Peter A. Milligan

Pharmacokinetics and Safety of Intravenous Voriconazole in Children after Single- or Multiple-Dose Administration

ABSTRACT We conducted a multicenter study of the safety, tolerability, and plasma pharmacokinetics of the parenteral formulation of voriconazole in immunocompromised pediatric patients (2 to 11 years old). Single doses of 3 or 4 mg/kg of body weight were administered to six and five children, respectively. In the multiple-dose study, 28 patients received loading doses of 6 mg/kg every 12 h on day 1, followed by 3 mg/kg every 12 h on day 2 to day 4 and 4 mg/kg every 12 h on day 4 to day 8. Standard population pharmacokinetic approaches and generalized additive modeling were used to construct the structural pharmacokinetic and covariate models used in this analysis. In contrast to that in adult healthy volunteers, elimination of voriconazole was linear in children following doses of 3 and 4 mg/kg every 12 h. Body weight was more influential than age in accounting for the observed variability in voriconazole pharmacokinetics. Elimination capacity correlated with the CYP2C19 genotype. Exposures were similar at 4 mg/kg every 12 h in children (median area under the concentration-time curve (AUC), 14,227 ng · h/ml) and 3 mg/kg in adults (median AUC, 13,855 ng · h/ml). Visual disturbances occurred in 5 (12.8%) of the 39 patients and were the only drug-related adverse events that occurred more than once. No withdrawals from the study were related to voriconazole. We conclude that pediatric patients have a higher capacity for elimination of voriconazole per kilogram of body weight than do adult healthy volunteers and that dosages of 4 mg/kg may be required in children to achieve exposures consistent with those in adults following dosages of 3 mg/kg.

Population Pharmacokinetic Analysis of Voriconazole Plasma Concentration Data from Pediatric Studies

ABSTRACT Voriconazole is a potent triazole with broad-spectrum antifungal activity against clinically significant and emerging pathogens. The present population pharmacokinetic analysis evaluated voriconazole plasma concentration-time data from three studies of pediatric patients of 2 to <12 years of age, incorporating a range of single or multiple intravenous (i.v.) and/or oral (p.o.) doses. An appropriate pharmacokinetic model for this patient population was created using the nonlinear mixed-effect modeling approach. The final model described voriconazole elimination by a Michaelis-Menten process and distribution by a two-compartment model. It also incorporated a statistically significant (P < 0.001) influence of the CYP2C19 genotype and of the alanine aminotransferase level on clearance. The model was used in a number of deterministic simulations (based on various fixed, mg/kg of body weight, and individually adjusted doses) aimed at finding suitable i.v. and p.o. voriconazole dosing regimens for pediatric patients. As a result, 7 mg/kg twice a day (BID) i.v. or 200 mg BID p.o., irrespective of body weight, was recommended for this patient population. At these doses, the pediatric area-under-the-curve (AUC) distribution exhibited the least overall difference from the adult AUC distribution (at dose levels used in clinical practice). Loading doses or individual dosage adjustments according to baseline covariates are not considered necessary in administering voriconazole to children.

Model-based drug development: a rational approach to efficiently accelerate drug development.

The pharmaceutical industry continues to face significant challenges. Very few compounds that enter development reach the marketplace, and the investment required for each success can surpass

Pharmacokinetics, Safety, and Tolerability of Voriconazole in Immunocompromised Children

1.8 billion. Despite attempts to improve efficiency and increase productivity, total investment continues to rise whereas the output of new medicines declines. With costs increasing exponentially through each development phase, it is failure in phase II and phase III that is most wasteful. In todays development paradigm, late‐stage failure is principally a result of insufficient efficacy. This is manifested as either a failure to differentiate sufficiently from placebo (shown for both novel and precedented mechanisms) or a failure to demonstrate sufficient differentiation from existing compounds. Set in this context, this article will discuss the role model‐based drug development (MBDD) approaches can and do play in accelerating and optimizing compound development strategies through a series of illustrative examples.

