The next generation of biopharmaceutical therapies promises to significantly advance human health by providing new ways to tackle some of the most complex diseases. While the innovation behind these novel therapies is cutting edge, many of the processes involved in their development and manufacturing have failed to keep pace with the modern approaches adopted by other industries.

The lifecycle and processes required to bring a therapy to market are complex and involve significant milestones such as regulatory approval. However, the vast majority are supported by manual tasks and in silos of data and knowledge. In an industry where patient safety is paramount and speed to market has a direct impact on human health, data and related insights hold the key to accelerating timelines and unlocking innovation.

To bring promising new therapies to the patients who need them faster, an approach for bringing people, processes, and data together across the full lifecycle is needed. By ingraining scientific understanding and know-how into a data backbone that supports the therapy on its journey from research to manufacturing, many of the bottlenecks and quality risks, triggered by manual tasks, can be engineered out. This approach results in a solid data foundation that unlocks the power of artificial intelligence (AI) and digital twins, delivering the insights needed to drive innovation, accelerate regulatory filing and technology transfer, and ultimately, bring high-quality therapies to market faster.

The need to bring new therapies to market faster was always present, but it became even more crucial with SARS-CoV-2. The Pfizer–BioNTech vaccine, which went from initial research to the first vaccination in nine months, raised the question, “Does it really need to take 10 years and $2 billion to deliver life-changing therapies to patients?” (1).

Towards the end of 2021, 74% of novel drugs approved by FDA (2) underwent an expedited development and quality assessment phase. Interestingly, not all of them were COVID-19 vaccines, demonstrating a fundamental shift toward accelerating development and manufacturing.

This is further amplified when considering the number of products that will go off-patent in the near future. Over the next eight years, 190 drugs and 69 blockbusters (with $236 billion revenue) are expected to hit a patent cliff (3). This eventuality places immense pressure on the biopharmaceutical industry to bridge the gap with next-generation blockbuster drugs.

Despite the huge investment in R&D and groundbreaking innovations in genomic medicine, the typical timeline for bringing a new therapy to market can be as long as 10 to 15 years. Many of the patients who require these cutting-edge personalized therapies are sick and need access to these treatments much sooner than that.

When focusing on the key components of the lifecycle, we find many manual, paper-based processes and data silos that are not well connected. The industry has adopted some digital technologies, but often these tools are not working in harmony. This results in experts wasting 20% of their time on data administration rather than improving and accelerating processes (4). Due to data inaccessibility, especially at the development stages of the lifecycle, 10–20% of the work has to be repeated (5), wasting time and resources unnecessarily. At the operational level, this is highly inefficient and introduces significant quality risks. From a strategic perspective, the opportunity to use high-quality data to drive innovation is lost, and, with so many manual tasks, only a small proportion of the data is typically used for advanced analytics.

Beyond the operational foundations, the milestones involved in regulatory submission and technology transfer from clinical production to commercial manufacturing require rapid access to high-quality data and the insights that help define and control the process as it scales. With the growing trend towards contract manufacturing, the problems of managing and transferring data and insight are extending beyond the corporate firewall.

The potential returns for biopharmaceutical companies are significant for those who can bring order to this complex data landscape. The International Council for Harmonisation (ICH) has laid out guidelines that underline the importance of data, insight, and quality throughout the lifecycle of a drug. By demonstrating a robust understanding of the process, the level of regulatory scrutiny can be reduced. In turn, this presents an opportunity to further optimize processes post-approval, which can drastically improve the yield while reducing the costs and time to get therapies to patients.

Some companies have tried to integrate their legacy systems through integration layers and data lakes. However, while data lake integration does bring silos together in a single location, it doesn’t inherently solve the challenge of how those data sets are related to each other and what their impact is on the process performance and product quality. In other words, data scientists need to use ontologies and semantic enrichment to contextualize the data. That means the data lake method does not suffice to drive concrete understanding that will generate a meaningful data backbone. Regardless of whether the data science team does eventually manage to align and contextualize data in their data lake, they would have nonetheless spent significant amounts of time in the process.

