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Quality Metrics

Susan Schniepp, Distinguished Fellow at Regulatory Compliance Associates®, discusses the quality metrics journey in determining the suitability of pharmaceutical products.

 

quality metrics

 

One of the most discussed and debated topics on today’s pharmaceutical landscape is the issue of quality metrics. Establishing, maintaining, and interpreting quality metrics to determine the suitability of pharmaceutical products has become a high priority for the FDA. To understand the issue of quality metrics it is important to start at the beginning.

 

FDASIA

 

The first stop on this journey took place in 2012 when Congress passed the Food Drug Administration Safety and Innovation Act (FDASIA) which enhanced FDA’s capability to proactively react to, prevent, and alleviate drug shortages. This direction is embedded in the language contained in Title VII—Drug Supply Chain and Title X—Drug Shortages.

 

Specifically, Title VII Section 705 of the Act states FDA:

 

“…shall inspect establishments described in paragraph that are engaged in the manufacture, preparation, propagation, compounding, or processing of a drug or drugs (referred to in this subsection as ‘drug establishments’) in accordance with a risk-based schedule established by the Secretary”.

 

QA Risk Factors

 

This section also describes the risk factors considered in establishing the inspection schedule. One of the risk factors listed in this section is:

 

“Any other criteria deemed necessary and appropriate by the Secretary for purposes of allocating inspection resources.”

 

Section 706 of the same act authorizes FDA to request certain company information in advance of or in lieu of inspections by stating

 

“Any records or other information that the Secretary may inspect under this section from a person that owns or operates an establishment that is engaged in the manufacture, preparation, propagation, compounding, or processing of a drug shall, upon the request of the Secretary, be provided to the Secretary by such person, in advance of or in lieu of an inspection…”

 

In addition, Title X section 506C-1 (Annual Reporting on Drug Shortages). Title X section 506C-1 requires FDA to annually provide Congress “a report on drug shortages…”

 

Quality Metrics Examples

 

The second part of the journey occurred in the Federal Register Notice. In that notice, FDA asked the industry to:

 

“assist the Food and Drug Administration in drafting a strategic plan on drug shortages as required by the Food and Drug Administration Safety and Innovation Act…”

 

This notice asked a series of thought-provoking questions about more specific quality metrics including:

 

“What metrics do manufacturers currently use to monitor production quality?” and “How frequently would such metrics need to be updated to be meaningful?”.

 

The industry reaction to this information was varied. Many trade organizations responded to the questions in the Federal Register. Some prepared white papers while others held meetings to discuss the issue with their members. The general consensus was that industry needed to seriously engage with FDA to define which quality metrics would provide information to the agency supporting efforts to eliminate drug shortages and to be used in establishing a risk-based approach to inspections.

 

FDA Quality Metrics

 

The third leg of the journey was the issuance of a draft guidance titled Request for Quality Metrics. In between the issuance of the Federal Register and the release of this original draft guidance there were many industry comments submitted to the agency suggesting various metrics that might be employed by the agency.

 

In addition, industry trade organizations held conferences and seminars to discuss the issue with the stakeholders and agency representatives. A number of these trade organizations published white papers based on the proceedings from these conferences and put forth the position that the need to submit metrics to the agency was redundant because of the requirement to submit annual product reviews. The agency listened to industry and some of the feedback was incorporated into the guidance.

 

Quality Performance Indicators

 

The quality performance indicators that were chosen as metrics for the updated guidance include:

 

  • Lot Acceptance Rate
  • Product Quality Complaint Rate
  • Invalidated OOS Rate
  • Annual Product Review
  • Product Quality Review
  • On Time Rate

 

The guidance also contained three optional metrics intended to measure quality culture:

 

  • Measuring Senior Management Engagement
  • CAPA Effectiveness
  • Process Capability/Performance

 

This version of the quality metrics guidance generated 83 comments from industry submitted to the docket. A number of these comments focused on the practicality of submitting the requested metrics. In response to this concern the agency issued the Quality Metrics Technical Conformance Guide: Technical Specifications Document that explained how companies would be expected to submit the data to the agency.

 

Quality Management

 

The fourth part of our journey came when the second version of the guidance, titled Submission of Quality Metrics Data Guidance for Industry was released for comment. The docket for commenting on this most recent version of the guidance closed. There were 25 comments submitted. 12 from trade associations, 10 from individual firms, one from a hospital group, one from an academic institution and one from a private citizen.

 

The guidance clearly stated the

 

“…metrics can be also useful to FDA: to help develop compliance and inspection policies and practices…. improve the agency’s ability to predict, and therefore, possibly mitigate future drug shortages; and to encourage the pharmaceutical industry to implement state-of-the-art, innovative quality management systems for pharmaceutical manufacturing.”

