ElevateBio

Powering the creation of cell & gene therapies at a speed the world deserves.

Automation / Innovation / Industrialization

by

A 98% batch success rate in cell therapy manufacturing is a system outcome, not a performance metric. In most industries, a rate like that is impressive but not surprising. In cell therapy manufacturing, it stops people mid-conversation.

At ElevateBio we’ve seen that reaction, and we understand it. These are living cells. Autologous programs start with patient-specific material that doesn’t tolerate deviation. Allogeneic programs must scale donor-derived cells while preserving potency and viability across increasingly large batches. Every batch carries weight scientifically, clinically, and for the patient waiting on the other end of it.

So, when ElevateBio’s team achieved a 98% batch success rate across cell therapy, viral vector, and mRNA programs, the natural question is: how? It isn’t one thing. It’s a system, explained in the whitepaper Engineering Reliability in Cell Therapy Manufacturing.

Planning Is Where Reliability Is Built or Lost

Not planning as a scheduling exercise, but planning as a continuous process of cross-functional alignment. Upcoming clinical enrollment, historical cycle times, QC testing capacity, raw material readiness, operator qualification: all of it unified into a single operational model that identifies conflicts weeks before they can affect execution. The goal, as the whitepaper puts it, is to prevent delays rather than explain them.

Digital Execution Catches What Manual Systems Miss

Electronic batch records and manufacturing execution systems guide operators through every step, and that’s not a paperwork upgrade. Paper-based systems let deviations go unnoticed until someone finds them later, and every manual transcription is a place where accuracy can slip. Ours enforce the sequence of operations, require verification before a step can proceed, and time-stamp the record as it happens. Paired with real-time dashboards, that same structure is what lets our team detect subtle trends across batches, move investigations faster, and give program leads actual visibility into what’s happening rather than a status update after the fact.

The System Is the Point

A 98% success rate doesn’t come from one good decision. It comes from planning and digital execution reinforcing each other, batch after batch, so reliability gets engineered in before a single cell is ever touched. Read the whitepaper for the full picture of how we built it.

Jeff Briganti Senior Director, Marketing

Jeff Briganti is a life sciences marketing and commercial leader with deep experience across biotechnology, advanced therapies, gene editing, and CDMO services. As Sr. Director of Marketing at ElevateBio, he leads marketing strategy and programs supporting the company’s advanced therapy manufacturing and gene editing capabilities. He has held senior leadership roles at Aldevron and Thermo Fisher Scientific and holds a BS in Genetics and an MBA from the University of Wisconsin–Madison.

by

Reverse transcriptase (RT) editing can expand precision gene editing by enabling targeted rewriting of DNA sequences using a specialized guide RNA, without relying on DNA double-strand breaks. This gives therapeutic developers a more versatile way to make precise DNA changes when target access, editing potency, or guide RNA performance limits other editing approaches.

At ElevateBio, we are developing RT editing systems by combining diverse CRISPR-associated RNA-guided nucleases, proprietary RTs, engineered guide RNAs, and proprietary LNPs for delivery. The goal is to help partners match the right editing system to the needs of a specific therapeutic target.

Diverse Enzyme Mining Creates Better Starting Points

Effective RT editing depends on both the targeting nuclease and the RTs. Instead of relying on a narrow set of standard tools, we mined a bioinformatic database of more than 10 billion natural proteins and identified a diverse collection of CRISPR-associated RNA-guided nucleases and proprietary RT enzymes.

That breadth matters for partners because different targets require different editing properties. A broader enzyme collection can increase target-site access, expand PAM compatibility, and create more opportunities to optimize potency and specificity for a program’s mutation or locus of interest.

RT Editing Uses the Guide as Both Address and Template

RT editing creates a targeted single-strand break and replaces the existing DNA sequence with new sequence encoded by the guide RNA. In this system, the guide RNA helps direct the editor to the genomic site and carries the template for the intended edit.

This architecture creates flexibility. Unlike approaches limited to specific base conversions, RT editing can support precise sequence rewriting across a broader range of edit types. That versatility makes it especially relevant for diseases where the desired correction cannot be addressed efficiently with a standard nuclease or base editor.

Engineering Improves Potency and Performance

RT editing performance depends on more than the initial enzyme. Both the RT editor and guide RNA can be engineered to improve activity.

Data (see technical poster) show that engineering proprietary RT editors increased RT editing at the Hao1 target from less than 0.1% to 25% in primary murine hepatocytes. Guide engineering achieved a greater than two-fold increase in RT editing efficiency, with more than 70% precise RT editing observed in primary murine hepatocytes. In vivo, ElevateBio achieved 28.4% precise RT editing using LNP delivery, which resulted in the reduction of the relevant biomarker to therapeutic levels.

RT Editing Gives Partners a More Flexible Path to Precise Sequence Rewriting

RT editing is not simply another editing modality. It expands the set of tools available for programs that require precise DNA rewriting, broader target access, and iterative optimization of both protein and guide components.

For partners, the value is practical: ElevateBio can help identify, engineer, and optimize RT editing systems around a specific therapeutic target, rather than forcing the target to fit a limited editing toolset.

View the Related Poster and Video Presentation

Drew Kelso
Senior Manager, Business Development Gene Editing Services

Drew Kelso is a Senior Manager of Business Development at ElevateBio, where he identifies and pursues potential partners to develop advanced therapies.

Since joining ElevateBio in 2021, Drew has progressed from bench scientist to business development leader. He began in R&D by characterizing and developing multiple gene editing systems and modalities, then advanced to scientific leadership roles where he directed partnership programs advancing therapeutic systems toward the clinic. In these roles, he oversaw research strategy and execution, advanced a novel genome-editing modality to preclinical validation. Now in business development, he leverages his deep scientific expertise to identify and evaluate partnership opportunities.

