Self-Driving Laboratories: A Paradigm Shift in Nanomedicine Development

Abstract

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.

Atlas: A Brain for Self-driving Laboratories

“…[1][2][3][4][5][6][7] SDLs can accelerate the rate at which advanced materials, functional molecules, and industrial processes are designed by enhancing productivity, throughput, accuracy, and reproducibility. Early-stage SDLs have targeted diverse research and development goals, including chemical reaction and process optimization, [8][9][10][11][12][13][14][15][16][17][18] the design of nanomaterials, [19][20][21][22] and light-harvesting materials, [23][24][25][26] to name a few. [27][28][29][30][31] The cornerstone of an SDL is its decision-making algorithm (here informally referred to as its "brain"), which is typically implemented as a data-driven experiment planning strategy.…”