Mining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metabolites

Purpose: The present study was performed to demonstrate that small amounts of routine clinical data allow to generate valuable knowledge. Concretely, the aims of this research were to build a joint population pharmacokinetic model for capecitabine and three of its metabolites (5-DFUR, 5-FU and 5-FUH2) and to determine optimal sampling times for therapeutic drug monitoring. Methods: We used data of 7 treatment cycles of capecitabine in patients with metastatic colorectal cancer. The population pharmacokinetic model was built as a multicompartmental model using NONMEM and was internally validated by visual predictive check. Optimal sampling times were estimated using PFIM 4.0 following D-optimality criterion. Results: The final model was a multicompartmental model which represented the sequential transformations from capecitabine to its metabolites 5-DFUR, 5-FU and 5-FUH2 and was correctly validated. The optimal sampling times were 0.546, 0.892, 1.562, 4.736 and 8 hours after the administration of the drug. For its correct implementation in clinical practice, the values were rounded to 0.5, 1, 1.5, 5 and 8 hours after the administration of the drug. Conclusions: Capecitabine, 5-DFUR, 5-FU and 5-FUH2 can be correctly described by the joint multicompartmental model presented in this work. The aforementioned times are optimal to maximize the information of samples. Useful knowledge can be obtained for clinical practice from small databases

Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory

Purpose: Physiologically-based pharmacokinetic (PBPK) models are essential in drug development, but require parameters that are not always obtainable. We developed a methodology to investigate the feasibility and requirements for precise and accurate estimation of PBPK parameters using population modelling of clinical data and illustrate this for two key PBPK parameters for hepatic metabolic clearance, namely whole liver unbound intrinsic clearance (CLint,u,WL) and hepatic blood flow (Qh) in children. Methods: First, structural identifiability was enabled through re-parametrization and the definition of essential trial design components. Subsequently, requirements for the trial components to yield precise estimation of the PBPK parameters and their inter-individual variability were established using a novel application of population optimal design theory. Finally, the performance of the proposed trial design was assessed using stochastic simulation and estimation. Results: Precise estimation of CLint,u,WL and Qh and their inter-individual variability was found to require a trial with two drugs, of which one has an extraction ratio (ER) ≤ 0.27 and the other has an ER ≥ 0.93. The proposed clinical trial design was found to lead to precise and accurate parameter estimates and was robust to parameter uncertainty. Conclusion: The proposed framework can be applied to other PBPK parameters and facilitate the development of PBPK models

varTestnlme: An R Package for Variance Components Testing in Linear and Nonlinear Mixed-Effects Models

The issue of variance components testing arises naturally when building mixed-effects models, to decide which effects should be modeled as fixed or random or to build parsimonious models. While tests for fixed effects are available in R for models fitted with lme4, tools are missing when it comes to random effects. The varTestnlme package for R aims at filling this gap. It allows to test whether a subset of the variances and covariances corresponding to a subset of the random effects, are equal to zero using asymptotic property of the likelihood ratio test statistic. It also offers the possibility to test simultaneously for fixed effects and variance components. It can be used for linear, generalized linear or nonlinear mixed-effects models fitted via lme4, nlme or saemix. Numerical methods used to implement the test procedure are detailed and examples based on different real datasets using different mixed models are provided. Theoretical properties of the used likelihood ratio test are recalled

