AJ Blackston’s Post

𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐓𝐨𝐨𝐥𝐬 𝐂𝐚𝐭𝐚𝐥𝐨𝐠 On May 13, 2024, FDA’s Office of Science and Engineering Laboratories (OSEL) launched an improved online Regulatory Science Tool (RST) Catalog to enhance search and browse capability as well as allow for long-term growth in published tools. The RST Catalog is now organized by research program and device type, cross referenced by both program and RST category, and an online portal for many OSEL-developed applications including the Device and Material Safety Evaluation Library. The RST Catalog will continue to provide peer-reviewed resources for medical device companies and innovators. A Catalog of Regulatory Science Tools (RSTs) was developed by OSEL to expand the scope of science-based approaches used in the development of emerging medical technologies. These RSTs can be used by the industry to drive innovation and assess emerging medical technologies. The Catalog includes more than 100 RSTs, including laboratory methods, tissue-mimicking phantoms, and computational modelling and simulations. These tools are intended to add speed and consistency to medical device development by streamlining the review process. However, the introduction to the Catalog states these RSTs do not replace FDA-recognized standards or qualified medical device development tools (MDDTs) since they have not been evaluated for suitability to any specific use. View the new Regulatory Science Tool Catalog here: https://cdrh-rst.fda.gov/ Visit our website for more industry news and articles. https://lnkd.in/eGrnpRdv

🔬 Harnessing DFSS and Axiomatic Design for Genomic Medical Device Innovation 🌟 In the rapidly evolving field of genomics, developing cutting-edge medical devices demands a robust design approach that ensures precision, reliability, and scalability. This is where Design for Six Sigma (DFSS) and Axiomatic Design comes into play—two robust methodologies that can transform how we approach the design of genomic medical devices. 🏆DFSS: A Pathway to Excellence DFSS is a data-driven methodology focused on designing products that meet customer needs immediately. By integrating statistical tools and Six Sigma principles, DFSS helps us minimize variability and defects in the product design phase long before production begins. This proactive approach ensures that the final product meets and exceeds performance expectations. 🧭 Axiomatic Design: Simplifying Complexity Axiomatic Design provides a framework to break down complex systems into manageable components systematically. It is built on two key axioms: The Independence Axiom: Maintain the independence of functional requirements (FRs) to avoid unintended interactions. The Information Axiom: Minimize the design's complexity (information content) to create a more robust and user-friendly product. 🩺 Application in Genomic Medical Devices When applied to a genomic medical device, these methodologies ensure that specific design parameters clearly define and independently address every functional requirement—the accuracy of cfDNA detection, turnaround time, or ease of use. Adhering to the Independence Axiom prevents potential design conflicts that could compromise device performance. DFSS complements this by applying rigorous analysis to ensure data drives our design choices. This ultimately results in a device that meets stringent quality standards and market needs. ✅ Why It Matters In an industry where precision can make the difference between a breakthrough and a setback, combining DFSS and Axiomatic Design allows us to innovate confidently. We can design genomic medical devices that are not only cutting-edge but also reliable, scalable, and aligned with the highest quality standards. 🔗 If you're passionate about combining advanced methodologies with innovative technologies to create life-changing medical devices, let's connect and share insights! #Genomics #MedicalDevices #DFSS #AxiomaticDesign #Innovation #QualityEngineering #SixSigma #HealthcareInnovation #ProductDevelopment #Biotech

 We are seeing a significant increase in healthcare innovations, especially with in-vitro diagnostics, and clinical decision support software. A few of the biggest challenges companies will face in the Medical Device and Life Science space in 2024 include but are not limited to: - Being competitive and innovative in a highly regulated environment - Understanding regulatory requirements in new or less mature regulated areas - Obtaining funding to support regulatory and development efforts Companies who are focused on aligning their product roadmap with a sound regulatory strategy will not only unlock funding but obtain revenue faster, which will allow them to leap ahead of the competition. BeanStock Ventures #medicaldevices #medicalequipment #SaMD #SiMD

Published! Extremely pleased to share news that the 'playbook' from the long-running effort to revolutionize medical device development through the application of in silico (computational) methods has finally been published. This is the primary outcome of the 'ENRICHMENT In Silico Clinical Trial' project run by FDA and Dassault Systèmes and on which I was honored to share in leadership duties for modeling and simulation. We started this journey four and a half years ago with a goal towards identifying and illustrating how computational methods can change the paradigm of medical device clinical trials in order to improve patient health and create value for medical device manufacturers. The technical details of the work are, no doubt, of great interest to all. You can review the paper and Appendix (where the real gory, nerdy details can be found!) and I'm happy to discuss at length with whomever cares to listen. Taking a step back, though, this work (together with a similar paper published by FDA authors last week and other recent impactful efforts, particularly those out of Europe) marks nothing short of a critical inflection point in medical device development. The use of computational methods to predict the response of populations of patients (and not just bench top analyses or individual digital twins) opens the door to radical improvements in understanding and tackling disease. Leveraging first principles modeling and simulation at scale with these methods will transform device development and clinical insights and I cannot wait to see what amazing products and techniques the amazing engineers of this industry can bring to bear as a result. On a personal note, kudos to the team (including the authors listed below), the many, many industry, scientific, and clinical advisors to the project, and the amazing interns who helped with our modeling work. Kudos, as well, to the pioneering work of Tina M. Morrison for her role in kickstarting this effort and helping to lead the industry in adoption of these methods. Paper can be found here: https://lnkd.in/estXY-VF Brent Craven, Kenneth Aycock, Steve Levine, Tom Battisti, Pras Pathmanathan, Ashley Peterson, Vice President at Thornton Tomasetti, Sanjay Pant, Claudio Capelli, David Hoganson

 Cannot wait to see the #impact as well! 🧬💊 #GoDassaultSystemes #LifeScienceIstheBest #HelpPatients #LessPain "The use of #computational #methods to predict the response of #populations #of #patients (and not just bench top analyses or individual digital twins) opens the door to radical #improvements in #understanding and #tackling #disease. Leveraging first principles modeling and simulation at scale with these methods will #transform #device #development and #clinical #insights and I cannot wait to see what #amazing #products and #techniques the amazing engineers of this industry can bring to bear as a result."

