Development Of Therapeutic Proteins Research Articles

Innovative Formulation Platform: Paving the Way for Superior Protein Therapeutics with Enhanced Efficacy and Broadened Applications.

Protein therapeutics offer high therapeutic potency and specificity; the broader adoptions and development of protein therapeutics, however, have been constricted by their intrinsic limitations such as inadequate stability, immunogenicity, suboptimal pharmacokinetics and biodistribution, and off-target effects. This review describes a platform technology that formulates individual protein molecules with a thin formulation layer of crosslinked polymers, which confers the protein therapeutics with high activity, enhanced stability, controlled release capability, reduced immunogenicity, improved pharmacokinetics and biodistribution, and ability to cross the blood brain barriers. Based on currently approved protein therapeutics, this formulating platform affords the development of a vast family of superior protein therapeutics with improved efficacy and broadened indications at significantly reduced cost.

Higher Order Structure Differences Among Insulin Crystalline Drugs Revealed by 2D heteronuclear NMR.

During therapeutic protein development, two-dimensional (2D) heteronuclear NMR spectra can be a powerful analytical method for measuring protein higher order structure (HOS) in solution since the spectra exhibit much higher resolution than homonuclear 1H spectra. However, 2D NMR capabilities for characterizing protein HOS in crystalline states remain to be assessed, given the low 13C natural abundance and intrinsically broader lines in solid-state NMR (SSNMR). Herein, high-resolution heteronuclear correlation (HETCOR) SSNMR was utilized to directly measure intact crystal drug products of insulin human, insulin analogs of insulin lispro and insulin aspart. The fingerprint regions in 2D 1H-13C HETCOR spectra were identified, which distinguished the insulin crystals in their primary structure, HOS heterogeneity and dynamics, as well as the manufacturing processes. The HOS heterogeneity in insulin analogs is consistent with their therapeutic effect of rapid action; while insulin human crystals showed more structural homogeneity, consistent with their slower pharmacokinetics (PK)peak time than insulin analogs. Therefore, heteronuclear NMR could be broadly applicable to study protein drug dosage forms from liquid to solid, yielding improved molecular level structure data for assessing drug HOS in biosimilar drug development.

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment.

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning.

Therapeutic antibodies such as monoclonal antibodies (mAbs), bispecific and multispecific antibodies are pivotal in therapeutic protein development and have transformed disease treatments across various therapeutic areas. The integrity of therapeutic antibodies, however, is compromised by sequence liabilities, notably deamidation, where asparagine (N) and glutamine (Q) residues undergo chemical degradations. Deamidation negatively impacts the efficacy, stability, and safety of diverse classes of antibodies, thus necessitating the critical need for the early and accurate identification of vulnerable sites. In this article, a comprehensive antibody deamidation-specific dataset (n = 2285) of varied modalities was created by using high-throughput automated peptide mapping followed by supervised machine learning to predict the deamidation propensities, as well as the extents, throughout the entire antibody sequences. We propose a novel chimeric deep learning model, integrating protein language model (pLM)-derived embeddings with local sequence information for enhanced deamidation predictions. Remarkably, this model requires only sequence inputs, eliminating the need for laborious feature engineering. Our approach demonstrates state-of-the-art performance, offering a streamlined workflow for high-throughput automated peptide mapping and deamidation prediction, with the potential of broader applicability to other antibody sequence liabilities.

Versatile and efficient protein association through chemically modified Sphingomyelin Nanosystems (SNs) for enhanced delivery.

Proteins are biological macromolecules well known to regulate many cellular signaling mechanisms. For instance, they are very appealing for their application as therapeutic agents, presenting high specificity and activity. Nonetheless, they suffer from unfolding, instability and low bioavailability making their administration through systemic and other routes very tough. To overcome these drawbacks, drug delivery systems and nanotechnology have arisen to deliver biomolecules in a sustained manner while, at the same time, increasing dose availability, protecting the cargo without compromising proteins' bioactivity, and enhancing intracellular delivery. In this work, we proposed the optimization of sphingomyelin nanosystems (SNs) for the delivery of a wide collection of proteins (ranging from 10-500 kDa and pI) using diverse chemical association strategies. We have further characterized SNs by varied analytical methodologies. We have also carried out in vitro experiments to validate the potential of the developed formulations. As the final goal, we aim to obtain evidence of the potential use of SNs for the development of protein therapeutics.

