#FDA Tag

The U.S. Food and Drug Administration (FDA) has organized a stakeholder call to discuss the DSCSA implementation, which took place on 29 November 2023.

The Drug Supply Chain Security Act (DSCSA) drug supply chain security requirements, ten years after its implementation, are intended to improve the FDA’s ability to detect and remove potentially dangerous drugs, whether: counterfeit, stolen, contaminated or otherwise, from the supply chain.

Among the topics covered:

  • 10-year anniversary of DSCSA implementation
  • Stabilization period and expectations for trading partners to achieve interoperable, electronic tracing of products at the package level
  • Recent key guidances for industry related to supply chain security requirements
  • Looking ahead

Also in late November, the FDA announced that the CDER NextGen Portal (CDER NextGen) includes a DSCSA portal that enables the FDA and trading partners to communicate when the FDA requests information related to investigations of suspect or illegitimate products or during a recall.

The DSCA portal is used to:

  • Confirm basic information and points of contact for trading partners
  • Notify trading partners when they have messages from the FDA
  • Enable trading partners to respond to FDA messages and upload documents

Additionally, the DSCSA enables to:

After the FDA had already published an initial discussion paper addressing Artificial Intelligence (AI) in the manufacturing of medicinal products, in early 2023, the EMA issued a draft reflection paper outlining the current thinking on the use of artificial intelligence (AI) to support the safe and effective development, regulation and use of human and veterinary medicines, on 19 July 2023.

This paper reflects on principles relevant to the application of AI and machine learning (ML) at any step of a medicines’ lifecycle, from drug discovery to the post-authorisation setting and reports the experience of the EMA in this context, in which scientific knowledge is rapidly evolving.

Recently the success of ChatGPT and related reporting have made the topic of Artificial Intelligence accessible to a wide audience.

 

General considerations

In general, it is mentioned that AI and ML, if used correctly, can effectively support the acquisition, transformation, analysis and interpretation of data within the medicinal products lifecycle.

A risk-based approach to the development, implementation and performance monitoring of AI and ML tools should enable developers to proactively define the risks to be managed during the life cycle of AI and ML tools.

AI and ML tools, when used properly, can effectively support the acquisition, transformation, analysis and interpretation of data within the medicinal product lifecycle. Section 5 of the document lists some guidelines and documents that may provide useful recommendations for implementing AI/ML applications.

It is essential to highlight that the marketing authorisation applicant or MAH is responsible for ensuring that the algorithms, models, datasets, etc. used are fit for purpose and meet ethical, technical, scientific and regulatory standards.

 

Content of the document

The document addresses the following topics:

  • AI in the lifecycle of medicinal products
    • Drug discovery
    • Non-clinical development
    • Clinical trials
    • Precision medicine
    • Product information
    • Manufacturing
    • Post-authorisation phase
  • Regulatory interactions
  • Technical aspects
    • Data acquisition and augmentation
    • Training, validation, and test data
    • Model development
    • Performance assessment
    • Interpretability and explainability
    • Model deployment
  • Governance
  • Data protection
  • Integrity aspects
  • Ethical aspects and trustworthy AI

 

Conclusion

The quickly developing field of AI and ML shows great promise for enhancing all phases of the medicinal product lifecycle.

Finally, the use of AI in the lifecycle of medicines should always comply with existing legal requirements, considering ethics and its underlying principles, and with due respect for fundamental rights. A human-centred approach should be adopted in the development and use of AI and ML.

SOURCES:

The Mutual Recognition Agreement (MRA) between Switzerland and the United States in Good Manufacturing Practice (GMP) for medicinal products has entered into force from 27 July 2023.

This agreement in principle establishes a mechanism whereby each country recognises GMP inspections carried out by the regulatory authority of the other, i.e. Swissmedic for Switzerland and the Food and Drug Administration (FDA) for the United States.

Both authorities are thus able to mutually use GMP inspections and their results in order to avoid duplicate inspections.

A significant aspect of this MRA is that it is not only limited to human medicines, but also includes veterinary medicines

In addition to the FDA and Swissmedic, the Office of the U.S. Trade Representative and the State Secretariat for Economic Affairs of Switzerland had also signed the agreement. These institutions play an important role in facilitating negotiations and cooperation between the two nations to ensure the proper implementation of the agreement.

The Mutual Recognition Agreement is based on the Food and Drug Administration Safety and Innovation Act, enacted in 2012, which allows the FDA to enter into agreements with other regulatory authorities to recognise inspections performed by them. This regulatory environment has created a favourable environment for international collaboration and mutual exchange of information.

SOURCES:

https://www.swissmedic.ch/swissmedic/en/home/news/mitteilungen/inkrafttreten-mra-swissmedic-fda.html

 

 

This guidance, published in March, is aimed at all those involved in the regulatory submission of medicinal product data. It supports the development and implementation of the International Organization for Standardization (ISO) Identification of Medicinal Products (IDMP) standards for substances, terminologies and other information used throughout the medicinal product development lifecycle worldwide.

The purpose of these standards is to make the international exchange of medicines information between stakeholders more accurate, complete, and consistent.

The five IDMP standards and corresponding technical specifications, were developed within the ISO network member organizations. The standards, originally published in 2012 by ISO, provide a framework (data models, terms, definitions, etc.) to uniquely identify and describe medicinal products with consistent documentation and terminologies to enable reliable exchange of product information between global regulators, manufacturers, suppliers, and distributors.

The FDA supports these standards for the identification and description of marketed non-investigational medicinal products, with the goal of harmonizing the standards for the international exchange of medicinal product data.

This guidance serves as a guidance document made available by the FDA and contains helpful but nonbinding recommendations.

SOURCES:

FDA Guidance “Identification of Medicinal Products – Implementation and Use“.

The U.S. Food and Drug Administration (FDA) has updated (Revision 1) its Guidance for Industry on Out-of-Specification (OOS) Results. The first version of the document was dated October 2006.

The guide follows a step-by-step approach consisting of three main phases for investigating OOS test results

  • LABORATORY INVESTIGATION
  • FULL-SCALE OOS INVESTIGATION
  • CONCLUDING THE INVESTIGATION

The process outlined in the text follows quite closely the requirements laid out in 21 CFR part 211.

Definition

The definition of “OOS” has not changed.

«the term “OOS results” includes all test results that fall outside the specifications or acceptance criteria established in drug applications, drug master files (DMFs), official compendia, or by the manufacturer.  The term also applies to all in-process laboratory tests that are outside of established specifications

Comparison with the 2006 version

The revision of the guideline contains some formal adjustments from the previous version, and also focuses on updating references to other FDA relevant guidelines and regulatory requirements (USP chapters, CFR paragraphs, etc.)

In addition, the new text contains some clarifications or rewordings from the 2006 edition, the following changes are worth mentioning:

  • section IV.C.2.: the wording on “Outlier Tests” has been amended as follows:
  • Version October 2006“Occasionally, an outlier test may be of some value in estimating the probability that the OOS result is discordant from a data set, and this information can be used in an auxiliary fashion, along with all other data from the investigation, to evaluate the significance of the result.”
  • Version May 2022“Occasionally, an outlier test may be of some value in understanding how discordant from a data set a result is, but can be used solely in an informational capacity in the course of an investigation to determine the distance of a result from the mean.”
  • Section V.B. – Cautions: has been divided into three subsections:
  1. Averaging results from multiple sample preparations from the original sample
  2. Averaging results from same final sample preparation
  3. Borderline results that are within specification

Both versions of the guideline can be downloaded from the FDA homepage.