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.

  • Quality Data Analytics implementation Framework

    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.

  • AI Powered Regulatory Intelligence

    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.

  • Clinical Safety Reporting

    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.

  • Anomaly Detection in Clinical Data

    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.

  • Audit Site Selection

    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.