A Structure-Constrained Neural Simulator for Population PK under Regimen Shift (ConstrainNODE-PK)

Ali Issa, Tarjinder Sahota, Núria Buil-Bruna, Joseph F Standing, Frank Kloprogge
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:661-689, 2026.

Abstract

Prospective dose selection requires simulating concentration distributions under regimens for which no concentrations have yet been observed. In this setting, success is not defined by low individual prediction error alone but by calibrated population simulation under regimen shift: a model can fit held-out individual observations yet still miscalibrate exposure distributions and underestimate high-exposure risk when the dosing schedule changes. We present ConstrainNODE-PK, a structure-constrained neural population pharmacokinetic simulator for intravenous bolus dosing under linear kinetics. The model expresses concentration as an exact superposition of dose events applied to a learned unit-dose impulse response, so transfer to a new regimen changes only the event sequence. The response is parameterized as a positive exponential mixture with a monotone residual neural ordinary differential equation, enforcing non-negativity, stability and monotone post-bolus decay; together with exact superposition, this gives exact dose proportionality while remaining flexible enough to capture one- to three-compartment-like behavior without per-compound compartment selection. We evaluate on six simulated settings and two public cohorts, using held-out subjects throughout and assessing both individualized prediction and population-level simulation calibration. The primary stress test is strict transfer from once-daily to three-times-daily dosing, with no target-regimen concentrations available at inference time. In this setting, ConstrainNODE-PK maintains near-nominal 95% prediction-interval coverage (95.6%), whereas a matched unconstrained dose-aware neural ordinary differential equation falls to 45.8%. Across settings, the model remains competitive with matched nonlinear mixed-effects references on individualized prediction and preserves near-nominal prior-predictive population calibration. Strict regimen-transfer evidence comes from controlled simulated intravenous-bolus settings; the African Research on Kidney Disease iohexol and Tobramycin cohorts provide complementary held-out within-cohort population-calibration checks rather than strict real-world transfer validation. More broadly, when a healthcare model is used to simulate a future intervention, the relevant validation target is calibration under the induced intervention shift, not only in-regimen fit.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-issa26a, title = {A Structure-Constrained Neural Simulator for Population PK under Regimen Shift (ConstrainNODE-PK)}, author = {Issa, Ali and Sahota, Tarjinder and Buil-Bruna, N\'{u}ria and Standing, Joseph F and Kloprogge, Frank}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {661--689}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/issa26a/issa26a.pdf}, url = {https://proceedings.mlr.press/v340/issa26a.html}, abstract = {Prospective dose selection requires simulating concentration distributions under regimens for which no concentrations have yet been observed. In this setting, success is not defined by low individual prediction error alone but by calibrated population simulation under regimen shift: a model can fit held-out individual observations yet still miscalibrate exposure distributions and underestimate high-exposure risk when the dosing schedule changes. We present ConstrainNODE-PK, a structure-constrained neural population pharmacokinetic simulator for intravenous bolus dosing under linear kinetics. The model expresses concentration as an exact superposition of dose events applied to a learned unit-dose impulse response, so transfer to a new regimen changes only the event sequence. The response is parameterized as a positive exponential mixture with a monotone residual neural ordinary differential equation, enforcing non-negativity, stability and monotone post-bolus decay; together with exact superposition, this gives exact dose proportionality while remaining flexible enough to capture one- to three-compartment-like behavior without per-compound compartment selection. We evaluate on six simulated settings and two public cohorts, using held-out subjects throughout and assessing both individualized prediction and population-level simulation calibration. The primary stress test is strict transfer from once-daily to three-times-daily dosing, with no target-regimen concentrations available at inference time. In this setting, ConstrainNODE-PK maintains near-nominal 95% prediction-interval coverage (95.6%), whereas a matched unconstrained dose-aware neural ordinary differential equation falls to 45.8%. Across settings, the model remains competitive with matched nonlinear mixed-effects references on individualized prediction and preserves near-nominal prior-predictive population calibration. Strict regimen-transfer evidence comes from controlled simulated intravenous-bolus settings; the African Research on Kidney Disease iohexol and Tobramycin cohorts provide complementary held-out within-cohort population-calibration checks rather than strict real-world transfer validation. More broadly, when a healthcare model is used to simulate a future intervention, the relevant validation target is calibration under the induced intervention shift, not only in-regimen fit.} }
Endnote
%0 Conference Paper %T A Structure-Constrained Neural Simulator for Population PK under Regimen Shift (ConstrainNODE-PK) %A Ali Issa %A Tarjinder Sahota %A Núria Buil-Bruna %A Joseph F Standing %A Frank Kloprogge %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-issa26a %I PMLR %P 661--689 %U https://proceedings.mlr.press/v340/issa26a.html %V 340 %X Prospective dose selection requires simulating concentration distributions under regimens for which no concentrations have yet been observed. In this setting, success is not defined by low individual prediction error alone but by calibrated population simulation under regimen shift: a model can fit held-out individual observations yet still miscalibrate exposure distributions and underestimate high-exposure risk when the dosing schedule changes. We present ConstrainNODE-PK, a structure-constrained neural population pharmacokinetic simulator for intravenous bolus dosing under linear kinetics. The model expresses concentration as an exact superposition of dose events applied to a learned unit-dose impulse response, so transfer to a new regimen changes only the event sequence. The response is parameterized as a positive exponential mixture with a monotone residual neural ordinary differential equation, enforcing non-negativity, stability and monotone post-bolus decay; together with exact superposition, this gives exact dose proportionality while remaining flexible enough to capture one- to three-compartment-like behavior without per-compound compartment selection. We evaluate on six simulated settings and two public cohorts, using held-out subjects throughout and assessing both individualized prediction and population-level simulation calibration. The primary stress test is strict transfer from once-daily to three-times-daily dosing, with no target-regimen concentrations available at inference time. In this setting, ConstrainNODE-PK maintains near-nominal 95% prediction-interval coverage (95.6%), whereas a matched unconstrained dose-aware neural ordinary differential equation falls to 45.8%. Across settings, the model remains competitive with matched nonlinear mixed-effects references on individualized prediction and preserves near-nominal prior-predictive population calibration. Strict regimen-transfer evidence comes from controlled simulated intravenous-bolus settings; the African Research on Kidney Disease iohexol and Tobramycin cohorts provide complementary held-out within-cohort population-calibration checks rather than strict real-world transfer validation. More broadly, when a healthcare model is used to simulate a future intervention, the relevant validation target is calibration under the induced intervention shift, not only in-regimen fit.
APA
Issa, A., Sahota, T., Buil-Bruna, N., Standing, J.F. & Kloprogge, F.. (2026). A Structure-Constrained Neural Simulator for Population PK under Regimen Shift (ConstrainNODE-PK). Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:661-689 Available from https://proceedings.mlr.press/v340/issa26a.html.

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