(M1030-07-47) A Data-Efficient Approach to Design Stable Solid-State Lipid Nanoparticles for Microneedle Application

Monday, November 10, 2025

10:30 AM - 11:30 AM CT

Presenting Author(s)

KT

Khanh T.M. Tran, Ph.D.

Postdoctoral Associate
Massachusetts Institute of Technology
Arlington, Massachusetts, United States

Main Author(s)

KT

Khanh T.M. Tran, Ph.D.

Postdoctoral Associate
Massachusetts Institute of Technology
Arlington, Massachusetts, United States

Co-Author(s)

JT

Jinbi Tian, BS

Graduate student
Massachusetts Institute of Technology
Cambridge, Massachusetts, United States
AL

Amy Lee, Ph.D.

Postdoctoral Associate
Massachusetts Institute of Technology
Cambridge, Massachusetts, United States
MK

Mina Konaković Luković, Ph.D.

Assistant Professor
Massachusetts Institute of Technology
Cambridge, Massachusetts, United States
RL

Robert Langer, Ph.D.

Institute Professor and Professor of ChemE & BE
Massachusetts Institute of Technology
Cambridge, Massachusetts, United States
Ana Jaklenec, Ph.D.

Principal Research Scientist
Massachusetts Institute of Technology
Cambridge, Massachusetts, United States

Purpose: In limited-resource settings such as developing countries and remote areas, maintaining the cold chain for biologics and ensuring availability of trained medical personnels are major barriers to global healthcare access. Dissolvable microneedle patches (MAP) have emerged as a promising solution: they can be self-administered with minimal training, generate no sharp waste, and easy to transport¹. Dry solid form of MAP also has more favorable characteristics like enhanced stability, preserved bioactivity, and longer shelf-life as compared to liquid form². However, preserving the bioactivity of loaded cargos in MAP presents significant challenges, especially for lipid nanoparticles (LNP) based vaccines and therapeutics. LNP have been widely used in FDA-approved products for encapsulating and delivering RNA vaccines and cancer drugs. When going through a phase transition from liquid to solid in MAP fabrication, LNPs are inherently unstable, prone to aggregation, structural loss, and degradation. Thus, the selection of appropriate protectants or excipients is critical to preserving essential properties such as particle size, dispersity, encapsulation efficiency, structural integrity, and biological activity of LNP within the microneedle matrix³. Yet, conventional manual screening methods are inefficient and resource-heavy, given the wide range of factors involved. These challenges in developing stable solid-state LNP-based therapeutics for microneedles formulation are often time-consuming, expensive, and require extensive refinement. Here, we aim to develop highly scalable protocols and data-driven approach to enable rapid formulation screening of LNP loaded MAP, ensuring their integrity and stability under long-term storage at ambient temperature.

Methods: We leveraged Artificial Intelligence (AI)-guided Bayesian optimization models and high throughtput in vitro cell assays to streamline the process of identifying suitable excipients or excipient compositions to stabilize LNP for MAP fabrication. We employed STING agonist protein loaded LNP as a model for the screening. We used THP1-Lucia ISG cells which secretes luciferase Lucia reporter gene under the control of an IRF-inducible promoter (STING agonists) as a measurable metric to assess LNP stability. Sugars, amino acids, buffers, and polymers encompass some of the wide variety of stabilizing agents that can be used individually or in combination when phase transitioning into solid-state samples. Preliminary material screenings were conducted to filter out unnecessary information that the model might encounter. We selected the materials to stabilize LNP and construct MAP based on preliminary experiments and existing literature search. We determined factors such as concentration range, buffer choice, specific excipients to help facilitating the search of the AI-model. With the data sets provided by the model, solid-state STING-LNPs MAP were made by mixing soluble STING-LNP with excipient compounds and vacuum-drying for 48-hour. Samples were then rehydrated and treated to cells. Bioluminescence data from in vitro assay was be fed into the AI model. The model optimized the data set and prompted the next conditions to evaluate. This loop was repeated until we obtained the most optimized formulations.

Results: Searching for an optimal solution while minimizing the number of experiments performed is crucial. Figure 1 describes the implemented high-throughput experimental framework to screening to identify optimized excipient composition. After solid-state formation of STING-LNP with excipients via vaccum drying at ambient temperature, samples will be rehydrated in PBS then treated to THP1-Lucia IGS cells for 24 hours. Bioluminescence signals were measured and normalized to fresh STING-LNPs. The data generated from experiments will be input to the AI models and the model will make predictions of the next sets of conditions. After 5 rounds of optimization, we were able to improve LNP stability to high 80% (Figure 2). The data demonstrated that the interactive feed-back loop will accelerate discovery by prioritizing candidates for experimental validation, reducing trial-and-error cycles and enhancing precision. Figure 3 presented the top performing excipient compounds from each round of optimizations. By using this approach, we can quickly identify excipient combinations that could preserve LNP activities after drying, which is suitable for MAP fabrication.

Conclusion: Current strategies to enhance the thermostability of LNP loaded MAP formulations focus on developing solid-state, water-free systems that minimize the risk of hydrolytic degradation and particle aggregation. Overcoming these formulation challenges could greatly enhance the applicability and effectiveness of MAP for therapeutics and vaccines products, ultimately expediting responses to global health issues. By harnessing the power of AI, we could facilitate discovery of stable solid-state LNPs for MAP formulations by 12-time faster, which will broaden their clinical applications. Additionally, high-throughput screening protocols could facilitate the search and narrow down stabilizing formulations in an efficient and timely manner.

References: 1Tran, Khanh TM, et al. "Transdermal microneedles for the programmable burst release of multiple vaccine payloads." Nature Biomedical Engineering 5.9 (2021): 998-1007.
2Tran, Khanh TM, et al. "Single-administration long-acting microarray patch with ultrahigh loading capacity and multiple releases of thermally stable antibodies." Molecular Pharmaceutics 20.5 (2023): 2352-2361.
3Han, J., Kanelli, M., Liu, Y., Daristotle, J. L., Pardeshi, A., Forster, T. A., ... & Jaklenec, A. (2025). On-patient medical record and mRNA therapeutics using intradermal microneedles. Nature Materials, 1-10.

Acknowledgements: Khanh Tran is a recipient of a postdoctoral fellowship from the Ludwig Center at MIT’s Koch Institute for Integrative Cancer Research. Jinbi Tian is a recipient of Siebel Scholars Foundation Fellowship. This abstract is based on research funded by (or in part by) the Gates Foundation. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the Gates Foundation.

High throughput screening and AI-guided optimization framework. Solid-state formulations of LNP were made by mixing LNP with excipient compositions provided by the algorithm and vacuum-dried overnight. LNP were reconstituted in PBS and treated to cells for 24 hours. Data from the experiments are normalized and fed into the algorithms. AI-algorithm optimizes the excipient compositions and provided the next sets of conditions to evaluate.

Stability improvements of Solid-state LNP for MAP formulation after 5 rounds of optimization. Normalized luminescence data from cells treated with reconstituted LNP, N=3 biological replicates.

Excipient compositions of top performing formulations from each round of optimization. Excipients include polymers, sugars, amino acid.