Nonlinear Mixed-Effect Models for Prostate-Specific Antigen Kinetics and Link with Survival in the Context of Metastatic Prostate Cancer: a Comparison by Simulation of Two-Stage and Joint Approaches

In metastatic castration-resistant prostate cancer (mCRPC) clinical trials, the assessment of treatment efficacy essentially relies on the time-to-death and the kinetics of prostate-specific antigen (PSA). Joint modelling has been increasingly used to characterize the relationship between a time-to-event and a biomarker kinetics but numerical difficulties often limit this approach to linear models. Here we evaluated by simulation the capability of a new feature of the Stochastic Approximation Expectation-Maximization algorithm in Monolix to estimate the parameters of a joint model where PSA kinetics was defined by a mechanistic nonlinear mixed-effect model. The design of the study and the parameter values were inspired from one arm of a clinical trial. Increasingly high levels of association between PSA and survival were considered and results were compared with those found using two simplified alternatives to joint model, a two-stage and a joint sequential model. We found that joint model allowed for a precise estimation of all longitudinal and survival parameters. In particular the effect of PSA kinetics on survival could be precisely estimated, regardless of the strength of the association. In contrast, both simplified approaches led to bias on longitudinal parameters and two-stage model systematically underestimated the effect of PSA kinetics on survival. In summary we showed that joint model can be used to characterize the relationship between a nonlinear kinetics and survival. This opens the way for the use of more complex and physiological models to improve treatment evaluation and prediction in oncology.Comment: The AAPS Journal, 2015, pp.1550-741

Powers of the likelihood ratio test and the correlation test using empirical bayes estimates for various shrinkages in population pharmacokinetics.

International audienceWe compared the powers of the likelihood ratio test (LRT) and the Pearson correlation test (CT) from empirical Bayes estimates (EBEs) for various designs and shrinkages in the context of nonlinear mixed-effect modeling. Clinical trial simulation was performed with a simple pharmacokinetic model with various weight (WT) effects on volume (V). Data sets were analyzed with NONMEM 7.2 using first-order conditional estimation with interaction and stochastic approximation expectation maximization algorithms. The powers of LRT and CT in detecting the link between individual WT and V or clearance were computed to explore hidden or induced correlations, respectively. Although the different designs and variabilities could be related to the large shrinkage of the EBEs, type 1 errors and powers were similar in LRT and CT in all cases. Power was mostly influenced by covariate effect size and, to a lesser extent, by the informativeness of the design. Further studies with more models are needed

Profils patients associés à la non conformité des décisions aux recommandations de prise en charge thérapeutique des cancers du sein : utilisation de l'analyse de concepts formels

International audienceLes systèmes d'aide à la décision médicale permettent d'améliorer le suivi des recommandations de pratique clinique. OncoDoc2 est un tel système s’appuyant sur des recommandations de prise en charge du cancer du sein. Malgré son utilisation en routine lors de réunions de concertation pluridisciplinaire de sénologie, des décisions non conformes aux recommandations subsistent. L'objectif est d'utiliser l'analyse de concepts formels afin de caractériser les profils patients associés aux deux modalités de la conformité. Deux étapes de pré-traitement permettant de simplifier les données à analyser sont proposées : une réduction d'attributs par suppression de ceux non statistiquement associés à la non conformité, et un gommage sélectif de valeurs. Parmi les décisions recueillies sur 3 ans à l'hôpital Tenon, 198 concernent la reprise chirurgicale et ont été analysées. Les profils patients associés à la non conformité retrouvés sont ceux pour lesquels il n'existe pas de preuve scientifique des recommandations. Mots-clés

A pharmacokinetic -- viral kinetic model describes the effect of alisporivir monotherapy or in combination with peg-IFN on 2 hepatitis C virologic response