Prior to ElevateBio, Drew spent two years as a scientist at Intellia Therapeutics, where he applied biochemical and molecular techniques to investigate genome editing strategies and characterized gene knockout and integration in primary cells, generating insights that directly informed therapeutic development decisions.

Drew earned his Ph.D. in Biochemistry and Molecular Biology at Clemson University, and M.S. in Biotechnology at the University of Alabama at Birmingham, and a B.S. in Biology at the University of Montevallo.

Outside of work, Drew enjoys running, hiking, playing games and sports, and, most importantly, spending time with family.

by

Epigenetic editing can regulate genes without changing DNA by using programmable DNA-binding systems to recruit activator or repressor machinery to specific genomic sites, tuning gene expression up or down while leaving the underlying sequence intact.

Epigenetic Editing Tunes Expression Without Cutting or Rewriting DNA

Many diseases are driven by abnormal gene activation or silencing. Epigenetic editing offers a way to address those diseases by regulating expression at the gene level without permanently altering the genetic code.

At ElevateBio, we are developing an RNA-guided epigenetic editing platform designed to program both gene activation and repression. This approach adds a controllable layer of gene modulation between transient gene expression and permanent DNA editing.

Repression Can Be Durable After Transient Delivery

Gene repression must be strong enough to reduce target expression and durable enough to have biological relevance after delivery. ElevateBio’s epigenetic repressors demonstrate B2M repression across multiple human cell types, including HEK293T cells, Jurkat cells, and primary human T cells (see full poster and all data).

In Jurkat cells, B2M repression persisted for at least four weeks after transient mRNA delivery. That result supports the potential for lasting gene expression changes without permanent DNA modification.

Activation Can Be Tuned to Physiologically Relevant Levels

Some diseases may benefit from restoring gene expression by only two- or three-fold, which requires tunability rather than maximal overexpression. Full overexpression can overshoot the desired biological range.

Across the platform, we demonstrate CD25 activation in Jurkat cells, activation of a metabolic gene target in mouse liver cells, and tunable activation of neurodevelopmental genes in Neuro2a cells.

Multiplexing Enables More Flexible Gene Control

Many diseases are not driven by a single gene moving in one direction. A useful epigenetic editing platform must be able to activate one target while repressing another.

ElevateBio’s platform includes distinct RNA-guided DNA-binding systems that support simultaneous regulation of multiple targets. In our studies, we activated CD25 while repressing B2M within the same Jurkat cell population.

Specificity and Delivery Optionality Matter

Precise gene regulation depends on on-target activity and delivery flexibility. In RNA-seq analysis, the intended target remained the dominant signal, with little to no detectable off-target transcriptional activity under the tested conditions.

In addition to mRNA delivery, ElevateBio’s compact epigenetic activators were packaged into an all-in-one AAV vector. Using CD25 activation as a readout, we confirmed functional AAV delivery in Jurkat cells.

Epigenetic Editing Expands the Gene Modulation Design Space

Epigenetic editing offers a programmable way to regulate gene expression without cutting or rewriting DNA. By enabling durable repression, tunable activation, multiplexed regulation, high on-target specificity, and multiple delivery strategies, ElevateBio’s platform expands the therapeutic design space for diseases driven by abnormal gene expression.

In the end, not every genetic disease requires permanent DNA editing. When the goal is to restore, reduce, or rebalance gene expression, epigenetic editing creates a flexible approach to gene control.

View the Related Poster and Video Presentation

Ryan Rickels, PhD

Ryan Rickels, PhD, is a Senior Scientist in the Technology Department within ElevateBio’s Gene Editing Services, where he leads a small team focused on developing next-generation editing technologies and enabling their application across therapeutic programs.

Since joining ElevateBio in 2022, Ryan has played a key role in advancing multiple editing modalities, including base editors, reverse transcriptase (RT) editors, and epigenetic editors, helping drive innovation across ElevateBio’s gene editing portfolio.

Before joining ElevateBio, Ryan completed a postdoctoral fellowship at Duke University, where he focused on genome engineering technologies and their therapeutic applications. He earned his PhD from Northwestern University Feinberg School of Medicine, where his research centered on epigenetics and chromatin biology. Ryan received a B.S. in Biochemistry and Molecular Biology from the University of Tennessee.

Outside of work, Ryan enjoys exercising, camping, and spending time with his children.

by

The biggest drivers of cell therapy cost of goods (COGs) are decisions made during process development, not manufacturing execution. Media consumption, manual touchpoints, facility throughput, and analytical testing burden are all downstream consequences of upstream design choices. Reducing COGS at scale without introducing quality risk means getting those early decisions right, not optimizing around them later.

The Process Design Decisions That Lock In Cost

Programs do not set out to build expensive processes. They build processes optimized for speed at discovery stage with high media volumes, manual interventions, custom analytic panels, and then carry those designs forward into clinical manufacturing. By the time anyone models the cost per dose, the process is fixed.

The decisions with the largest COGS impact are often made in the first months of process development: choice of activation method, media formulation and feeding strategy, expansion platform, and harvest timing. Each of these determines how much labor, material, and cleanroom time a single batch requires. A process designed around manual sampling and open manipulations does not become closed and automated as the program advances. It becomes expensive.

Labor and Facility Time Are the Levers Most Programs Underestimate

When reviewing program costs, raw materials get the attention, but it is the number of manual steps per batch and the days each batch occupies a suite that determine whether a process can scale economically. In autologous cell therapy, labor and facility occupancy typically account for 50–70% of COGS. A process that requires twelve operator interventions over nine days in a cleanroom has a fundamentally different cost structure than one designed for six interventions over six days, even if the cell product is equivalent.

