Establishment of a rapid identification of adverse drug reaction program in R language implementation based on monitoring data

doi:10.3785/j.issn.1008-9292.2020.03.07

J Zhejiang Univ (Med Sci)

2020, Vol. 49

Issue (2): 253-259 DOI: 10.3785/j.issn.1008-9292.2020.03.07

Establishment of a rapid identification of adverse drug reaction program in R language implementation based on monitoring data

HONG Dongsheng^1,²(

),NI Jian^2,³,SHAN Wenya^1,²,LI Lu^1,²,HU Xi^1,²,YANG Hongyu^1,²,ZHAO Qingwei^2,*(

),ZHANG Xingguo^1,*(

)

1. Department of Pharmacy, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
2. Key Laboratory for Drug Evaluation and Clinical Research of Zhejiang Province, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
3. Information Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China

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Abstract

Objective: To establish a clinically applicable model of rapid identification of adverse drug reaction program (RiADP) for risk management and decision-making of clinical drug use. Methods: Based on the theory of disproportion analysis, frequency method and Bayes method, a clinically applicable RiADP model in R language background was established, and the parameters of the model were interpreted by MedDRA coding. Based on the actual monitoring data of FDA, the model was validated by the assessing hepatotoxicity of lopinavir/ritonavir (LPV/r). Results: The established RiADP model included four parameters: standard value of adverse drug reaction signal information, empirical Bayesian geometric mean value, ratio of reporting ratio and number of adverse drug reaction cases. Through the application of R language parameter package "phViD", the model parameters could be output quickly. After being encoded by MedDRA, it was converted into clinical terms to form a clinical interpretation report of adverse drug reactions. In addition, the evaluation results of LPV/r hepatotoxicity by the model were matched with the results reported in latest literature, which also proved the reliability of the model results. Conclusion: In this study, a rapid identification method of adverse reactions based on post marketing drug monitoring data was established in R language environment, which is capable of sending rapid warning of adverse reactions of target drugs in public health emergencies, and providing intuitive evidence for risk management and decision-making of clinical drugs.

Key words： Coronavirus disease 2019 Severe acute respiratory syndrome coronavirus 2 Novel coronavirus pneumonia Adverse reaction monitoring Drug evaluation R language

Received: 06 March 2020 Published: 14 March 2020

CLC:

R969.3

Corresponding Authors: ZHAO Qingwei,ZHANG Xingguo E-mail: hdswell@zju.edu.cn;qwzhao@zju.edu.cn;1182008@zju.edu.cn

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	HONG Dongsheng
	NI Jian
	SHAN Wenya
	LI Lu
	HU Xi
	YANG Hongyu
	ZHAO Qingwei
	ZHANG Xingguo

Cite this article:

HONG Dongsheng,NI Jian,SHAN Wenya,LI Lu,HU Xi,YANG Hongyu,ZHAO Qingwei,ZHANG Xingguo. Establishment of a rapid identification of adverse drug reaction program in R language implementation based on monitoring data. J Zhejiang Univ (Med Sci), 2020, 49(2): 253-259.

URL:

http://www.zjujournals.com/med/10.3785/j.issn.1008-9292.2020.03.07 OR http://www.zjujournals.com/med/Y2020/V49/I2/253

基于监测数据的药物不良反应快速识别及R语言实现

目的: 构建药物不良反应信号快速识别（RiADP）模型，为临床用药的风险管理和科学决策提供帮助。方法: 在不相称性测定分析理论的基础上，结合频数法和贝叶斯法建立一种R语言环境下临床可用的RiADP模型，并通过国际医学用语词典（MedDRA）编码，实现模型参数的临床解读。以美国食品药品监督管理局实际监测数据为依据，利用建立的RiADP模型对拟用于2019冠状病毒病治疗的抗病毒药物洛匹那韦/利托那韦的肝毒性进行识别，从而对模型进行验证。结果: 本研究提出的RiADP模型包括4个模型参数：药物不良反应信号信息标准值、经验贝叶斯几何均值、报告比值比和不良反应报告例数。通过R语言参数包“PhViD”可以实现模型参数的快速输出，MedDRA编码后可转化为临床术语，形成药物不良反应的临床解释报告。模型对洛匹那韦/利托那韦肝毒性的评估结果与最新研究报道匹配，证明模型结果可靠。结论: 本研究在R语言环境下提出了一种基于上市后药物监测数据的不良反应信号快速识别方法，可以在突发公共卫生事件下实现目标药物不良反应的快速预警，为临床用药的风险管理和决策提供循证依据。

关键词： 2019冠状病毒病, 严重急性呼吸综合征冠状病毒2, 新型冠状病毒肺炎, 不良反应监测, 药物评价, R语言

Fig 1 Data analysis process of RiADP model

Tab 1 Monitoring data of abnormal liver function caused by lopinvir and ritonavir in FDA


[1]	GUAN W J , NI ZY , HU Y et al. Clinical characteristics of coronavirus disease 2019 in China[J]. N Engl J Med, 2020, doi: 10.1056/NEJMoa2002032