To overcome the drawbacks of current lab data management systems, a transition from legacy systems to cloud-based platforms is needed. Such platforms enable data integration from lab equipment and electronic notebooks without sacrificing the insight that the data conveys. The resulting digital data backbone could highlight the connection between process parameters and quality attributes, minimizing the time spent aligning and contextualizing data and allowing companies to shift focus to business outcomes, process optimization, and regulatory compliance.

A more intact digital data backbone leads to better predictive analytics in the manufacturing process, reducing the number of failed batches. Additionally, the increased quality of emerging data reduces the administrative burden on scientists and the need for excessive quality assurance.

Tech transfer with streamlined management

As explained in the ICH Q10 guidance (6), technology transfer is paramount to sharing product and process knowledge between suppliers and manufacturers, especially if outsourcing is involved. With hundreds of new contract manufacturing agreements made in 2021, outsourcing is undeniably on the rise.

Imagine a healthcare supplier that provides their contract development and manufacturing organization (CDMO) partner with poorly contextualized data. The CDMO would not be able to optimize the use of that data and would most likely set false expectations in terms of scale and timeline. Detection of faulty production would be discovered too late down the line, meaning the entire team would have to suspend their current projects and repeat previous work to address discrepancies.

A central cloud-based digital data backbone can mitigate the risks associated with outsourcing to a CDMO partner. By streamlining data management, biopharmaceutical companies improve their chances of partnering with the ideal CDMOs with the proper expertise. This helps ensure that the drug manufacturing process is efficient, cost-effective, and fast. Companies can then focus on their business goals while gaining access to the CDMO equipment and facilities.

However, CDMOs are only one part of the equation. The digital data backbone is extremely useful for seamless data sharing across all parties in the biopharmaceutical lifecycle. A good example is a collaboration between Cytiva and Biogen, which needed to assess the impact of raw material variability on manufacturing outcomes. By integrating process data from the manufacturer and the raw material data from the supplier, the companies could get a systemic perspective on the process, thereby detecting potential risks from raw material variability. These findings led to the process optimization to account for variability (7). Using similar integrative approaches paves the way for more reliant supplier-manufacturer relationships.

With streamlined data management, biopharmaceutical companies also gain access to more advanced technology. The premise of digital twins is particularly enticing. The ability to virtualize an entire lab experiment or manufacturing process means one can predict the outcome of possible safety issues, giving them the opportunity to optimize on a small scale. In this vein, the digital twin is a promising tool to reduce the drug development lifecycle and delivery to the patient.

However, constructing the “perfect” digital twin relies heavily on large volumes of enriched and contextualized data across different conditions. This is necessary to train the digital twin and improve its predictive and emulative power. The benefits of digital twins were demonstrated in messenger RNA (mRNA) vaccine research for SARS-CoV-2. Researchers used them to optimize the plasmid DNA (pDNA) to mRNA conversion, increase manufacturing capacity, and eliminate batch failures. In addition, establishing a digital twin allows researchers to repurpose existing data to study vaccine development for possible future variants (8).

Having worked on the Middle East respiratory syndrome coronavirus (MERS-COV), Moderna had the upper hand in vaccine development for SARS-CoV-2. However, it would not have been possible had they not retained the comprehensive experimental data from the MERS outbreak. Using the MERS-related data, Moderna could revive their insight into the mRNA-based vaccine development, dramatically accelerating the research phase (9).

Reducing manual quality assurance (QA) steps can also accelerate drug development. In particular, preclinical studies can be delayed due to the scope of data to be reported by contract research organizations (CROs) because legacy paper-based processes for recording study data involve multiple QA steps to ensure quality and reproducibility. Ultimately, this slows progress and extends timelines.

Data digitization can significantly reduce time spent on QA and overcome reporting bottlenecks (10). Digital workflows for recording data pertaining to laboratory process control, instruments, and business workflows differentiate between original and modified data or calculated versions and enable easy tracking and reassurance of the original data. This reduces the time to create study reports considerably, freeing up resources to run more studies per year.