 

Quality Metrics

 

The FDA also clarified the goal of the metrics program by stating

 

“As described in this guidance, FDA is initiating a voluntary reporting phase of the FDA quality metrics reporting program.”

 

This current version of the document requires companies to report on only three metrics:

 

  • Lot Acceptance Rate
  • Product Quality Complaint Rate
  • Invalidated OOS Rate

 

The optional metrics intended to measure quality culture were removed.

 

Total Quality Management

 

These proposed metrics are not new to the pharmaceutical industry. Many of them are currently being used by companies to measure their total quality management performance. In order to be successful, companies need to review and analyze the information the FDA is asking for, as well as other metrics they are collecting, and identify potential problem signals. Quality leadership teams should look to solve issues and self-correct before regulatory inspections occur.

 

The problem with the proposed metrics, however, is that they are lagging indicators of performance. There is no set requirement on which metrics a company should track to measure their overall performance. Each company should determine which metrics to track based on their operations, number of facilities they operate and where they are located, what types of products they manufacture, and what type of culture exists in their places of business. Any metrics chosen must be meaningful and written to provide a clear analysis of ongoing activities.

 

Quality Assurance Management

 

It is important for operations and quality to agree on any type of quality assurance metrics. Additionally, how to report the results to management is as important as the results themselves. The interpretation of the data is a crucial element because it may include a root-cause analysis of its own and may help to promote continuous process improvements.

 

When choosing a metric, it is important that the architects of the QA metric are aware of consequences that may inadvertently drive negative behavior. Management attempting to incentivize achievement of the goal such as offering a financial award if the goal is achieved may lead to inappropriate behaviors that do not address the real issue. In these cases, it is generally not the metric that will drive the behavior but rather use of behavioral rewards. Reward for achievement rather than analysis of the real underlying causes will not lead to sustainable positive change.

 

Quality Control Management 

 

When managed properly, metrics are an important tool to drive positive design controls and process improvements. Regardless of what metrics a company chooses to measure their performance, achieving a quality culture is important in assuring reported quality control metrics are accurate and reliable. A quality culture requires management and employees to establish an environment where responsibility, accountability, and reliability are paramount. This helps each person deliver a high-quality product to the customer and sustained performance over time.

 

Management must educate employees and provide the tools and environment where they can perform their functions in an atmosphere that encourages excellence and continuous improvement. Continuous improvement programs are, in fact, reliable indicators of the presence of a quality culture. Where the industry’s and the agency’s destination on this journey ends is yet to be seen. What is evident is that companies must have a robust quality metrics program so they may continue to supply quality products without interruptions to patients.

 

Planning Quality Management

 

Companies need to keep in mind that when planning a quality management program, they should evaluate numerous data input points. This would include, but not limited to:

 

  • Product-quality attributes
  • Manufacturing site performance
  • People metrics
  • Quality-system metrics

 

For product-quality metrics, companies should consider reporting on batch-specific data such as trending drug product, drug substance, and stability test results against customer complaint rates.

 

Internal Audit

 

Indirect product-quality metrics could include environmental monitoring, water trend results, and yield rates. When establishing site metrics, the company could look at inspection history including internal audit findings and maintenance history such as equipment age versus defect-failure rates.

 

People metrics should consider ongoing job-specific training and education, skills and experience assessments, and employee turnover rate by job function and site.

 

Root Cause

 

Quality systems metrics might look at change control, investigation root-cause trends, and release-testing cycle times. Companies that can successfully establish a robust metrics program that helps them continuously improve should have no trouble meeting the FDA metrics requirements.

 

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Risk Management for Machine Learning in Medical Devices

regulatory compliance

 

The quest is on for machine learning (ML) to turn raw data into useful medical devices that improve outcomes and reduce burden on the healthcare system. Supporting, and someday emulating, human thought processes enables ML devices to improve the decision-making process for patients and clinicians. Software designed to continually learn and improve poses both challenges and opportunities.

 

For example, in the not-so-distant future, patients may experience medical devices such as an intelligent insulin pump that more effectively manages patient needs in anticipation of a dessert about to be consumed. As ML matures, the possibilities to improve patient care are endless while the challenges are many.

 

ML Device Development

 

As device developers seek to harness ML for next generation products, it’s important to address the unique ML challenges in the pre-commercial stage, including:

 

  1. The research phase, where selecting the ML algorithm drives subsequent risk mitigation considerations
  2. The building phase, which addresses verification, validation, and risk management concerns specific to ML
  3. Regulatory processes and the nuances of working with regulatory bodies and novel technology

 

ML Algorithms

 

ML typically uses supervised or unsupervised algorithms to discover a data pattern and generate actions. In supervised learning, the developer guides the teaching process of the algorithm. This requires a known data set with inputs and outputs to train the machine to make predictions. The developer corrects the machine’s predictions in this learning cycle, and the system learns from the corrections.

