Review of machine learning for lipid nanoparticle formulation and process development

Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of pharmaceutical sciences 2024-09
Hauptverfasser: Dorsey, Phillip J., Lau, Christina L., Chang, Ti-chiun, Doerschuk, Peter C., D'Addio, Suzanne M.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title Journal of pharmaceutical sciences
container_volume
creator Dorsey, Phillip J.
Lau, Christina L.
Chang, Ti-chiun
Doerschuk, Peter C.
D'Addio, Suzanne M.
description Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular delivery for therapeutic proteins. In the pharmaceutical industry, establishing a robust formulation and process to achieve target product performance is a critical component of drug development. Fundamental understanding of the processes for making LNPs and their interactions with biological systems have advanced considerably in the wake of the COVID-19 pandemic. Nevertheless, LNP formulation research remains largely empirical and resource intensive due to the multitude of input parameters and the complex physical phenomena that govern the processes of nanoparticle precipitation, self-assembly, structure evolution, and stability. Increasingly, artificial intelligence and machine learning (AI/ML) are being applied to improve the efficiency of research activities through in silico models and predictions, and to drive deeper fundamental understanding of experimental inputs to functional outputs. This review will identify current challenges and opportunities in the development of robust LNP formulations of nucleic acids, review studies that apply machine learning methods to experimental datasets, and provide discussion on associated data science challenges to facilitate collaboration between formulation and data scientists, aiming to accelerate the advancement of AI/ML applied to LNP formulation and process optimization.
doi_str_mv 10.1016/j.xphs.2024.09.015
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3110913884</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0022354924004222</els_id><sourcerecordid>3110913884</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1527-6d4c322c2457f0d48cc60e601805215f1770c039fa2254dafa42c9f7b6168adb3</originalsourceid><addsrcrecordid>eNp9kMtqHDEQRUWwiSe2fyALo6U33SmppX5ANsHkYTAYjGctNFIpo6Fbaks9jv330TBOll4VFKcutw4hnxnUDFj7ZVe_zNtcc-CihqEGJj-QFZMcqhZYd0JWAJxXjRTDGfmU8w4AWpDyIzlrhkYwMXQrsn7AZ49_aHR00mbrA9IRdQo-_KYuJjr62VsadIizTos3Ix7W037Ui4-B6mDpnKLBnKnFZxzjPGFYLsip02PGy7d5TtY_vj_e_Kru7n_e3ny7q0yp2VWtFabh3HAhOwdW9Ma0gKV8D5Iz6VjXgYFmcJpzKax2WnAzuG7TsrbXdtOck-tjbunwtMe8qMlng-OoA8Z9Vg1jMLCm70VB-RE1Keac0Kk5-UmnV8VAHXSqnTroVAedCgZVdJajq7f8_WZC-__kn78CfD0CWL4sIpPKxmMwaH1Csygb_Xv5fwHhIYbY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3110913884</pqid></control><display><type>article</type><title>Review of machine learning for lipid nanoparticle formulation and process development</title><source>Alma/SFX Local Collection</source><creator>Dorsey, Phillip J. ; Lau, Christina L. ; Chang, Ti-chiun ; Doerschuk, Peter C. ; D'Addio, Suzanne M.</creator><creatorcontrib>Dorsey, Phillip J. ; Lau, Christina L. ; Chang, Ti-chiun ; Doerschuk, Peter C. ; D'Addio, Suzanne M.</creatorcontrib><description>Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular delivery for therapeutic proteins. In the pharmaceutical industry, establishing a robust formulation and process to achieve target product performance is a critical component of drug development. Fundamental understanding of the processes for making LNPs and their interactions with biological systems have advanced considerably in the wake of the COVID-19 pandemic. Nevertheless, LNP formulation research remains largely empirical and resource intensive due to the multitude of input parameters and the complex physical phenomena that govern the processes of nanoparticle precipitation, self-assembly, structure evolution, and stability. Increasingly, artificial intelligence and machine learning (AI/ML) are being applied to improve the efficiency of research activities through in silico models and predictions, and to drive deeper fundamental understanding of experimental inputs to functional outputs. This review will identify current challenges and opportunities in the development of robust LNP formulations of nucleic acids, review studies that apply machine learning methods to experimental datasets, and provide discussion on associated data science challenges to facilitate collaboration between formulation and data scientists, aiming to accelerate the advancement of AI/ML applied to LNP formulation and process optimization.