FDA post ICH S12: Nonclinical Biodistribution Considerations for Gene Therapy Products

 Dated for May, the FDA releaseed on 28April2023, the final version of ICH S12:

Nonclinical Biodistribution Considerations forGene Therapy Products

ICH released this in March of this year, so this was processed by the FDA rather quickly compared to other ICH guidelines. The objectives of the guideline are:

A. Objectives of the ICH S12 Guidance (1.1)

The objective of this guidance is to provide harmonized recommendations for the conduct of nonclinical biodistribution (BD) studies in the development of gene therapy (GT) products. This document provides recommendations for the overall design of nonclinical BD assessments. Considerations for interpretation and application of the BD data to support a nonclinical development program and the design of clinical trials are also provided. The recommendations in this guidance endeavor to facilitate the development of GT products while avoiding unnecessary use of animals, in accordance with the 3Rs (reduce/refine/replace) principles.

The Scope is interesting as it defines what is covered and what it does not (prophylactic vaccines. Chemically synthesized oligonucleotides or their analogues, which are not produced using a biotechnology-based manufacturing process):

Scope (1.3)

GT products within the scope of this guidance include products that mediate their effect by the expression (transcription or translation) of transferred genetic materials. Some examples of GT products can include purified nucleic acid (e.g., plasmids and RNA), microorganisms (e.g., viruses, bacteria, fungi) genetically modified to express transgenes (including products that edit the host genome), and ex vivo genetically modified human cells. Products that are intended to alter the host cell genome in vivo without specific transcription or translation (i.e., delivery of a nuclease and guide RNA by nonviral methods) are also covered in this guidance. Although not currently considered GT in certain regions, the principles outlined in this guidance are also applicable to oncolytic viruses that are not genetically modified to express a transgene.

This guidance does not apply to prophylactic vaccines. Chemically synthesized oligonucleotides or their analogues, which are not produced using a biotechnology-based manufacturing process, are also outside the scope of this guidance.

The release of a GT product outside the body via excreta and secreta (feces, urine, saliva, nasopharyngeal fluids, etc.), or through the skin (pustules, sores, wounds) is termed shedding. Evaluation of the nonclinical shedding profile of a GT product is outside the scope of this guidance. Assessment of genomic integration and germline integration of GT products is also outside the scope of this guidance. Considerations for these aspects of nonclinical data can be found in existing International Council for Harmonisation (ICH) consideration documents (1, 2).

 If you are working in gene therapy development, a must read.

FDA LEARNING OPPORTUNITY: Regulatory Education for Industry (REdI) Annual Conference 2023

 This 3 track FREE Conference has 3 tracks, small molecule drugs, biologics and devices. Registration and detailed agenda are available at the link.  

PLENARY

Federal law authorizes the FDA to collect user fees which help the FDA fulfill its mission of promoting and protecting the public health. Most major user fee programs run in five-year cycles, and the past year saw the launch of the current cycle for CDER, CBER, and CDRH. This session will take a closer look at the impact of user fee legislation, how the FDA advances programs through user fees support, and highlights of some of the exciting new efforts planned during this user fee cycle.

DRUGS TRACK

The drugs track will continue its focus on PDUFA VII commitments. Subject matter experts will provide cutting edge insights and perspectives on how several of these goals and initiatives are being implemented. Agenda topics will provide practical information and advances in bioinformatics (eCTD v4.0; ESG; data standards); digital health technologies, real-world evidence (RWE) & pilot programs.

Topics Include

• New Meeting Types, including What's New Under PDUFA, BsUFA, and OMUFA
• PDUFA VII Chemistry, Manufacturing, and Controls (CMC) Assessment Updates
• Use-Related Risk Analysis (URRA) and Human Factor (HF) Protocol Reviews: What to Submit for an Efficient Review

BIOLOGICS TRACK

The biologics track will focus on the developmental and regulatory topics relevant to advanced therapies, including cellular and gene therapies, tissue-engineered and other biological products. The speakers will present updates on programmatic enhancements mandated by PDUFA-VII, including new regulatory programs, stakeholder communications and much more.

Topics Include

• IND readiness: contents of preclinical and clinical parts; Expanded Access INDs
• Product development challenges and Chemistry, Manufacturing, and Controls (CMC) Developmental Readiness Pilot program
• Estimand and Complex Innovative Design (CID) Program in application to CBER biologics
  

   

  DEVICES TRACK

  The devices track will provide an overview and highlights of how to get a new medical device to market. It will also discuss some best practices for ensuring that medical devices maintain their quality and continue to be safe and effective once on the market; and offer technical topic updates on biocompatibility, artificial intelligence, and radiological health.

• Topics Include

• Artificial Intelligence and Machine Learning; FDA Medical Device Inspections & Form FDA 483
• Premarket Notification [510(k)] Program; Quality System Principles
• Tips and Best Practices for High Quality Regulatory Submissions

 

FDA publishes Q&A formatted guidance on "A Risk-Based Approach to Monitoring of Clinical Investigations"

The introduction highlights why the guidance (link) was needed and that is expands on a 2013 guidance.

