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Informing selection of drugs for COVID-19 treatment through adverse events analysis

Abstract

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic and there is an urgent need for safe and effective drugs for COVID-19 treatment. Since developing a new drug is time consuming, many approved or investigational drugs have been repurposed for COVID-19 treatment in clinical trials. Therefore, selection of safe drugs for COVID-19 patients is vital for combating this pandemic. Our goal was to evaluate the safety concerns of drugs by analyzing adverse events reported in post-market surveillance. We collected 296 drugs that have been evaluated in clinical trials for COVID-19 and identified 28,597,464 associated adverse events at the system organ classes (SOCs) level in the FDA adverse events report systems (FAERS). We calculated Z-scores of SOCs that statistically quantify the relative frequency of adverse events of drugs in FAERS to quantitatively measure safety concerns for the drugs. Analyzing the Z-scores revealed that these drugs are associated with different significantly frequent adverse events. Our results suggest that this safety concern metric may serve as a tool to inform selection of drugs with favorable safety profiles for COVID-19 patients in clinical practices. Caution is advised when administering drugs with high Z-scores to patients who are vulnerable to associated adverse events.

Introduction

Coronavirus disease 2019 (COVID-19), a newly emerged disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been rapidly spreading worldwide due to its contagious nature1,2,3. The World Health Organization (WHO) declared COVID-19 a pandemic on March 11, 2020. As of June 3, 2021, the outbreak has been reported in 213 countries with over 171 million confirmed cases and over 3.68 million deaths. The global spread of the virus has overwhelmed healthcare systems and caused unprecedented disruption to society as well as the economy. The severity of this pandemic demands safe and effective therapeutic approaches. Currently, only one drug, remdesivir under the brand name Veklury, is approved by the U.S. Food and Drug Administration (FDA) for adults and pediatric patients (12 years of age and older and weighing at least 40 kg) for the treatment of COVID-19 requiring hospitalization. Since developing a new drug usually takes more than a decade, many COVID-19 studies use an alternative strategy by repurposing approved or investigational drugs to treat COVID-194. Since approved drugs may have been on the market for years or even decades, their safety, toxicity, and pharmacokinetics are known. This knowledge can be used to shorten the time required for developing these drugs to treat COVID-19.

A remarkable number of drugs have been considered for treating COVID-19 patients5,6,7,8. As of June 3, 2021, 5849 COVID-19-related clinical trials had been registered with ClinicalTrials.gov and 29% (1693) involved the use of marketed drugs. The efficacy and safety of these drugs for treatment of COVID-19-related indications are being tested in ongoing clinical trials. Among these drugs, the antimalarial drugs hydroxychloroquine and chloroquine are widely used in clinical trials. Based on their potential efficacy9,10, the FDA and European Medicines Agency (EMA) issued authorization for emergency use of oral formulations of hydroxychloroquine sulfate and chloroquine phosphate in the treatment of COVID-19 patients in late March and early April 2020, respectively. However, on June 15, 2020, the FDA revoked the emergency use authorization (EUA) because these drugs may not be effective in treating COVD-19 and the drugs’ potential benefits for such use do not outweigh their known and potential risks11,12. Some drugs emerged in the virtual screening as potential COVID-19 drugs, such as dipyridamole13 which inhibits the SARS-CoV-2 main protease, peptides drugs14 and chronic disease drugs15 including candesartan, losartan, telmisartan. Using the chronic disease drugs as an example, these drugs are in the class of angiotensin II receptor blockers (ARBs) and have shown promising affinities against the COVID-19 main protease in molecular docking and molecular dynamics15. Since some clinical trials are still ongoing, the efficacy of those drugs is inconclusive or pending. Therefore, it is important for physicians to select drugs with favorable safety profiles.

Results and discussion

Drugs for COVID-19 treatment in clinical trials

To investigate the safety of drugs repurposed for COVID-19, we searched ClinicalTrials.gov on June 3, 2021 and found 5849 clinical trials for COVID-19. To focus on promising COVID-19 drugs, we excluded clinical trials that were marked as withdrawn, suspended, and terminated in the recruitment status and 5599 clinical trials remained. Among these 5599 clinical trials, 1526 were listed as intervention with drugs. Searching the 1526 clinical trials by common drug names and synonyms from the DrugBank database19 produced 1075 trials for 406 approved or investigational drugs. The remaining 451 clinical trials without approved or investigational drugs were associated with agents not covered in DrugBank, such as traditional Chinese medicines. These 451 trials were excluded. Detailed information on the clinical trials for the 406 drugs are provided in Supplementary Table 1. Figure 1 shows the distribution of clinical trials involving the 406 drugs. Of these 406 drugs, 207 are being tested in only one clinical trial, 79 in two, 33 in three, and 87 in more than three clinical trials.

