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J Psychopharmacol. 2018 Nov;32(11):1191-1196. doi: 10.1177/0269881118796809. Epub 2018 Sep 20.

Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare.

Author information

1
1 King's College London, Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, London, UK.
2
2 In Silico Biosciences, Berwyn, PA, USA.
3
3 South London and Maudsley NHS Trust, BRC Nucleus, London, UK.
4
4 King's College London, SGDP, Institute of Psychiatry, Psychology and Neuroscience, London, UK.

Abstract

BACKGROUND::

Computer-modelling approaches have the potential to predict the interactions between different antipsychotics and provide guidance for polypharmacy.

AIMS::

To evaluate the accuracy of the quantitative systems pharmacology platform to predict parkinsonism side-effects in patients prescribed antipsychotic polypharmacy.

METHODS::

Using anonymized data from South London and Maudsley NHS Foundation Trust electronic health records we applied quantitative systems pharmacology, a neurophysiology-based computer model of humanized neuronal circuits, to predict the risk for parkinsonism symptoms in patients with schizophrenia prescribed two concomitant antipsychotics. The performance of the quantitative systems pharmacology model was compared with 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 31 December 2014. The area under the receiver operating characteristic (AUROC) curve for the quantitative systems pharmacology model was 0.66 ( p = 0.01), while AUROCs for D2R, 1/Ksum and chlorpromazine equivalent dose were 0.52 ( p = 0.350), 0.53 ( p = 0.347) and 0.52 ( p = 0.330) respectively.

CONCLUSION::

Our results indicate that quantitative systems pharmacology has the potential to predict the risk of parkinsonism associated with antipsychotic polypharmacy from minimal source information, and thus might have potential decision-support applicability in clinical settings.

KEYWORDS:

Antipsychotic polypharmacy; antipsychotics; computer-modelling; concomitant; electronic health records

PMID:
30232932
DOI:
10.1177/0269881118796809

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