Self-driving laboratories: A paradigm shift in nanomedicine development

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date...

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Bibliographic Details
Published in:Matter 2023-04, Vol.6 (4), p.1071-1081
Main Authors: Hickman, Riley J., Bannigan, Pauric, Bao, Zeqing, Aspuru-Guzik, Alán, Allen, Christine
Format: Article
Language:English
Subjects:
active learning
automation
drug delivery
high-throughput experimentation
lipid-based nanoparticles
liposomes
machine learning
materials acceleration platforms
nanomedicines
polymer nanoparticles
self-driving laboratories
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Summary:Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial, in part due to the complexity associated with their preclinical development. Herein, we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next-generation nanomedicines but also encourage participation of the pharmaceutical science community in a large data curation initiative. [Display omitted] Recent advances in machine learning have led to the development of tools and techniques with the potential to make a transformative impact in the pharmaceutical sciences. In this perspective, we propose combining state-of-the-art machine-learning techniques with high-throughput experimentation to create a materials acceleration platform for nanomedicine development, NanoMAP. Development of such a platform requires interdisciplinary collaboration between the drug delivery and artificial intelligence communities. Currently, the lack of large robust datasets limits the use of these data-driven methods. To overcome this, NanoMAP includes a large data curation initiative made possible by a web-based application. We see the implementation of this platform as a means to improve bench-to-bedside translation of innovative medicines for patients who suffer from life-threatening diseases. Blueprint for a materials acceleration platform for nanomedicine development.
ISSN:2590-2385
2590-2393
2590-2385
DOI:10.1016/j.matt.2023.02.007