Ivelina Gueorguieva

First-in-human dose study of the novel transforming growth factor-β receptor I kinase inhibitor LY2157299 monohydrate in patients with advanced cancer and glioma.

Purpose: TGFβ signaling plays a key role in tumor progression, including malignant glioma. Small-molecule inhibitors such as LY2157299 monohydrate (LY2157299) block TGFβ signaling and reduce tumor progression in preclinical models. To use LY2157299 in the treatment of malignancies, we investigated its properties in a first-in-human dose (FHD) study in patients with cancer. Experimental Design: Sixty-five patients (58 with glioma) with measurable and progressive malignancies were enrolled. Oral LY2157299 was given as a split dose morning and evening on an intermittent schedule of 14 days on and 14 days off (28-day cycle). LY2157299 monotherapy was studied in dose escalation (part A) first and then evaluated in combination with standard doses of lomustine (part B). Safety was assessed using Common Terminology Criteria for Adverse Events version 3.0, echocardiography/Doppler imaging, serum troponin I, and brain natriuretic peptide (BNP) levels. Antitumor activity was assessed by RECIST and Macdonald criteria. Results: In part A, 16.6% (5/30) and in part B, 7.7% (2/26) of evaluable patients with glioma had either a complete (CR) or a partial response (PR). In both parts, 15 patients with glioma had stable disease (SD), 5 of whom had SD ≥6 cycles of treatment. Therefore, clinical benefit (CR+PR+SD ≥6 cycles) was observed in 12 of 56 patients with glioma (21.4%). LY2157299 was safe, with no cardiac adverse events. Conclusions: On the basis of the safety, pharmacokinetics, and antitumor activity in patients with glioma, the intermittent administration of LY2157299 at 300 mg/day is safe for future clinical investigation. Clin Cancer Res; 21(3); 553–60. ©2014 AACR.

A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis.

Population modeling of tumor size dynamics has recently emerged as an important tool in pharmacometric research. A series of new mixed‐effects models have been reported recently, and we present herein a synthetic view of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment. This selection of models will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology.

Optimal Design for Multivariate Response Pharmacokinetic Models

We address the problem of designing pharmacokinetic experiments in multivariate response situations. Criteria, based on the Fisher information matrix, whose inverse according to the Rao–Cramer inequality is the lower bound of the variance–covariance matrix of any unbiased estimator of the parameters, have previously been developed for univariate response for an individual and a population. We extend these criteria to design individual and population studies where more than one response is measured, for example, when both parent drug and metabolites are measured in plasma, multi-compartment models, where measurements are taken at more than one site, or when drug concentration and pharmacodynamic data are collected simultaneously. We assume that measurements made at distinct times are independent, but measurements made of each concentration are correlated with a response variance–covariance matrix. We investigated a number of optimisation algorithms, namely simplex, exchange, adaptive random search, simulated annealing and a hybrid, to maximise the determinant of the Fisher information matrix as required by the D-optimality criterion. The multiresponse optimal design methodology developed was applied in two case studies, where the aim was to suggest optimal sampling times. The first was a restrospective iv infusion experiment aimed to characterise the disposition kinetics of tolcapone and its two metabolites in healthy volunteers. The second was a prospective iv bolus experiment designed to estimate the tissue disposition kinetics of eight beta-blockers in rat.

Reducing Whole Body Physiologically Based Pharmacokinetic Models Using Global Sensitivity Analysis: Diazepam Case Study

AbtractThere are situations in drug development where one may wish to reduce the dimensionality and complexity of whole body physiologically based pharmacokinetic models. A technique for formal reduction of such models, based on global sensitivity analysis, is suggested. Using this approach mean and variance of tissue(s) and/or blood concentrations are preserved in the reduced models. Extended Fourier amplitude sensitivity test (FAST), a global sensitivity technique, takes a sampling approach, acknowledging parameter variability and uncertainty, to calculate the impact of parameters on concentration variance. We used existing literature rules for formal model reduction to identify all possible smaller dimensionally models. To discriminate among those competing mechanistic models extended FAST was used, whereby we treated model structural uncertainty as another factor contributing to the overall uncertainty. A previously developed 14 compartment whole body physiologically based model for diazepam disposition in rat was reduced to three alternative reduced models, with preserved arterial mean and variance concentration profiles.

