Influence of C-reactive protein on the pharmacokinetics of voriconazole in relation to the CYP2C19 genotype: a population pharmacokinetics analysis.

Voriconazole is a broad-spectrum triazole antifungal agent. A number of studies have revealed that the impact of C-reactive protein (CRP) on voriconazole pharmacokinetics was associated with the CYP2C19 phenotype. However, the combined effects of CYP2C19 genetic polymorphisms and inflammation on voriconazole pharmacokinetics have not been considered in previous population pharmacokinetic (PPK) studies, especially in the Chinese population. This study aimed to analyze the impact of inflammation on the pharmacokinetics of voriconazole in patients with different CYP2C19 genotypes and optimize the dosage of administration. Data were obtained retrospectively from adult patients aged ≥16 years who received voriconazole for invasive fungal infections from October 2020 to June 2023. Plasma voriconazole levels were measured via high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). CYP2C19 genotyping was performed using the fluorescence in situ hybridization method. A PPK model was developed using the nonlinear mixed-effect model (NONMEM). The final model was validated using bootstrap, visual predictive check (VPC), and normalized prediction distribution error (NPDE). The Monte Carlo simulation was applied to evaluate and optimize the dosing regimens. A total of 232 voriconazole steady-state trough concentrations from 167 patients were included. A one-compartment model with first order and elimination adequately described the data. The typical clearance (CL) and the volume of distribution (V) of voriconazole were 3.83 L/h and 134 L, respectively. The bioavailability was 96.5%. Covariate analysis indicated that the CL of voriconazole was substantially influenced by age, albumin, gender, CRP, and CYP2C19 genetic variations. The V of voriconazole was significantly associated with body weight. An increase in the CRP concentration significantly decreased voriconazole CL in patients with the CYP2C19 normal metabolizer (NM) and intermediate metabolizer (IM), but it had no significant effect on patients with the CYP2C19 poor metabolizer (PM). The Monte Carlo simulation based on CRP levels indicated that patients with high CRP concentrations required a decreased dose to attain the therapeutic trough concentration and avoid adverse drug reactions in NM and IM patients. These results indicate that CRP affects the pharmacokinetics of voriconazole and is associated with the CYP2C19 phenotype. Clinicians dosing voriconazole should consider the patient's CRP level, especially in CYP2C19 NMs and IMs.

Bridging the gap between two-stage and joint models: The case of tumor growth inhibition and overall survival models.

Many clinical trials generate both longitudinal biomarker and time-to-event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well-established methods exist for analyzing such data either sequentially (two-stage models) or simultaneously (joint models). Two-stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time-consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two-stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results.

Population Pharmacokinetics and Absolute Oral Bioavailability of Lasalocid after Single Intravenous and Intracrop Administration in Laying Hens.

Lasalocid sodium is a polyether carboxylic ionophore agent authorized by the EU for use as a coccidiostat in broilers, turkeys, and pullets up to 16 weeks of age, except for laying hens. However, laying hens are the most common nontarget species exposed to lasalocid sodium, mainly due to cross-contamination from feed mills. This exposure may result in potential drug residue deposition in eggs, which could potentially expose consumers to the drug. The breeds commonly used for commercial egg production in Poland are Isa Brown and Green-legged Partridge hens, which have been found to significantly differ in egg-laying performance. This variability may also affect the pharmacokinetics of lasalocid. Data on lasalocid plasma pharmacokinetics in laying hens are lacking. In this study, we aimed to determine typical population pharmacokinetic parameters, absolute oral bioavailability, and how breed may influence the pharmacokinetics of lasalocid. Twenty-layer hens of the two breeds were used in this study. Lasalocid was administered orally at a single dose of either 1 mg or 5 mg/kg body weight or intravenously at a dose of 1 mg/kg body weight, in a crossover design with a three-week washout period between study periods. Blood samples were collected for 72 h, and lasalocid concentrations were measured using high-performance liquid chromatography with fluorescence detection. A population pharmacokinetic analysis was conducted using nonlinear mixed effects modeling. Standard numerical and graphical criteria were used to select the best model, and a stepwise covariate modeling approach was used to determine any influencing factors. The best model was a three-compartment mammillary model with first-order absorption, transit compartments, and linear elimination. The estimated absolute oral bioavailability was low (36%). It was found that breed significantly influenced not only absorption but also the elimination of lasalocid. This study revealed that lasalocid absorption and elimination varied between the two breeds. This variability in pharmacokinetics may result in breed-related differences in drug residue accumulation in eggs, and ultimately, the risk associated with consumer exposure to drug residues may also vary.