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.

The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success and ensure robust drug supply beyond approval, it is essential to assess the developability profile of new potential drug candidates as early and broadly as possible in development (Jain et al. MAbs, 2023. https://doi.org/10.1016/j.copbio.2011.06.002 ). Predicting these properties in silico is expected to be the next leap in innovation as it would enable significantly reduced development timelines combined with broader screens at lower costs. However, developing predictive algorithms typically requires substantial datasets generated under very defined conditions, a limiting factor especially for new classes of therapeutic proteins that hold immense clinical promise. Here we describe a strategy for assessing the developability of a novel class of small therapeutic Anticalin® proteins using machine learning in conjunction with a knowledge-driven approach. The knowledge-driven approach considers developability attributes such as aggregation propensity, charge variants, immunogenicity, specificity, thermal stability, hydrophobicity, and potential post-translational modifications, to calculate a holistic developability score. Based on sequence-derived descriptors as input parameters we established novel statistical models designed to predict the developability scores for Anticalin proteins. The best models yielded low root mean square errors across the entire dataset and were further validated by removing input data from individual screening campaigns and predicting developability scores for those drug candidates. The adoption of the described workflow will enable significantly streamlined preclinical development of Anticalin drug candidates and could potentially be applied to other therapeutic protein scaffolds.

SiMPl-GS: Advancing Cell Line Development via Synthetic Selection Marker for Next-Generation Biopharmaceutical Production.

Rapid and efficient cell line development (CLD) process is essential to expedite therapeutic protein development. However, the performance of widely used glutamine-based selection systems is limited by low selection efficiency, stringency, and the inability to select multiple genes. Therefore, an AND-gate synthetic selection system is rationally designed using split intein-mediated protein ligation of glutamine synthetase (GS) (SiMPl-GS). Split sites of the GS are selected using a computational approach and validated with GS-knockout Chinese hamster ovary cells for their potential to enable cell survival in a glutamine-free medium. In CLD, SiMPl-GS outperforms the wild-type GS by selectively enriching high producers. Unlike wild-type GS, SiMPl-GS results in cell pools in which most cells produce high levels of therapeutic proteins. Harnessing orthogonal split intein pairs further enables the selection of four plasmids with a single selection, streamlining multispecific antibody-producing CLD. Taken together, SiMPl-GS is a simple yet effective means to expedite CLD for therapeutic protein production.

A generic anti-drug antibody assay for monoclonal antibody therapeutics with broad dynamic range eliminates the need for titer evaluation in preclinical studies

In preclinical protein therapeutic development studies, the emergence of anti-drug antibodies (ADA) can potentially impact drug pharmacokinetics and safety. While immunogenicity assessment is not mandatory in preclinical studies, banking samples can be valuable for interpreting unexpected pharmacological responses. Immunoassays that use generic reagents across different drug molecules can simplify ADA assessment and expedite sample evaluations. This work showcases the ability of the Gyrolab automated immunoassay platform to detect and quantify both drug-free and drug-bound (total) ADAs to monoclonal antibody (mAb) therapeutics in cynomolgus monkey preclinical studies. Compared to the previously reported total ADA ELISA, the Gyrolab assay exhibited a wider signal dynamic range and increased drug tolerance. Similar sensitivity, dynamic range and cut point factors were observed for four therapeutic mAbs of different isotypes using the Gyrolab assay. Here we present a comparison of ADA assays using bridging ELISA, total ADA ELISA and total ADA Gyrolab formats in a cynomolgus monkey study where the subjects were treated with a single dose of a mAb therapeutic. We demonstrate that the total ADA assays detected host ADA responses at earlier time points compared to the bridging ELISA. The Gyrolab assay has the best correlation between signal-to-noise (S/N) and titer over a wide ADA concentration range, highlighting the utility of Gyrolab in S/N reporting of ADA response to eliminate the need for secondary titer assays. Collectively, our results demonstrate that the generic ADA Gyrolab assay minimizes the necessity for extensive assay development and optimization for therapeutic mAbs, streamlining preclinical immunogenicity assessment to enable interpretation of pharmacological data.