A well-curated digital data backbone accelerates the next generation of therapeutics through the lifecycle and assures quality, bringing products to market faster. Lengthy manufacturing and quality assessment does not correlate with a positive impact on patients. On the contrary, delays in bringing therapeutics to the market cause a decline in the survival rate and quality of life in patients suffering from life-threatening diseases. Furthermore, the lack of a digital data backbone could pose unknown risks and adverse effects on patients despite extensive efforts to perform a quality assessment. Therefore, digitization of data management is necessary to speed up innovation that makes medicines safer and more effective.

Similarly, biopharmaceutical companies also benefit from advancements in data management. Research has demonstrated that, had rituximab for non-Hodgkin’s lymphoma and trastuzumab for breast cancer been delivered to market one year sooner, patient willingness to pay would have skyrocketed by $310 million and $8 billion for the two drugs, respectively (11). Another study showed that first-to-market products have a 9–12% market share advantage over their competitors for specialty and parenteral drugs (12).

Early entry into the pharmaceutical market—mediated by integrated digital lifecycle management—not only improves chances of success but also makes post-launch modifications easier to implement, enabling more agility and opening opportunities for further cost-of-goods-sold improvements. Furthermore, thanks to shortened delivery times and increases in return-on-investment, the company can allocate more time and resources to refine its product to supply patients with unmet needs.

2. P. Cavazzoni, “Many Important Drugs Approved in 2021 as COVID-19 Pandemic Continues,” 

3. J. Merrill, “The Next Big Patent Cliff Is Coming, and Time Is Running Out to Pad the Fall.”

4. IDBS, “Biopharma Development Data Management,” August 2021.

5. K. Moriss et al, “Making the Most of Drug Development Data,” 

7. Cytiva, “Industry 4.0: Embracing Digital Transformation in Bioprocessing,” 

9. Hill, Claire. “Covid-19’s Digital Legacy.” 

10. IDBS, “IDBS and AIT Bioscience Deliver First Paperless Bioanalysis Lab,” Press Release, Nov. 10, 2010,

Cost of Caution: The Impact on Patients of Delayed Drug Approvals

 (Manhattan Institute, 2nd Edition, June 2010).

12. M. Cha and F. Yu, “Pharma’s First-to-Market Advantage,” 

Pietro Forgione is the vice-president of strategy at IDBS.

Articles

Features

Innovation is driven through a strong digital backbone.

Accelerating Time-to-Insight Throughout the BioPharma Lifecycle

The BioPharma industry is in a state of flux. As the industry modernizes to deliver on the promise of a new generation of drug modalities, such as cell and gene therapies, it is also seeking to take advantage of recent advances in digital technologies. Many companies have digital transformation initiatives to leverage the potential of big data, cloud computing, machine learning/artificial intelligence, and the Internet of Things (IoT). The ultimate goal of these initiatives is to improve operational efficiency, reduce costs and time to market, and stay ahead of the competition. To enable this transformation, the so-called Industry 4.0 movement must be built on a solid foundation of good data governance while accommodating the industry’s stringent regulatory requirements.

In the race to all things digital, data governance, data integrity, and regulatory compliance may not be at the top of the C-Suite’s mind. Yet, digital initiatives often provide an opportunity to modernize these legacy processes, replacing labor intensive and time-consuming manual methods with integrated systems that can reduce effort and improve overall data quality.

Recent years have seen the rise of “483s,” FDA regulatory warning letters, with data integrity violations accounting for most of the notices. In 2019, almost half (47%) of all warning letters issued by FDA concerned data integrity. By the end of 2021, that number had increased to 65% (1). This trend has prompted forward-thinking organizations to re-evaluate their infrastructure and ways of working in efforts to maintain compliance and avoid future risk through digitization of their business and operational processes. In many cases, these efforts should be seen as complementary to programs that modernize data governance infrastructure to meet Industry 4.0 aspirations.

The industry has seen good adoption of tools to help manage operational data and improve data integrity, such as electronic lab notebooks (ELN), laboratory information management systems (LIMS), and manufacturing execution systems (MES). Larger organizations are also seeing a resurge in investments in building centralized data repositories, such as data lakes, to help drive their digitization initiatives. A core business objective of many of these data lakes is to break down data silos to create centralized repository of data for end users that is easily accessible, coherent, and complete. However, automating the integration of such varied systems, whilst ensuring data integrity and regulatory compliance, remains a significant industry challenge (2).

