Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

Hierarchical Nanobiosensors at the End of the SARS-CoV-2 Pandemic

Nanostructures have played a key role in the development of different techniques to attack severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Some applications include masks, vaccines, and biosensors. The latter are of great interest for detecting diseases since some of their features allowed us to find specific markers in secretion samples such as saliva, blood, and even tears. Herein, we highlight how hierarchical nanoparticles integrated into two or more low-dimensional materials present outstanding advantages that are attractive for photonic biosensing using their nanoscale functions. The potential of nanohybrids with their superlative mechanical characteristics together with their optical and optoelectronic properties is discussed. The progress in the scientific research focused on using nanoparticles for biosensing a variety of viruses has become a medical milestone in recent years, and has laid the groundwork for future disease treatments. This perspective analyzes the crucial information about the use of hierarchical nanostructures in biosensing for the prevention, treatment, and mitigation of SARS-CoV-2 effects.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

INTRODUCTION

Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED

the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION

The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.