As evidence of its interest in quality metrics, the FDA published an initial draft guidance in July of 2015 encouraging firms foster a culture of quality and continuous improvement, and signaling its intent to establish a quality metrics reporting program requiring the submission of quality metrics data. A year and a half later (November 2016), responding to comments and some pushback by industry stakeholders, the FDA released a revised draft guidance entitled “Submission of Quality Metrics Data: Guidance for Industry.”

In this latest revised draft guidance, the FDA initiates a voluntary reporting phase of the FDA quality metrics reporting program as a prelude to the eventual mandatory requirement for the submission of quality metrics data. Effectively, this revised draft guidance is a gift from the FDA that establishes a “practice” period for companies to get their quality management program and metrics in alignment with what will eventually become FDA rules on quality metric submission. As the FDA states in the revised guidance, “FDA does not intend to take enforcement action based on errors in a quality metrics data submission made as a part of this voluntary phase of the reporting program, provided the submission is made in good faith.”

While the voluntary program is focused on finished drug products and API manufacturing, all manufacturers may report quality metrics data (e.g., atypical active ingredients, excipient manufacturers). The FDA also notes that participation in the voluntary reporting program outlined in the draft guidance provides an opportunity to demonstrate a commitment to transparency and a willingness to proactively engage with the agency. In other words, participation in this “voluntary” program is a good way to create good relations with the FDA.

Pharmaceutical and biologics manufacturers would be well-advised to become familiar with the program outlined in the FDA’s revised draft guidance on quality metrics, so that they can begin to develop and implement appropriate standard operating procedures (SOPs) and quality management system (QMS) solutions. There are a number of important takeaways in the FDA’s revised draft guidance on quality metrics. In this blog, we’ll detail some of the more noteworthy aspects of the guidance that companies and labs should be paying attention to.

Overview of The FDA Quality Metrics Guidance

Some of the key details contained in the FDA revised guidance on quality metrics include:

The FDA’s objectives for the quality metrics program. As stated in the guidance document, the FDA’s goals behind the publication of this revised draft guidance are to:

• Establish a signal detection program as one factor in identifying establishments and products that may pose significant risk to consumers
• Identify situations in which there may be a risk for drug supply disruptions and engage proactively with manufacturers to mitigate the likelihood of their occurrence
• Improve the FDA’s evaluation of drug manufacturing and control operations
• Help prepare for, direct and improve the effectiveness of establishment inspections
• Use the calculated metrics as an element of the post-approval manufacturing change reporting program with an emphasis on encouraging lifecycle manufacturing improvement

Dates for Voluntary Quality Metrics Reporting. As described in the Notice of Availability (NOA) for the revised draft guidance, the FDA intends to open an electronic portal in January 2018 to begin receiving voluntary submissions of quality metrics data that was generated in 2017.

Metrics to be Reported for Voluntary Program. The quality metrics data described in this draft guidance is produced in the course of manufacturing drugs in compliance with cGMP. The metrics that the FDA is asking establishments to report in this voluntary program are:

• Lot acceptance rate (LAR) as an indicator of manufacturing process performance. LAR is the number of accepted lots in a timeframe divided by the number of lots started by the same establishment in the current reporting timeline.
• Product Quality Complaint Rate as an indicator of patient or customer feedback. PQCR is defined as the number of product quality complaints received for the product divided by the total number of dosage units distributed in the current reporting timeframe.
• Invalidated Out-of-Specification (OOS) Rate (IOOSR) as an indicator of laboratory operation and performance. IOOSR is defined as the number of OOS test results for lot release and long-term stability testing invalidated by the reporting establishment due to an aberration of the measurement process divided by the total number of lot release and long-term stability OOS test results in the current reporting timeframe.

Appendix B of the Guidance helps to define these metrics with clarifying examples which will help companies with their internal definitions. Any questions that an establishment may have about their specific situation when gathering this data can be emailed to: OPQ-OS-QualityMetrics@fda.hhs.gov.

Additional Quality Metrics Recommended. The quality metrics requested for the voluntary program are not intended to be all-inclusive. Manufacturers are encouraged to utilize additional quality metrics in their day-to-day QC operations that are deemed necessary to evaluate a product’s or manufacturer’s quality. Additional metrics may be added to the FDA’s future mandatory quality metrics reporting program. Also, additional metrics, or lack thereof, may be evaluated in an FDA inspection of manufacturing facilities.

