Completed Work Products
Audit Site Selection
Transforming Trial Audits: A Deep Dive Into Data-Driven Clinical Site Audit Selection in Pharmaceuticals
Published in RQA QUASAR #169, November 2024 – Transforming Trial Audits: A Deep Dive Into Data-
Driven Clinical Site Audit Selection in Pharmaceuticals. The paper was led by the Audit Site Selection Work Product Team, which includes authors Hangyu Liu, Elina Beletski, Michael Pelosi, Ofure Obazee, Bjorn Koneswarakantha, and Lucie Regne-Martos. The Work Product Team consists of 10+ IMPALA biopharmaceutical Member Organizations, each providing input on the evolution in data-driven clinical site audit selection in the pharmaceutical industry. This white paper delves into the transformative shift from traditional data-driven methods to more advanced strategies, exemplified by the challenges of automating clinical site audit selection and giving insights into the current practices of the IMPALA consortium member companies. It highlights the need for continuous innovation and industry collaboration to fully realize the potential of data-driven methodologies in enhancing the quality and integrity of clinical trials. In the article, IMPALA Members share a vision for a data-driven clinical site audit selection, led by innovative and efficient processes that revolutionize the quality assurance approach in clinical trials, to be safer, more compliant, and more successful.
Please note that RQA membership is required for access.
Anomaly Detection in Clinical Data
Open-source Clinical Trial Anomaly Spotter (CTAS) R Package
The Clinical Trial Anomaly Spotter (CTAS) is a powerful open source tool for Central Statistical Monitoring that identifies outliers and anomalies efficiently and accurately in clinical trial time series. Its main focus is on flagging sites with one or more study parameters whose profiles differ from those of the other sites. In addition, the results can be used to identify anomalies for individual subjects.
Development of the package was spearheaded by Pekka Tiikkainen, Principal Clinical Data Scientist at Bayer, and tested and adapted for user flexibility across organizations by members of IMPALA’s Anomaly Detection in Clinical Data Work Product Team. The underlying algorithm of the CTAS operates by defining one or more time series for each parameter. The algorithm summarizes time series into a set of features such as Mean, Standard deviation, Unique value count, and Autocorrelation. These features help in identifying individual subjects with suspicious data.
CTAS represents a significant advancement in clinical trial data analysis; however, IMPALA’s vision for CTAS extends beyond its current capabilities and usage. CTAS has the potential to be an industry-standard tool that can significantly enhance the integrity and reliability of clinical trial data, leading to more accurate research outcomes and ultimately, better patient care.
IMPALA proudly invites all interested partners to test, utilize, and provide feedback for this innovative package.
Clinical Safety Reporting
Clinical Safety Reporting {simaerep} Package
Accurate and timely reporting of adverse events (AEs) in clinical trials is crucial to ensuring data integrity and patient safety. However, AE under-reporting remains a challenge, often highlighted in Good Clinical Practice (GCP) audits and inspections. Traditional detection methods, such as on-site investigator audits via manual source data verification (SDV), have limitations. Addressing this, the open-source R package {simaerep} was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting and over-reporting at each clinical trial site. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials. With the v0.6.0 update in late 2024, the package also supports in-database processing, enabling seamless scaling with enterprise IT infrastructure. The statistical probability of a site under-reporting adverse events can be used to manage, target and focus quality assurance activities.
Education
Data Analytics for Quality Assurance in Pharmaceutical Development Framework
The “Data Analytics for Quality Assurance in Pharmaceutical Development Framework” is a collaborative whitepaper which offers insights and guidance on transforming Quality Assurance (QA) in Good Clinical Practice (GCP) and Good Pharmacovigilance Practice (GVP) through the application of advanced analytics.
This Framework reflects on the learnings and key decisions taken by the Intercompany Quality Analytics (IMPALA) Consortium to elevate the goal of advancing analytics in GCP and GVP. This framework can guide Quality Assurance in development, implementation and enhancement of their analytics and technology strategy, regardless of organizational maturity.
Goals/Outcomes
In addition to the Framework, the Education Work Product Team will consider ongoing materials to support the education and adoption of strategies, methodologies, and tools that further the mission of transforming the biopharmaceutical Quality Assurance process in GCP & GVP using data analytics.
ONGOING WORK PRODUCT TEAMS
AI Powered Regulatory Intelligence
Goal
In order to comply with evolving regulation guidelines, pharmaceutical companies are challenged with the labor-intensive process of monitoring and implementing regulatory changes, along with the risk of overlooking critical updates due to human error. This Work Product Team aims to use AI technology to streamline how regulatory updates are handled. By automatically analyzing new regulatory changes and comparing them with internal documents, the AI system will help to pinpoint which documents might be affected and identify any discrepancies or omissions within internal documentation to ensure compliance.
Expected Outcomes
A detailed playbook that outlines the entire process of developing and utilizing an AI-powered Regulatory Intelligence tool. This will include specifications for input formats, output details, AI modeling techniques, infrastructure requirements, and additional information.
RAPID Audit Methodology
Goal
Real-time Audit Package informed by Data (RAPID) is a data-driven approach that follows a pre-defined methodology to assess processes and identify systemic issues efficiently. This Work Package will generate a cross-industry audit methodology that relies heavily on data analytics, allowing auditors to analyze large volumes of data in a short period of time and draw conclusions based on evidence. By tailoring the objective questions of the RAPID audit and utilizing specific data analytics packages, the RAPID methodology will be designed to focus on key areas of risk and provide insights into the effectiveness of GCP and GVP processes and the compliance state of clinical development and marketed products.
Expected Outcomes
- Establish a cross-industry RAPID methodology.
- Develop job aids and templates for implementation by IMPALA members.
- White paper on RAPID methodology, including an assessment of impact of the methodology based on IMPALA members’ experience.
- Conference presentation on an innovative approach to managing quality.
Quality Briefs
Goal
A change in the Quality paradigm to reduce the burden of retrospective, time-consuming traditional QA activities, and ultimately accelerate approval and patient access to innovative products.
- To ensure quality maintains pace with technological advances, using an outcomes-based approach enabled by Quality Briefs* (QBs), supporting innovation to support risk-based assessment and decision making to get new products to patients without delays. To create QBs as a living entity, serving from molecule, filing, through to post-approval and inspections across the pipeline.
- To influence & engage Health Authorities through building trust and confidence via proactive provision of QBs with clear Quality Position conclusions based on risk-assessed Critical to Quality (CtQ) Factors ; ultimately reducing compliance delays on route to approval.
- To create a future of quality professionals acting as enablers of innovation rather than traditional gatekeepers.
Expected Outcomes
- Establish agreed Quality Brief methodology and template available for IMPALA members to pilot
- Establish implementation toolkit & guidance for QBs for IMPALA members to pilot implementation
- Pilot use of QBs with selected Health Authorities to engage, influence & ensure fit for purpose: building trust & confidence.
- White paper on QB methodology, including risk assessment of Critical to Quality Factors, associated Quality Strategy, evidence-based Quality Position Conclusions and outcomes-focus published for open-source availability to industry and Health Authorities
- Long term: Influence Policy so QBs are understood by Health Authorities in both a filing & inspection context
*Terminology may change as the Consortium brings together one-industry approach