Audit Site Selection


In the Annual Audit Plan (AAP), each planned audit has a target year and estimated duration, but it does not have a specific date to start the planned audit. So the objective of this audit scheduling workstream is to build an algorithm to optimize the timing of individual audits given certain factors, including but not limited to: (1) Timing factors: In general, we don’t want to schedule an audit when the site is super busy, so we will try to avoid conducting audits near the time of important study milestones (FPI, FSA, etc.) or near the holiday. (2) Resource factors: factor in the work schedule and workload of the audit manager, auditor, and other individuals involved in the audit; (3) Travel factors: optimize the travel distance and cost for planned audits.

Expected Outcomes

Our expected outcome from this workstream is an audit scheduling tool, that can provide insights for audit managers to decide when to start a planned audit. The specific output format could be either a dashboard or a flat-file (.xlsx, .csv, etc.) generated by a Python script. The operational plan of this tool will be: When the AAP is finalized and/or modified, our audit planning tool will pull all planned audits from the AAP, and for each audit, the tool will propose some recommended audit start dates given the optimization of all relevant factors

Anomaly Detection in Clinical Data


Across the industry, the integrity of data’s has been a focused area for numerous years. While in certain GxP area such as GMP, the definition of high level of data integrity can be monitored through a common ground of understanding  (e.g human error whether intended or not, ALCOA+ principle), in other GxP area such as GCP and PV, defining Data integrity remains rather ambiguous and subject to interpretation of each involved parties.

The purpose of this Work Product is therefore to establish a shared view across the pharmaceutical industry on how to define and evaluate the integrity of data for GCP area and in the Quality processes associated to it.

To do so we will:

  • Share the current practices/ or lack of across our different organizations
  • Identify the pain point and HA expectations based on observations history
  • Align on a minimum requirements for evaluation of data integrity in areas in scope

Expected Outcomes

Set of metrics and trending guidelines that establish the minimum evaluation level that should be performed to monitor the trustworthiness of our data.

Quality Briefs


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 fast-track new products to patients. 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 Impact Factors*; ultimately speeding up the time to approval.
  • To create a future of quality professionals acting as enablers of innovation rather than traditional gatekeepers.

Expected Outcomes

  • Establish agreed Quality Brief template available for IMPALA members to pilot
  • Pilot use of QBs with selected Health Authorities to engage, influence & ensure fit for purpose: building trust & confidence.
  • White paper on QB methodology, including Impact Factors, risk-based Quality Positions and outcomes published for open-source availability to industry and Health Authorities
  • Long term: Influence Policy so QBs are required/ expected for submissions and are familiar and understood in inspections

*Terminology may change as the Consortium brings together one-industry approach



To support the mission of IMPALA to transform the biopharmaceutical Quality Assurance process in GCP & GVP using data analytics, it is imperative to define the framework, including skills, capabilities and requirements needed to develop an analytics domain within biopharma quality organizations.

The Education Work Product Team’s goal is to deliver a framework enabling biopharma organizations to develop GCP/GVP QA data analytics capabilities.

Expected Outcomes

The Education Work Product team’s expected outcome is a framework with the following components:

  • Framework including but not limited to
  • Guide for developing analytics capabilities
  • Recommended competencies
  • Lessons Learned & Best Practices (e.g., Adoption Engagement, Implementation and Quality Analytics Champions, etc.)
  • Communication
  • Whitepapers
  • Conferences
  • Other forms of media such as Podcasts, etc.