Food and Drug Administration Quality Metrics Reporting Program; Establishment of a Public Docket; Request for Comments, 13295-13299 [2022-04972]
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and supplements thereto on the grounds
that new information, evaluated
together with the evidence available
when the application was approved,
showed there is a lack of substantial
evidence that the drug is effective under
the conditions of use prescribed,
recommended, or suggested in the
labeling. The Agency again invited
Glenwood, and any other interested
person(s) who would be adversely
affected by the withdrawal of approval
of NDA 007663, to submit: (1) On or
before September 19, 1977, a written
notice of appearance and request for
hearing and (2) on or before October 17,
1977, the data, information, and
analyses relied upon to justify a hearing.
On September 12, 1977, Glenwood
filed a written notice of appearance and
requested a hearing, and on October 17,
1977, Glenwood submitted data in
support of its hearing request. Along
with these submissions, Glenwood
requested that the Agency delay action
on the hearing request until the firm had
conducted another placebo-controlled
study. Subsequently, Glenwood
initiated a clinical trial at the Downstate
Medical Center of the State University
of New York and supplemented its
hearing request with additional data,
including a progress report on the
clinical trial of POTABA conducted at
the Downstate Medical Center.
Following a meeting between
Glenwood and FDA on November 18,
1985, Glenwood sponsored another
controlled clinical trial, and the final
study report was submitted on February
4, 1993.
By letter dated October 21, 2010, FDA
asked Glenwood whether it wanted to
pursue its pending hearing request
regarding POTABA. By letter dated
November 11, 2010, Glenwood affirmed
its hearing request.
By letter dated June 8, 2020, FDA
again asked Glenwood whether it
wanted to pursue its pending hearing
request regarding POTABA. By letter
dated July 2, 2020, Cheplapharm
Arzneimittel GmbH, successor-ininterest to Glenwood LLC, stated that it
did not wish to pursue the hearing
request for POTABA.
III. Conclusions and Order
There are no outstanding hearing
requests regarding potassium
aminobenzoate oral preparations under
Docket No. FDA–1977–N–0015, DESI
7663. Therefore, as proposed in the
NOOH, FDA withdraws approval of
NDA 007663 under section 505(e) of the
FD&C Act.
Shipment in interstate commerce of
any drug product identified in this
docket under DESI 7663, or any IRS
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product, that is not the subject of an
approved NDA or abbreviated new drug
application is unlawful as of the
effective date of this notice (see DATES).
Any person who wishes to determine
whether this notice covers a specific
product should write to Astrid LopezGoldberg at the Center for Drug
Evaluation and Research (see FOR
FURTHER INFORMATION CONTACT). Firms
should be aware that, after the
applicable date of this notice (see
DATES), FDA intends to take
enforcement action without further
notice against any firm that
manufactures or ships in interstate
commerce any unapproved product
covered by this notice.
IV. Discontinued Products
Firms must notify the Agency of
certain product discontinuations in
writing under section 506C(a) of the
FD&C Act (21 U.S.C. 356c). See https://
www.fda.gov/Drugs/DrugSafety/
DrugShortages/ucm142398.htm. Some
firms may have previously discontinued
manufacturing or distributing products
covered by this notice without
discontinuing the listing as required
under section 510(j) of the FD&C Act (21
U.S.C. 360(j)). Other firms may
discontinue manufacturing or
distributing listed products in response
to this notice. All firms are required to
electronically update the listing of their
products under 510(j) of the FD&C Act
to reflect discontinuation of unapproved
products covered by this notice (21 CFR
207.57(b)). Questions on electronic drug
listing updates should be sent to
eDRLS@fda.hhs.gov. In addition to the
required update, firms can also notify
the Agency of product discontinuation
by sending a letter, signed by the firm’s
chief executive officer and fully
identifying the discontinued product(s),
including the product National Drug
Code number(s), and stating that the
manufacturing and/or distribution of the
product(s) have been discontinued. The
letter should be sent electronically to
Astrid Lopez-Goldberg (see FOR FURTHER
INFORMATION CONTACT). FDA plans to
rely on its existing records, including its
drug listing records, the results of any
future inspections, or other available
information, when it identifies violative
products for enforcement action.
Dated: March 3, 2022.
Lauren K. Roth,
Associate Commissioner for Policy.
[FR Doc. 2022–04971 Filed 3–8–22; 8:45 am]
BILLING CODE 4164–01–P
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DEPARTMENT OF HEALTH AND
HUMAN SERVICES
Food and Drug Administration
[Docket No. FDA–2022–N–0075]
Food and Drug Administration Quality
Metrics Reporting Program;
Establishment of a Public Docket;
Request for Comments
AGENCY:
Food and Drug Administration,
HHS.
Notice; establishment of a
public docket; request for comments.
ACTION:
The Food and Drug
Administration (FDA or Agency) is
announcing the establishment of a
docket to solicit comments on changes
to FDA’s previously proposed quality
metrics reporting program (QM
Reporting Program). This notice
describes considerations for refining the
QM Reporting Program based on lessons
learned from two pilot programs with
industry that were announced in the
Federal Register in June 2018, a Site
Visit Program and a Quality Metrics
Feedback Program, as well as
stakeholder feedback on FDA’s 2016
revised draft guidance for industry
entitled ‘‘Submission of Quality Metrics
Data.’’ FDA is interested in responses to
the questions listed in section III of this
document, in addition to any general
comments on the proposed direction for
the program. This notice is not intended
to communicate our regulatory
expectations for reporting quality
metrics data to FDA but is instead
intended to seek input from industry to
inform the future regulatory approach.
DATES: Submit either electronic or
written comments by June 7, 2022.
ADDRESSES: You may submit comments
as follows. Please note that late,
untimely filed comments will not be
considered. Electronic comments must
be submitted on or before June 7, 2022.
The https://www.regulations.gov
electronic filing system will accept
comments until 11:59 p.m. Eastern Time
at the end of June 7, 2022. Comments
received by mail/hand delivery/courier
(for written/paper submissions) will be
considered timely if they are
postmarked or the delivery service
acceptance receipt is on or before that
date.
SUMMARY:
Electronic Submissions
Submit electronic comments in the
following way:
• Federal eRulemaking Portal: ≤
https://www.regulations.gov. Follow the
instructions for submitting comments.
Comments submitted electronically,
including attachments, to https://
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www.regulations.gov will be posted to
the docket unchanged. Because your
comment will be made public, you are
solely responsible for ensuring that your
comment does not include any
confidential information that you or a
third party may not wish to be posted,
such as medical information, your or
anyone else’s Social Security number, or
confidential business information, such
as a manufacturing process. Please note
that if you include your name, contact
information, or other information that
identifies you in the body of your
comments, that information will be
posted on https://www.regulations.gov.
• If you want to submit a comment
with confidential information that you
do not wish to be made available to the
public, submit the comment as a
written/paper submission and in the
manner detailed (see ‘‘Written/Paper
Submissions’’ and ‘‘Instructions’’).
