Identifying side effects of commonly used drugs in the treatment of Covid 19

Aygün, İrfan; Kaya, Mehmet; Alhajj, Reda

doi:10.1038/s41598-020-78697-1

Cited by 35 publications

(33 citation statements)

References 51 publications

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“…The rapid upsurge in active COVID-19 cases produced a significant health care crisis due to a lack of alertness to confront a sudden pandemic, especially in the developing nations (Blumenthal et al, 2020). At the onset of COVID-19, disease management faced several issues because of no availability of specific effective medication (Aygün et al, 2020;Elengoe, 2020). Hence, a hit and trial strategy of drug repositioning/repurposing with older medication invented for different indications was implanted to find an answer to this dreadful disease (Harrison, 2020;Li, 2019).…”

Section: Introductionmentioning

confidence: 99%

Adverse events reported from the COVID-19 vaccines: A descriptive study based on the WHO database (VigiBase®)

Dutta¹,

Kaur²,

Bhardwaj³

et al. 2021

J Appl Pharm Sci

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The emergency approval of a few COVID-19 vaccines provided a ray of hope to fight the deadly pandemic. However, their approval was solely based on limited data from the clinical trials in a short period, thereby imposing a demand for post-marketing surveillance studies to monitor beneficial and adverse events (AEs). This study focuses on observing the serious adverse events (SAEs) data reported in the World Health Organization database. The data from VigiBase ® was analyzed. The duplicates in the data were removed and analyzed based on age, gender, and SAEs at the system organ classification level and the individual preferred term level. A total of 103,954 AEs were reported. The majority of them were seen as females (80%), from Europe (83%), and were between 18 and 64 years (80.74%) of age. The most-reported AEs were of the nervous system (19.1%), musculoskeletal (11.2%), and elderly (>65 years) people. The reported SAEs from the COVID-19 vaccines were in line with the data published in the clinical trial reports. These SAEs to vaccines will need causality analysis and review of individual reports.

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Section: Introductionmentioning

confidence: 99%

Adverse events reported from the COVID-19 vaccines: A descriptive study based on the WHO database (VigiBase®)

Dutta¹,

Kaur²,

Bhardwaj³

et al. 2021

J Appl Pharm Sci

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“…Vaccination is a well-accepted reliable approach to prevent diseases 17 , and historically, it has proven to be one of the most effective strategies to control epidemics and pandemics, such as the SARS-CoV-2 outbreak 18 . All vaccines result in at least a small number of patients that demonstrate some kind of post-vaccination side effects 19 . Although vaccines are a life-saving medication, in some cases they can result in an after-effect, and sometimes even resulting in severe symptoms, although with a low probability.…”

Section: Discussionmentioning

confidence: 99%

“…At the early stage of the research,Md. Martuza Ahamad (MMA) and Sakifa Aktar (SA) had collected the data from VAERS repository on COVID- 19 MMA and SA act as a principle investigator of this work in contributed equally.…”

Section: Author Contributionsmentioning

confidence: 99%

Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and post-vaccination reactogenicity

Ahamad

Aktar

Uddin

et al. 2021

Preprint

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Good vaccine safety and reliability are essential to prevent infectious disease spread. A small but significant number of apparent adverse reactions to the new COVID-19 vaccines have been reported. Here, we aim to identify possible common causes for such adverse reactions with a view to enabling strategies that reduce patient risk by using patient data to classify and characterise patients those at risk of such reactions. We examined patient medical histories and data documenting post-vaccination effects and outcomes. The data analyses were conducted by different statistical approaches followed by a set of machine learning classification algorithms. In most cases, similar features were significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, allergic history, taking other medications, type-2 diabetes, hypertension and heart disease are the most significant pre-existing factors associated with risk of poor outcome and long duration of hospital treatments, pyrexia, headache, dyspnoea, chills, fatigue, various kind of pain and dizziness are the most significant clinical predictors. The machine learning classifiers using medical history were also able to predict patients most likely to have complication-free vaccination with an accuracy score above 85%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches. Important classifiers achieving these reactions notably included allergic susceptibility and incidence of heart disease or type-2 diabetes.

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“…which is highly protein-bound (>99%), so caution when giving with other highly protein-bound drugs with narrow therapeutic indices. Atazanavir [ 83 ] Protease inhibitors Diarrhea, nausea, vomiting, abdominal pain Indirect hyperbilirubinemia with overt jaundice Elevation of hepatic enzymes especially in patients with underlying HBV or HCV co-infection Inhibitor of CYP3A4 and CYP2C9 PPI decreases its concentrations. Tenofovir and efavirenz should not be co-administered with atazanavir Favipiravir [ 84 ] RNA-dependent RNA polymerase inhibitor Nausea/vomiting (5–15%), diarrhea (5%) Liver enzyme abnormalities Inhibitor for: CYP2C8 and aldehyde oxidase Interferon beta [ 85 ] Cytokines with anti-viral and immunomodulatory effects.…”

Section: Main Textmentioning

confidence: 99%

SARS-CoV-2-associated gastrointestinal and liver diseases: what is known and what is needed to explore

Sweed

Abdelsameea

Khalifa

et al. 2021

Egypt Liver Journal

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Background The pandemic of COVID19 which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first described in China as an unexplained pneumonia transmitted by respiratory droplets. Gastrointestinal (GI) and liver injury associated with SARS-CoV-2 infection were reported as an early or sole disease manifestation, mainly outside China. The exact mechanism and incidence of GI and liver involvement are not well elucidated. Main body We conducted a PubMed search for all articles written in the English language about SARS-CoV-2 affecting the GI and liver. Following data extraction, 590 articles were selected. In addition to respiratory droplets, SARS-CoV-2 may reach the GI system through the fecal-oral route, saliva, and swallowing of nasopharyngeal fluids, while breastmilk and blood transmission were not implicated. Moreover, GI infection may act as a septic focus for viral persistence and transmission to the liver, appendix, and brain. In addition to the direct viral cytopathic effect, the mechanism of injury is multifactorial and is related to genetic and demographic variations. The most frequently reported GI symptoms are diarrhea, nausea, vomiting, abdominal pain, and bleeding. However, liver infection is generally discovered during laboratory testing or a post-mortem. Radiological imaging is the gold standard in diagnosing COVID-19 patients and contributes to understanding the mechanism of extra-thoracic involvement. Medications should be prescribed with caution, especially in chronic GI and liver patients. Conclusion GI manifestations are common in COVID-19 patients. Special care should be paid for high-risk patients, older males, and those with background liver disease.

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Identifying side effects of commonly used drugs in the treatment of Covid 19

Cited by 35 publications

References 51 publications

Adverse events reported from the COVID-19 vaccines: A descriptive study based on the WHO database (VigiBase®)

Adverse events reported from the COVID-19 vaccines: A descriptive study based on the WHO database (VigiBase®)

Adverse effects of COVID-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and post-vaccination reactogenicity

SARS-CoV-2-associated gastrointestinal and liver diseases: what is known and what is needed to explore

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