Effects of the dual phosphatidylinositol 3-kinase/mammalian target of rapamycin inhibitor NVP-BEZ235 on the tumor vasculature: implications for clinical imaging.

Dysregulated angiogenesis and high tumor vasculature permeability, two vascular endothelial growth factor (VEGF)-mediated processes and hallmarks of human tumors, are in part phosphatidylinositol 3-kinase (PI3K) dependent. NVP-BEZ235, a dual PI3K/mammalian target of rapamycin (mTOR) inhibitor, was found to potently inhibit VEGF-induced cell proliferation and survival in vitro and VEGF-induced angiogenesis in vivo as shown with s.c. VEGF-impregnated agar chambers. Moreover, the compound strongly inhibited microvessel permeability both in normal tissue and in BN472 mammary carcinoma grown orthotopically in syngeneic rats. Similarly, tumor interstitial fluid pressure, a phenomenon that is also dependent of tumor permeability, was significantly reduced by NVP-BEZ235 in a dose-dependent manner on p.o. administration. Because RAD001, a specific mTOR allosteric inhibitor, was ineffective in the preceding experiments, we concluded that the effects observed for NVP-BEZ235 are in part driven by PI3K target modulation. Hence, tumor vasculature reduction was correlated with full blockade of endothelial nitric oxide (NO) synthase, a PI3K/Akt-dependent but mTORC1-independent effector involved in tumor permeability through NO production. In the BN472 tumor model, early reduction of permeability, as detected by K(trans) quantification using the dynamic contrast-enhanced magnetic resonance imaging contrasting agent P792 (Vistarem), was found to be a predictive marker for late-stage antitumor activity by NVP-BEZ235.

Exposure-effect population model of inolimomab, a monoclonal antibody administered in first-line treatment for acute graft-versus-host disease.

BACKGROUND AND OBJECTIVE: Inolimomab, a monoclonal antibody against interleukin (IL)-2Ralpha (CD25) has shown promising results in the treatment of corticosteroid-resistant acute graft-versus-host disease (GvHD). The objective of the present study was to characterise the pharmacokinetic and pharmacodynamic properties of inolimomab as first-line treatment in this condition. METHODS: The data came from 21 patients with acute GvHD (8 with an International Bone Marrow Transplant Registry [IBMTR] score of B, 11 with a score of C and 2 with a score of D) following haematopoietic stem cell transplantation after a median delay of 26 days (range 10-127 days). Inolimomab was administered at 0.1, 0.2, 0.3 or 0.4 mg/kg daily in association with methylprednisolone (2 mg/kg) for 8 or 16 days depending on the status at day 9. Then, for responder patients, administrations were continued three times weekly until day 28. Inolimomab concentrations and pharmacodynamic data (acute GvHD scores) were recorded during the study. The pharmacodynamic data were assessed in four grades according to the IBMTR and Glucksberg classification in parallel with Karnofsky scores. A population analysis was developed using a nonlinear mixed-effects model to define the pharmacokinetic model, to test covariates and, when apparent, to model the exposure-effect relationship by a proportional odds model. The modelling was finally qualified by a predictive check. RESULTS: The best pharmacokinetic model was two-compartmental. For each score, the most demonstrative exposure-effect graphics linked the cumulative area under the concentration-time curve to cumulated probabilities of observing a specific score. This relationship was identified as a maximum effect model for the skin (with two patient subpopulations: sensitive/less sensitive) and a linear model for the intestinal tract and liver. No covariate was identified as influencing any of these parameters. CONCLUSION: Inolimomab exposure-effect relationships as first-line treatment for acute GvHD have been identified and modelled. The discovered dose-effect relationship allows confirmation of the treatment response, thereby establishing the first step towards optimising the inolimomab dosage in future trials.

Are population pharmacokinetic and/or pharmacodynamic models adequately evaluated? A survey of the literature from 2002 to 2004.

Model evaluation is an important issue in population analyses. We aimed to perform a systematic review of all population pharmacokinetic and/or pharmacodynamic analyses published between 2002 and 2004 to survey the current methods used to evaluate models and to assess whether those models were adequately evaluated. We selected 324 articles in MEDLINE using defined key words and built a data abstraction form composed of a checklist of items to extract the relevant information from these articles with respect to model evaluation. In the data abstraction form, evaluation methods were divided into three subsections: basic internal methods (goodness-of-fit [GOF] plots, uncertainty in parameter estimates and model sensitivity), advanced internal methods (data splitting, resampling techniques and Monte Carlo simulations) and external model evaluation. Basic internal evaluation was the most frequently described method in the reports: 65% of the models involved GOF evaluation. Standard errors or confidence intervals were reported for 50% of fixed effects but only for 22% of random effects. Advanced internal methods were used in approximately 25% of models: data splitting was more often used than bootstrap and cross-validation; simulations were used in 6% of models to evaluate models by a visual predictive check or by a posterior predictive check. External evaluation was performed in only 7% of models. Using the subjective synthesis of model evaluation for each article, we judged the models to be adequately evaluated in 28% of pharmacokinetic models and 26% of pharmacodynamic models. Basic internal evaluation was preferred to more advanced methods, probably because the former is performed easily with most software. We also noticed that when the aim of modelling was predictive, advanced internal methods or more stringent methods were more often used.

Evaluation of uncertainty parameters estimated by different population PK software and methods.

The uncertainty associated with parameter estimations is essential for population model building, evaluation, and simulation. Summarized by the standard error (SE), its estimation is sometimes questionable. Herein, we evaluate SEs provided by different non linear mixed-effect estimation methods associated with their estimation performances. Methods based on maximum likelihood (FO and FOCE in NONMEM, nlme in Splus, and SAEM in MONOLIX) and Bayesian theory (WinBUGS) were evaluated on datasets obtained by simulations of a one-compartment PK model using 9 different designs. Bootstrap techniques were applied to FO, FOCE, and nlme. We compared SE estimations, parameter estimations, convergence, and computation time. Regarding SE estimations, methods provided concordant results for fixed effects. On random effects, SAEM and WinBUGS, tended respectively to under or over-estimate them. With sparse data, FO provided biased estimations of SE and discordant results between bootstrapped and original datasets. Regarding parameter estimations, FO showed a systematic bias on fixed and random effects. WinBUGS provided biased estimations, but only with sparse data. SAEM and WinBUGS converged systematically while FOCE failed in half of the cases. Applying bootstrap with FOCE yielded CPU times too large for routine application and bootstrap with nlme resulted in frequent crashes. In conclusion, FO provided bias on parameter estimations and on SE estimations of random effects. Methods like FOCE provided unbiased results but convergence was the biggest issue. Bootstrap did not improve SEs for FOCE methods, except when confidence interval of random effects is needed. WinBUGS gave consistent results but required long computation times. SAEM was in-between, showing few under-estimated SE but unbiased parameter estimations.