Written/Paper Submissions
Submit written/paper submissions as
follows:
• Mail/Hand delivery/Courier (for
written/paper submissions): Dockets
Management Staff (HFA–305), Food and
Drug Administration, 5630 Fishers
Lane, Rm. 1061, Rockville, MD 20852.
• For written/paper comments
submitted to the Dockets Management
Staff, FDA will post your comment, as
well as any attachments, except for
information submitted, marked and
identified, as confidential, if submitted
as detailed in ‘‘Instructions.’’
Instructions: All submissions received
must include the Docket No. FDA–
2022–N–0075 for ‘‘FDA Quality Metrics
Reporting Program; Establishment of a
Public Docket; Request for Comments.’’
Received comments, those filed in a
timely manner (see ADDRESSES), will be
placed in the docket and, except for
those submitted as ‘‘Confidential
Submissions,’’ publicly viewable at
https://www.regulations.gov or at the
Dockets Management Staff between 9
a.m. and 4 p.m., Monday through
Friday, 240–402–7500.
• Confidential Submissions—To
submit a comment with confidential
information that you do not wish to be
made publicly available, submit your
comments only as a written/paper
submission. You should submit two
copies total. One copy will include the
information you claim to be confidential
with a heading or cover note that states
‘‘THIS DOCUMENT CONTAINS
CONFIDENTIAL INFORMATION.’’ The
Agency will review this copy, including
the claimed confidential information, in
its consideration of comments. The
second copy, which will have the
claimed confidential information
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redacted/blacked out, will be available
for public viewing and posted on
https://www.regulations.gov. Submit
both copies to the Dockets Management
Staff. If you do not wish your name and
contact information to be made publicly
available, you can provide this
information on the cover sheet and not
in the body of your comments and you
must identify this information as
‘‘confidential.’’ Any information marked
as ‘‘confidential’’ will not be disclosed
except in accordance with 21 CFR 10.20
and other applicable disclosure law. For
more information about FDA’s posting
of comments to public dockets, see 80
FR 56469, September 18, 2015, or access
the information at: https://
www.govinfo.gov/content/pkg/FR-201509-18/pdf/2015-23389.pdf.
Docket: For access to the docket to
read background documents or the
electronic and written/paper comments
received, go to https://
www.regulations.gov and insert the
docket number, found in brackets in the
heading of this document, into the
‘‘Search’’ box and follow the prompts
and/or go to the Dockets Management
Staff, 5630 Fishers Lane, Rm. 1061,
Rockville, MD 20852, 240–402–7500.
FOR FURTHER INFORMATION CONTACT: Jean
Chung, Center for Drug Evaluation and
Research, Food and Drug
Administration, 10903 New Hampshire
Ave., Bldg. 75, Rm. 6655, Silver Spring,
MD 20993, 301–796–1874, jean.chung@
fda.hhs.gov.
SUPPLEMENTARY INFORMATION:
I. Background
A. Quality Metrics
For pharmaceutical manufacturing,
quality metrics are objective means of
measuring, evaluating, and monitoring
the product and process life cycle to
proactively identify and mitigate quality
risks; thereby managing operations at
higher levels of safety, efficacy,
delivery, and performance. Quality
metrics are used throughout the drug
and biological product industry to
monitor quality control systems and
processes and drive continuous
improvement efforts in manufacturing.
Quality metrics are important because
failure to update and innovate
manufacturing practices and lack of
operational reliability (i.e., state of
control) can lead to quality problems
that have a negative impact on public
health.
The minimum standard for ensuring
that a manufacturer’s products are safe
and effective is compliance with current
good manufacturing practice (CGMP)
requirements as outlined in current
regulations and as recommended in
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current policies (21 CFR parts 210 and
211 for drug products and the
International Conference on
Harmonisation guidance for industry
entitled ‘‘Q7 Good Manufacturing
Practice Guidance for Active
Pharmaceutical Ingredients’’ (September
2016); available at: https://www.fda.gov/
regulatory-information/search-fdaguidance-documents/q7-goodmanufacturing-practice-guidanceactive-pharmaceutical-ingredientsguidance-industry). However,
compliance with CGMP does not
necessarily indicate whether a
manufacturer is investing in
improvements and striving for
sustainable compliance, which is the
state of having consistent control over
manufacturing performance and quality.
Sustainable CGMP compliance is
difficult to achieve without a focus on
continual improvement.
An effective Pharmaceutical Quality
System (PQS) ensures both sustainable
CGMP compliance and supply chain
robustness. Quality metrics data can
contribute to a manufacturer’s ability to
develop an effective PQS because
metrics provide insight into
manufacturing performance and enable
the identification of opportunities for
updates and innovation to
manufacturing practices. Quality
metrics also play an important role in
supplier oversight and can be used to
inform the oversight of outsourced
activities and material suppliers as well
as appropriate monitoring activities to
minimize supply chain disruptions.
Quality metrics data provided by
establishments can also be useful to
FDA. These data can assist the Agency
in developing compliance and
inspection policies and practices to
improve the Agency’s ability to predict,
and therefore possibly mitigate, future
drug shortages, and to encourage the
pharmaceutical industry to implement
innovative quality management systems
for pharmaceutical manufacturing. For
example, quality metrics data can be
applied to FDA’s risk-based inspection
scheduling, reducing the frequency and/
or length of routine surveillance
inspections for establishments with
metrics data that suggest sustainable
compliance. Additionally, the
submission of quality metrics data can
provide ongoing insight into an
establishment’s operations between
inspections.
As part of FDA’s shift towards a riskbased approach to regulation, the
Agency proposed to develop and
implement a QM Reporting Program to
support its quality surveillance
activities, as described in section I.B of
this notice. Under this program, FDA
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intends to analyze the quality metrics
data submitted by establishments to: (1)
Obtain a more quantitative and objective
measure of manufacturing quality and
reliability at an establishment; (2)
integrate the metrics and resulting
analysis into FDA’s comprehensive
quality surveillance program; and (3)
apply the results of the analysis to assist
in identifying products at risk for
quality problems (e.g., quality-related
shortages and recalls).
B. FDA Guidance for Industry on the
Submission of Quality Metrics Data
In July 2015, FDA issued the draft
guidance entitled ‘‘Request for Quality
Metrics’’ (80 FR 44973), which
described a potential mandatory
program for product-based reporting of
quality metrics. Under this proposed
program, manufacturers would have
submitted four primary metrics (lot
acceptance rate (LAR), product quality
complaint rate (PQCR), invalidated/
overturned out-of-specification rate
(IOOSR), and annual product review
(APR) or product quality review on-time
rate) and three optional metrics (senior
management engagement, corrective and
preventative action (CAPA)
effectiveness, and process capability/
performance). Stakeholder comments on
the guidance included concerns
regarding the burden associated with
collecting, formatting, and submitting
data at a product level across multiple
establishments; technical comments on
the proposed metrics and definitions;
and legal concerns regarding the
proposed mandatory program.
Stakeholder commenters also suggested
a phased-in approach to allow learning
by both industry and FDA.