How to Submit Quality Metrics for the Voluntary Reporting Program. Reporting establishments should submit quality metrics reports where the data is segmented on a quarterly basis throughout a single calendar year. Appendix A of the draft guidance contains a description of the quality metrics data elements that are relevant for different business segments/types. Additionally, a revised version of the Quality Metrics Technical Conformance Guide that describes additional technical details will be released soon. Finally, the FDA expects to publish a Federal Register notice providing further instructions on the submission of voluntary reports no fewer than 30 days before the electronic portal is opened.

Both Product and Site Quality Metric Reports Will be Accepted. The FDA will permit establishments to submit data in this voluntary quality metrics reporting program in two formats – by site segmented by product, or by product segmented by site. This allows companies to submit the data in the way that works best for them. That said, the Agency does prefer data segmented by product, because it demonstrates effective control over the manufacturing process for drugs over the entire the supply chain.

Special Considerations for Products Imported/Manufactured Outside the United States. The FDA recognizes that it may be extremely difficult to identify started lots, rejected lots, and OOS results that are manufactured by CMOs that are not in the United States. The FDA therefore allows voluntary reports to contain data from lots not imported with the data from lots that are imported, provided that the manufacturing process for both uses the same process and controls data. Product quality complaint rate (PQCR) data, however, should be collected only for drugs that are imported, intended for import or manufactured in the United States.

Optional Comment Field Within Quality Metrics Report.  Reporting establishments can submit a comment of up to 300 words with their quality metrics report in order to explain anomalous data or report any plans for quality improvement. As the guidance explains, comments “may describe special situations, such as natural disasters, the use of emerging technology, or describe the manufacturing supply chain or a plan for improvement.” The FDA “may refer to the comments if unusual data trends are identified, or in preparation for an on-site inspection.”

Mandatory Quality Metrics Reporting Will Eventually be Implemented. After data collection in 2018, the portal accepting voluntary submissions of quality metrics data will be closed and the FDA will begin data analysis. Once this analysis is complete, the FDA will share on its website what it has learned from the voluntary phase of the reporting program, and also initiate notice and comment rulemaking to develop it’s mandatory reporting program.

Quality Metrics Reporters List Will be Published. Upon completing analysis of the data from the voluntary quality metrics reporting program, the FDA will publish a list of the companies that participated in this voluntary reporting phase on its website. The reporting establishments in this list will be broken down into product and site report categories, and then tiered based on how much quality metric data was reported. The FDA feels this list may be useful for:

• Establishments within the pharmaceutical manufacturing industry as one element of a robust outsourcer or supplier selection process.
• Healthcare purchasing organizations, healthcare providers, patients, and consumers in sourcing drugs.

Key Quality Management System Takeaways for Manufacturers

In order to submit data that is in alignment with this new revised quality metrics draft guidance, product owners will need to choose between submitting data organized by product or site. While the FDA prefers data organized by product, gathering the metrics by product across the entire supply chain will be more involved than by site. Manufacturers will likely need to redesign their quality metrics dashboards and management review process to be able to facilitate consistency in quality practices across the enterprise and partners. The following recommendations may be helpful in adjusting quality management systems to meet the new guidelines:

• Appropriate dashboard KPIs need to be determined and defined prior to implementing system that aggregates data for the dashboard.
• The quality system should contain policies and procedures that validate that information is funneling to the dashboard as intended and assure the appropriate level of data visibility by management provided by the dashboard.
• KPIs in the dashboard should align with requirements outlined in the FDA revised draft guidance, and include clear definitions that include data standards.
• Individuals manually entering source data should receive proper training on proper policies and procedures.
• Review of KPI targets should occur on a regular basis. Assessments should be regularly conducted to identify poor performance in order to drive corrective action plans.

