Notice2023-09985
Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products; Availability
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Published
May 11, 2023
Issuing agencies
Health and Human Services DepartmentFood and Drug Administration
Abstract
The Food and Drug Administration (FDA or Agency) is announcing the publication of a discussion paper entitled "Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products." To fulfill its mission of protecting, promoting, and advancing public health, FDA's Center for Drug Evaluation and Research (CDER), in collaboration with the Center for Biologics Evaluation and Research (CBER) and Center for Devices and Radiological Health (CDRH), including the Digital Health Center of Excellence (DHCoE), is issuing this document to facilitate a discussion with stakeholders on the use of artificial intelligence (AI) and machine learning (ML) in drug development to help inform the regulatory landscape in this area.
Full Text
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<title>Federal Register, Volume 88 Issue 91 (Thursday, May 11, 2023)</title>
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[Federal Register Volume 88, Number 91 (Thursday, May 11, 2023)]
[Notices]
[Pages 30313-30314]
From the Federal Register Online via the Government Publishing Office [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[FR Doc No: 2023-09985]
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
[Docket No. FDA-2023-N-0743]
Using Artificial Intelligence and Machine Learning in the
Development of Drug and Biological Products; Availability
AGENCY: Food and Drug Administration, HHS.
ACTION: Notice of availability.
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SUMMARY: The Food and Drug Administration (FDA or Agency) is announcing
the publication of a discussion paper entitled ``Using Artificial
Intelligence and Machine Learning in the Development of Drug and
Biological Products.'' To fulfill its mission of protecting, promoting,
and advancing public health, FDA's Center for Drug Evaluation and
Research (CDER), in collaboration with the Center for Biologics
Evaluation and Research (CBER) and Center for Devices and Radiological
Health (CDRH), including the Digital Health Center of Excellence
(DHCoE), is issuing this document to facilitate a discussion with
stakeholders on the use of artificial intelligence (AI) and machine
learning (ML) in drug development to help inform the regulatory
landscape in this area.
DATES: Either electronic or written comments on the framework must be
submitted by August 9, 2023.
ADDRESSES: You may submit comments as follows. Please note that late,
untimely filed comments will not be considered. The <a href="https://www.regulations.gov">https://www.regulations.gov</a> electronic filing system will accept comments until
11:59 p.m. Eastern Time at the end of August 9, 2023. Comments received
by mail/hand delivery/courier (for written/paper submissions) will be
considered timely if they are received on or before that date.
Electronic Submissions
Submit electronic comments in the following way:
<bullet> Federal eRulemaking Portal: <a href="https://www.regulations.gov">https://www.regulations.gov</a>.
Follow the instructions for submitting comments. Comments submitted
electronically, including attachments, to <a href="https://www.regulations.gov">https://www.regulations.gov</a>
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 <a href="https://www.regulations.gov">https://www.regulations.gov</a>.
<bullet> 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:
<bullet> 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.
<bullet> 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-2023-N-0743 for ``Using Artificial
[[Page 30314]]
Intelligence and Machine Learning in the Development of Drug and
Biological Products.'' 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
<a href="https://www.regulations.gov">https://www.regulations.gov</a> or at the Dockets Management Staff between
9 a.m. and 4 p.m., Monday through Friday, 240-402-7500.
<bullet> 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 <a href="https://www.regulations.gov">https://www.regulations.gov</a>.
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: <a href="https://www.govinfo.gov/content/pkg/FR-2015-09-18/pdf/2015-23389.pdf">https://www.govinfo.gov/content/pkg/FR-2015-09-18/pdf/2015-23389.pdf</a>.
