DRUG-SPECIFIC SYNAPTIC DYNAMICS OF ANTIPSYCHOTIC RESPONSE IN IPSC-DERIVED NEURONS IN SCHIZOPHRENIA: A SEX AWARE APPROACH

Clinical response to antipsychotic treatment in schizophrenia is highly heterogeneous, yet the neuronal mechanisms underlying treatment response remain poorly understood. While molecular studies have begun to capture this variability, it remains unclear whether response is reflected in fundamental functional properties of human neurons, particularly at the level of synaptic dynamics. Here, we report ongoing work using whole-cell patch-clamp electrophysiology to characterize spontaneous excitatory postsynaptic currents (sEPSCs) in iPSC-derived neurons generated from schizophrenia patients clinically classified as antipsychotic responders or non-responders. Neurons were examined under untreated conditions and following exposure to clozapine, olanzapine, or risperidone. sEPSCs were quantified per cell, and EPSC frequency, mean amplitude, rise time, and decay time were compared between untreated and treated conditions using non-parametric statistics. Antipsychotic treatment was associated with distinct, drug-specific synaptic effects selectively in responder-derived neurons. Clozapine response was associated with selective prolongation of EPSC decay time without changes in synaptic drive. Olanzapine response was marked by reduced EPSC frequency and amplitude with minimal effects on synaptic timing, consistent with attenuation of synaptic drive. Risperidone response was characterized by reduced EPSC frequency together with prolonged rise and decay times, indicating combined modulation of synaptic input and kinetics. In contrast, non-responder-derived neurons showed no significant treatment-associated changes across EPSC measures. Ongoing analyses aim to assess sex-stratified synaptic profiles and their contribution to variability in antipsychotic response. Together, these findings suggest that drug-specific synaptic features may provide biophysically interpretable markers of treatment-responsive states and a functional framework for predictive modeling of antipsychotic response.