Comparative Effects of CT Imaging Measurement on RECIST End Points and Tumor Growth Kinetics Modeling.

Quantitative assessments of tumor burden and modeling of longitudinal growth could improve phase II oncology trials. To identify obstacles to wider use of quantitative measures we obtained recorded linear tumor measurements from three published lung cancer trials. Model-based parameters of tumor burden change were estimated and compared with similarly sized samples from separate trials. Time-to-tumor growth (TTG) was computed from measurements recorded on case report forms and a second radiologist blinded to the form data. Response Evaluation Criteria in Solid Tumors (RECIST)-based progression-free survival (PFS) measures were perfectly concordant between the original forms data and the blinded radiologist re-evaluation (intraclass correlation coefficient = 1), but these routine interrater differences in the identification and measurement of target lesions were associated with an average 18-week delay (range, -20 to 55 weeks) in TTG (intraclass correlation coefficient = 0.32). To exploit computational metrics for improving statistical power in small clinical trials will require increased precision of tumor burden assessments.

Predicting Plasma Olanzapine Concentration Following a Change in Dosage: A Population Pharmacokinetic Study.

INTRODUCTION: Due to high inter-individual variability in peripheral pharmacokinetic parameters, dosing of antipsychotics currently relies on clinical trial-and-error, and predicting antipsychotic plasma concentrations before changing a dose has been a challenge. METHODS: Patients with schizophrenia receiving a stable dose of olanzapine were included. 2 plasma samples were collected at 2 given time points for the measurement of plasma olanzapine concentrations. At least 7 days after a dosage change of olanzapine, a third sample was collected. The plasma concentration of the third sample was predicted in a blinded fashion using a mixed-effects model with NONMEM(®), using the following information: the 2 baseline plasma concentrations, the interval between the last dose and blood draw, and clinical and demographic information. RESULTS: 31 subjects (mean±SD age=56.0±11.6; 19 men) were enrolled. The mean prediction (95% confidence interval) errors were 1.6 (-2.8 to 6.0) ng/mL. A highly significant correlation was observed between the observed and predicted concentrations of the third sample (r=0.91, p<0.001). DISCUSSION: Plasma olanzapine concentrations following an actual dosage change can be predicted in advance with a high degree of certainty.

Estimation of renal cell carcinoma treatment effects from disease progression modeling.

To improve future drug development efficiency in renal cell carcinoma (RCC), a disease-progression model was developed with longitudinal tumor size data from a phase III trial of sorafenib in RCC. The best-fit model was externally evaluated on 145 placebo-treated patients in a phase III trial of pazopanib; the model incorporated baseline tumor size, a linear disease-progression component, and an exponential drug effect (DE) parameter. With the model-estimated effect of sorafenib on RCC growth, we calculated the power of randomized phase II trials between sorafenib and hypothetical comparators over a range of effects. A hypothetical comparator with 80% greater DE than sorafenib would have 82% power (one-sided α = 0.1) with 50 patients per arm. Model-based quantitation of treatment effect with computed tomography (CT) imaging offers a scaffold on which to develop new, more efficient, phase II trial end points and analytic strategies for RCC.

Growth of screen-detected abdominal aortic aneurysms in men: a bayesian analysis.

There is considerable interindividual variability in the growth of abdominal aortic aneurysms (AAAs), but an individual's growth observations, risk factors, and biomarkers could potentially be used to tailor surveillance. To assess the potential for tailoring surveillance, this study determined the accuracy of individualized predictions of AAA size at the next surveillance observation. A hierarchical Bayesian model was fitted to a total of 1,732 serial ultrasound measurements from 299 men in whom ultrasound screening identified an AAA. The data were best described by a nonlinear model with a constant first derivative of the AAA growth rate with size. The area under the receiver operating characteristic (ROC) curves for predicting whether an AAA was ≥40 or ≥50 mm at the next observation were 0.922 and 0.979, respectively, and the median root mean squared error was 2.52 mm. These values were nearly identical for models with or without plasma D-dimer effects.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e12; doi:10.1038/psp.2012.13; advance online publication 24 October 2012.

Nonlinear population pharmacokinetics of sirolimus in patients with advanced cancer.

Sirolimus, the prototypical inhibitor of the mammalian target of rapamycin, has substantial antitumor activity. In this study, sirolimus showed nonlinear pharmacokinetic characteristics over a wide dose range (from 1 to 60 mg/week). The objective of this study was to develop a population pharmacokinetic (PopPK) model to describe the nonlinearity of sirolimus. Whole blood concentration data, obtained from four phase I clinical trials, were analyzed using a nonlinear mixed-effects modeling (NONMEM) approach. The influence of potential covariates was evaluated. Model robustness was assessed using nonparametric bootstrap and visual predictive check approaches. The data were well described by a two-compartment model incorporating a saturable Michaelis-Menten kinetic absorption process. A covariate analysis identified hematocrit as influencing the oral clearance of sirolimus. The visual predictive check indicated that the final pharmacokinetic model adequately predicted observed concentrations. The pharmacokinetics of sirolimus, based on whole blood concentrations, appears to be nonlinear due to saturable absorption.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e17; doi:10.1038/psp.2012.18; advance online publication 5 December 2012.

