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Pertinez H.,University of Liverpool | Chenel M.,Institute Of Recherches Internationales Servier | Aarons L.,University of Manchester
Pharmaceutical Research | Year: 2013

Purpose: To develop a physiologically based pharmacokinetic (PBPK) model to describe the disposition of Strontium - a bone seeking agent approved in 2004 (as its Ranelate salt) for treatment of osteoporosis in post-menopausal women. Methods: The model was developed using plasma and bone exposure data obtained from ovariectomised (OVX) female rats - a preclinical model for post-menopausal osteoporosis. The final PBPK model incorporated elements from literature models for bone seeking agents allowing for description of the heterogeneity of bone tissue and also for a physiological description of bone remodelling processes. The model was implemented in MATLAB in open and closed loop configurations, and fittings of the model to exposure data to estimate certain model parameters were carried out using nonlinear regression, treating data with a naïve-pooled approach. Results: The PBPK model successfully described plasma and bone exposure of Strontium in OVX rats with parameter estimates and model behaviour in keeping with known aspects of the distribution and incorporation of Strontium into bone. Conclusions: The model describes Strontium exposure in a physiologically rationalized manner and has the potential for future uses in modelling the PK-PD of Strontium, and/or other bone seeking agents, and for scaling to model human Strontium bone exposure. © 2013 Springer Science+Business Media New York.


Stein D.J.,University of Cape Town | Picarel-Blanchot F.,Institute Of Recherches Internationales Servier | Kennedy S.H.,University of Toronto
Human Psychopharmacology | Year: 2013

Objectives Anxiety in major depression is associated with increased morbidity. The antidepressant, agomelatine, which acts as an agonist at melatonin MT1 and MT2 receptors and as an antagonist at serotonin 5-HT2C receptors, has demonstrated efficacy and safety in both major depression and generalized anxiety disorder. Here, we investigated the efficacy of agomelatine in anxious depression. Methods Data from three placebo-controlled short-term trials of agomelatine and three comparative studies of agomelatine versus fluoxetine, sertraline, and venlafaxine were pooled. Effects of agomelatine on anxiety symptoms were assessed with the Hamilton Anxiety Rating Scale in four studies (one vs placebo and three vs active comparator) and with the Hamilton Depression Rating Scale (HAMD) anxiety subscore in all six studies. Anxiolytic and antidepressant efficacies of agomelatine were assessed in patients with more severe anxiety symptoms at baseline (score ≥5 on HAMD anxiety subscore). Results Agomelatine had a significantly greater effect on anxiety symptoms than both placebo and a number of comparator antidepressants. In more anxious depressed patients, agomelatine had a significantly greater effect on anxiety and depressive symptoms than both placebo and comparator antidepressants. Conclusion Once-a-day oral agomelatine is a new, efficacious alternative option for the treatment of anxiety in patients with major depression. Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd.


Bertrand J.,University Paris Diderot | Comets E.,University Paris Diderot | Chenel M.,Institute Of Recherches Internationales Servier | Mentre F.,University Paris Diderot
Biometrics | Year: 2012

Nonlinear mixed effects models allow investigating individual differences in drug concentration profiles (pharmacokinetics) and responses. Pharmacogenetics focuses on the genetic component of this variability. Two tests often used to detect a gene effect on a pharmacokinetic parameter are (1) the Wald test, assessing whether estimates for the gene effect are significantly different from 0 and (2) the likelihood ratio test comparing models with and without the genetic effect. Because those asymptotic tests show inflated type I error on small sample size and/or with unevenly distributed genotypes, we develop two alternatives and evaluate them by means of a simulation study. First, we assess the performance of the permutation test using the Wald and the likelihood ratio statistics. Second, for the Wald test we propose the use of the F-distribution with four different values for the denominator degrees of freedom. We also explore the influence of the estimation algorithm using both the first-order conditional estimation with interaction linearization-based algorithm and the stochastic approximation expectation maximization algorithm. We apply these methods to the analysis of the pharmacogenetics of indinavir in HIV patients recruited in the COPHAR2-ANRS 111 trial. Results of the simulation study show that the permutation test seems appropriate but at the cost of an additional computational burden. One of the four F-distribution-based approaches provides a correct type I error estimate for the Wald test and should be further investigated. © 2011, The International Biometric Society.


Galizzi J.-P.,Institute Of Recherche Servier | Lockhart B.P.,Institute Of Recherche Servier | Bril A.,Institute Of Recherches Internationales Servier
Drug Metabolism and Drug Interactions | Year: 2013

Translational research is a continuum between clinical and basic research where the patient is the center of the research process. It brings clinical research to a starting point for the drug discovery process, permitting the generation of a more robust pathophysiological hypothesis essential for a better selection of drug targets and candidate optimization. It also establishes the basis of early proof for clinical concept studies, preferably in phase I, for which biomarkers and surrogate endpoints can often be used. Systems biology is a prerequisite approach to translational research where technologies and expertise are integrated and articulated to support efficient and productive realization of this concept. The first component of systems biology relies on omics-based technologies and integrates the changes in variables, such as genes, proteins and metabolites, into networks that are responsible for an organism' s normal and diseased state. The second component of systems biology is in the domain of computational methods, where simulation and modeling create hypotheses of signaling pathways, transcription networks, physiological processes or even cell- or organism-based models. The simulations aim to show the origin of perturbations of the system that lead to pathological states and what treatment could be achieved to ameliorate or normalize the system. This review discusses how translational research and systems biology together could improve global understanding of drug targets, suggest new targets and approaches for therapeutics, and provide a deeper understanding of drug effects. Taken together, these types of analyses can lead to new therapeutic options while improving the safety and efficacy of new and existing medications.


Hubert S.,Agency for Science, Technology and Research Singapore | Abastado J.-P.,Agency for Science, Technology and Research Singapore | Abastado J.-P.,Institute Of Recherches Internationales Servier
Medecine/Sciences | Year: 2014

Metastasis is the main cause of cancer-related death. While the development of clinically detectable metastases occurs only at late time points, recent data obtained in mice and humans indicate that cancer cell dissemination is an early event in the progression of several types of cancer. However, disseminated cancer cells can remain dormant for prolonged periods of time. Then, how do cancer cells acquire the ability to disseminate so early? What are the selective pressures driving their dissemination? What are the signals controlling dormancy and why do some cancer cells eventually escape these controls? The present review presents our current understanding on these questions and how this novel paradigm could be translated to the clinic. © 2014 médecine/sciences-Inserm.

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