In response to this feedback, FDA
published a revised draft guidance in
November 2016 entitled ‘‘Submission of
Quality Metrics Data’’ (81 FR 85226).
The 2016 guidance described an initial
voluntary phase of the QM Reporting
Program, with participants reporting
data either by product or establishment,
through an FDA submission portal. FDA
removed one of the four metrics from
the 2015 draft guidance and requested
submission of the remaining three key
metrics: (1) LAR to measure
manufacturing process performance; (2)
IOOSR to measure laboratory
robustness; and (3) PQCR to measure
patient or customer feedback and
proposed incentives for participation.
This guidance also described how FDA
intended to utilize the submitted data.
Stakeholder comments on the guidance
indicated that the FDA-standardized
definitions remained a challenge and
incentives to participate in a voluntary
program needed to be strengthened (e.g.,
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direct collaboration with FDA to
develop the program was an example of
a strong incentive). Commenters
requested a better understanding of the
value and utility of the data to be
submitted to FDA and how FDA would
measure success of the program.
Commenters also expressed a preference
for a pilot program to gather industry
input before implementing a
widespread QM Reporting Program.
C. Lessons Learned From FDA’s Quality
Metrics Pilot Programs
In Federal Register notices issued on
June 29, 2018, FDA announced the
availability of two pilot programs, a
Quality Metrics Site Visit Program (83
FR 30751) and a Quality Metrics
Feedback Program (83 FR 30748) for any
establishment that has a quality metrics
program developed and implemented by
the quality unit and used to support
product and process quality
improvement.
The Quality Metrics Site Visit
Program offered experiential learning
for FDA staff and provided participating
establishments an opportunity to
explain the advantages and challenges
associated with implementing and
managing a Quality Metrics program.
For example, participants provided
feedback in the form of case studies to
demonstrate the differences between the
metric definitions proposed in the FDA
draft guidances and definitions
commonly used by industry for the
same metrics. They proposed changes to
the definitions, justifying why those
changes (if any) would be needed. FDA
toured the operations of 14
establishments worldwide and engaged
with establishments on topics such as:
How quality metrics data are collected,
analyzed, communicated (e.g.,
dashboards, business intelligence
platforms), and reported throughout the
organization in a structured and
centralized manner; how management
utilizes quality metrics data to monitor
the performance of their supply
network; how management leverages
metrics to promote data-driven
decisions; how an establishment
implements and monitors continuous
improvements based on metrics; how
various quality metrics are defined; how
actions were taken from observations
resulting from quality metrics data
reviews; and how efforts to proactively
mitigate and prevent shortages are
coordinated.
In the Quality Metrics Feedback
Program, participating establishments
presented their quality metrics programs
to FDA staff. The presentations were
followed by discussions and knowledge
sharing that focused on analytical
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strategies, exploratory data analyses,
data preparation and structure, and
visualizations for communication, as
well as demonstrations on how FDA
plans to analyze the data using
advanced analytical techniques (e.g.,
data/text mining, interactive
visualizations), sophisticated statistical
methods (e.g., control charts, time series
analysis), and machine learning (e.g.,
predictive analytics, natural language
processing). In these discussions, FDA
also obtained feedback on industry’s
anticipated challenges in applying the
approach described in FDA’s revised
draft guidance. Participants had the
opportunity to submit their quality
metrics data through an FDA
submission portal and provide feedback
on their user experience. The industry
participants represented different
sectors of the pharmaceutical industry
including innovator drug products,
generic drug products, nonprescription
(also known as over-the-counter (OTC))
drug products, and biological products.
The dedicated meetings with industry
during the two pilot programs that
focused on data analytics resulted in the
following key lessons learned for FDA,
which will inform the direction of the
QM Reporting Program:
1. Different industry sectors prefer
different metrics due to their individual
operations and business dynamics
needs. Therefore, it is necessary to
implement a program with sufficient
flexibility when choosing metrics.
Identifying critical practice areas (e.g.,
manufacturing process performance)
and allowing establishments to select
appropriate metrics from several options
is a more feasible approach.
2. Any metric chosen to be reported
should be meaningful to the practice
area being measured, and the data
collected on that metric should be able
to influence decision making about
process improvements and capital
investments.
3. In some instances, a combination of
metrics rather than a single metric is
preferred to assess a particular practice
area.
4. The majority of participants prefer
to report data at an establishment level
and have the capability to segment by
product, but some participants prefer
product-level reporting due to their
business structure (e.g., a vertically
integrated company).
5. Calculating LAR and PQCR based
on the definitions in the 2016 revised
draft guidance can result in
mathematical discrepancies such as
rates over 100 percent or invalid
calculations (i.e., dividing by zero)).
These discrepancies are caused by
inherent variabilities from real-time
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operations (e.g., lots may not be
dispositioned in the same quarter in
which they were started) or how
denominators are defined for a specified
period of time.
6. While LAR and IOOSR are quality
metrics that are routinely monitored by
establishments, they are not discerning
metrics due to limited variability over
time or limited scope and can result in
false positives by highlighting
nonexistent performance issues. Other
metrics should be identified as
surrogates for manufacturing process
performance and laboratory robustness.
Examples include, but are not limited
to, right-first-time rate, process
capability, and adherence to lead time.
7. The effectiveness of the quality
system is a critical component of a QM
Reporting Program as evidenced by
numerous establishments collecting
data around their PQS. Examples
include metrics related to the
effectiveness of CAPA programs, repeat
deviations, maintenance programs, and
timeliness.
8. Metrics related to quality culture
are important indicators of performance
and reliability, but unlike other quality
metrics, it is difficult to capture quality
culture at an establishment based on
numerical metrics alone. Both
numerical key performance indicators
(KPIs) (e.g., APR timeliness and near
misses) and qualitative summaries (e.g.,
descriptions of management
commitment or quality planning) can be
used to further understand quality
culture.
9. FDA’s analysis of the data
submitted during the Quality Metrics
Feedback Program indicates that the use
of statistical quality control applications
(e.g., statistical process control and
process capability) and machine
learning/natural language processing are
appropriate and meaningful analytical
strategies to assess quality metrics data
submitted by establishments.
II. Proposed Direction for an FDA QM
Reporting Program
FDA has applied the lessons learned
from the pilot programs and other
stakeholder feedback toward refining
the QM Reporting Program that was
presented in the 2016 revised draft
guidance. In this section, we summarize
a potential direction for the program,
and in section III we request input on
specific aspects of this approach.
FDA believes that a change in the
entities responsible for collecting and
submitting quality metrics data is not
needed. ‘‘Covered establishments,’’ as
defined in the 2016 revised draft
guidance, are establishments engaged in
the manufacture, preparation,
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propagation, compounding or
processing of a ‘‘covered drug product’’
(products subject to an approved
application under section 505 of the
Federal Food, Drug, and Cosmetic Act
(FD&C Act) (21 U.S.C. 355) or section
351 of the Public Health Service Act;
legally marketed pursuant to section
505G of the FD&C Act (21 U.S.C. 355h)
(nonprescription drugs marketed
without an approved drug application);
or marketed as unapproved finished
drug products) or an active
pharmaceutical ingredient used in the
manufacture of a covered drug product.
