Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response

Area of Science:

• Pharmacology
• Drug Discovery
• Computational Biology

Background:

• G protein-coupled receptors (GPCRs) offer therapeutic potential through diverse signaling.
• Tailoring drug responses requires understanding specific effector activation/avoidance.
• Identifying signaling profiles linked to desired clinical outcomes remains a challenge.

Purpose of the Study:

• To characterize the signaling diversity of mu opioid receptor (MOR) ligands.
• To classify MOR ligands based on their in vitro signaling profiles.
• To associate these classifications with clinical side effects using pharmacovigilance data.

Main Methods:

• Quantified MOR ligand signaling using logistic and operational parameters across ten in vitro assays.
• Employed unsupervised clustering on curve parameters to categorize ligands.
• Correlated ligand categories with adverse event frequencies from FDA pharmacovigilance data.
• Validated the classification approach using beta2-adrenergic receptor ligands.

Main Results:

• MOR ligands were classified into distinct categories based on their signaling profiles.
• Specific signaling profiles were associated with increased frequency of undesired clinical events.
• The classification method demonstrated predictive power for linking in vitro signaling to clinical outcomes.

Conclusions:

• In vitro signaling profiles of MOR ligands can be classified to predict clinical relevance.
• This approach aids in identifying ligands with improved therapeutic potential and reduced side effects.
• The methodology provides a framework for optimizing drug development by linking molecular signaling to clinical safety.