Conclusion

With the release of this revised guidance document of the submission of quality metrics data, the FDA continues to encourage pharmaceutical manufacturers to implement a modern, risk-based quality management system as part of its mission to protect public health. The FDA’s grand vision is for the pharmaceutical industry to shift from a culture of compliance and rules to a culture of quality, where quality protocols are built into every process. While this revised guidance outlines a voluntary program for submission of quality metric data, a mandatory program is coming. Manufacturers would therefore be wise to utilize this voluntary phase to take a hard look at their quality systems maturity and begin to focus on how quality metrics are defined, collected, organized, verified and reported. Manufacturers will need to review their quality metrics management and reporting systems, identify any gaps in data collection, and take steps to bring systems into alignment with the metrics program guidelines. Manufacturers who take these steps now will avoid the hassle of being forced to make these changes under accelerated timelines later, and will reap the financial and customer loyalty benefits that come with producing quality products

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Quality by Design

The more the organization understands regarding the key aspects of their products and processes, the better they can concentrate their efforts towards finding and implementing appropriate controls to improve quality. The approach should focus on incorporating design aspects and controls into the value chain at various phases and continuously rather than a holistic check towards the end of the process, thus attempting to mitigate risks throughout the process.

Data Integrity by Design

Data is centric to the organization. It needs to be visible to those that need to access it and leverage it across the organization. Validation is impacted by bad data and specifically when data is not integrated across functions of the organization. Integrated data is critical to Validation 4.0.

Integrated Environments

Current technologies like IoT have provided a means to capture and make visible key information across the entire value chain. This information is key to ensuring Validation 4.0.

Modern Documentation

A critical area to consider regarding validation is documentation. Going away from a paper-based approach to a digital approach where all the data is centralized versus in multiple places is imperative

The post Validation 4.0 and Quality 4.0 in the Life Sciences Industry appeared first on Astrix.

There are several important factors to consider to ensure a successful transition to CSA. They are focused on those areas that can provide a major impact to your CSA implementation.

• Leveraging Agile Methodologies

  • Leverage Scrum – Scrum is an approach that relies on teams working together in short phases, enabling rapid feedback, continual improvement, and fast adaptation to change to accomplish an objective. By incorporating this agile approach with the CSA implementation the organization will be able to bring in efficiencies and optimization into their processes supporting CSA.
  • Test-driven development (TDD) – TDD is a software development methodology that focuses on establishing unit test cases before writing actual code. It’s a method that combines programming, unit testing, and refactoring in an iterative. TDD will enable organizations and teams to reduce the amount of documented testing that would need to be performed as part of the system release.
  • Follow Behavioral-driven development- BDD is an agile software development process in which an application is documented and designed around the behavior that a user expects to see when interacting with it. BDD will help reduce the rigor and the complexities of developing test cases from the requirements alone – incorporating the learnings from the BDD sessions into the testing phase will greatly reduce the time for the testing phase as well as bring in efficiencies.
  • Introduce early testing techniques – By incorporating configuration and experimentation in lower environments to find defects early, organizations can improve process efficiencies across the SDLC as well get to release faster.

• Leveraging automation and digital technology

  • Continuous Integration – The method of automating the integration of code changes from various contributors into a single software project is known as continuous integration (CI). It’s a key DevOps best practice that allows developers to merge code changes into a common repository, from which builds and tests can be executed. Using this approach, organizations can test more holistically and focus more on the integration testing rather than the functional testing.
  • Continuous Delivery (CI/CD) – This refers to the ability to securely and swiftly deploy updates of any kind, such as new features,  configuration changes, bug fixes, and experimentation, into production or into the hands of users. By adopting CI/CD into the deployment / release processes, quality is incorporated and hence tested at various phases of the process, thus reducing the final testing that needs to be done prior to the release of the product. This further results in a reduction of the workload for the Quality team.
  • Employ Automated Controls and Quality Management System (QMS) tools – By leveraging automated controls throughout the organization the quality function can receive feedback quickly from systems that are able to collect that data and provide it in a consolidated fashion. A QMS or Enterprise Quality Management System (EQMS) is an important component to any digital quality framework. The objective of EQMS is to manage content and business processes for quality and compliance across the value chain. This EQMS platform integrates with the IT architecture and data model and facilitates cross-functional communication and collaboration.

It is essential that the EQMS is not siloed. Quality information should be collected as data and leveraged across the organization to make informed decisions.

The EQMS has to also have an interface to other systems whether it is ERP, PLM,  supplier quality, vendor management, or other enterprise systems integral to the organization. Those interfaces are critical because that is where data resides, and access to that data is vital for decision making.

EQMS also needs to be mobile. It can’t be at one particular location or region or within one area. The EQMS has to provide the ability to look at data wherever, whenever, and however needed. Having this visibility to the pertinent data allows teams to make decisions faster as well as implement controls that prevent issues and non-conformances further in the processes.