 

Natural language processing is an example of this. The developer enters a sentence, asks the machine what it means and over time, the machine learns the pattern and consequently makes smarter outputs.

 

Unsupervised Learning

 

The other type is unsupervised learning, where the developer does not provide teaching guidance along the way. Instead, the machine extracts general rules from the data using mathematical optimization and other techniques. An example involves the condition of peritonitis, a swelling of the peritoneal cavity. The machine takes pictures of the patient cavity and determines if infection is suggested based on its analysis of prior data.

 

Choosing to use either a supervised or unsupervised ML algorithm typically depends on factors. These include the structure and volume of the data, and the use case at hand of the medical device. The developer can introduce errors in the model if the underlying assumptions are untrue. For example, a machine could learn how to visually differentiate between a criminal and civilian if given a set of photographs. However, the resulting algorithm would be incorrect when applied to future photos because appearance doesn’t predict criminal behavior.

 

ML Validation and Verification

 

Besides choosing the best algorithm at the onset of new product development, the R&D professional needs to choose the right amount of data to validate the model. Mislabeled data, too little data, and too much data introduces risk into the machine. The risks are based on the type of algorithm in ML.

 

In supervised learning, the decision tree or statistics are used to teach the machine. It’s validated by using fault tree analysis that pairs with the decision tree to understand if the machine takes the wrong path based on input data. The challenge in validation is mathematically proving the error margin falls within the tolerance originally specified. The math requires an adequate data set where data points can be allocated between the learning the validation samples.

 

In Vitro Diagnostics

 

ML can make it difficult to determine appropriate data sizes due to the lack of standards and potential introduction of creative approaches. A developer, for example, might compare previous clinical studies to suggest sample sizes. In-vitro diagnostics (IVD) validation might require some 450,000 patient data sets for algorithm development and validation to ensure the sample size in the fault tree analysis.

 

Artificial Intelligence

 

In another example, IBM Watson allows developers to choose various AI algorithms. A developer searching for cancer tumors in a biopsy might choose a neural network, which can be difficult to understand and challenging to develop and validate.

 

The neural network is trained using sets of data like a list of blood test results that indicate the patient has a certain number of cancer cells. Or, the algorithm can be trained by supplying it with images of healthy cells and those afflicted with cancer. In this example, the algorithm can be validated by comparing the training data set to a reasonable clinical study, which compares blood test results to correct diagnoses, the developer asserts that the algorithm has been adequately trained.

 

Algorithms, which are developed by using a pre-developed AI model, can be validated by leveraging the recommendation of the original creator on the amount of data needed to test to meet the desired margin of error. Another way to determine sample size involves leveraging domain experts such as clinicians, who understand the frequency of all paths in the decision tree based on their knowledge of each tree node and its associated risks.

 

Cybersecurity 

 

Developers understand the need for security and privacy in healthcare applications. In ML, a new security risk involves the malicious introduction of bad data into the machine, which can lead to invalid and harmful outputs. Use of ethical hackers, however, can help mitigate the risk of bad data in supervised learning. These hackers specialize in simulating malicious acts that lead to limitations or boundaries on system learning, which ultimately protect against bad data.

 

Mitigation Tactics

 

The risk of bad data in unsupervised ML can be reduced by buying an established algorithm with embedded mitigation tactics (mathematical, programmatic, etc). However, a thorough review of the algorithm mitigations is necessary by cybersecurity specialists who understand medical devices and unsupervised machine learning algorithms.

 

Developers have long been wary of privacy issues related to protected health information in cloud applications. Since many ML platforms leverage cloud storage and therefore introduce new risks to the process, it’s important for ML developers to understand how their data is collated with other data sets. This shared data about the patient condition could be combined to violate privacy through a technique called inference by malicious entities.

 

Inference

 

Inference is an approach that combines different innocuous and non-sensitive data to gain sensitive information. Consider the aggregated data for an automobile accident patient. It’s possible an attorney might slice the data and discover information about the victim’s diabetes to blame the patient for the mishap due to a potential diabetic coma.

 

The use of polyinstantiation can mitigate these types of risks by slicing the data into sets for collation, and designing data silos so only the developer knows which piece goes into the algorithm, thereby preventing the disclosure of the entire patient database.