Docket: For access to the docket to read background documents or
the electronic and written/paper comments received, go to <a href="https://www.regulations.gov">https://www.regulations.gov</a> 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: Tala Fakhouri, Center for Drug
Evaluation and Research, Food and Drug Administration, 10903 New
Hampshire Ave., Bldg. 51, Rm. 6330, Silver Spring, MD 20993-0002, 301-
837-7407, <a href="/cdn-cgi/l/email-protection#8edaefe2efa0c8efe5e6e1fbfce7cee8eaefa0e6e6fda0e9e1f8"><span class="__cf_email__" data-cfemail="cd99aca1ace38baca6a5a2b8bfa48daba9ace3a5a5bee3aaa2bb">[email protected]</span></a>; Janice Maniwang, Center for Drug
Evaluation and Research, Food and Drug Administration, 10903 New
Hampshire Ave., Bldg. 51, Rm. 6316, Silver Spring, MD 20993-0002, 301-
796-3821, <a href="/cdn-cgi/l/email-protection#e9a38887808a8cc7a48887809e88878ea98f8d88c781819ac78e869f"><span class="__cf_email__" data-cfemail="460c27282f2523680b27282f3127282106202227682e2e3568212930">[email protected]</span></a>; or Hussein Ezzeldin, Center for
Biologics Evaluation and Research, Food and Drug Administration, 10903
New Hampshire Ave., Bldg. 71, Rm. 5246, Silver Spring, MD 20993-0002,
240-402-8629, <a href="/cdn-cgi/l/email-protection#96dee3e5e5f3fff8b8d3ececf3faf2fff8d6f0f2f7b8fefee5b8f1f9e0"><span class="__cf_email__" data-cfemail="afe7dadcdccac6c181ead5d5cac3cbc6c1efc9cbce81c7c7dc81c8c0d9">[email protected]</span></a>; or Brendan O'Leary, Center
for Devices and Radiological Health, Food and Drug Administration,
10903 New Hampshire Ave., Bldg. 66, Rm. 5530, Silver Spring, MD 20993-
0002, 301-796-6898, <a href="/cdn-cgi/l/email-protection#652717000b01040b4b2a290004171c250301044b0d0d164b020a13"><span class="__cf_email__" data-cfemail="0d4f7f6863696c63234241686c7f744d6b696c2365657e236a627b">[email protected]</span></a>.
SUPPLEMENTARY INFORMATION:
I. Background
FDA aims to ensure safety and effectiveness while facilitating
innovations in the development of drugs. Recent rapid technological
innovations in sophisticated data collection and generation tools,
combined with robust information management and exchange systems, and
advanced computing abilities may prove transformational in the way
drugs are developed and used.\1\ This evolving ecosystem presents
unique opportunities and challenges, and FDA is committed to working
across its medical product centers with partners domestically and
internationally to ensure that the full potential of these innovations
is realized for the benefit of the public.
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\1\ See <a href="https://pubmed.ncbi.nlm.nih.gov/35319833/">https://pubmed.ncbi.nlm.nih.gov/35319833/</a>.
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Developers, manufacturers, regulators, academic groups, and other
stakeholders are working to develop a shared understanding of where and
how specific innovations, such as AI and ML, can best be utilized
across the drug development process, including through the use of AI/
ML-enabled tools, which may include devices. FDA is publishing this
discussion paper as part of a multifaceted approach to enhance mutual
learning and to establish a dialogue with FDA stakeholders on this
topic. While AI and ML are not consistently defined across all
disciplines and stakeholders, AI can be generally described as a branch
of computer science, statistics, and engineering that uses algorithms
or models to perform tasks and exhibit behaviors such as learning,
making decisions, and making predictions. ML is generally considered a
subset of AI that allows ML models to be developed by ML training
algorithms through analysis of data, without models being explicitly
programmed. Additionally, there are a variety of ML methods and
different types of algorithms that may be utilized in a given context.
For the purposes of this discussion paper, AI and ML will be referenced
together as AI/ML, and references to drug development and the drug
development process include a wide scope of activities and phases,
including manufacturing and surveillance, among others.
This discussion paper, which considers the application of AI/ML in
the broad context of the drug development process, is not FDA guidance
or policy, and is not meant to endorse a specific AI/ML use or approach
in drug development. Rather, it is an initial communication with
stakeholders, including academic groups, that is intended to promote
mutual learning and discussion. Specifically, FDA is soliciting
feedback on the opportunities and challenges with utilizing AI/ML in
the development of drugs, as well as in the development of medical
devices intended to be used with drugs. This feedback will provide an
additional resource to help inform the regulatory landscape in this
area. Additionally, it is beneficial for researchers and technology
developers, particularly those new to drug development and human
subjects research, to recognize some of the initial thinking and
considerations involved with utilizing these technologies, including
having familiarity with FDA's current activities, initiatives,
practices, and potentially applicable regulations.
II. Electronic Access
Persons with access to the internet may obtain the discussion
paper, ``Using Artificial Intelligence and Machine Learning in the
Development of Drug and Biological Products: Discussion Paper'' at
<a href="https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development">https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development</a>.
Dated: May 5, 2023.
Lauren K. Roth,
Associate Commissioner for Policy.
[FR Doc. 2023-09985 Filed 5-10-23; 8:45 am]
BILLING CODE 4164-01-P
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