Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems

Abstract

The field of nanotechnology and personalised medicine is undergoing drastic changes in the approach and efficiency of experimentation. The COVID-19 pandemic has spiralled into mass stagnation of major laboratories around the globe and led to increased investment into remote systems for nanoparticle experiments. A significant number of laboratories now operate using automated systems; however, the extension to nanoparticle preparation and artificial intelligence-dependent databases holds great translational promise. The strive to combine automation with artificial intelligence (AI) grants the ability to optimise targeted therapeutic nanoparticles for unique cell types and patients. In this perspective, the current and future trends of automated approaches to nanomedicine synthesis are discussed and compared with traditional methods.

Keywords: Artificial intelligence; Automation; Microfluidics; Nanotechnology; Personalised medicine.

Fig. 1

Schematic diagram of an AI-automated workstation. A functional AI-guided robotic nanoparticle synthesis and analysis system will start with genetic analysis of individual patients and personalised treatment selection. The nanoparticle will then be formulated and tested in cells and organs-on-chip using automated systems. The AI system will ultimately be able to determine the best formulation and treatment for individual patients for maximum response and optimal outcome

Fig. 2

In the field of pathology, AI is already making major changes. Similar impact of AI can occur in the field of nanomedicine synthesis and prediction. Graphical representation of PubMed results based on two search queries—[“Machine Learning” AND “Laboratory Medicine” and “Machine Learning” AND “Pathology”]

Fig. 3

Graphical comparison of the hands-on and automated approaches to experimentation