 

Regulation in ML Technologies

 

Experienced medical device developers understand the well-established process for working with regulators and developing submissions. The challenge in ML surrounds the lack of precedence.

 

Regulators are used to working with established frameworks where a consistent set of inputs generates a reliable set of outputs, but in ML, the outputs are continuously evolving. Thus, device developers must help regulatory agencies establish ways to assess the safety and effectiveness of products. Some suggested tactics include:

 

  • Build a regulatory affairs team with experience in ML and multidisciplinary functions.
  • Conduct early and frequent meetings with regulatory authorities so both sides can learn from each other.
  • Find clinical and regulatory information throughout the world that is supportive of the desired goal. If negative information is uncovered, address it rather than ignore it.
  • Do not submit a “black box.” Develop ways to communicate how and why a particular result occurred.
  • Seek related credible sources, publications, guidance documents and subject matter experts, reference them, and utilize them.
  • Recognize that regulators are used to understanding the device’s Mechanism of Action. In ML and other novel technologies, it is difficult to describe how the device works, so seek alternatives such as Safety Assurance Cases to help effectively communicate risks and risk management activities.

 

Safety Assurance

 

When developing new medical device technologies, regulation compliance, risk identification, and risk management are all equally important. Safety assurance cases are an effective way of helping demonstrate device safety.
 
 
Assurance cases have been used successfully by other industries such as avionics to efficiently minimize product risk and expedite government reviews. The assurance case helps reviewers better understand risk management in a regulatory submission and recognize how the sponsor both mitigates risks and reduces the likelihood of a device harming end users.
 
 
Safety assurance cases can streamline processes for U.S. Food and Drug Administration (FDA) reviewers by improving their understanding of claims and supporting information, and elucidating the evidence supporting product safety and efficacy. This system is markedly different than the traditional method, which entails presenting FDA reviewers with supporting evidence sans guidance and rationale.
 
Such an approach, however,  can be problematic for regulators dealing with new technologies because there may not yet be any applicable review standards in place. The safety assurance case process enables reviewers to follow a structured map that focuses on specific evidence of safety claims, possibly resulting in faster submission evaluations.
 
 
Safety assurance cases are similar to legal cases, as they authorize product safety and serving as the logical glue for various parts of the regulatory submission. It is an overarching document that:
 
  1. Presents all claims that can be easily linked with supporting evidence to demonstrate the validity of safety claims
  2. Is a formal method used to demonstrate the validity of a claim. It is presented as a clear, understandable argument supported by scientific evidence
  3. Contains arguments based on statistical measurements of the system’s reliability and are grounded in risk-based and scientific methods to help discuss and draw conclusions

 

For regulators, safety assurance cases:

  1. Help to connect the dots in a structured way
  2. Helps them to see both claims and supporting evidence
  3. Helps them understand the “big picture”

 

For medical device manufacturers, safety assurance cases:

  1. Align medical device product development with FDA expectations.
  2. Help gain faster regulatory approvals. Medical device companies that move toward best practices by leveraging safety assurance case principles can clearly demonstrate product safety in a single document, making it easier for the FDA to review.

 

The three elements of an assurance case are claims, evidence, and arguments.

  1. The claim is a statement about a property of the system—typically, contained and/or driven by a requirements specification
  2. The evidence should provide information demonstrating the validity of the claim. This evidence may include verification and/or validation results including, but not limited to, test data, experiment results, and analysis. The evidence should also address the relevance to the claim, whether the evidence directly supports the claim, and whether it is providing sufficient coverage of the claim
  3. Arguments should link evidence to the claim and provide a detailed description of what is being proven. The arguments also should identify specific evidence that supports the claim
 
There are numerous examples, published by the FDA, industry, and academia that explain the reasoning for constructing an effective safety assurance case. It is important that companies understand the importance of a well-structured medical device product development process executed by experienced professionals, as well as the diligence and effective communication strategies that provide regulators, payers and medical professionals with the evidence and confidence needed to bring these new technologies to market.
 

Risk Management

 

Companies considering integrating ML into their products can boost their probability of success by developing a complete ML strategy (as opposed to a piecemeal or single-product approach). Working with experienced ML professionals helps, along with a multidisciplinary ML mindset incorporating R&D, regulatory, software, and cybersecurity expertise.
 
It also is wise to take a studied approach in selecting the ML algorithm, and develop a robust risk management plan that addresses the unique challenges in ML validation and cybersecurity. Most of all, companies should prepare themselves for the challenges surrounding safety and efficacy. This will be important during the regulatory submission process. Developers should be open to novel approaches such as safety assurance cases.

