Data mining methodology for response to hypertension symptomology-application to COVID-19-related pharmacovigilance

• Published in: eLife 2021-11, Vol.10
• Main Authors: Xu, Xuan, Kawakami, Jessica, Millagaha Gedara, Nuwan Indika, Riviere, Jim E, Meyer, Emma, Wyckoff, Gerald J, Jaberi-Douraki, Majid
• Format: Article
• Language: English
• Subjects: Adverse Drug Reaction Reporting Systems; Angiotensin; Angiotensin-Converting Enzyme Inhibitors - adverse effects; Antihypertensive Agents - adverse effects; Antihypertensive Agents - therapeutic use; Antihypertensives; Artificial intelligence; Bayes Theorem; Bayesian analysis; Blood pressure; Brand names; Calcium Channel Blockers - adverse effects; Classification; Clinical trials; Computational and Systems Biology; Coronaviruses; COVID-19; COVID-19 - complications; COVID-19-related pharmacovigilance; Data analysis; Data Mining - methods; data-driven methodology; Demography; Dictionaries; Disease transmission; Drug-Related Side Effects and Adverse Reactions; Endothelins; Enzymes; FDA ADE database; Fibrinolytic Agents - adverse effects; Humans; Hypertension; Hypertension - complications; Hypertension - drug therapy; Lung diseases; Machine learning; Medicine; Methods; Nifedipine; Patients; Pharmacovigilance; Prostacyclin; pulmonary symptomology; Renin; Respiratory function; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Sildenafil
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.70734

Potential therapy and confounding factors including typical co-administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs. Afterward, the graphical least absolute shrinkage and selection operator (GLASSO) captured drug associations based on pulmonary ADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO. Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pulmonary ADEs. Macitentan and bosentan were associated with 64% and 56% of pulmonary ADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pulmonary ADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pulmonary ADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy. We consider pulmonary ADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high risk