乳幼児を対象とした臨床試験でのファーマコメトリクスによる試験デザインの最適化

武蔵野大学2021年

Optimal Experimental Design Applied to Models of Microbial Gene Regulation

Microbial gene expression is a comparatively well understood process, but regulatory interactions between genes can give rise to complicated behaviours. Regulatory networks can exhibit strong context dependence, time-varying interactions and multiple equilibrium. The qualitative diagrammatic models often used in biology are not well suited to reasoning about such intricate dynamics. Fortunately, mathematics offers a natural language to model gene regulation because it can quantify the various system inter-dependencies with much greater clarity and precision. This added clarity makes models of microbial gene regulation a valuable tool for studying both natural and synthetic gene regulatory systems. However models are only as good as the knowledge and assumptions they are built on. Specifically, all models depend on unknown parameters -- constant that quantify specific rates and interaction strengths within the regulatory system. In systems biology parameters are generally fit, rather than measured directly, because their values are contextually dependent on state of the microbial host. This fitting requires collecting observations of the modeled system. Exactly what is measured, how many times and under what experimental conditions defines an experimental design. The experimental design is intimately linked to the accuracy of any resulting parameter estimates for a model, but determining what experimental design will be useful for fitting can be difficult. Optimal experimental design (OED) provides a set of statistical techniques that can be used make design choices that improve parameter estimation accuracy. In this thesis I examine the use of OED methods applied to models of microbial gene regulation. I have specifically focused on optimal design methods that combine asymptotic parametric accuracy objectives, based on the Fisher information matrix, with relaxed formulations of the design optimization problem. I have applied these OED methods to three biological case studies. (1) I have used these methods to implement a multiple-shooting optimal control algorithm for optimal design of dynamic experiments. This algorithm was applied to a novel model of transcriptional regulation that accounts for the microbial host's physiological context. Optimal experiments were derived for estimating sequence-specific regulatory parameters and host-specific physiological parameters. (2) I have used OED methods to formulate an optimal sample scheduling algorithm for dynamic induction experiments. This algorithm was applied to a model of an optogenetic induction system -- an important tool for dynamic gene expression studies. The value of sampling schedules within dynamic experiments was examined by comparing optimal and naive schedules. (3) I derived an optimal experimental procedure for fitting a steady-state model of single-cell observations from a bistable regulatory motif. This system included a stochastic model of gene expression and the OED methods made use of the linear noise approximation to derive a tractable design algorithm. In addition to these case-studies, I also introduce the NLOED software package. The package can perform optimal design and a number of other fitting and diagnostic procedures on both static and dynamic multi-input multi-output models. The package makes use automatic differentiation for efficient computation, offers a flexible modeling interface, and will make OED more accessible to the wider biological community. Overall, the main contributions of this thesis include: developing novel OED methods for a variety of gene regulatory scenarios, studying optimal experimental design properties for these scenarios, and implementing open-source numerical software for a variety of OED problems in systems biology

Mechanistic modelling of in vitro transporter processes to improve drug-drug interaction predictions

There is currently a need to evaluate the interaction of drugs in the liver and at the liver membrane, to determine whether the potential for a drug-drug interaction in the clinic could adversely affect a patients prognosis. The interactions of drugs or probe substrates with liver membrane transporters are currently poorly understood at a molecular level, and there is strong interest in terms of the pharmacology of the transporters and how we can examine and understand these interactions through mathematical models. Currently the dynamics of interactions through the use of micro-rate constants, where steady-state assumptions are not implied in data analysis are less favoured. Whilst modelling and data analysis conducted using Michaelis-Menten type kinetics (defined as macro-rate constant mechanistic models), under the assumption of rapid equilibration of substrate with the transporter (association with the transporter is almost instantaneous) are more common. The aim of this thesis is to improve the determination of transporter mediated drug-drug interactions (TrDDIs) in in vitro liver specific cellular systems through the use of structurally identifiable mechanistic models describing the dynamics of the interaction between substrates and inhibitors. This was done by the design of experiments to optimise the data collected for substrate and inhibitors for use within the mechanistic models across different cellular systems (human cell lines, rat and human hepatocytes) under different inhibition conditions. Mechanistic models were developed to obtain robust model fits that adequately described the interaction between substrates and inhibitors, whilst gaining an insight in terms of model selectivity, given the data available. The structural identifiability of the mechanistic models was assessed to ensure that the unknown parameters in the model could be estimated from the experimental data. The mode of inhibition was determined through the use of mechanistic models for each experimental chapter and compared with conclusions drawn in the in literature. The potential for a clinical TrDDI was evaluated for the experimental work in cryopreserved human hepatocytes (Chapter 5), through a worst case scenario static xviii drug interaction model at the entrance to the liver using an \R value", and through the use of a semi-quantitative physiologically based pharmacokinetic (PBPK) model. All the micro-rate constant mechanistic models were at least structurally locally identifiable with no parameters unknown. Conversely, the macro-rate constant mechanistic were only structurally locally identifiable if both substrate and inhibitor were measured (see Chapter 5). Otherwise one to two parameters had to be known for the macro-rate constant mechanistic models to be structurally locally identifiable. Concurrent with the structural identifiability analysis results, in each of the experimental chapters, the use of micro-rate constant mechanistic models were always the best fitting model to the experimental data based on goodness of fit statistics compared to the use of Michaelis-Menten macro-rate constant mechanistic models. Both the micro-rate constant and macro-rate constant mechanistic models were in agreement with regards to the mechanism of inhibition in all experimental cases, whilst the steady-state assumptions required for the use of the Michaelis-Menten equation were only valid for the micro-rate constants derived in Chapter 5. This supported the use of scaled micro-rate constant parameters in Chapter 5 to Michaelis-Menten parameters in the semi-quantitative mechanistic PBPK model in Chapter 6, where there was a potential for a clinical TrDDI given the in vitro data, which was at odds with the determined R value. In conclusion, this work strongly supports the use of micro-rate constants in mechanistic modelling of in vitro TrDDIs to formally test steady-state assumptions through more robust, structurally identifiable parameter estimates

