Purpose The three aims of this investigation were (1) to develop a population pharmacokinetic (PK) model for factor VIII (FVIII) in haemophilia A patients, with estimates of inter-occasion and ...inter-individual variance, (2) to investigate whether appropriate dosing of FVIII for regular prophylaxis can be calculated according to patient characteristics, and (3) to present dosing recommendations for initiating prophylactic treatment. Methods A population PK model was developed using data from four PK studies on patients aged 7-74 years. The model was tested on sparse FVIII data from 42 outpatient visits by haemophilia prophylaxis patients aged 3-66 years. Dose requirements for prophylaxis were calculated both according to the population model and from empirical Bayesian estimates of FVIII PK in the individual patients. Results The study data were well characterised by a two-compartment PK model. Body weight, age and type of FVIII preparation (plasma-derived or recombinant) were identified as significant covariates. Inter-occasion variance was lower than inter-individual variance for both clearance and volume of the central compartment. The model could reasonably predict FVIII PK in the sparse clinical data. Model-predicted doses (based on age and body weight) to maintain a recommended 0.01 U/mL trough level of FVIII with administration on alternate days started at around 60 U/kg in the small children, decreasing to 10 U/kg or less in middle age. However, “true” dose requirements, as estimated from individual PK parameter data, showed a much greater variation. Conclusion Appropriate dosing of FVIII for prophylactic treatment cannot be calculated only from body weight and/or age. However, plausible starting doses for most patients would be 1,000 U every other day. FVIII levels should then be checked for dose adjustment.
Glutathione PEGylated (GSH-PEG) liposomes were evaluated for their ability to enhance and prolong blood-to-brain drug delivery of the opioid peptide DAMGO (H-Tyr-d-Ala-Gly-MePhe-Gly-ol). An ...intravenous loading dose of DAMGO followed by a 2 h constant rate infusion was administered to rats, and after a washout period of 1 h, GSH-PEG liposomal DAMGO was administered using a similar dosing regimen. DAMGO and GSH-PEG liposomal DAMGO were also administered as a 10 min infusion to compare the disposition of the two formulations. Microdialysis made it possible to determine free DAMGO in brain and plasma, while the GSH-PEG liposomal encapsulated DAMGO was measured with regular plasma sampling. The antinociceptive effect of DAMGO was determined with the tail-flick method. All samples were analyzed using liquid chromatography-tandem mass spectrometry. The short infusion of DAMGO resulted in a fast decline of the peptide concentration in plasma with a half-life of 9.2 ± 2.1 min. Encapsulation in GSH-PEG liposomes prolonged the half-life to 6.9 ± 2.3 h. Free DAMGO entered the brain to a limited extent with a steady state ratio between unbound drug concentrations in brain interstitial fluid and in blood (Kp,uu) of 0.09 ± 0.04. GSH-PEG liposomes significantly increased the brain exposure of DAMGO to a Kp,uu of 0.21 ± 0.17 (p < 0.05). By monitoring the released, active substance in both blood and brain interstitial fluid over time, we were able to demonstrate that GSH-PEG liposomes offer a promising platform for enhancing and prolonging the delivery of drugs to the brain.
Comparison of the pharmacokinetics (PK) of a coagulation factor between groups of patients can be biased by differences in study protocols, in particular between blood sampling schedules. This could ...affect clinical dose tailoring, especially in children. The aim of this study was to describe the relationships of the PK of factor VIII (FVIII) with age and body weight by a population PK model. The potential to reduce blood sampling was also explored. A model was built for FVIII PK from 236 infusions of recombinant FVIII in 152 patients (1-65 years of age) with severe hemophilia A. The PK of FVIII over the entire age range was well described by a 2-compartment model and a previously reported problem, resulting from differences in blood sampling, to compare findings from children and adults was practically abolished. The decline in FVIII clearance and increase in half-life with age could be described as continuous functions. Retrospective reduction of blood sampling from 11 to 5 samples made no important difference to the estimates of PK parameters. The obtained findings can be used as a basis for PK-based dose tailoring of FVIII in clinical practice, in all age groups, with minimal blood sampling.