PKPD Modeling of VEGF, sVEGFR‐2, sVEGFR‐3, and sKIT as Predictors of Tumor Dynamics and Overall Survival Following Sunitinib Treatment in GIST

ABSTRACT The pharmacokinetics of voriconazole in children receiving 4 mg/kg intravenously (i.v.) demonstrate substantially lower plasma exposures (as defined by area under the concentration-time curve [AUC]) than those in adults receiving the same therapeutic dosage. These differences in pharmacokinetics between children and adults limit accurate prediction of pediatric voriconazole exposure based on adult dosages. We therefore studied the pharmacokinetics and tolerability of higher dosages of an i.v.-to-oral regimen of voriconazole in immunocompromised children aged 2 to <12 years in two dosage cohorts for the prevention of invasive fungal infections. The first cohort received 4 mg/kg i.v. every 12 h (q12h), then 6 mg/kg i.v. q12h, and then 4 mg/kg orally (p.o.) q12h; the second received 6 mg/kg i.v. q12h, then 8 mg/kg i.v. q12h, and then 6 mg/kg p.o. q12h. The mean values for the AUC over the dosing interval (AUCτ) for 4 mg/kg and 6 mg/kg i.v. in cohort 1 were 11,827 and 22,914 ng·h/ml, respectively, whereas the mean AUCτ values for 6 mg/kg and 8 mg/kg i.v. in cohort 2 were 17,249 and 29,776 ng·h/ml, respectively. High interpatient variability was observed. The bioavailability of the oral formulation in children was approximately 65%. The safety profiles were similar in the two cohorts and age groups. The most common treatment-related adverse event was increased gamma glutamyl transpeptidase levels. There was no correlation between adverse events and voriconazole exposure. In summary, voriconazole was tolerated to a similar degree regardless of dosage and age; the mean plasma AUCτ for 8 mg/kg i.v. in children approached that for 4 mg/kg i.v. in adults, thus representing a rationally selected dosage for the pediatric population.

PKPD Modeling of Predictors for Adverse Effects and Overall Survival in Sunitinib‐Treated Patients With GIST

The predictive value of longitudinal biomarker data (vascular endothelial growth factor (VEGF), soluble VEGF receptor (sVEGFR)‐2, sVEGFR‐3, and soluble stem cell factor receptor (sKIT)) for tumor response and survival was assessed based on data from 303 patients with imatinib‐resistant gastrointestinal stromal tumors (GIST) receiving sunitinib and/or placebo treatment. The longitudinal tumor size data were well characterized by a tumor growth inhibition model, which included, as significant descriptors of tumor size change, the model‐predicted relative changes from baseline over time for sKIT (most significant) and sVEGFR‐3, in addition to sunitinib exposure. Survival time was best described by a parametric time‐to‐event model with baseline tumor size and relative change in sVEGFR‐3 over time as predictive factors. Based on the proposed modeling framework to link longitudinal biomarker data with overall survival using pharmacokinetic–pharmacodynamic models, sVEGFR‐3 demonstrated the greatest predictive potential for overall survival following sunitinib treatment in GIST.

Modeling and simulations relating overall survival to tumor growth inhibition in renal cell carcinoma patients

A modeling framework relating exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)‐2, ‐3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was extended to include adverse effects (myelosuppression, hypertension, fatigue, and hand–foot syndrome (HFS)). Longitudinal pharmacokinetic–pharmacodynamic models of sunitinib were developed based on data from 303 patients with gastrointestinal stromal tumor. Myelosuppression was characterized by a semiphysiological model and hypertension with an indirect response model. Proportional odds models with a first‐order Markov model described the incidence and severity of fatigue and HFS. Relative change in sVEGFR‐3 was the most effective predictor of the occurrence and severity of myelosuppression, fatigue, and HFS. Hypertension was correlated best with sunitinib exposure. Baseline tumor size, time courses of neutropenia, and relative increase of diastolic blood pressure were identified as predictors of OS. The framework has potential to be used for early monitoring of adverse effects and clinical response, thereby facilitating dose individualization to maximize OS.

A non-parametric method to analyse time-course of effect in the absence of pharmacokinetic data: application to inhaled bronchodilators.

PurposeTo assess the link between tumor growth inhibition (TGI) and overall survival (OS) based on historical renal cell carcinoma (RCC) data. To illustrate how simulations can help to identify TGI thresholds based on target OS benefit [i.e., hazard ratio (HR) compared with standard of care] to support new drug development in RCC.MethodsTumor size (TS) data were modeled from 2552 patients with first-line or refractory RCC who received temsirolimus, interferon, sunitinib, sorafenib or axitinib in 10 Phase II or Phase III studies. Three model-based TGI metrics estimates [early tumor shrinkage (ETS) at week 8, 10 or 12, time to tumor growth (TTG) and growth rate] as well as baseline prognostic factors were tested in multivariate lognormal models of OS. Model performance was evaluated by posterior predictive check of the OS distributions and hazard ratio across treatments.ResultsTTG was the best TGI metric to predict OS. However, week 8 ETS had a satisfactory performance and was employed in order to maximize clinical utilization. The week 8 ETS to OS model was then used to simulate clinically relevant ETS thresholds for future Phase II studies with investigational treatments.ConclusionsThe published OS model and resultant simulations can be leveraged to support Phase II design and predict expected OS and HR (based on early observed TGI data obtained in Phase II or Phase III studies), thereby informing important mRCC development decisions, e.g., Go/No Go and dose regimen selection.

