Development of a Tumour Growth Inhibition Model to Elucidate the Effects of Ritonavir on Intratumoural Metabolism and Anti-tumour Effect of Docetaxel in a Mouse Model for Hereditary Breast Cancer

In a mouse tumour model for hereditary breast cancer, we previously explored the anti-cancer effects of docetaxel, ritonavir and the combination of both and studied the effect of ritonavir on the intratumoural concentration of docetaxel. The objective of the current study was to apply pharmacokinetic (PK)-pharmacodynamic (PD) modelling on this previous study to further elucidate and quantify the effects of docetaxel when co-administered with ritonavir. PK models of docetaxel and ritonavir in plasma and in tumour were developed. The effect of ritonavir on docetaxel concentration in the systemic circulation of Cyp3a knock-out mice and in the implanted tumour (with inherent Cyp3a expression) was studied, respectively. Subsequently, we designed a tumour growth inhibition model that included the inhibitory effects of both docetaxel and ritonavir. Ritonavir decreased docetaxel systemic clearance with 8% (relative standard error 0.4%) in the co-treated group compared to that in the docetaxel only-treated group. The docetaxel concentration in tumour tissues was significantly increased by ritonavir with mean area under the concentration-time curve 2.5-fold higher when combined with ritonavir. Observed tumour volume profiles in mice could be properly described by the PK/PD model. In the co-treated group, the enhanced anti-tumour effect was mainly due to increased docetaxel tumour concentration; however, we demonstrated a small but significant anti-tumour effect of ritonavir addition (p value <0.001). In conclusion, we showed that the increased anti-tumour effect observed when docetaxel is combined with ritonavir is mainly caused by enhanced docetaxel tumour concentration and to a minor extent by a direct anti-tumour effect of ritonavir.

Chemical composition of particulate matter and element fluxes in the eastern Norwegian Sea, NS Komsomolets wreck site area

This paper reports results of a geochemical study of suspended particulate matter and particle fluxes in the Norwegian Sea above the Bear Island slope. Concentrations of suspended particles and the main components of suspended matter were determined in the euphotic, intermediate (clean water), and bottom nepheloid layers. It was shown that biogenic components are predominant in water above the nepheloid layer, whereas suspended matter of the nepheloid layer is formed by resuspension of lithogenic components of bottom sediments. Chemical compositions of suspended matter and material collected in sediment traps are identical.

Utilización de modelos hidráulicos bidimensionales en la determinación del tiempo de concentración

