Classification based on extensions of LS-PLS using logistic regression: application to clinical and multiple genomic data.

BACKGROUND: To address high-dimensional genomic data, most of the proposed prediction methods make use of genomic data alone without considering clinical data, which are often available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions. We consider here methods for classification purposes that simultaneously use both types of variables but apply dimensionality reduction only to the high-dimensional genomic ones. RESULTS: Using partial least squares (PLS), we propose some one-step approaches based on three extensions of the least squares (LS)-PLS method for logistic regression. A comparison of their prediction performances via a simulation and on real data sets from cancer studies is conducted. CONCLUSION: In general, those methods using only clinical data or only genomic data perform poorly. The advantage of using LS-PLS methods for classification and their performances are shown and then used to analyze clinical and genomic data. The corresponding prediction results are encouraging and stable regardless of the data set and/or number of selected features. These extensions have been implemented in the R package lsplsGlm to enhance their use.

Methods and software tools for design evaluation in population pharmacokinetics-pharmacodynamics studies.

Population pharmacokinetic (PK)-pharmacodynamic (PKPD) models are increasingly used in drug development and in academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple-one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.

A Population Pharmacokinetic Model of Tocilizumab in Kidney Transplant Patients Treated for Chronic Active Antibody-Mediated Rejection: Comparison of Plasma Exposure Between Intravenous and Subcutaneous Administration Schemes.

BACKGROUND: Tocilizumab prevents the clinical worsening of chronic active antibody-mediated rejection (CAAMR) in kidney transplant recipients. Following a global shortage of the intravenous pharmaceutical form in 2022, patients were switched from monthly intravenous administration of 8 mg/kg to weekly subcutaneous injection of 162 mg, raising the question of bioequivalence between these schemes of administration. AIMS: We aimed to compare the areas under the curve (AUC) of tocilizumab in virtual simulations of populations treated with the two administration schemes and to identify the covariates that could contribute to pharmacokinetic variability of tocilizumab in kidney transplant patients with CAAMR who received tocilizumab as salvage treatment. METHODS: This retrospective monocentric study included 43 kidney transplant patients (202 tocilizumab concentrations) with CAAMR treated with intravenous or subcutaneous tocilizumab between December 2020 and January 2023. We developed a population pharmacokinetic model using nonlinear mixed effects modeling and identified the covariates that could contribute to tocilizumab AUC variability. Monte Carlo simulations were then performed to assess the subcutaneous and intravenous tocilizumab AUC for 0-28 days (M1), 56-84 days (M3), 140-168 days (M6), and 308-336 days (M12). Bioequivalence was defined by SC/IV AUC geometric mean ratios (GMRs) between 0.80 and 1.25. RESULTS: A two-compartment model with parallel linear and nonlinear elimination best described the concentration-time data. Significant covariates for tocilizumab clearance were body weight, urinary albumin-to-creatinine ratio (ACR), and inflammation status [C-reactive protein (CRP) ≥ 5 mg/L]. The GMR values and their 90% confidence intervals at M3, M6, and M12 were within the 0.8-1.25 margin for equivalence. Conversely, the 90% prediction intervals of the GMR were much wider than the 90% confidence intervals and did not fall within 0.8 and 1.25. CONCLUSIONS: From month 3 of treatment, the subcutaneous and intravenous tocilizumab administration schemes provided average bioequivalent pharmacokinetic exposure at the population level but not at the individual level. Body weight, inflammation, ACR, and administration scheme should be considered to personalize the dose of tocilizumab for patients with CAAMR. Further studies are required to determine the target of tocilizumab exposure in kidney transplant patients with CAAMR.

Design evaluation and optimisation in crossover pharmacokinetic studies analysed by nonlinear mixed effects models.

Bioequivalence or interaction trials are commonly studied in crossover design and can be analysed by nonlinear mixed effects models as an alternative to noncompartmental approach. We propose an extension of the population Fisher information matrix in nonlinear mixed effects models to design crossover pharmacokinetic trials, using a linearisation of the model around the random effect expectation, including within-subject variability and discrete covariates fixed or changing between periods. We use the expected standard errors of treatment effect to compute the power for the Wald test of comparison or equivalence and the number of subjects needed for a given power. We perform various simulations mimicking crossover two-period trials to show the relevance of these developments. We then apply these developments to design a crossover pharmacokinetic study of amoxicillin in piglets and implement them in the new version 3.2 of the r function PFIM.

Daunorubicin and Its Active Metabolite Pharmacokinetic Profiles in Acute Myeloid Leukaemia Patients: A Pharmacokinetic Ancillary Study of the BIG-1 Trial.