 
Incorporating ML into medical devices offers life sciences companies unparalleled opportunities to impact health and create sustainable differentiation from competitors. As with any emerging technology, there are risks along the product lifecycle during the R&D, regulatory, and validation processes. A prudent approach involves careful planning combined with a solid risk management strategy that brings in seasoned experts to augment internal capabilities.
 
 

About the Article

 
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Conducting an Internal Audit

The internal audit function of life science companies is one cornerstone of an effective and efficient quality management system. There are several types of audits that comprise a robust internal control program including supplier audits, internal audits, and regulatory audits.

 

Compliance Audit

 

Compliance audit documents to be reviewed include the quality manual, list of standard operating procedures, open deviations, and corrective and preventive actions. Any additional supporting evidence an ISO audit team may choose to review will help the certified auditor team assess and decide the final facility compliance status.

 

Forensic Audit

 

Preparing for a forensic audit or regulatory audit typically consists of the regulatory inspector providing the facility being audit agenda. This listing would include any areas of the audit mentioned in a FDA 483 and audit trail documents. The audit report will focus on life science departments to be inspected. This can include incoming raw materials, quality control, chemistry and microbiology laboratories and manufacturing. 

 

IT Audit

 

Conducting an IT audit is also critical in times like these of high cybersecurity concerns. Internal audits are performed by the company as a self-assessment for the purpose of identifying areas/issues that might affect their IT compliance status. This specific audit committee may include employees from across the company to intentionally examine IT process and quality from a cross-functional perspective. 

 

Audit Committees

 

Preparing for an internal audit requires the same discipline as preparing for supplier and regulatory audits. This includes audit committees and the number of employees involved in the process. During the COVID-19 pandemic, many companies reduced the number of employees allowed at an audit site. Many of the other audit committee who helped conduct the audit were allowed to work remotely. It is important to consider where employees are globally and the role they each play during an internal audit. 

 

Internal audits are part of management team responsibilities. Conducting an internal audit is different from the other audit types, whether it is pre-, during, or post-pandemic. If designed and implemented appropriately, there is great value in the internal audit. It allows the company to find vulnerabilities in their systems and remediate before they are discovered by an external auditor.

 

Audit Procedures

 

Internal audits can provide valuable information that can be used to prevent issues before they become compliance concerns. Audit procedures often help develop a remediation plan to take action to mitigate compliance problems. Having corrective actions in place before others identify the issue may lessen the impact of the observation. Most importantly, show there is a process in place for continuous improvement. In addition, the internal audit can be used for training staff and communicating valuable information to the organization.

 

Audit Schedule

 

The ideal tone for an internal audit should be a collaborative team-oriented activity that is instructive, informative, open, honest, and inclusive. There are several factors that help contribute to establishing this tone. The most successful is hiring a certified information system auditor to help guide the process. 

 

Another way to set the proper tone is to publish the audit schedule or agenda in advance. This makes sure the functional areas personnel are informed of the time schedule. During a pandemic, the agenda takes on another level of importance because it ensures the proper documents are ready to go. Teams should upload all data either before or during the audit. Prior planning precludes poor performance in this area.

 

Audit Office

 

Each of these specific audits requires preparation to make sure the forensic audit is productive and accomplishes its intended purpose. In the manufacturing world, the goal of the audit office is to ensure facilities are manufacturing fit-for-use products in full adherence. This includes meeting current good manufacturing practice (CGMP) requirements.

 

Audit Trail

 

Supplier audits are performed to confirm the audit trail of raw materials, packaging, labeling components, etc.. An effective audit trail should provide documentation of a continuous, uninterrupted supply of materials that are compliant with CGMPs. Regulatory authorities perform inspections to determine if the manufacturing company is providing materials that comply to CGMPs.

 

Audit Risk

 

An operations audit conducted requires documents be shared electronically to the auditor using secure electronic systems. This electronic exchange helps reduce audit risk by increasing the efficiency between independent auditor and a facility being audited. The quality audit documentation can be reviewed by the auditor, and questions can be communicated to the audit manager via email, conference calls or virtual technology. While this may not be ideal, because it eliminates the audit planning in-person interaction, it is still an effective way to conduct a system audit.

 

Facility Audit Tour

 

internal audit

Touring the facility is challenging when a virtual audit or external audit is conducted. These challenges can be overcome with some flexibility and ingenuity. Live video feed could be streamed to the auditor while the company’s audit manager and/or subject matter experts are available. This can help answer questions that might arise during the live videoconference.

 

Audit Video Recording

 

Additionally, the operational audit could be recorded, and that recording could be provided to the auditor. The understanding would include the audit manager being available to answer any questions upon the review of the video. The recorded version of the tour has both positives and negatives. For example, a certified auditor needs to see things in as real time as much as possible. However, it does allow for the auditor to pause and go back to review audit control processes in more detail if warranted.

