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Challenges

Introduction

Data collection nowadays has risen to unprecedented levels and at an incredible pace. Simultaneously, the technology used has been greatly developed and new Artificial Intelligence (AI) and Machine Learning (ML) approaches are being created. In other words, Real-World Data (RWD) has been shown to be extremely promising and a possible future key factor in common regulatory decision-making. However, there are some hurdles in their utilization. The major issues when utilizing RWD are addressed and outlined below.

Environment

As stated in other subsections of the “Real World”, although RWD and Real-World Evidence (RWE) provide a great advantage in the research and development (R&D) world of new medicines and therapies, but it has some major setbacks one of which being the lack of awareness and acceptance regarding RWD (1). This can be viewed in regulators,  Health care providers, and people who have a certain condition like CDG. Regarding the decision-making committees, this can be supported with the results of the company “Syneos health” from their 2019 survey conducted to different committee members from the US and Europe who are regularly involved in the regulatory decision making of rare diseases trials (2). In the US 55% were familiar with the term “Real World Evidence”, but it is not often used in their institutions. In Europe 37 % of the respondents stated it is not common terminology used in their institutions on their daily basis. So, stakeholders who have a comment in the design and function of a clinical trial in a rare disease field like CDG either do not intend to use it or are unaware of it which has a huge impact in the progress of accelerating orphan drugs approvals (3). The internal preferences for gold standard procedures like Randomized Clinical Trials (RCTs) have already shown that they are impractical in rare diseases clinical trials so a replacement of this method is extremely needed. This mindset has to be changed in order to improve rare diseases individuals' condition like CDG and their overall life quality. However, it was seen in another survey that some pharmaceutical companies are showing interest in RWD. According to a survey from 2017 by Deloitte (4) conducted to biopharmaceuticals companies, 54% of respondents are investing in RWE programs and many are targeting the use of RWE to assist Research and Development (R&D) and leveraging the design of their trials; so there is some improvement regarding RWD usage, regarding pharmaceuticals. This change in the pharmaceutical world is of extreme importance, but it has to be a global change in the stakeholders spectrum. Nonetheless, It is important to stress that the sample size of each survey is distinct given that Syneos Health had 64 respondents and Deloitte had 15 and the target audience might have been more precisely chosen in the Deloitte’s in comparison to the Syneos Health survey, so there is a possibility that this numbers are not representative of each stakeholder.
Another stakeholder that is important to point out is the people who live with a certain rare disease like a CDG. There is often a lack of awareness regarding individuals data handling (5). This raises moral and personal privacy issues as a consequence of the unknown usage of their data. It is usually stored in large databases that can be used to study a specific case or rare disease and it can help in the understanding of diseases that are impossible to study when recurring to Randomized Controlled Trials (RCTs). However, people are afraid that their data is not safe and will be used for commercial gain (5). This occurs because they are not accurately informed on how their data is going to be managed and how it might benefit the community they live in. People consentement to share their health information is an important step towards scientific advancements, obviously there is some regulation that must be followed with data sharing in order to not violate an individual's privacy.

Accessibility

Firstly, let’s define data accessibility. This concept relates to the possibility of exploring through different aggregates of data. This also includes external availability that is not necessarily associated with clinical matters. This raises some ethical issues regarding unallowed shared data, such as regarding privacy like it was mentioned prior. As it was stated in a different section of “Real World“, RWD is originated from different sources and has various qualities. The data from health-care centres such as hospitals, usually does not have the primary objective of performing targeted observational research (studies that follow a group of people over time who have a disease like a CDG and are observed in order to understand that specific disease in depth) (6); Thus, it is extremely necessary to manage it in different logical sections. This will ease the data accessibility and optimize the utilization of RWD and its quality. In Europe, the databases which regard medical, fulfil the minimum regulatory requirements and are accessibility are extremely low (7). Thus; nowadays, there is an apparent  deficiency of organization between different entities which difficulties computer storage infrastructure. This subject is sustained by a Deloitte survey (4) given that 64% of the respondents answered that there is a deficit of infrastructure to manage collaboration and analyze data which intensifies the importance of amending this concern worldwide.  Another survey, the Inteliquet 2019 Real-World Data survey (8), shows that 44% of the respondents have problems with accessing robust data, 39% in accessing primary infirm journey data and 37% find it difficult to gain internal knowledge/understanding of where RWD/E analyses can be applied.

Balance

One major challenge related to these concepts is the lack of balance between what is expected and what is possible. As we previously said, the resources in databases and information healthcare experts have access is limited and, most of the time, it is not specific. This poses a major setback in the manner that it is expected illimited benefits and applications of data which are not possible to match. Therefore, it is important to raise awareness of data sharing, not only between people living with certain conditions, but it also is crucial between researches, pharmaceuticals.

