Indirect measurement of hepatic drug clearance by fitting dynamical models

Abstract

We present an indirect signal processing-based measurement method for biological quantities in humans that cannot be directly measured. We develop the method, focusing on estimating hepatic enzyme and drug transporter activity through breath samples collected clinically during the erythromycin breath test (EBT): a small dose of radiolabeled drug is injected and the subsequent content of radiolabeled CO ₂ is measured repeatedly in exhaled breath; the resulting time series is analyzed. To model the EBT, we developed a 14-variable nonlinear reduced-order dynamical model that describes the behavior of the drug and its metabolites in the human body well enough to capture all biological phenomena of interest. Based on this system of coupled nonlinear ordinary differential equations (ODEs) we treat the measurement problem as an inverse problem: we estimate the ODE parameters of individual patients from the measured EBT time series. These estimates then provide model-based estimates of the hepatic activities of interest. The parameters are hard to estimate as the ODEs are stiff and the problem needs to be regularized to ensure stable convergence. We develop a formal operator framework to capture and treat the specific nonlinearities present, and perform perturbation analysis to establish properties of the estimation procedure and its solution. Development of the method required 150,000 CPU hours at a supercomputing center, and a single production run requires approximately 24 CPU-hours. We introduce and analyze the method in the context of future precision dosing of drugs for vulnerable patients (e.g., oncology, nephrology, or pediatrics) to eventually ensure efficacy and avoid toxicity.