El tiempo de concentración de una cuenca sigue siendo relativamente desconocido para los ingenieros. El procedimiento habitual en un estudio hidrológico es calcularlo según varias fórmulas escogidas entre las existentes para después emplear el valor medio obtenido. De esta media se derivan los demás resultados hidrológicos, resultados que influirán en el futuro dimensionamiento de las infraestructuras. Este trabajo de investigación comenzó con el deseo de conseguir un método más fiable y objetivo que permitiera obtener el tiempo de concentración. Dada la imposibilidad de poner en práctica ensayos hidrológicos en una cuenca física real, ya que no resulta viable monitorizar perfectamente la precipitación ni los caudales de salida, se planteó llevar a cabo los ensayos de forma simulada, con el empleo de modelos hidráulicos bidimensionales de lluvia directa sobre malla 2D de volúmenes finitos. De entre todos los disponibles, se escogió InfoWorks ICM, por su rapidez y facilidad de uso. En una primera fase se efectuó la validación del modelo hidráulico elegido, contrastando los resultados de varias simulaciones con la formulación analítica existente. Posteriormente, se comprobaron los valores de los tiempos de concentración obtenidos con las expresiones referenciadas en la bibliografía, consiguiéndose resultados muy satisfactorios. Una vez verificado, se ejecutaron 690 simulaciones de cuencas tanto naturales como sintéticas, incorporando variaciones de área, pendiente, rugosidad, intensidad y duración de las precipitaciones, a fin de obtener sus tiempos de concentración y retardo. Esta labor se realizó con ayuda de la aceleración del cálculo vectorial que ofrece la tecnología CUDA (Arquitectura Unificada de Dispositivos de Cálculo). Basándose en el análisis dimensional, se agruparon los resultados del tiempo de concentración en monomios adimensionales. Utilizando regresión lineal múltiple, se obtuvo una nueva formulación para el tiempo de concentración. La nueva expresión se contrastó con las formulaciones clásicas, habiéndose obtenido resultados equivalentes. Con la exposición de esta nueva metodología se pretende ayudar al ingeniero en la realización de estudios hidrológicos. Primero porque proporciona datos de manera sencilla y objetiva que se pueden emplear en modelos globales como HEC-HMS. Y segundo porque en sí misma se ha comprobado como una alternativa realmente válida a la metodología hidrológica habitual. Time of concentration remains still fairly imprecise to engineers. A normal hydrological study goes through several formulae, obtaining concentration time as the median value. Most of the remaining hydrologic results will be derived from this value. Those results will determine how future infrastructures will be designed. This research began with the aim to acquire a more reliable and objective method to estimate concentration times. Given the impossibility of carrying out hydrological tests in a real watershed, due to the difficulties related to accurate monitoring of rainfall and derived outflows, a model-based approach was proposed using bidimensional hydraulic simulations of direct rainfall over a 2D finite-volume mesh. Amongst all of the available software packages, InfoWorks ICM was chosen for its speed and ease of use. As a preliminary phase, the selected hydraulic model was validated, checking the outcomes of several simulations over existing analytical formulae. Next, concentration time values were compared to those resulting from expressions referenced in the technical literature. They proved highly satisfactory. Once the model was properly verified, 690 simulations of both natural and synthetic basins were performed, incorporating variations of area, slope, roughness, intensity and duration of rainfall, in order to obtain their concentration and lag times. This job was carried out in a reasonable time lapse with the aid of the parallel computing platform technology CUDA (Compute Unified Device Architecture). Performing dimensional analysis, concentration time results were isolated in dimensionless monomials. Afterwards, a new formulation for the time of concentration was obtained using multiple linear regression. This new expression was checked against classical formulations, obtaining equivalent results. The publication of this new methodology intends to further assist the engineer while carrying out hydrological studies. It is effective to provide global parameters that will feed global models as HEC-HMS on a simple and objective way. It has also been proven as a solid alternative to usual hydrology methodology.

Potentiation of the bioavailability of daidzin by an extract of Radix puerariae.

The dose effect of pure daidzin on the suppression of ethanol intake in Syrian golden hamsters was compared with that of crude daidzin contained in a methanol extract of Radix puerariae (RP). EC50 values estimated from the graded dose-response curves for pure daidzin and RP extract daidzin are 23 and 2.3 mg per hamster per day, respectively. Apparently the antidipsotropic activity of the RP extract cannot be accounted for solely by its daidzin content (22 mg/g). In addition to daidzin, six other isoflavones were identified in the RP extract and quantified--namely, puerarin (160 mg per g of extract), genistin (3.7 mg/g), daidzein (2.6 mg/g), daidzein-4',7-diglucoside (1.2 mg/g), genistein (0.2 mg/g), and formononetin (0.16 mg/g). None of these, administered either alone or combined, contributes in any significant way to the antidipsotropic activity of the extract. Plasma daidzin concentration-time curves determined in hamsters administered various doses of pure daidzin or RP extract by i.p.injection indicate that the crude extract daidzin has approximately 10 times greater bioavailability than the pure compound. Reconstruction of the dose-response effects for pure and crude daidzin using bioavailable daidzin rather than administered dose gives a single curve. Synthetic daidzin added to the RP extract acquires the bioavailability of the endogenous daidzin that exists naturally in the extract. These results show that (i) daidzin is the major active principle in methanol extracts of RP, and (ii) additional constituents in the methanol extract of RP assist uptake of daidzin in golden hamsters.