💻 Online webinar about "In Silico Medicine for Medical Devices", that was held by MathWorks, TÜV SÜD and Virtonomy.io, is now available online. During the webinar, Visa Suomi, DPhil, Martin Witte and Dr. Simon J. Sonntag discuss the use of computational modeling and simulation for the design, testing, and clinical evaluation of medical devices, and the current industry and regulatory view on such methods. As we've seen recently with FDA releasing a guidance on the use of in silico methods for R&D of medical devices, the regulators are also using and supporting CM&S activities as supportive regulatory evidence for market approvals. However, if you are new to the topic, this rountable is a good place to start. Some of the things discussed were: ⭕ Validity and Reliability: Are the computational models and simulations used for medical devices valid and reliable? ⭕ Optimization and Design: How can computational methods optimize the design and performance of medical devices? ⭕ Regulatory Compliance: What are the regulatory requirements for using computational models in medical device development? ⭕ Clinical Translation: How can computational simulations bridge the gap between preclinical testing and clinical trials? ... and more. If you would like us to help you get started with drafting a virtual testing plan, or would like a free consultation, send us a message. For more information: https://lnkd.in/dkb72syD

Exploring the future of medical devices in Brazil through AI and machine learning is like opening a new chapter in healthcare innovation. Brazil, with its diverse population and advanced infrastructure, stands at the forefront of this transformation. 1️⃣ Diverse Population: Brazil's genetic diversity offers a unique opportunity to develop personalized medical devices, especially for rare diseases. 2️⃣ Regulatory Advancements: Recent changes in Brazil's medical device regulations streamline trials, making it a prime location for early feasibility studies. 3️⃣ bioaccess®: As the only Medtech CRO in Latin America, we empower startups to navigate these promising opportunities efficiently. Together, these elements make Brazil an ideal place for pioneering medical device trials. By leveraging AI and machine learning, we are not only innovating but also ensuring that these advancements reach those who need them most more quickly. 🌍💡 #Medtech #ClinicalTrials

STATUS OF FDA APPROVAL OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING -ENABLED MEDICAL DEVICES: The U.S. Food and Drug Administration (FDA) recently updated its list of approved AI/ML-enabled medical devices, adding 191 new devices and bringing the total to 882. Most of these new approvals are in the radiology sector, with 128 being radiology-focused. The FDA emphasizes that these devices have met premarket safety and effectiveness requirements, including diverse study evaluations. Notable companies with newly approved devices include Siemens Medical Solutions, GE, Philips, Canon, Viz.ai, Butterfly Network, Lunit, and Aidoc Medical. Of the new approvals, 151 were cleared between August 2023 and March 2024, while 40 were from prior periods after further refinements. The number of AI/ML medical devices approved by the FDA has significantly increased, growing from about 500 at the start of 2023 to nearly 700 by the end of the year. Radiology, where AI/ML can have immediate clinical impact, comprises almost 80% of these devices. However, FDA clearance does not guarantee insurance coverage. The Centers for Medicare & Medicaid Services (CMS) currently covers only around 10 of these devices. Medical organizations and patient advocacy groups are lobbying Congress to establish a pathway for broader CMS coverage of AI in medical settings. #digitalhealth #AI #MAIC #digitaltransformation #FDA #diagnostics #cancer #Bayer #artificialintelligence #AI/ML #Machinelearning

🔔 Hi there! The study I’d like to share with you today emphasizes the importance of credible, validated computational models in the development and assessment of medical devices. Thus, the authors explore the use of in silico clinical trials (ISCTs) for medical devices, evaluate the credibility results, and propose a structured workflow for credibility assessment. Among the key findings they highlight, and I totally agree with, is the fact that ISCTs offer an efficient alternative for medical device evaluation, potentially reducing the reliance on traditional clinical trials. They came up with the idea that it’s crucial to establish a standardized, rigorous approach to verify and validate ISCTs (one of the reasons is the variability in methods and complexity among different ISCTs).  In the result, they present a hierarchical workflow for ISCT credibility assessment. You can learn more about it and the details of the study via the link below. Moreover, the study emphasizes the importance of integrating real-world data and computational predictions to enhance the reliability of ISCTs. Indeed, credibility assessment in in silico clinical trials is a hot topic. Regulatory bodies like the FDA and EMA are increasingly considering evidence from in silico trials to approve drugs or medical devices. However, they demand that these models be rigorously validated and shown to be credible. Credibility ensures that the model's predictions are accurate and reliable for decision-making, which is critical for patient safety.   Establishing a standardized framework for ISCT validation not only strengthens trust but also accelerates the adoption of these models, allowing innovations to reach the market faster and safer. By the way, have you conducted in silico trials? If yes, considering the clear advantages like customization of virtual patients and personalized testing, what would you highlight as the main advantage of moving to such trials? https://lnkd.in/dZt_TFeq #insilicotrials #clinicaltrials #medicalresearch

Why are #medicaldevices so cool? ONWARD Medical have a device that uses AI/ML to bridge spinal injury. This stuff feels like #startrek medicine. Designing medical devices with embedded AI/ML can be a real regulatory bugbear- Ketryx is hosting a webinar on how to use CI/CD to meet the FDA's new PCCP process - https://lnkd.in/epUQWBUP