Alisporivir is a cyclophilin inhibitor with demonstrated in vitro and in vivo activity against hepatitis C 11 virus (HCV). We estimated antiviral effectiveness of alisporivir alone or in combination with 12 pegylated-Inteferon (peg-IFN) in 88 patients infected with different HCV genotypes treated for four 13 weeks. The pharmacokinetics of both drugs were modeled and used as driving functions for the viral 14 kinetic model. Genotype was found to significantly affect pegylated-Inteferon effectiveness ($\epsilon$= 86.3% 15 and 99.1% in genotype-1/4 and genotype-2/3, respectively, p\textless{}10 -7) and infected cells loss rate ($\delta$= 16 0.22 vs 0.39 day -1 in genotype-1/4 and genotype-2/3, respectively, p\textless{}10 -6). Alisporivir effectiveness 17 was not significantly different across genotype and was high for doses $\ge$600 mg QD. We simulated 18 virologic responses with other alisporivir dosing regimens in HCV genotype-2/3 patients using the 19 model. Our predictions consistently matched the observed responses, demonstrating that this model 20 could be a useful tool for anticipating virologic response and optimize alisporivir-based therapies

Bone events and evolution of biologic markers in Gaucher disease before and during treatment

25 pagesInternational audienceINTRODUCTION : Known biomarkers of Gaucher-disease activity are platelets, chitotriosidase, angiotensin-converting enzyme (ACE), tartrate-resistant acid phosphatase (TRAP) and ferritin. The aim of this study was to retrospectively evaluate the frequency of bone events (BE) and biomarker changes during two periods: diagnosis to first enzyme-replacement therapy (ERT) and the latter to the closing date. METHODS : BE of 62 treated patients, among the 73-patient cohort followed at Beaujon Hospital, Clichy, France, were described with Kaplan-Meier curves, and linear-mixed models were used to analyze their biomarker changes and the influence of several covariates (splenectomy, diagnosis year, genotype, age at diagnosis and sex). RESULTS : BE occurred before (54 events in 21 patients), but also during, ERT (12 events in 10 patients), with respective frequencies (95% confidence interval) at 10 years of 22.4% (13.3 to 36.3) and 20.0% (10.2 to 36.9). Biomarker slope changes before and during ERT differed significantly for platelets (+190/mm3/year and 7,035/mm3/year, respectively; P < 0.0001) and ferritin (+4% and -14%; P < 0.0001). High ferritin levels and low platelet counts at ERT onset were significantly associated with BE during ERT (P = 0.019 and 0.039, respectively). Covariates significantly influenced biomarker changes (baseline and/or slope): splenectomy affected platelets (baseline and changes), TRAP changes and chitotriosidase changes; diagnosis date influenced ACE and TRAP baseline values; and genotype influenced chitotriosidase baseline and changes. CONCLUSIONS : Platelet counts and ferritin levels and their slope changes at ERT onset seem to predict BE during treatment. Biomarker baseline values and changes are dependent on several covariables

Reconstructing Social Interactions Using an unreliable Wireless Sensor Network

International audienceIn the very active field of complex networks, research advances have largely been stimulated by the availability of empirical data and the increase in computational power needed for their analysis. These works have led to the identification of similarities in the structures of such networks arising in very different fields, and to the development of a body of knowledge, tools and methods for their study. While many interesting questions remain open on the subject of static networks, challenging issues arise from the study of dynamic networks. In particular, the measurement, analysis and modeling of social interactions are first class concerns. In this article, we address the challenges of capturing physical proximity and social interaction by means of a wireless network. In particular, as a concrete case study, we exhibit the deployment of a wireless sensor network applied to the measurement of Health Care Workers' exposure to tuberculosis infected patients in a service unit of the Bichat-Claude Bernard hospital in Paris, France. This network has continuously monitored the presence of all HCWs in all rooms of the service during a 3 month period. We both describe the measurement system that was deployed and some early analysis on the measured data. We highlight the bias introduced by the measurement system reliability and provide a reconstruction method which not only leads to a significantly more coherent and realistic dataset but also evidences phe- nomena a priori hidden in the raw data. By this analysis, we suggest that a processing step is required prior to any adequate exploitation of data gathered thanks to a non-fully reliable measurement architecture

