Physiologically Based Pharmacokinetic Predictions of Tramadol Exposure Throughout Pediatric Life: an Analysis of the Different Clearance Contributors with Emphasis on CYP2D6 Maturation (original) (raw)
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The AAPS Journal, 2018
In pediatric PBPK models, age-related changes in the body are known to occur. Given the sparsity of and the variability associated with relevant physiological parameters, different PBPK software providers may vary in their system's data. In this work, three commercially available PBPK software packages (PK-Sim®, Simcyp®, and Gastroplus®) were investigated regarding their differences in system-related information, possibly affecting clearance prediction. Three retrograde PBPK clearance models were set up to enable prediction of pediatric tramadol clearance. These models were qualified in terms of total, CYP2D6, and renal clearance in adults. Tramadol pediatric clearance predictions from PBPK were compared with a pooled popPK model covering clearance ranging from neonates to adults. Fold prediction errors were used to evaluate the results. Marked differences in liver clearance prediction between PBPK models were observed. In general, the prediction bias of total clearance was greatest at the youngest population and decreased with age. Regarding CYP2D6 and renal clearance, important differences exist between PBPK software tools. Interestingly, the PBPK model with the shortest CYP2D6 maturation half-life (PK-Sim) agreed best with the in vivo CYP2D6 maturation model. Marked differences in physiological data explain the observed differences in hepatic clearance prediction in early life between the various PBPK software providers tested. Consensus on the most suited pediatric data to use should harmonize and optimize pediatric clearance predictions. Moreover, the combination of bottom-up and top-down approaches, using a convenient probe substrate, has the potential to update system-related parameters in order to better represent pediatric physiology.
Physiology-Based IVIVE Predictions of Tramadol from in Vitro Metabolism Data
Pharmaceutical Research, 2014
Purpose To predict the tramadol in vivo pharmacokinetics in adults by using in vitro metabolism data and an in vitro-in vivo extrapolation (IVIVE)-linked physiologically-based pharmacokinetic (PBPK) modeling and simulation approach (Simcyp®). Methods Tramadol metabolism data was gathered using metabolite formation in human liver microsomes (HLM) and recombinant enzyme systems (rCYP). Hepatic intrinsic clearance (CLint H ) was (i) estimated from HLM corrected for specific CYP450 contributions from a chemical inhibition assay (model 1); (ii) obtained in rCYP and corrected for specific CYP450 contributions by study-specific intersystem extrapolation factor (ISEF) values (model 2); and (iii) scaled back from in vivo observed clearance values (model 3). The modelpredicted clearances of these three models were evaluated against observed clearance values in terms of relative difference of their geometric means, the fold difference of their coefficients of variation, and relative CYP2D6 contribution. Results Model 1 underpredicted, while model 2 overpredicted the total tramadol clearance by −27 and +22%, respectively. The CYP2D6 contribution was underestimated in both models 1 and 2. Also, the variability on the clearance of those models was slightly underpredicted. Additionally, blood-to-plasma ratio and hepatic uptake factor were identified as most influential factors in the prediction of the hepatic clearance using a sensitivity analysis. Conclusion IVIVE-PBPK proved to be a useful tool in combining tramadol's low turnover in vitro metabolism data with systemspecific physiological information to come up with reliable PK predictions in adults.
Determinants of Drug Metabolism in Early Neonatal Life
Current Clinical Pharmacology, 2007
Clinical pharmacology intends to predict drug-specific effects and side effects based on pharmacokinetics (i.e. absorption, distribution, metabolism and elimination) and pharmacodynamics (i.e. dose/effect relationship). Developmental pharmacology focuses on the maturational aspects of these phenomena during perinatal life and later stages of infancy. In general, phenotypic variation in drug metabolism is based on constitutional, environmental and genetic factors but in early neonatal life, it mainly reflects ontogeny. Although the major site of drug metabolism is the liver, the gastrointestinal tract, blood cells and other organs like kidneys or lungs might also be involved in drug metabolism. Important alterations in hepatic drug metabolism occur in early neonatal life. These alterations are of relevance when agedependent aspects of pharmacokinetics,-dynamics or toxicology are considered. Age dependent maturation of various phase I and II processes will be illustrated based on recently reported observations on the in vivo disposition of various analgesics (paracetamol, tramadol) in human neonates and young infants. However, age only in part explains the interindividual variability observed. Therefore, concerted efforts should be developed to simultaneously assess the impact of age, environmental factors, comorbidity and polymorphisms in this specific population. The implementation of multivariable models like non-linear mixed effects (NONMEM) models hereby provide us with the tools to disentangle the impact of various co-variables in this specific population.
