Work Products

Leads

• Haleh Valian
• David Boy

Goal

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

Leads

• Pekka Tiikkainen

Goal

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.