TABLE 1.
Summary of examples featuring artificial intelligence and clinical pharmacology
| Description | Clinical pharmacology feature | Reference |
|---|---|---|
| PK/PD modeling | ||
| Latent hybridization model integrating expert PK/PD models with hospital observation data through neural ODEs | Informing clinical decisions with expert pharmacological models; potential for improving the PK/PD model based on clinical observation variables | Qian Z, Zame WR, Fleuren LM, Elbers P, van der Schaar M. Integrating expert ODEs into neural ODEs: pharmacology and disease progression. 2021. doi:10.48550/ARXIV.2106.02875 |
| An automated tool to distil closed‐form ODEs from observed trajectories | Discovering an interpretable set of differential equations that corresponds to the PK/PD model of a drug | Qian Z, Kacprzyk K, van der Schaar M. D‐CODE: discovering closed‐form ODEs from observed trajectories. Presented at: International Conference on Learning Representations, 2022. https://openreview.net/forum?id=wENMvIsxNN [accessed 04 Oct 2022] |
| Nonpharmacometric models to predict longitudinal changes in tumor size | Predict changes in tumor trajectory and optimize the treatment options | Talianu A, Johnson M. Long short‐term memory recurrent neural networks to predict longitudinal changes in tumor size. Presented at: Population Approach Group Europe 29. 2021. Abstract 9815. https://www.page‐meeting.org/?abstract=9815 [accessed 2–3 Sep 2021] |
| Algorithm exploring different dosing regimens for cancer treatment | Potential for personalized dosing regimen balancing safety and efficacy | Sotto Mayor T, Irurzun Arana I, Johnson M. Developing a reinforcement learning algorithm to determine an optimal dosing regimen for cancer treatment. Presented at: Population Approach Group Europe 30. 2022. Abstract 10151. https://www.page‐meeting.org/?abstract=10151 [accessed 28 Jun 2022] |
| Apply machine‐learning algorithms to develop population PK models | High potential for automated PK model selection | Sale M, Ismail M, Wang F, et al. Comparison of robustness and efficiency of four machine learning algorithms for identification of optimal population pharmacokinetic models. Presented at: Population Approach Group Europe 30. 2022. Abstract 10053. https://www.page‐meeting.org/?abstract=10053 [accessed 28 Jun 2022] |
| Clinical trials | ||
| A Bayesian framework for finding the maximum tolerated dose for drug combinations in the presence of safety constraints | Better clinical designs for testing most optimal dose combinations while having a constrained number of patients in a safe, informative, and efficient way | Lee H‐S, Shen C, Zame WR, Lee J‐W, van der Schaar M. SDF‐Bayes: cautious optimism in safe dose‐finding clinical trials with drug combinations and heterogeneous patient groups. Presented at: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021; April 13–15, 2021; San Diego, CA. |
| Safe efficacy exploration dose allocation: a model for maximizing the cumulative efficacies while satisfying the toxicity constraints with high probability | State‐of‐the‐art clinical designs that find the optimal dose at phase I with a higher success rate and fewer patients while preserving the validity of the study | Shen C, Wang Z, Villar S, Van Der Schaar M. Learning for dose allocation in adaptive clinical trials with safety constraints. In: Daumé H III, Singh A, eds. Proceedings of the 37th International Conference on Machine Learning. Vol. 119. Omnipress. 2020:8730–8740. |
| Contextual constrained clinical trial algorithm for dose finding under a multitude of budget and safety constraints | Learning both about toxicity and efficacy at phase I while reducing costs; balancing the trade‐off between learning and treatment | Lee H‐S, Shen C, Jordon J, van der Schaar M. Contextual constrained learning for dose‐finding clinical trials. 2020. doi:10.48550/ARXIV.2001.02463 |
| Other applications | ||
| A state space model for disease progression that can build on the entire patient history as opposed to the current state only | Disease progression could be used for finding better drug administration regimens depending on the stage of disease | Alaa AM, van der Schaar M. Attentive state‐space modeling of disease progression. In: Wallach H, et al., eds. Advances in Neural Information Processing Systems (NEROIPS). Vol. 32. Curran Associates, Inc.; 2019:1–11 |
| A machine‐learning method for partitioning patients into subgroups with uncertainty quantification | Ability to identify patient subpopulations that would benefit from a drug faster and make trials more successful and efficient | Lee H‐S, Zhang Y, Zame W, Shen C, Lee J‐W, van der Schaar M. Robust recursive partitioning for heterogeneous treatment effects with uncertainty quantification. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. Advances in Neural Information Processing Systems (NEROIPS). Vol. 33. Curran Associates, Inc.; 2020:2282–2292 |
Abbreviations: ODE, ordinary differential equation; PD, pharmacodynamic; PK, pharmacokinetic.