Unlocking the potential of synthetic DNA 

Raquel Sanches-Kuiper, Vice President of Science and Applications at Evonetix, and Clare Whitewoods, Marketing Communications Manager at Evonetix, look at the benefits synthetic DNA brings to pharmaceutical development. 

Engineering biology is reshaping the world, from enabling the rapid development of vaccines to providing alternatives to petroleum fuels. The rapid progress made over the past two decades has demonstrated the enormous potential of bioengineering to solve some of the world’s greatest challenges, spanning healthcare, sustainability, materials, and agriculture1. 

Central to engineering biology is the design – or redesign – and assembly of biological components to generate a desired function. A robust supply of synthetic DNA is essential for this process, particularly as frequent iteration is generally required as part of the Design-Build-Test-Learn (DBTL) development cycle to optimise results. Many DNA synthesis technologies have emerged to serve engineering biology, but demand still outstrips supply and relieving the gene synthesis bottleneck will be essential to scale the field2.

The opportunity for personalised healthcare 

Advancements in engineering biology and access to synthetic DNA will be transformative in medicine, especially for personalised therapeutics. Biologics represents the fastest-growing sector of the pharmaceutical industry, with hundreds of therapies approved in recent years3,4. mRNA vaccines and antibody therapies in particular are promising areas that are already having significant impact across the healthcare industry, with enormous potential to address unmet medical needs. However, for both these technologies, their dependence on rapid access to synthetic DNA means that further scaling depends on a step-change in access to accurate, gene-length DNA synthesis technology.   

The Covid-19 pandemic demonstrated the potential for mRNA vaccines. The vaccines developed by Pfizer/BioNTech and Moderna have been instrumental to the global vaccination programmes that prevented the deaths of millions and enabled the relaxation of population restrictions5. The rapid delivery of the Covid-19 vaccines proved that mRNA vaccines have the potential to deliver significant time and cost savings in comparison with traditional vaccine technologies, increasing overall process efficiency and enabling development cycles of months instead of years.  

Their short development timeline gives mRNA vaccines several distinct advantages over traditional vaccines. They are well suited to rapidly responding to emerging infectious disease threats – a growing need as the rate of pandemic-capable diseases increases6. Their speed also allows for their potential use in personalised therapy for diseases such as cancer. Indeed, prior to the pandemic, mRNA vaccine technology had been in development for years as scientists explored their application in a range of cancers. Such cancer vaccines work by training the immune system to attack cells displaying cancer-specific antigens. Personalised cancer vaccines require a particularly fast turnaround time to allow the unique profile of the patient’s cancer to be reflected in the treatment and avoid delays in initiating treatment as the disease progresses. 

Monoclonal antibodies represent another biotherapeutic class that has been proven to be safe and effective for the treatment of a range of conditions. Approximately half of the almost 200 antibodies either approved or in regulatory approval are therapies for cancer indications, with other therapeutic areas including infectious diseases and immune, cardiovascular and neurological disorders7. The growth and success of monoclonal antibody-based therapies has been made possible by extensive progress in antibody engineering, resulting in more effective drugs with fewer side effects. Clinical use of monoclonal antibodies requires them to have several properties that ensure their efficacy, safety, and ease of use, including specifications for antigen binding affinity and specificity, biological efficacy, pharmacokinetics, and immunogenicity – all of which must be enhanced through cycles of screening and optimisation8,9.  By improving access to accurate synthetic DNA, screening timelines can be reduced and a new generation of antibody therapeutics made possible.  

Artificial intelligence: accelerating engineering biology applications  

Progress in machine learning (ML) and other artificial intelligence (AI)-based tools has given scientists new approaches for rapid, sophisticated protein design. To date, AI has generally enabled optimisation of existing protein structures to achieve desired properties; however, more recent developments have seen tools built that can develop entirely new proteins not present in nature, providing a huge opportunity to explore sequence space and find solutions to biomedical, industrial and agricultural problems that evolution has not been required to solve10,11.” 

AI will accelerate engineering biology by guiding design, but in doing so, will also significantly increase the demand for synthetic DNA. 

The need for accessible synthesis of accurate gene-length DNA  

Engineering biology has already showcased its ability to address global problems in healthcare. Synthetic DNA, a key enabler in the biology revolution, is in increasing demand, and addressing the gene synthesis bottleneck is essential to further scale the field. But despite the clear need, increasing access to accurate, gene-length DNA for use in research isn’t straightforward.  

Emerging technologies, including benchtop gene synthesis platforms, will go a long way to providing a solution to this bottleneck, by enabling affordable and flexible gene synthesis at scale, simplifying and accelerating each iteration of an experiment, and driving the DBTL (Design, Build, Test, Learn) cycle. These systems must integrate each stage of the gene synthesis process, from DNA synthesis and assembly to error removal, if the error rate and overall assembly time are to be significantly reduced. 

The integration of AI in drug discovery has further accelerated progress in engineering biology, supporting protein design and development, but this advancement further potentiates the need for accurate, gene-length synthetic DNA. In addition, with biologics such as mRNA vaccines and monoclonal antibodies emerging as powerful therapeutics, there is great potential to rapidly respond to new diseases and to enable personalised medicines, particularly against cancer.  

Technologies that enable access to accurate, gene-length DNA will be pivotal to maximising these opportunities and will greatly expand engineering biology capabilities to tackle the world’s greatest challenges.  

About the authors  

Raquel Sanches-Kuiper, PhD is Vice President of Science and Applications at Evonetix, with a background in Protein Engineering and Product Development, including the early development of NGS technologies at Solexa and Illumina. 

Clare Whitewoods, PhD, is Marketing Communications Manager at Evonetix, and has a background in Biochemistry and Scientific Communications.  


  1. Garner, K. L. Principles of synthetic biology. Essays Biochem 65, 791-811, doi:10.1042/EBC20200059 (2021).
  2. Hoose, A., Vellacott, R., Storch, M., Freemont, P. S. & Ryadnov, M. G. DNA synthesis technologies to close the gene writing gap. Nat Rev Chem 7, 144-161 (2023) 
  3. Ebrahimi S. & Samanta D., Engineering protein-based therapeutics through structural and chemical design. Nature Communications, 14, 2411 (2023).   
  4. Otto E. et al., Rapid growth in biopharma: Challenges and opportunities. (McKinsey Global Institute 2014).   
  5. Watson, O. et al., Global impact of the first year of COVID-19 vaccination: a mathematical modeling study. The Lancet Infectious Disease 9, 1293-1302 (2022).   
  6. Tollefson, J., Why deforestation and extinctions make pandemics more likely. Nature 584, 175–176 (2020).  
  7. The Antibody Society. Therapeutic monoclonal antibodies approved or in review in the EU or US. (date accessed 5 September 2023).   
  8. Ducancel & Muller, Molecular engineering of antibodies for therapeutic and diagnostic purposes. mAbs, 2012, 4, 445-457.   
  9. Wang et al., Optimization of therapeutic antibodies. Antibody Therapeutics, 2021, 4, 45-54.  
  10. Yang, K. et al., Machine-learning-guided directed evolution for protein engineering. Nature Methods, 16, 687-694 (2019). 
  11. Eisenstein M., AI-enhanced protein design makes proteins that have never existed. Nature Biotechnology, 41, 303-305 (2023).   


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