 

Audit Issues

 

The auditors should work with the functional area and talk with as many employees as possible to identify the issues of concern. Individuals who are responsible for performing the day-to-day activities often have the best insight. Questions would include what is currently working and what needs to be improved.

 

Excluding them from participating in the audit process might result in overlooking a serious issue. As a result, this could come up or inadvertently lead the auditor to think the site is hiding something. To be able to get the most valuable information about the potential compliance issues facing the organization, internal audits should not be judgmental or antagonistic. 

 

Audit Questions

 

Auditors should be direct and avoid asking questions designed to intentionally stump people. Another important behavior is the ability of the auditor to listen to the answers and refrain from judging. The exact same behavior defined for the auditor should also be the behavior displayed by the auditees.

 

Auditees should be direct and avoid deflecting or obfuscating answers. They should take the time to explain why they do things the way they do them. Performance audit answers should be proactive, point out things of concern and seek advice on how to remediate them. Both parties need to remember they are not enemies, rather they are the partners in improving the organization.

 

Conclusion

 

Conducting these types of each technical audit presents a multitude of challenges. Today’s audit risk model has allowed the life science industry to creatively utilize technology-based applications to communicate and perform an effective system audit. The documentation and supporting evidence review can be conducted remotely, and confidentiality can be maintained. After reviewing the documentation and supporting evidence, the auditor can request interviews with various personnel.

 

Integrated audit interviews can then be scheduled via Zoom or online video conferencing. With appropriate planning and the proper use of technology, remote auditing can have the same audit quality as in-person auditing. 

 

 

BioPharm International

Vol. 34, No. 2

Pages: 44-45

 

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Quality Inspection: Making the Most of Your Processes

Quality inspection results should be included as part of the batch release documentation, says Susan J. Schniepp, distinguished fellow at Regulatory Compliance Associates.

 

Q: My company is a contract manufacturer. Our final product inspection process consistently delays timely release of the manufactured products to our clients. Is there a best process/practice for performing a quality inspection?

 

A: This is not a new problem for the industry. To me, some of the issue is the fact that we call the specific activity of the overall physical product evaluation the “final product inspection.” In my opinion, the physical product evaluation should start at the beginning: the receipt of the individual components (stoppers, vials, labels, etc.) that are used in manufacturing.

 

Quality Inspection

 

Each component should pass an incoming quality inspection with defined acceptable quality limits (AQL) and clear directions for rejection of the component should it exceed the AQL. The specific elements to be looked at on incoming inspection will vary depending on the component.

 

Let’s assume your company manufactures sterile injectable products and the major component you will use in manufacturing is a glass vial. The first step in the process is to perform an incoming inspection of the glass vials. The number of vials to inspect from the shipment will depend on the number of vials contained in the shipment. The sample size to be inspected should be recorded in the appropriate standard operating procedure (SOP). The specific defects to be inspected should also be included in the SOP.

 

Quality Control Inspection

 

When evaluating quality control defects, most companies employ definitions of critical, major, and minor to the reject/accept criteria. In the case of vials, critical might be defined as likely to cause harm to the patient; major might be defined as leading to impairment to the patient; and minor would be defined as cosmetic defect causing no threat to the patient. Defining the acceptance criteria upfront may save you a lot of time at the end of the process.

 

Once the vial is accepted, it will be used in the manufacturing process. Defects that exist in the vial after initial acceptance may be detected during this step of the process. Many manufacturing lines have automatic sensors that detect vial imperfections and eliminate the vial from the batch before it is sent for labeling. The line operators need to be trained to recognize when there is an increase in rejected vials because this could mean that the incoming inspection did not pick up the vial defect. Again, if the defect can be detected and categorized at this process stage, it will save time at the product release stage.

 

Quality Assurance Inspection

 

The last quality assurance stage before labeling would be visual inspection of the stoppered vial. Some companies perform this step manually with trained visual inspectors while others utilize visual inspection equipment to perform the final inspection of the vial before labeling. Companies using vial inspectors typically focus those inspectors on looking for particulates in the vial, but they should also be trained to recognize vial defects. Regardless of which inspection format is used, there should be SOPs and AQLs governing the inspection process.

 

Final product inspection results should be included as part of your , so it is important to have repeatable, defined process/processes in place at all stages of the manufacturing process that is/are suitable for the product being inspected. The more time you spend upfront in the component inspection process, the more efficient your quality checks in manufacturing and batch release process will be.

