A Translational Model‐Based Meta‐Analysis to Predict Tremor Incidence Associated with Serotonin Reuptake Transporter Inhibition

The serotonin reuptake transporter (SERT) is responsible for the removal and recycling of the neurotransmitter serotonin from neuronal synapses and is an important pharmacological target for treating a variety of CNS disorders. However, excessive levels of extrasynaptic serotonin resulting from SERT inhibition can lead to serotonin toxicity, which manifests as a spectrum of adverse events (AEs) termed “serotonin syndrome” (SS), ranging in severity from mild to life‐threatening. We hypothesized that, by performing a model‐based meta‐analysis (MBMA) of the data in the literature, the dose at which tremors (a characteristic manifestation of SS) occur could be predicted based on the pharmacokinetic properties and SERT inhibitory potency of a given drug. To investigate the relationship between tremors and the predicted relative strength of SERT inhibition, a literature survey was performed to collate observed tremor data, pharmacokinetic parameters, and SERT potency data for known SERT inhibitors. Using these data for 20 SERT inhibitors, an E max model relationship between tremor incidence and the ratio between brain unbound exposure and SERT IC50 was observed. The identified relationship provides a valuable tool to predict the likelihood of tremor incidence for investigational drugs with SERT inhibitory activity and to inform safety assessment and dose selection.