Population pharmacokinetics of TLD-1, a novel liposomal doxorubicin, in a phase I trial.

STUDY OBJECTIVES: TLD-1 is a novel pegylated liposomal doxorubicin (PLD) formulation aiming to optimise the PLD efficacy-toxicity ratio. We aimed to characterise TLD-1's population pharmacokinetics using non-compartmental analysis and nonlinear mixed-effects modelling. METHODS: The PK of TLD-1 was analysed by performing a non-compartmental analysis of longitudinal doxorubicin plasma concentration measurements obtained from a clinical trial in 30 patients with advanced solid tumours across a 4.5-fold dose range. Furthermore, a joint parent-metabolite PK model of doxorubicinentrapped, doxorubicinfree, and metabolite doxorubicinol was developed. Interindividual and interoccasion variability around the typical PK parameters and potential covariates to explain parts of this variability were explored. RESULTS: Medians  ± standard deviations of dose-normalised doxorubicinentrapped+free Cmax and AUC0-∞ were 0.342 ± 0.134 mg/L and 40.1 ± 18.9 mg·h/L, respectively. The median half-life (95 h) was 23.5 h longer than the half-life of currently marketed PLD. The novel joint parent-metabolite model comprised a one-compartment model with linear release (doxorubicinentrapped), a two-compartment model with linear elimination (doxorubicinfree), and a one-compartment model with linear elimination for doxorubicinol. Body surface area on the volumes of distribution for free doxorubicin was the only significant covariate. CONCLUSION: The population PK of TLD-1, including its release and main metabolite, were successfully characterised using non-compartmental and compartmental analyses. Based on its long half-life, TLD-1 presents a promising candidate for further clinical development. The PK characteristics form the basis to investigate TLD-1 exposure-response (i.e., clinical efficacy) and exposure-toxicity relationships in the future. Once such relationships have been established, the developed population PK model can be further used in model-informed precision dosing strategies. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov-NCT03387917-January 2, 2018.

Population pharmacokinetics of intravenous daptomycin in critically ill patients: implications for selection of dosage regimens.

Daptomycin is gaining prominence for the treatment of methicillin-resistant Staphylococcus aureus infections. However, the dosage selection for daptomycin in critically ill patients remains uncertain, especially in Chinese patients. This study aimed to establish the population pharmacokinetics of daptomycin in critically ill patients, optimize clinical administration plans, and recommend appropriate dosage for critically ill patients in China. The study included 64 critically ill patients. Blood samples were collected at the designated times. The blood daptomycin concentration was determined using validated liquid chromatography-tandem mass spectrometry. A nonlinear mixed-effects model was applied for the population pharmacokinetic analysis and Monte Carlo simulations of daptomycin. The results showed a two-compartment population pharmacokinetic model of daptomycin in critically ill adult Han Chinese patients. Monte Carlo simulations revealed that a daily dose of 400 mg of daptomycin was insufficient for the majority of critically ill adult patients to achieve the anti-infective target. For critically ill adult patients with normal renal function (creatinine clearance rate >90 mL/min), the probability of achieving the target only reached 90% when the daily dose was increased to 700 mg. For patients undergoing continuous renal replacement therapy (CRRT), 24 h administration of 500 mg met the pharmacodynamic goals and did not exceed the safety threshold in most patients. Therefore, considering its efficacy and safety, intravenous daptomycin doses are best scaled according to creatinine clearance, and an increased dose is recommended for critically ill patients with hyperrenalism. For patients receiving CRRT, medication is recommended at 24 h intervals.

Population pharmacokinetics of nirmatrelvir in Chinese patients with COVID-19: Therapeutic drug monitoring and dosing regimen selection in clinical practice.

