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Evaluation of Antipsychotic-Induced Neuroleptic Malignant Syndrome Using a Self-Organizing Map

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Journal Cureus
Date 2024 Oct 1
PMID 39350836
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Abstract

Introduction In neuropsychiatric pharmacotherapy, neuroleptic malignant syndrome (NMS) is a potentially serious side effect of antipsychotics characterized primarily by fever, disorientation, extrapyramidal disorders, and autonomic nervous system imbalance, which can lead to death if left untreated. We visualized the NMS profile of antipsychotics using a self-organizing map (SOM). We combined it with decision tree analysis to discriminate between 31 antipsychotics in more detail than typical antipsychotic (TAP) and atypical antipsychotic (AAP) classifications. Method A total of 20 TAPs and 11 AAPs were analyzed. We analyzed NMS reports extracted from the Japanese Adverse Drug Event Report (JADER) database based on standardized Medical Dictionary for Regulatory Activities (MedDRA) queries (Standardized MedDRA Queries (SMQ) code: 20000044, including 68 preferred terms). The SOM was applied using the SOM package in R version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria). Results The Japanese Adverse Drug Event Report (JADER) database contained 887,704 reports published between April 2004 and March 2024. The numbers of cases of NMS (SMQ code: 20000044) reported for risperidone, aripiprazole, haloperidol, olanzapine, and quetiapine were 1691, 1294, 1132, 1056, and 986, respectively. After the antipsychotics were classified into six units using SOM, they were adapted for decision tree analysis. First, 31 antipsychotics branched off into groups with loss of consciousness, with one group (10 TAPs) consisting entirely of TAPs, and the other consisting of antipsychotics that were further separated into two groups with coma induced by TAPs and AAPs. Conclusion The results of this study provide a reference for healthcare providers when predicting the NMS characteristics induced by each drug in patients, thereby facilitating the effective treatment of schizophrenia.

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References
1.
Hu X, Yan A . In silico prediction of rhabdomyolysis of compounds by self-organizing map and support vector machine. Toxicol In Vitro. 2011; 25(8):2017-24. DOI: 10.1016/j.tiv.2011.08.002. View

2.
Tse L, Barr A, Scarapicchia V, Vila-Rodriguez F . Neuroleptic Malignant Syndrome: A Review from a Clinically Oriented Perspective. Curr Neuropharmacol. 2015; 13(3):395-406. PMC: 4812801. DOI: 10.2174/1570159x13999150424113345. View

3.
Kawakami J . [Visualization and analysis of drug information on adverse reactions using data mining method, and its clinical application]. Yakugaku Zasshi. 2014; 134(1):105-18. DOI: 10.1248/yakushi.13-00201. View

4.
Meltzer H, Massey B . The role of serotonin receptors in the action of atypical antipsychotic drugs. Curr Opin Pharmacol. 2011; 11(1):59-67. DOI: 10.1016/j.coph.2011.02.007. View

5.
Meltzer H, Li Z, Kaneda Y, Ichikawa J . Serotonin receptors: their key role in drugs to treat schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2003; 27(7):1159-72. DOI: 10.1016/j.pnpbp.2003.09.010. View