By Iris Loew-Friedrich, chief medical officer, UCB
The Hippocratic oath "primum non nocere," or “first do no harm,” is the basis of all medical care. However, it is about ensuring that the benefit of a treatment outweighs the risk for the patient. To make this assessment, we need access to both clear evidence of efficacy and a robustly defined safety profile of the drug being prescribed. Historically, there has been more focus on quantifying benefit, while risk has often been defined more qualitatively due to the infrequency of many safety events. This is now changing. Today, we have access to more data than ever before, and in an ever-advancing technological landscape, it is increasingly important to adopt a safety-centric monitoring approach that ensures we can measure safety data in real time and in a standardized manner across the industry. The challenge now is how to extract and leverage the quality data from the large volume that we have access to.
The Challenge Of Disparate Data
One of the significant challenges faced by the pharmaceutical industry when it comes to safety monitoring is that data comes from various sources, such as, for example, multiple clinical trials, multiple indications, healthcare records, devices, and more, all from different systems and in different forms. More and more data are emerging from multiple sources beyond the clinical trial. At the core of any safety assessment is good clinical and pharmacovigilance practice and a focus on data quality. Implementing robust data validation procedures and quality control measures becomes more challenging when dealing with diverse data sources. In the post-approval space, the data is even more disparate and the quality even more variable. This makes it difficult to aggregate safety information effectively into a unified data set.
When combining disparate data sources, challenges arise from variations in data formats, coding systems, and terminologies. Ensuring data quality and accuracy across different sources is crucial for robust data analysis and avoiding errors or biases. Data sources provide data of variable levels of completeness and quality and therefore bring different levels of scientific value.
Clinical trial data remains the gold standard, as clinical trials generate high-quality, robust data sets but of limited size. Post-approval trials generate real-world data sets large enough to assess an emerging safety signal. However, due to their reliance on self-reporting, spontaneous adverse events reports can be both incomplete and unverifiable, limiting their usefulness beyond large and broad analyses of comparative reporting frequencies. Research by Transcelerate1 indicated that certain data sources, such as market research programs and social media, provide data of lower completeness than others, as well as providing no new information over and above traditional methods. This points to an opportunity for a more risk-based approach that reflects the weight each type of data can bring to the assessment and identification of a safety signal.
Healthcare data, including clinical trial data, often contains sensitive patient information, making data security and privacy critical considerations. Each data source brings its unique risk of data breaches or unauthorized access. So, implementing robust security measures and ensuring compliance with data protection regulations are vital to maintain patient confidentiality. This becomes especially important when considering the use of electronic health records (EHRs).
The Value Of Standardized Data Collection
UCB’s global safety database for all reported adverse events is already one of the more standardized forms of data collection due to the requirements of the E2B (R3) guidelines issued by the FDA regarding regulatory reporting of adverse events. However, the data that fall outside the E2B (R3) domains (for example, endpoints related to safety and remote ECG data) and the differences in coding practices, as well as the versioning of dictionaries, still create significant challenges for the safety reporting. This adverse event database is distinct from the clinical database for all safety data collected during the clinical trials.
Standardizing data collection in clinical trials is essential to ensure data consistency and integrity across different sites and studies. The value of standardized data collection extends beyond individual clinical trials or programs. When the data can be merged at a disease or product level it allows different questions to be answered. Merging data sets between sponsors to create larger comparator data sets can enable novel trial designs, between study comparisons and provide a better opportunity to assess safety at the drug class level. This also has positive implications for placebo study arms, by either reducing or eliminating the need for placebo arms or combining placebo arms to provide insights into population background comorbidity.
By adopting standardized data collection practices with uniform data formats, metrics, and defined terminologies, we can streamline the process of collecting and integrating safety data and create more robust data sets. This also will create efficiencies for clinical practice and research sites. If we can align this within clinical practice, then this opens the wider horizon of trials being better integrated into everyday healthcare. In addition, ensuring the adopted practices meet regulatory data standards will help organizations meet regulatory requirements and could streamline the approval process and reduce potential delays.
Safety oversight and assessment are built on a wide set of data from many sources beyond classical clinical trials, such as the spontaneous and solicited individual safety reports and real-world evidence (RWE) studies that utilize claims data, medical records, registries, literature reviews, and more. The data from these sources is submitted and collected in different formats and often with a different purpose in mind than the safety review being performed. The challenge of integrating this disparate data is magnified by the lack of data standardization in the collection and reporting of safety data. This problem is likely to be compounded in the future as additional sources of data, such as data collected from mobile devices, EHRs, and insurance claims, become part of the wealth of safety information that we may leverage to ensure public health. The inappropriate pooling of data into a larger unified database also can have unintended consequences and might inadvertently mask safety signals when the different types of data cannot be separated to eliminate bias.2
If data is standardized and built correctly, it is possible to efficiently leverage emerging technologies and platforms. This allows the transfer of data across devices, systems, and institutions. It also facilitates long-term data management, archiving, and reuse in the future. Long-term data management is becoming increasingly important in areas such as post-market surveillance, pharmacovigilance, and RWE generation.
