Optimising Inductive Linearisation for nonlinear ODEs with an application to the Michaelis-Menten model: a stochastic simulation-estimation study

Background: Solving systems of nonlinear differential equations is of significant interest in applying pharmacokinetic-pharmacodynamic and systems pharmacology. Inductive Linearisation (IndLin) [1] is a numerical solver that has been developed for generating approximated solutions to nonlinear systems based on iterative linearisation to yield a linear time-varying (LTV) ODE. The method is then paired with the integrator […]

Exploring and optimising the computational efficiency of Inductive Linearisation for estimation

Background: Determining the optimal choice of a dose of medicine to meet the patient’s need is a complicated mathematical and statistical problem. Large between-subject variability in metabolic, excretion processes, and physiological systems involving drug effect, as well as complicated nonlinearities, are at the heart of the problem. This leads to nonlinear differential equations that need […]