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models

International audienceBackground and objective: Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. Methods: Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic / pharmacody-namic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. Results: The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. Conclusion: PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in phar-macometrics and is freely available at http://www.pfim.biostat.fr

Personalización del tratamiento antineoplásico con capecitabina en pacientes con carcinoma colorrectal

Objetivo: desarrollar y validar un modelo farmacocinético – farmacodinámico (PKPD) poblacional de capecitabina que proporcione información útil en la personalización del tratamiento en pacientes con carcinoma colorrectal. Métodos: estudio prospectivo observacional post-autorización desarrollado en el Hospital Universitario Doctor Peset de Valencia entre febrero de 2015 y agosto 2016 en pacientes con carcinoma colorrectal. Las determinaciones de las concentraciones plasmáticas de CAP y sus metabolitos (5-DFUR y 5-FU) se realizaron a partir de muestras de sangre obtenidas 1h, 2h y 3h post-administración. Los recuentos absolutos de neutrófilos (RAN) se realizaron entre los días 15-24 post-administración. Resultados: 48 pacientes fueron incluidos en el estudio farmacocinético (PK) realizándose 432 determinaciones de niveles plasmáticos. Se asumió la absorción y el metabolismo de primer orden a partir de CAP a 5-FU a través de 5-DFUR y se asumieron procesos de eliminación de primer orden para CAP y 5-FU. Los valores de aclaramiento de CAP, 5'-DFUR y 5-FU fueron 294, 8,97 y 12,8 L/h, respectivamente. El volumen aparente de distribución de CAP (Vd2) fue de 449 L, mientras que V3 (5-DFUR) y V4 (5-FU) fueron fijados a 1L. Se recogieron un total de 85 covariables (78 polimorfismos y 7 variables demográficas, antropométricas, biométricas y relacionadas con el tratamiento). El modelo PK final incorporó las siguientes covariables: oxaliplatino en el periodo de latencia, rs6720173 en el aclaramiento de 5-DFUR y rs2271862 en el aclaramiento de 5-FU. 48 pacientes fueron incluidos en el estudio farmacodinámico (PD) realizándose 349 RAN. La base estructural siguió lo descrito en la publicación de Friberg et al. El basal de RAN (E0) fue de 3,87 x109/L y la pendiente de 0,0309 ml/mcg. El modelo PD incorporó como covariable el oxaliplatino sobre la pendiente. Según los ejercicios de simulación realizados el riesgo de neutropenia es inferior si presentan genotipo rs2271862 mutado, por lo que en estos pacientes podría incrementarse las dosis de capecitabina sin comprometer la seguridad. Este hallazgo cobra mayor importancia cuando el tratamiento con capecitabina se realiza de forma concomitante con oxaliplatino, dado que en estos casos, los pacientes salvajes podrían recibir dosis de hasta 588 mg/m2/24h, mientras que los pacientes mutados podrían recibir dosis de hasta 1.176 mg/m2/24h. Conclusión: la determinación genética del polimorfismo de nucleótido único rs2271862 presente en el gen ABC y la monitorización farmacocinética de 5-FU tras la administración de capecitabina por vía oral permitiría disponer de información útil para personalizar el tratamiento con capecitabina en pacientes con carcinoma colorrectal