The Neural Mixed Effects algorithm: leveraging machine learning for pharmacokinetic modelling | |
A. Janssen(1), F.W.G. Leebeek(2), M.H. Cnossen(3), and R.A.A Mathôt(1) for the OPTI-CLOT study group and SYMPHONY consortium | |
(1) Department of Hospital Pharmacy - Clinical Pharmacology, Amsterdam University Medical Center, Amsterdam, The Netherlands. (2) Department of Hematology, Erasmus MC, Erasmus University Medical Center Rotterdam, The Netherlands. (3) Department of Pediatric Hematology, Erasmus MC Sophia Children’s Hospital, Erasmus University Medical Center Rotterdam, The Netherlands. | |
Oral: Methodology - New Modelling Approaches | |
PDF poster/presentation: | ![]() |
Objectives: There is much interest in the application of machine learning (ML) algorithms for the personalisation of treatment. Nonlinear mixed-effects (NLME) models are pivotal for current therapeutic drug monitoring strategies but may also have limitations. One such limitation is in handling particularly high dimensional and complex data. For genomics data for example, interactions between a multitude of genes are difficult to represent in an algebraic form. Instead, ML algorithms can learn these relationships directly from data, providing a solution to this issue. Unfortunately, ML algorithms are difficult to apply due to the following three issues:
To resolve the abovementioned issues, we present the neural mixed effects (NME) algorithm. This algorithm incorporates a neural network (NN) into the mixed effects statistical framework. We will present a dataset of simulated haemophilia A patients receiving blood clotting factor VIII (FVIII) to test the accuracy of the NME algorithm. Methods: The NME algorithm was developed in the Julia programming language (Julia Computing, Inc., v1.6.0). The basis of the NME algorithm is a NN that predicts the parameters for a system of ordinary differential equations (ODEs) representing a compartment model. This allows us to pass dosing information directly to the ODE solver. Next, we use the first order approximation of the extended least squares objective function [1]. Using this objective function we can simultaneously update the weights of the NN as well as the population parameters representing IIV. The resulting NME algorithm represents the best of two worlds: it provides a measure of IIV allowing individualized predictions like in NLME models, while using the function approximation capabilities of NNs for the implementation of covariates. Results: On the validation set, 68.5% of typical FVIII predictions by the NME model were within 0.05 IU mL-1 of the true concentration. For the individual predictions, 92.0% were within this range. The NME model accurately predicted IIV parameters for CL (ω2 = 0.126 vs 0.129) and V1 (ω2 = 0.0677 vs 0.0705). In addition, the model predicted variance of residual error was 0.0385, close to the true value 0.04 used in the simulation. Conclusion: Our results show that the NME algorithm accurately predicts FVIII levels and IIV based on limited drug measurements. In contrast to other ML algorithms [3,4], the NME algorithm supports any dosing scheme and incorporates prior knowledge about drug dynamics through a compartment model. |