In this study, performance of the estimation methods available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation methods in addition to the classical methods.
NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The results advocates for the use of bivariate MHMM models when implementation is possible.
The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model. A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. Parameter precision was better with higher magnitudes of the transition probability parameters.
Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error > 150%). A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs. Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example.
O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Get Quantitative Pharmacology and Individualized Therapy Strategies in Development of Therapeutic Proteins for Immune-Mediated Inflammatory Diseases now with O’Reilly online learning. While several other software packages for nonlinear mixed effect modeling are available, NONMEM remains the standard in the pharmaceutical industry. NONMEM is the NONlinear Mixed Effect Modeling package originally developed by Stuart Beal, Lewis Sheiner, and Alison Boeckmann and is now being developed by Robert Bauer.
This chapter is intended to describe how to apply the NONMEM software application and NONMEM estimation methods to the TMDD equations. This results in long computational run times and numerical difficulties, requiring careful attention to the selection of software, estimation methods, and its parameters.
In mathematics such systems are called “stiff.” System parameters are often poorly identifiable either due to stiffness of the differential equations (for the full TMDD model) or due to limitations of the available data. Numerical methods for solving these equations are numerically unstable, unless the step size is taken to be extremely small. Differential equations of the TMDD model describe processes with very different characteristic timescales, from few minutes for binding processes to several weeks for the elimination of the drug. The target‐mediated drug disposition ( TMDD) model is a nonlinear system of differential equations, with multiple fixed and random effect parameters that describe complex biological processes. 8 Tutorial: Numerical (NONMEM) Implementation of the Target‐Mediated Drug Disposition Model 8.1 Introduction