Research scientists, some of whom affiliated with Université de Montréal (UdeM), conducted a study to demonstrate how they can predict the potential clinical implications of new therapeutic compound based on simple cellular responses. The team conducted the work at Centre de Recherche de l’Hôpital Ste-Justine.
Publishing their results in journal Nature Communications, the team may be on to a major breakthrough that can actually accelerate drug development.
First: The Problem
Developing new drugs is a long, complex, and costly process. It starts with identifying the molecule or “ligand” (such as a drug, hormone, or neurotransmitter) that can activate or block the target or “receptor” involved in a disease. Compound identification and validation is one of the most important steps in ensuring that a new drug provides an effective clinical response with the fewest possible side effects.
The Needle in the Haystack
The researchers needed to devise a method that would enable drug development researchers to identify and improve drug candidate selection. This requires the ability to find a way to categorize large numbers of drug candidates based on similarities in initiating cellular responses that leads investigators to the therapeutic action of new targets.
The team understood that drugs produce desired or undesired clinical actions by changing basic signals within cells. By grouping drugs with known clinical actions and new ligands, they can infer the clinical actions of new compounds by comparing similarities and differences in their signals with known drugs to promote desired clinical responses while avoiding side effects. This method of analysis was developed by using opioid analgesics as prototypes. This made it possible for the team to associate simple cellular signals produced by opioids such as oxycodone, morphine and fentanyl with the frequency with which respiratory depression and other undesirable side effects of these drugs were reported to the Food and Drug Administration’s pharmacovigilance program. At the height of the opioid epidemic, when the risk of death by respiratory depression is at its highest, the team believes this new analytical strategy could lead to the development of safer opioids.
Professor Michel Bouvier reports that their findings helped them to classify large numbers of compounds all the while factoring in a huge array of cellular signals into account. Now the team has a better handle on the predictive value for clinical responses.
“Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response” was published in Nature Communications on September 9, 2019. The co-lead author is Besma Benredjem, a doctoral student in pharmacology studying under Graciela Piñeyro. The co-senior author is Graciela Piñeyro, a researcher at Centre de recherche du CHU Sainte-Justine and associate professor in UdeM’s Department of Pharmacology and Physiology. The study was completed in collaboration with Pfizer Inc. and was financed by the Natural Sciences and Engineering Research Council of Canada, the Canadian Institutes of Health Research, the National Institutes of Health, the Fonds de recherche du Québec – Santé and Mitacs Globalink.