Representations of Lipid Nanoparticles Using Large Language Models for Transfection Efficiency Prediction

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2024 May 29

PMID 38810107

Authors

Saeed Moayedpour

Jonathan Broadbent

Saleh Riahi

Michael Bailey

Hoa V Thu

Dimitar Dobchev

Akshay Balsubramani

Ricardo N D Santos

Lorenzo Kogler-Anele

Alejandro Corrochano-Navarro

Sizhen Li

Fernando U Montoya

Vikram Agarwal

Ziv Bar-Joseph

Sven Jager

Affiliations

Soon will be listed here.

Abstract

Motivation: Lipid nanoparticles (LNPs) are the most widely used vehicles for mRNA vaccine delivery. The structure of the lipids composing the LNPs can have a major impact on the effectiveness of the mRNA payload. Several properties should be optimized to improve delivery and expression including biodegradability, synthetic accessibility, and transfection efficiency.

Results: To optimize LNPs, we developed and tested models that enable the virtual screening of LNPs with high transfection efficiency. Our best method uses the lipid Simplified Molecular-Input Line-Entry System (SMILES) as inputs to a large language model. Large language model-generated embeddings are then used by a downstream gradient-boosting classifier. As we show, our method can more accurately predict lipid properties, which could lead to higher efficiency and reduced experimental time and costs.

Availability And Implementation: Code and data links available at: https://github.com/Sanofi-Public/LipoBART.

Citing Articles

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery.

Paloncyova M, Valerio M, Dos Santos R, Kuhrova P, Srejber M, cechova P Mol Pharm. 2025; 22(3):1110-1141.

PMID: 39879096 PMC: 11881150. DOI: 10.1021/acs.molpharmaceut.4c00744.

References

Li Y, Li R, Chakraborty A, Ogurlu R, Zhao X, Chen J . Combinatorial Library of Cyclic Benzylidene Acetal-Containing pH-Responsive Lipidoid Nanoparticles for Intracellular mRNA Delivery. Bioconjug Chem. 2020; 31(7):1835-1843. PMC: 9069324. DOI: 10.1021/acs.bioconjchem.0c00295. View

Li B, Luo X, Deng B, Wang J, McComb D, Shi Y . An Orthogonal Array Optimization of Lipid-like Nanoparticles for mRNA Delivery in Vivo. Nano Lett. 2015; 15(12):8099-107. PMC: 4869688. DOI: 10.1021/acs.nanolett.5b03528. View

Mouchlis V, Afantitis A, Serra A, Fratello M, Papadiamantis A, Aidinis V . Advances in de Novo Drug Design: From Conventional to Machine Learning Methods. Int J Mol Sci. 2021; 22(4). PMC: 7915729. DOI: 10.3390/ijms22041676. View

Eygeris Y, Gupta M, Kim J, Sahay G . Chemistry of Lipid Nanoparticles for RNA Delivery. Acc Chem Res. 2021; 55(1):2-12. DOI: 10.1021/acs.accounts.1c00544. View

Sun D, Lu Z . Structure and Function of Cationic and Ionizable Lipids for Nucleic Acid Delivery. Pharm Res. 2023; 40(1):27-46. PMC: 9812548. DOI: 10.1007/s11095-022-03460-2. View

Gaulton A, Bellis L, Bento A, Chambers J, Davies M, Hersey A . ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2011; 40(Database issue):D1100-7. PMC: 3245175. DOI: 10.1093/nar/gkr777. View

Winter B, Winter C, Schilling J, Bardow A . A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing. Digit Discov. 2022; 1(6):859-869. PMC: 9721150. DOI: 10.1039/d2dd00058j. View

Liu S, Cheng Q, Wei T, Yu X, Johnson L, Farbiak L . Membrane-destabilizing ionizable phospholipids for organ-selective mRNA delivery and CRISPR-Cas gene editing. Nat Mater. 2021; 20(5):701-710. PMC: 8188687. DOI: 10.1038/s41563-020-00886-0. View

Aimo L, Liechti R, Hyka-Nouspikel N, Niknejad A, Gleizes A, Gotz L . The SwissLipids knowledgebase for lipid biology. Bioinformatics. 2015; 31(17):2860-6. PMC: 4547616. DOI: 10.1093/bioinformatics/btv285. View

10.

Kauffman K, Dorkin J, Yang J, Heartlein M, DeRosa F, Mir F . Optimization of Lipid Nanoparticle Formulations for mRNA Delivery in Vivo with Fractional Factorial and Definitive Screening Designs. Nano Lett. 2015; 15(11):7300-6. DOI: 10.1021/acs.nanolett.5b02497. View

11.

Dara T, Vatanara A, Meybodi M, Alsadat Vakilinezhad M, Sadegh Malvajerd S, Vakhshiteh F . Erythropoietin-loaded solid lipid nanoparticles: Preparation, optimization, and in vivo evaluation. Colloids Surf B Biointerfaces. 2019; 178:307-316. DOI: 10.1016/j.colsurfb.2019.01.027. View

12.

Zhang X, Zhao W, Nguyen G, Zhang C, Zeng C, Yan J . Functionalized lipid-like nanoparticles for in vivo mRNA delivery and base editing. Sci Adv. 2020; 6(34). PMC: 7442477. DOI: 10.1126/sciadv.abc2315. View

13.

Kon E, Elia U, Peer D . Principles for designing an optimal mRNA lipid nanoparticle vaccine. Curr Opin Biotechnol. 2021; 73:329-336. PMC: 8547895. DOI: 10.1016/j.copbio.2021.09.016. View

14.

Rajan K, Zielesny A, Steinbeck C . DECIMER: towards deep learning for chemical image recognition. J Cheminform. 2020; 12(1):65. PMC: 7590713. DOI: 10.1186/s13321-020-00469-w. View

15.

Kim J, Eygeris Y, Gupta M, Sahay G . Self-assembled mRNA vaccines. Adv Drug Deliv Rev. 2021; 170:83-112. PMC: 7837307. DOI: 10.1016/j.addr.2020.12.014. View

16.

Ucak U, Ashyrmamatov I, Lee J . Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization. J Cheminform. 2023; 15(1):55. PMC: 10228139. DOI: 10.1186/s13321-023-00725-9. View

17.

Hou X, Zaks T, Langer R, Dong Y . Lipid nanoparticles for mRNA delivery. Nat Rev Mater. 2021; 6(12):1078-1094. PMC: 8353930. DOI: 10.1038/s41578-021-00358-0. View

18.

Han X, Zhang H, Butowska K, Swingle K, Alameh M, Weissman D . An ionizable lipid toolbox for RNA delivery. Nat Commun. 2021; 12(1):7233. PMC: 8668901. DOI: 10.1038/s41467-021-27493-0. View

19.

Baden L, El Sahly H, Essink B, Kotloff K, Frey S, Novak R . Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine. N Engl J Med. 2020; 384(5):403-416. PMC: 7787219. DOI: 10.1056/NEJMoa2035389. View

20.

Rogers D, Hahn M . Extended-connectivity fingerprints. J Chem Inf Model. 2010; 50(5):742-54. DOI: 10.1021/ci100050t. View