Additive Manufacturing and Bioprinting

  • Additive Manufacturing (AM). AM refers to technologies that build three-dimensional objects by adding layer upon layer of material. AM encompasses 3D printing, rapid prototyping, direct digital manufacturing, layered manufacturing, and additive fabrication.
  • Bioprinting. Bioprinting is the technique by which successive layers of biopolymers or cell-laden hydrogels are applied to a surface layer by layer to fabricate viable tissue. There are currently three bioprinting methodologies:
    • Laser assisted. Laser bioprinting delivers a laser pulse to a donor slide, which results in the transfer of a cell-laden hydrogel to a donor slide.
    • Ink jet printing. Inkjet printing delivers cell-laden hydrogels or biopolymers to a surface through the use of thermal energy or a piezoelectric motor.
    • Extrusion dispensing. Extrusion dispensing uses pistons or air pressure to deliver cell-laden hydrogels or biopolymers to a surface.
  • Regulatory Concerns for AM and Bioprinting. At present, bioprinting is not used clinically. Regulatory agencies, such as the FDA, identify AM as a rapidly growing technology.  As such, the use of AM-derived products is being closely monitored by the FDA.  Technical considerations such as biocompatibility, structural integrity, and durability must be addressed prior to submitting FDA applications.  The FDA’s guidance on AM can be viewed or downloaded here.
  • 3D Printing of Medical Devices.  3D printing is a process that creates a three-dimensional object by building successive layers of raw material. Each new layer is attached to the previous one until the object is complete. Objects are produced from a digital 3D file, such as a computer-aided design (CAD) drawing or a Magnetic Resonance Image (MRI).
  • Technical Considerations for Additive Manufactured Medical Devices.
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  • Patient-Matched Device

Source

  • Vijayavenkataraman S, Fuh JYH, Lu WF. 3D printing and 3D bioprinting in pediatrics.  Bioengineering.  2017 Jul 13; 4(3): 63.

Combination Products

As the name implies, combination products are therapeutic or diagnostic products that combine drugs, devices, and/or biological products.

  • Regulatory Concerns for Combination Products.  Based upon the product’s primary mode of action, the FDA’s Office of Combination Products (OCP) determines which FDA center would be the lead center for the product’s marketing.

Digital Health / Artificial Intelligence / Software as a Device

  • Digital Health. Digital medicine relies on high-quality hardware and software to obtain biometric data from which health care decisions are made.  Wearable devices (e.g., the Apple Watch®, glucose monitors), telemedicine, and virtual reality are all examples of the rapidly growing field of digital health. The recent pandemic has accelerated the use of digital medicine.  For example, the use of telemedicine claims in September of 2020 was 5%, compared to only 0.16% in September of 2019.
  • Medical X-Ray Imaging Devices, Conformance with IEC Standards.  Manufacturers and importers of medical x-ray imaging equipment must follow the current Electronic Product Radiation Control (EPRC) regulations and procedures or provide a declaration of conformity to equivalent International Electrotechnical Commission (IEC) standards.  More information can be found here.
  • Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices. Information to industry regarding the documentation recommended by the FDA for premarket submissions for software devices, including standalone software applications and hardware-based devices that incorporate software, can be found here.
  • Artificial Intelligence (AI). AI is the ability of a machine to imitate human intelligence and thought pattern. Within the AI field are subsets, which include Machine Learning (ML) and Deep Learning (DL).  ML is a process where the program incorporates algorithms that enable it to modify their output by responding to input data.  DL involves a layered network of algorithms, which closely mimics a biological neural network.  This layered network allows the program to alter these algorithms independent of human programming.  With respect to pediatrics, the application of AI remains at an early stage of development.
  • Artificial Intelligence and Machine Learning in Software as a Medical Device. Information on this topic and be found here.
  • Regulatory Concerns for Digital Medicine and AI. Digital health and the use of AI in medicine raises a number of ethical and practical questions.  Data storage and ownership will remain an important issue as personal health care information gets stored and processed in a cloud-based platform.  In addition, AI results may come in conflict with an institution’s established standard of care.  In response to this growing technology, the FDA has issued a discussion paper on the use of AI/ML-based software as a medical device.  In general, the FDA requires a new premarket notification (510(k)) whenever there will be a significant change to such software that may alter the risk to patients or users.  To assist with this decision, the FDA website has posted Deciding When to Submit a 510(k) for a Software Change to an Existing Device.

Sources

  • Farheath.org.
  • Schuman, AJ. AI in pediatrics: Past, present and future. Contemporary PEDS Journal. 2019 July 14; Vol 36 No 7, Volume 36, Issue 5.
  • S. Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD): Discussion paper and request for feedback.
  • Chen A, Punn R, Collins RT, Chen JH, Stauffer KJ, Wang R, Alexander S, MacMillen Lechich K, Murphy DJ, Chung S, Selamet Tierney ES. Tele-clinic visits in pediatric patients with Marfan syndrome using parentally acquired echocardiography. The Journal of Pediatrics. 2021; doi: https:// doi.org/10.1016/j.jpeds.2021.01.004.

Pediatric Extrapolation for Devices (PEDS)

Due to a relatively small and geographically diverse patient cohort, and to the difficulty of obtaining informed consent, pediatric clinical trials are difficult to perform. Extrapolation refers to leveraging existing data from controlled studies of adult devices to support an application in the pediatric patient population.

  • Benefits of Extrapolation:
    • Facilitates the process of obtaining a pediatric use claim
    • Can result in more products being allowed for the pediatric patient population
    • Enhances and encourages more development of pediatric devices
    • Increases availability of medical devices with appropriate pediatric labelling, thus enhancing safety
  • When Is Extrapolation Appropriate?
    • Similarity exists between the adult population and the intended pediatric patient.
    • Sufficient quality, with respect to study design and data analysis, has been exhibited in the adult study.
    • Reasonable assurance can be made of safety and effectiveness.
  • Additional Information Regarding Extrapolation:
    • The FDA has sponsored a webinar on the subject. View or download PDF here.
    • An FDA guidance paper is available to view or download here.