Daunorubicin pharmacokinetics (PK) are characterised by an important inter-individual variability, which raises questions about the optimal dose regimen in patients with acute myeloid leukaemia. The aim of the study is to assess the joint daunorubicin/daunorubicinol PK profile and to define an optimal population PK study design. Fourteen patients were enrolled in the PK ancillary study of the BIG-1 trial and 6-8 samples were taken up to 24 h after administration of the first dose of daunorubicin (90 mg/m2/day). Daunorubicin and daunorubicinol quantifications were assessed using a validated liquid chromatography technique coupled with a fluorescence detector method. Data were analysed using a non-compartmental approach and non-linear mixed effects modelling. Optimal sampling strategy was proposed using the R function PFIM. The median daunorubicin and daunorubicinol AUC0-tlast were 577 ng/mL·hr (Range: 375-1167) and 2200 ng/mL·hr (range: 933-4683), respectively. The median metabolic ratio was 0.32 (range: 0.1-0.44). Daunorubicin PK was best described by a three-compartment parent, two-compartment metabolite model, with a double first-order transformation of daunorubicin to metabolite. Body surface area and plasma creatinine had a significant impact on the daunorubicin and daunorubicinol PK. A practical optimal population design has been derived from this model with five sampling times per subject (0.5, 0.75, 2, 9, 24 h) and this can be used for a future population PK study.

Design evaluation and optimization for models of hepatitis C viral dynamics.

Mathematical modeling of hepatitis C viral (HCV) kinetics is widely used for understanding viral pathogenesis and predicting treatment outcome. The standard model is based on a system of five non-linear ordinary differential equations (ODE) that describe both viral kinetics and changes in drug concentration after treatment initiation. In such complex models parameter estimation is challenging and requires frequent sampling measurements on each individual. By borrowing information between study subjects, non-linear mixed effect models can deal with sparser sampling from each individual. However, the search for optimal designs in this context has been limited by the numerical difficulty of evaluating the Fisher information matrix (FIM). Using the software PFIM, we show that a linearization of the statistical model avoids most of the computational burden, while providing a good approximation to the FIM. We then compare the precision of the parameters that can be expected using five study designs from the literature. We illustrate the usefulness of rationalizing data sampling by showing that, for a given level of precision, optimal design could reduce the total number of measurements by up 50 per cent. Our approach can be used by a statistician or a clinician aiming at designing an HCV viral kinetics study.

Pharmacokinetic design optimization in children and estimation of maturation parameters: example of cytochrome P450 3A4.

The aim of this work was to determine whether optimizing the study design in terms of ages and sampling times for a drug eliminated solely via cytochrome P450 3A4 (CYP3A4) would allow us to accurately estimate the pharmacokinetic parameters throughout the entire childhood timespan, while taking into account age- and weight-related changes. A linear monocompartmental model with first-order absorption was used successively with three different residual error models and previously published pharmacokinetic parameters ("true values"). The optimal ages were established by D-optimization using the CYP3A4 maturation function to create "optimized demographic databases." The post-dose times for each previously selected age were determined by D-optimization using the pharmacokinetic model to create "optimized sparse sampling databases." We simulated concentrations by applying the population pharmacokinetic model to the optimized sparse sampling databases to create optimized concentration databases. The latter were modeled to estimate population pharmacokinetic parameters. We then compared true and estimated parameter values. The established optimal design comprised four age ranges: 0.008 years old (i.e., around 3 days), 0.192 years old (i.e., around 2 months), 1.325 years old, and adults, with the same number of subjects per group and three or four samples per subject, in accordance with the error model. The population pharmacokinetic parameters that we estimated with this design were precise and unbiased (root mean square error [RMSE] and mean prediction error [MPE] less than 11% for clearance and distribution volume and less than 18% for k(a)), whereas the maturation parameters were unbiased but less precise (MPE < 6% and RMSE < 37%). Based on our results, taking growth and maturation into account a priori in a pediatric pharmacokinetic study is theoretically feasible. However, it requires that very early ages be included in studies, which may present an obstacle to the use of this approach. First-pass effects, alternative elimination routes, and combined elimination pathways should also be investigated.

Fisher information matrix for nonlinear mixed effects multiple response models: evaluation of the appropriateness of the first order linearization using a pharmacokinetic/pharmacodynamic model.

We focus on the Fisher information matrix used for design evaluation and optimization in nonlinear mixed effects multiple response models. We evaluate the appropriateness of its expression computed by linearization as proposed for a single response model. Using a pharmacokinetic-pharmacodynamic (PKPD) example, we first compare the computation of the Fisher information matrix with approximation to one derived from the observed matrix on a large simulation using the stochastic approximation expectation-maximization algorithm (SAEM). The expression of the Fisher information matrix for multiple responses is also evaluated by comparison with the empirical information obtained through a replicated simulation study using the first-order linearization estimation methods implemented in the NONMEM software (first-order (FO), first-order conditional estimate (FOCE)) and the SAEM algorithm in the MONOLIX software. The predicted errors given by the approximated information matrix are close to those given by the information matrix obtained without linearization using SAEM and to the empirical ones obtained with FOCE and SAEM. The simulation study also illustrates the accuracy of both FOCE and SAEM estimation algorithms when jointly modelling multiple responses and the major limitations of the FO method. This study highlights the appropriateness of the approximated Fisher information matrix for multiple responses, which is implemented in PFIM 3.0, an extension of the R function PFIM dedicated to design evaluation and optimization. It also emphasizes the use of this computing tool for designing population multiple response studies, as for instance in PKPD studies or in PK studies including the modelling of the PK of a drug and its active metabolite.

Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0.

Nonlinear mixed effect models (NLMEM) with multiple responses are increasingly used in pharmacometrics, one of the main examples being the joint analysis of the pharmacokinetics (PK) and pharmacodynamics (PD) of a drug. Efficient tools for design evaluation and optimisation in NLMEM are necessary. The R functions PFIM 1.2 and PFIMOPT 1.0 were proposed for these purposes, but accommodate only single response models. The methodology used is based on the Fisher information matrix, developed using a linearisation of the model. In this paper, we present an extended version, PFIM 3.0, dedicated to both design evaluation and optimisation for multiple response models, using a similar method as for single response models. In addition to handling multiple response models, several features have been integrated into PFIM 3.0 for model specification and optimisation. The extension includes a library of classical analytical pharmacokinetics models and allows the user to describe more complex models using differential equations. Regarding the optimisation algorithm, an alternative to the Simplex algorithm has been implemented, the Fedorov-Wynn algorithm to optimise more practical D-optimal design. Indeed, this algorithm optimises design among a set of sampling times specified by the user. This R function is freely available at http://www.pfim.biostat.fr. The efficiency of this approach and the simplicity of use of PFIM 3.0 are illustrated with a real example of the joint PKPD analysis of warfarin, an oral anticoagulant, with a model defined by ordinary differential equations.

Intracellular Pharmacokinetics of Antiretroviral Drugs in HIV-Infected Patients, and their Correlation with Drug Action.

In patients infected by HIV, the efficacy of highly active antiretroviral (ARV) therapy through the blockade of different steps of the retrovirus life cycle is now well established. As HIV is a retrovirus that replicates within the cells of the immune system, intracellular drug concentrations are important to determine ARV drug efficacy and toxicity. Indeed, nucleoside reverse transcriptase inhibitors (NRTIs), non-NRTIs (NNRTIs), newly available integrase inhibitors and protease inhibitors (PIs) act on intracellular targets. NRTIs are prodrugs that require intracellular anabolic phosphorylation to be converted into their active form of triphosphorylated NRTI metabolites, most of which have longer plasma half-lives than their parent compounds. The activity of intracellular kinases and the expression of uptake transporters, which may depend on cell functionality or their activation state, may greatly influence intracellular concentrations of triphosphorylated NRTI metabolites. In contrast, NNRTIs and PIs are not prodrugs, and they exert their activity by inhibiting enzyme targets directly. All PIs are substrates of cytochrome P450 3A, which explains why most of them display poor pharmacokinetic properties with intensive presystemic first-pass metabolism and short elimination half-lives. There is evidence that intracellular concentrations of PIs depend on P-glycoprotein and/or the activity of other efflux transporters, which is modulated by genetic polymorphism and coadministration of drugs with inhibiting or inducing properties. Adequate assay of the intracellular concentrations of ARVs is still a major technical challenge, together with the isolation and counting of peripheral blood mononuclear cells (PBMCs). Furthermore, intracellular drug could be bound to cell membranes or proteins; the amount of intracellular ARV available for ARV effectiveness is never measured, which is a limitation of all published studies. In this review, we summarize the findings of 31 studies that provided results of intracellular concentrations of ARVs in HIV-infected patients. Most studies also measured plasma concentrations, but few of them studied the relationship between plasma and intracellular concentrations. For NRTIs, most studies could not establish a significant relationship between plasma and triphosphate concentrations. Only eight published studies reported an analysis of the relationships between intracellular concentrations and the virological or immunological efficacy of ARVs in HIV patients. In prospective studies that were well designed and had a reasonable number of patients, virological efficacy was found to correlate significantly with intracellular concentrations of NRTIs but not with plasma concentrations. For PIs, the only prospectively designed trial of lopinavir found that virological efficacy was influenced by both trough plasma concentrations and intracellular concentrations. ARVs are known to cause important adverse effects through interference with cellular endogenous processes. The relationship between intracellular concentrations of ARVs and their related toxicity was investigated in only four studies. For zidovudine, the relative strength of the association between a decrease in haemoglobin levels and plasma zidovudine concentrations, as compared with intracellular zidovudine triphosphate concentrations, is still unknown. Similarly, for efavirenz and neuropsychological disorders, methodological differences confound the comparison between studies. In conclusion, intracellular concentrations of ARVs play a major role in their efficacy and toxicity, and are influenced by numerous factors. However, the number of published clinical studies in this area is limited; most studies have been small and not always adequately designed. In addition, standardization of assays and PBMC counts are warranted. Larger and prospectively designed clinical studies are needed to further investigate the links between intracellular concentrations of ARVs and clinical endpoints.