Programs that address this early do three things: design for closed processing, reduce in-process sampling, and select expansion platforms that shorten culture duration and/or employ automation. That discipline builds cost efficiency into the process rather than trying to engineer it in after clinical data is generated.

ElevateBio Is Building Toward Manufacturing Efficiency as a Design Principle

At ElevateBio, the investment in manufacturing technology and platform architecture is oriented around this problem. Process development, MSAT and manufacturing operate as a connected system, so that decisions about cost, throughput, and quality are made together rather than sequentially. The goal is not to optimize a process after it is built. It is to design processes where efficiency is structural — where closed systems, shortened culture durations, and streamlined analytics are built in from the start, not retrofitted under commercial pressure.

The Programs That Reduce COGS Are the Ones That Design for It

Cost reduction in cell therapy is not a manufacturing problem. It is a process development problem. The programs that reach commercial scale with viable unit economics are the ones that treated COGS as a design constraint from day one, not the ones that built a process first and asked what it costs later. The earlier manufacturing economics enter the conversation, the more room there is to act on them.

Chris Shumway, M.S.
Senior Director, Manufacturing

Chris Shumway joined ElevateBio in 2024 as Senior Director of Manufacturing, where he oversees manufacturing processes at ElevateBio BaseCamp, ensuring efficiency, compliance, and high-quality production. He has more than 20 years of experience in process development, validation, quality systems, and driving continuous improvement in manufacturing operations.

Prior to his role at ElevateBio, Chris held multiple manufacturing and leadership positions at Avid Bioservices, NantKwest, and AbbVie. He received his M.S. in Engineering Management from Syracuse University and his B.S. in Chemical Engineering from Lafayette College.

by

One assay is not enough for gene editing off-target safety assessment because each method detects a different subset of potential edits, and off-target profiles can change by editing modality, cell type, donor context, and dose. The most comprehensive safety profiles combine biochemical, computational, and cell-based approaches, followed by deep sequencing confirmation and biological risk annotation.

Different Methods Detect Different Off-Target Events

Biochemical approaches such as Digenome-Seq help nominate potential off-target sites by testing editor activity against genomic DNA. Computational tools identify sites based on homology patterns. While both assays are useful for nominating genomic sites for further safety evaluation in a cell-free manner, a direct measure of in-cell editing activity is missing. In our assessments at ElevateBio, whole exome sequencing (WES) identifies off-target events down to 1% allele frequency. WES acts as an unbiased safety net, nominating and confirming edits across all coding regions without requiring prior knowledge of where to look.

Off-Target Profiles Can Change by Cell Type and Editing Modality

The same guide RNA and CRISPR editor can generate different off-target patterns in different cell types, for instance primary T cells versus primary hepatocytes. A nuclease and base editor using identical guides can create distinct off-target signatures. These biological variables mean that safety assessment in a single cell type or with a single detection method provides false confidence. Primary cells from both male and female donors reveal the true editing landscape that immortalized cell lines may mask.

Assessment Data Should Feed Back Into Editor Engineering

Off-target reduction happens through iterative cycles of detection and protein engineering. At ElevateBio, improvements to adenosine deaminase domains have reduced both the number of off-target sites and editing magnitude at each site. This optimization is enabled by a constant feedback loop between off-target assessment and engineering to accelerate the development of safer, more specific therapeutics.

Orthogonal Methods Turn Detection Data Into Safety Insight

Combining Digenome-Seq, computational prediction through tools like Calitas, and whole exome sequencing captures the broadest spectrum of potential off-target events. Each confirmed site undergoes clinical variant analysis using ACMG/AMP guidelines and comparison to databases of known genetic variation to determine biological risk (see the technical poster, with data). Dose-response curves in primary cells establish whether off-target editing occurs at therapeutically relevant doses. This integrated framework transforms raw detection data into risk assessments that guide both engineering decisions and regulatory submissions.

Off-target safety assessment should not rely on one assay or one model system. Programs that generate the most useful safety profiles layer orthogonal detection methods, evaluate therapeutically relevant primary cells, and use the results to guide editor engineering before IND-enabling decisions are locked.

View the Related Poster and Video Presentation

Allie Crawley, PhD, Associate Director, Computational Biology

Allie oversees teams building applied genomics platforms in the CRISPR medicines field. She has worked to establish bioinformatic software and workflows, including design of correction strategies for genetic diseases, evaluation of editing, and off-target safety measurements for genomic medicines. After receiving her PhD in Functional Genomics from North Carolina State University, she joined AgBiome and helped build the foundational technology platform owned by ElevateBio Life Edit.

by

Potent, tolerable, and development-ready gene editing is not achieved through a single design decision. It improves through iteration: optimizing the editor, RNA chemistry, guide design, delivery vehicle, and target biology as one connected system.

This is especially true in the liver. LNP-mediated delivery has created a practical path for transient RNA delivery, but performance still depends on many interacting variables. A strong editor is not enough if the RNA composition is weak. A potent lipid nanoparticle (LNP) is not enough if the guide is underperforming. Development depends on making each component better, then carrying those learnings forward.

Drug Substance Optimization Can Unlock Potency

For in vivo editing, the drug substance is more than a single variable. It is the combined performance of the RNA payload, guide RNA, chemical modifications, and their compatibility with the LNP delivery system.

At ElevateBio, HAO1 has served as a useful in vivo liver target for optimizing LNP-RNA drug substances. In mouse studies, internal mRNA modifications and guide RNA modifications increased editing potency, with individual changes producing meaningful gains. These results show why optimization cannot stop at editor selection (see presentation and data). RNA design and chemistry can materially change in vivo editing outcomes.

