Engineering Repeat-Dose RNA: Engineering Around Immune Recognition In Repeat-Dose RNA Therapeutics

By Jyotsna Jajula, research assistant, Wayne State University

Repeat dosing remains one of the most important unresolved challenges in RNA therapeutics. A first dose may generate strong expression, encouraging pharmacology, or an early clinical signal, yet subsequent doses often produce weaker activity, greater reactogenicity, or both. For developers trying to move RNA beyond one-time use and into durable treatment paradigms, immune recognition is not a side issue. It is a core product design constraint.

Too often, repeat dosing is framed as a downstream clinical problem that appears only after a candidate has already been optimized for potency, manufacturability, and delivery. In practice, the opposite is true. The probability of successful redosing is shaped much earlier by decisions made across sequence design, nucleotide chemistry, impurity control, formulation strategy, route of administration, and translational testing.^1,3

That makes repeat dosing an engineering problem as much as an immunology problem. Teams that want to improve redosing performance should stop treating immune recognition as a late-stage complication to manage and start treating it as an early constraint to design around.^1,2

Why Repeat Dosing Still Breaks Programs

The promise of RNA therapeutics rests in part on programmability. Developers can redesign sequences, modify chemical architecture, and pair payloads with evolving delivery systems more quickly than with many traditional modalities. But repeat dosing exposes the gap between what is programmable in theory and what is sustainable in practice.¹

A product may perform well after one administration because the system has not yet been conditioned by prior exposure. Once the same payload or delivery vehicle is administered again, the biological context changes. Innate sensing pathways may activate more rapidly, inflammatory responses may become harder to control, and productive expression may decline even when the dose remains constant. In some cases, the treatment burden required to restore performance begins to outweigh the therapeutic benefit.^1,4

This pattern matters because many clinically meaningful RNA applications will require more than a single administration. Chronic disease, protein replacement, regenerative strategies, and certain oncology settings all increase the pressure to build platforms that can be used repeatedly, not just once. If repeat dosing fails, the limitation is not only immunological. It becomes strategic, commercial, and clinical.¹

Immune Recognition Has Multiple Sources

One of the most common development mistakes is to search for a single explanation for repeat-dose failure. In reality, immune recognition usually emerges from several overlapping contributors rather than one isolated cause.

The RNA payload itself can be part of the problem. Sequence composition, length, secondary structure, untranslated region design, and chemical modification choices can all influence how the molecule is perceived in vivo. A construct optimized primarily for strong initial expression may carry liabilities that become more apparent only after repeated exposure.^4,7

Manufacturing-related impurities can further amplify those liabilities. Double-stranded RNA species, truncated products, residual reagents, and other process-derived contaminants may not prevent an early proof of concept, but they can narrow the tolerability window and complicate redosing.⁷

The delivery system adds another layer of complexity. Lipid nanoparticles and other carriers are not immunologically neutral, and their composition, biodistribution, and clearance behavior can shape how the body responds to each administration.^3,6

Dose level, route of administration, and dosing interval also matter. The relevant question is not simply whether a product is immunogenic in absolute terms. It is whether the full treatment strategy repeatedly introduces the same molecular and formulation signals into the same biological environment under conditions that increase the likelihood of recognition.^3,6

Design The Payload For Redosing

If a program is intended for repeat administration, payload design should reflect that goal from the beginning. Sequence optimization cannot focus only on maximizing first-dose expression. Developers should also evaluate how codon usage, untranslated regions, structural features, and sequence motifs influence both performance and immune visibility over time.^8,9,10

That requires a broader definition of optimization. A payload that delivers the strongest initial expression is not automatically the best candidate for a repeat-dose product. In some cases, slightly lower first-dose output may be an acceptable trade-off if it supports better tolerability, more consistent performance across administrations, or a wider therapeutic window.^8,9

Chemical modification strategy deserves the same discipline. Modified nucleotides can reduce innate activation and improve translation in some settings, but they are not universal solutions. Their value depends on the modality, target tissue, delivery system, and manufacturing process. The right question is not whether a modification is broadly beneficial, but whether it improves the balance among potency, durability, tolerability, and manufacturability in the intended dosing context.^2,7