</description><identifier>ISSN: 0022-3549</identifier><identifier>ISSN: 1520-6017</identifier><identifier>EISSN: 1520-6017</identifier><identifier>DOI: 10.1016/j.xphs.2024.09.015</identifier><identifier>PMID: 39341497</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>AI/ML ; Artificial intelligence ; Formulation and process development ; Lipid nanoparticle(s) ; LNP ; Machine learning ; Optimization</subject><ispartof>Journal of pharmaceutical sciences, 2024-09</ispartof><rights>2024 American Pharmacists Association</rights><rights>Copyright © 2024. Published by Elsevier Inc.</rights><rights>Copyright © 2024 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1527-6d4c322c2457f0d48cc60e601805215f1770c039fa2254dafa42c9f7b6168adb3</cites><orcidid>0000-0002-0711-4428 ; 0000-0002-6464-0758 ; 0000-0002-4549-3481 ; 0009-0005-7388-6169 ; 0000-0002-4517-6582</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27929,27930</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39341497$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dorsey, Phillip J.</creatorcontrib><creatorcontrib>Lau, Christina L.</creatorcontrib><creatorcontrib>Chang, Ti-chiun</creatorcontrib><creatorcontrib>Doerschuk, Peter C.</creatorcontrib><creatorcontrib>D'Addio, Suzanne M.</creatorcontrib><title>Review of machine learning for lipid nanoparticle formulation and process development</title><title>Journal of pharmaceutical sciences</title><addtitle>J Pharm Sci</addtitle><description>Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular delivery for therapeutic proteins. In the pharmaceutical industry, establishing a robust formulation and process to achieve target product performance is a critical component of drug development. Fundamental understanding of the processes for making LNPs and their interactions with biological systems have advanced considerably in the wake of the COVID-19 pandemic. Nevertheless, LNP formulation research remains largely empirical and resource intensive due to the multitude of input parameters and the complex physical phenomena that govern the processes of nanoparticle precipitation, self-assembly, structure evolution, and stability. Increasingly, artificial intelligence and machine learning (AI/ML) are being applied to improve the efficiency of research activities through in silico models and predictions, and to drive deeper fundamental understanding of experimental inputs to functional outputs. This review will identify current challenges and opportunities in the development of robust LNP formulations of nucleic acids, review studies that apply machine learning methods to experimental datasets, and provide discussion on associated data science challenges to facilitate collaboration between formulation and data scientists, aiming to accelerate the advancement of AI/ML applied to LNP formulation and process optimization.</description><subject>AI/ML</subject><subject>Artificial intelligence</subject><subject>Formulation and process development</subject><subject>Lipid nanoparticle(s)</subject><subject>LNP</subject><subject>Machine learning</subject><subject>Optimization</subject><issn>0022-3549</issn><issn>1520-6017</issn><issn>1520-6017</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kMtqHDEQRUWwiSe2fyALo6U33SmppX5ANsHkYTAYjGctNFIpo6Fbaks9jv330TBOll4VFKcutw4hnxnUDFj7ZVe_zNtcc-CihqEGJj-QFZMcqhZYd0JWAJxXjRTDGfmU8w4AWpDyIzlrhkYwMXQrsn7AZ49_aHR00mbrA9IRdQo-_KYuJjr62VsadIizTos3Ix7W037Ui4-B6mDpnKLBnKnFZxzjPGFYLsip02PGy7d5TtY_vj_e_Kru7n_e3ny7q0yp2VWtFabh3HAhOwdW9Ma0gKV8D5Iz6VjXgYFmcJpzKax2WnAzuG7TsrbXdtOck-tjbunwtMe8qMlng-OoA8Z9Vg1jMLCm70VB-RE1Keac0Kk5-UmnV8VAHXSqnTroVAedCgZVdJajq7f8_WZC-__kn78CfD0CWL4sIpPKxmMwaH1Csygb_Xv5fwHhIYbY</recordid><startdate>20240927</startdate><enddate>20240927</enddate><creator>Dorsey, Phillip J.</creator><creator>Lau, Christina L.</creator><creator>Chang, Ti-chiun</creator><creator>Doerschuk, Peter C.</creator><creator>D'Addio, Suzanne M.