Conclusion

As we’ve outlined, there are multiple methodologies and technologies to consider when the organization is looking to transition to CSA. By leveraging the right mix of tools and methodologies, the organization’s move to CSA will result in lower risks during the transition.

Additionally, it is equally important to have the right skill sets (internal and external) to assist with implementing these approaches and tools.  Knowing the various methods and technology and being able to apply them are effectively two distinct requirements.

Why it Matters to You

Organizations making the transition from Computer System Validation (CSV) to Computer Systems Assurance (CSA) will benefit from this information in the following ways. It will:

• Assist in identifying and implementing efficiencies and optimization of the processes supporting CSA.
• Enable organizations and teams to reduce the amount of documented testing that would need to be performed as part of the system release.
• Provide a way to greatly reduce the time for the testing phase as well as provide for efficiencies.
• Enable tests to be done more holistically and to focus on the integration testing rather than the functional testing.
• Reduce the final testing that needs to be done prior to the release of the product.

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation & dedicated staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.

The post Computer Systems Assurance – Methodologies and Technologies to Consider appeared first on Astrix.

Cloud technology is no longer thought of as an elusive and uncertain technology environment. The cloud has become mainstream with a computing infrastructure offering extensive computing power, copious data storage, scalability, stability, and a reduced data security risk. Given its benefits across the organization, the quality function is a perfect area to leverage this technology to improve operational efficiencies.

The Cloud’s Impact on the Quality Function

Moving certain key aspects of the quality function to the cloud provides life sciences organizations the ability to centralize data, become more flexible, and enables the empowerment of the employees. Cloud-based quality management systems provide a means to centralize data and better integrate systems, to provide flexibility in operations, as well as a means to ensure knowledge sharing and remote capability.

Enables Centralization of Data and Systems

By leveraging the cloud and centralizing the quality data of the business, it standardizes information and also provides greater visibility into the data across the organization. It also has the ability to fundamentally transform the business.

By centralizing the quality data, it establishes a shared method of interaction across the business and eliminates the confusion that is caused by different locations and groups having their own systems, naming conventions and standard operating procedures.

System-to-system integration is also facilitated along with streamlined reporting and data analysis. With standardized naming conventions, the quality management system can easily collect data across various facilities without sophisticated database requests. This provides for the ability to gain additional insights on the quality of processes and systems across the enterprise.

Provides Flexibility To Adapt to External or Internal Factors

All companies strive to maximize flexibility with their operations so that they can change direction quickly when either internal or external factors affect the organization. Businesses want to have the ability to adjust to demand increases or decreases and hopefully safeguard profitability. A cloud quality management system provides this agility and flexibility. The system can provide the essential data people need to collaborate and work efficiently across functions and geographies. By incorporating a cloud quality management system, you gain maximum flexibility in pursuing the organization’s quality goals.

Provides for Cross-Pollination of Knowledge

Employees of the organization attain knowledge of processes and systems over the years. For example, Plant floor supervisors who have worked in the organization for years, have an intuition as to how to run their production operations. This knowledge is engrained in these experienced employees.

With a cloud-based quality system, that experience and knowledge can be transformed into a digital knowledgebase. In cloud-based quality systems, it is simple to capture and convert that inherent knowledge into explicit rules, procedures, processes, and workflows to ensure that they are consistently applied. This cross-pollination enables the organization to keep the processes running when faced with issues such as resource availability, system downtime, etc.

Facilitates Remote Operations

Providing the ability for employees to work remote is becoming an important reality. Most manufacturers are limiting the number of employees on the shop floor at one time. They are separating the floor into zones and limiting who can enter each zone. With this type of setup, it might require that a production supervisor do their job from an office on site without physically walking the floor.

Cloud-based Quality management systems of today provide the means to do this. Many also integrate with other plant software systems.

Easily Deployed and Maintained

An on-premises quality system requires hardware, licenses, and infrastructure. With a Cloud-based system, a majority of the overhead is eliminated. You only need a computer and to buy subscriptions to use the cloud-based system. The deployment is extremely easy from the user setup perspective.

With a cloud-based quality system, updates are done without a major interaction of company resources. Additionally, there are no servers to purchase and setup and no software to install. There is also no requirement to do time-consuming software upgrades. The organization always has the latest and greatest software version on any device with a browser.

