Closing the Gap in Exposure-Response Data: A Pharmaverse Framework

The Missing Standard

CDISC released the Population Pharmacokinetic (PopPK) Implementation Guide in 2023, giving the clinical programming community a clear structural blueprint for PK analysis datasets. But Exposure-Response (ER) modeling — which builds directly on PopPK outputs to characterize relationships between drug exposure, safety, and efficacy — has no equivalent standard.

The result is predictable: different studies, different variable names, different exposure metrics, different dataset structures. Every ER analysis team starts more or less from scratch. That makes cross-study pooling, automation, and programming more difficult than necessary, particularly with ever-quickening turnaround times in drug development.

A Framework Built on What We Already Have

ER datasets share a lot of structural DNA with PopPK datasets — numeric covariates, relative time variables, pharmacokinetic exposure metrics. That overlap is the starting point for this framework: extending CDISC ADaM principles already established for PopPK into the ER space.

Early discussions are underway with the CDISC ADaM working group about moving this framework forward as a Knowledge Article or Examples Document. The working group has expressed interest in positioning ER datasets as a subclass of ADPPK — grounding the framework within existing CDISC standards architecture and providing a clear lineage from the 2023 PopPK Implementation Guide. Nothing is formalized yet, but the direction is encouraging.

The result of the new framework is four specialized datasets, each targeting a different aspect of ER analysis:

Dataset	Purpose
`ADER`	Exposure foundation — comprehensive PK metrics, transformations, and baseline covariates
`ADEE`	Exposure-Efficacy — time-to-event efficacy outcomes linked to drug exposure
`ADES`	Exposure-Safety — adverse event occurrence, severity, and time-to-onset by exposure
`ADTRR`	Exposure-Tumor Response Rate — categorical tumor response (CR, PR, SD, PD) by exposure

Each dataset builds on standard ADaM datasets (ADSL, ADRS, ADTTE, ADAE, ADLB, ADVS) and incorporates PK parameters from ADPC/ADPP, producing analysis-ready datasets without additional data wrangling.

The framework was presented as paper DS12 at PHUSE US Connect 2026 in Austin, TX. The paper and slides are now available in the PHUSE archive.

Why the Pharmaverse Ecosystem?

The framework is implemented using {admiral}, {metacore}, {metatools}, and {xportr} — the same toolchain used across the pharmaverse for ADaM dataset development. That choice was intentional.

{admiral}’s modular derivation functions map naturally onto how ER datasets are built incrementally. Its assert_* functions catch errors at the point of derivation rather than burying them downstream. {metacore} keeps specs and code in sync. {metatools} provides utility functions for metadata management and validation. {xportr} handles CDISC compliance at the point of export.

The pharmaverse ecosystem did not just make implementation easier — it made the framework more trustworthy and maintainable. And because it is open-source, every improvement feeds back to the community.

The Examples Page

The working R code is now live on the pharmaverse examples site.

The ADER+ page covers all four datasets in a single tabbed page, with:

A shared introduction explaining the ER framework and its relationship to the PopPK Implementation Guide
Common derivations used across all four datasets
Dataset-specific derivation code for ADER, ADEE, ADES, and ADTRR
Full variable listings and metadata

The code uses {pharmaverseadam} as source data, making it immediately reproducible. Think of it as a template — a starting point you can adapt for your own studies.

We Need Your Feedback

This framework is a proposal, not a finished standard. Any formal ER ADaM standard would ultimately be owned and ratified by CDISC — the community can propose, pilot, and advocate, but the path to an official standard requires active collaboration with CDISC. The groundwork for that is community validation: pilot testing across therapeutic areas, working group discussion, and real-world use.

That means we need two kinds of input:

Clinical programmers — try the code. Does the derivation logic hold up? What edge cases are we missing? Open an issue or PR on the pharmaverse examples repository.

ER modelers and pharmacometricians — this one is especially for you. Does this dataset structure actually serve your modeling needs? Are the exposure metrics the right ones? Is the dataset grain appropriate for the analyses you run? You are the end users of these datasets, and your perspective is exactly what’s needed to make this framework scientifically sound, not just technically compliant.

The discussion is open. Let’s keep it going.

Last updated

2026-05-04 13:35:46.92611

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BibTeX citation:

@online{dickinson2026,
  author = {Dickinson, Jeff},
  title = {Closing the {Gap} in {Exposure-Response} {Data:} {A}
    {Pharmaverse} {Framework}},
  date = {2026-04-30},
  url = {https://pharmaverse.github.io/blog/posts/2026-04-17-closing-the-gap-in/closing-the-gap-in-exposure-response-data-a-pharmaverse-framework.html},
  langid = {en}
}

For attribution, please cite this work as:

Dickinson, Jeff. 2026. “Closing the Gap in Exposure-Response Data: A Pharmaverse Framework.” April 30, 2026. https://pharmaverse.github.io/blog/posts/2026-04-17-closing-the-gap-in/closing-the-gap-in-exposure-response-data-a-pharmaverse-framework.html.