Do you want others to use your models?

Well, you SAY you only use NONMEM (although I don’t believe that - see below) but ultimately I’m sure that you’d like other people to use your model and/or the inferences based on it.

The use of modelling and simulation has expanded into quantitative pharmacology, pharmacology (Pop PK and PKPD), disease modelling, model-based meta-analysis (MBMA), cost-effectiveness and many more. Many different quantitative disciplines are now involved in building models, evaluating models, using models and modelling outputs to interpret data, plan studies or make decisions. For a given model to be useful (and used!) across all of these groups we need to find a way that we can express the model such that anybody else that wants to use it can do so for their own purposes, whatever those are.

How do you share models?

I share via (NMTRAN / other) code

A recent survey by the International Society of Pharmacometrics found that ~75% of respondents primarily use NONMEM as their main tool for analysis. This would support the strategy for sharing NMTRAN models within the pharmacometrics community, but what about outside of it?

Within the statistics community there is a growing use of R as a tool for analytical work, sometimes calling other tools e.g. C++, Java, etc. but still, within industry, SAS is primarily used as the clinical trial reporting tool of choice. So if you’re interested in getting statisticians to use your model, then what language should you choose for sharing? Do you expect them to learn and understand the NMTRAN code? Do you both try to translate to R? What about SAS? Will you learn R or SAS sufficient to be able to share your model?

I share via publications

Writing down the maths of a model is a great way to share the model between quantitative disciplines. It’s a universal language and brings with it rigour and a lack of ambiguity. BUT while two modellers can agree on the maths of a problem, the implementation may vary between two software tools such that it may be easy to implement in one tool, but very difficult (impossible?) in another.

The problem of reproducibility

Reproducibility means many things to many different people. For now, let’s focus on being able to re-run an estimation step for a given set of data and the ability to confirm that the parameter estimates match those presented by the original analyst. Even in this minimalist definition of reproducibility we can face problems reproducing the original analyst’s work if we don’t get exactly the code they ran, match the same software and environment settings, etc. The problem gets worse if we’re trying to convert from one software tool to another along the way or if we’re trying to implement the mathematical and statistical models presented in a publication if we don’t have access to the original code.


NONMEM and associated tools e.g. PsN have some limitations in available functionality, for example optimal trial design. And while NONMEM can be used for prediction and simulation of new designs, regimens and scenarios there are a growing number of freely available, open-source tools that can also do this and arguably more easily given that the model is encoded in the appropriate input format for that tool.

Now, either we have to write a load of NMTRAN to translators (some exist e.g. Pirana) or we have to use or develop a master language that can easily translate to NMTRAN, but also to other tools e.g. Certara / Pharsight’s PML or DDMoRe’s MDL.

Open-source tools are growing in use

There is a rising number of free, open-source software for nonlinear mixed effects modelling and simulation (e.g. saemix, nlmixr, mlxR / simulx, mrgsolve, RxODE, PKPDsim, PFIM, PopED ) and while use of these tools may be limited today, they provide a means for performing the majority of modelling and simulation tasks with no cost to the analyst. More integration of these tools into pharmacometrics toolset and workflow would benefit many, especially those who cannot get access to NONMEM licenses, yet still may wish to use disease and therapeutic drug models in their work or for predicting outcomes for patients.

About 10 years ago, it would have been hard to imagine the case that S-Plus would be thoroughly supplanted by R, to the point where it ceased to be a supported product by the company (Tibco) that bought it from Insightful. And yet, here we are. R is rising in popularity as an analytical engine (often coupled with other technologies). So to dismiss open-source solutions as “irrelevant” may be regarded as a short-sighted view.

(Open-source) Standards can shape the discipline

The advent and growth of [SBML] ( has had a profound effect on the systems biology modelling world - impacting model sharing and tool development. With SBML as a basis for import and export of models to and from software tools, the community now has a means to quickly and easily share models, regardless of the preferred tools for individuals, laboratories or companies. This sparked development of many open-source tools, but also forced integration of SBML into existing and novel commercial tools.

The DDMoRe model exchange standards could be similarly revolutionary for model sharing and interoperability within the pharmacometrics discipline but also beyond into other quantitative disciplines e.g. statistics.

Unified workflow agnostic to software tool choice is a good aim

At present, any time the modeller (or anyone using a given model) needs to transcode the model from one software tool to another, we should ensure that the transcoded model is equivalent to the original before making any new inferences from it. This adds an overhead of time and resource into the modelling and simulation process and brings with it the possibility of errors introduced while translating from one implementation to another.

The ideal workflow would be agnostic to the tool used for a given task. It would be ideal to be able to substitute one tool for another in the workflow and carry on with subsequent down-stream activities, with no intervention from the user to activities outside of the workflow. This would greatly improve reproducibility and portability of workflow from one analyst to another and also across quantitative disciplines.

MDL, along with the DDMoRe model exchange standards, has achieved this aim using the Interoperability Framework. In this example we show end-to-end workflow using a variety of tools to perform exploratory analysis, estimation, model diagnostics & qualification, prediction, simulation and optimal design all without having to recode the model in any way.

The future with MDL and the DDMoRe model exchange standards

There has never been a better time to get engaged with the DDMoRe model exchange standards through the DDMoRe Language Community. We have shown that the concepts of interoperability are achievable, we have >100 models on the DDMoRe Model Repository and we have made these languages open-source so that the community can capitalise on work already done and contribute to the future state of these standards.


Thanks to Vijay Ivaturi and kylebmetrum for comments and additions to this blog post.