OBJECTIVES: To establish a population pharmacokinetics (PopPK) model of nirmatrelvir in Chinese COVID-19 patients and provide reference for refining the dosing strategy of nirmatrelvir in patients confirmed to be infected with SARS-CoV-2. METHODS: A total of 80 blood samples were obtained from 35 mild to moderate COVID-19 patients who were orally administered nirmatrelvir/ritonavir tablets. The PopPK model of nirmatrelvir was developed using a nonlinear mixed effects modelling approach. The stability and prediction of the final model were assessed through a combination of goodness-of-fit and bootstrap method. The exposure of nirmatrelvir across various clinical scenarios was simulated using Monte Carlo simulations. RESULTS: The pharmacokinetics of nirmatrelvir was well characterised by a one-compartment model with first-order absorption, and with creatinine clearance (Ccr) as the significant covariate. Typical population parameter estimates of apparent clearance and distribution volume for a patient with a Ccr of 95.5 mL·min-1were 3.45 L·h-1 and 48.71 L, respectively. The bootstrap and visual predictive check procedures demonstrated satisfactory predictive performance and robustness of the final model. CONCLUSION: The final model was capable of offering an early prediction of drug concentration ranges for different nirmatrelvir dosing regimens and optimise the dose regimen of nirmatrelvir in individuals with confirmed SARS-CoV-2 infection.

Dose regimen optimization of cephalothin for surgical prophylaxis against Staphylococcus aureus and coagulase negative staphylococci in dogs by pharmacokinetic/pharmacodynamic modeling.

First generation cephalosporins such cephalothin of cefazolin are indicated for antimicrobial prophylaxis for clean and clean contaminated surgical procedures because its antimicrobial spectrum, relative low toxicity and cost. Anesthesia and surgery could alter the pharmacokinetic behavior of different drugs administered perioperative by many mechanisms that affect distribution, metabolism or excretion processes. Intravenous administration of the antimicrobial within 30 and 60 min before incision is recommended in order to reach therapeutic serum and tissue concentrations and redosing is recommended if the duration of the procedure exceeds two half-life of the antimicrobial. To the author's knowledge there are no pharmacokinetic studies of cephalothin in dogs under anesthesia/surgery conditions. The aim of this study was (1) to evaluate the pharmacokinetics of cephalothin in anesthetized dogs undergoing ovariohysterectomy by a nonlinear mixed-effects model and to determine the effect of anesthesia/surgery and other individual covariates on its pharmacokinetic behavior; (2) to determine the MIC and conduct a pharmacodynamic modeling of time kill curves assay of cephalothin against isolates of Staphylococcus spp. isolated from the skin of dogs; (3) to conduct a PK/PD analysis by integration of the obtained nonlinear mixed-effects models in order to evaluate the antimicrobial effect of changing concentrations on simulated bacterial count; and (4) to determine the PK/PD endpoints and PK/PDco values in order to predict the optimal dose regimen of cephalothin for antimicrobial prophylaxis in dogs. Anesthesia/surgery significantly reduced cephalothin clearance by 18.78%. Based on the results of this study, a cephalothin dose regimen of 25 mg/kg q6h by intravenous administration showed to be effective against Staphylococcus spp. isolates with MIC values ≤2 µg/mL and could be recommended for antimicrobial prophylaxis for clean surgery in healthy dogs.

Model-Informed Precision Dosing of Isoniazid: Parametric Population Pharmacokinetics Model Repository.

Introduction: Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use. Methods: A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca. Results: Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00-200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC0-24 and 83.2% decrease in Cmax (Infants), and a 49.3% reduction in AUC0-24 and 73.5% reduction in Cmax (Adults). Discussion: Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.

Population Pharmacokinetics of Tenofovir Alafenamide Fumarate and Its Metabolite Tenofovir in Healthy Chinese Volunteers.

