Evaluation of α2-Integrin Expression as a Biomarker for Tumor Growth Inhibition for the Investigational Integrin Inhibitor E7820 in Preclinical and Clinical Studies

E7820 is an orally active inhibitor of α2-integrin mRNA expression, currently tested in phases I and II. We aimed to evaluate what levels of inhibition of integrin expression are needed to achieve tumor stasis in mice, and to compare this to the level of inhibition achieved in humans. Tumor growth inhibition was measured in mice bearing a pancreatic KP-1 tumor, dosed at 12.5–200 mg/kg over 21 days. In the phase I study, E7820 was administered daily for 28 days over a range of 0–200 mg, followed by a 7-day washout period. PK-PD models were developed in NONMEM. α2-Integrin expression measured on platelets, corresponding to tumor stasis at t = 21 in 50% and 90% of the mice (Iint,50, Iint,90) were calculated. It was evaluated if these levels of inhibition could be achieved in patients at tolerable doses. One hundred nineteen α2-Integrin measurements and 210 tumor size measurements were available from mice. The relationship between PK and α2-integrin expression was modeled using an indirect-effect model, subsequently linked to an exponential tumor growth model. Iinh,50 and Iinh,90 were 14.7% (RSE 7%) and 17.9% (RSE 8%). Four hundred sixty two α2-integrin measurements were available from 29 patients. Using the schedule of 100 mg qd (MTD), α2-integrin expression was inhibited more strongly than the Iint,50 and Iint,90 in greater than 95% and greater than 50% of patients, respectively. Moderate inhibition of α2-integrin expression corresponded to tumor stasis in mice, and similar levels could be reached in patients with the dose level of 100 mg qd

A model of hypertension and proteinuria in cancer patients treated with the anti-angiogenic drug E7080

Hypertension and proteinuria are commonly observed side-effects for anti-angiogenic drugs targeting the VEGF pathway. In most cases, hypertension can be controlled by prescription of anti-hypertensive (AH) therapy, while proteinuria often requires dose reductions or dose delays. We aimed to construct a pharmacokinetic–pharmacodynamic (PK–PD) model for hypertension and proteinuria following treatment with the experimental VEGF-inhibitor E7080, which would allow optimization of treatment, by assessing the influence of anti-hypertensive medication and dose reduction or dose delays in treating and avoiding toxicity. Data was collected from a phase I study of E7080 (n = 67), an inhibitor of multiple tyrosine kinases, among which VEGF. Blood pressure and urinalysis data were recorded weekly. Modeling was performed in NONMEM, and direct and indirect response PK–PD models were evaluated. A previously developed PK model was used. An indirect response PK–PD model described the increase in BP best, while the probability of developing proteinuria toxicity in response to exposure to E7080, was best described by a Markov transition model. This model may guide clinical interventions and provide treatment recommendations for E7080, and may serve as a template model for other drugs in this class

Relationships between sirolimus dosing, concentration and outcomes in renal transplant recipients

AimTo explore relationships between sirolimus dosing, concentration and clinical outcomes.MethodsData were collected from 25 kidney transplant recipients (14 M/11 F), median 278 days after transplantation. Outcomes of interest were white blood cell (WBC) count, platelet (PLT) count, and haematocrit (HCT). A naive pooled data analysis was performed with outcomes dichotomized (Mann-Whitney U-tests).ResultsSeveral patients experienced at least one episode when WBC (n = 9), PLT (n = 12), or HCT (n = 21) fell below the lower limits of the normal range. WBC and HCT were significantly lower (P ConclusionsGiven this relationship between sirolimus concentration and effect, linked population pharmacokinetic-pharmacodynamic modelling using data from more renal transplant recipients should now be used to quantify the time course of these relationships to optimize dosing and minimize risk of these adverse outcomes

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