Just as data checking and review are important components of data management, so is the step of documenting how these tasks were accomplished. Creating a plan for how to review the data before it is collected or compiled allows a researcher to think systematically about the kinds of errors, conflicts, and other data problems they are likely to encounter in a given data set. When associated with the resulting data and metadata, these documented quality control procedures help provide a complete picture of the content of the dataset. A helpful approach to documenting data checking and review (often called Quality Assurance, Quality Control, or QA/QC) is to list the actions taken to evaluate the data, how decisions were made regarding problem resolution, and what actions were taken to resolve the problems at each step in the data life cycle.
Quality control and assurance should include:
- determining how to identify potentially erroneous data
- how to deal with erroneous data
- how problematic data will be marked (i.e. flagged)
- Review and approval of Analytical & Formulation technology transfer documents like MFCs, MPCs, EBSPs, EBDPPs, HTPs, IUSPs, Scale up Reports & PDRs.
- Analytical method validation protocol and reports of API & Formulation.
- Analytical method transfer protocols & executed data of API & Formulations,
- Trending of AMT on Quarterly basis
- Analytical method qualification and Analytical Reports,
- Review of qualification documents of DQ/IQ/OQ & PQ of formulation equipment
- Handling of Deviations and Incidents of drug products.
- Lab visits and Ensuring compliance in area of SOP review of system, operation, cleaning and Engineering
- Line clearances, review and monitoring of Pilot batch manufacturing activities
- Review and monitoring of qualification activities of formulation equipment
- Review and monitoring of validation activities of cleaning, Water & HVAC
Experience: 8 to 12 years
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