[2]	PARASRAMPURIA D A , BENET L Z , SHARMA A . Why drugs fail in late stages of development: case study analyses from the last decade and recommendations[J]. AAPS J, 2018, 20 (3): 46 doi: 10.1208/s12248-018-0204-y

[3]	LONERGAN M , SENN S J , MCNAMEE C et al. Defining drug response for stratified medicine[J]. Drug Discov Today, 2017, 22 (1): 173- 179 doi: 10.1016/j.drudis.2016.10.016

[4]	PERINO J , MOTTAL N , BOHBOT Y et al. Cardiac failure in patients treated with azacitidine, a pyrimidine analogue: Case reports and disproportionality analyses in Vigibase[J]. Br J Clin Pharmacol, 2020, doi: 10.1111/bcp.14211

[5]	MARWITZ K , JONES S C , KORTEPETER C M et al. An evaluation of postmarketing reports with an outcome of death in the US FDA adverse event reporting system[J]. Drug Saf, 2020, doi: 10.1007/s40264-020-00908-5

[6]	GENTILI M , POZZI M , PEETERS G et al. Review of the methods to obtain paediatric drug safety information: spontaneous reporting and healthcare databases, active surveillance programmes, systematic reviews and meta-analyses[J]. Curr Clin Pharmacol, 2018, 13 (1): 28- 39 doi: 10.2174/1574884713666180206164634

[7]	BEHERA S K , DAS S , XAVIER A S et al. Comparison of different methods for causality assessment of adverse drug reactions[J]. Int J Clin Pharm, 2018, 40 (4): 903- 910 doi: 10.1007/s11096-018-0694-9

[8]	BATE A . Bayesian confidence propagation neural network[J]. Drug Saf, 2007, 30 (7): 623- 625 doi: 10.2165/00002018-200730070-00011

[9]	KUMAR A , AHUJA J , SHRIVASTAVA TP et al. Statistical signal process in R language in the pharmacovigilance programme of India[J]. Ther Innov Regul Sci, 2018, 52 (3): 329- 333 doi: 10.1177/2168479017728988

[10]	TUBERT-BITTER P , BéGAUD B , AHMED I . Comparison of two drug safety signals in a pharmacovigilance data mining framework[J]. Stat Methods Med Res, 2016, 25 (2): 615- 629 doi: 10.1177/0962280212462295

[11]	TRIPPE Z A , BRENDANI B , MEIER C et al. Identification of substandard medicines via disproportionality analysis of individual case safety reports[J]. Drug Saf, 2017, 40 (4): 293- 303 doi: 10.1007/s40264-016-0499-5

[12]	AHMED I , THIESSARD F , MIREMONT-SALAMé G et al. Early detection of pharmacovigilance signals with automated methods based on false discovery rates: a comparative study[J]. Drug Saf, 2012, 35 (6): 495- 506 doi: 10.2165/11597180-000000000-00000

[13]	TIMBROOK T T , MCKAY L , SUTTON J D et al. Disproportionality analysis of safety with nafcillin and oxacillin with the FDA Adverse Event Reporting System (FAERS)[J]. Antimicrob Agents Chemother, 2020, 64 (3): doi: 10.1128/AAC.01818-19

[14]	SAKAEDA T , TAMON A , KADOYAMA K et al. Data mining of the public version of the FDA Adverse Event Reporting System[J]. Int J Med Sci, 2013, 10 (7): 796- 803 doi: 10.7150/ijms.6048

[15]	VERDEN A , DIMBIL M , KYLE R et al. Analysis of spontaneous postmarket case reports submitted to the FDA regarding thromboembolic adverse events and JAK inhibitors[J]. Drug Saf, 2018, 41 (4): 357- 361 doi: 10.1007/s40264-017-0622-2

[16]	BROWN E G . Methods and pitfalls in searching drug safety databases utilising the Medical Dictionary for Regulatory Activities (MedDRA)[J]. Drug Saf, 2003, 26 (3): 145- 158 doi: 10.2165/00002018-200326030-00002

[17]	WHO. Global Health Observatory (GHO) data[DB/OL].[2020-03-10]. https://www.who.int/gho/database/en/.

[18]	MANSUR J M . Medication safety systems and the important role of pharmacists[J]. Drugs Aging, 2016, 33 (3): 213- 221 doi: 10.1007/s40266-016-0358-1

[19]	LINDQUIST M . Use of triage strategies in the WHO signal-detection process[J]. Drug Saf, 2007, 30 (7): 635- 637 doi: 10.2165/00002018-200730070-00014

[20]	SZARFMAN A , TONNING J M , DORAISWAMY P M . Pharmacovigilance in the 21st century: new systematic tools for an old problem[J]. Pharmacotherapy, 2004, 24 (9): 1099- 1104 doi: 10.1592/phco.24.13.1099.38090

[21]	SCHOLL J H , VAN PUIJENBROEK E P . The value of time-to-onset in statistical signal detection of adverse drug reactions: a comparison with disproportionality analysis in spontaneous reports from the Netherlands[J]. Pharmacoepidemiol Drug Saf, 2016, 25 (12): 1361- 1367 doi: 10.1002/pds.4115

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