A Translational Model-Based Meta-Analysis to Predict Tremor Incidence Associated with Serotonin Reuptake Transporter...

Area of Science:

• Pharmacology
• Neuroscience
• Drug Development

Background:

• The serotonin reuptake transporter (SERT) regulates synaptic serotonin levels, making it a key target for central nervous system (CNS) disorders.
• Inhibition of SERT can cause serotonin toxicity, leading to serotonin syndrome (SS), a range of adverse events from mild to life-threatening.

Purpose of the Study:

• To develop a model-based meta-analysis (MBMA) to predict the dose-dependent incidence of tremors, a characteristic SS manifestation.
• To establish a relationship between drug pharmacokinetic properties, SERT inhibitory potency, and tremor occurrence.

Main Methods:

• A literature survey was conducted to gather data on tremor incidence, pharmacokinetic parameters, and SERT inhibitory potency (IC50) for 20 known SERT inhibitors.
• An Emax model was employed to analyze the relationship between tremor incidence and the ratio of brain unbound exposure to SERT IC50.

Main Results:

• A quantifiable relationship was identified between tremor incidence and the ratio of brain unbound exposure to SERT IC50 for SERT inhibitors.
• This model effectively predicts the likelihood of tremor occurrence based on drug characteristics.

Conclusions:

• The developed MBMA provides a valuable predictive tool for assessing the risk of tremors in investigational drugs targeting SERT.
• Findings can inform safety assessments and guide dose selection for new CNS disorder therapeutics.