Tenofovir alafenamide fumarate (TAF) is a first-line drug for treating hepatitis B virus infection. This study aimed to establish the prodrug-metabolite population pharmacokinetic (PK) model for TAF and its metabolite tenofovir (TFV) in healthy Chinese volunteers and evaluate the factors affecting the PK. Using 1043 TAF and 1198 TFV plasma sample concentrations collected from 67 healthy volunteers, a population PK model was developed using the nonlinear mixed-effects model. The 1-compartment model containing 4 transit compartments and the 2-compartment model accurately described the PK of TAF and TFV, respectively. Covariates such as meal state and sex were found to be statistically significant and potentially clinically relevant. Both internal and external validations demonstrated good stability and predictive performance of the connected model. This study elucidated the PK process by which TAF was absorbed, converted, and finally metabolized and eliminated as TFV, and explored the sources of interindividual variability between TAF and TFV.

Dosing Optimization of Posaconazole in Lung-Transplant Recipients Based on Population Pharmacokinetic Model.

Although posaconazole tablets show relatively low variability in pharmacokinetics (PK), the proportion of patients achieving the PK/PD target at the approved uniform dose for both prophylaxis and therapy is not satisfactory. The aim of this study was to develop a posaconazole population PK model in lung-transplant recipients and to propose a covariate-based dosing optimization for both prophylaxis and therapy. In this prospective study, 80 posaconazole concentrations obtained from 32 lung-transplant patients during therapeutic drug monitoring were analyzed using nonlinear mixed-effects modelling, and a Monte Carlo simulation was used to describe the theoretical distribution of posaconazole PK profiles at various dosing regimens. A one-compartment model with both linear absorption and elimination best fit the concentration-time data. The population apparent volume of distribution was 386.4 L, while an apparent clearance of 8.8 L/h decreased by 0.009 L/h with each year of the patient's age. Based on the covariate model, a dosing regimen of 200 mg/day for prophylaxis in patients ˃60 years, 300 mg/day for prophylaxis in patients ˂60 years and for therapy in patients ˃60 years, and 400 mg/day for therapy in patients ˂60 years has been proposed. At this dosing regimen, the PK/PD target for prophylaxis and therapy is reached in 95% and 90% of population, respectively, representing significantly improved outcomes in comparison with the uniform dose.

Nonlinear multilevel joint model for individual lesion kinetics and survival to characterize intra-individual heterogeneity in patients with advanced cancer.

In advanced cancer patients, tumor burden is calculated using the sum of the longest diameters (SLD) of the target lesions, a measure that lumps all lesions together and ignores intra-patient heterogeneity. Here, we used a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify heterogeneity in lesion dynamics and measure their impact on survival. Using a nonlinear model of tumor growth inhibition, we estimated that dynamics differed greatly among lesions, and inter-lesion variability accounted for 21% and 28% of the total variance in tumor shrinkage and treatment effect duration, respectively. Next, we investigated the impact of individual lesion dynamics on survival. Lesions located in the liver and in the bladder had twice as much impact on the instantaneous risk of death compared to those located in the lung or the lymph nodes. Finally, we evaluated the utility of individual lesion follow-up for dynamic predictions. Consistent with results at the population level, the individual lesion model outperformed a model relying only on SLD, especially at early landmark times and in patients with liver or bladder target lesions. Our results show that an individual lesion model can characterize the heterogeneity in tumor dynamics and its impact on survival in advanced cancer patients.

Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model.

The transmission rate is a central parameter in mathematical models of infectious disease. Its pivotal role in outbreak dynamics makes estimating the current transmission rate and uncovering its dependence on relevant covariates a core challenge in epidemiological research as well as public health policy evaluation. Here, we develop a method for flexibly inferring a time-varying transmission rate parameter, modeled as a function of covariates and a smooth Gaussian process (GP). The transmission rate model is further embedded in a hierarchy to allow information borrowing across parallel streams of regional incidence data. Crucially, the method makes use of optional vaccination data as a first step toward modeling of endemic infectious diseases. Computational techniques borrowed from the Bayesian spatial analysis literature enable fast and reliable posterior computation. Simulation studies reveal that the method recovers true covariate effects at nominal coverage levels. We analyze data from the COVID-19 pandemic and validate forecast intervals on held-out data. User-friendly software is provided to enable practitioners to easily deploy the method in public health research.

Smoothed simulated pseudo-maximum likelihood estimation for nonlinear mixed effects models with censored responses.