‘‘Covered establishments’’ include
contract laboratories, contract
sterilizers, and contract packagers.
FDA is considering changes to other
aspects of the QM Reporting Program.
Stakeholders have indicated that
different industry sectors may prefer
different quality metrics. To provide
flexibility to manufacturers, FDA would
focus less on standardization of quality
metrics and definitions. Instead, FDA
would identify practice areas that are
critical to ensure sustainable product
quality and availability and would
permit manufacturers to select a
metric(s) from each practice area that
are meaningful and enable
establishments to identify continual
improvement opportunities. The metric
definitions would not specify how
establishments calculate particular
metrics. Rather, the reporting
establishment would select the most
appropriate metric(s) from each practice
area and inform FDA how it was
calculated. Through the collective
feedback gathered from pilot
participants, FDA has identified the
following four general practice areas as
appropriate at this time for the QM
Reporting Program: (1) Manufacturing
Process Performance, (2) PQS
Effectiveness, (3) Laboratory
Performance, (4) Supply Chain
Robustness. Examples of quality metrics
associated with each practice include
the following:
1. Manufacturing Process Performance
• Process Capability/Performance
Indices (Cpk/Ppk): A measure that
compares the output of a process to the
specification limits and can be
calculated as a proportion (e.g., total
number of attributes with Ppk greater
than 1.33 divided by total number of
attributes where Ppk is used). It is
important to consider standard
deviation measurements using a
reasonable sample size.
• LAR: A measure of the proportion
of lots that were accepted in a given
time period. Examples of inputs that can
be used to calculate LAR include lots
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completed, lots dispositioned, lots
attempted, lots rejected, lots released,
lots approved, abandoned lots, and
parallel/backup lots.
• Right-First-Time Rate: A measure of
the proportion of lots manufactured
without the occurrence of a nonconformance. Examples of inputs that
can be used to calculate a right-firsttime rate include number of deviations,
lots dispositioned, lots attempted,
number of nonconformances, and lots
approved in the first pass.
• Lot Release Cycle Time: A measure
of the amount of time it takes for the lot
disposition process. Lot release cycle
time can be calculated with an
appropriate unit of measurement such
as number of hours or days.
2. PQS Effectiveness
• CAPA Effectiveness: A measure of
the proportion of CAPA plan
implemented and deemed effective (i.e.,
effectiveness verifications closed as
effective). Examples of inputs that can
be used to calculate CAPA effectiveness
include number of CAPAs initiated,
CAPAs closed on time, CAPAs closed as
‘‘effective,’’ overdue CAPAs, and CAPAs
resulting in retraining.
• Repeat Deviation Rate: A measure
of the proportion of recurring deviation
measures. Examples of inputs that can
be used to calculate repeat deviation
rate include total number of deviations
and number of deviations with the same
assignable root cause.
• Change Control Effectiveness: A
measure of timeliness and effectiveness
of implemented changes to GMP
facilities, systems, equipment, or
processes. Examples of inputs that can
be used to calculate this metric include
on-time closure of the change, total
number of late effectiveness checks,
total number of changes initiated,
number of changes that are initiated
reactively versus proactively, and total
number of changes deemed effective.
• Overall Equipment Effectiveness: A
measure of operating productivity,
utilizing planned production time.
Overall equipment effectiveness can be
calculated using inputs related to
availability (e.g., planned production
time, operating time), performance (e.g.,
production capacity), and quality (e.g.,
production output that does not result
in acceptable product).
• Unplanned Maintenance: A
measure of the proportion of
maintenance time that was not planned
or scheduled. Examples of inputs that
can be used to calculate this metric
include total maintenance hours and
planned maintenance hours.
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3. Laboratory Performance
• Adherence to Lead Time: A
measure of the proportion of tests in the
laboratory that are completed on time
according to schedule requirements.
Adherence to lead time can be
calculated, for example, by tracking
initiation and testing turnover time in
release and stability tests (i.e., the
number of days between the start date
and completion date for quality control
(QC)); tracking data review and
documentation; tracking final result
reporting prior to batch disposition; or
comparing QC testing completion date
against the target date.
• Right-First-Time Rate: A measure of
the proportion of tests conducted
without the occurrence of a deviation.
Right-first-time rate as a metric for
laboratory performance can be
calculated, for example, by tracking the
invalid assay rate, the number of assays
invalidated due to human errors, or
CGMP documentation errors during
review.
• IOOSR: A measure that indicates a
laboratory’s ability to accurately
perform tests. Examples of inputs that
can be used to calculate this metric
include total number of tests conducted
and total number of out-of-specification
results invalidated due to an aberration
of the measurement process.
• Calibration Timeliness: A measure
of a laboratory’s adherence to
inspecting, calibrating, and testing
equipment for its intended purposes as
planned. This metric can be measured
by tracking calibration criteria and
schedules.
jspears on DSK121TN23PROD with NOTICES1
4. Supply Chain Robustness
• On-Time In-Full (OTIF): A measure
of the extent to which shipments are
delivered to their destination containing
the correct quantity and according to the
schedule specified in the order. This
metric can be calculated using inputs
such as the number of orders shipped,
number of past due orders, or number
of orders shipped within tolerance.
• Fill Rate: A measure that quantifies
orders shipped as a percentage of the
total demand for a given period.
Examples of inputs that can be used to
calculate this metric include total
number of orders shipped, the number
of orders placed, and the number of
orders received.
• Disposition On-Time: A measure of
the proportion of lots in which the
disposition was carried out on time.
Examples of inputs that can be used to
calculate this metric include the total
number of lots dispositioned and the
total number of lots dispositioned on
time.
VerDate Sep<11>2014
17:44 Mar 08, 2022
Jkt 256001
• Days of Inventory On-Hand: A
measure of how a company utilizes the
average inventory available. It is the
number of days that inventory remains
in stock.
Given that the majority of participants
in the pilot programs prefer to report
data at an establishment level, FDA is
considering an approach for aggregating
and reporting quality metrics data at the
establishment level, with the option to
segment by manufacturing train,
product type, or product level (e.g.,
application number or product family).
Once the data are submitted, FDA
intends to analyze the information with
statistical and machine learning
methods to provide useful insights for
inspection resource allocation.
Examples include examination of
product trends and clusters; exploratory
and time-series analyses for signal
identification, thereby monitoring the
health of the establishment over time;
and utilizing quality metrics data as an
input into machine learning models to
assist in determining an establishment’s
overall PQS effectiveness.
III. Request for Comments
We are seeking comment on the
following aspects of FDA’s proposed
direction for its QM Reporting Program.
We note that the questions posed in this
section are not meant to be exhaustive.
We are also interested in any other
pertinent information that stakeholders
and any other interested parties would
like to provide on FDA’s QM Reporting
Program. FDA encourages stakeholders
to provide the rationale for their
comments, including available
examples and supporting information.
