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The authors discuss how their research will help FDA in its identification of areas of emphasis in pre- and postapproval evaluation of products and processes.
This is the second in a series of papers reporting on the Recall Root Cause Research (RRCR) project. The first article in this series provided an overview of the project and was published in Pharmaceutical Technology in August 2009 (1). These papers describe retrospective research on drug-product recalls performed by the US Food and Drug Administration's Center for Drug Evaluation and Research's (CDER) Division of Manufacturing and Product Quality (DMPQ) in collaboration with other CDER components including the Division of Compliance Risk Management and Surveillance, and Office of Pharmaceutical Science. DMPQ introduced the project and its objectives at the FDA/PDA Joint Regulatory Conference on Sept. 9, 2008, in Washington, DC (2).
Quality defects in pharmaceutical products may have no effect on consumers' health or can have effect ranging from a reversible adverse event to the gravest consequences. The consumer expects every unit of a drug product to be safe and effective, and it is therefore essential for a firm's daily operations to be robustly conducted so that each batch uniformly meets that standard.
The principle objective of the RRCR project is to retrospectively assess why significant defects happen. More specifically, our research endeavors to answer the following questions:
To answer these questions, DMPQ collected information on Class I and II1 drug recalls and selected defect types, and then compiled data for each recall by searching several databases.
The project had two especially complex aspects. First, the nature of the data posed a number of challenges. Most of the information in the databases studied was free-form text that ranged from several words to several thousand words. As a result, significant and time-intensive data mining was required. The text material included a combination of information supplied by the recalling firm and FDA investigation reports. This resulted in a mix of data, facts, symptoms, reasons, and hypotheses mingled with possible root causes. In addition, because the information used was primarily text, the same topic or concept could be expressed in many different terms. These terms were then used to search the data. For example, there are several ways to say a product lacks sterility assurance, positive sterility, microbial contamination, lack of sterility, contains bacteria, and sterility failure. To facilitate the data-mining process, the different terms were consolidated into a smaller list of free-form fields. These fields were then converted into fixed-form fields for tabulation and into case studies for illustration of the root causes.
It is important to note that some events leading to recalls are not inadvertent mistakes or errors but intentional fraud by unscrupulous entities. For example, FDA undertook sample collections of Chinese herbal over-the-counter (OTC) products. The FDA sampling program found some drugs had an undeclared active pharmaceutical ingredient (API) that enhances male performance. The undeclared API was the same as or similar (analog) to the active ingredient in approved drugs. A large percentage of Class I recalls of OTC and unapproved new drugs have occurred due to this intentional act of using illegal ingredients in dietary supplements. In contrast, most other recalls have been due to substandard product and poor process design or operational errors.
Study populations. The study population includes products regulated by CDER that have been recalled.
Data acquisition. For purposes of this project, data acquisition means the collection of numerical and non-numerical data from the relevant databases and sources. Given the nature of the information collected, the primary activities were extracting the material, summarizing free-form text from the sources, and entering it into a data set for analysis. The recalls evaluated are considered the complete populations of interest, not samples from larger populations. Thus, sampling variability is not considered.
Root-cause analysis. Root-cause analysis (RCA) is an invaluable tool used in our research. The goal of RCA is to find the true cause of an observed defect, failure, or problem and use that information to correct it. RCA is a problem-solving tool widely used in a variety of situations and industries. In the pharmaceutical industry, it is a prelude to determining the appropriate corrective and preventive action (CAPA) to be taken for an unexpected occurrence.
RCA starts by having a good understanding of the problem, coupled with available information (both quantitative and qualitative), data, and facts. The process for determining root cause is systematic and consistent, as the root cause must be supported by evidence. Additionally, a defect, failure, or other problem may occur due to a combination of factors. It is very possible to identify more than one root cause or probable root cause for a given situation. Of course, careful documentation is essential at a company (and, by extension, FDA) to enable later "forensic" identification of the cause.
Note also that there can be multiple levels of causation. For example, in industry, the root cause of many recalls is the failure of management to implement and maintain a robust quality system (also known as a "quality-management system"). The test of an industrial quality system is its capacity and effectiveness in preventing manufacturing inconsistencies and quality defects. This central objective is met through sound design, control, monitoring, detection, and adaptive improvement practices throughout the product lifecycle. Both elusive and easily identified manufacturing problems can cause substandard product to enter the market, and it is essential for a manufacturer to institute robust performance monitoring and CAPA programs to prevent such occurrences (4, 5). Thus, RCA in this project attempts to drill down to identify the mechanistic manufacturing cause(s) that should have been detected at the design stage (i.e., during experimentation or scale-up) and/or by the quality system2 prior to distribution. In accord with the RCA concept, the cause(s) should be defined with enough precision and certainty to suggest a clear course of action. Our research project realized the importance of this objective, even as we observed that the root causes posited by the company at times significantly changed in the course of the iterative investigations we reviewed and that the inspection findings occasionally revealed other likely causes. All available relevant information was considered in our root cause determinations.