Cross-Modality Learning Accelerates Development

The value of an integrated platform is that improvements in one modality can inform the next. Learnings from HAO1-targeted nuclease experiments helped guide more complex RNA payload strategies, including RT editing. In mouse studies, our internal formulation, LNP1, delivered an RT editor targeting HAO1, and the addition of an accessory protein improved editing.

The same feedforward logic supported base editing development. A proprietary LNP and adenine base editor were used to target PCSK9 in vivo, producing efficient editing with reductions in serum PCSK9 and total cholesterol. Those learnings helped inform subsequent NHP-ready editing work.

In Vivo Readiness Requires More Than Potency

Potency matters, but it is only one part of the development equation. Advanced therapy programs also need tolerability, translatability, and a path to improved follow-on editors.

ElevateBio’s LNP1-delivered A-base editor produced functional editing and target protein reduction in NHPs, with minimal transient liver enzyme changes across evaluated doses. Platform feedforward then enabled faster selection of follow-up editors, including engineered variants and alternate systems that improved in vitro potency.

In vivo gene editing advances fastest when optimization is iterative and integrated. By connecting RNA design, guide chemistry, LNP formulation, protein engineering, and preclinical validation, ElevateBio helps partners move from early editing concepts toward in vivo-ready programs with greater speed and higher potency without compromise.

View the Related Poster and Video Presentation

Tyler McCullough, Ph.D

Tyler McCullough, Ph.D., is the Associate Director of Translational Biology at ElevateBio’s Life Edit Therapeutics. He brings 11 years of extensive experience in gene editing with a focus on Lipid Nanoparticle (LNP) and Adeno-Associated Virus (AAV) delivery systems targeting various organs, including the eye, muscle and liver. His expertise includes IND-enabling studies, from mice up to non-human primates (NHP).

Previously, Dr. McCullough held several roles at Life Edit, such as Senior Scientist, Preclinical Development. In this role, he was responsible for conducting preclinical studies, including first-in-mouse editing with proprietary systems and delivery methods. His work helped generate critical in vivo editing data that contributed to securing key partnerships.

During his academic career, Dr. McCullough collaborated with industry leaders in gene editing, independently designing and executing studies that led to one of the first therapeutic CRISPR-based editing studies in NHPs. He completed his post-doctoral fellowship at Duke University and earned his Ph.D. in Genetics from the University of Florida. Additionally, he holds a Master of Health Science from John Hopkins University and Bachelor of Science in Microbiology and Cell Science from the University of Florida.

by

The future of advanced therapies won’t be defined by a single technology. For years, our industry has searched for the next breakthrough, whether that’s a more precise gene editor, a more efficient manufacturing platform, a faster analytical method, or an emerging modality poised to transform medicine. Each innovation matters, but none alone is enough to address the complexity of developing the next generation of therapies.

Too often, these technologies have evolved independently, with discovery, manufacturing, analytics, and emerging modalities advancing along separate paths. Enabling the next generation of therapies will require continuous investment across the entire advanced therapy lifecycle, from therapeutic discovery and process development to manufacturing, quality, release, and the technologies that will define the future of medicine.

At ElevateBio, that’s the philosophy driving our technology strategy. We are continuously investing in the capabilities needed to advance therapies from discovery through commercialization, because meaningful innovation happens when every stage of the lifecycle is strengthened. Every investment we make is intended to solve today’s challenges while building the infrastructure needed for tomorrow’s therapies.

Gene Editing Requires Multiple Approaches

Gene editing has transformed our understanding of what is therapeutically possible. That’s why ElevateBio built a diversified gene editing portfolio that includes novel nucleases and base editors, programmable gene insertion technologies, epigenetic editors, AI-designed enzymes, and the manufacturing capabilities needed to translate these discoveries into clinical development. This breadth allows us to approach complex disease targets with greater flexibility while reducing development risk and increasing the likelihood of identifying the optimal therapeutic strategy.

AI is accelerating this evolution even further. By combining a proprietary library of more than 10 billion proteins with generative AI and machine learning, we’re expanding protein engineering beyond the limits of naturally occurring biology. Rather than relying solely on existing proteins, we can computationally design and optimize therapeutic candidates with improved precision, potency, and safety. As AI continues to reshape biotechnology, its greatest impact may not simply be making discoveries faster, but helping scientists explore entirely new possibilities in biology that were previously inaccessible.

Together, these investments represent a continued commitment to expanding the possibilities of gene editing. By building capabilities across multiple technologies, platforms, and approaches, we are creating the foundation to address increasingly complex therapeutic challenges.

Automation Is Redefining Cell Therapy Manufacturing

Scientific innovation only creates value if it can be manufactured consistently and at scale. Cell therapy manufacturing remains one of the industry’s greatest operational challenges. Manual workflows can cause variability, limit scalability, and contribute significantly to costs. Addressing these challenges requires more than incremental process improvement; it requires rethinking how manufacturing itself is designed.

We recently completed building our new advanced therapies manufacturing facility in Pittsburgh and expect it to be operational next year. At this facility, we are taking a fresh look at automating manufacturing to reduce complexity, improve scalability, and lower cost of goods sold. This includes fully integrating and leveraging all the data and process systems we utilize to minimize human error, improve process consistency, accelerate production timelines, and create a foundation for more efficient manufacturing. We’ve also collaborated with companies such as Sartorius and OriBiotech on the optimization and implementation of cell therapy platforms. The objective is not simply to replace manual steps with automated ones, but to leverage systems that are inherently more predictable, reproducible, and efficient.

As advanced therapies move toward broader commercialization, manufacturing excellence will become just as important as scientific excellence.

