RT Journal Article
SR Electronic
T1 Efficient Precision Dosing Under Estimated Uncertainties via Koopman Expectations of Bayesian Posteriors with Pumas
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.01.25.428134
DO 10.1101/2021.01.25.428134
A1 Chris Rackauckas
A1 Vaibhav Dixit
A1 Adam R. Gerlach
A1 Vijay Ivaturi
YR 2021
UL http://biorxiv.org/content/early/2021/01/27/2021.01.25.428134.abstract
AB Personalized precision dosing is about mathematically determining effective dosing strategies that optimize the probability of containing a patientâ€™s outcome within a therapeutic window. However, the common Monte Carlo approach for generating patient statistics is computationally expensive because thousands of simulations need to be computed. In this manuscript we describe a new method which utilizes the Koopman operator to perform a direct computation of expected patient outcomes with respect to quantified uncertainties of Bayesian posteriors in a nonlinear mixed effect model framework. We detail how quantities such as the probability of being within the therapeutic window can be calculated with a choice of loss function on the Koopman expectation. We demonstrate a high performance parallelized implementation of this methodology in PumasÂ® and showcase the ability to accelerate the computation of these expectations by 50x-200x over Monte Carlo. We showcase how dosing can be optimized with respect to probabilistic statements respecting variable uncertainties using the Koopman operator. We end by demonstrating an end-to-end workflow, from estimating variable uncertainties via Bayesian estimation to optimizing a dose with respect to parametric uncertainty.Competing Interest StatementC.R., V.D, and V.I. are affiliated with Pumas-AI.