key: cord-0685221-hv64ze76
authors: Gurwitz, David
title: Repurposing current therapeutics for treating COVID‐19: A vital role of prescription records data mining
date: 2020-05-18
journal: Drug Dev Res
DOI: 10.1002/ddr.21689
sha: 726469a2138653f4a19761deacd3c000f50dfab7
doc_id: 685221
cord_uid: hv64ze76

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.

At time of writing this commentary (late April 2020), humanity is struggling with the SARS-Cov-2 pandemic which has already infected more than 3.7 million people and caused over 250,000 fatalities globally, mostly among older individuals. Even the most optimistic scenarios estimate that it may take until mid-2021 to develop, receive approval, and start population-wide distribution of an effective SARS-Cov-2 vaccine (Callaway, 2020; Gewin, 2020) . Meanwhile, many million further individuals will likely be infected, some with fatal outcome. Drug repurposing, also termed drug repositioning, attempts to apply drugs already approved for certain indications for a new indication; this approach is by far less costly and more efficient compared with developing a new drug (Koromina, Pandi, & Patrinos, 2019; Xu et al., 2015; Xu, Li, Jiang, & Chen, 2020) . A notable example is the demonstration, based on prescription records data mining, that the diabetes drug metformin is protective against many solid cancers: decreased mortality following a cancer diagnosis was observed in patients prescribed metformin (Xu et al., 2015) . The number of new drugs approved per billion US dollars spent for drug research and development was halved every 9 years since 1950, decreasing about 80-fold from 1950 to 2012 after adjustment for inflation (Scannell, Blanckley, Boldon, & Warrington, 2012) . It was recently estimated that the average cost for developing a new drug ranges from US $2 billion to $3 billion, while on average 13-15 years are required for its approval (Xu et al., 2020) . These numbers illustrate the need for considering drug repurposing for drastically reducing development costs, improving success rates, and reducing toxicity risks, as the latter are, for most drugs, already established.

As of late April 2020, the National Institutes of Health (NIH) website for clinical trials (ClinicalTrials.gov) lists over thousand registered trials in COVID-19 patients. Some of these trials examine the potential to repurpose (reposition) therapeutics approved for other conditions for COVID-19 patients; notable examples are listed in Table 1 . However, clinical trials typically take many months to complete, require high level of funding, and are usually done with small cohorts (fewer than 100 patients in each study arm) and at a single site. A by far faster and more efficient method for rapidly identifying approved therapeutics and repurpose them for COVID-19 patients is data mining of electronic health and prescription records of COVID-19 patients. Electronic health records (EHRs) of COVID-19 patients were already useful for identifying and assessing hypertension and diabetes as its major fatality risks (T. Chen, Wu et al., 2020; Li et al., 2020) . Health records were also useful for studying infectivity among children (Qiu et al., 2020) and in embryos of infected pregnant women (H. Chen, Guo et al., 2020; Li et al., 2020) , and COVID-19 neurological manifestations (Mao et al., 2020) .

Many health providers maintain EHRs which include, in addition to detailed longitudinal clinical phenotypes, prescription records of their customers. For example, Maccabi Healthcare, the second largest Israeli healthcare provider, maintains such records and has utilized them for epidemiologic studies (Levkovitch-Verbin, Goldshtein, Chodick, Zigman, & Shalev, 2014). Drug prescription records data mining is also useful for identifying adverse events due to drug interactions (Hansen et al., 2016; Zhan, Roughead, Liu, Pratt, & Li, 2018) . Making such prescription record datasets valuable for clinical and epidemiological research requires a common data collection and encryption modes that enable rapid, comparable, and systematic analyses across unrelated observational data sources for identifying and evaluating the safety and efficacy of therapeutics and their combinations for various clinical morbidities (Reisinger et al., 2010; Shabo, 2010 : Shabo, 2014 ). This remains an unmet need for improved international collaboration of prescription records data mining.

Applying data mining of prescription records for COVID-19

patients for whom rich phenotypic information is available on the course of their disease, starting with early phase prior to hospital admission, seems a promising method for identification of drug candidates that can be repurposed for COVID-19 patients. Such combined data mining may assist in identifying the most suitable existing therapeutics, possibly including drug combinations, that may protect SARS-CoV-2 infected individuals from life-threatening symptoms. (e.g., Gurwitz, 2020; Rothlin et al., 2020) (Wambier & Goren, 2020) .

This suggestion is based on the well-established androgen-mediated upregulation of TMPRSS2, coding for a protease which is essential for ARS-CoV-2 cell entry subsequent to the binding of its spike protein to the ACE2 receptor (Clinckemalie et al., 2013) . This suggestion is supported by the comparatively low rates of COVID-19 fatalities among children, whose androgen levels remain low until puberty.

Access of health informatics researchers to EHRs, including to individual prescription records, is key for efficient and time-saving drug repurposing studies. An effective and fast response for reducing COVID-19 morbidity and fatalities requires novel expedited ways of applying information technologies for supporting clinical needs (Grange et al., 2020) . In Taiwan, the success of combating COVID-19 was in part thanks to its advanced information technology capacity (Lin et al., 2020) . As discussed above, applying health informatics is in particular valuable for drug repurposing (drug repositioning). During the ongoing pandemic, and as long as a safe and effective vaccines are not available, applying medical and prescription records data mining for drug repurposing requires a reasonable degree of easing current data access regulations, as summarized in 

The author declares no potential conflict of interest.

https://orcid.org/0000-0002-9363-1869

T A B L E 2 Proposed protocol of utilization of prescription records for repurposing therapeutics for COVID-19 patients • Applying prescription records for drug repurposing is far cheaper and faster compared with prospective interventional clinical trials. This approach has been successful for several drugs, for example, for metformin (Xu et al., 2015) . • Health and prescription records must be anonymized by removing any potential identifiers (e.g., date and place of birth, names of treating clinicians, etc.). Records should include rich phenotypic data, including age, sex, ethnicity, current comorbidities, BMI, and so forth (while all potential identifiers are removed). • Access to raw data should be provided to registered qualified users following commitment to not breach patients' identity, not provide raw data access to third parties, and publish findings only as aggregated data. Publication of individual patient records must be strictly prohibited along with the policies for performing human genome research (Joly, Dyke, Knoppers, & Pastinen, 2016) . • To facilitate timely drug repurposing research, institutional review boards should consider waiving the informed consent of patients for accessing their recent prescription records.

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