Author(s): Tomé, Inês ; Francisco, Vitor ; Fernandes, Hugo ; Ferreira, Lino
Date: 2021
Persistent ID: https://hdl.handle.net/10316/103692
Origin: Estudo Geral - Universidade de Coimbra
Project/scholarship: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID/PT;
The use of pharmacologically active compounds to manage and treat diseases is of utmost relevance in clinical practice. It is well recognized that spatial-temporal control over the delivery of these biomolecules will greatly impact their pharmacokinetic profile and ultimately their therapeutic effect. Nanoparticles (NPs) prepared from different materials have been tested successfully in the clinic for the delivery of several biomolecules including non-coding RNAs (siRNA and miRNA) and mRNAs. Indeed, the recent success of mRNA vaccines is in part due to progress in the delivery systems (NP based) that have been developed for many years. In most cases, the identification of the best formulation was done by testing a small number of novel formulations or by modification of pre-existing ones. Unfortunately, this is a low throughput and time-consuming process that hinders the identification of formulations with the highest potential. Alternatively, high-throughput combinatorial design of NP libraries may allow the rapid identification of formulations with the required release and cell/tissue targeting profile for a given application. Combinatorial approaches offer several advantages over conventional methods since they allow the incorporation of multiple components with varied chemical properties into materials, such as polymers or lipid-like materials, that will subsequently form NPs by self-assembly or chemical conjugation processes. The current review highlights the impact of high-throughput in the development of more efficient drug delivery systems with enhanced targeting and release kinetics. It also describes the current challenges in this research area as well as future directions.
ERA Chair project (ERA@UC, Ref. No. 669088) through EU Horizon 2020 program, Nos. POCI-01-0145-FEDER-016390 (acronym: CANCEL STEM). Project No. 47081 (BioRobotBeads) funded by the Operational programs POCI þ POR Lisboa