Population pharmacokinetic analysis of mycophenolic acid in renal transplant recipients following oral administration of mycophenolate mofetil

Aim To develop a population pharmacokinetic model for mycophenolic acid in adult kidney transplant recipients, quantifying average population pharmacokinetic parameter values, and between- and within-subject variability and to evaluate the influence of covariates on the pharmacokinetic variability. Methods Pharmacokinetic data for mycophenolic acid and covariate information were previously available from 22 patients who underwent kidney transplantation at the Princess Alexandra Hospital. All patients received mycophenolate mofetil 1 g orally twice daily. A total of 557 concentration-time points were available. Data were analysed using the first-order method in NONMEM (version 5 level 1.1) using the G77 FORTRAN compiler. Results The best base model was a two-compartment model with a lag time (apparent oral clearance was 271 h(-1), and apparent volume of the central compartment 981). There was visual evidence of complex absorption and time-dependent clearance processes, but they could not be successfully modelled in this study. Weight was investigated as a covariate, but no significant relationship was determined. Conclusions The complexity in determining the pharmacokinetics of mycophenolic acid is currently underestimated. More complex pharmacokinetic models, though not supported by the limited data collected for this study, may prove useful in the future. The large between-subject and between-occasion variability and the possibility of nonlinear processes associated with the pharmacokinetics of mycophenolic acid raise questions about the value of the use of therapeutic monitoring and limited sampling strategies.

Sampling times for monitoring tacrolimus in stable adult liver transplant recipients

The aim of this study was to determine the most informative sampling time(s) providing a precise prediction of tacrolimus area under the concentration-time curve (AUC). Fifty-four concentration-time profiles of tacrolimus from 31 adult liver transplant recipients were analyzed. Each profile contained 5 tacrolimus whole-blood concentrations (predose and 1, 2, 4, and 6 or 8 hours postdose), measured using liquid chromatography-tandem mass spectrometry. The concentration at 6 hours was interpolated for each profile, and 54 values of AUC(0-6) were calculated using the trapezoidal rule. The best sampling times were then determined using limited sampling strategies and sensitivity analysis. Linear mixed-effects modeling was performed to estimate regression coefficients of equations incorporating each concentration-time point (C0, C1, C2, C4, interpolated C5, and interpolated C6) as a predictor of AUC(0-6). Predictive performance was evaluated by assessment of the mean error (ME) and root mean square error (RMSE). Limited sampling strategy (LSS) equations with C2, C4, and C5 provided similar results for prediction of AUC(0-6) (R-2 = 0.869, 0.844, and 0.832, respectively). These 3 time points were superior to C0 in the prediction of AUC. The ME was similar for all time points; the RMSE was smallest for C2, C4, and C5. The highest sensitivity index was determined to be 4.9 hours postdose at steady state, suggesting that this time point provides the most information about the AUC(0-12). The results from limited sampling strategies and sensitivity analysis supported the use of a single blood sample at 5 hours postdose as a predictor of both AUC(0-6) and AUC(0-12). A jackknife procedure was used to evaluate the predictive performance of the model, and this demonstrated that collecting a sample at 5 hours after dosing could be considered as the optimal sampling time for predicting AUC(0-6).

Free mycophenolic acid should be monitored in renal transplant recipients with hypoalbuminemia

The current approach for therapeutic drug monitoring in renal transplant recipients receiving mycophenolate mofetil (MMF) is measurement of total mycophenolic acid (MPA) concentration. Because MPA is highly bound, during hypoalbuminemia the total concentration no longer reflects the free (pharmacologically active) concentration. The authors investigated what degree of hypoalbuminemia causes a significant change in protein binding and thus percentage free MPA. Forty-two renal transplant recipients were recruited for the study. Free and total concentrations of MPA (predose, and 1, 3, and 6 hours post-MMF dose samples) and plasma albumin concentrations were determined on day 5 posttransplantation. Six-hour area under the concentration-time curve (AUC(0-6)) values were calculated for free and total MPA, and percentage free MPA was determined for each patient. The authors found a significant relationship between low albumin concentrations and increased percentage free MPA (Spearman correlation = -0.54, P < 0.0001). Receiver operating characteristic (ROC) curve analysis was performed on the albumin versus percentage free MPA data. The cutoff value of albumin determined from the ROC analysis that differentiated normal from elevated percentage free MPA (defined as greater than or equal to3%) in this patient population was 31 g/L. At this cutoff value albumin was found to be a good predictor of altered free MPA percentage, with a sensitivity and specificity of 0.75 and 0.80, respectively, and an area under the ROC curve of 0.79. To rationalize MMF dosing regimens in hypoalbuminemic patients (plasma albumin less than or equal to 31 g/L), clinicians should consider monitoring the free MPA concentration.