Rational design of diblock copolymer enables efficient cytosolic protein delivery

Polymer-mediated cytosolic protein delivery demonstrates a promising strategy for the development of protein therapeutics. Here, we propose a new designed diblock copolymer which realizes efficient cytosolic protein delivery both in vitro and in vivo. The polymer contains one protein-binding block composed of phenylboronic acid (PBA) and N-(3-dimethylaminopropyl) (DMAP) pendant units for protein binding and endosomal escape, respectively, followed by the response to ATP enriched in the cytosol which triggers the protein release. The other block is PEG designed to improve particle size control and circulation in vivo. By optimizing the block composition, sequence and length of the copolymer, the optimal one (BP20) was identified with the binding block containing 20 units of both PBA and DMAP, randomly distributed along the chain. When mixed with proteins, the BP20 forms stable nanoparticles and mediates efficient cytosolic delivery of a wide range of proteins including enzymes, toxic proteins and CRISPR/Cas9 ribonucleoproteins (RNP), to various cell lines. The PEG block, especially when further modified with folic acid (FA), enables tumor-targeted delivery of Saporin in vivo, which significantly suppresses the tumor growth. Our results shall inspire the design of novel polymeric vehicles with robust capability for cytosolic protein delivery, which holds great potential for both biological research and therapeutic applications.

Half-life extension via ABD-fusion leads to higher tumor uptake of an affibody-drug conjugate compared to PAS- and XTENylation.

A critical parameter during the development of protein therapeutics is to endow them with suitable pharmacokinetic and pharmacodynamic properties. Small protein drugs are quickly eliminated by kidney filtration, and in vivo half-life extension is therefore often desired. Here, different half-life extension technologies were studied where PAS polypeptides (PAS300, PAS600), XTEN polypeptides (XTEN288, XTEN576), and an albumin binding domain (ABD) were compared for half-life extension of an anti-human epidermal growth factor receptor 2 (HER2) affibody-drug conjugate. The results showed that extension with the PAS or XTEN polypeptides or the addition of the ABD lowered the affinity for HER2 to some extent but did not negatively affect the cytotoxic potential. The half-lives in mice ranged from 7.3 h for the construct including PAS300 to 11.6 h for the construct including PAS600. The highest absolute tumor uptake was found for the construct including the ABD, which was 60 to 160% higher than the PASylated or XTENylated constructs, even though it did not have the longest half-life (9.0 h). A comparison of the tumor-to-normal-organ ratios showed the best overall performance of the ABD-fused construct. In conclusion, PASylation, XTENylation, and the addition of an ABD are viable strategies for half-life extension of affibody-drug conjugates, with the best performance observed for the construct including the ABD.

Identification and characterization of rabbit scFv antibodies suitable for immuno-affinity separation of recombinant human kynureninase from Escherichia coli cell lysate

