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 COVID19, 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 highthroughput 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 datadriven 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.

Automated summary

Nanomedicines have successfully turned potential therapeutic agents into clinically approved medicines with optimized safety and efficacy, as exemplified by mRNA vaccines against COVID-19 using lipid nanoparticle technology. However, the design of nanomedicines remains complex, especially in preclinical development. To address this, we propose NanoMAP, a platform that combines high-throughput experimentation, artificial intelligence techniques, and web-based data sharing to streamline the development process. The deployment of NanoMAP demands interdisciplinary collaboration between drug delivery and AI experts, enabling a data-driven design approach and encouraging participation from the pharmaceutical science community in large-scale data curation.