Improving development troubleshooting

A common challenge in biological processes is assigning an accurate probable root cause to an unexpected event/occurrence, excursion, or deviation in the process. The ability to make data-driven decisions and having the correct expertise can increase accuracy when assigning a probable root cause. There are several steps to process troubleshooting, and while all are critical to achieving the desired outcome, the focus of this article will be on root cause analysis (RCA).

Defining the problem

It is important to define the event/occurrence, excursion, or deviation properly. To establish a corrective or preventative action plan for the investigated problem, or to decide no action is needed (in some cases), the goals and direction of conducting root cause analysis within the organization should be understood well.

It should be also noted that monitoring the process and verifying the reoccurrence of the problem indicates the effectiveness of the action or the accuracy of probable root cause assignment.

Data-driven assessment

For a comprehensive RDA, the ability to obtain, compile, and store accurate process/analytical data is needed. This is the costliest step for data decision-driven organizations, but greatly increases process understanding, and therefore, the accuracy of probable root causes.
The compilation of process inputs and outputs can also help determine potential cause and effect, correlation, or assessment of multi-factorial root causes. The raw data, accompanying defined meta-data, can be translated into variables (which can also include calculations from raw data, e.g. potency ratios or yield per cm2) used for statistical analysis.

Having the correct expertise and robust systems

In the absence of data, it is vital to have the correct expertise, regardless of organizational level. First step of an expert to conduct RCA with no available data is to build a complex process understanding mapping. Process understanding can come from multiple sources and personnel levels (process timing, bottlenecks, movements, equipment details, etc.), and increase the likelihood of an accurate root cause assignment.

This step is important because even when the cause of an unexpected event is apparent, making immediate assumptions on the probable root cause can be detrimental to its assignment.

Secondly, clever experimental design can be leveraged to either confirm the root cause or solve the problem entirely. It is helpful especially when the root cause is likely to be cellular or in production level.

Although it is more important in the laboratory, it is necessary to have robust systems (such as proper aseptic techniques and practices, autoclave type and instructions, storage of critical reagents, equipment maintenance and calibration, etc.) to avoid the problem outright.

Common tools of root cause analysis

The 6M method can be applied to start a root cause (cause and effect) analysis.

  • Manpower
    (i.e., human error, number of technicians)
  • Machine
    (i.e., qualification or maintenance)
  • Material
    (i.e., raw material impurities)
  • Method
    (i.e., incorrect process SOP)
  • Measurement
    (i.e., incorrect analytical method or sample)
  • Mother Nature (Environment)
    (i.e., hurricane, storm) (less traditionally 8M including Management, Maintenance)

The 6M categories can be placed on the Ishikawa / Fish Bone Diagram with the problem stated (or effect) and subsequent probable root cause per category.

In this example, a completely new start-up CDMO without all the proper systems in place was asked to assess the cell growth of a new cell line in the bioreactor system. Upon the first run with a new cell line, hey had much lower cell growth than expected even though there was no change in the turbidity of the media.

The probable root causes can be verified or analyzed with information or data (for example, the temperature data from the bioreactor, equipment logbooks for calibration, review of procedural SOP mistakes, review of media quality release documents, process control run charts or other data analysis methods). Once a verification method is used for assessment with the best available information, the probable causes can be scored or determined by the likelihood of the occurrence and detection.

The selected probable root cause should be subjected to the 5 Why method to find the root of the problem. The method suggests to the investigators to ask “Why” to the 5th level and answer these questions step by step.

Example: After reviewing the most likely root causes and reviewing available information for each likely root cause, the task force team found that there was an error in the SOP.

Probable Root Cause

Incorrect Inoculum Preparation Dilution in the SOP

  • Why was the dilution written incorrectly?
    The author copied a procedure from development
  • Why did the author copy the procedure from development?
    The procedure in the notebook represents what was performed in the laboratory.
  • Why was the procedure performed in the laboratory incorrect?
    The same dilution was used in a similar but different inoculum preparation procedure for a different cell line.
  • Why did the author/owner of the notebook not correct the mistake?
    The entry in the laboratory notebook was assumed to be correct.
  • Why was there not someone else to find the mistake?
    There are no reviewed entries in the laboratory notebooks.

Thanks to 5-Why method, it is concluded that entries in the laboratory notebooks should have been reviewed. By asking a series of five simple questions, it can lead to the root cause of a problem, but it requires critical thinking and careful analysis.

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