Echinacea alkamide disposition and pharmacokinetics in humans after tablet ingestion

Echinacea is a widely used herbal remedy for the treatment of colds and other infections. However, almost nothing is known about the disposition and pharmacokinetics of any of its components, particularly the alkamides and caffeic acid conjugates which are thought to be the active phytochemicals. In this investigation, we have examined serial plasma samples from 9 healthy volunteers who ingested echinacea tablets manufactured from ethanolic liquid extracts of Echinacea angustifolia and Echinacea purpurea immediately after a standard high fat breakfast. Caffeic acid conjugates could not be identified in any plasma sample at any time after tablet ingestion. Alkamides were rapidly absorbed and were measurable in plasma 20 min after tablet ingestion and remained detectable for up to 12 h. Concentration-time curves for 2,4-diene and 2-ene alkamides were determined. The maximal concentrations for the sum of alkamides in human plasma were reached within 2.3 h post ingestion and averaged 336 +/- 131 ng eq/mL plasma. No obvious differences were observed in the pharmacokinetics of individual or total alkamides in 2 additional fasted subjects who took the same dose of the echinacea preparation. This single dose study provides evidence that alkamides are orally available and that their pharmacokinetics are in agreement with the one dose three times daily regimen already recommended for echinacea.

The population pharmacokinetics of citalopram after deliberate self-poisoning: A Bayesian approach

Defining the pharmacokinetics of drugs in overdose is complicated. Deliberate self-poisoning is generally impulsive and associated with poor accuracy in dose history. In addition, early blood samples are rarely collected to characterize the whole plasma-concentration time profile and the effect of decontamination on the pharmacokinetics is uncertain. The aim of this study was to explore a fully Bayesian methodology for population pharmacokinetic analysis of data that arose from deliberate self-poisoning with citalopram. Prior information on the pharmacokinetic parameters was elicited from 14 published studies on citalopram when taken in therapeutic doses. The data set included concentration-time data from 53 patients studied after 63 citalopram overdose events (dose range: 20-1700 mg). Activated charcoal was administered between 0.5 and 4 h after 17 overdose events. The clinical investigator graded the veracity of the patients' dosing history on a 5-point ordinal scale. Inclusion of informative priors stabilised the pharmacokinetic model and the population mean values could be estimated well. There were no indications of non-linear clearance after excessive doses. The final model included an estimated uncertainty of the dose amount which in a simulation study was shown to not affect the model's ability to characterise the effects of activated charcoal. The effect of activated charcoal on clearance and bioavailability was pronounced and resulted in a 72% increase and 22% decrease, respectively. These findings suggest charcoal administration is potentially beneficial after citalopram overdose. The methodology explored seems promising for exploring the dose-exposure relationship in the toxicological settings.

Analysis of population pharmacokinetic data using NONMEM and WinBUGS

The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the posterior distribution of the log-likelihood and a posterior predictive check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance-covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the posterior predictive check. Again, the full between-subject variance-covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.

Relationships of tacrolimus pharmacokinetic measures and adverse outcomes in stable adult liver transplant recipients

Alternative measures to trough concentrations [non-trough concentrations and limited area under the concentration-time curve (AUC)] have been shown to better predict tacrolimus AUC. The aim of this study was to determine if these are also better predictors of adverse outcomes in long term liver transplant recipients. The associations between tacrolimus trough concentrations (C-0), non-trough concentrations (C-1, C-2, C-4, C-6/8), and AUC(0-12) and the occurrence of hypertension, hyperkalaemia, hyperglycaemia and nephrotoxicity were assessed in 34 clinically stable liver transplant patients. The most common adverse outcome was hypertension, prevalence of 36%. Hyperkalaemia and hyperglycaemia had a prevalence of 21% and 13%, respectively. A sequential population pharmacokinetic/pharmacodynamic approach was implemented. No significant association between predicted C-0, C-1, C-2, C-4, C-6/8 or AUC(0-12) and adverse effects could be found. Tacrolimus concentrations and AUC measures were in the same range in patients with and without adverse effects. Measures reported to provide benefit, preventing graft rejection and minimizing acute adverse effects in the early post-transplant period, were not able to predict adverse effects in stable adult liver recipients whose trough concentrations were maintained in the notional target range.

