Virtual Patients

A working group addressing barriers: Combining simulations and experiments to inform clinical trials.

How it began. First, we built a collaboration of MDIC members including FDA, nonprofits, software and device manufactures who volunteered their engineers, statisticians, regulatory professionals, and medical doctors. Next, leveraging FDA guidance for the use of Bayesian statistics in medical device clinical trials, we created a framework to augment clinical trial design with virtual patients.

Now, the working group is conducting a mock submission, demonstrating how to implement this framework in an example trial design and IDE submission.

Here are some specifics on the virtual patient framework and its possible benefits

The framework creates potential for smaller, shorter, and more cost-efficient clinical trials. Incorporation of prior knowledge in clinical trial design, most commonly through use of Bayesian statistics, has benefits of decreased sample size and trial length while maintaining the same study endpoints. A common source of prior information is historical data from similar trials, such as outside-the-US device use, or field performance of similar therapies. When validated models that can predict safety and effectiveness outcomes are available, these models can be a superior source of prior information. Use of a model as this prior knowledge can increase the power of a clinical trial and reduce its size, duration, and cost.

There are two important features of this framework to point out. First, the computational model must predict safety and/or effectiveness outcomes as well as uncertainty in the prediction. We call this model a virtual patient, and the model is most likely a model of the device and anatomy impacted by the device. Second, the number of virtual patients used to enhance the trial is based on the agreement between the model and real patient results. Better agreement, more virtual patients.

Additional things to consider.

  • Predictive models require patient data to model anatomy, physiology, and disease progression. Access to data is a barrier.
  • This framework can enrich clinical trials for small populations like pediatrics, how do we encourage and support the creation of validated models for these under represented populations?
  • How do we engage patients to understand their support for this type of clinical trial?

Our mock submission progress.

Q-sub introduction memo, MDIC Q-sub informational meeting memo
Q-sub request letter, MDIC Q-sub informational meeting cover letter
VP- Qsub1 slides 001_MDIC mock submission informal request slides 27 may 2015
HHS Acknowledgment Letter Q-sub Q150804
VP- Qsub2 request letter 001_MDIC engineering model pre-submission meeting Cover Letter 4 Nov 2015
VP- Qsub2 summary of engineering model overview 002_2014VP Engineering Model – 5 Nov 2015
HHS Acknowledgment Letter Q-sub Q150804-S001
VP_Qsub3 MDIC clinical study pre-submission meeting Cover Letter 6 June 2016
VP_Qsub3 MDIC statistical methods mock submission pre-sub 6 June 2016