Data Should Accelerate Decisions, Not Delay Them

Innovation doesn’t stop once manufacturing is complete. Release testing remains one of the most time-intensive stages of advanced therapy manufacturing, and every additional day spent waiting for analytical results delays treatment and adds operational complexity. We see an opportunity to fundamentally change this process by making analytics a more integrated, data-driven, and efficient part of the manufacturing workflow.

By investing in developing next-generation sequencing (NGS), rapid analytics, automation, and connected data workflows, we’re building release testing capabilities that compress timelines while maintaining the rigorous analytical standards these therapies require. Traditional release can often take weeks, representing upwards of 50% of overall manufacturing time. Whereas at ElevateBio, new sequencing assays can deliver results in as little as four days, helping reduce a critical bottleneck and accelerate the path to release. Our vision extends beyond implementing new instruments. We’re creating integrated analytical workflows where sequencing, data analysis, interpretation, and reporting work together to enable faster release decisions, reduce testing complexity, and improve manufacturing efficiency.

In the future, analytics won’t simply verify quality; they will become a driver of manufacturing innovation. The data generated throughout manufacturing and release can provide valuable insights to optimize processes, strengthen platform capabilities, and continuously improve future therapies.

Investing Ahead of the Next Wave of Innovation

Perhaps the most important technology investments are the ones the market hasn’t fully demanded yet. Driving innovation requires looking beyond today’s challenges and building the capabilities that will enable tomorrow’s therapies. The field continues to evolve rapidly, with mRNA and lipid nanoparticle (LNP) technologies emerging as foundational tools that extend well beyond vaccines, including in vivo cell therapies.

Recognizing that shift, we’ve continued investing in building capabilities spanning our lipid library, formulation, process development, quality systems, and manufacturing for mRNA and LNP-based programs. We have already manufactured over 25 drug substance or bulk drug product mRNA batches for both vaccine and in vivo therapeutic applications. We have also developed our own end-to-end mRNA manufacturing process, which we anticipate making available to partners by early 2027. Just as importantly, we’re building flexibility into our manufacturing model. Some partners arrive with established manufacturing processes ready for GMP execution, while others need support much earlier in development. Our vision is to meet innovators wherever they are by providing an integrated path from development through commercial production. Reducing technical transitions ultimately reduces development risk, shortens timelines, and helps innovative therapies move more efficiently toward patients.

Advancing Innovation Across the Advanced Therapy Lifecycle

Advanced therapies are becoming increasingly sophisticated. The innovations that define the next decade will likely require continued investments across multiple technologies and capabilities, rather than standalone platforms, making flexibility itself a critical capability for enabling the future of medicine.

At ElevateBio, we believe driving innovation requires building capabilities today that can address the challenges of tomorrow. By continuously investing across the advanced therapy lifecycle, we are strengthening the tools and technologies that enable discovery and development, improving manufacturing scalability, accelerating decision-making, and preparing for the therapies of the future.

Mike Paglia, Chief Technology Officer

Michael Paglia is the Chief Technology Officer at ElevateBio, a technology-driven company commercializing its enabling technologies, manufacturing capabilities, and industry-leading expertise to accelerate the development of genetic medicines to treat human diseases. He has more than two decades of experience in facility design, start up, and operations ensuring the highest standards of quality, safety, and regulatory compliance. 

At ElevateBio, Michael led the design, construction and operations of the BaseCamp manufacturing facility that was recognized as the Facility of the Year, Operational Excellence by International Society for Pharmaceutical Engineering (ISPE) in 2021.  Michael established the process development and manufacturing capability and leads manufacturing operations, CMC regulatory, process/analytical development, and the advancement of innovative process technologies. 

Prior to ElevateBio, Michael was the Vice President of CMC Operations at Oncorus responsible for the development and manufacturing of novel genetically modified oncolytic herpes virus for the treatment of cancer and prior to that, Head of Technical Operations, Cellular Process Development and Manufacturing Operations at bluebird bio where he led the early process development, manufacturing, and global technology transfer of four approved genetically modified autologous cell therapies.  Early in his career at Tolerx, Michal lead process development, and late-stage manufacturing of novel therapeutic antibody products designed to treat patients by reprogramming the immune system.

Michael received his undergraduate degree from Providence College and a Master’s of Science in Biochemistry and Cellular Biology from the University of New Hampshire where he was honored with the Distinguished Alumni Award from the College of Life Science and Agriculture (COLSA) in 2023 for his career guidance and ongoing initiatives in COLSA to enhance STEM workforce development initiatives.

by

Targeted large gene insertion is becoming one of the most important frontiers in genomic medicine. Many therapeutic applications benefit from or require inserting a full gene into a defined genomic location.

In many oncology or autoimmune therapies, this technology enables delivery of CARs. It also matters in monogenic disease, where inserting a functional gene may address multiple disease-causing variants with one therapeutic strategy.

The Field Needs Fit-for-Purpose Insertion Systems

Large gene insertion is not one problem. Payload size, delivery, genomic target access, insertion efficiency, specificity, and fit for either in vivo or ex vivo use are important considerations for the correct approach. No single enzyme class can address all potential applications.

Large serine recombinases (LSRs), CRISPR-LSR systems, and retrotransposons each bring different strengths. LSRs can insert large cargo without exposed double-strand breaks and are not limited by payload size. Retrotransposons offer an all-RNA delivery path. CRISPR-LSR approaches can make insertion more programmable, but with added complexity that may be better suited for ex vivo engineering.

The right question is not which modality is best. It is which modality best fits the biology, delivery route, target site, and development path.