Many biopharmaceutical processes lack agreed-upon standards for data representation and transmission. While standards are emerging, most have yet to be widely adopted by hardware and software vendors due to the lack of industry consensus or the immaturity of the standards. The problem is compounded as many hardware and software vendors fail to provide adequate programmatic interfaces to enable automated system-to-system integration, creating silos of critical data.

A less appreciated and more nuanced data integrity issue is data contextualization. Even if an operator can extract data from a specific system (e.g., chromatography), the data may be of little use without combining it with data stored in other systems, such as the experimental conditions under which the sample was generated, or how the sample was stored or subsequently processed. Maintaining this information context or “chain of custody” is not only necessary for interpreting experimental observations (e.g., mechanism of action), but also critical when attempting to gain the higher level of business intelligence needed to optimize a drug candidate’s attributes or a process for drug manufacture.

Human operators are routinely required to manually transcribe and combine data from multiple systems, often using intermediary tools, such as spreadsheets. This human-centric process is time consuming and error prone, and the potential for errors escalates with each additional transcription required in data transfer. Additionally, manual data transcription workflows require extensive additional quality checks to ensure data integrity and meet regulatory requirements, such as 21 

 Part 11 or good laboratory/manufacturing practices (GxP) (3).

Moreover, generating and accessing high-quality contextualized data is a major bottleneck to implementing machine learning and other advanced analytic techniques. Highly educated data scientists can spend an inordinate amount of time on a project simply searching for, combining, and cleaning data to generate datasets for training and validating models.

Improving data governance practices and systems integration is a critical pre-requisite to implementation of the automation and analytics aspirations of Industry 4.0.

FDA uses the acronym ALCOA to describe its expectations of data integrity to help industry technicians stay compliant with 21 

 Part 11. Per ALCOA, data must be attributable, legible, contemporaneous (i.e., recorded in real-time when generated with a date and time stamp), original, and accurate. The concepts were expanded to ALCOA+, which incorporates additional features and specifies that data must be complete, consistent, enduring, and available (4).

The principles of ALCOA+ and requirements of 21 

 Part 11 for maintaining data integrity are well established within the biopharmaceutical industry. More recently, similar concepts are being advocated to address broader data integration and system automation challenges by a movement known as the F.A.I.R Principles for scientific data management and stewardship (5). Established in 2014 by participants of a seminar called the Lorentz Workshop “Jointly Designing Data Fairport”, F.A.I.R ensures data are findable, accessible, interoperable, and reusable. More of a design principle than a standard, it recommends or relies on operators to subscribe to one system that enables crosstalk between two or more different systems in a machine-readable format. The output generates data that are comprehensible, reusable, and contextualizable. (6).

F.A.I.R. and ALCOA+ complement each other to help tie the data together. F.A.I.R. focuses on infrastructure, namely metadata, to increase the reliability of electronic data capture, while ALCOA+ addresses data integrity challenges to improve the trustworthiness of the data output in the process.

The current state of biopharmaceutical informatics is heavily dictated by larger organizations which leverage considerable purchasing power. A high proportion of these organizations have made substantial investments in a trove of disparate systems stitched together with in-house integration code, bespoke data lakes/warehouses, and a variety of analytics tools. They are understandably hesitant to undertake the cost and risk associated with wholesale change, especially if the systems in question are validated to a GxP standard. Progress is often incremental, and this can stymie innovation by incentivizing incumbent vendors to maintain the status quo and creating a barrier to entry for newer, more disruptive technologies. There are, however, several emerging technologies to help automate the capture, integration, and contextualization of data from these legacy systems that can both improve data integrity and drive broader Industry 4.0 initiatives, such as implementation of IoT, robotics, and the creation of digital twins for predictive modeling.

Systems integration remains a perennial problem. Some organizations have the necessary IT/software development skills in-house to integrate their digital topography by working directly with individual vendors to create custom solutions. This approach is not ideal for many companies, however, because it is resource-intensive, time consuming, and creates custom software code that must be maintained in perpetuity, creating a resource overhead debt. Its viability is also dependent on support from hardware/software vendors for system integration, which varies considerably from vendor to vendor and should be a decisive factor when procuring a new system or platform.