In vivo recovery (IVR) is traditionally used as a parameter to characterize the pharmacokinetic properties of coagulation factors. It has also been suggested that dosing of factor VIII (FVIII) and ...factor IX (FIX) can be adjusted according to the need of the individual patient, based on an individually determined IVR value. This approach, however, requires that the individual IVR value is more reliably representative for the patient than the mean value in the population, i.e. that there is less variance within than between the individuals. The aim of this investigation was to compare intra‐ and interindividual variance in IVR (as U dL−1 per U kg−1) for FVIII and plasma‐derived FIX in a cohort of non‐bleeding patients with haemophilia. The data were collected retrospectively from six clinical studies, yielding 297 IVR determinations in 50 patients with haemophilia A and 93 determinations in 13 patients with haemophilia B. For FVIII, the mean variance within patients exceeded the between‐patient variance. Thus, an individually determined IVR value is apparently no more informative than an average, or population, value for the dosing of FVIII. There was no apparent relationship between IVR and age of the patient (1.5–67 years). For FIX, the mean variance within patients was lower than the between‐patient variance, and there was a significant positive relationship between IVR and age (13–69 years). From these data, it seems probable that using an individual IVR confers little advantage in comparison to using an age‐specific population mean value. Dose tailoring of coagulation factor treatment has been applied successfully after determination of the entire single‐dose curve of FVIII:C or FIX:C in the patient and calculation of the relevant pharmacokinetic parameters. However, the findings presented here do not support the assumption that dosing of FVIII or FIX can be individualized on the basis of a clinically determined IVR value.
Aims To create a general physiologically based pharmacokinetic (PBPK) model for drug disposition in infants and children, covering the age range from birth to adulthood, and to evaluate it with ...theophylline and midazolam as model drugs.
Methods Physiological data for neonates, 0.5‐, 1‐, 2‐, 5‐, 10‐ and 15‐year‐old children, and adults, of both sexes were compiled from the literature. The data comprised body weight and surface area, organ weights, vascular and interstitial spaces, extracellular body water, organ blood flows, cardiac output and glomerular filtration rate. Tissue: plasma partition coefficients were calculated from rat data and unbound fraction (fu) of the drug in human plasma, and age‐related changes in unbound intrinsic hepatic clearance were estimated from CYP1A2 and CYP2E1 (theophylline) and CYP3A4 (midazolam) activities in vitro. Volume of distribution (Vdss), total and renal clearance (CL and CLR) and elimination half‐life (t1/2) were estimated by PBPK modelling, as functions of age, and compared with literature data.
Results The predicted Vdss of theophylline was 0.4‐0.6 l kg−1 and showed only a modest change with age. The median prediction error (MPE) compared with literature data was 3.4%. Predicted total CL demonstrated the time‐course generally reported in the literature. It was 20 ml h−1 kg−1 in the neonate, rising to 73 ml h−1 kg−1 at 5 years and then decreasing to 48 ml h−1 kg−1 in the adult. Overall, the MPE was − 4.0%. Predicted t1/2 was 18 h in the neonate, dropping rapidly to 4.6‐7.2 h from 6 months onwards, and the MPE was 24%. The predictions for midazolam were also in good agreement with literature data. Vdss ranged between 1.0 and 1.7 l kg−1 and showed only modest change with age. CL was 124 ml h−1 kg−1 in the neonate and peaked at 664 ml h−1 kg−1 at 5 years before decreasing to 425 ml h−1 kg−1 in the adult. Predicted t1/2 was 6.9 h in the neonate and attained ‘adult’ values of 2.5‐3.5 h from 1 year onwards.
Conclusions A general PBPK model for the prediction of drug disposition over the age range neonate to young adult is presented. A reference source of physiological data was compiled and validated as far as possible. Since studies of pharmacokinetics in children present obvious practical and ethical difficulties, one aim of the work was to utilize maximally already available data. Prediction of the disposition of theophylline and midazolam, two model drugs with dissimilar physicochemical and pharmacokinetic characteristics, yielded results that generally tallied with literature data. Future use of the model may demonstrate further its strengths and weaknesses.