In this study we successfully developed an on-demand affinity chromatographic resin for manufacturing non-Fc-based biopharmaceuticals. Affinity chromatography columns with immobilized rabbit single-chain variable fragments (scFvs) were used for directly purifying the recombinant human kynureninase (KYNase) as a model target therapeutic protein from Escherichia coli cell lysates. Among the 38 different anti-KYNase scFv clones identified, four unique clones were selected as candidates for further characterization owing to their relatively low KYNase binding affinity at pH 4.0, thereby facilitating enzyme elution. Subsequently, all four clones were successfully produced and purified, followed by covalent coupling to NHS-activated HiTrap HP columns. While KYNase was specifically adsorbed to all four scFv-immobilized columns and was eluted at pH 4.0, the respective levels of static binding capacity (SBC) and recovery among the four scFv clones were different at this elution pH. That is, the scFv-immobilized columns captured KYNase with SBC ranging from 1.15 to 2.68 mg/cm3-bed with clone R2-47 exhibiting the highest level of SBC, with a ligand utilization of 39.4%. Moreover, using the scFv column of R2-47, 90.7% of the captured human KYNase was recovered in the first elution step at pH 4.0, and approximately 67% of enzymatic activity was retained. In summary, high-purity human KYNase was obtained from the E.coli cell lysate by one-step affinity purification, and 89.7% of KYNase was recovered in the first elution step. The methodology demonstrated in the current study could be applied for the purification and development of various therapeutic proteins.

Unraveling the Microscopic Mechanism of Molecular Ion Interaction with Monoclonal Antibodies: Impact on Protein Aggregation.

Understanding and predicting protein aggregation represents one of the major challenges in accelerating the pharmaceutical development of protein therapeutics. In addition to maintaining the solution pH, buffers influence both monoclonal antibody (mAb) aggregation in solution and the aggregation mechanisms since the latter depend on the protein charge. Molecular-level insight is necessary to understand the relationship between the buffer-mAb interaction and mAb aggregation. Here, we use all-atom molecular dynamics simulations to investigate the interaction of phosphate (Phos) and citrate (Cit) buffer ions with the Fab and Fc domains of mAb COE3. We demonstrate that Phos and Cit ions feature binding mechanisms, with the protein that are very different from those reported previously for histidine (His). These differences are reflected in distinctive ion-protein binding modes and adsorption/desorption kinetics of the buffer molecules from the mAb surface and result in dissimilar effects of these buffer species on mAb aggregation. While His shows significant affinity toward hydrophobic amino acids on the protein surface, Phos and Cit ions preferentially bind to charged amino acids. We also show that Phos and Cit anions provide bridging contacts between basic amino acids in neighboring proteins. The implications of such contacts and their connection to mAb aggregation in therapeutic formulations are discussed.

Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development

The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though no study has reported a conclusive application of these algorithms, the efforts continue with much optimism. We intended to test the application of these algorithms in rank-ordering therapeutic proteins for their instability during the pre-translational modification stages, as may be predicted according to the confidence of the structure predicted by these algorithms. The selected molecules were based on a harmonized category of licensed therapeutic proteins; out of the 204 licensed products, 188 that were not conjugated were chosen for analysis, resulting in a lack of correlation between the confidence scores and structural or protein properties. It is crucial to note here that the predictive accuracy of these algorithms is contingent upon the presence of the known structure of the protein in the accessible database. Consequently, our conclusion emphasizes that these algorithms primarily replicate information derived from existing structures. While our findings caution against relying on these algorithms for drug discovery purposes, we acknowledge the need for a nuanced interpretation. Considering their limitations and recognizing that their utility may be constrained to scenarios where known structures are available is important. Hence, caution is advised when applying these algorithms to characterize various attributes of therapeutic proteins without the support of adequate structural information. It is worth noting that the two main algorithms, AlfphaFold2 and ESMFold, also showed a 72% correlation in their scores, pointing to similar limitations. While much progress has been made in computational sciences, the Levinthal paradox remains unsolved.