Programs designed for chronic or semi-chronic use should therefore adopt a redosing-oriented design framework. Instead of asking only which payload performs best after the first administration, teams should ask which payload remains most viable after repeated administrations under clinically relevant conditions.^7,8,9

Treat Purity As A Biological Variable

In many organizations, purity is still discussed primarily as a CMC matter. It is linked to analytical methods, release specifications, and lot consistency. All of those are essential. But in repeat-dose RNA development, purity also functions as a biological variable.^7,11

Impurities such as double-stranded RNA can act as potent drivers of innate immune activation. Even when a material passes internal quality thresholds, its residual impurity profile may still meaningfully affect the feasibility of repeat administration. This is especially important when a program depends on maintaining activity over multiple doses rather than maximizing performance after a single one.^7,11,12

That reality should change how teams evaluate purification strategies. Yield, scalability, cost, and robustness remain important, but they should not be the only decision criteria. A process that produces materially cleaner product and better repeat-dose tolerability may create more long-term value than one that delivers higher output with a narrower biological margin.^7,12,13

The practical implication is straightforward: analytical characterization should connect more directly to biological readouts. If one purification approach reduces immunostimulatory species and produces more stable performance over repeated dosing, that is not a secondary process benefit. It is a core product advantage.^7,12,13

Build Delivery Systems For Reuse

Delivery systems are often optimized for uptake, biodistribution, and acute expression after a first administration. Those are necessary priorities, but they are not enough when repeat dosing is part of the intended product profile. Developers should also ask whether a delivery system is built for reuse.^3,6

A carrier that appears highly effective after one administration may become less attractive after repeated exposure if it contributes to cumulative inflammatory burden, unfavorable tissue responses, or declining functional performance. Formulation teams should therefore evaluate repeat-use tolerability alongside first-dose potency. The goal is not simply to achieve delivery but to sustain useful delivery across a clinically meaningful dosing schedule.^4,6

This is where siloed development can become costly. Payload design, formulation design, and dosing strategy are often optimized separately, even though repeat-dose performance depends on their interaction. A modest formulation adjustment, a different route of administration, or a longer interval between doses may improve redosing feasibility as much as a payload redesign does.³

For that reason, developers should optimize the treatment strategy as a system rather than as a set of disconnected variables. A delivery platform that works once is not necessarily a platform that can support durable therapy over time.^3,6

Test For Repeat-Dose Failure Earlier

Another reason repeat-dose problems emerge late is that development programs often ask the wrong question. Many preclinical packages are designed to show whether a candidate works after a first administration. Far fewer are structured to determine whether that performance can survive repeat use.^1,4

For repeat-dose candidates, translational testing should be built to detect more than acute expression or short-term pharmacology. Teams should look for declining activity across administrations, changes in pharmacodynamic response, evidence of innate immune activation, and cumulative tolerability issues that become more apparent over time. Those signals are often more informative for long-term product viability than a strong first-dose readout alone.^4,6

It is also important to compare alternative payloads, formulations, and purification strategies under repeat-dose conditions early enough to influence development decisions. If developers wait until late-stage studies to ask whether a product can withstand repeated administration, they risk preserving the wrong version of the candidate.^1,4

The goal is not to create a perfect predictive model for every program. It is to generate decision-relevant evidence early enough to shape design choices. In repeat-dose RNA development, a candidate that looks strong after one administration may still prove fundamentally weak if repeat-use performance is not tested soon enough.^1,4

What RNA Developers Should Do Now

Teams pursuing repeat-dose RNA therapies should act on five priorities. First, define repeat dosing as a product requirement early. If a therapy is likely to need multiple administrations, that reality should shape development choices from the start rather than being treated as a later clinical concern.^1,4

Second, optimize payloads for redosing, not just for peak first-dose expression. The strongest initial output is not always the best long-term development choice if it comes with liabilities that become more visible after repeated administration.¹

Third, elevate impurity control from a manufacturing issue to a biological design lever. Cleaner product may improve more than analytical quality. It may also expand the tolerability window and strengthen repeat-dose feasibility.⁷

