Repurposing current therapeutics for treating COVID-19: A vital role of prescription records data mining.
David GurwitzPublished in: Drug development research (2020)
Since its outbreak in late 2019, the SARS-Cov-2 pandemic already infected over 3.7 million people and claimed more than 250,000 lives globally. At least 1 year may take for an approved vaccine to be in place, and meanwhile millions more could be infected, some with fatal outcome. Over thousand clinical trials with COVID-19 patients are already listed in ClinicalTrials.com, some of them for assessing the utility of therapeutics approved for other conditions. However, clinical trials take many months, and are typically done with small cohorts. A much faster and by far more efficient method for rapidly identifying approved therapeutics that can be repurposed for treating COVID-19 patients is data mining their past and current electronic health and prescription records for identifying drugs that may protect infected individuals from severe COVID-19 symptoms. Examples are discussed for applying health and prescription records for assessing the potential repurposing (repositioning) of angiotensin receptor blockers, estradiol, or antiandrogens for reducing COVID-19 morbidity and fatalities. Data mining of prescription records of COVID-19 patients will not cancel the need for conducting controlled clinical trials, but could substantially assist in trial design, drug choice, inclusion and exclusion criteria, and prioritization. This approach requires a strong commitment of health provides for open collaboration with the biomedical research community, as health provides are typically the sole owners of retrospective drug prescription records.
Keyphrases
- sars cov
- clinical trial
- healthcare
- coronavirus disease
- public health
- respiratory syndrome coronavirus
- mental health
- health information
- small molecule
- electronic health record
- phase ii
- big data
- phase iii
- human health
- emergency department
- risk assessment
- study protocol
- open label
- drug induced
- angiotensin ii
- machine learning
- cross sectional
- angiotensin converting enzyme
- depressive symptoms
- climate change
- minimally invasive
- adverse drug
- binding protein
- cell fate