Faculty Bibliography

INTRODUCTION/BACKGROUND:Schizophrenia has a prevalence of approximately 1% in the general population, with 15.2 per 100,000 persons affected. Iloperidone is a second-generation antipsychotic drug approved for the treatment of schizophrenia in adults. It acts primarily by D2/5HT2a receptor antagonism, with greater affinity for the 5HT2a receptor than for the D2 receptor. AREAS COVERED/METHODS:This article discusses iloperidone and aims to provide useful information for clinicians to determine which circumstances would best suit the use of iloperidone to treat schizophrenic patients. In this review, the authors briefly discuss schizophrenia and its treatment, before they discuss properties of iloperidone, its indications, approval process, and adverse effects. Finally, the authors review the specific strengths and weaknesses of the medication. EXPERT OPINION/CONCLUSIONS:Iloperidone would be an attractive option in patients who are particularly prone to EPS, or who are showing prominent negative symptoms, as well as cognitive deficits. Its availability only in an oral formulation makes it a better option for patients with good medication adherence, and though it could be useful in patients prone to weight gain or hepatic dysfunction on other second generation antipsychotics, it should be used with caution in patients prone to side effects related to alpha adrenergic blockade.

Numerous intrinsic currents are known to collectively shape neuronal membrane potential dynamics, or neuronal signatures. Although how sets of currents shape specific signatures such as spiking characteristics or oscillations has been studied individually, it is less clear how a neuron's suite of currents jointly shape its entire set of signatures. Biophysical conductance-based models of neurons represent a viable tool to address this important question. We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. This insight into the neurobiological organization of currents also suggests an intuitive, systematic, and robust methodology to develop biophysical single-cell models with multiple biological characteristics applicable for both hand- and automated-tuning approaches. We illustrate the methodology using two example case rodent pyramidal neurons, from the lateral amygdala and the hippocampus. The methodology also helped reveal that a single-core compartment model could capture multiple neuronal properties. Such biophysical single-compartment models have potential to improve the fidelity of large network models.