Galit Meshulam-Simon is the associate director of Commercial Applications at Elegen.
COVID-19 brought messenger RNA (mRNA) into the global spotlight, demonstrating its speed, flexibility, and efficacy in vaccine development. However, mRNA’s potential extends far beyond infectious disease prevention. As a programmable and transient genetic platform, it is increasingly being explored for a wide range of therapeutic applications, including treatments for cancer, autoimmune disease, and rare genetic disorders.
One of mRNA’s greatest advantages is the ability to rapidly iterate and optimize sequences, accelerating drug discovery and development. Advances in cell-free gene synthesis and in vitro transcription workflows are streamlining this process, enabling faster production of high-quality mRNA while maintaining precision and scalability. AI is further enhancing these capabilities by optimizing sequence design, predicting greater sequence stability, and refining manufacturing processes, thus helping researchers develop more effective and targeted therapies.
As innovations in gene synthesis, AI-driven optimization, and delivery continue to progress, mRNA is emerging as a transformative platform for the next generation of therapeutics, with the potential to revolutionize treatment across a wide array of diseases. This article explores the expanding role of mRNA in therapeutic development, the latest advancements in synthesis and AI-driven optimization, and how these innovations are shaping the future of genetic medicine.
Advantages of Therapeutic mRNA
mRNA as a therapeutic is not a new idea—scientists have explored its potential since the 1990s.1 The groundbreaking work of Karikó and Weissman2 enabled the unprecedented rapid development of the mRNA COVID-19 vaccines. mRNA vaccines offer several advantages over traditional protein-based vaccines, including eliciting a stronger immune response, featuring reconfigurable designs, and enabling rapid development on accelerated timelines.3 Its programmability enables precise antigen targeting, and with advances in AI and gene synthesis, researchers can rapidly optimize mRNA designs to encode disease-specific epitopes. Many of these benefits—particularly transient and controlled protein expression, along with ease of design and manufacturing—extend beyond vaccines to other mRNA-based therapies.
Additionally, mRNA production via in vitro transcription is scalable, with well-defined parameters, enabling faster development cycles. These attributes position mRNA as a safe, adaptable, and rapidly deployable platform, revolutionizing personalized and pandemic-responsive medicine (below).
Accelerated Gene Expression Feedback Loop. Innovations in cell-free gene synthesis decrease turnaround times for obtaining full-length, complex, NGS-verified DNA from weeks or months to as few as 6-8 business days. Cell-free DNA speeds workflows screening candidates for efficacy, stability, and safety, generating large volumes of experimental data. This data can then be rapidly fed back into AI models, refining predictions and design rules iteratively. This synergy of AI-guided optimization and cell-free gene synthesis can accelerate vaccine candidates to reach clinical trials in weeks while improving safety and efficacy. 4-6
Illustrated by Andrew LaMorte, https://www.andrewlamorte.com
Recent breakthroughs in cell-free gene synthesis have drastically reduced the time and cost of producing customized mRNA sequences. Coupled with AI-driven design and optimization, researchers can now rapidly fine-tune mRNA structures for improved stability and efficacy. These advancements have propelled mRNA technology forward, making it a powerful platform for vaccines, cancer treatments, and personalized genetic therapies.
Cell-Free Gene Synthesis and AI Optimization Improve mRNA as a Therapeutic Platform
The combination of high-throughput cell-free gene synthesis and AI is revolutionizing therapeutic development by enabling rapid optimization of mRNA sequences for enhanced stability, translational efficiency, and tailored immune responses.
Cell-free gene synthesis eliminates the need for bacterial cloning, which has been the conventional method for high-quality gene-length DNA production. Cloning involves lengthy culturing steps, is vulnerable to endotoxin contamination and plasmid instability, and is constrained by complex sequence designs. By eliminating bacterial cloning, full-length cell-free gene synthesis enables scientists to investigate a broader and more complex sequence space not only faster but also more cost-efficiently (below).
Conventional Cell-Based versus Cell-Free Gene Synthesis. Conventional cell-based gene synthesis requires a more complicated, time- and resource-intensive workflow to assemble, sequence, amplify, and purify a single DNA sequence. In contrast, next-generation cell-free gene synthesis technologies produce long, complex, high-accuracy DNA fast and with high-throughput, enabling researchers to shift the effort normally spent building DNA to analyzing downstream results, improving designs, and exploring a broader sequence space instead.
Illustrated by Andrew LaMorte, https://www.andrewlamorte.com
This cell-free approach allows the production of previously "unclonable" sequences, such as viral genomes or synthetic gene circuits, which are essential for mRNA vaccines and CRISPR-based therapies.7 Additionally, removing cellular contaminants common in bacterial cloning simplifies purification and reduces immunogenicity risks. Cell-free gene synthesis accelerates timelines, enabling rapid synthesis and validation of DNA templates in days rather than weeks or months.4
AI tools such as AlphaFold for predicting 3D protein structures,8 Active Learning-Assisted Directed Evolution (ALDE) for enabling broader sampling of the protein fitness landscape,9 ImmunoBERT for predicting immunogenicity to minimize adverse immune responses,10 and language models for predicting mRNA translation efficiency11 are increasing the speed and improving the quality of screening candidates. With cell-free gene synthesis eliminating the bottlenecks imposed by cloning, these AI-designed constructs can be produced more rapidly, further accelerating the pace and lowering the cost of identifying leads to advance to the clinic.
Future Implications for mRNA Therapeutics
The fusion of cell-free gene synthesis and AI-driven optimization is redefining what’s possible in mRNA therapeutic development. By removing the bottlenecks of traditional cloning, researchers can now access longer, high-complexity, sequence-perfect DNA quickly—empowering rapid design, synthesis, and testing. As these technologies continue to advance, together with advanced mRNA delivery technologies,12 mRNA is poised to become a cornerstone of genetic medicines, driving innovations across vaccines, cancer therapies, and rare disease treatments.
- Wolff JA, et al. Direct gene transfer into mouse muscle in vivo. Science. 1990;247(4949 Pt 1):1465-1468.
- Karikó K, et al. Suppression of RNA recognition by Toll-like receptors: the impact of nucleoside modification and the evolutionary origin of RNA. Immunity. 2005;23(2):165-175.
- Wu Y, et al. Comparison of immune responses elicited by SARS-CoV-2 mRNA and recombinant protein vaccine candidates. Front Immunol. 2022;13.
- Polack FP, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med. 2020;383(27):2603-2615.
- Zhang H, et al. Algorithm for optimized mRNA design improves stability and immunogenicity. Nature. 2023;621(7978):396-403.
- Leppek K, et al. Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics. Nat Commun. 2022;13(1):1536.
- Carlson R. The changing economics of DNA synthesis. Nat Biotechnol. 2009;27(12):1091-1094.
- Jumper J, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583-589.
- Yang J, et al. Active learning-assisted directed evolution. Nat Commun. 2025;16(1):714.
- Ruffolo JA, et al. Fast, accurate antibody structure prediction from deep learning on a massive set of natural antibodies. Nat Commun. 2023;14(1):2389.
- Chu Y, et al. A 5' UTR language model for decoding untranslated regions of mRNA and function predictions. Nat Mach Intell. 2024;6(4):449-460.
- McKinlay CJ, et al. Enhanced mRNA delivery into lymphocytes enabled by lipid-varied libraries of charge-altering releasable transporters. Proc Natl Acad Sci U S A. 2018;115(26):E5859-E5866.