A. Reporting Levels
1. Do you agree that reporting should
be aggregated at an establishment level?
2. Would reporting at an
establishment level facilitate submission
of quality metrics data by contract
manufacturing organizations?
3. If you normally assess metrics by
product family at an establishment,
what are useful definitions of ‘‘product
family’’ from your industry sector?
B. Practice Areas and Quality Metrics
1. If you think the general practice
areas listed in section II of this notice
would not meet the objectives of FDA
QM Reporting Program, what other
practice areas should FDA consider?
2. If FDA were to consider Quality
Culture as one of the general practice
areas, what are the critical components
of a robust quality culture and can any
of these components be measured
quantitatively? If so, how do you
recommend quality culture information
PO 00000
Frm 00049
Fmt 4703
Sfmt 9990
13299
be captured as a quantitative metric
(e.g., near misses, APR on-time, binary
response to Quality Culture survey, or
other numerical metrics/KPIs)?
3. Do you think that any of the
examples of quality metrics proposed by
FDA would not be an appropriate
measure for the designated practice
area?
4. What other metrics should FDA
consider for a designated practice area?
5. FDA is interested in an
establishment’s experience with
implementing process capability and
performance metrics. For example, how
would you report Cpk and/or Ppk to
FDA as part of the QM Reporting
Program (e.g., reporting Cpk and/or Ppk
for certain products, aggregated at the
establishment level)?
6. A metric may need to be changed
or adjusted by an establishment to better
monitor PQS effectiveness, inform
appropriate business strategy, or capture
insightful trends, thereby driving
continual improvement behaviors. What
criteria should be applied to justify
changing or modifying a quality metric
(by either the establishment or by FDA)?
How frequently would you expect
changes or modifications to be needed?
7. When would you rely on multiple
metrics versus a single metric as an
indicator when assessing a particular
practice area (e.g., two metrics are
considered in combination because one
metric influences the other)? What
combination of metrics have been
meaningful and useful?
C. Other Considerations
1. Are there considerations unique to
specific product categories (e.g., generic
drug products, OTC drug products, or
biological products) that should be
addressed in the QM Reporting
Program?
2. What would be the optimal
reporting frequency for quality metrics
data submissions (e.g., monthly,
quarterly, or yearly, and segmented by
quarter or month)?
3. In instances where a manufacturer
is not able to extract domestic data and
its submission to FDA contains both
U.S. and foreign data, how can these
data be submitted to FDA in a manner
that would still be informative?
4. Are there any other aspects of
FDA’s proposed direction for the
program that FDA should address in
future policy documents?
Dated: February 28, 2022.
Lauren K. Roth,
Associate Commissioner for Policy.
[FR Doc. 2022–04972 Filed 3–8–22; 8:45 am]
BILLING CODE 4164–01–P
E:\FR\FM\09MRN1.SGM
09MRN1
Agencies
[Federal Register Volume 87, Number 46 (Wednesday, March 9, 2022)]
[Notices]
[Pages 13295-13299]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-04972]
-----------------------------------------------------------------------
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
[Docket No. FDA-2022-N-0075]
Food and Drug Administration Quality Metrics Reporting Program;
Establishment of a Public Docket; Request for Comments
AGENCY: Food and Drug Administration, HHS.
ACTION: Notice; establishment of a public docket; request for comments.
-----------------------------------------------------------------------
SUMMARY: The Food and Drug Administration (FDA or Agency) is announcing
the establishment of a docket to solicit comments on changes to FDA's
previously proposed quality metrics reporting program (QM Reporting
Program). This notice describes considerations for refining the QM
Reporting Program based on lessons learned from two pilot programs with
industry that were announced in the Federal Register in June 2018, a
Site Visit Program and a Quality Metrics Feedback Program, as well as
stakeholder feedback on FDA's 2016 revised draft guidance for industry
entitled ``Submission of Quality Metrics Data.'' FDA is interested in
responses to the questions listed in section III of this document, in
addition to any general comments on the proposed direction for the
program. This notice is not intended to communicate our regulatory
expectations for reporting quality metrics data to FDA but is instead
intended to seek input from industry to inform the future regulatory
approach.
DATES: Submit either electronic or written comments by June 7, 2022.
ADDRESSES: You may submit comments as follows. Please note that late,
untimely filed comments will not be considered. Electronic comments
must be submitted on or before June 7, 2022. The https://www.regulations.gov electronic filing system will accept comments until
11:59 p.m. Eastern Time at the end of June 7, 2022. Comments received
by mail/hand delivery/courier (for written/paper submissions) will be
considered timely if they are postmarked or the delivery service
acceptance receipt is on or before that date.
Electronic Submissions
Submit electronic comments in the following way:
Federal eRulemaking Portal: https://www.regulations.gov. Follow the instructions for submitting comments.
Comments submitted electronically, including attachments, to https://
[[Page 13296]]
www.regulations.gov will be posted to the docket unchanged. Because
your comment will be made public, you are solely responsible for
ensuring that your comment does not include any confidential
information that you or a third party may not wish to be posted, such
as medical information, your or anyone else's Social Security number,
or confidential business information, such as a manufacturing process.
Please note that if you include your name, contact information, or
other information that identifies you in the body of your comments,
that information will be posted on https://www.regulations.gov.
If you want to submit a comment with confidential
information that you do not wish to be made available to the public,
submit the comment as a written/paper submission and in the manner
detailed (see ``Written/Paper Submissions'' and ``Instructions'').
Written/Paper Submissions
Submit written/paper submissions as follows:
Mail/Hand delivery/Courier (for written/paper
submissions): Dockets Management Staff (HFA-305), Food and Drug
Administration, 5630 Fishers Lane, Rm. 1061, Rockville, MD 20852.
For written/paper comments submitted to the Dockets
Management Staff, FDA will post your comment, as well as any
attachments, except for information submitted, marked and identified,
as confidential, if submitted as detailed in ``Instructions.''
Instructions: All submissions received must include the Docket No.
FDA-2022-N-0075 for ``FDA Quality Metrics Reporting Program;
Establishment of a Public Docket; Request for Comments.'' Received
comments, those filed in a timely manner (see ADDRESSES), will be
placed in the docket and, except for those submitted as ``Confidential
Submissions,'' publicly viewable at https://www.regulations.gov or at
the Dockets Management Staff between 9 a.m. and 4 p.m., Monday through
Friday, 240-402-7500.
Confidential Submissions--To submit a comment with
confidential information that you do not wish to be made publicly
available, submit your comments only as a written/paper submission. You
should submit two copies total. One copy will include the information
you claim to be confidential with a heading or cover note that states
``THIS DOCUMENT CONTAINS CONFIDENTIAL INFORMATION.'' The Agency will
review this copy, including the claimed confidential information, in
its consideration of comments. The second copy, which will have the
claimed confidential information redacted/blacked out, will be
available for public viewing and posted on https://www.regulations.gov.