Data

An equally significant aspect of this topic is who should manage the collected information. A few experts suggest that it is better to have a specific kind of approach in which private and public collaborators should work to identify what information is crucial which means a correlation between internal and external data (9) between different parties. By taking this kind of approach, the amount of people who have access to information is narrowed; thus raising data’s security. However, it also provides a more accessible procedure for researchers to meet the evidence and data required as well as avoiding the entry of personal data by third parties with unauthorized access (9). Even though there is still a lot of data aggregation, most of it is not as useful as it may seem. Since, it is not possible to generate evidence out of it. This poses a problem since the exploration of data should be invested in order to specify the data and to store specific data according to a certain disease, getting rid of the unnecessary data aggregated.

Modeling Complexity and Capacity

A big hurdle in the application of RWE is how the data is treated due to the huge complexity that comes with it. The assessment of RWD might be biased because of excessive arbitrary while filtering the data which might affect the credibility of external control arms (as it was mentioned in the benefits section) used in clinical trials (1). The stakeholders counter this point with the use of epidemiological approaches such as the creation of inclusion and exclusion criteria which can mitigate this problem during data’s analysis (1). This can be made in the form of an algorithm such as through innovative methods of Machine Learning, a type of Artificial Intelligence (Like it was mentioned in another section of this website). However, this has its own limitations because most rare diseases like CDG are extremely unknown so it is quite hard to select the right parameters. Therefore, to achieve a certain quality it is important to have a broad range of criterion (10).

Organizational

There is a lack of expertise on how the data should be handled and also a scarcity of which criteria should be followed during a research in light of the fact that most rare diseases are extremely unknown. Therefore, choosing the right parameters is difficult and data quality will be affected. Moreover, although the Food Drug Administration (FDA) has published some recommendations on how the data should be processed (11), a big problem of aggregated data is that it generates a lack of standardization on how this is assessed by different stakeholders. Therefore, the data quality of different companies and research centers is not standardized. This raises the possibility of lack of credibility in the approval of new orphan drugs. Therefore, this issue might make this data unsuitable for research purposes or discoveries with different quality sets. 

 

For a better understanding, the  following videos are available:

  • A youtube channel called Understanding Patient Data has developed a video which shows a range of stakeholders “ Why do we need to talk about patient data? “ that is ideal to everyone who wants to understand the usefulness of people's medical data. Watch it below
  • The youtube channel Inside Digital Health has developed a video which is outline one of the drawbacks of RWD assessment. This video is called “Bias Is a Main Risk When Interpreting Real-World Data” that is ideal to everyone who wants to understand how this might affect the quality of our data. Watch it below

 

Bibliography

  1. Rudrapatna, V. A., & Butte, A. J. (2020). Opportunities and challenges in using real-world data for health care. Journal of Clinical Investigation, 130(2), 565–574. https://doi.org/10.1172/jci129197
  2. https://www.syneoshealth.com/sites/default/files/thought-leadership-articles/pdf/Real%20World%20Value%20-%20Advancing%20Payer%20Understanding%20of%20RWE%20in%20Rare%20Disease.pdf Accessed March 2021
  3. Polak TB, van Rosmalen J, Uyl – de Groot CA. Expanded Access as a source of real-world data: An overview of FDA and EMA approvals. Br J Clin Pharmacol. 2020;86:1819–1826. https://doi.org/10.1111/bcp.14284
  4. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/life-sciences-health-care/us-ls-2017-real-world-evidence-survey-031617.pdf Accessed March 2021
  5. https://www.hdruk.ac.uk/what-is-health-data-research/why-is-health-data-research-important/ Accessed March 2021
  6. Wu J, Wang C, Toh S, Pisa FE, Bauer L.(2020) Use of real-world evidence in regulatory decisions for rare diseases in the United States-Current status and future directions. Pharmacoepidemiol Drug Saf.(10):1213-1218. doi: 10.1002/pds.4962. Epub 2020 Jan 30. PMID: 32003065.
  7. Collier, S. et al. (2017). Monitoring safety in a phase III real‐world effectiveness trial: use of novel methodology in the Salford Lung Study. Pharmacoepidemiol. Drug Saf. 26, 344–352.
  8. https://www.businesswire.com/news/home/20200212005009/en/Life-Sciences-Industry-Rapidly-Adopting-Real-World-Data-but-Access-to-Robust-Data-a-ConcernBarrier-According-to-Inteliquet-Survey Accessed March 2021
  9. Hildesheim, Elmar .(2018).  “Real World Evidence - Impact on Regulatory Decision Making.”(Master's Thesis); Rheinische Friedrich-Wilhelms-Universität Bonn Available: https://www.dgra.de/media/pdf/studium/masterthesis/master_wegener_elmar_2018.pdf
  10. https://www.syneoshealth.com/insights-hub/the-real-world-evidence-equation Acessed April 2021
  11. https://www.fda.gov/media/120060/download Acessed March 2021
  12. https://www.slideshare.net/qocandeslideshare/real-world-evidence-pharmaceutical-industrypl Acessed April 2021
     

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Authors

Alexandre Gil and Pedro Granjo from Sci and Volunteer Program Nova School of Science and Technology 2021.

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Page modified at Tuesday, May 11, 2021 - 05:33