It is known that pentoxifylline inhibits platelet aggregation
in vitro, but the effects from pentoxifylline and its main metabolites: 3,7-dimetyl-1(5´hydroxyhexyl)xanthine (R-M1 and S-M1), ...3,7-dimetyl -1(4-carboxybutyl)xanthine (M4), 3,7-dimetyl -1(3-carboxypropyl)xanthine (M5), on platelet aggregation in whole blood
in vitro and
in vivo have not been studied. We found that pentoxifylline,
rac-M1, R-M1, S-M1 and M4 significantly inhibit ADP induced platelet aggregation in whole blood
in vitro in a concentration-dependent manner, R-M1 being the most potent followed by
rac-M1, S-M1, pentoxifylline, and M4. In this series of experiments the effects on aggregation induced ATP-release were less pronounced and were only significant after treatment with pentoxifylline,
rac-M1 and R-M1, but the potency order appears to be the same. Since the metabolites are not available for use in humans, and also since each substance would be extensively metabolised
in vivo, we made an attempt to estimate the relative contribution of each substance to the total effect of pentoxifylline
in vivo. Previously published concentrations of pentoxifylline and these metabolites in humans, after administration of pentoxifylline, were used in combination with the potency ratios from this study. The findings from these calculations were that the main effect
in vivo comes from S-M1 followed by pentoxifylline, the other metabolites contribute less than 10% each. In conclusion: in the following potency order R-M1,
rac-M1, pentoxifylline, S-M1 and M4 all have significant effects on platelet aggregation in whole blood
in vitro. However, it appears that the main effects
in vivo are caused by S-M1 and pentoxifylline.
Physiologically based pharmacokinetic (PBPK) models can be used to predict drug disposition in humans from animal data and the influence of disease or other changes in physiology on the ...pharmacokinetics of a drug. The potential usefulness of a PBPK model must however be balanced against the considerable effort needed for its development. Proposed methods to simplify PBPK modeling include predicting the necessary tissue:blood partition coefficients (kp) from physicochemical data on the drug instead of determining them in vivo, formal lumping of model compartments, and replacing the various kp values of the organs and tissues by only two values, for "fat" and "lean" tissues, respectively. The aim of this study was to investigate the effects of simplifying complex PBPK models on their ability to predict drug disposition in humans. Arterial plasma concentration curves of fentanyl and pethidine were simulated by means of a number of successively reduced models. Median absolute prediction errors were used to evaluate the performance of each model, in relation to arterial plasma concentration data from clinical studies, and the Wilcoxon matched pairs test was used for comparison of predictions. An originally diffusion-limited model for fentanyl was simplified to perfusion-limitation, and this model was either lumped, reducing 11 organ/tissue compartments to six, or changed to a model based on only two kp values, those of fat (used for fat and lungs) and muscle (used for all other tissues). None of these simplifications appreciably changed the predictions of arterial drug concentrations in the 10 patients. Perfusion-limited models for pethidine were set up using either experimentally determined Gabrielsson et al. 1986 or theoretically calculated Davis and Mapleson 1993 kp values, and predictions using the former were found to be significantly better. Lumping of the models did not appreciably change the predictions; however, going from a full set of kp values to only two ("fat" and "lean") had an adverse effect. Using a kp for lungs determined either in rats or indirectly in humans Persson et al. 1988, i.e., a total of three kp values, improved these predictions. In conclusion, this study strongly suggested that complex PBPK models for lipophilic basic drugs may be considerably reduced with marginal loss of power to predict standard plasma pharmacokinetics in humans. Determination of only two or three kp values instead of a "full" set can mean an important reduction of experimental work to define a basic model. Organs of particular pharmacological or toxicological interest should of course be investigated separately as needed. This study also suggests and applies a simple method for statistical evaluation of the predictions of PBPK models.
Abstract 1416
A population pharmacokinetic (PK) model of a recombinant FVIII (rFVIII) was established on ADVATE® (Antihemophilic Factor (Recombinant), Plasma/Albumin-Free Method) studies in pediatric ...and adult patients with hemophilia A. The objective of this analysis was to evaluate the effect of reduced PK sampling time points on the estimated PK parameters in the population PK model.
Plasma FVIII activity PK data were collected for 3 ADVATE® clinical trials in previously treated patients: 184 full PK data sets (11 time points) for 100 adults/adolescents, aged 10 to 65 years, and from 52 reduced sample PK data sets (5 time points) for 52 children, aged 1 to 6 years. A population PK analysis was conducted on a two-compartment structure model and the covariate effect of age and weight was explored.
Four reduced sampling scenarios from the full 10 post-infusion sampling time points, were investigated: 1) Reduced to 4 (1 hr, 9 hr, 24 hr, and 48 hr), 2) Reduced to 3 (6 hr, 24 hr, and 48 hr), 3) Reduced to 2 (6 hr and 24 hr), and 4) Reduced to 1 sampling time points (24 hr post-infusion). After applying the reduced sampling on a random 10% of sampling set at a time in the population PK model, the differences in model estimates and individual PK estimates between full and reduced sampling, were evaluated.
The two-compartment population PK model adequately described the data. Clearance (CL) was significantly correlated with age and body weight and central volume of distribution was also related with body weight.
