Thu Thuy Nguyen

Correlation between Fecal Concentrations of Ciprofloxacin and Fecal Counts of Resistant Enterobacteriaceae in Piglets Treated with Ciprofloxacin: toward New Means To Control the Spread of Resistance?

ABSTRACT We assessed in a piglet model the relationship between fecal ciprofloxacin concentrations and ciprofloxacin-resistant Enterobacteriaceae counts. Twenty-nine piglets were orally treated with placebo or with 1.5 or 15 mg ciprofloxacin/kg of body weight/day from day 1 (D1) to D5. Areas under the curve (AUC) of concentrations increased sharply with dose and correlated positively with AUC of resistant bacteria log counts between D1 and D9. Removing residual colonic quinolones could help to control the emergence of resistance in fecal flora.

Mathematical Modeling of Bacterial Kinetics to Predict the Impact of Antibiotic Colonic Exposure and Treatment Duration on the Amount of Resistant Enterobacteria Excreted

Fecal excretion of antibiotics and resistant bacteria in the environment are major public health threats associated with extensive farming and modern medical care. Innovative strategies that can reduce the intestinal antibiotic concentrations during treatments are in development. However, the effect of lower exposure on the amount of resistant enterobacteria excreted has not been quantified, making it difficult to anticipate the impact of these strategies. Here, we introduce a bacterial kinetic model to capture the complex relationships between drug exposure, loss of susceptible enterobacteria and growth of resistant strains in the feces of piglets receiving placebo, 1.5 or 15 mg/kg/day ciprofloxacin, a fluoroquinolone, for 5 days. The model could well describe the kinetics of drug susceptible and resistant enterobacteria observed during treatment, and up to 22 days after treatment cessation. Next, the model was used to predict the expected amount of resistant enterobacteria excreted over an average piglets lifetime (150 days) when varying drug exposure and treatment duration. For the clinically relevant dose of 15 mg/kg/day for 5 days, the total amount of resistant enterobacteria excreted was predicted to be reduced by 75% and 98% when reducing treatment duration to 3 and 1 day treatment, respectively. Alternatively, for a fixed 5-days treatment, the level of resistance excreted could be reduced by 18%, 33%, 57.5% and 97% if 3, 5, 10 and 30 times lower levels of colonic drug concentrations were achieved, respectively. This characterization on in vivo data of the dynamics of resistance to antibiotics in the colonic flora could provide new insights into the mechanism of dissemination of resistance and can be used to design strategies aiming to reduce it.

The Emergence of Linezolid Resistance among Enterococci in Intestinal Microbiota of Treated Patients Is Unrelated to Individual Pharmacokinetic Characteristics

ABSTRACT Linezolid is an antimicrobial agent for the treatment of multiresistant Gram-positive infections. We assessed the impact of linezolid on the microbiota and the emergence of resistance and investigated its relationship with plasma pharmacokinetics of the antibiotic. Twenty-eight patients were treated for the first time with linezolid administered orally (n = 17) or parenterally (n = 11) at 600 mg twice a day. Linezolid plasma pharmacokinetic analysis was performed on day 7. Colonization by fecal enterococci, pharyngeal streptococci, and nasal staphylococci were assessed using selective media with or without supplemental linezolid. The resistance to linezolid was characterized. The treatment led to a decrease of enterococci, staphylococci, and streptococci in the fecal (P = 0.03), nasal, and pharyngeal (P < 0.01) microbiotas. The appearance of resistant strains was observed only in enterococci from the fecal microbiota between the 7th and 21st days of treatment in four patients (14.3%). The resistance was mainly due for the first time to the mutation G2447T in the 23S rRNA gene. No pharmacokinetic parameters were significantly different between the patients, regardless of the appearance of resistance. The emergence of linezolid resistance during treatment was observed only in the intestinal microbiota and unrelated to pharmacokinetic parameters. However, colonization by Gram-positive bacteria was reduced as a result of treatment in all microbiotas.

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

Alisporivir is a cyclophilin inhibitor with demonstrated in vitro and in vivo activity against hepatitis C virus (HCV). We estimated the antiviral effectiveness of alisporivir alone or in combination with pegylated interferon (peg‐IFN) in 88 patients infected with different HCV genotypes treated for 4 weeks. The pharmacokinetics of the two drugs were modeled and used as driving functions for the viral kinetic model. Genotype was found to significantly affect peg‐IFN effectiveness (ɛ = 86.3 and 99.1% for genotypes 1/4 and genotypes 2/3, respectively, P < 10−7) and the loss rate of infected cells (δ = 0.22 vs. 0.39 per day in genotype 1/4 and genotype 2/3 patients, respectively, P < 10−6). Alisporivir effectiveness was not significantly different across genotypes and was high for doses ≥600 mg q.d. We simulated virologic responses with other alisporivir dosing regimens in HCV genotype 2/3 patients using the model. Our predictions consistently matched the observed responses, demonstrating that this model could be a useful tool for anticipating virologic response and optimizing alisporivir‐based therapies.

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models

BACKGROUND AND OBJECTIVE Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. METHODS Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. RESULTS The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. CONCLUSION PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr.

