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Strengthening a Weak Link in QC Microbial Testing (Part III)

POSTED BY Rapid Micro Biosystems | 11 minute read

By Mark Newton and David L. Jones

Microbial enumeration testing in the pharmaceutical QC lab is based around well proven traditional manual methods that have changed little since the creation of the first pharmacopeia. These manual processes rely heavily on the training, expertise and judgment of the individuals who perform them. Improvements have been made to the processes to minimize variation such as: use of commercial media, validated incubators, verification signatures and electronic batch records; however, the ability to accurately see and enumerate colonies, then accurately record the information to paper or electronic system, is still a weak link in the forensic data trail.

In this four-part series, we will explore the unprecedented capabilities of automated plate counting: consistent counting, time savings and reduced fatigue for analysts, and automated capturing of complete, original data for review, monitoring and trending analysis. Along the way, we will also suggest key considerations for QC labs evolving from manual plate counts to automation.

Key Considerations of Digital Systems

Digital systems reduce manual transcription errors which are always present when people are involved. McDowall1 cites studies of “fat finger” error rates that range from 0.3% up to 39%; he believes the realistic rate in most analytical labs to be in the 0.3% – 3% range. These errors are eliminated with digital data collection and transfer.

It is important that digital systems collect all raw (original) data generated during the conduct of the assay, so a reviewer can determine if all decisions made by the analyst(s) can be scientifically justified. There are two actions that add complexity which must be included in the data set:

  1. Permitting an original (raw) plate count to be manually adjusted by a qualified person;
  2. Permitting a plate (or image of a plate) to be re-processed.

The regulatory expectation is set forth in the FDA Data Integrity Guidance2 on page 10:

FDA expects processes to be designed so that data required to be created and maintained cannot be modified without a record of the modification.

Original Data: First Plate Image Capture

For automated (digital) plate counting systems, the original data required to reconstruct the test would include original plate images plus workflow configuration plus manual adjustments made by a scientist. It is the combination of these three that determines a reportable result value. For the first image capture of a plate, the workflow settings must be captured and linked to the image, along with the automated result counts. The image and workflow settings permit the result counts to be reconstructed at any point in the future.

Original Data: Time Series Image Capture

Advanced systems can capture plate images over time and use algorithms to determine if an original “colony” was something inanimate such as a particle or a viable, growing colony. This capability permits more accurate colony counts. For this technology two options exist:

  1. The data is extracted from each image then compiled to generate a final cfu result but the images are not displayed. The images are deleted after data extraction. As no images are kept and the operator has no access to the data used to calculate the final validated answer and no recourse to recalculate the result the final cfu generated is the reportable value. The accuracy of the final result has been validated through the PQ/MQ phase of the system validation.
  2. The images are stored and at the end of the analysis the operator must check the result against the image series and correct any errors. Any such additional time series images must be preserved requiring large data storage capacity and the date/time of such an image must become part of the record in such a manner that a data reviewer would know that additional time images exist for a given plate identity.

Original Data: Manual Adjustments (Overrides) of Captured Images

In addition to a time series of images, it is possible for an analyst to examine the images (or the plate) and determine that the reported result must be adjusted, based on their observation. From a data integrity perspective, this manual adjustment (override) is a high-risk activity. As a result, the system must preserve the original (automated) values and the new values entered by an authorized scientist. Of course, this override capability must be restricted to very few individuals. The system must require a change reason to be manually entered by the authorized scientist, and the change reason (along with the original and override values, date and time stamp, and the scientist’s digital identity) must be preserved. Ideally, the system would set a flag to indicate a manual override has been recorded. This flag is an aide to the second person reviewer. 

Raw Data File Size Considerations

Early automated plate count systems often captured a plate image, processed the image to produce a colony count, then deleted the image. Images were not retained due to storage limitations – thousands of plate images required storage space beyond the capabilities of most firms. This was considered as equivalent to the manual process since microbial plates were discarded several days after viewing. Cloud storage vendors now make it feasible to store all captured images online. This is a large step forward, as it permits review and re-calculation of results months after a plate has been physically discarded. If questions arise about the process, original data is available to permit a complete review of the testing record. This has important implications when sterile operations and their risks to patient safety are implicated in manufacturing issues.

The complete record of original data would be:

Original plate capture (plus workflow settings, date/time stamp, analyst ID)
All time sequence images (plus workflow settings, date/time stamp, analyst ID)
Manual adjustments made by authorized scientist (plus automated values prior to adjustment, scientist ID, date/time stamp, change justification)

  • Sampling Record
    • Sample (sample ID, type, location, exposure time, sampling date; batch is optional)
    • Media (batch ID, media type, preparation date, expiry date)
    • Analyst (analyst ID, user ID if different, full name, security role; secondary: login date/time, logout date/time)
    • Image of plate (photo ID, pixel size, file type, file size, date created, date modified, created by, modified by, date archived, archived by)
    • Processing method (method ID, method version, date of last revision; method ID/version will link to a unique set of configuration attributes)
    • Current result (result ID, total count, counting unit of measure)
    • Modified result? (yes/no). If yes then refer to audit trail for data about manual activity
    • Start date (date/time) – start of incubation colony count
    • End date (date/time) – end of incubation colony count
  • Test Result Audit Trail
    For each modified colony count result, an entry must be made to provide a complete record
    • Date/time (timestamp from server)
    • Analyst (analyst ID, User ID)
    • Sample (sample ID)
    • Processing method (if manual override, this would be ‘Manual’)
    • Original result value
    • New result value
    • Change reason (free text field)

In the concluding post of our four-part series, we will discuss best practices for security and control of automated plate counting systems. Look for it soon!

Mark Newton is an associate senior quality assurance consultant at Eli Lilly and Company. David L. Jones is director of marketing and industry affairs at Rapid Micro Biosystems. This content previously appeared in edited form in the April 2022 issue of Cleanroom Technology.

Part I  |  Part II  |  Part III  Part IV


1McDowall, Robert. Fat Finger, Falsification, or Fraud? LCGC Europe. 24(4): 194-200. April, 2012. https://www.chromatographyonline.com/view/fat-finger-falsification-or-fraud

2Food and Drug Administration (FDA). Data Integrity and Compliance With Drug cGMP: Questions and Answers. Guidance for Industry. December 2018. https://www.fda.gov/media/119267/download