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InQuest Science has developed a cutting-edge expert data management system for evaluating product defect issues along with providing training and guidance on particulate matter control, optimized inspection and 21CFR part 11 compliant electronic particulate and physical container/closure defect tracking systems. Focusing on Parenteral, Ophthalmic and Medical Device manufacturing to achieve a robust product lifecycle knowledge and control basis.

These modular based data management and monitoring systems are designed to assure routine particulate and defect control compliance for raw materials, API, incoming single use materials, primary components, 100% in-process inspection and AQL inspection meeting USP<1790>, USP<790>, USP<771, USP<787>, USP<788> and USP<789> requirements.

In Addition, we have developed logical control processes for inspector training and qualification standards management which is unsurpassed in the industry. All this, in conjunction with the appropriate forensics for defect identification and CAPA remediation leads to a high degree of sophistication in day to day process monitoring for companies of all sizes.

Regulatory gaps or assessment points by the FDA:

• Consolidate multi-sourced data electronically from all departments to allow quality assurance management oversight, and to view the full upstream product prior to releasing or rejecting batches.
• Monitor each functional area in the manufacturing train to recognize drift or inconsistencies that may lead to a batch recall.
• Facilitate historical evaluation of inspection or defect testing data to establish realistic control limits. Produce routine SPC or annual product reports for 100% and AQL defect testing, and sub-visible or destructive particulate matter testing.
• Provide a master defect library (photos and descriptions) expected by regulators.
• Maintain a secure (blinded) repository of actual defect standards and test sets. Required for the inspection training and qualification assessments of manual or automated methods.
• Manage qualification data and schedule periodic challenge testing for visual inspectors or vision inspection systems.
• Automate the documentation of manual inspection station setup and system suitability of automated vision systems.
• Provide a simplified mechanism for documenting rejected product characterization to analyze the predominant defects and define action plans for mitigation.
• Capture images and information on particulate matter identification in a standardized three step approach, (levels: 1 (In-situ), 2 (Microscopic), 3 (Spectroscopic).

“Big Data” is the new buzz word in the pharmaceutical industry and the term refers to capturing as much information as available from all sources possible to build comprehensive knowledge about a product or process. This approach has evolved because cloud computing and inexpensive data storage memory is now readily available.

In the 20th century, paper based data was not stored in an electronic database because of the limited availability of “validated” systems primarily due to the expense involved with the large memory demands needed for storing this diverse array of product quality information. Unfortunately these former limitations also forced companies periodically to manually collect this scattered information for input into tools such as unvalidated spreadsheets or statistical programs that were not designed specifically for evaluating and monitoring this information. Also the data was not available “Real Time” and most often evaluated by trending long after the defects or process deviations occurred.

In the last 10 years we all have witnessed a quantum shift towards inexpensive unlimited digital memory and increasing reliance on cloud based applications. Building on the availability of unlimited data storage and cloud based applications it is now possible to apply a modern 21st century concept to crunch this information into useable forms.

In just the next 5 years at the pace that digital technology is proceeding it will be impossible for the pharma industry or other technology sectors to resist pursuing paperless electronic solutions applied to “Big Data” for evaluating process or product quality. Obviously this saves time and money while reducing rejected product losses and minimizing the potential of product recalls from distribution.

InQuest Science fully embraces this transition and seeks to be the first to capitalize on this technology evolution with the development of the Identifier™ system for data mining applied to Inspection, particulate and defect life cycle monitoring.