Absolute deviations (%) from the estimates using full PK sampling in the Individual PK estimates (CL, Vss, and Half-life) using each of the reduced sampling time points were showed in the below table.
Median Absolute Deviation % from Full SamplingReduced to 4Reduced to 3Reduced to 2Reduced to 1Clearance mL/(Kg*hr)2.793.153.065.34Volume dL/kg2.233.883.944.20Half-life hr2.673.754.034.43
It appears that PK parameters estimated using population PK model are robust to reduced sampling time points. Accurate measurement of PK on reduced samples gives patients and clinicians the opportunity to design treatment regimens that are better tailored to individuals.
Oh:Baxter: Employment. Björkman:Baxter: Consultancy; Octapharma: Consultancy. Schroth:Baxter: Employment. Fritsch:Baxter: Employment. Collins:NovoNordisk: Consultancy, Honoraria, The EACH2 registry was funded by Novonordisk; Baxter Healthcare: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Fischer:Baxter: Consultancy; NovoNordisk: Consultancy. Blanchette:Bayer: Consultancy; Baxter: Research Support. Casey:Baxter: Employment. Spotts:Baxter: Employment. Ewenstein:Baxter Bioscience: Employment.
Correct dosing of drugs in neonates, infants and children is hampered by a general lack of knowledge about drug disposition in this population. Suggested methods to improve our knowledge without ...performing conventional full-scale investigations include population pharmacokinetic studies, allometric scaling of drug disposition according to bodyweight and in silico prediction of pharmacokinetics. The last method entails scaling of pharmacokinetic parameters according to age-dependent changes in drug absorption and elimination capacity, plasma protein binding and physiological characteristics of the subjects. Maturation (or ontogeny) of the drug-metabolising part of the cytochrome P450 (CYP) enzyme system is thus an important factor in the calculations for most drugs. The aim of this commentary is to test and critically examine the proposed methods to estimate hepatic clearance (CL) as a function of age (0-20 years), with CYP3A-mediated metabolism as the case in point. Midazolam and alfentanil were used as model drugs. Allometric scaling failed to predict the CL of midazolam and alfentanil in neonates. Calculations using in vitro findings on CYP maturation gave better estimates for neonates but very divergent ones for older infants and children. This was chiefly due to very different data on CYP3A4/5 ontogeny in three published studies. In the age range where full adult CYP activity per gram of liver could be assumed, allometric scaling and in silico predictions gave similar results. These predictions were also in approximate agreement with clinical data.The findings with the two model drugs can very probably be generalised to most drugs cleared by CYP-dependent hepatic metabolism. Allometric scaling accounts for development of body size and function but not for the fact that the drug-metabolising capacity of the liver is generally low at birth. The crucial question in the prediction of CL is thus when the activity of the applicable CYP isoform(s) attains adult levels. There are still not enough data on this, particularly when different studies even on the same CYP isoform have given very divergent results. It may also be pointed out that CYP ontogeny is an area where we have at least some information. There are several other important developmental changes about which we know practically nothing. Thus, while allometric scaling is generally unreliable for prediction in neonates and infants, the alternative method of in silico prediction can at present be used only to obtain tentative initial estimates of drug CL. Neither of the methods can be used as a substitute for actual clinical studies.
The aim of this study was to evaluate the use of limited blood sampling and Bayesian analysis to estimate the pharmacokinetics (PK) and tailor the dose of factor VIII (FVIII) in an individual ...patient. In a Bayesian analysis, PK parameters are estimated from only a few plasma concentration measurements, using a previously established PK model. First the necessary model was created using intense blood sampling FVIII data from 10 patients. Then FVIII data from another 21 patients were used for ‘clinical’ evaluation. Three scenarios were created retrospectively by reduction of the original 7‐sample data set; blood sampling at 4, 24 and 48 h, at 8 and 30 h and at 24 h after the infusion. PK parameters were estimated for each individual using Bayesian analysis and compared with those obtained using conventional methods from the full data. The accuracy of predictions of FVIII levels during prophylactic treatment 5–17 months later and implications for dose tailoring were also investigated. Blood sampling at 4, 24 and 48 h was found to give practically the same PK information as a full, conventional (7–10‐sample) study. Even a single 24‐h FVIII level provided adequate data for initial dose tailoring and gave predictions of FVIII levels 5–17 months later that were not appreciably worse than predictions based on the full PK analysis. By contrast, dose tailoring based on body weight failed completely. In conclusion, PK‐based dose tailoring of FVIII can be performed using limited blood sampling during prophylactic treatment.
PDF
