Document ID: FDA-2023-N-0743-0001
Agency: fda
Document Type: Notice
Title: Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products;
Availability
Posted Date: 2023-05-11T04:00Z

[Federal Register Volume 88, Number 91 (Thursday, May 11, 2023)]
[Notices]
[Pages 30313-30314]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[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 https://www.regulations.gov 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:
     Federal eRulemaking Portal: https://www.regulations.gov. 
Follow the instructions for submitting comments. Comments submitted 
electronically, including attachments, to https://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-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 
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: 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, [email protected]; 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, [email protected]; 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, [email protected]; 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, [email protected].

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 https://pubmed.ncbi.nlm.nih.gov/35319833/.
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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 
https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development.

    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