When procuring new informatic systems or instruments intended to be integrated with your digital topography, key questions that should be asked include:

How good are the vendors’ APIs and documentation?

Do they offer services or have certified partners that support integration?

Can they provide references to examples of successful integration projects?

Increasingly, procurement teams are placing an emphasis on integration support, putting pressure on hardware/software vendors to ascribe to F.A.I.R. principles when designing their products. For many small to mid-sized organizations, building an integrated ecosystem of hardware and software to drive automation is beyond their technical or budgetary reach. To address this need, a number of companies have developed softwares designed to simplify the integration of popular laboratory hardware and software platforms, by creating libraries of connectors to common laboratory equipment and key informatics applications, and providing the mechanism to automate the exchange of data between systems (7).

Cleaning, contextualizing, and aligning the data pulled from different systems presents a challenge as daunting as accessing the source data itself. Recreating the information “chain of custody” typically requires combining partial data sets from different systems, harmonizing terms and identifiers, and ensuring data are aligned correctly. This is one of the most tedious and error-prone steps, creating significant data integrity risks, particularly when done manually with a human operator.

Automating this process may be accomplished often in conjunction with some form of centrally-managed metadata/ontology library to automatically annotate data or create knowledge graphs that map terms from different systems and join together the data. The availability of ontology management, semantic enrichment, and knowledge graph tools continues to expand apace, led by open-source initiatives as well as a number of commercial vendors that specialize in this market.

Systems integration and automated data contextualization focus on integration of hardware, their control systems, and other operational tools, such as ELN, LIMS, or MES. However, the dynamic of wet-lab work does not always provide access to software interfaces for capturing all observations. Some data are still manually documented in hand-written notes, which can be critical to properly qualify an experiment or analytical run. This information may or may not become part of the electronic information chain of custody; it can be lost, erroneously transcribed, or intentionally omitted from the record.

To address this issue, some companies have introduced scientifically intelligent digital voice assistants to provide a more efficient, hands-free user experience for lab workflow and data capture (8). For example, the assistant can prompt the user through a complex protocol with voice instruction, import data at critical steps directly from lab equipment, and capture key observations and ancillary notes by transcribing the operator’s voice dictation. These tools can operate both independently as well as integrate directly with other systems such as ELN.

As small-molecule drug development gives way to newer drug modalities, the suitability of legacy informatics and hardware systems comes into question. Combined with a growing purchasing power of mid-market biotechnology companies, which are often greenfield sites with little or no legacy infrastructure, the opportunity for a more innovative and disruptive change to the traditional informatics landscape will emerge.

Many of these newer technologies seek to embrace a holistic integrated approach to BioPharma Lifecycle Management, by combining elements of out-of-the box workflow execution, preconfigured system and hardware integrations, contextualized data stores built on F.A.I.R principles, and integrated analytics to drive business intelligence through an integrated digital platform. Their adoption will be driven on whether they can be implemented quickly, deliver business benefits immediately, reduce total cost of ownership, and provide a scalable foundation of data lifecycle management to accelerate Industry 4.0 initiatives.

Technology is finally maturing to address the challenge of integration and automation in the lab and manufacturing plant, whilst ensuring data integrity and regulatory compliance. Regardless of how organizations reevaluate and modernize their current processes, the solution starts with a willingness to embrace new-world approaches to these old-world problems.

1. J. Eglovitch, “Experts Say FDA Enforcement Focus Unchanged, Use Of Alternative Tools To Grow,” 

 Title 21, Part 11 (Government Printing Office, Washington DC) 1-9.

5. IDBS, “The FAIR Principles: A Quick Introduction,” 

6. Labfolder, “Setting the Standard: FAIR and ALCOA+ in Research During the Pandemic,”

7. D. Levy, “Machine Learning Adoption And Implementation In /The Lab,”

8. P. de Matos, “Lab of the Future Post-COVID-19: Bringing User Experience to the Forefront,” 

Scott Weiss is vice-president of Business Development and Open Innovation at IDBS.

