Development of Antipsychotic Medications with Novel Mechanisms of Action Based on Computational Modeling of Hippocampal Neuropathology

Abstract

A large number of cellular level abnormalities have been identified in the hippocampus of schizophrenic subjects. Nonetheless, it remains uncertain how these pathologies interact at a system level to create clinical symptoms, and this has hindered the development of more effective antipsychotic medications. Using a 72-processor supercomputer, we created a tissue level hippocampal simulation, featuring multicompartmental neuron models with multiple ion channel subtypes and synaptic channels with realistic temporal dynamics. As an index of the schizophrenic phenotype, we used the specific inability of the model to attune to 40 Hz (gamma band) stimulation, a well-characterized abnormality in schizophrenia. We examined several possible combinations of putatively schizophrenogenic cellular lesions by systematically varying model parameters representing NMDA channel function, dendritic spine density, and GABA system integrity, conducting 910 trials in total. Two discrete “clusters” of neuropathological changes were identified. The most robust was characterized by co-occurring modest reductions in NMDA system function (-30%) and dendritic spine density (-30%). Another set of lesions had greater NMDA hypofunction along with low level GABA system dysregulation. To the schizophrenic model, we applied the effects of 1,500 virtual medications, which were implemented by varying five model parameters, independently, in a graded manner; the effects of known drugs were also applied. The simulation accurately distinguished agents that are known to lack clinical efficacy, and identified novel mechanisms (e.g., decrease in AMPA conductance decay time constant, increase in projection strength of calretinin-positive interneurons) and combinations of mechanisms that could re-equilibrate model behavior. These findings shed light on the mechanistic links between schizophrenic neuropathology and the gamma band oscillatory abnormalities observed in the illness. As such, they generate specific falsifiable hypotheses, which can guide postmortem and other laboratory research. Significantly, this work also suggests specific non-obvious targets for potential pharmacologic agents.

Decreased cortical thickness in drug naïve first episode schizophrenia: In relation to serum levels of BDNF

“…Our study found similar correlation pattern. In a recent longitudinal study in 14 first episode schizophrenia patients, Rizos et al found a positive association between changes of BDNF levels and changes of left hippocampal volume after 8 months of antipsychotic treatment (Rizos et al, 2014) suggesting that neuroprotective effect through modulation of BDNF levels could in turn protect against brain tissue loss (Siekmeier and vanMaanen, 2013;Wakade et al, 2002). Based on these findings, an absence or reduction of the positive relationship between brain morphology of insula and serum levels of BDNF that present in the healthy cohort may suggest a breakdown in the protective role or neurotrophic effect of BDNF in these brain regions in psychosis, which is consistent with the neurotrophic theory of schizophrenia (Weinberger, 1987).…”

Multifactorial Modeling of Impairment of Evoked Gamma Range Oscillations in Schizophrenia

“…Here we not only reduced the weights to 75 and 50% but also increased them to 125 and 150% (see also the computational study of Siekmeier and vanMaanen, 2013), because there is evidence of post-synaptic upregulation as a compensatory means for a reduced GABAergic tone due to reduced connectivity (Lisman et al, 2008). …”

Multifactoriality in Psychiatric Disorders: A Computational Study of Schizophrenia

“…Our results are akin to what has been shown in a simple model of the lobster enteric nervous system 20 and in a smaller scale study of gamma frequency alterations related to schizophrenia in a detailed hippocampal model, which varied 3 parameters in combination. 30 In this latter study, most schizophrenia-like phenotypes were found to cluster around reductions in both NMDA receptor function and pyramidal cell connectivity. However, by using more simplified networks that allowed us to vary a larger number of parameters, we found that schizophrenia-like alterations could occur in a much broader range of parametric combinations.…”

“…Thus, the usual process involves (a) generating a computational model of a network phenotype related to a disorder and (b) studying whether one or a few specific alterations (eg, in neurotransmitter receptors, channel conductances, or brain connectivity) can account for this phenotype. [25][26][27][28][29] However, the possibility that other combinations of molecular and network features might produce the same effect is rarely investigated (but see Siekmeier and vanMaanen 30 for a significant exception). To evaluate the hypothesis that similar network alterations can be produced by a myriad of low-level causes, we performed a large-scale parametric exploration of 2 published network models of schizophrenia.…”

“…To evaluate the effect of combined variations of multiple parameters 20,30 (figure 3), we ran 100 simulations for each of 5625 parameter combinations in the spontaneous and persistent activity protocols, setting background excitation and conductances for AMPA, GABA, and NMDA at 1 of 5 different levels (90%-110% of normal, with 5% steps) and excitatory and inhibitory connectivity at 1 of 3 different levels (90%, 95%, or 100%). We selected as "valid networks" those in which the median FR during the last second in nonselective excitatory (ie, not pertaining to the S1 or S2 pools) and inhibitory neurons was within the observed range of 2000 simulations of spontaneous activity using the original model parameters for There are also connections from 800 external neurons firing at 3 Hz, simulated as a 2400-Hz background excitatory input to each neuron.…”