Fourth, evaluate delivery systems based on reuse, not simply acute efficiency. A carrier that performs well once is not necessarily one that can support durable therapy across a clinically meaningful schedule.^4,6

Fifth, build translational packages that test the intended dosing strategy early enough to change direction when needed. In repeat-dose development, early evidence of durability, tolerability, and immune visibility is often more valuable than a strong first-dose signal in isolation.^1,4,7

Repeat dosing will not be solved by a single innovation. It will be improved through tighter integration across molecular design, process development, formulation strategy, and translational testing. Developers that adopt that mindset early will be better positioned to build RNA therapies that work not just once but as often as patients need them.^1,3,4,6

References:

1. Rohner E, Yang R, Foo KS, et al. Unlocking the promise of mRNA therapeutics. Nat Biotechnol. 2022;40(11):1586-1600. doi:10.1038/s41587-022-01491-z.
2. Karikó K, Buckstein M, Ni H, Weissman D. 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. doi:10.1016/j.immuni.2005.06.008.
3. Hou X, Zaks T, Langer R, Dong Y. Lipid nanoparticles for mRNA delivery. Nat Rev Mater. 2021;6(12):1078-1094. doi:10.1038/s41578-021-00358-0.
4. Kenjo E, Hozumi H, Makita Y, et al. Low immunogenicity of LNP allows repeated administrations of CRISPR-Cas9 mRNA into skeletal muscle in mice. Nat Commun. 2021;12(1):7101. doi:10.1038/s41467-021-26714-w.
5. Xiao Y, Lian X, Sun Y, et al. High-density brush-shaped polymer lipids reduce anti-PEG antibody binding for repeated administration of mRNA therapeutics. Nat Mater. 2025;24(11):1840-1851. doi:10.1038/s41563-024-02116-3.
6. Lee Y, Jeong M, Park J, Jung H, Lee H. Immunogenicity of lipid nanoparticles and its impact on the efficacy of mRNA vaccines and therapeutics. Exp Mol Med. 2023;55(10):2085-2096. doi:10.1038/s12276-023-01086-x.
7. Karikó K, Muramatsu H, Ludwig J, et al. Generating the optimal mRNA for therapy: HPLC purification eliminates immune activation and improves translation of nucleoside-modified, protein-encoding mRNA. Nucleic Acids Res. 2011;39(21):e142. doi:10.1093/nar/gkr695.
8. Leppek K, et al. Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics. Nat Commun. 2022;13(1):1536. doi:10.1038/s41467-022-28776-w.
9. Zhang H, Zhang L, Lin A, et al. Algorithm for optimized mRNA design improves stability and immunogenicity. Nature. 2023;621(7978):396-403. doi:10.1038/s41586-023-06127-z.
10. Kirshina A, Vasileva O, Kunyk D, et al. Effects of Combinations of Untranslated-Region Sequences on Translation of mRNA. Biomolecules. 2023;13(11):1677. doi:10.3390/biom13111677.
11. Mu X, Hur S. Immunogenicity of In Vitro-Transcribed RNA. Acc Chem Res. 2021;54(21):4012-4023. doi:10.1021/acs.accounts.1c00521.
12. Siew YY, Zhang W. Removing immunogenic double-stranded RNA impurities post in vitro transcription synthesis for mRNA therapeutics production: A review of chromatography strategies. J Chromatogr A. 2025;1740:465576. doi:10.1016/j.chroma.2024.465576.
13. Baiersdörfer M, Boros G, Muramatsu H, et al. A Facile Method for the Removal of dsRNA Contaminant from In Vitro-Transcribed mRNA. Mol Ther Nucleic Acids. 2019;15:26-35. doi:10.1016/j.omtn.2019.02.018.

About The Author:

Jyotsna Jajula is a research assistant at Wayne State University. Her work broadly explores RNA delivery mechanisms in oncology cell models, with a focus on internalization and cytoplasmic fate of therapeutic peptides. Jajula holds a master’s degree in pharmaceutical sciences and has prior research experience in lipid nanoparticles, RNA stability, and biodistribution strategies across oncology, immunology, and gene-therapy applications.