Hydroxychloroquine and ivermectin have been utilized in 119 and 63 clinical trials, respectively. It is not surprising that hydroxychloroquine has been extensively studied because it was initially reported to have some inhibitory activity against SARS-CoV-212. The antiparasitic drug, ivermectin, has also been intensively studied for COVID-19 treatment, especially when combined with hydroxychloroquine8,20. Other intensively studied drugs include remdesivir with 50 clinical trials, azithromycin with 48 clinical trials, tocilizumab with 47 clinical trials, favipiravir with 33 clinical trials, ritonavir with 33 clinical trials, lopinavir with 31 clinical trials and heparin with 30 clinical trials.

To examine the preferred type of drugs repurposed for COVID-19 treatment, drugs were grouped using the anatomical therapeutic chemical (ATC) classification system. We grouped 237 drugs out of 406 into 14 main pharmacological groups using their first level ATC codes (Supplementary Table 2). The remaining 169 investigational drugs or biological products such as remdesivir and favipiravir were excluded since no ATC codes were assigned to them. All 14 ATC classes are covered by COVID-19 drugs. The antineoplastic and immunomodulating agent class is the largest group with 51 drugs, and blood and blood forming organs is the second largest with 38 drugs. The distribution of clinical trials in the 14 ATC classes is shown in Supplementary Fig. 1. Not surprisingly, the antiparasitic products class contains 239 clinical trials (the most among all 14 classes) due to the large number of clinical trials for hydroxychloroquine (119) and chloroquine (12). The class of anti-infectives for systemic use contains 192 clinical trials for 30 drugs such as azithromycin, lopinavir/ritonavir, umifenovir, and ribavirin. Lopinavir is used with ritonavir to treat HIV-1 infection. Lopinavir/ritonavir’s antiviral effects against SARS-CoV-2 have been reported in some clinical trials and in vitro studies7,21. These two drugs have been used as an emergency treatment for COVID-19 patients in many countries including the U.S., Singapore and Japan7. However, recent studies have not shown clinical benefit of this combination of drugs for COVID-195,22.

Adverse events associated with drugs for the treatment of COVID-19

To measure the safety concerns for drugs that have been used for COVID-19 treatment in clinical trials, we extracted adverse events in post-market surveillance from the FAERS database. Of the 406 drugs found in clinical trials, 296 showed adverse events reported in FAERS; no adverse events were found for the other 110 drugs (Supplementary Table 1). Extracted adverse events coded using MedDRA low level term (LLT) and preferred term (PT) were grouped into 27 SOCs for statistical analysis. The 27 SOCs and their abbreviations used in this study are shown in Supplementary Table 3. All 14 ATC classes of drugs showed adverse events in all 27 SOCs. The number of adverse events for the 14 ATC classes of drugs are provided in Fig. 2. Interestingly, the drugs in the antineoplastic and immunomodulating agents class showed the most adverse events reported in FAERS, which is consistent with the common understanding that anticancer drugs are more likely to cause various adverse events.

Our safety concern metrics can be used for guidance in avoiding improper prescription of drugs to patients, and can also help to optimize treatment for COVID-19. For example, in patients with hypertension, our results (Supplementary Table 4) not only inform physicians to avoid prescription of trimetazidine (Z-score = 3.7), ulinastatin (Z-score = 3.6), adenosine (Z-score = 3.2), bivalirudin (Z-score = 3.2), tirofiban (Z-score = 3.0), dexmedetomidine (Z-score = 2.8), sevoflurane (Z-score = 2.8), chloroquine (Z-score = 2.4), angiotensin ii (Z-score = 2.4), molgramostim (Z-score = 2.4), amiodarone (Z-score = 2.3), propofol (Z-score = 2.1), verapamil (Z-score = 2.1), escin (Z-score = 2.1) and tenecteplase (Z-score = 2.1) because adverse events of cardiac disorders are significantly frequent with these drugs, but our results also suggest some other drugs like clavulanic acid (Z-score =  − 0.7) and tenofovir alafenamide (Z-score =  − 0.9) have less frequent adverse events of cardiac disorders compared to other reported adverse events of these drugs. This information can facilitate healthcare providers benefit-risk evaluations for these patients to make the best choice when selecting a drug among those that have a similar degree of efficacy.

