The IMPALA consortium is an engine of practical innovation, assembling expert teams to tackle high-priority challenges in the biopharmaceutical quality domain. These work product teams translate collaboration directly into impact, producing concrete, deployable assets like open-source software, novel methodology frameworks, and definitive guidance for immediate use.
With the increasing use of Generative AI in the pharmaceutical industry, and the evolving regulations and guidance from various regulatory authorities, IMPALA aims to outline some key principles and guidelines helping the industry to adopt GenAI while assessing the risks and establishing the right controls to ensure safe use.
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.
This 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.
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.
In order to comply with evolving regulation guidelines, pharmaceutical companies are challenged with the resource-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.
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. To address this, we aim to develop an analytics approach that can facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting and over-reporting at each clinical trial site.
To maintain high-quality data in clinical trials, it is crucial to be able to detect systematic anomalies in time series data at the site and subject level, which often stem from protocol misinterpretation or device miscalibration. Traditional approaches can be slow and resource intensive. We therefore aim to develop an analytics approach that can be run with minimal effort and with a high degree of reliability.
New workstream focused on leveraging Generative AI (GenAI) to support the drafting of audit findings . This initiative is part of a broader shift from exploratory discussions to structured implementation of GenAI in quality assurance.
To support Good Clinical Practice (GCP) and ensure clinical trial quality and integrity, sponsors need to conduct investigator site audits. However, due to resource constraints, it is not possible to audit every site in a clinical trial, and those that are selected for audits need to be selected based on a variety of risk factors. To improve audit site selection efficiency and accuracy, a shift towards data-driven is needed.