Reducing Lentiviral Vector Manufacturing Timelines with dbDNA

 

Lentiviral vectors are essential for some of today’s most transformative cell and gene therapies, from CAR-T to modified stem cell treatments. But making them efficiently and at scale is still a challenge. Traditional plasmid DNA (pDNA) manufacturing is slow, labour-intensive, and prone to batch variability, hurdles that slow timelines and increase costs. 

dbDNA offers a better alternative

As a linear, enzymatically amplified DNA construct, dbDNA is produced without bacterial fermentation, eliminating antibiotic resistance sequences and bacterial-derived impurities. The result? Highly pure DNA, ready for research or GMP manufacturing, with a faster, scalable process without the use of animal derived components. 

Proven performance across lentiviral vectors generations

In collaboration with Expression Manufacturing, Touchlight’s dbDNA was compared with pDNA in both 2.5th- and 3rd-generation lentiviral vectors systems. The findings were clear: 

• Comparable infectious titres to pDNA when delivering a GFP transgene 

• Up to 40% greater infectious titres for a clinical-stage ET3 transgene using dbDNA 

• Consistent performance across vector system generations 

Why it matters

Switching to dbDNA reduces production time, enhances batch consistency, and removes unwanted impurities, paving the way for more efficient lentiviral vectors manufacturing, and accelerating the path from development to clinic. Touchlight’s cell-free DNA platform is already helping clients bring innovative therapies to patients faster. 

Read the full application note to explore how dbDNA can optimise your LVV production.

How AI Assisted Bioprocessing Can Transform Biotech

 

Biotechnology is evolving, but some methods haven’t caught up. Slow experiments, costly materials, and outdated assumptions are holding back innovation. As demand grows for faster, smarter, and more scalable processes, the industry is turning to Artificial Intelligence (AI), modelling, and data-driven strategies to break through the bottlenecks. From regulatory shifts to machine learning breakthroughs, it’s time to rethink how AI assisted bioprocessing can help build the future of biotech. 

Old Methods: Slow, Costly, Limited 

The industry relies on statistical methods to improve biological systems, but traditional approaches like Design of Experiments (DoE) and one-factor-at-a-time experiments are often slow and resource-heavy. Limited data and high material costs make these methods less effective, especially when scaling new modalities. 

To meet growing demands for faster development and scalable processes, both regulators and industry leaders are turning to data-driven decision-making. The FDA’s 2025 draft guidance on AI highlights the need for model transparency and risk awareness in regulated environments. 

Despite its promise, AI and Machine Learning (ML) are often misunderstood. One myth is that they require massive datasets, when in fact, data quality is just as important. Another is that one model fits all, but the best approach depends on the data, process complexity, and specific goals. 

AI in MSAT: Predict, Optimise, Scale 

Manufacturing Science and Technology (MSAT) teams are increasingly using AI and modelling tools to improve bioprocess efficiency. Techniques like ML, Bayesian optimisation, together with empirical models help predict outcomes, reduce lab work, and support scale-up. Their uses include: 

• ML models: Predict DNA-based therapeutic yield using only the sequence, replacing wet lab experiments and modality-based heuristics. 

• Bayesian optimisation (using Gaussian Processes): Identifies optimal restriction digest conditions with far fewer experiments than traditional DoE, saving time and costly reagents. 

• Empirical models: Translate lab observations into predictive tools for scale-up using power-law models for viscosity or concentration and polarisation models for TFF flux prediction. 

Touchlight’s Approach: Flexible, Predictive, Proven 

Touchlight manufactures DNA, from discovery to GMP, to support the development of genetic medicines. One of our challenges is predicting how long each step in the process will take. Since our operations run on a fixed working day, accurate timing is essential for scheduling. 

A critical step of our process involves tangential flow filtration (TFF) using hollow fiber membranes. These membranes are ideal for processing DNA, but the time it takes to run a batch can vary depending on factors like membrane size, dimensions, DNA concentration, and shear rate. 