Enantioselective pharmacokinetics of tramadol in CYP2D6 extensive and poor metabolizers
European Journal of Clinical Pharmacology, 2006
Objective: To describe in detail the intravenous, single oral and multiple oral dose enantioselective pharmacokinetics of tramadol in CYP2D6 extensive metabolizers (EMs) and poor metabolizers (PMs). Methods: Eight EMs and eight PMs conducted three phases as an open-label cross-over trial with different formulations; 150 mg single oral tramadol hydrochloride, 50 mg single oral tramadol hydrochloride every 8 h for 48 h (steady state), 100 mg intravenous tramadol hydrochloride. Urine and plasma concentrations of (+/−)-tramadol and (+/−)-M1 were determined for 48 h after administration. Results: In all three phases, there were significant differences between EMs and PMs in AUC and t 1/2 of (+)-tramadol (P≤0.0015), (−)-tramadol (P≤0.0062), (+)-M1 (P≤0.0198) and (−)-M1 (P≤0.0370), and significant differences in C max of (+)-M1 (P<0.0001) and (−)-M1 (P≤0.0010). In Phase A and C, significant differences in t max were seen for (+)-M1 (P≤0.0200). There were no statistical differences between the absolute bioavailability of tramadol in EMs and PMs. The urinary recoveries of (+)-tramadol, (−)-tramadol, (+)-M1 and (−)-M1 were statistically significantly different in EMs and PMs (P<0.05). Median antimodes of the urinary metabolic ratios of (+)-tramadol / (+)-M1 and (−)-M1 were 5.0 and 1.5, respectively, hereby clearly separating EMs and PMs in all three phases. Conclusion: The impact of CYP2D6 phenotype on tramadol pharmacokinetics was similar after single oral, multiple oral and intravenous administration displaying significant pharmacokinetic dif-ferences between EMs and PMs of (+)-tramadol, (−)tramadol, -(+)-M1 and (−)-M1. The O-demethylation of tramadol was catalysed stereospecific by CYP2D6 in the way that very little (+)-M1 was produced in PMs.
Pharmacological Research, 2007
Objective: Our objective was to evaluate the effect of CYP2D6 phenotype in the enantioselective metabolism of tramadol in Spanish healthy human volunteers. Methods: A single oral 100 mg dose of racemic tramadol was administered to five subjects who were poor metabolizers (PMs) and 19 subjects who were extensive metabolizers (EMs), whose phenotypes were determined by the use of the racemic tramadol metabolic rate. The pharmacokinetic parameters were estimated from plasma concentrations of the enantiomers of tramadol and their main phase I metabolites, O-desmethyltramadol (M1) and N-desmethyltramadol (M2). Epinephrine plasma concentrations were also determinated. Results: The plasma concentrations of both tramadol enantiomers were consistently higher in PMs than in EMs of CYP2D6, with 1.98-and 1.74-fold differences in the mean area under the plasma concentration-time curves (AUC), respectively. The values for oral clearance of (+)-and (−)-tramadol were 1.91-and 1.71-fold greater in PMs, which were related to differences in both O-desmethylation and N-desmethylation in the two CYP2D6 metabolizer phenotypes. The mean AUC values of (+)-M1 and (−)-M1 were 4.33-and 0.89-fold greater in EMs, and it was related to similar differences in the formation rate constant. On the other hand, the differences were 7.40-and 8.69-fold greater in PMs for M2 enantiomers due to the involvement of CYP2D6 in their subsequent biotransformation. The time course of epinephrine systemic concentrations was completely different between both groups of metabolizers. In EMs plasma concentrations of epinephrine increased after tramadol administration whereas in PMs no effect was observed.