Nonlinear mixed effects models have been widely applied to analyses of data that arise from biological, agricultural, and environmental sciences. Estimation of and inference on parameters in nonlinear mixed effects models are often based on the specification of a likelihood function. Maximizing this likelihood function can be complicated by the specification of the random effects distribution, especially in the presence of multiple random effects. The implementation of nonlinear mixed effects models can be further complicated by left-censored responses, representing measurements from bioassays where the exact quantification below a certain threshold is not possible. Motivated by the need to characterize the nonlinear human immunodeficiency virus RNA viral load trajectories after the interruption of antiretroviral therapy, we propose a smoothed simulated pseudo-maximum likelihood estimation approach to fit nonlinear mixed effects models in the presence of left-censored observations. We establish the consistency and asymptotic normality of the resulting estimators. We develop testing procedures for the correlation among random effects and for testing the distributional assumptions on random effects against a specific alternative. In contrast to the existing variants of expectation-maximization approaches, the proposed methods offer flexibility in the specification of the random effects distribution and convenience in making inference about higher-order correlation parameters. We evaluate the finite-sample performance of the proposed methods through extensive simulation studies and illustrate them on a combined dataset from six AIDS Clinical Trials Group treatment interruption studies.

Model-based joint curve registration and classification.

In this paper, we consider the problem of classification of misaligned multivariate functional data. We propose to use a model-based approach for the joint registration and classification of such data. The observed functional inputs are modeled as a functional nonlinear mixed effects model containing a nonlinear functional fixed effect constructed upon warping functions to account for curve alignment, and a nonlinear functional random effects component to address the variability among subjects. The warping functions are also modeled to accommodate common effect within groups and the variability between subjects. Then, a functional logistic regression model defined upon the representation of the aligned curves and scalar inputs is used to account for curve classification. EM-based algorithms are developed to perform maximum likelihood inference of the proposed models. The identifiability of the registration model and the asymptotical properties of the proposed method are established. The performance of the proposed procedure is illustrated via simulation studies and an analysis of a hyoid bone movement data application. The statistical developments proposed in this paper were motivated by the hyoid bone movement study, the methodology is designed and presented generality and can be applied to numerous areas of scientific research.

Incorporating stand density effects and regression techniques for stem taper modeling of a Larix principis-rupprechtii plantation.

Stem form is the shape of the trunk, differs among tree species and mainly affected by stand density factor. Accurate taper equations are crucial for estimating the stem diameter, form and tree volume, which is conducive to timber utilization and sustainable forest management and planning. Larch (Larix principis-rupprechtii Mayr.) is a valuable afforestation species under large-scale development in North China, but no study on the effect of density on its stem taper has been reported yet. The dataset included 396 analytical trees from 132 standard plots of larch plantation in Saihanba, Hebei Province. Based on 12 different forms of models, we explored the optimal basic equation for plantations and the effects of the stand density, basal area, canopy density and different forms of stand density on the prediction accuracy of the variable-exponent models. The variable-exponent taper equation that includes Sd (stand density) was constructed by using nonlinear regression, a nonlinear mixed effect model and the nonlinear quantile regression method. The results indicate that the Kozak's 2004 variable-exponent taper equation was the best basic model for describing changes in the stem form of larch plantations, and the density factor in the form of S d improved the prediction accuracy of the basic model. Among the three regression methods, the quantile regression method had the highest fitting accuracy, followed by the nonlinear mixed effect model. When the quantile was 0.5, the nonlinear quantile regression model exhibited the best performance which provides a scientific basis for the rational management of larch plantations.

Parametric population pharmacokinetics of linezolid: A systematic review.

