Evidence map›Paper›PMID 42806112›Full record

ArticleNature biotechnology2026

Accelerated discovery of thermostable mRNA-lipid nanoparticle vaccines using data-efficient AI.

Jinbi Tian, Khanh T M Tran, Brett H Pogostin, Olivia Sheridan, Sevinj Mursalova, Amy H Lee, Shuai Liu, Jaya Hamkins, Daniel Antov, Alana L Power and 5 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Jinbi Tian *David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Khanh T M Tran *David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-5331-4068
Brett H PogostinDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Olivia SheridanDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sevinj MursalovaDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Amy H LeeDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Shuai LiuDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Jaya HamkinsDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Daniel AntovDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0009-0001-7301-7212
Alana L PowerDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Zane S DashDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Dongsoo YunDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-9495-1613
Mina Konaković LukovićDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-2895-0206
Robert S LangerDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. rlanger@mit.edu.ORCID http://orcid.org/0000-0003-4255-0492
Ana JaklenecDavid H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. jaklenec@mit.edu.ORCID http://orcid.org/0000-0001-6096-0538

Funding

Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation) INV-061250
6 · The paper itself

Abstract

The instability of mRNA-lipid nanoparticles (LNPs) necessitates ultra-cold storage, limiting global distribution and their broader application in advanced delivery systems. Solid-state, water-free formulations enhance thermostability and enable integration into emerging delivery modalities such as microneedle patches. Previous efforts to stabilize mRNA-LNPs have been constrained by narrow formulation scope and low-throughput screening methods. Here we introduce Algorithm-Guided Experimental design for lipid Nanoparticle Thermostabilization (AGENT), an artificial intelligence (AI)-driven framework that couples high-throughput experimentation with Bayesian optimization to identify thermostable mRNA-LNP formulations. AGENT extracts maximal information from sparse experimental datasets, enabling efficient formulation optimization in six iterations completed within 1 month. We stabilized mRNA vaccines with two clinically relevant LNPs representative of the Moderna (SM-102-based) and Pfizer-BioNTech (ALC-0315-based) compositions into solid-state formulations that retained 100% bioactivity after storage at 37 °C for more than 2 months. In rodents and non-human primates, thermostable, solid-state vaccine formulations induced antigen-specific immune responses non-inferior to those elicited by intramuscular delivery of freshly prepared soluble vaccines.

Identifiers

PMID42806112

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.