# Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modeling

arXiv: https://arxiv.org/abs/2010.11769 · HTML: https://mlsanity.com/paper/2010.11769/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data](https://mlsanity.com/paper/2212.04630/) (2022)
- [Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODE](https://mlsanity.com/paper/2308.01362/) (2023)
- [Amortized In-Context Mixed Effect Transformer Models: A Zero-Shot Approach for Pharmacokinetics](https://mlsanity.com/paper/2508.15659/) (2025)
- [From Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE](https://mlsanity.com/paper/2403.03274/) (2024)
- [Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics](https://mlsanity.com/paper/2604.17670/) (2026)

## Abstract (arXiv)

The longitudinal analysis of patient response time course following doses of therapeutics is currently performed using Pharmacokinetic/Pharmacodynamic (PK/PD) methodologies, which requires significant human experience and expertise in the modeling of dynamical systems. By utilizing recent advancements in deep learning, we show that the governing differential equations can be learnt directly from longitudinal patient data. In particular, we propose a novel neural-PK/PD framework that combines key pharmacological principles with neural ordinary differential equations. We applied it to an analysis of drug concentration and platelet response from a clinical dataset consisting of over 600 patients. We show that the neural-PK/PD model improves upon a state-of-the-art model with respect to metrics for temporal prediction. Furthermore, by incorporating key PK/PD concepts into its architecture, the model can generalize and enable the simulations of patient responses to untested dosing regimens. These results demonstrate the potential of neural-PK/PD for automated predictive analytics of patient response time course.