There are two critical differences between a De Novo classification request and a 510k submission. First, 510k clearance is based upon a substantial equivalence comparison of a device and a predicate device that is already marketed in the USA, while a De Novo classification is based upon a benefit-risk analysis of a device’s clinical benefits compared with the risk of harm to users and patients. Second, 510k clearance usually does not require clinical data to demonstrate safety and efficacy, while a De Novo classification request usually does require clinical data to demonstrate safety and efficacy. Therefore, it makes sense that the two most common challenges for innovative medical device companies are: 1) learning how to write a benefit-risk analysis, and 2) designing a clinical study. Success with both of these tasks can be significantly improved by requesting a De Novo pre IDE meeting with the FDA.
Benefit-Risk Analysis Questions to Ask During a De Novo pre IDE Meeting
Most device companies are only familiar with substantial equivalence comparisons–not a benefit/risk analysis. The statement “the benefits outweigh the risks” is not a benefit/risk analysis. The European MDD requires a benefit/risk analysis (mentioned eight times), while Regulation (EU) 2017/745 mentions benefit/risk 69 times. Despite the obvious increased emphasis on benefit/risk analysis in the new EU Regulations, the new ISO 14971 standard that is expected to be released next month still does not require a benefit/risk analysis for all risks as required by the regulations. The international standard also does not clearly explain how to perform a benefit/risk analysis. The best explanation for how to perform a benefit/risk analysis is provided in the FDA guidance.
In addition to reading that guidance, you will need to systematically identify all of the current alternative methods of treatment, diagnosis, or monitoring for your intended use. Therefore, you should ask in a pre-submission meeting if there are any additional devices or treatments that the FDA feels should be considered. You should review each of the alternative treatments for clinical studies that may help you in the design of your clinical study. You should carefully review the available clinical data for alternative treatments to help you quantify the risks and benefits associated with those treatments too. Finally, you should consider whether one or more of these alternative treatments might be a suitable control for your clinical study. Ideally, your clinical study design will show that the benefits of your device are greater, and the risks are less, but either may be enough for approval of your classification request. If you think the risks of your device are significantly less than alternative treatments, then ask the FDA about using this factor as an endpoint in your study design.
Clinical Study Design Considerations
Ideally, there is already a well-accepted clinical model for assessing efficacy for your desired indications. This means multiple, published, peer-reviewed journal articles. You might have a better method for evaluating subjects, but don’t propose that method instead of a “gold standard.” If you feel strongly that your method is more appropriate, propose both methods of evaluation. You also need multiple evaluators who can be objective. Randomization, blinding, and monitoring of clinical studies is critical to ensure an unbiased evaluation of clinical results.
You also need to design your study with realistic expectations. Murphy’s law is always active. That means, “things will go wrong in any given situation if you give them a chance.” Therefore, you must avoid optimism and devise methods for detecting errors quickly. This is why electronic data capture systems and eSource is preferred for data collection instead of the manual collection of data on paper case study forms. Not only does it reduce errors in data collection, but it also facilitates remote monitoring of clinical sites. This includes asking questions that are open-ended or quantitative–instead of Yes/No questions or qualitative evaluations that encourage subjectivity. You can always anticipate every mistake that will be made, and open-ended questions often capture essential data that would otherwise be lost. Asking the quantitative questions also will provide you with additional data you can analyze, which may reveal unexpected relationships or help you to explain unexpected results. To help facilitate the development of these questions, try asking yourself how you could detect an error for each data point you are collecting. Then add a detection mechanism to your data collection plan wherever and whenever you can.
Goals of De Novo pre IDE Meeting
A pre-IDE meeting is not typically your first pre-submission meeting with the FDA. Usually, your first pre-submission meeting is to verify that the FDA agrees that the regulatory pathway is a De Novo classification request rather than a 510(k) submission. Hopefully, you also were able to review your overall testing plan with the FDA during your first pre-submission meeting. You may have even reviewed a clinical synopsis with the FDA during your initial pre-submission meeting. During the pre-IDE meeting, your goal is to finalize your clinical study protocol. That doesn’t mean that the FDA should agree 100% with your draft protocol. You want positive and negative feedback on all aspects of your protocol before the IDE submission. During the IDE review, changes will be made.
The most important aspects of getting right before the IDE submission are the fundamentals. Most of my De Novo clients feel that a control group is not possible, because they think that test subjects will know when a sham is used. However, trying to avoid a control group is nearly impossible. The most important factors for why a control group is needed are:
- you need to minimize differences between experimental and control subjects, but you can’t do that if you are relying on data from other clinical studies
- you also need to ensure that your evaluation methods are identical, which is nearly impossible when performed by different people, at different facilities, using slightly different protocols
Another area of weakness in most draft clinical protocols is the method of evaluation. Specifically:
- Who is doing evaluations?
- Which endpoints are important?
- When are your endpoints?
- What are your acceptance criteria?
The last area to consider in a pre-IDE meeting is your statistical plan. You need a statistical plan, but the statistical analysis seldom appears to be the reason for the rejection of clinical data. The reason is that changes can be made to your statistical analysis of data after the study is completed, but you can’t change the data once the study is over. The FDA is now accepting adaptive designs that allow the company to analyze data during the study to recalculate the ultimate sample size needed based upon actual data rather than initial assumptions.
Other De Novo Classification Request Resources
On Thursday, October 17, we presented a live webinar showing medical device companies on how to avoid a stunning disaster. Click here to access the webinar recording. We recorded another webinar about the preparation De Novo Classification Requests that you can download from our website. I wrote a blog about De Novo classification requests. You can also learn a lot about how to Design your own De Novo clinical study by reviewing the Decision Summaries published by the FDA for each De Novo in the list of De Novo classification requests. Finally, the FDA pre-sub guidance 2019 is an invaluable resource for preparing any pre IDE meeting request.