Evaluation of model-based bioequivalence approach for single sample pharmacokinetic studies.

In a traditional pharmacokinetic (PK) bioequivalence (BE) study, a two-way crossover study is conducted, PK parameters (namely the area under the time-concentration curve [AUC] and the maximal concentration [ C max ]) are obtained by noncompartmental analysis (NCA), and the BE analysis is performed using the two one-sided test (TOST) method. For ophthalmic drugs, however, only one sample of aqueous humor, in one eye, per eye can be obtained in each patient, which precludes the traditional BE analysis. To circumvent this issue, the U.S. Food and Drug Administration (FDA) has proposed an approach coupling NCA with either parametric or nonparametric bootstrap (NCA bootstrap). The model-based TOST (MB-TOST) has previously been proposed and evaluated successfully for various settings of sparse PK BE studies. In this paper, we evaluate, via simulations, MB-TOST in the specific setting of single sample PK BE study and compare its performance to NCA bootstrap. We performed BE study simulations using a published PK model and parameter values and evaluated multiple scenarios, including study design (parallel or crossover), sampling times (5 or 10 spread across the dosing interval), and geometric mean ratio (of 0.8, 0.9, 1, and 1.25). Using the simulated structural PK model, MB-TOST performed similarly to NCA bootstrap for AUC. For C max , the latter tended to be conservative and less powerful. Our research suggests that MB-TOST may be considered as an alternative BE approach for single sample PK studies, provided that the PK model is correctly specified and the test drug has the same structural model as the reference drug.

Pharmacokinetics of Curative Tranexamic Acid in Parturients Undergoing Cesarean Delivery.

The aim of this study was to evaluate the population pharmacokinetics of tranexamic acid (TXA) administered intravenously at a single dose of 0.5 or 1 g in parturients undergoing active hemorrhagic cesarean delivery and to evaluate the influence of patient variables on TXA pharmacokinetics. Subjects from three recruiting centers were included in this PK sub-study if randomized in the experimental group (i.v TXA 0.5 g or 1 g over one minute) of the TRACES study. Blood samples and two urinary samples were collected within 6 h after TXA injection. Parametric non-linear mixed-effect modeling (Monolix v2020R1) was computed. The final covariate model building used 315 blood and 117 urinary concentrations from seventy-nine patients. A two-compartment model with a double first-order elimination from the central compartment best described the data. The population estimates of clearance (CL), central volume of distribution (V1), and half-life for a typical 70 kg patient with an estimated renal clearance of 150 mL/min (Cockroft-Gault) were 0.14 L/h, 9.25 L, and 1.8 h. A correlation between estimated creatinine clearance and CL, body weight before pregnancy, and V1 was found and partly explained the PK variability. The final model was internally validated using a 500-run bootstrap. The first population pharmacokinetic model of TXA in active hemorrhagic caesarean section was successfully developed and internally validated.

Efficient model-based bioequivalence testing.

The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard, the PK parameters area under the curve (AUC) and $C_{\max}$ are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency, the formulations are declared to be sufficiently similar if the $90\%$ confidence interval for these ratios falls between $0.8$ and $1.25 $. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of $\rm AUC$ and $C_{\max}$ using nonlinear mixed effects models. Here we propose another, more powerful test than the TOST and demonstrate its superiority through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of $0.05$, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable.

Antibiotic Use in Periodontal Therapy among French Dentists and Factors Which Influence Prescribing Practices.

The aim of the present survey is to investigate the use of antibiotics during periodontal therapy among French dentists with a focus on exploring potential differences between various groups of practitioners. A self-administered questionnaire was distributed to different groups of practitioners including members of (i) the French Society of Periodontology and Implantology; (ii) the College of University Teachers in Periodontology and, (iii) private practitioners participating in the French general dental practice-based research network. 272 questionnaires were included in the analysis. Prescription patterns were globally in line with the current recommendations. Systemic antibiotics are most frequently used as a first-line therapy in necrotizing periodontitis (92%) and aggressive periodontitis (53.3% to 66.1%). However, malpractice still exists, including in the management of periodontal abscesses. Antibiotics are prescribed (i) less frequently for periodontal abscesses and (ii) more frequently for generalized aggressive periodontitis by members of the periodontal society and University college (p < 0.05). Amoxicillin (59.9%) and the amoxicillin + metronidazole (59.6%) combination were the most frequently prescribed molecules. Providing a high number of periodontal treatments per week, being more recently graduated, having a post-graduate certificate in periodontology and holding or having held an academic position/hospital practice were all factors associated with a better knowledge of and/or more adequate antibiotic use.

New Model-Based Bioequivalence Statistical Approaches for Pharmacokinetic Studies with Sparse Sampling.

In traditional pharmacokinetic (PK) bioequivalence analysis, two one-sided tests (TOST) are conducted on the area under the concentration-time curve and the maximal concentration derived using a non-compartmental approach. When rich sampling is unfeasible, a model-based (MB) approach, using nonlinear mixed effect models (NLMEM) is possible. However, MB-TOST using asymptotic standard errors (SE) presents increased type I error when asymptotic conditions do not hold. In this work, we propose three alternative calculations of the SE based on (i) an adaptation to NLMEM of the correction proposed by Gallant, (ii) the a posteriori distribution of the treatment coefficient using the Hamiltonian Monte Carlo algorithm, and (iii) parametric random effects and residual errors bootstrap. We evaluate these approaches by simulations, for two-arms parallel and two-period, two-sequence cross-over design with rich (n = 10) and sparse (n = 3) sampling under the null and the alternative hypotheses, with MB-TOST. All new approaches correct for the inflation of MB-TOST type I error in PK studies with sparse designs. The approach based on the a posteriori distribution appears to be the best compromise between controlled type I errors and computing times. MB-TOST using non-asymptotic SE controls type I error rate better than when using asymptotic SE estimates for bioequivalence on PK studies with sparse sampling.

Robust designs in longitudinal studies accounting for parameter and model uncertainties - application to count data.

Nonlinear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal designs based on the expected Fisher information matrix (FIM) can be used. A method evaluating the FIM using Monte-Carlo Hamiltonian Monte-Carlo (MC-HMC) has been proposed and implemented in the R package MIXFIM using Stan. This approach, however, requires a priori knowledge of models and parameters, which leads to locally optimal designs. The objective of this work was to extend this MC-HMC-based method to evaluate the FIM in NLMEMs accounting for uncertainty in parameters and in models. When introducing uncertainty in the population parameters, we evaluated the robust FIM as the expectation of the FIM computed by MC-HMC over the distribution of these parameters. Then, the compound D-optimality criterion (CD optimality), corresponding to a weighted product of the D-optimality criteria of several candidate models, was used to find a common CD-optimal design for the set of candidate models. Finally, a compound DE-criterion (CDE optimality), corresponding to a weighted product of the normalized determinants of the robust FIMs of all the candidate models accounting for uncertainty in parameters, was calculated to find the CDE-optimal design which was robust on both parameters and model. These methods were applied in a longitudinal Poisson count model. We assumed prior distributions on the population parameters, as well as several candidate models describing the relationship between the logarithm of the event rate parameter and the dose. We found that assuming uncertainty in parameters could lead to different optimal designs, and misspecification of models could induce designs with low efficiencies. The CD- or CDE-optimal designs therefore provided a good compromise for different candidate models. Finally, the proposed approach allows for the first time optimization of designs for repeated discrete data accounting for parameter and model uncertainties.