An integrated approach to the data lifecycle is key to successful digital transformation.

Integrated Approaches to the Data Lifecycle

Bio-Rad Laboratories recently introduced a range of type 1 antibodies that are designed to inhibit the binding of evolcumab to its target, human proprotein convertase subtilisin/kexin type 9 (PCSK9). These sequence-defined, recombinant antibodies are intended to develop highly selective and sensitive pharmacokinetic (PK) and anti-drug antibody (ADA) assays for evolcumab and its biosimilars.

Evolocumab is a monoclonal antibody (mAb) that has been used for the treatment of hyperlipidemia, or elevated levels of cholesterol and other lipoproteins. It binds to PCSK9, which, according to an Aug. 25, 2022 company press release, has been shown to play a key role in the regulation of cholesterol levels. By preventing the interaction between PCSK9 and low-density lipoprotein receptors, it intends to reduce circulating cholesterol and other lipoproteins.

This range of five anti-evolocumab antibodies includes fully human immunoglobulin G1 clones and recombinant human combinatorial antibody libraries antibodies with SpyTag (a technology for irreversible conjugation of recombinant proteins) technology incorporated into their heavy chain; this is designed to enable site-directed conjugation or fast-switching to a bivalent Fab or a full-length Ig-like format within an hour. The antibodies can also be used to develop PK bridging, enzyme-linked immunoassays to measure free drug or as a surrogate positive control or reference standard in an ADA assay.

"Bio-Rad's range of ready-made and well-characterized anti-idiotypic antibodies keeps growing, offering researchers enhanced flexibility for the development of custom bioanalytical drug assays against a wide range of marketed biologic drugs,' said Hilary Latham, marketing director, Life Science Group, Bio-Rad, in the press release. "Generated under stringent quality control for batch-to-batch consistency, these antibodies can produce translatable and reproducible results, ensuring assay reliability."

New Technology

Bio-Rad’s new antibodies are designed to develop highly selective pharmacokinetic (PK) and anti-drug antibody (ADA) assays for evolocumab and its biosimilars.

Bio-Rad Launches Anti-Evolocumab Antibodies

Roche recently announced the launch of its first digital polymerase chain reaction (PCR) system, the Digital LightCycler System (DLS). The DLS is designed to detect disease and accurately quantify trace amounts of specific DNA and RNA targets that are not typically detectable by conventional PCR methods.

According to an Aug. 23, 2022 press release, the DLS will allow clinical research to divide DNA and RNA from a clinical sample into up to 100,000 microscopic individual reactions. This provides opportunities for high precision analysis, including identifying ultra-rare targets or accurately quantifying how much of the target DNA/RNA there is at a molecular level.

"Understanding the hidden characteristics of serious diseases is fundamental for ensuring that the most effective treatment is selected for each patient," said Thomas Schinecker, CEO, Roche Diagnostics, in the press release. “The Digital LightCycler System will support clinical researchers and laboratories in identifying rare and emerging disease mutations. This can be instrumental in early diagnosis and therapy decisions."

New Technologies

Roche’s Digital LightCycler System is designed to aid in diagnostics for cancer, genetic disease, and infection.

Roche Launches Digital PCR System

The National Institute of Health (NIH) announced on Aug. 25, 2022, that scientists funded in part by the NIH have successfully generated a mouse embryo model, or embryoid, that develops beyond the formation of the neural tube. Because the model closely mirrors natural mouse embryos 8.5 days after fertilization, this embryoid could potentially be used for research into factors affecting mammalian embryonic development and disease.

According to an agency press release, the researchers combined mouse embryonic stem cells (ESCs) with two types of mouse extraembryonic stem cells that yield the yolk sac and placenta. This enabled development of all brain regions, as opposed to models that just use ESCs. The structure was developed within a yolk sac that formed blood islands, structures designed to support the embryo and its circulatory system. In addition to the neural tubes, a gut tube and precursor cells for sperm formation developed.