To improve predictability, we have developed a hybrid model that combines physics with machine learning: 

• Mechanistic Model: The core of the model is based on mass transfer theory, incorporating concentration polarisation effects to describe solute transport through the membrane. This layer captures the fundamental physics governing flux behaviour, influenced by shear rate, membrane dimensions, and solute concentration gradients. 

• Discrepancy Modelling: While mechanistic models offer valuable insight, they may fail to capture all real-world effects.  To overcome this, we implemented a Gaussian Process (GP) model to learn the discrepancy between theoretical predictions and observed data. This discrepancy model captures residual behaviours not accounted for by the mass transfer framework, such as non-ideal flow patterns, membrane fouling, or subtle interactions between operating parameters. 

Advantages Over Traditional Approaches 

• Enhanced Predictive Accuracy: By correcting for model bias, hybrid models outperform purely mechanistic or empirical approaches in real-world scenarios. 
• Generalisation across scales: The mechanistic layer supports extrapolation to new equipment and scales, while the GP model adapts to specific operational contexts. 

• Uncertainty quantification: GP modelling enables probabilistic predictions, supporting risk-aware decision-making and robust scheduling. 

By combining these approaches, we can accurately predict processing times across different scales and equipment setups. This enables more efficient process design, improved planning, and accelerated delivery, ultimately helping genetic medicines reach patients faster.
 

Want to fast-track your project with cell-free DNA?  

Why Cell-Free DNA Is Replacing Plasmids in Next-Gen Genetic Medicine

In the fast-moving world of genetic medicine, cell-free DNA production has the potential to significantly advance the industry by making DNA faster, safer, and with greater purity. That was the message at the recent Outsourced Pharma webinar, “From Plasmids to Cell-Free DNA Using Megabulb DNA: How Touchlight and ElevateBio Are Enabling Safer and More Effective Cell and Gene Therapies.” 

The session brought together two innovators reshaping the foundation of advanced therapies: Touchlight, the London-based CDMO pioneering enzymatic, cell-free DNA manufacture, and ElevateBio, a leading US company driving process and technology innovation in cell and gene therapy. 

Moving Beyond Plasmids

For decades, plasmid DNA has been the workhorse of genetic medicine, but it comes with challenges in consistency, scalability, and regulatory complexity. Touchlight’s cell-free DNA platform takes a different approach, replacing bacterial fermentation with a fully enzymatic process that produces high-purity DNA at scale. 

As Dr Elena Stoyanova, Principal Scientist at Touchlight, explained, our technology isn’t just an incremental improvement; it’s a reinvention. Touchlight’s portfolio includes dbDNA, a linear, double-stranded DNA vector used across therapeutic modalities, and its newest innovation, mbDNA + Custom Circles, a suite of novel circular DNA architectures designed to unlock new levels of precision and safety in genome editing. 

The Power of mbDNA^TM

mbDNA offers a compelling alternative to plasmids and even viral vectors, providing higher homology-directed repair (HDR) efficiency and lower toxicity. This means that not only are cells engineered more effectively, but that they recover faster and expand more robustly, crucial advantages for therapeutic development. 

Dr. Stoyanova showcased how mbDNA’s circular, single-stranded design is enabling new frontiers in non-viral gene editing, while remaining scalable and GMP-ready, two qualities essential for real-world therapeutic manufacturing. 

ElevateBio’s Next-Generation Engineering

Taking the innovation from concept to process, Dr Chesney Michaels, Director of Emerging Technology for Cell and Gene Therapy at ElevateBio, demonstrated how mbDNA is being integrated into non-viral T cell engineering workflows. 

Through detailed case studies, he highlighted how ElevateBio’s ecosystem, from its BaseCamp manufacturing hub to its Life Edit gene editing platform, is developing the next generation of cell therapies. The collaboration with Touchlight shows how mbDNA performs across multiple delivery systems, including electroporation and lipid nanoparticles (LNPs), offering flexibility for both research and clinical production. 

A Glimpse of What’s Next
As both speakers emphasised, the partnership between Touchlight and ElevateBio reflects an industry shift from complex, often virus-dependent systems toward simpler and safer DNA technologies. With innovations like mbDNA, the path from design to clinic is becoming faster, more efficient, and ultimately more accessible for patients in need. 