Submit both copies to the Dockets Management Staff. If you do not wish
your name and contact information to be made publicly available, you
can provide this information on the cover sheet and not in the body of
your comments and you must identify this information as
``confidential.'' Any information marked as ``confidential'' will not
be disclosed except in accordance with 21 CFR 10.20 and other
applicable disclosure law. For more information about FDA's posting of
comments to public dockets, see 80 FR 56469, September 18, 2015, or
access the information at: https://www.govinfo.gov/content/pkg/FR-2015-09-18/pdf/2015-23389.pdf.
Docket: For access to the docket to read background documents or
the electronic and written/paper comments received, go to https://www.regulations.gov and insert the docket number, found in brackets in
the heading of this document, into the ``Search'' box and follow the
prompts and/or go to the Dockets Management Staff, 5630 Fishers Lane,
Rm. 1061, Rockville, MD 20852, 240-402-7500.
FOR FURTHER INFORMATION CONTACT: Jean Chung, Center for Drug Evaluation
and Research, Food and Drug Administration, 10903 New Hampshire Ave.,
Bldg. 75, Rm. 6655, Silver Spring, MD 20993, 301-796-1874,
[email protected].
SUPPLEMENTARY INFORMATION:
I. Background
A. Quality Metrics
For pharmaceutical manufacturing, quality metrics are objective
means of measuring, evaluating, and monitoring the product and process
life cycle to proactively identify and mitigate quality risks; thereby
managing operations at higher levels of safety, efficacy, delivery, and
performance. Quality metrics are used throughout the drug and
biological product industry to monitor quality control systems and
processes and drive continuous improvement efforts in manufacturing.
Quality metrics are important because failure to update and innovate
manufacturing practices and lack of operational reliability (i.e.,
state of control) can lead to quality problems that have a negative
impact on public health.
The minimum standard for ensuring that a manufacturer's products
are safe and effective is compliance with current good manufacturing
practice (CGMP) requirements as outlined in current regulations and as
recommended in current policies (21 CFR parts 210 and 211 for drug
products and the International Conference on Harmonisation guidance for
industry entitled ``Q7 Good Manufacturing Practice Guidance for Active
Pharmaceutical Ingredients'' (September 2016); available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/q7-good-manufacturing-practice-guidance-active-pharmaceutical-ingredients-guidance-industry). However, compliance with CGMP does not necessarily
indicate whether a manufacturer is investing in improvements and
striving for sustainable compliance, which is the state of having
consistent control over manufacturing performance and quality.
Sustainable CGMP compliance is difficult to achieve without a focus on
continual improvement.
An effective Pharmaceutical Quality System (PQS) ensures both
sustainable CGMP compliance and supply chain robustness. Quality
metrics data can contribute to a manufacturer's ability to develop an
effective PQS because metrics provide insight into manufacturing
performance and enable the identification of opportunities for updates
and innovation to manufacturing practices. Quality metrics also play an
important role in supplier oversight and can be used to inform the
oversight of outsourced activities and material suppliers as well as
appropriate monitoring activities to minimize supply chain disruptions.
Quality metrics data provided by establishments can also be useful
to FDA. These data can assist the Agency in developing compliance and
inspection policies and practices to improve the Agency's ability to
predict, and therefore possibly mitigate, future drug shortages, and to
encourage the pharmaceutical industry to implement innovative quality
management systems for pharmaceutical manufacturing. For example,
quality metrics data can be applied to FDA's risk-based inspection
scheduling, reducing the frequency and/or length of routine
surveillance inspections for establishments with metrics data that
suggest sustainable compliance. Additionally, the submission of quality
metrics data can provide ongoing insight into an establishment's
operations between inspections.
As part of FDA's shift towards a risk-based approach to regulation,
the Agency proposed to develop and implement a QM Reporting Program to
support its quality surveillance activities, as described in section
I.B of this notice. Under this program, FDA
[[Page 13297]]
intends to analyze the quality metrics data submitted by establishments
to: (1) Obtain a more quantitative and objective measure of
manufacturing quality and reliability at an establishment; (2)
integrate the metrics and resulting analysis into FDA's comprehensive
quality surveillance program; and (3) apply the results of the analysis
to assist in identifying products at risk for quality problems (e.g.,
quality-related shortages and recalls).
B. FDA Guidance for Industry on the Submission of Quality Metrics Data
In July 2015, FDA issued the draft guidance entitled ``Request for
Quality Metrics'' (80 FR 44973), which described a potential mandatory
program for product-based reporting of quality metrics. Under this
proposed program, manufacturers would have submitted four primary
metrics (lot acceptance rate (LAR), product quality complaint rate
(PQCR), invalidated/overturned out-of-specification rate (IOOSR), and
annual product review (APR) or product quality review on-time rate) and
three optional metrics (senior management engagement, corrective and
preventative action (CAPA) effectiveness, and process capability/
performance). Stakeholder comments on the guidance included concerns
regarding the burden associated with collecting, formatting, and
submitting data at a product level across multiple establishments;
technical comments on the proposed metrics and definitions; and legal
concerns regarding the proposed mandatory program. Stakeholder
commenters also suggested a phased-in approach to allow learning by
both industry and FDA.
In response to this feedback, FDA published a revised draft
guidance in November 2016 entitled ``Submission of Quality Metrics
Data'' (81 FR 85226). The 2016 guidance described an initial voluntary
phase of the QM Reporting Program, with participants reporting data
either by product or establishment, through an FDA submission portal.
FDA removed one of the four metrics from the 2015 draft guidance and
requested submission of the remaining three key metrics: (1) LAR to
measure manufacturing process performance; (2) IOOSR to measure
laboratory robustness; and (3) PQCR to measure patient or customer
feedback and proposed incentives for participation. This guidance also
described how FDA intended to utilize the submitted data. Stakeholder
comments on the guidance indicated that the FDA-standardized
definitions remained a challenge and incentives to participate in a
voluntary program needed to be strengthened (e.g., direct collaboration
with FDA to develop the program was an example of a strong incentive).
Commenters requested a better understanding of the value and utility of
the data to be submitted to FDA and how FDA would measure success of
the program. Commenters also expressed a preference for a pilot program
to gather industry input before implementing a widespread QM Reporting
Program.
C. Lessons Learned From FDA's Quality Metrics Pilot Programs
In Federal Register notices issued on June 29, 2018, FDA announced
the availability of two pilot programs, a Quality Metrics Site Visit
Program (83 FR 30751) and a Quality Metrics Feedback Program (83 FR
30748) for any establishment that has a quality metrics program
developed and implemented by the quality unit and used to support
product and process quality improvement.