Fuzzy simulation of pharmacokinetic models: case study of whole body physiologically based model of diazepam.

The aim of the present study is to develop and implement a methodology that accounts for parameter variability and uncertainty in the presence of qualitative and semi-quantitative information (fuzzy simulations) as well as when some parameters are better quantitatively defined than others (fuzzy-probabilistic approach). The fuzzy simulations method consists of (i) representing parameter uncertainty and variability by fuzzy numbers and (ii) simulating predictions by solving the pharmacokinetic model. The fuzzy-probabilistic approach includes an additional transformation between fuzzy numbers and probability density functions. To illustrate the proposed method a diazepam WBPBPK model was used where the information for hepatic intrinsic clearance determined by in vitro–in vivo scaling was semi-quantitative. The predicted concentration time profiles were compared with those resulting from a Monte Carlo simulation. Fuzzy simulations can be used as an alternative to Monte Carlo simulation.

A Phase II randomized study of galunisertib monotherapy or galunisertib plus lomustine compared with lomustine monotherapy in patients with recurrent glioblastoma

BACKGROUND The combination of galunisertib, a transforming growth factor (TGF)-β receptor (R)1 kinase inhibitor, and lomustine was found to have antitumor activity in murine models of glioblastoma. METHODS Galunisertib (300 mg/day) was given orally 14 days on/14 days off (intermittent dosing). Lomustine was given as approved. Patients were randomized in a 2:1:1 ratio to galunisertib + lomustine, galunisertib monotherapy, or placebo + lomustine. The primary objective was overall survival (OS); secondary objectives were safety, pharmacokinetics (PKs), and antitumor activity. RESULTS One hundred fifty-eight patients were randomized: galunisertib + lomustine (N = 79), galunisertib (N = 39), and placebo + lomustine (N = 40). Baseline characteristics were: male (64.6%), white (75.3%), median age 58 years, ECOG performance status (PS) 1 (63.3%), and primary glioblastoma (93.7%). The PKs of galunisertib were not altered with lomustine, and galunisertib had a median half-life of ∼8 hours. Median OS in months (95% credible interval [CrI]) for galunisertib + lomustine was 6.7 (range: 5.3-8.5), 8.0 (range: 5.7-11.7) for galunisertib alone, and 7.5 (range: 5.6-10.3) for placebo + lomustine. There was no difference in OS for patients treated with galunisertib + lomustine compared with placebo + lomustine [P (HR < 1) = 26%]. Median progression-free survival of ∼2 months was observed in all 3 arms. Among 8 patients with IDH1 mutation, 7 patients were treated with galunisertib (monotherapy or with lomustine); OS ranged from 4 to 17 months. Patients treated with galunisertib alone had fewer drug-related grade 3/4 adverse events (n = 34) compared with lomustine-treated patients (10% vs 26%). Baseline PS, post-discontinuation of bevacizumab, tumor size, and baseline levels of MDC/CCL22 were correlated with OS. CONCLUSIONS Galunisertib + lomustine failed to demonstrate improved OS relative to placebo + lomustine. Efficacy outcomes were similar in all 3 arms. CLINICAL TRIAL REGISTRATION NCT01582269, ClinicalTrials.gov.

Development of a whole body physiologically based model to characterise the pharmacokinetics of benzodiazepines. 1: Estimation of rat tissue-plasma partition ratios.