The full webinar dives deeper into the data, process optimisation, and the regulatory implications of this paradigm shift. 

Watch the full webinar on demand to see how Touchlight and ElevateBio are transforming the future of DNA manufacturing and gene therapy innovation. 

How to Scale DNA Production Effectively to Meet the Needs of Genetic Medicines

 

DNA is the blueprint of life, carrying the molecular instructions that tell every cell how to function. As we learn more about this code, our ability to rewrite it – and correct problems when they arise – continues to grow.

Genetic medicines use DNA to reprogramme cells, repair or replace lost functions, and train the immune system to fight disease with remarkable precision. From cancer and neurodegenerative disorders to infectious diseases, these DNA-based therapies are opening up exciting new possibilities in healthcare.

However, progress in this field relies on a reliable, scalable supply of high-quality DNA, and traditional manufacturing methods simply can’t keep up. In this blog, we explore the growing demand for genetic therapies, why reliance on plasmid DNA creates bottlenecks, and how our dbDNA technology provides a faster, more cost-effective, and scalable solution.

The Rising Demand for DNA in Genetic Medicines

The demand for genetic medicines is significantly growing due to advancements in genetic technologies, the growing burden of genetic disorders, and the push for more personalised and precise healthcare.

The global cell and gene therapy market is rapidly expanding, estimated at USD 25.03 billion in 2025 and projected to reach USD 117.46 billion by 2034. These medicines offer powerful new ways to treat a range of conditions, from rare inherited disorders to complex neurodegenerative diseases. By correcting or replacing faulty genes, gene therapies hold the potential not only to treat but even to cure diseases once thought untreatable.

Although mRNA vaccines, had been in development for many years, they came to global attention during the COVID-19 pandemic, when they were quickly developed and deployed at scale. Their success demonstrated the power of using genetic instructions to train the immune system. Since then, research has expanded rapidly beyond COVID-19 into areas such as cancer, infectious diseases, genetic disorders and cardiovascular diseases.

Currently, mRNA is being investigated for personalised cancer vaccines, a form of cancer immunotherapy. Unlike chemotherapy and radiotherapy, which affect both healthy and cancerous cells, these vaccines are designed to target only the tumour. By sequencing a patient’s tumour to identify unique mutations, scientists can create a bespoke mRNA vaccine that trains the immune system to exclusively recognise and eliminate cancer cells while sparing healthy tissue. Although still in early clinical trials, personalised cancer vaccines could offer highly precise, individualised treatments, providing better outcomes and fewer side effects.

Cell therapies are also advancing the field of cancer immunotherapy. CAR-T cell treatments have shown highly promising results in blood cancers, and researchers are now working to improve their ability to treat solid tumours. If successful, these therapies could provide patients with effective, targeted options where few currently exist.

But as demand for these types of genetic medicines grows, so too does the need for plasmid DNA, the critical starting material used to manufacture them. However, reliance on plasmid DNA has become a major bottleneck, limiting the speed and scale needed to keep pace with modern genetic medicine.

Why Plasmid DNA is Holding Back Genetic Medicine

Currently, most plasmid DNA is made by inserting the desired genetic sequence into a plasmid, growing it in bacteria, and then extracting and purifying the DNA. This process is slow, costly, and can take up to a year for GMP-grade material, due to complex fermentation, purification, and quality control steps. It also depends on expensive specialist facilities, equipment, and highly trained staff, all of which restrict the scalability of large-scale production.

Additionally, because plasmid DNA is produced in bacteria, it often carries unwanted bacterial sequences such as antibiotic resistance genes, which can raise safety concerns and delay development. Plasmid DNA also faces fidelity issues, as certain DNA elements are structurally unstable in E. coli, leading to deletions or rearrangements that compromise sequence integrity and reduce consistency.

Meeting the demand for genetic medicines with dbDNA technology

We know that to realise the potential of genetic medicines, we must come up with a better way to make DNA. That’s why we developed our dbDNA technology, an advanced cell-free DNA platform that produces synthetic dbDNA through an in vitro enzymatic process rather than using bacterial cells. This platform supports a range of applications, including viral vectors, non-viral, mRNA, gene editing and vaccine applications, offering safer, scalable and more economical alternatives to traditional plasmid-based methods.