Experimental procedures to investigate fibrillation of proteins

The unwanted phenomenon of protein fibrillation is observed in vivo and during therapeutic protein development in the industry. Protein aggregation is associated with various degenerative disorders and might induce immune-related challenges post-administration of biopharmaceutics. A pipeline for early detection, identification, and removal of pre-formed fibrils is needed to improve the quality, efficacy, and effectiveness of the formulation. Protein fibril formation is accompanied by unfolding, secondary structural changes and the formation of larger aggregates. However, most detection processes come with extensive sample preparation steps and inefficient repeatability, incurring a financial burden on research. The current article summarizes and critically discusses six simple yet powerful methods to detect aggregation phenomena in the line of detecting fibrillar aggregates in heat-induced bovine serum albumin protein. Comparing the native and heat-induced protein samples would provide insights about aggregates. Easy, inexpensive and optimized protocols for detecting the fibrillation of proteins are explained. The procedures mentioned here detected the appearance of β-sheet-rich fibrils in the heat-induced protein sample. The aggregation is characterized by enhanced thioflavin-T fluorescence, alteration in the intrinsic fluorescence, decrease in helicity and subsequent increase in β-sheet and appearance of particles with larger hydrodynamic diameters.•This article summarizes various analytical techniques to easily characterize the fibrillation of proteins.•Various techniques to detect the formation of β-sheet rich structures, changes in the secondary structures and size of aggregates have been discussed.•The stated methodologies are validated on a model protein, albumin.

Improving Protein Therapeutic Development through Cloud-based Data Integration.

Pharma 4.0 is a digital evolution of the pharmaceutical industry that automates scientists' traditional workflows with the implementation of modern technologies like cloud pipelines, artificial intelligence, robotic platforms, and augmented reality. Lab data capture (LDC) is an essential strategy for initiating Pharma 4.0 that aggregates and harmonizes siloed lab data from analytical instruments, reporting systems, and operational platforms. This publication describes the execution of LDC within a quantitative PCR (qPCR) workflow using the Tetra Data Platform (TDP). We selected this workflow because the qPCR instrument, the ViiA7, generates discrete file-based data that documents execution of individual assays for quantifying residual DNA throughout biologics process development and product profiling. TDP executes LDC through the deployment of file scanning software agents, scanning and ingestion processes, and a cloud-based parsing pipeline that harmonizes source data. Web applications were developed to query, visualize, and interpret harmonized qPCR data for automated experiment data processing and process control charting from the TDP platform. Our implementation of LDC enables analytical researchers to harness FAIR (Findable, Accessible, Interoperable, Reproducible) data practices across the organization and establishes a "compliance-by-code" culture in development labs.

A graph-based machine learning framework identifies critical properties of FVIII that lead to hemophilia A.

Introduction: Blood coagulation is an essential process to cease bleeding in humans and other species. This mechanism is characterized by a molecular cascade of more than a dozen components activated after an injury to a blood vessel. In this process, the coagulation factor VIII (FVIII) is a master regulator, enhancing the activity of other components by thousands of times. In this sense, it is unsurprising that even single amino acid substitutions result in hemophilia A (HA)-a disease marked by uncontrolled bleeding and that leaves patients at permanent risk of hemorrhagic complications. Methods: Despite recent advances in the diagnosis and treatment of HA, the precise role of each residue of the FVIII protein remains unclear. In this study, we developed a graph-based machine learning framework that explores in detail the network formed by the residues of the FVIII protein, where each residue is a node, and two nodes are connected if they are in close proximity on the FVIII 3D structure. Results: Using this system, we identified the properties that lead to severe and mild forms of the disease. Finally, in an effort to advance the development of novel recombinant therapeutic FVIII proteins, we adapted our framework to predict the activity and expression of more than 300 in vitro alanine mutations, once more observing a close agreement between the in silico and the in vitro results. Discussion: Together, the results derived from this study demonstrate how graph-based classifiers can leverage the diagnostic and treatment of a rare disease.

Hydrodynamic considerations for spring-driven autoinjector design

In recent years, significant progress has been made in the studies of the spring-driven autoinjector, leading to an improved understanding of this device and its interactions with tissue and therapeutic proteins. The development of simulation tools that have been validated against experiments has also enhanced the prediction of the performance of spring-driven autoinjectors. This paper aims to address critical hydrodynamic considerations that impact the design of spring-driven autoinjectors, with a specific emphasis on sloshing and cavitation. Additionally, we present a framework that integrates simulation tools to predict the performance of spring-driven autoinjectors and optimize their design. This work is valuable to the pharmaceutic industry, as it provides crucial insights into the development of spring-driven autoinjectors and therapeutic proteins. This work can also enhance the efficacy and safety of the delivery of therapeutic proteins, ultimately improving patient outcomes.

