PEtab extension for NLME

[paulflang]

PEtab extension for NLME

[eraimundez]

Hi everyone,

I think this would be a great idea! I was also thinking that this could be a very nice feature to have.
And I agree with @paulflang that what is mainly missing is the possibility of defining inter-patient variability (random effects) parameters.

Regarding experience with NLME: If this is upvoted, I willing to collect ideas from my expert colleagues (and also myself 😄, although I am not an expert yet) on how to best design the extension.

However, what I see here as a potential obstacle is actually how "useful" this extension would be, i.e. whether the tool developers (e.g. NONMEM, Monolix, nlmixr, Pumas, etc) would be willing to support the NLME-PEtab format as a direct input. Maybe this is something that should also be noted when deciding whether to move forward with this idea.

What are your thoughts?

[rikardn]

I was one of the developers of tooling around the PharmML and currently a developer of the pharmacometrics Python package https://github.com/pharmpy/pharmpy and maintainer of PsN (mentioned earlier). Pharmpy has a json format for nlme-models and also for dataset metadata. I would be happy to learn more about your work and discuss options so I signed myself up on the doodle for the 9th.

[eraimundez]

Hi @paulflang
I am bringing up here the discussion started on this spreadsheet, regarding the potential idea of having a covariates table. I think it is easier to keep on track the ideas here 😄

Indeed, the covariate mathematical expression will be part of the mathematical model.
And, indeed, if there is any condition-specific covariate the place to code this would be the condition table.

When I was suggesting a covariates table, I had in mind that, (at least) in the pharmacometrics field, it is quite common to

1. need to test several covariates to find those that are significant,
2. have different possible mathematical ways to define the relationship,
3. may have even, e.g., sex specific trafos
4. may adapt the e.g. basal/average value that is used in the trafo e.g. 70kg of body weigth

So, I can see this could become a "painful" situation if every time one would need to modify directly the SBML or condition table.

Of course, this is already trying to go one step further than having a way to store the model (which then would be fine as it is already, only including the IIV extension as mentioned in this thread). To store the model, just the final version with the finally selected model structure, noise model and covariates would need to be stored. And the covariates could be just part of the SBML and condition table as you pointed out.

But I think, if it is decided that the effort will be invested, going a step further and having a flexible framework already since the beginning that could be parsed by existing tools would be optimal.

Quickly, I could imagine something like the observables table:

covariateId covariateFormula conditionId parameterId estimate  # as an example, to be further refined

which could be parsed by already existing engines such as in Pumas or PsN (see Stepwise Covariate Model-building).

[FFroehlich]

The PharmML probably covers most relevant usecases, so it's probably a good idea to have this as a starting point for the things that need to be covered. I agree that having something more human readable and, hence, integration with PEtab makes sense.

I like the idea of extending parameter + condition tables with noise models + covariates. For the random effect covariance structure, there are some better strategies for parametrisation (see https://doi.org/10.1016/j.celrep.2021.109507 and maybe Pauls Thesis?). Regarding encoding, maybe consider how this was done in PharmML and try to come up with a human readable variant?

[dweindl]

I am not (yet) working on NLME and won't be able to contribute much, but I'd be happy to see it covered by PEtab.

regarding PharmML: It seems https://github.com/pharmml/pharmml-spec contains specifications for up to version 0.4. This doesn't seem to be latest version, since I found references to a PharmML version 0.9 elsewhere (without specification). Maybe one of the original PharmML authors can provide more recent information...

[dilpath]

Just a heads up, we will have a 1 hr PEtab breakout session at COMBINE 2023 in a few weeks. It's great to see this discussion here, I'm also interested in this particular extension, and I'm keen to discuss this in the breakout session. I'll try to create a hybrid event to support a virtual audience via Zoom, if possible. I'll share the Zoom details via the community mailing list, when more details about the event are known.

https://groups.google.com/u/1/g/petab-discuss

[matthiaskoenig]

Yes, we often have multiple datasets and also in the context of PBPK models we often use the data from many different studies. The idea is to have a general model which can be applied to multiple datasets, instead of having a model for a single dataset (but the latter also happens quit often).
As a consequence it makes sense to split things up into model equations (SBML), optimization (PETab), simulations (dosing protocols), and the information about the priors/non-linear mixed effects (these could also vary depending on the questions/datasets). Perfect separation is not possible but there are different building blocks instead of everything combined for a single dataset.
You want to optimize a certain subset of data with a given model structure and priors/NLME for a subset of simulation experiments/conditions corresponding to the protocol how the data was generated. This gives you much more flexibility and reusability and tools can focus on certain subsets of the problem.

[paulflang]

If upvoted, support from you and your colleagues would indeed be great, @eraimundez .

Regarding usefulness, I would distinguish between intrinsic and realized usefulness. I know that pharma is a rather static field, but if we believe a PEtab extension has intrinsic usefulness, it is quite likely that at least one existing PEtab compliant tool (pyPesto, PEtab.jl, Pumas(QSP), etc.) will take it up. And that might start a process, where the other tools don't want to be left behind and the intrinsic usefulness gets realized.

[eraimundez]

Hi @paulflang @FFroehlich
A quick follow up: I was just trying to have a look at the suggested PharmML format, however I am afraid this is no longer maintained (at all) ... Their website(s) are disabled (http://repository.ddmore.eu/ and http://ddmore.eu/pharmml ), and even redirecting to some spam site (http://www.pharmml.org/).
So, I am not really sure 😅

[paulflang]

Interesting. I was not aware. But it should still be possible to reuse some of their ideas described in their paper, their PAGE poster or GH repo (I haven't had the opportunity to deeply look into any of that myself yet).

[eraimundez]

Hi @rikardn
Thank you so much for reaching out! This is great to have you also on board for this initial discussion meeting.

Anyone else: Here is the link to the doodle https://doodle.com/meeting/participate/id/bWqxJnge
It will be active until this Friday 22/09/2023.

[matthiaskoenig]

Is there a date/time/link for the meeting?

[dweindl]

Is there a date/time/link for the meeting?

You got mail.

[rikardn]

Thanks all for the very interesting discussion! After getting to know the PEtab format a bit better I just wanted to share my thoughts.

In pharmacometrics much of what is in the PEtab would be considered being part of the model. The experimental conditions, the expression for the observations and noise (called the error model in pharmacometrics) and the parameters. Also the dataset, although not regarded as a part of the model, is very tightly linked to the model. Most often a model is developed for one dataset (experiment or trial) and will never see any other datasets (apart from slight modifications of the dataset, for example exclusion of outliers). This reduces the need for a file format such as PEtab for models in pharmacometrics. To me, correct me if I am wrong, it seems as if an SBML model doesn't have the same tight coupling to one single dataset and is designed to allow multiple experiments for the same model.

[eraimundez]

Just to add on @matthiaskoenig reply: Even for more "simple" popPK models (simple w.r.t to PBPK models 😉) that could rely on a single dataset this would still be benefitial.

For example:

• The development of the initial structural model (without covariates) can be substantially speed up as you could just reuse the model file from different compounds. As it will only contain the model equations.
• Afterwards, adding covariates (which are more specific to a given dataset) could be easily done by adding a covariates table that will be automatically linked to your model without needing to touch the structural model file.

It is a different point of view: what in pharmacometrics is considered to be a model (and having all in a single file, at least in NONMEM), in PEtab is considered as a modular "problem" where the yaml file puts all pieces together and allowing the user to reuse any of the individual parts.