476PA MODEL RELATING OVERALL SURVIVAL TO TUMOR GROWTH INHIBITION IN RENAL CELL CARCINOMA

In spite of the extensive use of long-acting beta(2)-agonist (LABA) bronchodilators in asthma, the actual mechanism of their in vivo duration of action is not well understood, primarily due to limitations of standard pharmacokinetic-pharmacodynamic (PKPD) analysis methodologies. We have developed a novel method of analysing lung efficacy vs. time profiles for LABAs that can be used to provide comparative information on the lung PK. We hypothesised that for compounds that do not differ in their PK at the site of PD action, but differ in their in vivo potencies, the relationship between the area under the effect curve (AUEC) and the observed maximum effect (OME) at different doses is described by the same sigmoid curve. We have illustrated this property for standard PKPD models by obtaining analytical solution and through simulations. Anaesthetised dog in vivo effect vs. time profiles were gathered for six inhaled LABA candidates that differ in their in vitro potencies. Neither lung nor systemic PK was available for any compound. Analysis of the AUEC vs. OME data, derived from the efficacy profiles, using nonlinear mixed effects modelling indicated that for four compounds, the observed differences in in vivo duration of action was due to differences in their in vivo potencies and not because of lung PK differences. Therefore, it was concluded that for these compounds, characterisation of lung PK was unlikely to differentiate their PKPD characteristics. Thus, the proposed approach helped focus resources during translational research leading to lead candidate selection.

A population pharmacokinetic analysis of sildenafil citrate in patients with erectile dysfunction

ABSTRACT Aim: Tumor growth inhibition (TGI) metrics estimated with longitudinal tumor size (TS) models have been shown to be predictive of overall survival (OS) in a variety of tumor types. Methods: TS data from 2490 patients with 1st line or refractory RCC who received temsirolimus, interferon, sunitinib, sorafenib or axitinib in 10 Phase 2 or Phase 3 studies were used. TGI metrics (Early tumor shrinkage (ETS) at week 8, 10, 12, time to growth (TTG)) as well as baseline prognostic factors were tested in a multivariate log-normal model of OS. Model performances were evaluated by posterior predictive check of the OS and hazard ratio distributions. Results: TTG was the best TGI metric to predict OS (days), but Week 8 ETS, an earlier measure, had satisfactory performance, and was employed due to its ease of clinical utility. The parameter estimates of the model with Week 8 ETS are: Parameter Estimate (SE) p-value Intercept 8.07 (0.270) TS ratio at week 8 -1.99 (0.135) Baseline hemoglobin (g/L) 0.133 (0.111) Baseline ECOG = 1 -0.400 (0. 048) Baseline ECOG = (2, 3) -0.163 (0.077) 0.033 Log(# baseline metastases) -0.209 (0.032) Baseline cor. calcium (mg/dL) -0.104 (0.019) Time from diagnosis (day) 8.0E-5 (1.7E-5) Baseline LDH (U/L) -3.7E-4 (9.2E-5) Lung metastases (yes) -0.138 (0.046) 0.002 Log(scale) -0.107 (0.020) This model was then used in simulation mode to define a clinically relevant ETS target for future Phase II studies with investigational treatments. Conclusions: A TGI-based OS model is proposed for patients with RCC. The model demonstrates good performance when fitted to data from 10 different Phase II and Phase III clinical trials. Simulations with this model help with identification of relevant ETS targets in clinical trial design. Disclosure: F. Mercier:is employed by Pharsight Consulting Services (a division of Certara, L.P.), a company that received financial support from Pfizer to complete this work; B. Houk: is employed by Pfizer; L. Claret: is employed by Pharsight Consulting Services (a division of Certara, L.P.), a company that received financial support from Pfizer to complete this work; P. Milligan: is employed by Pfizer; R. Bruno: is employed by Pharsight Consulting Services (a division of Certara, L.P.), a company that received financial support from Pfizer to complete this work.