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JUL-AUG 2017

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INTECH JULY/AUGUST 2017 29 AUTOMATION IT and distribute medicines and products at very high standards. All of the industry's supply chains are underpinned by an ever-growing magnitude of data. Regulatory focus and ex- pectations are increasing on the data life cycle within the broader area of managing and main- taining data integrity. Automation and IT sys- tems can support a number of the technical and business processes in this area. Importance of data Organizations create huge amounts of data, typically hundreds and hundreds of gigabytes a day. This includes enterprise systems and fac- tory systems and may include paper data as well as electronic data. A lot of people think of data integrity as purely electronic, but it is important to remember that it includes the paper as well. Manufacturers have the obligation to maintain the integrity of the data all the way through the supply chain to the patient. For example, two years after delivery to the customer there could be a regulatory review. The manufacturer needs to prove that the medicine produced was cor- rect using data genealogy and that the controls delivered a product that met all specifications and quality requirements. Data provides direct evidence that produced products are safe and effective—telling the story of the medicines long after they have been shipped. Data integrity Data integrity is about trust in the data through all the process steps. Massingham discussed the acronym ALCOA, which has been around since the 1990s and is used as a framework for ensuring data integ- rity. It is key to good documentation practice (GDP). ALCOA relates to data, whether paper or electronic, and is defined by the U.S. Food and Drug Administration guidance as "attributable, legible, contemporaneous, original, and accu- rate." These simple principles should be part of the data life cycle, GDP, and data integrity ini- tiatives. He provided a timely refresh on the fundamentals. Attributable All data generated or collected must be at- tributable to the per- son generating the data. This includes who performed an ac- tion and when it was done. This information can be recorded manually by initialing and dating a paper record or by an audit trail in an electronic system. For example: l During a validation exercise, test results should be initialed and dated by the person executing the test. l Adjustment of a set point on a process or mon- itoring system should be made by an autho- rized user, and the details of the change logged in an audit trail. l A correction on a lab record should be ini- tialed and dated to show the time and the person who made the adjustment. Note: It is important to maintain a signature log to identify the signatures, initials, and aliases of people completing paper records. Legible All data recorded must be legible (readable) and permanent. Ensuring records are read- able and permanent assists with accessibility throughout the data life cycle. This includes storing human-readable metadata that may be recorded to support an electronic record. For example: l GDP will always promote the use of indelible ink when completing records. l When making corrections to a record, use a single line to strike out the old record. This ensures the record is still legible. l Control your paper records and forms and format them with ample room to record the information. FAST FORWARD l Patients and consumers rely upon the pharmaceutical industry to deliver medicines and products at very high standards. l Regulatory expectations are increasingly focused on the information life cycle and maintaining data integrity. l Procedures, education, automation, and IT systems can support a number of the technical and business processes for data integrity. Management of compliance training Product quality life cycle mgt. and core stds. Receipt, storage, and returns Maintenance and calibration Production and packing control Inspection and testing Product and material release Periodic product review Corrective and preventive action . Training records . Critical quality attributes . Critical process parameters . Receipts . Calibration checks . Equipment logs . Manufacturing batch records . Packaging batch records . Analytical test results . Certificates of analysis . Change controls . Product data trends . Deviation and investigation reports

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