Development of a semimechanistic model to describe the pharmacokinetics of gentamicin in patients receiving Hemodialysis

The aim of this study was to ascertain the most suitable dosing schedule for gentamicin in patients receiving hemodialysis. We developed a model to describe the concentrationtime course of gentamicin in patients receiving hemodialysis. Using the model, an optimal dosing schedule was evaluated. Various dosing regimens were compared in their ability to achieve maximum concentration (C-max, >= 8 mg/L) and area under the concentration time-curve (AUC >= 70 mg(.)h/L and <= 120 mg(.)h/L per 24 hours). The model was evaluated by comparing model predictions against real data collected retrospectively. Simulations from the model confirmed the benefits of predialysis dosing. The mean optimal dose was 230 mg administered immediately before dialysis. The model was found to have good predictive performance when simulated data were compared to data observed in real patients. In summary, a model was developed that describes gentamicin pharmacokinetics in patients receiving hemodialysis. Predialysis dosing provided a superior pharmacokinetic profile than did postdialysis dosing.

Dosing of gentamicin in patients with end-stage renal disease receiving hemodialysis

The aim of this study was to evaluate dosing schedules of gentamicin in patients with end-stage renal disease and receiving hemodialysis. Forty-six patients were recruited who received gentamicin while on hemodialysis. Each patient provided approximately 4 blood samples at various times before and after dialysis for analysis of plasma gentamicin concentrations. A population pharmacokinetic model was constructed using NONMEM (version 5). The clearance of gentamicin during dialysis was 4.69 L/h and between dialysis was 0.453 L/h. The clearance between dialysis was best described by residual creatinine clearance (as calculated using the Cockcroft and Gault equation), which probably reflects both lean mass and residual clearance mechanisms. Simulation from the final population model showed that predialysis dosing has a higher probability of achieving target maximum concentration (C-max) concentrations (> 8 mg/L) within acceptable exposure limits (area under the concentration-time curve [AUC] values > 70 and < 120 mg.h/L per 24 hours) than postdialysis dosing.

Bayesian population pharmacokinetic analysis of sirolimus

Objective: It is usual that data collected from routine clinical care is sparse and unable to support the more complex pharmacokinetic (PK) models that may have been reported in previous rich data studies. Informative priors may be a pre-requisite for model development. The aim of this study was to estimate the population PK parameters of sirolimus using a fully Bayesian approach with informative priors. Methods: Informative priors including prior mean and precision of the prior mean were elicited from previous published studies using a meta-analytic technique. Precision of between-subject variability was determined by simulations from a Wishart distribution using MATLAB (version 6.5). Concentration-time data of sirolimus retrospectively collected from kidney transplant patients were analysed using WinBUGS (version 1.3). The candidate models were either one- or two-compartment with first order absorption and first order elimination. Model discrimination was based on computation of the posterior odds supporting the model. Results: A total of 315 concentration-time points were obtained from 25 patients. Most data were clustered at trough concentrations with range of 1.6 to 77 hours post-dose. Using informative priors, either a one- or two-compartment model could be used to describe the data. When a one-compartment model was applied, information was gained from the data for the value of apparent clearance (CL/F = 18.5 L/h), and apparent volume of distribution (V/F = 1406 L) but no information was gained about the absorption rate constant (ka). When a two-compartment model was fitted to the data, the data were informative about CL/F, apparent inter-compartmental clearance, and apparent volume of distribution of the peripheral compartment (13.2 L/h, 20.8 L/h, and 579 L, respectively). The posterior distribution of the volume distribution of central compartment and ka were the same as priors. The posterior odds for the two-compartment model was 8.1, indicating the data supported the two-compartment model. Conclusion: The use of informative priors supported the choice of a more complex and informative model that would otherwise have not been supported by the sparse data.