A state-of-the-art research conducted by a faculty member of the College of Medicine (CMED) at Qatar University (QU) in collaboration with researchers from the University of Montreal, Canada and scientists from Pfizer Inc., an American multinational pharmaceutical corporation, allowed the development of a new method that could help in identifying more effective drugs with fewer side effects. Researchers hope their findings, published in the journal Nature Communications, could be used to speed up the lengthy process of drug discovery.
Drug discovery is the process by which new therapeutic drugs are identified, which starts by screening large chemical libraries for the purpose of finding a new molecule or ‘ligand’ that could regulate (stimulate or inhibit) in cells a specific target protein or ‘receptor’ involved in a particular disease. Once accomplished, ligands selected from these screens are tested in animal models of a particular disease to guarantee a high level of safety and medicinal efficacy before studies are carried out to clinical trials and tested on human subjects.
QU Assistant Professor of Pharmacology at CMED, Dr. Karim Nagi commented saying “Unfortunately, it is very unlikely that a ligand selected from the early cellular screening runs will develop into a therapeutic drug. This poor success rate in clinical trials is caused by either a lack of clinical efficacy or the development of undesirable side effects produced by those ligands when tested on human subject, therefore making drug discovery a lengthy, challenging, and expensive process.” He added, “To address that issue, we have developed a new classification method that could predict the clinical responses of ligands very early in the drug discovery process and using simple cellular responses.”
Our understanding of how receptors produce their actions has significantly evolved over the past few years. Once activated by ligands, those small target proteins undergo ligand-specific conformational changes then act as signaling hubs that integrate a multitude of signaling cascades into cellular responses. Researchers of this study took advantage of this signaling diversity by identifying signaling pathways that should be specifically activated or avoided to promote desired clinical responses and avoid side effects.
To further access this knowledge and apply it to drug discovery, researchers also designed a new strategy that was able to classify ligands according to similarities in multidimensional signaling profiles and to associate the resulting ligand categories with the frequency of undesired events reported in the FDA’s pharmacovigilance database.
Furthermore, by using this classification method to group drugs with known clinical effects and new compounds selected from the early cellular screening runs, it would be possible to infer the clinical activities of new ligands by associating specific in vitro signaling profiles to clinically relevant responses. "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 Prof. Graciela Pineyro, co-senior author of the study and a researcher at the University of Montreal.
It is important to note that this new classification method was developed by using opioid ligands as prototypes. Although opioids have been successfully used to treat moderate to severe pain for decades, these last few years have seen an incredible increase in opioid prescriptions that unfortunately led to opioid misuse, abuse and deaths due to overdose. In the United States alone, opioid prescriptions tripled from 76 million to 219 million prescriptions from 1991 to 2011. In parallel with this increase and over the same time-period, opioid-related deaths also nearly tripled. This significant increase in the use of opioids has now reached a crisis level and the risks of opioid use and misuse continues to receive global attention. Therefore, the need to develop new opioid medications that can produce analgesia with fewer side effects, like tolerance and dependence that lead to opioid overdose and death, is a request by worldwide federal governments that recognize this serious threat.
Researchers of this study accepted to undertake that challenge and have contributed to achieve several milestones in fulfilling that request. Dr. Karim Nagi said, “This discovery is expected to greatly improve the screening process of not only new opioid analgesics, but also of new drug candidates for all diseases by predicting those more effective and better-tolerated ligands.”
Prof. Michel Bouvier, the study's co-senior author and a Principal Investigator of Molecular Pharmacology and Chief Executive Officer of the University of Montreal’s Institute for Research in Immunology and Cancer said, “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. We think we can help patients by speeding up the drug discovery process so clinical trials can start earlier.”
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