Three methods for estimation of the equilibrium tissue-to-plasma partition ratios (Kp values) in the presence of tissue concentration time data have been investigated. These are the area method, the open loop (tissue specific) method and the whole body model(closed loop) method, each with different model assumptions. Additionally, multiple imputations, a technique for dealing with deficiencies in data sets (i.e., missing tissues) is used. The estimated Kp values by the three methods have been compared and the limitations and advantages of each approach drawn. The area method, which is essentially model free, gives only a crude estimate of Kp without making any statement of its uncertainty; whereas both the open and closed loop methods provide an estimate of this. The closed loop method, where the most assumptions are made, is the approach that gives the best overall estimates of Kp, which was confirmed by comparing the predicted concentration–time profiles with experimental data. Although the estimates from the closed loop method, as well as the other two methods, are conditioned on the data, they are the most reliable for both propagating parameter variability and uncertainty through a whole body physiologically based model, as well as for extrapolation to human. A series of benzodiazepines, namely alprazolam, chlordiazepoxide, clobazam, diazepam, flunitrazepam, midazolam and triazolam in rat is used as a case study in the current investigation.

The use of a modified Fedorov exchange algorithm to optimise sampling times for population pharmacokinetic experiments

We propose a new algorithm for optimising sampling times for population pharmacokinetic experiments using D-optimality. The algorithm was used in conjunction with the population Fisher information matrix as implemented in MATLAB (PFIM 1.1 and 1.2) to evaluate population pharmacokinetic designs. The new algorithm based on the classical Fedorov exchange algorithm optimises the determinant of the population Fisher information matrix. The performance of the new algorithm has been compared with other existing algorithms including simplex, simulated annealing and adaptive random search. The new algorithm performed better especially when dealing with complex designs at the expense of longer computing times.

Diazepam Pharamacokinetics from Preclinical to Phase I Using a Bayesian Population Physiologically Based Pharmacokinetic Model with Informative Prior Distributions in Winbugs

Modelling is an important applied tool in drug discovery and development for the prediction and interpretation of drug pharmacokinetics. Preclinical information is used to decide whether a compound will be taken forwards and its pharmacokinetics investigated in human. After proceeding to human little to no use is made of these often very rich data. We suggest a method where the preclinical data are integrated into a whole body physiologically based pharmacokinetic (WBPBPK) model and this model is then used for estimating population PK parameters in human. This approach offers a continuous flow of information from preclinical to clinical studies without the need for different models or model reduction. Additionally, predictions are based upon single parameter values, but making realistic predictions involves incorporating the various sources of variability and uncertainty. Currently, WBPBPK modelling is undertaken as a two-stage process: (i) estimation (optimisation) of drug-dependent parameters by either least squares regression or maximum likelihood and (ii) accounting for the existing parameter variability and uncertainty by stochastic simulation. To address these issues a general Bayesian approach using WinBUGS for estimation of drug-dependent parameters in WBPBPK models is described. Initially applied to data in rat, this approach is further adopted for extrapolation to human, which allows retention of some parameters and updating others with the available human data. While the issues surrounding the incorporation of uncertainty and variability within prediction have been explored within WBPBPK modeling methodology they have equal application to other areas of pharmacokinetics, as well as to pharmacodynamics.

Uncertainty Analysis in Pharmacokinetics and Pharmacodynamics: Application to Naratriptan

PurposeThe aim of the study was to predict pain relief of migraine in patients following naratriptan oral (tablet) administration by using uncertainty analysis. The analysis was based on phase I pharmacokinetic naratriptan data, sumatriptan pharmacodynamic data, and naratriptan preclinical (animal) potency information, together with general knowledge as to how migraine affects oral absorption.MethodsA previously developed pharmacokinetic (PK)/pharmacodynamic (PD) model for naratriptan disposition and effect was used. The uncertain parameters in the model, which were associated with absorption and scaling between first-in-class compound sumatriptan and naratriptan, were modeled using fuzzy sets theory. Global sensitivity analysis was then used to investigate the impact of each PK/PD parameter on the responses.ResultsAcknowledging parametric uncertainty did not improve prediction of the probability of pain relief. Global sensitivity analysis demonstrated that predictions were heavily influenced by interindividual variability in pharmacodynamics, as the dose response relationship was relatively insensitive to the pharmacokinetics.ConclusionsTo predict the probability of pain relief following oral (tablet) administration of naratriptan, a simple dose response, instead of the PK/PD model, would have yielded very similar predictions. The naratriptan PK/PD model may be improved by either refining the PD model or better still by specifying the interindividual error by additional data collecting with an improved design.