Conclusion

The cloud provides a way for organizations to significantly improve their quality function. By leveraging the cloud a business can centralize their data and integrate their systems to improve operations and reporting.

Cloud applications also provide for flexibility in operations, cross-pollination of information, and the ability for employees to easily work remotely. Additionally, cloud based quality management systems require the least IT resources and cost, and are incredibly easy to deploy and maintain.

Why It Matters to You

Life Sciences organizations have an opportunity leveraging the cloud-based quality management systems to:

• Lower their cost of operations
• Centralize data and better integrate systems and processes
• Improve reporting across the organization
• Improve flexibility of the quality function and cross-pollination of knowledge

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation & dedicated staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.

The post Quality Management in the Cloud The Technology Advantage and How it Impacts the Quality Function appeared first on Astrix.

• Determine how the software will be used in the organization

Is the software affecting the quality of the product or the safety of the patients? If this isn’t the case, you won’t need the same level of assurance as you would for software that affects the product or patient safety. Document management, change management, and audit management software, for example, do not require the same level of testing as device software.

• Determine if there is the potential to influence product quality, patient safety, or system integrity.

CSV validation of computer systems focuses on evaluating the severity of the impact and the likelihood of failure, which leads to risk prioritization. Conversely, with CSA, the emphasis is on calculating the risk/impact on patient safety and product quality, as well as the implementation approach of the software program functionality.

The first step is to determine the degree of impact of the specific software on patient risk and product quality. If the risk is high in one or both of these areas,  more time should be spent in the testing phase. If the impact is very low on these areas, testing can be reduced.

When looking at the software itself, there are different degrees of implementation risk depending on the software’s origin. If the software is out-of-the-box with the pertinent documentation, then it should most likely be of lower risk.  If however there is a need to configure or completely customize the software, there is then a significant increase in the risk  to the situation.

• Wherever feasible, make use of vendor documentation.

If the software vendor’s documentation is audited and validated, then there is no need to reproduce this documentation. Use the vendor’s documentation and validation if it is of high quality, based on an initial assessment of the vendor provided documentation.

• Based on risk, conduct the specific level of testing required for that system or function.

With CSA, which is a risk-based approach, the focus is on critical thinking and professionals determining which systems and functions provide the greatest risk to patient safety and product quality. The highest level of testing should be applied to these areas. To those other low to no risk areas a significantly lower level of testing should be applied.

Testing with Computer System Assurance

Now that we’ve defined the process to follow to determine which areas are high-risk to patient safety or product quality, the next question to answer is, how is testing performed in a CSA context?

In the past, test scripts were written in great detail and were not as concerned with whether or not the system and its functions had a direct impact on patient safety or product quality. With CSA, this has changed. There are now new approaches to testing i.e., Scripted Testing , Unscripted Testing, and Ad-hoc Testing.

Scripted Testing is the traditional form of testing that has been done with CSV. It requires a step-by-step test procedure, required results, and it is pass/fail. However, the difference with CSA is that Scripted Testing is only applied to higher risk systems and features that directly impact the product or patient safety.

Unscripted Testing is less detail oriented than Scripted Testing. It is used to test the lower risk systems or functions that do not directly impact the product or patient safety. They do however impact the quality system. With Unscripted Testing there needs be a test goal and a pass/fail, however, there is no step-by-step test procedure.

Ad-hoc Testing – a third testing method can be used on those systems and functions that are low business risk to the quality area. This testing is performed without planning and required documentation.

Summary

The new CSA risk-based approach to systems validation requires the professional to spend more time focused on critical thinking and less time on documentation. The objective is to focus on those areas that have the largest impact to patient safety, product quality, or the quality system overall.

With this new process, CSA involves different levels of testing. Scripted Testing is used for those systems and functions of high risk to patient safety and product quality. Unscripted Testing is used for those systems and functions with low impact to these areas, however, they do have an impact to the quality system. And finally, Ad-hoc testing is used on those systems with low risk to the business.

Why it Matters to You

This new CSA risk-based approach is important for you to learn about because:

• It will potentially lower the cost of quality by requiring less time in testing and documentation.
• It will drive the team to achieve higher, quality, and productivity.
• Helps to maximize the use of validation and project resource expertise.

 About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation & dedicated staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere

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