Way To Accelerate Drug Discovering Process Found

Developing more effective drugs with fewer side effects is now a dream come true by this method which helps millions across the Globe to treat ailments.

The researchers conducted their work at Centre de Recherche de l’Hôpital Ste-Justine and published their findings in the prestigious journal Nature Communications.

Developing new drugs is a long, complicated, 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 are one of the most critical steps in ensuring that a new drug provides an adequate clinical response with the fewest possible side effects.

"Most new drugs tested on human subjects fail in clinical trials because the therapeutic response is insufficient. Developing a strategy that infers potential clinical responses early in the drug discovery process would significantly improve drug candidate selection," said Besma Benredjem, the study’s co-lead author and a doctoral student in pharmacology at UdeM.

Finding the needle in a haystack

"Our main goal was finding a way to categorize a large number of drug candidates based on similarities in their effectiveness in triggering a multiplicity of cellular responses that help identify the therapeutic action of new compounds," said Professor Graciela Piñeyro, co-senior author of the study and a researcher at CHU Sainte-Justine. To accomplish this, she worked with Dr. Olivier Lichtarge of Baylor College of Medicine, who uses advanced bioinformatics analysis to compare and group ligands according to fairly comprehensive signaling profiles.

Drugs produce desired or undesired clinical actions by changing essential signals within cells. By grouping drugs with known clinical effects and new ligands, we can infer the clinical activities of new compounds by comparing the similarities and differences in their signals with known drugs to promote desired clinical responses and avoid 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. "Thanks to our findings, we can now classify a large number of compounds while taking a multitude of cellular signals into account.

The wealth of comparisons this provides increases this classification’s predictive value for clinical responses," said Professor Michel Bouvier, the study’s co-senior author and a principal investigator of molecular pharmacology and Chief Executive Officer of UdeM’s Institute for Research in Immunology and Cancer. "We think we can help patients by speeding up the drug discovery process so clinical trials can start earlier."

"Our next goal is to use a similar approach to test cannabis products that may produce harmful neuropsychiatric actions among young people, and identify which cannabis extracts are most effective at treating chronic pain," added Besma Benredjem.Source-Eurekalert