 

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Pharmaceutical Technology
Vol. 47, No. 12
Page: 34

 

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Biologics Investigation

biologicsContract manufacturing organizations that manufacture traditional and biotech pharmaceutical products are responsible for performing investigations and reporting the results to their clients. But how do biologics consultants recognize the differences between performing an investigation of a biologic product versus pharmaceutical products?

 

Biologics Manufacturing

 

The short answer is there is no process difference when performing deviation investigations for traditional pharmaceutical products vs. biologic products. The differences lie in the complexity of the manufacturing processes. Further, these are the variables that should be considered regarding what could impacted the deviation.

 

Biotechnology Process

 

Chemical processes, although sometimes quite complex, often have fewer variables even though many of the categories are the same. For instance, when investigating an unknown impurity in a biological process from an oligopeptide fermentation process, the considerations are many. This often includes understanding the fermentation conditions, such as time, temperature, oxygen uptake and byproduct production.

 

Contamination

 

Moreover, potential contamination of reactants can include master cell banks and fermentation reagents, equipment integrity, and overall performance. Further considerations for the downstream purification process variables and the effect of a final configuration (e.g., folding) also need to be considered.

 

The purpose of a deviation investigation is to determine why the deviation happened and what its impact was on the product quality. To determine the impact of the deviation on biotechnology engineering in general, it is critically important to find the deviation root cause.

 

Root Cause Analysis

 

Conducting a root cause analysis is especially important when considering the COVID-19 pandemic and global impact on biological matrix supply chain. Small cap or start up biologics companies have virtually no room for production errors in this type of environment. For example, patients may be waiting for biologic medications and any production interruption can impact more than just the manufacturer. 

 

The process used in the industry to determine root cause is, of course, the investigation procedure. This procedure, regardless of whether the product is biotech or traditional, should require the investigator to review various production systems. Equally important, the system review during the investigation should help determine whether they were the cause of the deviation under investigation.

 

Engineering Validation

 

Every biologics consultant understands it is important to remember when performing an investigation to keep in mind a few general rules. Naturally, one size does not fit all. Simple errors require simple corrections while serious deviations require broader investigations. Validating the investigation is related not only to the seriousness of the deviation but also to the complexity of the factors that could influence the outcome.

 

Fishbone Diagram

 

The best tool to have during any investigation is inquisitiveness. Continuing to ask questions and avoid assumptions will lead to a better outcome. Using other tools, such and fishbone diagrams and determination of most probable number (MPN), are always encouraged. Undeniably, they do not take the place of a biologics consultant asking questions.

 

Biologic Performance

 

In performing an investigation, it is important for the biologic investigator to widen their performance perspective and look for ways to relate similar issues. The best way to ensure events are not related is to try and relate them, not the other way around. Keep in mind that human error is rarely a true root cause. There is usually something in the process that causes that human error.

 
And finally, always verify the facts of the investigation. It is also important to include a historical review. This review should determine if the deviation occurred with this or other products, with the specific manufacturing line or other manufacturing lines, and/or with the operators.

 

Biopharma Tools

 

The historical review can help to prioritize the resources and detailed system review. In addition, many biopharma companies make use of tools (fishbone diagram, MPN) to help prioritize resources. These tools, if used correctly, can be helpful in determining root cause. However, keep in mind they are just tools and do not take the place of thinking.

 

The detailed investigation should include a review of various systems. The systems most often reviewed are equipment and machinery, the manufacturing process, the raw materials used in manufacturing, the specifications, the environment, and finally, the operators.

 

Finally, this is not to imply that these systems are the only areas you should look at during the investigation. These are simply the most probable areas where you will uncover the root cause of the deviation.

 

CAPA & Corrective Action

 

Each investigation must address the following elements: root cause, impact to the material or product, the immediate correction taken, the corrective action to prevent re-occurrence for specific product/operation, and the preventive action taken to prevent re-occurrence for all products/operations.

 

Once these elements have been investigated, results from the investigation must be documented. The written narrative should clearly explain what happened, when it happened, and who was involved or observed what happened. The narrative documents the solution and rationale for the root cause that was determined through the investigation process.
 

Quality Assurance

 
The key to any successful investigation is not assuming you have the solution prior to completing the investigation. Increase your quality assurance and compliance by asking questions until you can think of no more questions to ask. Be sure to document the answers to your questions.
 
 
If you follow your investigation procedure and thoroughly document your results, you should have an acceptable investigation regardless of whether you are manufacturing a traditional product or a biotech product. 

 

 

BioPharm International
Vol. 28, No. 11
Page: 46–47

 

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Compendial Methods & Pharmacopoeias

Experts Susan J. Schniepp, distinguished fellow for Regulatory Compliance Associates® (RCA), and Steven J. Lynn, executive vice-president of Pharmaceuticals for RCA, discuss the verification of compendial methods.

