Cardiovascular drug development and real world data

Cardiovascular disease (CVD) is the number one cause of death globally. More people die annually due to CVD than from any other cause, with an estimated 17.5 million CVD related deaths in 2012, representing 31% of all deaths globally (1). Of these, an estimated 7.4 million were due to coronary heart disease and 6.7 million were due to stroke (1).

Globally, death due to CVD increased by 41% between the years 1990 and 2013, despite a 39%

decrease in age-specific death rates (2). Still, a reduction in CVD related deaths has been observed during the recent years in high-income countries. This reduction is likely due to the combined effect of less exposure to tobacco smoking, changes in diet, and improved treatment by increased use of evidence-based drug therapies, for example blood pressure lowering drugs, statins, platelet inhibitors, and anticoagulants for both cardiovascular risk factors and cardiovascular disease (2).

However, despite this, there is still a global overall increase in the prevalence of cardiovascular disease, even in high-income countries, and cardiovascular disease is still one of the major reasons for death and reduced health globally, and continued need for development of new effective and safe cardiovascular drugs is present.

In the development of new drug therapies, randomized controlled trials (RCTs) are from a methodology perspective considered to be the gold standard (3). However, in the development phase of a drug, there are several research questions might be more comprehensively studied using other research methods than randomised controlled trial design. In addition to this, there is a recognized and increasing demand from regulatory authorities and payers for additional data from real-life treatment settings to complement and support the results from RCTs. Accordingly, there is an increased focus within the pharma industry of the importance of Real-World Data (RWD) (4).

RWD are collected from sources outside of traditional (randomized) clinical trials. These sources may include large trials, or pragmatic clinical trials, prospective observational or register studies,

retrospective database studies, case reports, administrative and healthcare claims, electronic health records, data obtained as part of a public health investigation or routine public health surveillance, and registries (e.g., device, procedural, or disease registries) (Figure 1) (5). Real-World Evidence (RWE) is used to refer to the product of aggregation and analysis of RWD (5).

7 4.1.1 Sources of RWE

1. International Society for Pharmacoeconomics and Outcomes Research. ISPOR Using ‘Real World’ Data Task Force. Draft Report. 2006. Available from: www.ispor.org/workpaper/RWD_TF/RWTFDraftReport.pdf (Accessed 04 September 2017) 2. Gliklich, Dreyer, editors. Registries for evaluating patient outcomes: a user’s guide. 2nd edn. Rockville: Agency for Healthcare Research and Quality, 2010

3. Tunis et al. JAMA 2003;290:1624‒32 RCT, randomized controlled trial; RWE, real-world evidence

Historically, from a pharma industry development perspective, RWD has primarily an important role in post-marketing drug safety surveillance, where register data with large numbers of unselected patients with generally long follow up time are utilized to study the extended safety profile of drugs.

These studies can be initiated by the company itself, or be requested by regulatory authorities (post-authorisation safety studies (PASS)). A classical example of this type of studies is the safety follow up programme for acid suppressive drugs and potential increase risk of cancer (6).

However, as mentioned earlier, an increased demand for RWD is also evident from regulatory authorities and payers. The increase need for RWE in the different phases of a drug life cycle was presented by the European Medicine Agency at a meeting in 2016, as illustrated below (Figure 2) (4).

8 4.1.2 Need of RWE in different phases of a drug life cycle

For regulatory authorities, pre-launch RWE data on prevalence and incidence, current treatment and disease outcome have become an essential part of the documentation package for a new drug application and/or new indications (7-9). Overall, addition of RWE data may allow for a broader understanding of the data from RCTs, e.g. adding information on the estimated overall size of target patient population, and providing a possible assessment of the generalizability of the results from the RCTs caused by selection of study patients and follow-up vs. real-life populations (10). From a health economic perspective, data from real-world studies have become essential in reimbursement dossiers as a part of cost-effectiveness analyses for new drugs and indications or drugs on the marked facing potential price reductions (11).

When the drug is available on the market, treatment reality studies provide important information with data on treatment prescription patterns, i.e. is the drug prescribed according to

recommendations and reimbursement criteria, patients’ persistence to therapy, and monitoring of potential safety signals (12-14).

RWD is also considered to be a valuable data source for example in sample size estimations for planned randomised trials, and the generation of research questions regarding underlying disease patterns to be tested in randomized trials. Another potential and important area of use for RWD is the evaluation of changes in risk predictors, as this might not be possible to study with a randomised study design where randomisation into different follow up groups might not be feasible. Examples for this include changes in body mass index and association with cardiovascular disease risk after being diagnosed with type 2 diabetes (15), and changes in high density lipoprotein cholesterol after initiation of statin therapy (16).

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Furthermore, comparative effectiveness studies where one treatment is compared with another relevant treatment in a real-life setting regarding outcome is becoming a major part of the data needed during a drug’s life cycle. These studies allow outcome in larger, unselected patient

populations to be described as a complement to RCT outcome studies (Figure 3), or as an alternative to RCT data when RCT outcome data are not available (17-19). Payer authorities in many countries are starting to request comparative effectiveness data and cost data as a part of negotiations for gaining or maintaining reimbursement for drugs.

4.1.3 Real world evidence complements data from randomized controlled trials

Adopted from Taylor and Gordon (2007). Handbook of research methods in abnormal and clinical psychology RWE, real-world evidence; RCT, randomized controlled trial

Based on the importance of RWD for the pharma industry there is a continued increased search for registries and countries where high quality studies of this type can be performed. The typical limitations of RWD register sources include incomplete historical or geographic coverage, restriction to selected patient groups and lack of complete long term follow-up. In addition, insufficient linkage possibilities with other data sources (for example data on socio-economic status) might also reduce the usefulness of data. Furthermore, the quality of register-based research largely depends on the data validity. The requirements of data completeness and validity are even more critical for comparative effectiveness studies, as there is a risk that residual, unmeasured confounding, or confounding by indication may have affect the results. Furthermore, access to data on health care costs, for example what are main cost drivers within a therapeutic area, the cost for treating selected patient groups are critical information that ideally should be directly retrieved from register data.

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