</creator><general>Elsevier Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-0711-4428</orcidid><orcidid>https://orcid.org/0000-0002-6464-0758</orcidid><orcidid>https://orcid.org/0000-0002-4549-3481</orcidid><orcidid>https://orcid.org/0009-0005-7388-6169</orcidid><orcidid>https://orcid.org/0000-0002-4517-6582</orcidid></search><sort><creationdate>20240927</creationdate><title>Review of machine learning for lipid nanoparticle formulation and process development</title><author>Dorsey, Phillip J. ; Lau, Christina L. ; Chang, Ti-chiun ; Doerschuk, Peter C. ; D'Addio, Suzanne M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1527-6d4c322c2457f0d48cc60e601805215f1770c039fa2254dafa42c9f7b6168adb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>AI/ML</topic><topic>Artificial intelligence</topic><topic>Formulation and process development</topic><topic>Lipid nanoparticle(s)</topic><topic>LNP</topic><topic>Machine learning</topic><topic>Optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dorsey, Phillip J.</creatorcontrib><creatorcontrib>Lau, Christina L.</creatorcontrib><creatorcontrib>Chang, Ti-chiun</creatorcontrib><creatorcontrib>Doerschuk, Peter C.</creatorcontrib><creatorcontrib>D'Addio, Suzanne M.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of pharmaceutical sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dorsey, Phillip J.</au><au>Lau, Christina L.</au><au>Chang, Ti-chiun</au><au>Doerschuk, Peter C.</au><au>D'Addio, Suzanne M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Review of machine learning for lipid nanoparticle formulation and process development</atitle><jtitle>Journal of pharmaceutical sciences</jtitle><addtitle>J Pharm Sci</addtitle><date>2024-09-27</date><risdate>2024</risdate><issn>0022-3549</issn><issn>1520-6017</issn><eissn>1520-6017</eissn><abstract>Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular delivery for therapeutic proteins. In the pharmaceutical industry, establishing a robust formulation and process to achieve target product performance is a critical component of drug development. Fundamental understanding of the processes for making LNPs and their interactions with biological systems have advanced considerably in the wake of the COVID-19 pandemic. Nevertheless, LNP formulation research remains largely empirical and resource intensive due to the multitude of input parameters and the complex physical phenomena that govern the processes of nanoparticle precipitation, self-assembly, structure evolution, and stability. Increasingly, artificial intelligence and machine learning (AI/ML) are being applied to improve the efficiency of research activities through in silico models and predictions, and to drive deeper fundamental understanding of experimental inputs to functional outputs. This review will identify current challenges and opportunities in the development of robust LNP formulations of nucleic acids, review studies that apply machine learning methods to experimental datasets, and provide discussion on associated data science challenges to facilitate collaboration between formulation and data scientists, aiming to accelerate the advancement of AI/ML applied to LNP formulation and process optimization.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>39341497</pmid><doi>10.1016/j.xphs.2024.09.015</doi><orcidid>https://orcid.org/0000-0002-0711-4428</orcidid><orcidid>https://orcid.org/0000-0002-6464-0758</orcidid><orcidid>https://orcid.org/0000-0002-4549-3481</orcidid><orcidid>https://orcid.org/0009-0005-7388-6169</orcidid><orcidid>https://orcid.org/0000-0002-4517-6582</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0022-3549
ispartof Journal of pharmaceutical sciences, 2024-09
issn 0022-3549
1520-6017
1520-6017
language eng
recordid cdi_proquest_miscellaneous_3110913884
source Alma/SFX Local Collection
subjects AI/ML
Artificial intelligence
Formulation and process development
Lipid nanoparticle(s)
LNP
Machine learning
Optimization
title Review of machine learning for lipid nanoparticle formulation and process development
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-11T13%3A07%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Review%20of%20machine%20learning%20for%20lipid%20nanoparticle%20formulation%20and%20process%20development&rft.jtitle=Journal%20of%20pharmaceutical%20sciences&rft.au=Dorsey,%20Phillip%20J.&rft.date=2024-09-27&rft.issn=0022-3549&rft.eissn=1520-6017&rft_id=info:doi/10.1016/j.xphs.2024.09.015&rft_dat=%3Cproquest_cross%3E3110913884%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3110913884&rft_id=info:pmid/39341497&rft_els_id=S0022354924004222&rfr_iscdi=true