Data-driven methodology for discovery and response to pulmonary symptomology in hypertension through AI and machine learning: Application to COVID-19 related pharmacovigilance
Authors: Xu Xuan , Kawakami Jessica , Millagaha Gedara Nuwan Indika , Riviere Jim , Meyer Emma , Wyckoff Gerald J. , Jaberi-Douraki Majid 
Published: June 2021

Background

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.

Methods

In this study, we implemented adaptive signal detection approaches to correct spurious association, hidden factors, and confounder misclassification when the covariates are unknown or unmeasured on medications affecting the renin-angiotensin system (RAS), potentially creating an increased risk of life-threatening outcomes in high-risk patients.

Results

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 pADEs. Macitentan and bosentan were associates with 64% and 56% of pADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by our 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.

Conclusions

We consider pulmonary ADE (pADE) profiles in a long-standing group of therapeutics, RAS-acting agents, in patients with hypertension associated with high-risk for COVID-19. Using these techniques, we confirmed our hypothesis that drugs from the same drug class could have very different pADE profiles affecting outcomes in acute respiratory illness. We found that several indidvual drugs have significant differences between their drug classes and compared to other drug classes.

Funding

GJW and MJD accepted funding from BioNexus KC for funding on this project but BioNexus KC had no direct role in this article.

Clinical trial number

N/A

Author Summary

Underlying comorbidities continue to negatively affect COVID-19 patients. A recent focus has been on medications affecting RAS. Therefore, with the advent of COVID-19 acute respiratory distress syndrome (ARDS) in high-risk patients with hypertension, identifying specific RAS medications with the lowest incidence of pADEs would be beneficial. For this purpose, we curated the FDA ADE database to search for information related to human pADEs. As part of post-marketing drug safety surveillance, state/federal regulatory agencies and other institutions provide massive collections of ADE reports, these large data-sets present an opportunity to investigate ADEs to provide patient management based on comparative population data analysis. The abundance and prevalence of ADEs are not always detectable during randomized clinical trials and before a drug receives FDA approval for use in the clinic, which may appear with more widespread use. This is especially true for specific agents or diseases since there are simply too few events to be assessed, even in a large clinical trial for side effect profiles of specific disease states. For this purpose, we employed a novel method identifying extraneous causes of differential reporting including sampling variance and selection biases by reducing the effect of covariates.