Discovering Intrinsic Multi-Compartment Pharmacometric Models Using Physics-Informed Neural Networks

Основні поняття

A novel data-driven framework called PKINNs that efficiently discovers and models intrinsic multi-compartment pharmacometric structures, reliably forecasting their derivatives.

Анотація

The paper introduces a novel pharmacokinetic-informed neural network model called PKINNs that can efficiently discover and model intrinsic multi-compartment pharmacometric structures from data.
Key highlights:

PKINNs combines physics-informed neural networks (PINNs) and symbolic regression (SR) techniques to enable interpretable and explainable model discovery.
The framework accurately and robustly predicts the intrinsic derivatives of the underlying pharmacokinetic (PK) model, even in the presence of noise in the data.
PKINNs performs well in extrapolation prediction scenarios, demonstrating its potential for enhancing model-informed drug discovery.
The discovered models using SR techniques like PySR and SINDy provide insights into the functional forms governing the PK system.
The data-driven approach explores the function space, offering parsimonious models for practitioners to choose from, serving as a valuable starting point for further model refinement.

Статистика

The drug quantities in the depot compartment, central compartment, and peripheral compartment are denoted as X0, X1, and X2 respectively.
Initially, X0 = 1 and both X1 and X2 are zero in all simulations.
Simulations were run to t = 10 to generate the PK datasets.
Gaussian noise of different strengths (N(0, 0.005), N(0, 0.01), N(0, 0.02)) was added to the simulated data to create low, medium, and high noise datasets.

Цитати

"PKINNs efficiently discovers and models intrinsic multi-compartment-based pharmacometric structures, reliably forecasting their derivatives."
"The resulting models are both interpretable and explainable through Symbolic Regression methods."
"This framework holds the potential to significantly enhance model-informed drug discovery."

Ключові висновки, отримані з

Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks

by Imran Nasim,... о arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00166.pdf

Глибші Запити

How can PKINNs be extended to handle more complex pharmacokinetic-pharmacodynamic (PKPD) and quantitative systems pharmacology (QSP) models

To extend PKINNs to handle more complex pharmacokinetic-pharmacodynamic (PKPD) and quantitative systems pharmacology (QSP) models, several key enhancements can be implemented. Firstly, incorporating additional compartments and parameters into the neural network architecture can allow for the modeling of more intricate PKPD systems. This expansion would involve adjusting the network structure to accommodate the increased complexity of the models, potentially requiring deeper or wider networks to capture the dynamics accurately.
Furthermore, integrating more sophisticated differential equations representing diverse drug interactions, metabolism pathways, and physiological responses would enhance the model's capability to simulate a broader range of pharmacological scenarios. By incorporating a wider array of ODEs into the training process, PKINNs can learn to predict complex drug behaviors and interactions within the body more effectively.
Additionally, leveraging domain-specific knowledge and domain adaptation techniques can help tailor the PKINNs framework to specific pharmacological contexts. By incorporating domain-specific constraints, such as physiological constraints or known pharmacological interactions, the model can be fine-tuned to better represent real-world scenarios. This adaptation process can involve incorporating expert knowledge into the model training process or utilizing transfer learning techniques to adapt pre-trained models to new PKPD or QSP contexts.

What are the potential limitations of the current PKINNs framework, and how can it be further improved to handle real-world pharmacometric data with more complex dynamics

While PKINNs shows promise in discovering intrinsic multi-compartment pharmacometric models, there are potential limitations that need to be addressed for handling real-world pharmacometric data with more complex dynamics. One limitation is the scalability of the current framework to larger and more diverse datasets. As the complexity of pharmacological systems increases, the model may struggle to generalize effectively to unseen data or capture the full range of dynamics present in real-world scenarios.
To improve the framework, techniques such as ensemble learning or model ensembling can be employed to enhance the robustness and generalizability of PKINNs. By combining multiple models trained on different subsets of data or with different hyperparameters, the overall performance and reliability of the framework can be improved, especially when dealing with complex and noisy pharmacometric data.
Moreover, incorporating uncertainty quantification methods, such as Bayesian neural networks or Monte Carlo dropout, can provide insights into the model's confidence levels and help account for uncertainty in the predictions. By estimating and incorporating uncertainty measures into the model outputs, PKINNs can offer more reliable and interpretable predictions, especially in scenarios with complex dynamics or noisy data.

How can the insights gained from the discovered models using symbolic regression be leveraged to inform the design of new drug candidates or optimize existing therapies

The insights gained from the discovered models using symbolic regression can be leveraged to inform the design of new drug candidates or optimize existing therapies in several ways. Firstly, the extracted functional forms from symbolic regression can provide valuable insights into the underlying mechanisms of drug interactions, metabolism, and physiological responses. By understanding the mathematical relationships between different variables in the pharmacometric models, researchers can identify key factors influencing drug efficacy, toxicity, and pharmacokinetic properties.
These insights can guide the design of new drug candidates by highlighting critical parameters or pathways that need to be targeted for optimal therapeutic outcomes. By incorporating the discovered models into the drug development process, researchers can prioritize compounds that align with the identified mechanisms and have the highest likelihood of success in preclinical and clinical trials.
Furthermore, the symbolic regression results can be used to optimize existing therapies by identifying potential areas for dose adjustments, combination therapies, or personalized treatment strategies. By leveraging the interpretable models generated through symbolic regression, healthcare providers can tailor treatments to individual patients based on their unique pharmacokinetic profiles, genetic factors, and disease characteristics, leading to more effective and personalized healthcare interventions.