Network models incorporating chloride dynamics predict optimal strategies for terminating status epilepticus

Status epilepticus (SE), seizures lasting beyond five minutes, is a medical emergency commonly treated with benzodiazepines which enhance GABA_A receptor (GABA_AR) conductance. Despite widespread use, benzodiazepines fail in over one-third of patients, potentially due to seizure-induced disruption of neuronal chloride (Cl^-) homeostasis. Understanding these changes at a network level is crucial for improving clinical translation. Here, we address this using a large-scale spiking neural network model incorporating Cl^- dynamics, informed by clinical EEG and experimental slice recordings. Our simulations confirm that the GABA_AR reversal potential (E_GABA) dictates the pro- or anti-seizure effect of GABA_AR conductance modulation, with high E_GABA rendering benzodiazepines ineffective or excitatory. We show SE-like activity and E_GABA depend non-linearly on Cl^- extrusion efficacy and GABA_AR conductance. Critically, cell-type specific manipulations reveal that pyramidal cell, not interneuron, Cl^- extrusion predominantly determines the severity of SE activity and the response to simulated benzodiazepines. Leveraging these mechanistic insights, we develop a predictive framework mapping network states to Cl^- extrusion capacity and GABAergic load, yielding a proposed decision-making strategy to guide therapeutic interventions based on initial treatment response. This work identifies pyramidal cell Cl^- handling as a key therapeutic target and demonstrates the utility of biophysically detailed network models for optimising SE treatment protocols.