Key advantages of dbDNA technology for your workflows include:

• Rapid production – Multi-gram quantities manufactured in weeks compared with the many months for plasmid DNA, bringing therapies to market quicker.
• High purity – A fully cell-free process eliminates bacterial contaminants such as antibiotic resistance genes, improving safety and simplifying downstream workflows.
• Scalability – Easily scaled from research to clinical and commercial supply, without the need for specialist equipment and extensive fermentation processes.
• High fidelity – Enzymatic amplification preserves complex or unstable DNA regions that are difficult to maintain in plasmids, ensuring consistent sequence integrity.
• Cost efficiency – High-expression performance reduces DNA input requirements, and the streamlined process lowers overall development and manufacturing costs.

Summary

Genetic medicines hold enormous promise for the future of healthcare, with the potential to deliver long-term, disease-modifying treatments across many conditions. Most excitingly, they open the door to more precise and personalised approaches that can drastically improve outcomes for patients.

Realising this potential depends on fast, scalable DNA production. Technologies like dbDNA make this possible. By producing DNA rapidly, cleanly, and at scale, dbDNA technology removes the bottlenecks of plasmid production and provides the reliable foundation needed to accelerate life-changing therapies for patients.

To learn more about how our dbDNA can accelerate your genetic medicine projects, get in touch or explore our technology further.

CRISPR HDR Breakthrough with mbDNA: ssDNA Template Boosts Cell Viability & Scalability

 

 

CRISPR has transformed our ability to edit genomes, enabling targeted insertions, deletions, and base corrections. Yet implementing homology-directed repair (HDR) in therapeutic settings remains fraught with technical and logistical hurdles. From donor-to-donor variability to immunogenic responses, existing DNA templates often struggle to balance editing efficiency, cell viability, and manufacturing scalability.   

The CRISPR HDR Bottleneck in Cell Therapy

Delivering reliable, high-efficiency HDR in immune or stem cells typically encounters:   

• Donor-to-donor variability in cell viability, recovery, and yield   

• Suboptimal knockout/knock-in (KO/KI) rates that lengthen development cycles   

• Immunogenic sequences triggering unwanted immune activation   

• Inefficient nuclear import of non-viral DNA   

• Size constraints limiting cargo size to a few kilobases   

Overcoming these intertwined challenges demands a next-generation template designed from the ground up for precision and performance.   

Introducing mbDNA: A Tailored ssDNA HDR Platform

mbDNA (Megabulb DNA) rethinks CRISPR HDR templates as circular, single-stranded DNA molecules with a brief, user-defined double-stranded stem (~30 bp).

This architecture:

• Eliminates phage and bacterial sequences to reduce immunogenicity   

• Supports large gene-of-interest (GOI) cargos up to 20 kb   

• Includes a short-hairpin structure for robust Cas nuclease binding   

• Promotes active nuclear import for efficient template delivery   

By combining precise gene insertion with a minimal immunogenic footprint, mbDNA enables consistent cell viability and high KI rates across diverse donors.   

Scalable, Cell-Free Manufacturing

Traditional ssDNA synthesis at gene length is laborious and low yielding. Touchlight’s proprietary cell-free DNA platform addresses this by:   

• Utilising enzymatic amplification for rapid, high-fidelity ssDNA production   

• Offering full control over sequence composition and batch consistency   

• Accelerating timelines for clinical applications with GMP coming soon

This streamlined process bridges research and manufacturing, ensuring that HDR templates are available at scale without compromising quality.   

Proven Performance in the Generation of CAR-T Cells

In collaboration with an autologous ex vivo CAR-T cell partner, GMP-representative mbDNA was benchmarked against dbDNA and plasmid DNA (pDNA). Key findings included:   

• Knock-in efficiency reaching up 70% across a broad concentration range (see Figure 1)    

• Cell counts on par with DNA-free controls, indicating minimal cytotoxicity (see Figure 2) 

• Robust GOI integration at the TRAC locus, measured eight days post-electroporation