A Cascade Enzymatic Reaction Scheme for Irreversible Transpeptidative Protein Ligation.

Enzymatic peptide ligation holds great promise in the study of protein functions and development of protein therapeutics. Owing to their high catalytic efficiency and a minimal tripeptide recognition motif, peptidyl asparaginyl ligases (PALs) are particularly useful tools for bioconjugation. However, as an inherent limitation of transpeptidases, PAL-mediated ligation is reversible, requiring a large excess of one of the ligation partners to shift the reaction equilibrium in the forward direction. Herein, we report a method to make PAL-mediated intermolecular ligation irreversible by coupling it to glutaminyl cyclase (QC)-catalyzed pyroglutamyl formation. In this method, the acyl donor substrate of PALs is designed to have glutamine at the P1' position of the Asn-P1'-P2' tripeptide PAL recognition motif. Upon ligation with an acyl acceptor substrate, the acyl donor substrate releases a leaving group in which the exposed N-terminal glutamine is cyclized by QC, quenching the Gln Nα-amine in a lactam. Using this method, PAL-mediated ligation can achieve near-quantitative yields even at an equal molar ratio between the two ligation partners. We have demonstrated this method for a wide range of applications, including protein-to-protein ligations. We anticipate that this cascade enzymatic reaction scheme will make PAL enzymes well suited for numerous new uses in biotechnology.

Using New Peak Detection to Solve Sequence Variants Analysis Challenges in Bioprocess Development.

Recombinant therapeutic proteins have become the major class of drugs to treat various human diseases in recent years. Low levels of protein sequence variants (SVs) have been reported to be present in recombinant therapeutic proteins. The consequences of potential unwanted immune response from SVs of recombinant therapeutic proteins have increasingly drawn attention from regulatory authorities and the biopharmaceutical industry. It is highly desirable to detect low-level SVs during clone selection and early process development as part of the control strategy. Peptide mapping with LC-MS/MS analysis has been applied as a powerful tool to characterize post-translation modifications of therapeutic proteins. Despite the recent advancements in mass spectrometry hardware and software, it is still quite challenging and time-consuming to detect and identify low-level SVs. In this study, we present an optimized approach using new peak detection to detect and identify low level SVs with high confidence and high speed. The new approach makes sequence variants analysis by LC-MS/MS broadly applicable and practical in bioprocess development of therapeutic proteins.

Accelerated CMC workflows to enable speed to clinic in the COVID-19 era: A multi-company view from the biopharmaceutical industry.

The COVID-19 pandemic has placed unprecedented pressure on biopharmaceutical companies to develop efficacious preventative and therapeutic treatments, which is unlikely to abate in the coming years. The importance of fast progress to clinical evaluation for treatments, which tackle unmet medical needs puts strain on traditional product development timelines, which can take years from start to finish. Although previous work has been successful in reducing phase 1 timelines for recombinant antibodies, through utilization of the latest technological advances and acceptance of greater business risk or costs, substantially faster development is likely achievable without increased risk to patients during initial clinical evaluation. To optimize lessons learned from the pandemic and maximize multi-stakeholder (i.e., patients, clinicians, companies, regulatory agencies) benefit, we conducted an industry wide benchmarking survey in September/October 2021. The aims of this survey were to: (i) benchmark current technical practices of key process and product development activities related to manufacturing of therapeutic proteins, (ii) understand the impact of changes implemented in COVID-19 accelerated Ab programs, and whether any such changes can be retained as part of sustainable long-term business practices and (iii) understand whether any accelerative action(s) taken have (negatively) impacted the wider development process. This article provides an in-depth analysis of this data, ultimately highlighting an industry perspective of how biopharmaceutical companies can sustainably adopt new approaches to therapeutic protein development and production.