This guidance contains recommendations on planning a monitoring approach, developing the content of a monitoring plan, and addressing and communicating monitoring results. This guidance expands on the guidance for industry Oversight of Clinical Investigations – A Risk-Based Approach to Monitoring (August 2013) (the 2013 RBM guidance) by providing additional information to facilitate sponsors’ implementation of risk-based monitoring. 

 

But it is within the 'Background' section that the basis for using risk-based approaches are truly needed within clinical study oversight operations:

"to ensure adequate protection of the rights, safety, and welfare of participants in the clinical investigation and the integrity of the data submitted to FDA"

 

With this in mind the guidance notes that a risk-based monitoring plan should start with the protocol development, be used in site selection, and include onsite and remote (central) monitoring. The plan and process needs to be flexible in that not only risks that are identified during the protocol planning stage are addressed, but issues identified during the conduct have a process for reporting, assessing and implementing updated procedures to correct the practices that were risking patients or data integrity.  One section is devoted to the issue of assessing the adherence to study blinding - a key parameter for data integrity in blinded studies.

 

While many companies are in compliance with the 2013 guidance that laid out the foundation for risk-based monitoring, the FDA must have seen a number of best practices and gaps over the years; prompting them to write this Q&A formatted guidance.

FDA new DRAFT Guidance: Patient-Focused Drug Development: Incorporating Clinical Outcome Assessments Into Endpoints For Regulatory Decision-Making

 The draft guidance is available here.  It is the fourth guidance in a series:

• Methods to collect patient experience data that are accurate and representative of the intended patient population (Guidance 1)

• Approaches to identifying what is most important to patients with respect to their experience as it relates to burden of disease/condition and burden of treatment (Guidance 2)

• Approaches to selecting, modifying, developing, and validating clinical outcome assessments (COAs) to measure outcomes of importance to patients in clinical trials (Guidance 3)

• Methods, standards, and technologies for collecting and analyzing COA data for regulatory decision-making, including selecting the COA-based endpoint and determining clinically meaningful change in that endpoint (Guidance 4)

The scope for this guidance is :

This guidance focuses on COA (clinical outcome assessments) issues associated with clinical trial (study) endpoints, design, conduct, and analysis and will be of most relevance for those designing and conducting trials using COAs as well as analyzing and interpreting the trial data.

 

The draft guidance spends quite a bit of time presenting different types of endpoints, their meaning and planning for assessment by the Agency. Aspects of data collection are reviewed, as well as how to handle 'errors and missing data'.  Fifteen (15) pages are dedicated to "EVALUATING THE MEANINGFULNESS OF TREATMENT BENEFIT" before the last section that discusses a variety of topics related to optimizing the clinical trial design for robust data collection for the safety and efficacy endpoint assessments.

FDA Draft Guidance: Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions

With the rapid, and what some are calling hasty and precipitous, growth in artificial intelligence applications, and especially those in the medical field, the FDA is recognizing in its most recent draftguidance on the topic, that machine learning-enabled device software functions (ML-DSFs) are expected to learn and undergo iterative phases of development as the systems learn. As such, the FDA wants to ensure that they continue to provide important support to healthcare providers while at the same time ensuring the safety and efficacy of the product. The Introduction is pretty straightforward, but much of the very detailed body of the document relies on an understanding of the device approval process (which I don’t have). So for those of you interested in AI, ML, devices and FDA approval – please read the draft guidance.

 

From the Introduction:

FDA has a longstanding commitment to develop and apply innovative approaches to the regulation of medical device software and other digital health technologies to ensure their safety and effectiveness. As technology continues to advance all facets of healthcare, medical software incorporating artificial intelligence (AI), and specifically the subset of AI known as machine learning (ML) (henceforth referred to as machine learning-enabled device software functions or ML-DSFs), has become an important part of many medical devices. This draft guidance is intended to provide a forward-thinking approach to promote the development of safe and effective medical devices that use ML models trained by ML algorithms.

 

ML-enabled technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. Medical device manufacturers are using ML technologies to innovate their products to better assist healthcare providers and improve patient care. Examples of ML applications in medicine include earlier disease detection and diagnosis, development of personalized diagnostics and therapeutics, and development of assistive functions to improve the use of devices with the goal of improving user and patient experience.

 

FDA recognizes that the development of ML-DSFs is an iterative process. This draft guidance proposes a least burdensome approach to support iterative improvement through modifications to an ML-DSF while continuing to provide a reasonable assurance of device safety and effectiveness. As such, this draft guidance demonstrates FDA’s broader commitment to developing innovative approaches to the regulation of device software functions as a whole. Specifically, this draft guidance provides recommendations on the information to be included in a Predetermined Change Control Plan (PCCP) provided in a marketing submission for an ML- DSF. This draft guidance recommends that a PCCP describe the planned ML-DSF modifications; the associated methodology to develop, implement, and validate those modifications; and an assessment of the impact of those modifications. The PCCP is reviewed as part of a marketing submission to ensure the continued safety and effectiveness of the device without necessitating additional marketing submissions for implementing each modification described in the PCCP.