The Quality Metrics Site Visit Program offered experiential
learning for FDA staff and provided participating establishments an
opportunity to explain the advantages and challenges associated with
implementing and managing a Quality Metrics program. For example,
participants provided feedback in the form of case studies to
demonstrate the differences between the metric definitions proposed in
the FDA draft guidances and definitions commonly used by industry for
the same metrics. They proposed changes to the definitions, justifying
why those changes (if any) would be needed. FDA toured the operations
of 14 establishments worldwide and engaged with establishments on
topics such as: How quality metrics data are collected, analyzed,
communicated (e.g., dashboards, business intelligence platforms), and
reported throughout the organization in a structured and centralized
manner; how management utilizes quality metrics data to monitor the
performance of their supply network; how management leverages metrics
to promote data-driven decisions; how an establishment implements and
monitors continuous improvements based on metrics; how various quality
metrics are defined; how actions were taken from observations resulting
from quality metrics data reviews; and how efforts to proactively
mitigate and prevent shortages are coordinated.
In the Quality Metrics Feedback Program, participating
establishments presented their quality metrics programs to FDA staff.
The presentations were followed by discussions and knowledge sharing
that focused on analytical strategies, exploratory data analyses, data
preparation and structure, and visualizations for communication, as
well as demonstrations on how FDA plans to analyze the data using
advanced analytical techniques (e.g., data/text mining, interactive
visualizations), sophisticated statistical methods (e.g., control
charts, time series analysis), and machine learning (e.g., predictive
analytics, natural language processing). In these discussions, FDA also
obtained feedback on industry's anticipated challenges in applying the
approach described in FDA's revised draft guidance. Participants had
the opportunity to submit their quality metrics data through an FDA
submission portal and provide feedback on their user experience. The
industry participants represented different sectors of the
pharmaceutical industry including innovator drug products, generic drug
products, nonprescription (also known as over-the-counter (OTC)) drug
products, and biological products.
The dedicated meetings with industry during the two pilot programs
that focused on data analytics resulted in the following key lessons
learned for FDA, which will inform the direction of the QM Reporting
Program:
1. Different industry sectors prefer different metrics due to their
individual operations and business dynamics needs. Therefore, it is
necessary to implement a program with sufficient flexibility when
choosing metrics. Identifying critical practice areas (e.g.,
manufacturing process performance) and allowing establishments to
select appropriate metrics from several options is a more feasible
approach.
2. Any metric chosen to be reported should be meaningful to the
practice area being measured, and the data collected on that metric
should be able to influence decision making about process improvements
and capital investments.
3. In some instances, a combination of metrics rather than a single
metric is preferred to assess a particular practice area.
4. The majority of participants prefer to report data at an
establishment level and have the capability to segment by product, but
some participants prefer product-level reporting due to their business
structure (e.g., a vertically integrated company).
5. Calculating LAR and PQCR based on the definitions in the 2016
revised draft guidance can result in mathematical discrepancies such as
rates over 100 percent or invalid calculations (i.e., dividing by
zero)). These discrepancies are caused by inherent variabilities from
real-time
[[Page 13298]]
operations (e.g., lots may not be dispositioned in the same quarter in
which they were started) or how denominators are defined for a
specified period of time.
6. While LAR and IOOSR are quality metrics that are routinely
monitored by establishments, they are not discerning metrics due to
limited variability over time or limited scope and can result in false
positives by highlighting nonexistent performance issues. Other metrics
should be identified as surrogates for manufacturing process
performance and laboratory robustness. Examples include, but are not
limited to, right-first-time rate, process capability, and adherence to
lead time.
7. The effectiveness of the quality system is a critical component
of a QM Reporting Program as evidenced by numerous establishments
collecting data around their PQS. Examples include metrics related to
the effectiveness of CAPA programs, repeat deviations, maintenance
programs, and timeliness.
8. Metrics related to quality culture are important indicators of
performance and reliability, but unlike other quality metrics, it is
difficult to capture quality culture at an establishment based on
numerical metrics alone. Both numerical key performance indicators
(KPIs) (e.g., APR timeliness and near misses) and qualitative summaries
(e.g., descriptions of management commitment or quality planning) can
be used to further understand quality culture.
9. FDA's analysis of the data submitted during the Quality Metrics
Feedback Program indicates that the use of statistical quality control
applications (e.g., statistical process control and process capability)
and machine learning/natural language processing are appropriate and
meaningful analytical strategies to assess quality metrics data
submitted by establishments.
II. Proposed Direction for an FDA QM Reporting Program
FDA has applied the lessons learned from the pilot programs and
other stakeholder feedback toward refining the QM Reporting Program
that was presented in the 2016 revised draft guidance. In this section,
we summarize a potential direction for the program, and in section III
we request input on specific aspects of this approach.
FDA believes that a change in the entities responsible for
collecting and submitting quality metrics data is not needed. ``Covered
establishments,'' as defined in the 2016 revised draft guidance, are
establishments engaged in the manufacture, preparation, propagation,
compounding or processing of a ``covered drug product'' (products
subject to an approved application under section 505 of the Federal
Food, Drug, and Cosmetic Act (FD&C Act) (21 U.S.C. 355) or section 351
of the Public Health Service Act; legally marketed pursuant to section
505G of the FD&C Act (21 U.S.C. 355h) (nonprescription drugs marketed
without an approved drug application); or marketed as unapproved
finished drug products) or an active pharmaceutical ingredient used in
the manufacture of a covered drug product. ``Covered establishments''
include contract laboratories, contract sterilizers, and contract
packagers.
FDA is considering changes to other aspects of the QM Reporting
Program. Stakeholders have indicated that different industry sectors
may prefer different quality metrics. To provide flexibility to
manufacturers, FDA would focus less on standardization of quality
metrics and definitions. Instead, FDA would identify practice areas
that are critical to ensure sustainable product quality and
availability and would permit manufacturers to select a metric(s) from
each practice area that are meaningful and enable establishments to
identify continual improvement opportunities. The metric definitions
would not specify how establishments calculate particular metrics.
Rather, the reporting establishment would select the most appropriate
metric(s) from each practice area and inform FDA how it was calculated.
Through the collective feedback gathered from pilot participants, FDA
has identified the following four general practice areas as appropriate
at this time for the QM Reporting Program: (1) Manufacturing Process
Performance, (2) PQS Effectiveness, (3) Laboratory Performance, (4)
Supply Chain Robustness. Examples of quality metrics associated with
each practice include the following:
1. Manufacturing Process Performance
Process Capability/Performance Indices (Cpk/Ppk): A
measure that compares the output of a process to the specification
limits and can be calculated as a proportion (e.g., total number of
attributes with Ppk greater than 1.33 divided by total number of
attributes where Ppk is used). It is important to consider standard
deviation measurements using a reasonable sample size.
LAR: A measure of the proportion of lots that were
accepted in a given time period. Examples of inputs that can be used to
calculate LAR include lots completed, lots dispositioned, lots
attempted, lots rejected, lots released, lots approved, abandoned lots,
and parallel/backup lots.
Right-First-Time Rate: A measure of the proportion of lots
manufactured without the occurrence of a non-conformance. Examples of
inputs that can be used to calculate a right-first-time rate include
number of deviations, lots dispositioned, lots attempted, number of
nonconformances, and lots approved in the first pass.
