The Transcontinental Computational Psychiatry Workgroup (TCPW) organizes a monthly web-based meeting and a computational psychiatry satellite meeting with the Society of Biological Psychiatry. We hope to foster discussion and exchange between those involved in computational psychiatry—a rapidly growing, highly multidisciplinary field.

Videos of past meetings are here.


Next workgroup meeting

A biophysically realistic computer model of the human basal ganglia to mitigate the incidence of EPS motor side effects with polypharmacy in clinical practice with schizophrenia patients

Hugo Geerts

  • Wednesday, December 18th 2019 at 16:00 - 17:00 UTC (Other timezones)
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Background: Although antipsychotic polypharmacy remains common in clinical practice, the pharmacodynamic interactions between antipsychotics remain poorly understood. Computer-modeling approaches have the potential to predict these interactions and provide guidance for polypharmacy.
Methods: We applied Quantitative Systems Pharmacology (QSP), a neurophysiology-based computer model of humanized neuronal basal ganglia circuits in the cortico-striatal-thalamocortical motor loop, to build a classifier for predicting the risk for parkinsonism symptoms in patients with schizophrenia prescribed two concomitant antipsychotics, solely based on names and doses of the two drugs. This was achieved retrospectively using anonymised data from South London and Maudsley NHS Foundation Trust (SLAM) electronic health records. The performance of the QSP model was compared to the performance of simple parameters such as: combination of affinity constants (1/Ksum); sum of D2R occupancies (D2R) and chlorpromazine equivalent dose.
Results: We identified 832 patients with schizophrenia who were receiving two antipsychotics for six or more months, between 1 January 2007 and 31st December 2014. We will compare the Area under the Receiver Operating Characteristic (AUROC) for this new classifier to other predictors such as for D2R occupancy, 1/Ksum and chlorpromazine equivalent dose.
Discussion: Our results indicate that QSP has the potential to predict the risk of parkinsonism associated with antipsychotic polypharmacy from minimal source information, and thus might have potential decision-support for optimal treatment guidance.

Hugo Geerts, Ph.D., Bach.Med., MBA
Chief Scientist
In Silico Biosciences

Disclaimer: TCPW welcomes scientific contributions from industry. However, presentations at the TCPW are not intended to express any form of support or endorsement for any products.