Enzyme Diversity Creates More Paths to Success

Large insertion becomes more powerful when developers are not limited to a small set of naturally known enzymes. ElevateBio has built a discovery and engineering pipeline designed to expand that starting set.

Using a catalog of more than 10 billion proteins, ElevateBio mined more than 30,000 LSR candidates, and identified more than 100 active LSRs. Many demonstrated higher activity than the literature benchmark Bxb1 (view presentation and data).

This breadth creates optionality. Different LSRs have different recognition sites, activity levels, and integration profiles. A larger enzyme set gives developers more chances to find an insertion system that fits the therapeutic program instead of forcing the program to fit the tool.

Programmability Is the Next Barrier

LSRs are limited by the sites where they naturally integrate their cargo. Most natural or un-engineered LSRs do not have the desired insertion profiles into human genomes needed for therapeutic applications, which means the LSR technology cannot become broadly useful until engineered. ElevateBio is applying active learning, rational design, directed evolution, and generative AI to improve enzyme performance. The long-term goal is straightforward: choose the desired insertion location, then engineer or design an enzyme system matched to that site.

By leveraging our proprietary CRISPR technology to install landing pads, ElevateBio can make LSR-mediated large insertion programmable at defined genomic sites.  We developed this CRISPR LSR approach in primary T cells and have shown installation of LSR landing pad with greater than 90% efficiency at the TRAC locus. Addition of our LSR components achieved 67% installation of a CD19 CAR under the endogenous TRAC promoter. This approach is an attractive avenue for ex vivo manufacturing applications that require superb programmability.

The Future Is Fit-for-Purpose Insertion

A potent and diverse targeted insertion platform gives developers multiple paths to the same goal: inserting the right cargo, at the right site, in the right cell type, using a system matched to the therapeutic need.

Large gene insertion is not just another editing modality. It is a way to expand what genomic medicines can be designed to do.

View the Related Poster and Video Presentation

David Wiley, Director, Nucleic Acid Technology

LinkedIn

David Wiley is the Director of Nucleic Acid Technology at ElevateBio. His work focuses on guide RNA engineering, mRNA engineering, and production optimization, and he is currently leading the development of next-generation large gene insertion technologies, focusing on R2 retrotransposons and large serine recombinases technology.

Before ElevateBio, David was at Prime Medicine, where he led efforts in pooled screening platform development and early mRNA optimization. Prior to that, he was at Vertex Pharmaceuticals, working in the Functional Genomics group to identify potential therapeutic targets using CRISPR-based pooled screening approaches.

David earned his B.S. in Cell Biology and Genetics from the University of Georgia and his Ph.D. in Cell and Molecular and Developmental Biology from the University of North Carolina at Chapel Hill. He completed his postdoctoral training at Boston Children’s Hospital.

In his free time, David enjoys carpentry and spending time outdoors.

by

AI-directed protein engineering can optimize proteins for genomic medicines when machine learning models are trained on high-quality experimental activity data and paired with rapid design-build-test cycles.

At ElevateBio, we combine automation, protein language models, and active learning to identify protein variants with improved activity for genomic medicine applications.

Gene insertion by large serine recombinases (LSRs) or retrotransposases can make safer, more potent CAR T cell therapies and can be the solution to broadly curing monogenic diseases caused by a variety of DNA changes within the patient population.

Better Starting Proteins Create a Stronger Engineering Foundation

Our computational biologists mined ElevateBio’s large, curated bioinformatic databases to identify a diverse set of novel large serine recombinases and their corresponding attachment sites. Most of these wildtype proteins mediated large gene insertion in human cells, with several outperforming the literature standard Bxb1 molecule. More protein diversity means we can select the optimal LSRs to engineer for each therapeutic challenge.

Engineering in Mammalian Cells Improves Human-Relevant Performance

Engineering in mammalian cells avoids the common trap of finding proteins that work well in bacteria, but not in humans. At ElevateBio, we use experimentally derived activity data from mammalian cells to intelligently discover more potent molecules than those selected by nature, which translate into superior therapies.

Protein Language Models Focus the Search on Higher-Value Design Space

The limited throughput of mammalian cell wet lab experiments makes probing the vast protein design space difficult. Our use of protein language models combined with experimental data enables exploration of the vast and otherwise unmanageable protein fitness activity landscape. By identifying high fitness protein domains near activity maxima, we can rapidly focus our search on protein modifications that improve function.

Automation Accelerates the Design-Build-Test Cycle

With ElevateBio’s automation platform, we can synthesize protein variants in-house, reducing cost and speeding up the design-build-test cycle. After two rounds of machine-learning guided protein optimization completed in just 4 calendar weeks, we found many single mutations that improved large gene insertion, including one that was 2.4-fold better than wildtype protein from nature. By generating pairwise combinations of the most beneficial mutations, we found a new double-mutant variant 3.4-fold more active than wildtype (view and download poster on this topic). These data additionally measured interactions between pairs of individual mutations to facilitate design of higher order mutants which will further improve activity.

The takeaway is clear: protein engineering is most powerful when combining protein language models, machine learning, automation, and mammalian cell-based activity profiling as one integrated system. At ElevateBio, that integrated system helps accelerate protein optimization for genomic medicine development and deliver more effective solutions for our partners.

View the Related Poster and Video Presentation

Gavin Ellis, Senior Scientist, Translational Biology

LinkedIn

Gavin Ellis is a Senior Scientist at ElevateBio, where he has worked cross-functionally to improve our best-in-class base editing, large gene insertion, and epigenetic editing modalities.
Before ElevateBio, Gavin spent nine years at the Center for Cellular Immunotherapies at the University of Pennsylvania generating novel CAR and TCR transgenic Treg therapeutics for autoimmune disease and allotransplantation.

by

Optimized lipid nanoparticles can outperform electroporation in applications where cell health, durable expression, DNA delivery, and large gene insertion matter. That may not hold true in every T-cell engineering workflow today.