Benefits Of Safety-Centric Monitoring
At UCB, patients are at the center of everything we do. Consistent standardized data collection allows for the identification of adverse events, treatment responses, and potential risks associated with investigational interventions and the use of products post-approval. It facilitates evidence-based decision-making and assessment of benefit versus risk, leading to improved patient care and safety.
Standardized safety data collection allows for a holistic view of risks across the industry. By pooling the vast amount of data that we have, we can better identify rarer side effects, adverse drug interactions, and disparities in treatment across different patient populations. Lessons learned in these areas can be shared, which can turbocharge the development of better solutions that may have fewer side effects and can better address patients’ unmet needs. Larger combined data sets will reveal new insights into background comorbidity, which will influence clinical management, treatment choices, and interpretation of the safety data in clinical trials.
Underpinning all prescribing decisions is the individualized benefit-risk consideration. By collecting safety data in a more standardized way, utilizing accepted grading scales and focusing on key safety topics of interest we will be able to provide more quantitative and detailed benefit-risk assessments. This will continue to evolve as we begin to run trials with seamless access to EHRs and leverage emerging technology to create accessible real-time assessments of emerging benefits and safety over time.
The Path Forward — Collaboration And Technology
We at UCB understand the value, challenges, and the opportunity this diversity of data brings and are actively ensuring that our data meets the high-quality standards and robustness required for its use in important decisions around safety. As the pace of change accelerates, there is a wider speculation about the future of safety data standardization, especially in clinical trials and the potential for collaboration across industry: both in standardizing data collection and sharing data collected. Indeed, there are already some opportunities to share data between companies, such as the TransCelerate1 Historical Trial Data Sharing Initiative, which enables the sharing of anonymized data to maximize the value of clinical data collected historically in the control arms of clinical trials.
Data standardization facilitates sharing and collaboration between researchers, institutions, and regulatory bodies. It enables easier data exchange, sharing of best practices, and collaborative research initiatives. By using common data standards, researchers can contribute to larger data sets and participate in multi-site or multinational studies, leading to more robust and generalizable research outcomes. This is also important from a regulatory standpoint, and I hope that a culture of partnership will continue, where interactions with regulatory agencies are frequent and collaborative.
Additionally, leveraging emerging technology will be crucial to the future of automating data collection processes and ensuring data consistency in clinical trials. We are already seeing an explosion in innovation around electronic data capture (EDC) systems, mobile health applications, and remote monitoring devices. Apps validated by independent scientific groups, approved by regulators, and usable across all clinical studies could mean that all data related to a medicine and its control group are routed in real time to an independent data monitoring committee. At predefined milestones, analyses could be performed and communicated to the clinical team and regulators, improving regulatory science and decision-making. This also creates an opportunity for real-time sharing of information, which could enable a joint evaluation of the data followed by data driven discussions about the next steps. As new data analytics, machine learning, and artificial intelligence systems develop, the scope of real-time monitoring, predictive analytics, and proactive safety interventions will expand to push the boundaries of what we can capture. This means that to be truly effective this data standardization needs to be embraced beyond the clinical trial and become a global expectation in healthcare.
This connectivity with electronic records brings with it the prospect of a trial that is seamlessly integrated into clinical care or the “invisible clinical trial.” After patient consent, the treatment journey could be followed as an integrated part of their healthcare journey through direct access to their EHRs. This could not only enable better access to novel therapies for patients but also a fuller and more complete real-time picture of the safety profile and efficacy of emerging therapies. It would also bring significant value in the post marketing space where it would enable closer safety monitoring of newly approved therapies in a way that has limited impact on patients and providers. In the future we could see clinical trials and research as an integrated part of the healthcare continuum.
- Jokinen, J., Bertin, D., Donzanti, B., Hormbrey, J., Simmons, V., Li, H., Dharmani, C., Kracht, K., Hilzinger, T.S. and Verdru, P. (2019). Industry Assessment of the Contribution of Patient Support Programs, Market Research Programs, and Social Media to Patient Safety. Therapeutic Innovation & Regulatory Science, [online] 53(6), pp.736–745. doi:https://doi.org/10.1177/2168479019877384
- Jeremy D. Jokinen, Rosalind J. Walley, Michael W. Colopy, Thomas S. Hilzinger, Peter Verdru (2019). Pooling Different Safety Data Sources: Impact of Combining Solicited and Spontaneous Reports on Signal Detection In Pharmacovigilance Drug Safety (2019) 42:1191–1198 https://doi.org/10.1007/s40264-019-00843-0 Published online: 12 June 2019
About The Author:
Iris Loew-Friedrich is chief medical officer for UCB, a member of the company’s executive committee, and head of development solutions. She provides strategic global leadership for worldwide clinical development, medical affairs, regulatory affairs, quality assurance, statistical innovation, real-world evidence, and patient safety/pharmacovigilance. Her mission is to lead UCB’s Development Solutions, ensuring high-quality, innovative, cost-effective development of objectively differentiated patient solutions with proven superior and sustainable value for clearly defined patient populations.