Assessment of pain during labor with pupillometry: a prospective observational study.: Pupillometry and labor pain

International audienceBACKGROUND: Pain intensity is usually self-rated by patients with a numeric rating scale (NRS) but this scale cannot be used for noncommunicating patients. In anesthetized patients, experimental noxious stimulus increases pupillary diameter (PD) and pupillary light reflex amplitude (PLRA), the difference between PD before and after light stimulation. Labor pain is an intense acute nonexperimental stimulus, effectively relieved by epidural analgesia. In this prospective observational study, we therefore describe the effects of labor pain and pain relief with epidural analgesia on PD and PLRA, determine their association with pain intensity and determine the ability of a single measurement of PD or PLRA to assess pain. METHODS: In the first stage, pain (11-point NRS), PD, and PLRA were measured in 4 conditions in 26 laboring women: before and after epidural analgesia and in the presence and absence of a uterine contraction. Pupillometry values among the 4 conditions were compared, and the strength of the association between absolute values of pain and PD or PLRA and between pain and changes in PD or PLRA brought about by uterine contraction was assessed with r(2). In the second stage, 1 measurement was performed in 104 laboring women. The strength of the association between pain and PD or PLRA was assessed with r(2). The ability of PD or PLRA to discriminate pain (NRS > 4) was also assessed. RESULTS: In the first stage, a statistically significant increase in pain, PD, and PLRA was observed during a contraction, and this change was abolished after epidural analgesia. The r(2) for the association between pain and changes in PD (r(2) = 0.25 [95% confidence interval, 0.07-0.46] or PLRA (r(2) = 0.34 [0.14-0.56]) brought about by a uterine contraction was higher than the r(2) for the association between pain and absolute values of PD (r(2) = 0.14 [0.04-0.28]) or PLRA (r(2) = 0.22 [0.10-0.37]) suggesting a stronger association for changes than for absolute values. In the second stage, r(2) was 0.23 [0.10-0.38] for PD and 0.26 [0.11-0.40] for PLRA and the area under the receiver operating characteristics curve was 0.82 [0.73-0.91] and 0.80 [0.71-0.89], respectively. CONCLUSIONS: Changes in PD and PLRA brought about by a uterine contraction may be used as a tool to assess analgesia in noncommunicating patients

Modelling the influence of MDR1 polymorphism on digoxin pharmacokinetic parameters.

OBJECTIVES: Digoxin is a well-known probe for the activity of P-glycoprotein. The objective of this work was to apply different methods for covariate selection in non-linear mixed-effect models to study the relationship between the pharmacokinetic parameters of digoxin and the genotype for two major exons located on the multi-drug-resistance 1 (MDR1) gene coding for P-glycoprotein. METHODS: Thirty-two healthy volunteers were recruited in three pharmacokinetic drug interaction studies. The data after a single oral administration of digoxin alone were pooled. All subjects were genotyped for the MDR1 C3435T and G2677T/A genotypes. The concentration-time profile of digoxin was established using 12-16 blood samples taken between 15 min and 72 h after administration. We modelled the pharmacokinetics of digoxin using non-linear mixed-effect models. Parameter estimation was performed using the stochastic approximation EM method (SAEM). We used three methods to select the covariate model: selection from a full model using Wald tests, forward inclusion using the log-likelihood ratio test and model selection using the Bayesian Information Criterion. RESULTS: The three covariate inclusion methods led to the same final model. Carriers of two T alleles for the C3435T polymorphism in exon 26 of MDR1 had a lower apparent volume of distribution than carriers of a C allele. The only other covariate effect was a shorter absorption time-lag in women. CONCLUSION: The apparent volume of distribution of digoxin is lower in TT subjects, probably reflecting differences in bioavailability. Non-linear mixed-effect models can be useful for detecting the influence of covariates on pharmacokinetic parameters