LNPs Offer a Non-Viral Alternative to Electroporation

Electroporation has long been a standard non-viral method for delivering genetic payloads into primary T cells. It is widely used because it is flexible and familiar, but it can also introduce cell stress, reduce viability, and limit performance with challenging payloads such as large DNA cargos.

At ElevateBio, we are developing lipid nanoparticle platforms designed to support efficient RNA and DNA delivery into primary human T cells. The goal is not simply to replace one delivery method with another. The goal is to expand what non-viral T-cell engineering can achieve when delivery efficiency, cell health, and payload flexibility are all prioritized.

LNPs Can Improve mRNA Delivery and CAR Expression

Efficient mRNA delivery is a core requirement for many T-cell engineering workflows. In primary human T cells, ElevateBio LNPs delivered eGFP mRNA more efficiently than a commercial T-cell transfection reagent while maintaining strong cell viability.

The same delivery advantage appeared in CAR mRNA engineering. When CD19 CAR mRNA was delivered by LNP, CAR expression remained more durable over time than with electroporation (see data in a technical poster of this work). Our proprietary LNPs achieved approximately 99% CAR-positive T cells on Day 2 and approximately 60% on Day 4, supporting the potential for LNPs to improve transient mRNA-based engineering workflows.

LNPs Can Support Functional Genome Editing

T-cell engineering requires more than expression. Delivery systems must also support functional genome editing when editors, guide RNAs, or other gene-editing components are introduced into cells.

ElevateBio RNA LNPs delivered nuclease and guide RNA payloads for TRAC knockout in primary human T cells. In these studies, LNP delivery supported potent TRAC knockout, with editing efficiency greater than 85%. That result shows that optimized LNPs can support functional editing performance, not only transient transgene expression.

DNA Delivery Is Where LNPs May Create the Greatest Advantage

Large DNA delivery remains one of the hardest problems in T-cell engineering. Electroporation can deliver DNA, but large cargos often reduce efficiency and increase stress on primary cells.

Our LNP platform, optimized for plasmid delivery into primary human T cells, delivered eGFP nanoplasmid DNA and achieved greater than 90% GFP-positive T cells.

The advantage became most apparent in large gene insertion. In an LSR-mediated CAR insertion workflow, RNA LNPs delivered LSR mRNA while DNA LNPs delivered the CAR donor DNA payload. The LNP-based system achieved up to approximately 88% CD19-CAR-positive T cells, compared with less than 20% insertion efficiency using electroporation.

LNPs Expand the Design Space for T-Cell Engineering

Electroporation remains an important tool, but optimized LNP systems can offer meaningful advantages in workflows that require efficient delivery, strong cell health, and flexible RNA and DNA payload handling.

LNPs are not just an alternative transfection method. For next-generation T-cell engineering, especially workflows involving large DNA insertion, LNPs may expand the range of non-viral engineering strategies available to developers.

View the Related Poster and Video Presentation

Xiuying Li, Ph.D., Senior Scientist, Technology Development

LinkedIn

Xiuying Li is a Senior Scientist specializing in lipid nanoparticle (LNP) delivery systems for gene editing and mRNA therapeutics, with expertise in nanomedicine, biomaterials, and nucleic acid delivery.

At ElevateBio, she has been central to developing proprietary LNP platforms for ex vivo and in vivo delivery of gene editing machinery and nucleic acid therapeutics, spanning formulation development, targeted delivery, process development, and translational research.

Previously at Immorna Therapeutics, she focused on the discovery, optimization, and scale-up of LNP systems for mRNA vaccines and therapeutics targeting infectious diseases and oncology diseases.

Xiuying holds a Ph.D. in Pharmaceutical Sciences and has built her career at the intersection of drug delivery, gene editing, and translational medicine. Motivated by the impact of rare genetic disease within her family, she is passionate about developing therapies that transform patients’ lives.

by

Autologous cell therapies have transformed what’s possible for patients. Now it’s time to rethink the testing methods used to release them.

For many cell therapy programs, the manufacturing processes have been engineered to compress timelines where possible. Cells are collected, engineered, expanded, tested, and prepared for reinfusion on timelines where even small delays matter. But contaminant detection remains one of the areas where the field still depends heavily on growth-based methods for sterility, mycoplasma, and adventitious agent testing.

That mismatch creates a practical problem. Advanced therapies are increasingly personalized, time-sensitive, and operationally complex. Release testing needs to evolve with the products it supports.

Growth-Based Testing Was Not Built for Modern Cell Therapy Timelines

Traditional contaminant testing is effective because it is conservative, familiar, and rooted in established regulatory expectations. But it is also slow by design. Many methods depend on giving bacteria, fungi, mycoplasma, or viruses enough time to grow to detectable levels.

That time requirement creates a major bottleneck for autologous cell therapies. When the full vein-to-vein process may take about a month, waiting for safety results consumes a meaningful share of the overall timeline. The issue is not that legacy methods lack value. The issue is that cell therapy manufacturing has outpaced the speed of the testing model.

Sequencing-Based Analytics Could Create a Faster Detection Model

Sequencing-based contaminant detection offers a different model: identify microbial or viral nucleic acids directly rather than waiting for organisms to grow.

At ElevateBio, we are developing NGS-based rapid analytics for cell therapy release testing. The workflow extracts DNA or RNA from a representative cell therapy product, prepares sequencing libraries, uses hybrid capture to enrich relevant targets, and applies custom bioinformatics to identify bacterial, fungal, or viral species. In early feasibility studies, this approach detected two fungal and three bacterial contaminants at approximately 10 CFU per 75,000 cells, as well as five viral contaminants at 1–10 genome copies per 7,500 cells (see the data).

