A computational algorithm for personalized medicine in schizophrenia
| Autor: | Yena Lee, Beom S. Lee, Sabrina Singh, Marie McPherson, Roger S. McIntyre, David A. Chiriboga, James E. Gentle, Nan Sook Park |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Oncology
Olanzapine medicine.medical_specialty Perphenazine Pharmacogenomic Variants medicine.medical_treatment Polymorphism Single Nucleotide Sensitivity and Specificity 03 medical and health sciences 0302 clinical medicine Internal medicine medicine Humans Ziprasidone Precision Medicine Antipsychotic Biological Psychiatry Risperidone Positive and Negative Syndrome Scale business.industry 030227 psychiatry Drug Therapy Computer-Assisted Psychiatry and Mental health Schizophrenia Quetiapine Personalized medicine business 030217 neurology & neurosurgery Algorithms Clinical psychology medicine.drug Antipsychotic Agents |
| Zdroj: | Schizophrenia research. 192 |
| ISSN: | 1573-2509 |
| Popis: | Despite advances in sequencing candidate genes and whole genomes, no method has accurately predicted who will or will not benefit from a specific antipsychotic medication among patients with schizophrenia. We propose a computational algorithm that utilizes a person-centered approach that directly identifies individual patients who will respond to a specific antipsychotic medication. The algorithm was applied to the data obtained from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. The predictors were either (1) 13 single-nucleotide polymorphisms (SNPs) and 53 baseline variables or (2) 25 SNPs and the same 53 baseline variables, depending on the existing findings and data availability. The outcome variables were either (1) improvement in the Positive and Negative Syndrome Scale (PANSS) (Yes/No) or (2) completion of phase 1/1A (Yes/No). Each of those four predictor-outcome combinations was tried for each of the five antipsychotic medications (Perphenazine, Olanzapine, Quetiapine, Risperidone, and Ziprasidone), leading to 20 prediction experiments. For 18 out of 20 experiments, all three performance measures were greater than 0.50 (sensitivity 0.51-0.79, specificity 0.52-0.79, accuracy 0.52-0.74). Notably, the model provided a promising prediction for Ziprasidone for the case involving completion of phase 1/1A (Yes/No) predicted by 13 SNPs and 53 baseline variables (sensitivity 0.75, specificity 0.74, accuracy 0.74). The proposed algorithm simultaneously used both genetic information and clinical profiles to predict individual patients' response to antipsychotic medications. As the method is not disease-specific but a general algorithm, it can be easily adopted in many other clinical practices for personalized medicine. |
| Databáze: | OpenAIRE |
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