Individual Bayesian Information Matrix for Predicting Estimation Error and Shrinkage of Individual Parameters Accounting for Data Below the Limit of Quantification

PurposeIn mixed models, the relative standard errors (RSE) and shrinkage of individual parameters can be predicted from the individual Bayesian information matrix (MBF). We proposed an approach accounting for data below the limit of quantification (LOQ) in MBF.MethodsMBF is the sum of the expectation of the individual Fisher information (MIF) which can be evaluated by First-Order linearization and the inverse of random effect variance. We expressed the individual information as a weighted sum of predicted MIF for every possible design composing of measurements above and/or below LOQ. When evaluating MIF, we derived the likelihood expressed as the product of the likelihood of observed data and the probability for data to be below LOQ. The relevance of RSE and shrinkage predicted by MBF in absence or presence of data below LOQ were evaluated by simulations, using a pharmacokinetic/viral kinetic model defined by differential equations.ResultsSimulations showed good agreement between predicted and observed RSE and shrinkage in absence or presence of data below LOQ. We found that RSE and shrinkage increased with sparser designs and with data below LOQ.ConclusionsThe proposed method based on MBF adequately predicted individual RSE and shrinkage, allowing for evaluation of a large number of scenarios without extensive simulations.

Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory

PurposePhysiologically-based pharmacokinetic (PBPK) models are essential in drug development, but require parameters that are not always obtainable. We developed a methodology to investigate the feasibility and requirements for precise and accurate estimation of PBPK parameters using population modelling of clinical data and illustrate this for two key PBPK parameters for hepatic metabolic clearance, namely whole liver unbound intrinsic clearance (CLint,u,WL) and hepatic blood flow (Qh) in children.MethodsFirst, structural identifiability was enabled through re-parametrization and the definition of essential trial design components. Subsequently, requirements for the trial components to yield precise estimation of the PBPK parameters and their inter-individual variability were established using a novel application of population optimal design theory. Finally, the performance of the proposed trial design was assessed using stochastic simulation and estimation.ResultsPrecise estimation of CLint,u,WL and Qh and their inter-individual variability was found to require a trial with two drugs, of which one has an extraction ratio (ER) ≤ 0.27 and the other has an ER ≥ 0.93. The proposed clinical trial design was found to lead to precise and accurate parameter estimates and was robust to parameter uncertainty.ConclusionThe proposed framework can be applied to other PBPK parameters and facilitate the development of PBPK models.

Change in bacterial diversity of fecal microbiota drives mortality in a hamster model of antibiotic-induced Clostridium difficile colitis

Abstract Background C. difficile (C diff) infection results from antibiotic-induced changes in colonic microbiota. DAV131A, an oral adsorbent-based product, can sequester antibiotic (AB) residues in the gut and reduce mortality in a hamster model of moxifloxacin (MXF) or clindamycin (CM) induced C diffcolitis. We studied the link between changes of the bacterial diversity within the fecal microbiota and mortality in this model. Methods Male Syrian hamsters were administered 30 mg/kg MXF or 5 mg/kg CM subcutaneously once a day for 5 days (D1 to D5) and orally infected at D3 with 104C diffspores. They were orally administered various doses of DAV131A (0, and 200 to 900 mg/kg twice a day), from D1 to D8. Survival was monitored up to D16 and feces were collected (D1 and D3) to characterize the microbiota by 16S rRNA gene profiling. Changes of various α- (Shannon, Observed OTUs and Chao1) and β- (Bray-Curtis dissimilarity and [un]weighted UniFrac) diversity indices between D1 and D3 were obtained for each animal. We analyzed links between (i) DAV131A dose and changes of bacterial diversity and (ii) changes of bacterial diversity and mortality using non parametric tests and logistic regression. Results Data from 70 and 60 animals were available in the MXF and CM studies, among which 10 and 28 died, respectively. Increasing doses of DAV131A reduced mortality from 100% to 0% and reduced changes in bacterial diversity of the fecal microbiota. Very strong predictors of mortality were changes in Shannon and unweighted UniFrac indices, which were markedly less affected in hamsters who survived (see table below median (min; max) according to vital status and area under the ROC curve, AUROC). Died Survived p AUROC [95% CI] MXF n 10 60 Shannon -1.7 (-3.0; -1.0) -1.0 (-1.9; 0.1) <10-4 0.91 [0.80; 0.99] Unweighted UniFrac 0.61 (0.56; 0.76) 0.51 (0.37; 0.65) <10-6 0.95 [0.89; 0.99] CM n 28 32 Shannon -2.2 (-4.3; -0.4) -1.1 (-2.6; 0.0) <10-7 0.88 [0.78; 0.96] Unweighted UniFrac 0.71 (0.59; 0.84) 0.60 (0.49; 0.68) <10-10 0.94 [0.87; 0.98] Conclusion The extent of AB-induced changes in gut bacterial diversity correlated with increased mortality in a hamster model of C diff colitis. Higher doses of DAV131A protected fecal microbiota disruption and hence mortality. Disclosures C. Burdet, Da Volterra: Consultant and Research Contractor, Consulting fee; N. Saint-Lu, Da Volterra: Employee, Salary; S. Sayah-Jeanne, Da Volterra: Employee, Salary; P. Hugon, Da Volterra: Employee, Salary; F. Sablier-Gallis, Da Volterra: Employee, Salary; S. Ferreira, Genoscreen: Employee, Salary; A. Andremont, Da Volterra: Consultant, Consulting fee; F. Mentré, Da Volterra: Consultant and Research Contractor, Consulting fee; J. De Gunzburg, Da Volterra: Consultant and Shareholder, Consulting fee