 

Q. Are compendial methods considered validated?

 

A. According to the pharmacopoeias, compendial methods are validated—that’s about as simple as it gets. The United States of Pharmacopeia National Formulary (USP–NF) states,

 

“…users of analytical methods described in USP–NF are not required to validate the accuracy and reliability of these methods but merely verify their suitability under actual conditions of use”

 

European Pharmacopoeia

 

The European Pharmacopoeia (Ph.Eur.) and the (JP) also consider their methods validated. Ph.Eur. states:

 

“The analytical procedures given in an individual monograph have been validated in accordance with accepted scientific practice and recommendations on analytical validation. Unless otherwise stated in individual monograph or in the corresponding general chapter, validation of these procedures by the user is not required”

 

Japanese Pharmacopoeia

 

The Japanese Pharmacopoeia says:

 

“when an analytical procedure is to be newly carried in the Japanese Pharmacopoeia, when a test carried in the Japanese Pharmacopoeia is to be revised, and when the test carried in the Japanese Pharmacopoeia is to be replaced with a new test according to regulations in General Notices, analytical procedures employed for these tests should be validated according to this document”.

 

Validation

 

Supporting validation information for pharmacopeia methods is retained by the compendial authorities and not by the users of the methods. However, this does not change the fact that the official methods in these publications are supported by validation information.

 

Verification

 

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The methods need to be verified as suitable for use in the user’s laboratory. The USP–NF and the Ph.Eur. both describe the requirements for verification in their respective compendia.

 

Compendial Procedures

 

The USP–NF and Ph.Eur. explicitly require that compendial procedures demonstrate suitability under actual conditions of use. This information can be found in General Chapter, Verification of Compendial Procedures <1226> in the USP–NF, and General Notices in the Ph.Eur. (1,2).

 

On the other hand, the European Directorate for the Quality of Medicines (EDQM) stipulates on their website:

 

“When implementing a Ph.Eur. analytical procedure, the user must assess whether and to what extent its suitability under the actual conditions of use needs to be demonstrated according to relevant monographs, general chapters, and quality systems.”

 

In other words, it is the user’s responsibility to transfer the procedure correctly” 

 

Monograph 

 

Once a monograph has been established and it is accepted that the published method is validated, users of the monograph need to verify that the method is suitable for determining the product quality. Moreover, the purpose of verification is to establish that the official method is reproducible when used by others.

 

Lab Equipment

 

The monograph sponsor proves the method works for its product with the manufacturer’s analysts using the firm’s laboratory equipment. They have already met the basic International Council for Harmonisation (ICH) requirements for reproducibility, repeatability, and intermediate precision. 

 

The task for the user of the USP monograph is to prove the published method is reproducible. Additionally, any analyst for their company’s product should be capable of testing using the lab equipment.

 

Test Methodology

 

There is no absolute guidance for verification requirements. Companies must decide for themselves how they will establish the method is verified and suitable for their product. Lastly, the test methodology verification will depend and fluctuate based on complexity of the test method.

 

Chromatographic

 

Chromatographic methods should, at a minimum, meet the system suitability requirements defined in the official method. Conversely, other method parameters, such as accuracy and precision, may be considered.

 

Microbiology

 

Method performance can be accomplished by using performance characteristics such as blanks in chemistry or un-inoculated media in microbiology. Finally, laboratory control samples and spiked samples for chemistry, or positive culture controls for microbiology to assess accuracy.

 

Training Records

 

Technique-dependent methodologies should not require verification. These methods include but are not limited to loss on drying, pH, residue on ignition, etc. Technicians should be trained, and their training records maintained, demonstrating their ability to perform the method regardless of the material being tested.

 

Lab Procedures

 

To simplify activities for their analysts, many companies translate monograph instructions into laboratory procedures. Although this practice has its benefits, it can also lead to compliance concerns.

 

Nevertheless, the author recommends that a baseline comparison be made between the standards and the internal testing documents to minimize this risk. Further, the comparison does not need to elaborate, but should focus on critical parameters for test methods.

 

System Requirements

 

Equally important, the reader may also want to consider reference standard usage, system suitability requirements, or other parameters that may help establish equivalency to the compendial methods.

 

Critical to Quality (CTQ)

 

Finally, the criteria listed above should not be considered all-inclusive. Establishing an internal method that complies with the official pharmacopeial method is critical to quality (CTQ). After all, many different users may have other requirements to consider for their specific laboratory usage.

 

 

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Article details

Pharmaceutical Technology
Volume 46, Number 4
Page: 58

 

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