Since pharmacokinetics and pharmacodynamics might differ between men and women, gender plays an important role in adverse events related to a drug. To help physicians understand potential adverse events specifically for female and male patients, we provided the Z-scores on 27 SOCs for female and male patients in Supplementary Tables 5 and 6, respectively. We identified 11,420,242 adverse events reported for 268 drugs for female patients and 6,033,116 adverse events for 269 drugs for male patients. The remaining adverse events were either reported without sex information or have sex values of “Intersex”, “Transgender”, “Prefer not to disclose” or “Unknown”. As shown in Supplementary Tables 5 and 6, the Z-scores of SOC adverse events for each identified drug were categorized into 5 groups with colors representing likelihood levels of the occurrence of SOC adverse events. These two tables could be used to inform selection of drugs for treating female and male COVID-19 patients. For example, for male patients, Supplementary Table 6 suggests lopinavir and ritonavir are relatively safe without any significant frequent adverse events reported. However, both drugs have significant frequent adverse events for female patients (Supplementary Table 5): injury, poisoning and procedural complications (Z-score = 3.2 and 2.1), informing that more caution is needed when prescribing these two drugs to female patients.

We examined whether the significantly frequent adverse events are distributed differently in the ATC classes of drugs. Significantly frequent adverse events for the 14 ATC classes of drugs are summarized in Supplementary Fig. 2. Both total and averaged significantly frequent adverse events were not dramatically different among the ATC classes of drugs except for the drugs in the ATC various class which contained fewer significantly adverse events than other classes of drugs. Although drugs in the ATC various class displayed less safety concern, our analysis did not support evaluating safety concerns by ATC classes. Our data suggests that the Z-score safety concern metrics for individual drugs in Supplementary Table 4 could be used to inform physicians when selecting optimal drugs for patients and identifying appropriate patients for a specific drug.

There are some limitations in this study. First, in our analysis, information regarding concomitant use of drugs and adverse events specifically resulting from drug-drug interactions were not captured. Second, our analysis did not dissect the sub-demographics (e.g., race) that could impact results; some drugs may show significant sex/race difference in adverse events. Third, we did not have access to the efficacy side of the equation for drug selection. If the health benefit of a drug outweighs its safety concern, drugs with severe toxicity and/or high Z-score may still be used in clinical practice. Fourth, dose was not included in our study because we had difficulties obtaining dose information from clinical trials. Furthermore, because the majority of the COVID-19 trials are still ongoing, our safety metrics did not include safety information from COVID-19 patients in these trials. Lastly, we did not consider the severity of the adverse events which is an important clinical consideration.

In summary, we investigated safety concerns for COVID-19 drugs by analyzing adverse events reported in FAERS. We found 406 drugs registered in ClinicalTrials.gov, of which 296 drugs showed 28,597,464 SOCs adverse events in FAERS. We calculated Z-scores for SOCs of adverse events to qualitatively measure safety concerns for 296 drugs. Physicians may need to be cautious when prescribing drugs with high Z-scores for adverse events in SOCs in which patients have vulnerabilities. New evidence is evolving every day on clinical outcomes and different treatment options for patients infected with SARS-CoV-2. Since our original analyses, additional options have become available for the management of COVID-19 (e.g. https://www.covid19treatmentguidelines.nih.gov/whats-new/). Physicians should be cognizant of the Z-scores as the treatment algorithms evolve.

