Initial interactions with the FDA on developing a validation dataset as a medical device development tool.

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

[Introduction and Reflection on Novel Medical Device Regulatory Science Tool MDDT].

In recent years, emerging technology medical devices have developed rapidly. How to more scientifically and more efficiently regulate these novel medical devices so as to improve access to advanced medical technology while ensuring safety and effectiveness is a new challenge faced by regulatory authorities, and is also the core topic of regulatory science. New tools, new standards and new methods are important means to achieve regulatory science. "Medical Device Development Tool" proposed by the U.S. FDA is a novel medical device regulatory science tool, which can help medical device developers to predict and evaluate product performance more efficiently. It is also helpful for regulatory authorities to make regulatory decisions more efficiently. This study introduces the concept, qualification process, role of MDDT in medical device regulation and MDDT examples, and makes some discussion on the device evaluation from the perspective of reliability and validity. MDDT can facilitate the developing of novel medical device.

Framework for creating a qualified medical device development tool of autoinjectors.

Objectives: Autoinjectors are pivotal for precise self-administration of medications across a wide range of medical conditions. Nevertheless, the absence of a dedicated Medical Device Development Tool (MDDT) for autoinjectors represents a gap that may result in variations in the quality and regulatory compliance of autoinjectors as components of combination products. This research aim is to utilize the recently introduced Primary Functions outlined in ISO 11608-1:2022 with the title "Needle-based injection systems for medical use. Requirements and test methods. Part 1: Needle-based injection systems" to create a comprehensive MDDT framework tailored specifically for autoinjectors. Methods: To support the creation of the framework, the analysis of the FDA MDDTs that were already approved, FDA's design controls regulations, FDA's guidance related to autoinjectors, and the Primary functions outlined in ISO 11608-1:2022 were utilized. Results: The research identifies the Primary Functions in autoinjector to be Holding Force, Cap Removal Force, Activation Force, Extended Needle Length, Injection Time, Dose Accuracy and Needle Guard Lockout. Leveraging these Primary Functions and the FDA's MDDT approach, the research aims to bridge the gap by proposing a structured framework for the development of a specific MDDT tailored to autoinjectors. Conclusion: This study presents a MDDT framework tailored to the development of autoinjectors for drug delivery. This framework provides a structured methodology to support predictability and effectiveness of the autoinjector development and support regulatory review process, thereby expediting FDA approval for autoinjectors as part of combination product.

Interrater Reliability of National Institutes of Health Traumatic Brain Injury Imaging Common Data Elements for Brain Magnetic Resonance Imaging in Mild Traumatic Brain Injury.

The National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS) Traumatic Brain Injury (TBI) Imaging Common Data Elements (CDEs) are standardized definitions for pathological intracranial lesions based on their appearance on neuroimaging studies. The NIH-NINDS TBI Imaging CDEs were designed to be as consistent as possible with the U.S. Food and Drug Administration (FDA) definition of biomarkers as "an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention." However, the FDA qualification process for biomarkers requires proof of reliable biomarker test measurements. We determined the interrater reliability of TBI Imaging CDEs on subacute brain magnetic resonance imaging (MRI) performed on 517 mild TBI patients presenting to 11 U.S. level 1 trauma centers. Three U.S. board-certified neuroradiologists independently evaluated brain MRI performed 2 weeks post-injury for the following CDEs: traumatic axonal injury (TAI), diffuse axonal injury (DAI), and brain contusion. We found very high interrater agreement for brain contusion, with prevalence- and bias-adjusted kappa (PABAK) values for pairs of readers from 0.92 [95% confidence interval, 0.88-0.95] to 0.94 [0.90-0.96]. We found intermediate agreement for TAI and DAI, with PABAK values of 0.74-0.78 [0.70-0.82]. The near-perfect agreement for subacute brain contusion is likely attributable to the high conspicuity and distinctive appearance of these lesions on T1-weighted images. Interrater agreement for TAI and DAI was lower, because signal void in small vascular structures, and artifactual foci of signal void, can be difficult to distinguish from the punctate round or linear areas of slight hemorrhage that are a common hallmark of TAI/DAI on MRI.