Mining and analysis of lorlatinib-induced adverse drug event signals
- VernacularTitle:洛拉替尼的药物不良事件信号挖掘与分析
- Author:
Xia LONG
1
;
Mengwen HUANG
2
;
Shiyun PU
1
;
Lichen WANG
3
;
Mengjiao TANG
4
;
Houfeng ZHOU
1
Author Information
1. Dept. of Pharmacy，Chengdu Fifth People’s Hospital，Chengdu 611130，China
2. School of Pharmacy，Chengdu University of Traditional Chinese Medicine，Chengdu 611137，China
3. College of Pharmacy，North Sichuan Medical College，Sichuan Nanchong 637100，China
4. College of Pharmacy，Southwest Medical University，Sichuan Luzhou 646000，China
- Publication Type:Journal Article
- Keywords:
lorlatinib;
adverse drug event;
signal mining
- From:
China Pharmacy
2023;34(20):2513-2518
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To provide references for the safe use of lorlatinib in clinical practice. METHODS The reporting odds ratio （ROR） method， Medicines and Healthcare Products Regulatory Agency comprehensive standard method （referred to as “MHRA method”） and the Bayesian confidence propagation neural network （BCPNN） method were used to detect adverse drug events （ADEs） signals of lorlatinib in the FDA Adverse Event Reporting System from the first quarter of 2019 to the fourth quarter of 2022. RESULTS & CONCLUSIONS Totally 114 overlapping ADEs signals of lorlatinib were detected by the three methods， among which there were 73 new suspicious ADEs signals which were not covered in the instruction of lorlatinib. When using loratinib in clinical practice， special attention should be paid to ADEs with a high number of cases and signals， such as various neurological diseases， psychiatric diseases， respiratory system， thoracic and mediastinal diseases； clinical manifestations included cerebral edema， cerebral infarction， pulmonary hypertension， mutism， decreased sexual desire， pleural effusion. The signals of mobile thrombophlebitis， radiation necrosis， mutism， vesicoureteral reflux not mentioned in the instructions were all strong in BCPNN detection with high specificity， to which we should pay attention in clinical application.