The general categories of root causes are:
An ideal RCA should:
Examples of root-cause analysis
With that brief background on RCA, the following hypothetical but realistic examples may be useful to illustrate the typical output of our RCAs5 and how persistently asking the question "why" can lead to a specific answer:
RCA Example 1.
Who: Tim's PharmCo
What: Tim's PinkApple tablets
Where: Springfield, MO
Why: Some tablets contain no active ingredient.
The question to ask in this situation is "Why is there no active ingredient in the tablet?"
During the investigation it was determined that tablet lots manufactured on a Plover 78 press since Mar. 19, 2005, lacked API and instead "pink layer only" tablets were produced. (The "pink layer" provides bulk, color, and acts as a barrier to the other layers. When only the pink layer is present, this signifies that the active layer is missing.)
The next question should logically be, "Why are 'pink layer only' tablets not detected?"
The deviation appeared to be due to an intermittent manufacturing equipment malfunction. So, the authors would ask, "Why is there a malfunction?"
There is incomplete rejection of "pink layer only" tablets during routine sampling at the compression stage.
Why is there incomplete rejection of "pink layer only" tablets?
The physical act of sampling interferes with the synchronicity in the compression operation.
Why does sampling interfere with compression?
The operator stops the machine to take a sample.
Why does the operator stop the machine?
The automated sampling shoot does not always work; it is intermittent.
What is the recall root cause?
Fundamentally, a grave coating-process operational flaw must be corrected to assure the presence of active ingredient. The further failure of final QC checks to detect this serious defect is due to a loss of compression machinery synchronization with a sampling chute that needs repair.
RCA Example 2.
Who: Enervite Pharmaceuticals
What: EstraStick Oral solution 0.03 mg/day
Where: New York, NY
Why: Labeling issue
The patient booklet contained in each calendar-pack of nine systems does not include the required current black box warning for the key ingredient as contained in the approved NDA labeling. The black-box warning was updated in August 2003.
Why is the black-box warning not included in the patient booklet?
The firm's label control program did not assure that the patient booklet reflected the latest warning associated with the product.
Why was the booklet not updated?
Following approval of the new patient packaging insert (PPI) black-box labeling, the firm put new labeling into use for the pharmacy shelf box. However, the firm did not properly update the packaging bill of materials and resulting packaging orders to replace patient booklets with the new PPI.
Why were the packaging bill of materials and packaging order not properly updated?
Jounne Smythe was out on sick leave and his replacement, Mack T. Nive, did not know to update the packaging bill of materials and the packaging order.
Why didn't Mack know to do the update?
The standard operating procedure (SOP) did not describe how to do the updates.
Why didn't the SOP describe how to do the update?
Jounne was the only person ever to do updates and the procedure had never been documented.
What is the recall root cause?
The firm needs to write an SOP and train the staff to ensure that packaging materials are updated and obsolete labeling is not used.
Use of systematic RCA is effective at determining the origin of a defect or problem. DMPQ has made substantial progress in the initial investigation of selected recall areas using data mining, statistical analysis, and RCA. A few root cause determinations are presented in this paper to illustrate the use of RCA and the type of data amassed for each incident through data mining. Future papers in this series will discuss the root causes of specific defect focus areas such as Class I recalls, B. cepacia contamination, subpotency, and dissolution.
1 Please see FDA guidances for additional information on recalls (3).
2 that is, during management review of suitability of the process for launch, or during subsequent process performance monitoring
Richard Friedman is division director, Michael Smedley is branch chief, and Israel Santiago is a compliance officer in the Recalls, Shortages and Certificates Branch; all in the Division of Manufacturing and Product Quality, Office of Compliance, Center for Drug Evaluation and Research, US Food and Drug Administration. Lynn Torbeck* is the principal statistician at Torbeck and Assoc., 2000 Dempster, Evanston, IL 60202-1017, 847.424.1314, Lynn@Torbeck.org. He is also a member of Pharmaceutical Technology's editorial advisory board.
*To whom all correspondence should be addressed.
Submitted: Sept. 30, 2010. Accepted Nov. 19, 2010.
1. L. Torbeck et al., Pharm.Technol. 33 (8), 42–45 (2009).
2. L. Torbeck, FDA/PDA Joint Regulatory Conference (Washington DC, Sept. 9, 2008).
3. FDA, Guidance for Industry: Product Recalls (Rockville, MD, Feb. 2009).
4. ICH, ICH Q10, Pharmaceutical Quality System, June 2008.
5. FDA, Guidance for Industry: FDA Quality Systems Approach to Pharmaceutical CGMP Regulations (Rockville, MD, Sept. 2006).
This article represents the views of the authors and not of FDA.