After testing hundreds of chemicals, Paul Ehrlich, a German physician and bacteriologist that won the 1908 Nobel Prize for Medicine, discovered a substance that could kill selectively Treponema pallidum bacteria, the one responsible for syphilis.
After that, the expert kept investigating along the same research line with the aim of discovering more “magic bullets” able to attack invader pathogens without causing damage to the host cells. He was not aware, but he was laying the foundations for the pharmacology. Indeed, the Ehrlich’s idea of selective drugging, popularized as the “magic bullet”, made the scientist’s fortune and it is still the cornerstone of modern medicine.
Now, a group of researchers at Institute for Bioengineering of Catalonia (IBEC) also affiliated with the University College of London (UCL) have developed, in collaboration with experts at the Imperial College of London (ICL) and the Anhui University in China, a theoretical framework for the precision nanomedicine based on the idea of selectivity that Ehrlich defended.
In their work, published recently in the journal Science Advances, the authors, led by the ICREA Research Professor Giuseppe Battaglia, explain that what makes nanoparticles –particles 1 million times smaller than the thickness of a hair- adhere to other particles are the characteristics of their ligands.
Concretely, the results of the study show that particle size, ligand number, and polymer brush length can be computed together with ligand affinity and receptor volume to identify the most efficient formulations to achieve selectivity based on the cell combination of receptors.
Moreover, the model proves that the combination of multiple ligands can indeed be “bar-coded” onto medicines to selectively target specific cell populations while leaving the other untouched. In this regard, the team proved their applicability on targeting brain endothelial cells. Using the model, the experts proved that the right combination of ligands increases selectivity and allows for phenotypic targeting.
According to the researchers, the model provides not only a very powerful tool to design personalized nanomedicines but also gives important insights into how biological systems can achieve such high selectivity. In this regard, the theoretical framework proposed now by the scientists helps for a better understand how cells, viruses, bacteria, protein, and nucleic acid interact with each other, hence adding a powerful tool to the existing system biology approaches.