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Phoenix WinNonlin Software Review—Part 1

WinNonlin by Certara has been a fixture in pharmacokinetic analysis software for over 20 years. While it has been known as a tool for non-compartmental analysis and model-based analysis of single subject data, the new Phoenix WinNonlin creates an entirely new platform for pharmacokinetic and pharmacodynamic analysis. Similar to my other reviews. I will be evaluating features and usability of the Phoenix WinNonlin software from a user’s perspective.

Part 1 will review the Phoenix platform and integration with other tools. Part 2 will review the non-compartmental and individual pharmacokinetic model fitting tools. Finally Part 3 will review the new nonlinear mixed effects module (NLME).

The installation of Phoenix was simple and easy. A standard Windows installation program was used with the default options on computers with Windows Vista, Windows 7, and a Mac running Windows Vista through a Virtual Machine. WinNonlin is not natively supported on operating systems other than Windows (e.g. Linux, Mac OS X, and UNIX).

The new Phoenix platform is best described with a picture.

Phoenix Workflow
Phoenix Workflow

The newly designed interface has a centerpiece called the “workflow”. The left side of the image shows the object browser. This is where you have a list of all the objects in your file, and it is organized much like a set of nested folders. Users who are familiar with the Windows File Explorer or the SPlus statistical package will be immediately comfortable with the object browser. The right side of the image shows the workflow space. Within this white space you can place objects and then cause them to interact with one another. The orange box titled “External Sources” is a collection of data sets from external sources. Those data sets act as the input for 5 different non-compartmental analysis (NCA) objects that each have their own properties and output. The NCA in the lower left of the image is then the source of a summary statistics worksheet titled “Descriptive Stats”.

The types of objects available to use in Phoenix include: worksheets, plots, NCA, nonlinear modeling, nonlinear mixed effects modeling, in vitro-in vivo correlation tools, tables, NONMEM, SAS shell, SigmaPlot shell, SPlus script, R scripts, and other workflow objects. Each object in the workflow (or box on the white space) has its own inputs, results, and outputs. Each of these outputs can then be directed to become the input of another object (e.g. a set of final PK parameters from an NCA object can be sent to a table object). These workflow connections are illustrated by arrows and are saved in the single Phoenix project file. This allows a single workflow to be used as a template. For example, you could set up a template workflow for a drug-drug interaction study that includes the following:

  • NCA analysis for Drug 1
  • NCA analysis for Drug 2
  • Summary statistics worksheet for Drug 1
  • Summary statistics worksheet for Drug 2
  • Statistical comparison of drug-drug interaction
  • Tables for summary statistics of Drug 1, Drug 2, and drug-drug interaction
  • Plots with individual and mean concentration-time data

This workflow could be saved as a Phoenix template file and then when a new study is conducted, the concentration-time data can be added to the workflow, linked to the NCA analyses and a single button click will perform all analyses, calculate summary statistics, and produce the desired tables and figures. This ability to automate can revolutionize traditional pharmacokinetic analysis to simplify the work, standardize output, and allow for faster data analysis.

A new feature with Phoenix is is the ability to incorporate different analysis types on a single workflow. A single workflow can contain NCA, individual nonlinear models, and nonlinear mixed effects or population models. No need to switch back and forth between multiple model files for different analyses on a single set of data! You can conduct your NCA for initial estimates, along with 1- and 2-compartment model fits on the same workflow.

In addition to the workflow feature, Phoenix integrates well with other software packages such as NONMEM, SAS, R, SPlus, and ODBC-compliant databases like Watson LIMS. This integration is achieved through the Phoenix Connect module that allows seamless transfer of Phoenix output to selected software programs, and then the ability to receive output from those same programs. An example of this is the export of AUC values to SAS for statistical analysis followed by the import of the bioequivalence summary statistics into Phoenix for inclusion in a table object. This allows the Phoenix workflow to control data analysis procedures from beginning to end, while allowing a user to interact with their preferred software solution.

Overall, the new workflow layout and design is a significant advance in pharmacokinetic software. And although the new Phoenix user interface is a departure from the previous one, the flexibility and power of the new workflow will create a great opportunity for users to streamline their work processes and simplify data analysis.

More to come in Part 2 (NCA and individual model fitting) and Part 3 (NLME) of my review of Phoenix WinNonlin.

An evaluation copy of Phoenix was provided by Certara with the WinNonlin, Connect & NLME modules. You can learn more about Phoenix WinNonlin by calling your local Certara representative, or by requesting information from Certara.

The methods used to characterize the pharmacokinetics (PK) and pharmacodynamics (PD) of a compound can be inherently complex and sophisticated. PK/PD analysis is a science that requires a mathematical and statistical background, combined with an understanding of biology, pharmacology, and physiology. PK/PD analysis guides critical decisions in drug development, such as optimizing the dose, frequency and duration of exposure, so getting these decisions right is paramount. Selecting the tools for making such decisions is equally important. Fortunately, PK/PD analysis software has evolved greatly in recent years, allowing users to focus on analysis, as opposed to algorithms and programming languages. Read our white paper to learn about the key considerations when selecting software for PK/PD analysis.


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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