AIMS: Linezolid is often used for the infections caused by drug-resistant Gram-positive bacteria. Recent studies suggest that large between-subject variability (BSV) and within-subject variability could alter drug pharmacokinetics (PK) during linezolid therapy due to pathophysiological changes. This review synthesized information on linezolid population PK studies and summarized the significant covariates that influence linezolid PK. METHODS: A literature search was performed using PubMed, Web of Science and Embase from their inception to 30 September 2021. Published studies were included if they contained data analysing linezolid PK parameters in humans using a population approach with a nonlinear mixed-effects model. RESULTS: Twenty-five studies conducted in adults and five in paediatrics were included. One- and two-compartment models were the commonly used structural models for linezolid. Body size (weight, lean body weight and body surface area), creatinine clearance (CLcr) and age significantly influenced linezolid PK. The median clearance (CL) values (ranges) in infants (0.128 L/h/kg [0.121-0.135]] and children (0.107 L/h/kg [0.088-0.151]] were higher than in adults (0.098 L/h/kg [0.044-0.237]]. For patients with severe renal impairment (CLcr ≤ 30 mL/min), the CL was 37.2% (15.2-55.3%) lower than in patients with normal renal function. CONCLUSION: The optimal linezolid dosage should be adjusted based on the patient's body size, renal function and age. More studies are needed to explore the exact mechanism of linezolid elimination and evaluate the PK characteristics in paediatric patients.

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.

Assessing the impact of organ-specific lesion dynamics on survival in patients with recurrent urothelial carcinoma treated with atezolizumab or chemotherapy.

BACKGROUND: Tumor dynamics typically rely on the sum of the longest diameters (SLD) of target lesions, and ignore heterogeneity in individual lesion dynamics located in different organs. PATIENTS AND METHODS: Here we evaluated the benefit of analyzing lesion dynamics in different organs to predict survival in 900 patients with metastatic urothelial carcinoma treated with atezolizumab or chemotherapy (IMvigor211 trial). RESULTS: Lesion dynamics varied largely across organs, with lymph nodes and lung lesions showing on average a better response to both treatments than those located in the liver and locoregionally. A benefit of atezolizumab was observed on lung and liver lesion dynamics that was attributed to a longer duration of treatment effect as compared to chemotherapy (P value = 0.043 and 0.001, respectively). The impact of lesion dynamics on survival, assessed by a joint model, varied greatly across organs, irrespective of treatment. Liver and locoregional lesion dynamics had a large impact on survival, with an increase of 10 mm of the lesion size increasing the instantaneous risk of death by 12% and 10%, respectively. In comparison, lymph nodes and lung lesions had a lower impact, with a 10-mm increase in the lesion size increasing the instantaneous risk of death by 7% and 5%, respectively. Using our model, we could anticipate the benefit of atezolizumab over chemotherapy as early as 6 months before the end of the study, which is 3 months earlier than a similar model only relying on SLD. CONCLUSION: We showed the interest of organ-level tumor follow-up to better understand and anticipate the treatment effect on survival.

Needle-free iontophoresis-driven ß-adrenergic sweat rate test.

OBJECTIVES: Two CFTR-dependent ß-adrenergic sweat rate tests applying intradermal drug injections were reported to better define diagnosis and efficacy of CFTR-directed therapies. The aim of this work was to develop and test a needle-free image-based test and to provide an accurate analysis of the responses. METHODS: The modified method was conducted by applying two successive iontophoresis sessions using the Macroduct device. Efficiency of drug delivery was tested by evaporimetry. Cholinergically stimulated sweating was evoked by pilocarpine iontophoresis. ß-adrenergically stimulated sweating was obtained by iontophoresis of isoproterenol and aminophylline in the presence of atropine and ascorbic acid. A nonlinear mixed-effects (NLME) approach was applied to model volumes of sweat and subject-specific effects displaying inter- and intra-subject variability. RESULTS: Iontophoresis provided successful transdermal delivery of all drugs, including almost neutral isoproterenol and aminophylline. Pilocarpine was used at a concentration ∼130-times lower than that used in the classical Gibson and Cooke sweat test. Addition of ascorbic acid lowered the pH of the solution, made it stable, prevented isoproterenol degradation and promoted drug iontophoresis. Maximal secretory capacity and kinetic rate of ß-adrenergic responses were blunted in CF. A cutoff of 5.2 minutes for ET50, the time to reach the half maximal secretion, discriminated CF from controls with a 100% sensitivity and specificity. Heterozygous showed an apparently reduced kinetic rate and a preserved secretory capacity. CONCLUSION: We tested a safe, well-tolerated needle-free image-based sweat test potentially applicable in children. Modelling responses by NLME allowed evaluating metrics of CFTR-dependent effects reflecting secretory capacity and kinetic rate.