Methods

Identification of drugs for COVID-19 treatment in clinical practice

ClincialTrials.gov is a registry database for clinical studies which provides researchers access to ongoing COVID-19 clinical trials worldwide. On June 3, 2021, we downloaded 5849 COVID-19-related clinical trials in a CSV file from ClinicalTrials.gov. The csv file contained information such as clinical trial title, recruitment status, study results and details of interventions. To identify the potential drugs in the downloaded clinical trials, we performed the following steps. First, we excluded trials that were marked as withdrawn, suspended, and terminated in the recruitment status to focus on promising COVID-19 drugs. Then, for the remaining retrieved clinical trials we only included clinical trials that listed drugs as interventions. Since drug names in clinical trials are listed along with drug dosage and form in the Intervention field and they are not standardized, they may be full names, trade names, abbreviations, active ingredients or synonyms. We compiled a comprehensive list of drug names using synonyms and common drug names from DrugBank19. This comprehensive list of drug names was then used to identify drugs used in clinical trials. After these steps, we identified 406 approved or investigational drugs from 1075 clinical trials.

To further examine drugs at the organ level, we grouped the drugs using anatomical therapeutic chemical (ATC) codes. The ATC classification system divides drugs into different groups according to the organ or system on which they act. There are five levels in the ATC classification system. The first level has 14 codes corresponding to 14 anatomical main groups. ATC codes for drugs were extracted from the National Center for Biomedical Ontology (NCBO) BioPortal ontologies24. In our study, 308 ATC codes were found for 237 drugs and the drugs were classified into 14 anatomical groups using the first letter of their ATC codes. The reason that a drug may have more than one ATC code is that a bottom-level code only stands for a single use and a drug may have multiple uses. Some drugs investigated that didn’t have ATC codes assigned were not considered in our ATC analysis.

Extraction of adverse events from the FAERS database

The adverse events in the FAERS database are reported using preferred terms (PTs) or lowest level terms (LLTs) in MedDRA25,26. We used MedDRA V22.1 to group the reported LLTs and PTs into 27 SOCs. The adverse events that were not identified as PTs or LLTs in MedDRA V22.1 were discarded in our analysis. SOCs are the highest level of MedDRA hierarchy. LLT and PTs are grouped into SOCs by their physiological system, manifestation site or purpose. Since one PT may be mapped to different SOCs, only the primary SOC was considered in the analysis. The 27 SOCs and their abbreviations used in this study are listed in Supplementary Table 3. The number of adverse events of each SOC for each drug was then aggregated from the drug-event combination.

Statistical analysis

To examine whether the occurrence of an SOC of adverse events was significantly frequent for a drug, we calculated the Z-score for the SOC and the drug using the following equation.

$$Z_{i}^{j} = ~\frac{{N_{i}^{j} - \upmu _{j} }}{{\upsigma _{j} }}$$

where $${\text{N}}_{i}^{j}$$ is number of adverse events in SOC i that were found in FAERS for drug j, $$\upmu _{j}$$ is average number of adverse events in a SOC for drug j and is calculated by dividing the total number of adverse events by 27 (number of SOCs), and $$\upsigma _{j}$$ is the standard deviation of adverse events for drug j. If the Z-score for a SOC for a specific drug is larger than 2, this SOC is a significantly frequent adverse event among all 27 SOCs. Similarly, this SOC is a frequent adverse event if its Z-score is between 1 and 2, and a slightly frequent adverse event if Z-score is between 0 and 1; likely infrequent adverse event if Z-score is between -1 and 0 and an infrequent adverse event if Z-score is less than − 1.

All data processing and statistical analyses were performed using in-house Python scripts in Python 3.6 (Python Software Foundation, http://python.org) (Python Software Foundation, Beaverton, OR, USA).

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Acknowledgements

The authors thank Dr. William Slikker, Shirley K. Seo, and Doanh Tran for their constructive suggestions. This research was supported in part by an appointment to the Research Participation Program at the National Center for Toxicological Research (Wenjing Guo, Bohu Pan, Zuowei Ji) administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration. The views presented in this article do not necessarily reflect those of the US Food and Drug Administration.

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Authors

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H.H. and W.G conceived of and designed the study. W.G., B.P., S.S., Z.J. and G.Y. curated the data and conducted the data analysis. W.G. T.E.K., Y.L., M.L., W.T., T.A.P. and H.H. wrote the manuscript. All authors approved the manuscript.

Corresponding author

Correspondence to Huixiao Hong.

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Guo, W., Pan, B., Sakkiah, S. et al. Informing selection of drugs for COVID-19 treatment through adverse events analysis. Sci Rep 11, 14022 (2021). https://doi.org/10.1038/s41598-021-93500-5

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