The neural tube was flanked by somites, cells that lead to vertebrae, skeletal muscle, cartilage, and so on. The neural tube responded to a developmental challenge in a similar manner to natural mouse embryos. This evaluation was done by depriving the embryo of Pax6, a protein needed for brain and eye development; the resulting neural tubes appeared identical to those that develop in natural mouse embryos lacking the protein. According to the release, the researchers believe that this could be used to explore the genetic and epigenetic factors regulating development, as well as screen for the effects of various chemicals on mammalian embryo development.

This research is being led by Magdalena Zernicka-Goetz, of the California Institute of Technology and University of Cambridge. It is partially supported by Eunice Kennedy Shriver, of the NIH’s National Institute of Child Health and Human Development.

News

Scientists have developed a mouse model that could be a promising model system for research into factors affecting mammalian embryonic development and disease.

NIH-Funded Scientists Develop Mouse Embryo Model That Generates Neural Tubes

Canvax, a European supplier of enzymes, recombinant proteins, and similar products, announced the release of a new inorganic pyrophosphate on Aug. 25, 2022. The new enzyme, isolated from E. coli, is a DNA replication enhancer with ≥ 95% purity and has an enhanced capacity for increasing RNA yield in 

The enzyme has been stable under temperatures below 80º and is free of endonucleases, exonucleases, and RNAses. According to a company press release, this makes it ideal for use in RNA IVT reactions because it can reduce the inhibitory effects of protein-protein interactions. The company also stated in the release that it is suitable for messenger RNA-based vaccine manufacturing, RNA labeling, and enhancement of DNA replication.

“We're glad to contribute with this new launch, significantly to supporting mRNA vaccines manufacturing, and we're pleased that the research community has embraced our pyrophosphatase and our RNA enzymes portfolio,” said Jesus C. Morales, business development manager, Canvax, in the release.

Canvax’s new inorganic pyrophosphate is designed for in-vitro transcription reactions.

Canvax Releases New Inorganic Pyrophosphate

CYTENA, a BICO group company and supplier of high-precision laboratory equipment for handling biological cells, recently unveiled the next generation of B.SIGHT, a fully automated single-cell dispenser. The B.SIGHT instrument is designed to streamline single-cell isolation in microbiology research, with emphasis placed on handling bacteria, yeast, and other microorganisms.

“The new B.SIGHT brings isolation of bacteria and yeast to the next level,” said Julian Riba, CEO, CYTENA, in an Aug. 8, 2022 company press release.. “This second generation single-cell dispenser is really the outcome of a great collaboration between our team and our customers.”

“The new instrument provides more automation, faster processing speeds, higher resolution, and precise dispensing,” said Hussein Hamzeh, the product manager in charge of B.SIGHT, in the release. “Researchers using the B.SIGHT not only will be able to accelerate microorganism isolation workflows but also significantly reduce plastic waste.”

CYTENA’s B.SIGHT cell dispenser is designed to streamline single-cell isolation in microbiology research.

CYTENA Unveils Fully Automated Single-Cell Dispenser for Microorganisms

New England Biolabs (NEB), a supplier of enzymes for molecular biology applications, announced the release of the Faustovirus Capping Enzyme (FCE), a messenger RNA (mRNA) capping solution, on Aug. 9, 2022. FCE is designed to improve capping efficiency and scalability for mRNA manufacturing workflows, including vaccine generation.

FCE is a single-subunit enzyme that contains the enzymatic activities to achieve a Cap-0 structure, which is necessary in eukaryotic mRNA maturation. According to a company press release, FCE is active over a broader temperature range (20 °C–55 °C) than traditional enzymatic capping approaches and demonstrates increased capping efficiency across a variety of mRNA 5´ structures.

"The biological versatility of mRNA continues to propel its rise as a therapeutic modality within infectious disease, oncology, and beyond," said Breton Hornblower, product portfolio manager, NEB, in the press release. "We are very excited to introduce FCE—an enzyme discovered at NEB—to our portfolio of products for mRNA synthesis. This new product offers a streamlined, scalable, and cost-effective solution for mRNA capping for both research and therapeutic applications."

"For large-scale mRNA production, I have always favored post-transcriptional enzymatic capping due to the significant advantages in terms of yield," said Andrew Geall, chief development officer, Replicate Bioscience, in the release. “This new enzyme with improved activity at lower temperatures promises great utility for the production of our large (9–16 kb) self-replicating RNA vector.”

