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Feedback from the Phoenix Community: Our Visits with the FDA

We recently completed a week-long set of meetings with the FDA, where we met with over 300 FDA reviewers from 7 of the 11 FDA centers that use Phoenix.

Here are a few topics that took center-stage during our visits:

Q: How can we create Phoenix workflow templates that are reusable across different studies with varying numbers of parameters, such as treatments, analytes, matrices, and doses?

A: Creating workflow templates in Phoenix—containing a single object (like an XY plot) or multiple objects (eg, an entire workflow or set of objects)—is an effective way to increase your productivity. An example of a workflow template that is of particular interest for generic drug development is one that can be used for reference-scaled average bioequivalence (RSABE) methodology, which is increasingly used to demonstrate bioequivalence for Highly Variable Drugs and Drug Products (HVDs/HVDPs). For more information on workflow templates, be sure to check out our blog posts on how to create a Phoenix workflow template and how to use the Phoenix RSABE templates. If a more complex automated template is required, learn about implementing custom solutions using our Phoenix Technology Services.

Q: What is the advantage of using the QRPEM engine for Population PK/PD (Pop PK/PD) analysis that was introduced in the latest version of NLME 7.0?

A: The Quasi-random Parametric Expectation Maximization (QRPEM) algorithm is the most advanced and fastest accurate likelihood expectation maximization (EM) algorithm available, ideal for converging complex models such as those used in population pharmacokinetic/pharmacodynamic (pop PK/PD) modeling. QRPEM addresses problems typically encountered in the Pop PK/PD NLME domain, resulting in the ability to achieve N-1 behavior, and greatly improves computational efficiency for models where fixed effects cannot be driven by a simple EM update based on the estimated mean and covariance matrix of the posterior distributions for each subject. Download our white paper for a comprehensive overview of the NLME QRPEM algorithm.

Q: How easy is it to set up a grid?

A: The performance and scalability of software and hardware always constrains a PK/PD modeler’s productivity. The explosion of cloud computing resources has provided access to significant computing power to solve these complex models. However, accessing these cloud computing systems can be complex and confusing. And using these systems generally requires knowledge of command-line tools. To improve the performance of computationally intensive algorithms, parallel computing functionality was introduced in Phoenix NLME 7.0. This innovation enables modelers to easily access the power of these computing environments from the comfort of their desktop.

To learn more about setting up grid computing in NLME 7.0, watch our recent webinar that provides an overview on this topic.

About the author

Nathan Teuscher
By: Nathan Teuscher
Dr. Teuscher has been involved in clinical pharmacology and pharmacometrics work since 2002. He holds a PhD in Pharmaceutical Sciences from the University of Michigan and has held leadership roles at biotechnology companies, contract research organizations, and mid-sized pharmaceutical companies. Prior to joining Certara, Dr. Teuscher was an active consultant for companies and authored the Learn PKPD blog for many years. At Certara, Dr. Teuscher developed the software training department, led the software development of Phoenix, and now works as a pharmacometrics consultant. He specializes in developing fit-for-purpose models to support drug development efforts at all stages of clinical development. He has worked in multiple therapeutic areas including immunology, oncology, metabolic disorders, neurology, pulmonary, and more. Dr. Teuscher is passionate about helping scientists leverage data to aid in establishing the safety and efficacy of therapeutics.

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