Journal of Theoretical Biology, 2008
In this work we propose a mathematical model for the kinetics of tramadol, a synthetic opioid commonly used for treating moderate to severe pain. This novel theoretical framework could result in an objective criterion on how to adjust the assigned dose, depending on the genetic polymorphisms of CYP2D6. The model describes the coupled dynamics of tramadol and the metabolite O-desmethyltramadol. The effect of diffusion of the drug in the blood is here accounted for and we further hypothesize the existence of a time delay in the process of chemical translation from tramadol into metabolites. The system of coupled differential equations is solved numerically and the free parameters adjusted so to interpolate the experimental time series for the intravenous injection setting. Theoretical curves are shown to reproduce correctly the experimental profiles obtained from clinical trials. This enables in turn to extract an estimate of the metabolization rate. A difference in metabolization rate between CYP2D6 poor and extensive metabolizers is also found, and the stereoselectivity in the O-demethylation of tramadol highlighted. Our results allow one to quantify the dose of ðþÞ-tramadol (resp. ðÀÞ-tramadol) administered to poor or extensive metabolizers, if the same effect is sought. The latter is here quantified through the blood concentration of ðþÞ-metabolites (resp. ðÀÞ-metabolites).
Impact of CYP2D6 Genetic Polymorphism on Tramadol Pharmacokinetics and Pharmacodynamics
Background and objective: Tramadol is metabolized by the highly polymorphic enzyme cytochrome P450 Abstract (CYP)2D6. Patients with different CYP2D6 genotypes may respond differently to tramadol in terms of pain relief and adverse events. In this study, we compare the pharmacokinetics and effects of tramadol in Malaysian patients with different genotypes to establish the pharmacokinetic-pharmacodynamic relationship of tramadol. Study design and setting: All patients received an intravenous dose of tramadol 100mg as their first postoperative analgesic. Blood was sampled at 0 minutes and subsequently at 15 and 30 minutes, 1, 2, 4, 8, 16, 20, and 24 hours for serum tramadol and analyzed by high-performance liquid chromatography (HPLC). Patients were genotyped for CYP2D6*1, *3, *4, *5, *9, *10, and *17 alleles and duplication of the gene by means of an allele-specific PCR. Pain was measured using the Visual Analog Scales, and adverse effects were recorded. Results: About half of the patients had the wild-type allele (CYP2D6*1), with the 'Asian' CYP2D6*10 allele accounting for most of the rest (40%). None of the genotypes predicted poor metabolism. Twenty-seven percent of the patients were intermediate metabolizers (IM) and 2.9% were ultra-rapid (UM) metabolizers; the remaining 70% were extensive metabolizers (EM). The mean total clearance (CL) predicted by the model was lower (19 L/ hour) and the half-life longer (5.9 hours) than those reported in Western populations. This may due to the high frequency of the CYP2D6*10 allele amongst Malaysian patients. The UM and EM groups had 2.6-and 1.3-times faster CL, respectively, than the IM. CL was 16, 18, 23, and 42 L/hour while mean half-lives were 7.1, 6.8, 5.6, and 3.8 hours among the IM, EM1, EM2, and UM groups, respectively. However, the analgesic effects of tramadol were not measured adequately among the postoperative patients to establish its full therapeutic effects. There were significant differences in the adverse-effect profiles amongst the various genotype groups, with the IM group experiencing more adverse effects than the EM, and the EM having more adverse effects than the UM. Conclusion: CYP2D6 activity may play an important role in determining the pharmacokinetics of tramadol and in predicting its adverse effects. If these results can be confirmed in a larger population, genotyping may be an important tool in determining the dose of tramadol.
Maturation of cytochrome P450 3A mediated drug metabolism: Towards individualized dosing in children
2013
Chapter 1 Maturation of Cytochrome P450 3A Mediated Drug Metabolism-General introduction PART I Mechanism-based modeling to develop rational dosing guidelines in children: CYP3A maturation Chapter 2 Tailor-made drug treatment for children: creation of an infrastructure for data-sharing and population PK-PD modeling Chapter 3 Developmental changes in the expression and function of cytochrome P450 3A isoforms: evidence from in vitro and in vivo investigations PART II Development and application of a maturation function for CYP3A using midazolam as an in vivo probe Chapter 4 Critical illness is a major determinant of midazolam clearance in children aged 1 month to 17 years Chapter 5 A Novel Maturation Function for Clearance of the Cytochrome P450 3A Substrate Midazolam from Preterm Neonates to Adults Chapter 6 Population pharmacokinetic analysis on oral and intravenous midazolam across the human lifespan from preterm neonates to adults Chapter 7 Extrapolation of the midazolam CYP3A maturation function to cisapride: towards a semi-physiological approach for pharmacokinetics modelling in children PART III General discussion and summary Chapter 8 Maturation of Cytochrome P450 3A Mediated Drug Metabolism-Summary conclusions and perspectives