According to the release, FCE is manufactured through environmentally friendly manufacturing practices, avoids the use of organic solvents and without producing significant volumes of hazardous waste. A good manufacturing practice-grade format of the enzyme will be available in early 2023.

The Faustovirus Capping Enzyme launched by New England Biolabs is designed to increase capping efficiency and scalability of mRNA manufacturing workflows.

New England Biolabs Releases Novel Enzymatic mRNA Capping Solution

Merck, known as MSD outside of the United States and Canada, and Cervance, a Boston biotechnology startup, announced that they have entered a strategic research collaboration on August 9, 2022. The deal will see the companies work together to identify novel targets for Alzheimer’s disease via Cerevance’s Nuclear Enriched Transcript Sort sequencing (NETSseq) platform.

NETSseq isolates cell populations from human brain tissue with the aim of exposing biological pathways underlying neurodegenerative and psychiatric diseases that may be difficult to see in animal models or differentiated human stem cells. According to a company press release, Cerevance will receive a $25 million upfront payment from Merck for the rights to use the platform. Cerevance is also eligible to receive development and commercial milestone payments, totaling approximately $1.1 billion, as well as additional royalties on sales of approved products derived from the collaboration.

“The establishment of this collaboration with Merck, which comes on the heels of our positive Phase 2 data for CVN424 in patients with Parkinson’s disease, represents a significant milestone for Cerevance and reinforces the promise of our NETSseq technology platform,” said Mark Carlton, chief scientific officer, Cerevance, in the release. “We believe we are well-positioned to identify novel targets for neurodegenerative diseases and look forward to collaborating with Merck to potentially bring forward transformative therapeutics for patients with Alzheimer’s disease.”

“Progress in our understanding of the biology of neurodegenerative diseases continues to reveal compelling new mechanisms for potential therapeutic intervention,” said Jason M. Uslaner, vice-president and head of neuroscience discovery, Merck Research Laboratories, in the release. “We look forward to advancing the discovery program as well as taking advantage of the NETSseq platform to identify new targets with the team at Cerevance.”

Merck, known as MSD outside of the United States and Canada, will collaborate with Cerevance to identify novel targets for Alzheimer’s disease.

Merck Enters Alzheimer’s Collaboration with Cerevance

Carterra, a provider of technologies enabling high-throughput biology, and the La Jolla Institute for Immunology (LJI), a non-profit entity focused on immunology studies, announced that LSJ acquired Cartera’s LSA antibody discovery and characterization platform on Aug. 1, 2022. LJI first worked with Carterra and the LSA platform on the Coronavirus Immunotherapy Consortium (CoVIC), a project that attempted to find the most potent antibodies against COVID-19.

The LSA platform is designed to enable rapid characterization of hundreds of antibodies in the early stages, then provide in-depth analysis of leads during later stages of discovery. According to a company press release, relative to traditional methods, the LSA generates results 100 times faster while using 99% less sample. In addition to autoimmune and cancer targets, it is also designed to enable rapid characterization of sera samples from vaccine studies.

“The LSA was key to mapping the epitope landscape of the SARS-CoV2 spike, using the 400 antibodies in the consortium. That information was instrumental to understanding which target sites are still effective against emerging variants of concern,” said Erica Ollmann Saphire, professor at La Jolla Institute for Immunology, director of the CoVIC consortium, in the release. “Having seen what the LSA can do first-hand through our collaboration with Carterra as part of the CoVIC, we now look forward to using this instrument to propel our own discovery efforts.”

“We’re proud to see the benefit of Carterra’s LSA platform being brought to bear for global good,” says Tim Germann, chief commercial officer, Carterra, in the release. “Working with Erica and her team at LJI has been truly enlightening—they understand the importance of breaking down the old paradigm of slow and inefficient biologics discovery. Bringing the LSA to LJI represents their commitment to leading the effort to end the suffering from pandemics like Ebola and COVID-19.”

La Jolla’s acquisition of Carterra’s LSA Platform is designed to advance their immunological and infectious disease research.

Pharmaceutical Technology's In the Lab eNewsletter, September 2022