PIPELINE 4 Listicle Outputs, such as the cell viability, product titer and metabolite concentrations, such as glucose, can be predicted and better optimized for the process. These optimized models can also be implemented with a real-time digital twin. Recently, researchers at Sungkyunkwan University published a practical guideline for selecting optimal combinations of data-driven elements to predict Chinese hamster ovary cell culture’s process profile.9 This was achieved through a systematic framework for collectively evaluating various AI algorithms, forecasting strategies and model inputs. The utility of this framework was then demonstrated on an array of cell culture datasets under various conditions, selecting the most predictive algorithm for said condition. The choice of model elements that are considered will impact both computation load and accuracy of the model, the researchers found. The suitability of the model will depend on data availability, the complexity of the modeled culture conditions and model input variables. This framework should be used as a rational and practical guide on what should be considered to build the best forecasting data-driven model. Sponsored by References: 1. Tihanyi B, Nyitray L. Recent advances in CHO cell line development for recombinant protein production. Drug Discov Today Technol. 2020;38:25–34. doi: 10.1016/j.ddtec.2021.02.003 2. D’Amico CI, Robbins G, Po I, et al. Screening clones for monoclonal antibody production using droplet microfluidics interfaced to electrospray ionization mass spectrometry. J Am Soc Mass Spectrom. 2023;34(6):1117–1124. doi: 10.1021/ jasms.3c00055 3. Tiwold EK, Gyorgypal A, Chundawat SPS. Recent advances in biologic therapeutic N-glycan preparation techniques and analytical methods for facilitating biomanufacturing automation. J Pharm Sci. 2023;112(6):1485–1491. doi: 10.1016/j. xphs.2023.01.012 4. Lambiase G, Klottrup-Rees K, Lovelady C, et al. An automated, low volume, and high-throughput analytical platform for aggregate quantitation from cell culture media. J Chromatogr A. 2023;1691:463809. doi: 10.1016/j.chroma.2023.463809 5. Zhang L, Parasnavis S, Li Z, Chen J, Cramer S. Mechanistic modeling based process development for monoclonal antibody monomer-aggregate separations in multimodal cation exchange chromatography. J Chromatogr A. 2019;1602:317– 325. doi: 10.1016/j.chroma.2019.05.056 6. Fan J, Sripada SA, Pham DN, et al. Purification of a monoclonal antibody using a novel high-capacity multimodal cation exchange nonwoven membrane. Sep Purif Technol. 2023;317:123920. doi: 10.1016/j.seppur.2023.123920 7. Millán-Martín S, Jakes C, Carillo S, Rogers R, Ren D, Bones J. Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing. Nat Protoc. 2023;18(4):1056–1089. doi: 10.1038/s41596-022-00785-5 8. Bhattacharya S, Joshi S, Rathore AS. A native multi-dimensional monitoring workflow for at-line characterization of mAb titer, size, charge, and glycoform heterogeneities in cell culture supernatant. J Chromatogr A. 2023;1696:463983. doi: 10.1016/j.chroma.2023.463983 9. Park S-Y, Kim S-J, Park C-H, Kim J, Lee D-Y. Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins. Biotechnol Bioeng. 2023;120:2494–2508. doi: 10.1002/bit.28405