Browsing by Subject "Pharmacovigilance"

Now showing 1 - 4 of 4
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    A theoretical model for detecting drug interaction with awareness of timing of exposure
    (Springer Nature, 2025-04-21) Shi, Yi; Sun, Anna; Yang, Yuedi; Xu, Jing; Li, Justin; Eadon, Michael; Su, Jing; Zhang, Pengyue; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Drug-drug interaction-induced (DDI-induced) adverse drug event (ADE) is a significant public health burden. Risk of ADE can be related to timing of exposure (TOE) such as initiating two drugs concurrently or adding one drug to an existing drug. Thus, real-world data based DDI detection shall be expanded to investigate precise adverse DDI with a special awareness on TOE. We developed a Sensitive and Timing-awarE Model (STEM), which was able to optimize the probability of detection and control false positive rate for mining all two-drug combinations under case-crossover design, in particular for DDIs with TOE-dependent risk. We analyzed a large-scale US administrative claims data and conducted performance evaluation analyses. We identified signals of DDIs by using STEM, in particular for DDIs with TOE-dependent risk. We also observed that STEM identified significantly more signals than the conditional logistic regression model-based (CLRM-based) methods and the Benjamini-Hochberg procedure. In the performance evaluation, we found that STEM demonstrated proper false positive control and achieved a higher probability of detection compared to CLRM-based methods and the Benjamini-Hochberg procedure. STEM has a high probability to identify signals of DDIs in high-throughput DDI mining while controlling false positive rate, in particular for detecting signals of DDI with TOE-dependent risk.
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    Drug-induced anaphylaxis in China: a 10 year retrospective analysis of the Beijing Pharmacovigilance Database
    (Springer, 2018-10) Zhao, Ying; Sun, Shusen; Li, Xiaotong; Ma, Xiang; Tang, Huilin; Sun, Lulu; Zhai, Suodi; Wang, Tiansheng; Epidemiology, School of Public Health
    Background Few studies on the causes of drug-induced anaphylaxis (DIA) in the hospital setting are available. Objective We aimed to use the Beijing Pharmacovigilance Database (BPD) to identify the causes of DIA in Beijing, China. Setting Anaphylactic case reports from the BPD provided by the Beijing Center for Adverse Drug Reaction Monitoring. Method DIA cases collected by the BPD from January 2004 to December 2014 were adjudicated. Cases were analyzed for demographics, causative drugs and route of administration, and clinical signs and outcomes. Main outcome measure Drugs implicated in DIAs were identified and the signs and symptoms of the DIA cases were analyzed. Results A total of 1189 DIA cases were analyzed. The mean age was 47.6 years, and 732 (61.6%) were aged from 18 to 59 years. A total of 627 patients (52.7%) were females. There was a predominance of cardiovascular (83.8%) followed by respiratory (55.4%), central nervous (50.1%), mucocutaneous (47.4%), and gastrointestinal symptoms (31.3%). A total of 249 different drugs were involved. DIAs were mainly caused by antibiotics (39.3%), traditional Chinese medicines (TCM) (11.9%), radiocontrast agents (11.9%), and antineoplastic agents (10.3%). Cephalosporins accounted for majority (34.5%) of antibiotic-induced anaphylaxis, followed by fluoroquinolones (29.6%), beta-lactam/beta-lactamase inhibitors (15.4%) and penicillins (7.9%). Blood products and biological agents (3.1%), and plasma substitutes (2.1%) were also important contributors to DIAs. Conclusion A variety of drug classes were implicated in DIAs. Patients should be closely monitored for signs and symptoms of anaphylaxis when medications are administered especially with antibiotics, TCM, radiocontrast and antineoplastic agents.
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    Improved Adverse Drug Event Prediction Through Information Component Guided Pharmacological Network Model (IC-PNM)
    (IEEE, 2021) Ji, Xiangmin; Wang, Lei; Hua, Liyan; Wang, Xueying; Zhang, Pengyue; Shendre, Aditi; Feng, Weixing; Li, Jin; Li, Lang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Improving adverse drug event (ADE) prediction is highly critical in pharmacovigilance research. We propose a novel information component guided pharmacological network model (IC-PNM) to predict drug-ADE signals. This new method combines the pharmacological network model and information component, a Bayes statistics method. We use 33,947 drug-ADE pairs from the FDA Adverse Event Reporting System (FAERS) 2010 data as the training data, and the new 21,065 drug-ADE pairs from FAERS 2011-2015 as the validations samples. The IC-PNM data analysis suggests that both large and small sample size drug-ADE pairs are needed in training the predictive model for its prediction performance to reach an area under the receiver operating characteristic curve (\textAUROC)= 0.82(AUROC)=0.82. On the other hand, the IC-PNM prediction performance improved to \textAUROC= 0.91AUROC=0.91 if we removed the small sample size drug-ADE pairs from the prediction model during validation.
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    Study designs and statistical methods for pharmacogenomics and drug interaction studies
    (2016-04-01) Zhang, Pengyue; Li, Lang; Boukai, Benzion; Shen, Changyu; Zeng, Donglin; Liu, Yunlong
    Adverse drug events (ADEs) are injuries resulting from drug-related medical interventions. ADEs can be either induced by a single drug or a drug-drug interaction (DDI). In order to prevent unnecessary ADEs, many regulatory agencies in public health maintain pharmacovigilance databases for detecting novel drug-ADE associations. However, pharmacovigilance databases usually contain a significant portion of false associations due to their nature structure (i.e. false drug-ADE associations caused by co-medications). Besides pharmacovigilance studies, the risks of ADEs can be minimized by understating their mechanisms, which include abnormal pharmacokinetics/pharmacodynamics due to genetic factors and synergistic effects between drugs. During the past decade, pharmacogenomics studies have successfully identified several predictive markers to reduce ADE risks. While, pharmacogenomics studies are usually limited by the sample size and budget. In this dissertation, we develop statistical methods for pharmacovigilance and pharmacogenomics studies. Firstly, we propose an empirical Bayes mixture model to identify significant drug-ADE associations. The proposed approach can be used for both signal generation and ranking. Following this approach, the portion of false associations from the detected signals can be well controlled. Secondly, we propose a mixture dose response model to investigate the functional relationship between increased dimensionality of drug combinations and the ADE risks. Moreover, this approach can be used to identify high-dimensional drug combinations that are associated with escalated ADE risks at a significantly low local false discovery rates. Finally, we proposed a cost-efficient design for pharmacogenomics studies. In order to pursue a further cost-efficiency, the proposed design involves both DNA pooling and two-stage design approach. Compared to traditional design, the cost under the proposed design will be reduced dramatically with an acceptable compromise on statistical power. The proposed methods are examined by extensive simulation studies. Furthermore, the proposed methods to analyze pharmacovigilance databases are applied to the FDA’s Adverse Reporting System database and a local electronic medical record (EMR) database. For different scenarios of pharmacogenomics study, optimized designs to detect a functioning rare allele are given as well.