Genetic variation in CYP3A43 explains racial difference in olanzapine clearance.

The antipsychotic drug, olanzapine, one of the most widely used drugs in clinical medicine, has a high rate of discontinuation due to inefficacy and/or adverse effects. We identified a single nucleotide polymorphism in the drug metabolizing enzyme, cytochrome P450 3A43 (CYP3A43; rs472660), that highly significantly predicted olanzapine clearance in the Clinical Antipsychotic Trials of Intervention Effectiveness trial (P=5.9e(-7)). Moreover, at standard antipsychotic doses, 50% of individuals with the high clearance genotype (AA) have trough blood levels below the therapeutic range. Interestingly, a much higher proportion of African Americans carry the A allele compared with Caucasians (allele frequency 67 vs 14%). After accounting for CYP3A43 genotype, race is no longer a significant predictor of olanzapine clearance. Olanzapine clearance was associated with measures of clinical response. Patients with greater clearance had higher symptom ratings and were more likely to discontinue treatment due to an inadequate response. Our data identify a genetic mechanism for variation in olanzapine response and demonstrate that blood level monitoring of olanzapine treatment is advisable.

Predicting age-specific dosing of antipsychotics.

Antipsychotic medications are the standard of care for both acute and maintenance treatment of schizophrenia, the latter requiring an indefinite treatment across a patient's life span. However, dosing and tolerability of antipsychotics have been studied primarily in younger patients, and very limited data are available for age- and phase-specific dosing. This leaves the clinician with no guidance on dose adjustment as patients grow older-an issue of critical importance, especially in light of recent concerns about increased morbidity and mortality associated with antipsychotics.

Mathematics for understanding disease.

The application of mathematical models to reflect the organization and activity of biological systems can be viewed as a continuum of purpose. The far left of the continuum is solely the prediction of biological parameter values, wherein an understanding of the underlying biological processes is irrelevant to the purpose. At the far right of the continuum are mathematical models, the purposes of which are a precise understanding of those biological processes. No models in present use fall at either end of the continuum. Without question, however, the emphasis in regards to purpose has been on prediction, e.g., clinical trial simulation and empirical disease progression modeling. Clearly the model that ultimately incorporates a universal understanding of biological organization will also precisely predict biological events, giving the continuum the logical form of a tautology. Currently that goal lies at an immeasurable distance. Nonetheless, the motive here is to urge movement in the direction of that goal. The distance traveled toward understanding naturally depends upon the nature of the scientific question posed with respect to comprehending and/or predicting a particular disease process. A move toward mathematical models implies a move away from static empirical modeling and toward models that focus on systems biology, wherein modeling entails the systematic study of the complex pattern of organization inherent in biological systems.

Administration of enoxaparin by continuous infusion in a naturalistic setting: analysis of renal function and safety.

STUDY OBJECTIVE: To describe the clinical use and safety of continuous infusion (CI) enoxaparin in a naturalistic setting and to evaluate the influence of renal function on enoxaparin elimination. DESIGN: Retrospective medical record review. SETTING: 1000-bed tertiary care teaching centre. PATIENTS: Hospitalized patients that received enoxaparin by CI during a 2-year period. INTERVENTIONS: None. MEASUREMENTS: Specific details of dosage and monitoring were collected. Adverse drug reactions (ADR) were recorded. Creatinine clearance (CrCl) was calculated using Cockroft and Gault and Brater equations. A population pharmacokinetic analysis was performed using the non-linear mixed effect model (NONMEM). For patients located in the intensive care unit (ICU) and ward, POSTHOC pharmacokinetic parameter estimates were evaluated using the Wilcoxon rank-sum. Pearson correlation coefficient was calculated to determine the association between renal function and anti-Xa clearance. MAIN RESULTS: Sixty-seven patients received enoxaparin by CI of which 61.2% were in the ward and 38.8% in the ICU. The average initial rate and duration of infusion were 5.2 mg/h and 5.6 days, respectively. The number of anti-Xa concentration measurements averaged five per patient. Nine patients experienced an ADR. The most frequent ADR was gastrointestinal bleeding (n = 4). Among the 67 patients, 48 had available anti-Xa concentrations and were included in the NONMEM model. The anti-Xa CL and volume of distribution for ICU and ward patients averaged 0.64 +/- 0.34 L/h, 10.6 +/- 1.55 L and 1.01 +/- 0.39 L/h, 9.08 +/- 1.17 L, respectively. CrCl was not a significant covariate when included in the NONMEM model, and the association between CrCl and anti-Xa clearance was not significant (R2 = 0.0005; P = 0.8916). CONCLUSIONS: This study is the first to report the use and safety of prolonged CI enoxaparin. Pharmacokinetic parameters of enoxaparin differ in ICU vs. ward patients. Overall, we found the safety of CI to be comparable to subcutaneous administration. Also, we found no effect of renal function on enoxaparin elimination.