Here we highlight the top three emerging technologies in the world of pharma. This segment features recent innovations in novel electronic sensors, predictive computer models, and shape modifying microrobots.
ULTRASOFT ELECTRONIC SENSOR
A novel ultrasoft electronic sensor has been created by researchers from the University of Tokyo, Tokyo Women’s Medical University, and RIKEN in Japan. This new sensor allows for the monitoring of beating heart cells produced by researchers via direct contact with the tissue, without affecting their behaviour. The nanomesh sensor has significant benefits over current monitoring techniques, which impede the natural movement of the heart cells. The device could ultimately enable more accurate analyses of the effect of drugs on the heart, as well as aiding the study of other cells, organs, and medicines.
Tiny robots that can modify their shape to pass through narrow blood vessels or intricate structures could be set to revolutionise targeted drug delivery. The creation, by researchers from Ecole Polytechnique Fédérale de Lausanne and ETH Zurich in Switzerland, appears to have circumvented the problems researchers have previously faced when attempting to navigate narrow winding blood vessels with a microrobot. The bots, which are made from a hydrogel nanocomposite containing magnetic nanoparticles, have deformations in their structure, meaning they can automatically assume the most efficient shape when navigating a particular obstacle.
PREDICTIVE COMPUTATIONAL MODEL
A computational technique can predict how drug molecules in molecular crystals arrange themselves under changing energetic conditions. This technology can be used to predict the likelihood of a drug ceasing to function properly; this has been an issue in some new treatments because most drugs are marketed in a solid state. The method, developed by researchers in collaboration with Avant-garde Materials Simulation GmbH, has the potential to be of major benefit to the pharma industry by reducing the chances of expensive development failures, production errors, and litigation. The team are now looking to advance the technique further and combine it with machine learning to enhance its computational efficiency.