Identifying an optimal regulatory strategy is a multi-variate optimization problem with many variables including budget, timeline, investor milestones, marketing claims, data availability, ease of clinical assessment, presence of a strong predicate.

Our engineers, clinical, and regulatory experts will work with you to find the most optimal solution to get you on the market as soon as possible, following these steps:

1. Understanding the Business Goals
   • What are you trying to accomplish by bringing the SaMD to market?
   • What markets do you want to sell it in and what is their relative importance?
   • What marketing claims do you need to be able to make about the device’s performance to accomplish these business goals?
   • How will the software be used (e.g., within the clinical workflow or directly by patients)?
   • Draft a rough intended use statement for the device.
2. Understanding the Software and Clinical Workflow
   • What work has been done on the software?
   • What do you hope to implement in the next year or two?
   • What functionality is the highest risk?
3. Researching the Regulatory Strategy
   • Determine the product code for the device
   • Identify applicable FDA guidance and Standards
   • Determine available market pathways (e.g., 510(k), De Novo, PMA)
   • If a 510(k), identify possible predicate devices.
   • Will the SaMD need clinical performance testing, and roughly what form this would take?
   • Identify key regulatory risks and ensure they’re inline with the business goals.
   • Determine any architectural design constraints that could reduce how much of the software is considered part of the device (per the 2020 Multiple Function Device Product FDA Guidance).
   • If the device is a series of modules will each module have it’s own IU statement? Will there be an overall software ‘Platform’ which serves as the user interface and which may perform some functions (i.e., image viewer, report generation)?
4. Develop a Clinical Validation Strategy
   1. The exact scope of this will vary based on the device and may include Performance Testing Study, Multi-Reader Study if needed, Technical Performance Assessments, Standalone Testing, etc.
   • Determine if the product is a CADq (quantification), CADe, CADt, CADx, or CADp (prognostic) device.
   • Explore the data you already have available to ascertain if it can be reused for the study.
   • Work with you to tweak your device’s indications for use and marketing claims to make the clinical validation study significantly easier and faster without sacrificing too much marketing value.
   • Conduct a statistical power study to justify the anticipated sample size.
   • Design the standalone performance study design
   • Design the clinical performance study design if necessary
   • Provide a proposal for the cost necessary to conduct the study(ies)
5. Write the pre-sub request, including
   • Meeting Purpose
   • Device Description
   • For each algorithm component:
     • Algorithm description and verification strategy
   • Standalone Performance Testing Plan
   • Clinical Performance Testing Plan
   • Training Data Description
     • Demographics, sample size, number of Institutions, qualification of the annotators
   • Validation Data Description
     • Demographics, sample size, number of Institutions, qualification of the annotators
   • Proposed Predicate Device
   • Proposed Indications and Intended Use
   • Predicate Device Comparison (for a 510(k))

For additional details about our process, you may take a look at our published resource “How to Document AI/ML Algorithms in FDA Presubmissions (Q-Sub)”. Note that the approach is continually evolving and may not follow this document exactly.

The primary deliverable of this phase is the pre-sub documents that are sent to FDA. We’ll also include a regulatory strategy document that includes most of the same information, as well as a few additional information that is not always shared with FDA:

• Key marketing claims
• Key regulatory risks (i.e., issues that may prevent getting the SaMD to market, how likely they are to occur, and how to respond to FDA comments if they arise)
• Strategy related to future submissions (although the focus of the strategy will be on the first submission)

Pricing depends on your device, timeline, and scope. Contact us for a custom quote.

If your AI model is used for triage, to detect disease, to diagnose disease, or is involved in guiding therapeutic decisions, then you will likely need to validate its performance using clinical data. That is, a reference standard that has been defined by clinical experts.

This is true even if your device as a clear predicate.

Yes! We have seen many companies receive funding and/or get acquired after getting a BDD. It is a positive market signal.

A Pre-Sub is a formal method for getting feedback from the FDA. See FDA Pre-Subs: Best Practices, FAQs, and Examples for more details.

In many cases, we can find a path to market and an acceptable clinical validation strategy with a single Pre-Sub. Sometimes after the first Pre-Sub, there is remaining regulatory risk that requires further FDA interaction.

Our AI Regulatory Strategy service is meant for companies who have a somewhat well-defined intended use and have made some progress developing their model. If you’re very early stage, feel free to read our resources to seek guidance on your pathway.

Absolutely! We frequently consult with in house teams on topics related to AI/ML, software, and cybersecurity.

Unfortunately, no. Although most of our team members have experience with CE marking, Health Canada, UKCA, and other regulatory submissions—we’ve decided to focus just on the US market so that we can be experts in this jurisdiction.

We have partners we can coordinate with for international regulatory consulting.