• Prediction of anti-epileptic d...

*Result*: Prediction of anti-epileptic drug response of patients based on peripheral blood RNA profiles and machine learning.

Title:
Prediction of anti-epileptic drug response of patients based on peripheral blood RNA profiles and machine learning.
Authors:
Liu ZD, Kou ZY, Guo L
Source:
International journal of clinical pharmacology and therapeutics [Int J Clin Pharmacol Ther] 2026 Feb; Vol. 64 (2), pp. 75-82.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Dustri-Verlag Dr. K. Feistle Country of Publication: Germany NLM ID: 9423309 Publication Model: Print Cited Medium: Print ISSN: 0946-1965 (Print) Linking ISSN: 09461965 NLM ISO Abbreviation: Int J Clin Pharmacol Ther Subsets: MEDLINE
Imprint Name(s):
Publication: Munchen : Dustri-Verlag Dr. K. Feistle
Original Publication: Mùˆnchen : Dustri-Verlag Dr. K. Feistle, c1994-
MeSH Terms:
Anticonvulsants*/therapeutic use , Epilepsy*/drug therapy , Epilepsy*/genetics , Epilepsy*/blood , Epilepsy*/diagnosis , RNA*/blood , RNA*/genetics , Support Vector Machine* , Machine Learning*, Valproic Acid/therapeutic use ; Phenytoin/therapeutic use ; Carbamazepine/therapeutic use ; Humans ; Female ; Male ; Adult ; Middle Aged ; Principal Component Analysis ; ROC Curve ; Treatment Outcome ; Young Adult ; Predictive Value of Tests
Substance Nomenclature:
0 (Anticonvulsants)
614OI1Z5WI (Valproic Acid)
6158TKW0C5 (Phenytoin)
63231-63-0 (RNA)
33CM23913M (Carbamazepine)
Entry Date(s):
Date Created: 20251211 Date Completed: 20260123 Latest Revision: 20260123
Update Code:
20260130
DOI:
10.5414/CP204862
PMID:
41378850
Database:
MEDLINE

*Further Information*

*Objective: In this study, we aimed to develop a method for predicting the response of patients to three commonly used anti-epileptic drugs (AEDs), namely, carbamazepine, phenytoin, and valproate, using machine learning models, based on the patients' peripheral blood RNA profiles.
Materials and Methods: A data set from the Gene Expression Omnibus Series database (GSE143272) was utilized that included the peripheral blood RNA information and some clinical features (age, weight, sex, epilepsy type, drug response) of 57 epilepsy patients. 22 classification models were constructed and trained, in which the peripheral blood RNA information, age, weight, sex, and epilepsy type served as predictors, and the patient' response to anti-epileptic drug as the outcome. The predicting capacity was evaluated by utilizing the sensitivity, the specificity, and the receiver operating characteristic curve of the models.
Results: Among the 22 trained models, the model of a quadratic support vector machine with a pretreatment of principal component analysis displayed the highest accuracy at 0.75, and the highest value of area under ROC curve at 0.81.
Conclusion: The model of a quadratic support vector machine with a pretreatment of principal component analysis is a potential tool for predicting the response of patients with epilepsy to drug treatment.*