Sequential Updating of a New Dynamic Pharmacokinetic Model for Caffeine in Premature Neonates

AbstractBackground and objective: Caffeine treatment is widely used in nursing care to reduce the risk of apnoea in premature neonates. To check the therapeutic efficacy of the treatment against apnoea, caffeine concentration in blood is an important indicator. The present study was aimed at building a pharmacokinetic model as a basis for a medical decision support tool. Methods: In the proposed model, time dependence of physiological parameters is introduced to describe rapid growth of neonates. To take into account the large variability in the population, the pharmacokinetic model is embedded in a population structure. The whole model is inferred within a Bayesian framework. To update caffeine concentration predictions as data of an incoming patient are collected, we propose a fast method that can be used in a medical context. This involves the sequential updating of model parameters (at individual and population levels) via a stochastic particle algorithm. Results: Our model provides better predictions than the ones obtained with models previously published. We show, through an example, that sequential updating improves predictions of caffeine concentration in blood (reduce bias and length of credibility intervals). The update of the pharmacokinetic model using body mass and caffeine concentration data is studied. It shows how informative caffeine concentration data are in contrast to body mass data. Conclusion: This study provides the methodological basis to predict caffeine concentration in blood, after a given treatment if data are collected on the treated neonate.

[1]  J. Bircher,et al.  Assessment of the cytochrome P-448 dependent liver enzyme system by a caffeine breath test , 2004, European Journal of Clinical Pharmacology.

[2]  J. Gouyon,et al.  Renal effects of caffeine in preterm infants. , 1990, Biology of the neonate.

[3]  J. M. Lanao,et al.  Population pharmacokinetics of caffeine in premature neonates , 1997, European Journal of Clinical Pharmacology.

[4]  W. Daily,et al.  Apnea in premature infants: monitoring, incidence, heart rate changes, and an effect of environmental temperature. , 1969, Pediatrics.

[5]  Christian P. Robert,et al.  Bayesian-Optimal Design via Interacting Particle Systems , 2006 .

[6]  A Schumitzky,et al.  Population pharmacokinetics/pharmacodynamics modeling: parametric and nonparametric methods. , 2000, Therapeutic drug monitoring.

[7]  Y. Lebranchu,et al.  Bayesian estimation of cyclosporin exposure for routine therapeutic drug monitoring in kidney transplant patients. , 2005, British journal of clinical pharmacology.

[8]  J. Aranda,et al.  Pharmacologic considerations in the therapy of neonatal apnea. , 1981, Pediatric clinics of North America.

[9]  R. Schumacher,et al.  Treatment of Apnea of Prematurity , 2003, Paediatric drugs.

[10]  L B Sheiner,et al.  Estimating population kinetics. , 1982, Critical reviews in biomedical engineering.

[11]  D. Rubin.  Using the SIR algorithm to simulate posterior distributions , 1988 .

[12]  D. Henderson-smart,et al.  Methylxanthine treatment for apnea in preterm infants. , 2008, The Cochrane database of systematic reviews.

[13]  J. Valentin.  Basic anatomical and physiological data for use in radiological protection: reference values , 2002, Annals of the ICRP.

[14]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[15]  Hong Chang,et al.  Model Determination Using Predictive Distributions with Implementation via Sampling-Based Methods , 1992 .

[16]  L Tierney,et al.  Some adaptive monte carlo methods for Bayesian inference. , 1999, Statistics in medicine.

[17]  J. Kadane,et al.  Experiences in elicitation , 1998 .

[18]  Joseph G. Ibrahim,et al.  Prior elicitation for model selection and estimation in generalized linear mixed models , 2003 .

[19]  T. Turmen,et al.  Effect of caffeine on control of breathing in infantile apnea. , 1983, The Journal of pediatrics.

[20]  C. Paré,et al.  Maturational changes of caffeine concentrations and disposition in infancy during maintenance therapy for apnea of prematurity: influence of gestational age, hepatic disease, and breast-feeding. , 1985, Pediatrics.

[21]  M. Richard,et al.  Maturation of caffeine metabolic pathways in infancy , 1988, Clinical pharmacology and therapeutics.

[22]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[23]  A. Thomson,et al.  Population pharmacokinetics of caffeine in neonates and young infants. , 1996, Therapeutic drug monitoring.

[24]  R. Gorodischer,et al.  Tissue distribution of caffeine in premature infants and in newborn and adult dogs. , 1981, Pediatric pharmacology.

[25]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[26]  Eric P. Fox.  Bayesian Statistics 3 , 1991 .

[27]  G. Pons,et al.  Developmental changes of caffeine elimination in infancy. , 1988, Developmental pharmacology and therapeutics.

[28]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[29]  Robert L. Williams,et al.  Dose‐related sleep disturbances induced by coffee and caffeine , 1976, Clinical pharmacology and therapeutics.

[30]  L. Tierney.  Markov Chains for Exploring Posterior Distributions , 1994 .

[31]  R. Goldstein,et al.  Effect of caffeine on ventilatory responses to hypercapnia, hypoxia, and exercise in humans. , 1990, Journal of applied physiology.

[32]  Roger W. Jelliffe,et al.  A Bayesian Approach to Tracking Patients Having Changing Pharmacokinetic Parameters , 2004, Journal of Pharmacokinetics and Pharmacodynamics.

[33]  David Madigan,et al.  A Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets , 2003, Data Mining and Knowledge Discovery.

[34]  J. Aranda,et al.  Maturation of caffeine elimination in infancy. , 1979, Archives of disease in childhood.

[35]  A. O'Hagan,et al.  Statistical Methods for Eliciting Probability Distributions , 2005 .

[36]  J. Geweke,et al.  Bayesian Inference in Econometric Models Using Monte Carlo Integration , 1989 .

[37]  Bayesian Nonparametric Population Models: Formulation and Comparison with Likelihood Approaches , 2004, Journal of Pharmacokinetics and Biopharmaceutics.

[38]  R Jouvent,et al.  Nonparametric Estimation of Population Characteristics of the Kinetics of Lithium from Observational and Experimental Data: Individualization of Chronic Dosing Regimen Using a New Bayesian Approach , 1994, Therapeutic drug monitoring.

[39]  A. Guglielmi,et al.  Methylxanthines increase renal calcium excretion in preterm infants. , 1995, Biology of the neonate.

[40]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[41]  P. Ljungman,et al.  Population pharmacokinetic analysis resulting in a tool for dose individualization of busulphan in bone marrow transplantation recipients , 2001, Bone Marrow Transplantation.

[42]  P. Steer,et al.  Population pharmacokinetics of intravenous caffeine in neonates with apnea of prematurity , 1997, Clinical pharmacology and therapeutics.

[43]  N. Chopin.  A sequential particle filter method for static models , 2002 .

[44]  T. Gunn,et al.  Efficacy of caffeine in treatment of apnea in the low-birth-weight infant. , 1977, The Journal of pediatrics.