Lot Release Cycle Time: A measure of the amount of time it
takes for the lot disposition process. Lot release cycle time can be
calculated with an appropriate unit of measurement such as number of
hours or days.
2. PQS Effectiveness
CAPA Effectiveness: A measure of the proportion of CAPA
plan implemented and deemed effective (i.e., effectiveness
verifications closed as effective). Examples of inputs that can be used
to calculate CAPA effectiveness include number of CAPAs initiated,
CAPAs closed on time, CAPAs closed as ``effective,'' overdue CAPAs, and
CAPAs resulting in retraining.
Repeat Deviation Rate: A measure of the proportion of
recurring deviation measures. Examples of inputs that can be used to
calculate repeat deviation rate include total number of deviations and
number of deviations with the same assignable root cause.
Change Control Effectiveness: A measure of timeliness and
effectiveness of implemented changes to GMP facilities, systems,
equipment, or processes. Examples of inputs that can be used to
calculate this metric include on-time closure of the change, total
number of late effectiveness checks, total number of changes initiated,
number of changes that are initiated reactively versus proactively, and
total number of changes deemed effective.
Overall Equipment Effectiveness: A measure of operating
productivity, utilizing planned production time. Overall equipment
effectiveness can be calculated using inputs related to availability
(e.g., planned production time, operating time), performance (e.g.,
production capacity), and quality (e.g., production output that does
not result in acceptable product).
Unplanned Maintenance: A measure of the proportion of
maintenance time that was not planned or scheduled. Examples of inputs
that can be used to calculate this metric include total maintenance
hours and planned maintenance hours.
[[Page 13299]]
3. Laboratory Performance
Adherence to Lead Time: A measure of the proportion of
tests in the laboratory that are completed on time according to
schedule requirements. Adherence to lead time can be calculated, for
example, by tracking initiation and testing turnover time in release
and stability tests (i.e., the number of days between the start date
and completion date for quality control (QC)); tracking data review and
documentation; tracking final result reporting prior to batch
disposition; or comparing QC testing completion date against the target
date.
Right-First-Time Rate: A measure of the proportion of
tests conducted without the occurrence of a deviation. Right-first-time
rate as a metric for laboratory performance can be calculated, for
example, by tracking the invalid assay rate, the number of assays
invalidated due to human errors, or CGMP documentation errors during
review.
IOOSR: A measure that indicates a laboratory's ability to
accurately perform tests. Examples of inputs that can be used to
calculate this metric include total number of tests conducted and total
number of out-of-specification results invalidated due to an aberration
of the measurement process.
Calibration Timeliness: A measure of a laboratory's
adherence to inspecting, calibrating, and testing equipment for its
intended purposes as planned. This metric can be measured by tracking
calibration criteria and schedules.
4. Supply Chain Robustness
On-Time In-Full (OTIF): A measure of the extent to which
shipments are delivered to their destination containing the correct
quantity and according to the schedule specified in the order. This
metric can be calculated using inputs such as the number of orders
shipped, number of past due orders, or number of orders shipped within
tolerance.
Fill Rate: A measure that quantifies orders shipped as a
percentage of the total demand for a given period. Examples of inputs
that can be used to calculate this metric include total number of
orders shipped, the number of orders placed, and the number of orders
received.
Disposition On-Time: A measure of the proportion of lots
in which the disposition was carried out on time. Examples of inputs
that can be used to calculate this metric include the total number of
lots dispositioned and the total number of lots dispositioned on time.
Days of Inventory On-Hand: A measure of how a company
utilizes the average inventory available. It is the number of days that
inventory remains in stock.
Given that the majority of participants in the pilot programs
prefer to report data at an establishment level, FDA is considering an
approach for aggregating and reporting quality metrics data at the
establishment level, with the option to segment by manufacturing train,
product type, or product level (e.g., application number or product
family).
Once the data are submitted, FDA intends to analyze the information
with statistical and machine learning methods to provide useful
insights for inspection resource allocation. Examples include
examination of product trends and clusters; exploratory and time-series
analyses for signal identification, thereby monitoring the health of
the establishment over time; and utilizing quality metrics data as an
input into machine learning models to assist in determining an
establishment's overall PQS effectiveness.
III. Request for Comments
We are seeking comment on the following aspects of FDA's proposed
direction for its QM Reporting Program. We note that the questions
posed in this section are not meant to be exhaustive. We are also
interested in any other pertinent information that stakeholders and any
other interested parties would like to provide on FDA's QM Reporting
Program. FDA encourages stakeholders to provide the rationale for their
comments, including available examples and supporting information.
A. Reporting Levels
1. Do you agree that reporting should be aggregated at an
establishment level?
2. Would reporting at an establishment level facilitate submission
of quality metrics data by contract manufacturing organizations?
3. If you normally assess metrics by product family at an
establishment, what are useful definitions of ``product family'' from
your industry sector?
B. Practice Areas and Quality Metrics
1. If you think the general practice areas listed in section II of
this notice would not meet the objectives of FDA QM Reporting Program,
what other practice areas should FDA consider?
2. If FDA were to consider Quality Culture as one of the general
practice areas, what are the critical components of a robust quality
culture and can any of these components be measured quantitatively? If
so, how do you recommend quality culture information be captured as a
quantitative metric (e.g., near misses, APR on-time, binary response to
Quality Culture survey, or other numerical metrics/KPIs)?
3. Do you think that any of the examples of quality metrics
proposed by FDA would not be an appropriate measure for the designated
practice area?
4. What other metrics should FDA consider for a designated practice
area?
5. FDA is interested in an establishment's experience with
implementing process capability and performance metrics. For example,
how would you report Cpk and/or Ppk to FDA as part of the QM Reporting
Program (e.g., reporting Cpk and/or Ppk for certain products,
aggregated at the establishment level)?
6. A metric may need to be changed or adjusted by an establishment
to better monitor PQS effectiveness, inform appropriate business
strategy, or capture insightful trends, thereby driving continual
improvement behaviors. What criteria should be applied to justify
changing or modifying a quality metric (by either the establishment or
by FDA)? How frequently would you expect changes or modifications to be
needed?
7. When would you rely on multiple metrics versus a single metric
as an indicator when assessing a particular practice area (e.g., two
metrics are considered in combination because one metric influences the
other)? What combination of metrics have been meaningful and useful?
C. Other Considerations
1. Are there considerations unique to specific product categories
(e.g., generic drug products, OTC drug products, or biological
products) that should be addressed in the QM Reporting Program?
2. What would be the optimal reporting frequency for quality
metrics data submissions (e.g., monthly, quarterly, or yearly, and
segmented by quarter or month)?
3. In instances where a manufacturer is not able to extract
domestic data and its submission to FDA contains both U.S. and foreign
data, how can these data be submitted to FDA in a manner that would
still be informative?
4. Are there any other aspects of FDA's proposed direction for the
program that FDA should address in future policy documents?
Dated: February 28, 2022.
Lauren K. Roth,
Associate Commissioner for Policy.
[FR Doc. 2022-04972 Filed 3-8-22; 8:45 am]
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