Those results do not eliminate the need for further development. They do show that sequencing-based methods detect a diverse range of potential contaminants in a cell therapy-relevant background.

The Bigger Opportunity Is One Analytical Framework for Multiple Contaminant Classes

The value of sequencing is not only speed. It is breadth.

A sequencing-based approach has the potential to consolidate detection across bacteria, fungi, and adventitious viruses within a common analytical framework. It also produces digital, species-level information that may support more informative investigations when a signal is detected.

That matters because rapid release is not simply about getting to a pass/fail answer faster. It is about creating a more responsive quality model: one that can detect risk earlier, characterize it more precisely, and support better decisions before product release.

Rapid Release Will Require Both Innovation and Validation

Sequencing-based analytics are not a shortcut around safety expectations. They must be characterized, qualified, and validated with the same rigor expected of any release-relevant method.

The next work is clear: further define limits of detection, reduce cost and turnaround time, evaluate additional technologies, and demonstrate performance across more representative product types. The long-term opportunity is equally clear. Faster contaminant detection could help cell therapy programs reduce release bottlenecks while maintaining the safety standards patients depend on.

Rapid release starts with faster, more informative contaminant detection. Sequencing-based analytics are not just a new test. They are a path toward a release model better matched to the timelines and complexity of modern cell therapy.

View the Related Poster and Video Presentation

Gary Sommerville Ph.D., Principal Scientist, NGS, Technical Services

LinkedIn

Gary Sommerville is a Principal Scientist in ElevateBio’s Next-Generation Sequencing core lab, where he leverages over a decade of genomics expertise as the wet lab lead to drive cross-functional collaboration supporting internal R&D initiatives. He also leads assay development and onboards external sequencing clients seeking to utilize ElevateBio’s NGS technical capabilities.

Prior to ElevateBio, Gary spent six years as a Senior Scientist at Dana-Farber Cancer Institute’s Molecular Biology Core Facilities, where he led sequencing data generation for internal stakeholders and Harvard Medical School collaborators.

Gary earned his Ph.D. in Molecular Medicine from Trinity College Dublin, Ireland, investigating molecular signatures of cancer stem cells across various cancer types with and without BRAF V600E mutations.

In his free time, Gary enjoys traveling, attending sporting events with his children, and pursuing an ever-elusive scratch handicap in golf.

by

Base editing will not reach its full therapeutic potential if the field is limited to enzymes nature has already evolved. To expand what adenine base editors can do, we need to design deaminases for the job, not just search for better natural starting points. 

The Bottleneck Is No Longer Imagination, It Is Experimental Throughput 

Protein sequence space is too large to explore exhaustively. Even ambitious directed evolution campaigns test only a fraction of what is theoretically possible, and many enzymes that show early activity still carry unwanted attributes, including bystander editing profiles that limit targetability. Better enzymes exist. The challenge is finding them quickly enough, and with enough precision, to matter. 

Generative AI Breaks Evolutionary Constraints 

We wanted to explore beyond evolution. We trained a generative model on 370,000 putative adenine deaminases curated from ElevateBio’s 10+ billion protein catalog. Every training sequence was validated computationally, including protein folding with ESMFold, then filtered for quality before inclusion in the training body. The model learned the design grammar of this enzyme family, then generated diverse sequences while maintaining the active site, binding sites, and core folds required for functional deaminases.  

While generating new sequences, selection of sampling parameters is what separates useful diversity from noise. More deterministic settings produced high duplication rates and sequences nearly identical to training data. Balancing creativity with determinism produced more diverse protein sequences ready for experimental validation. 

Generation, Filtering, and Optimization Need to Work as One Loop 

Generating novel sequences is only the first step. We pair sequence generation with strict computational filtering and a machine learning optimization pipeline that uses experimental feedback to improve candidates quickly. Using the “generate, test, optimize” feedback loop, we mitigate much of the inherent risk of de novo generation by treating protein design as an iterative engineering problem.  

Although here we chose to focus on base editors, this integrated platform enables us to design purpose-built editors that can be matched to specific therapeutic targets or disease mechanisms across the full spectrum of gene editing modalities. 

One Round of Optimization Creates a Precision Editor 

Our top de novo deaminase candidate started at less than 2% on-target A→G editing. After a single round of ML-based optimization, it exceeded 20%. The results: 

  • Greater than 20% on-target A→G base editing after one optimization cycle (view poster and data) 
  • Minimal bystander profile across a panel of therapeutically-relevant genes. Examples include less than 1% bystander editing just two nucleotides from the target base 

The specificity profile is key. Bystander editing at nearby positions is the factor that makes many genomic targets intractable with current base editing tools. A deaminase that edits its target base while leaving the +2 position unchanged opens sites that were previously untouchable.  

The next leap in base editing will not come only from finding better natural enzymes. It will come from designing fit-for-purpose deaminases, testing them quickly, and improving them through experimental feedback. That shift moves enzyme discovery from a search problem to an engineering discipline. 

View the Related Poster and Video Presentation

Matt Nethery, Principal Data Scientist, Computational Biology

LinkedIn

Matt Nethery works as a Principal Data Scientist in ElevateBio’s gene editing services, responsible for the implementation of generative AI for discovery and engineering of novel genome editing tools. He has 15 years of wide-ranging industry experience, from building software applications and mobile analytics in the healthcare industry, to bioinformatics and generative AI. Matt completed his PhD in Functional Genomics at North Carolina State University researching microbial genomics, CRISPR biology, and phage engineering.

MENU