Population pharmacokinetic analyses of methotrexate in pediatric patients: a systematic review.

BACKGROUND AND OBJECTIVES: Methotrexate is widely utilized in the chemotherapy of malignant tumors and autoimmune diseases in the pediatric population, but dosing can be challenging. Several population pharmacokinetic models were developed to characterize factors influencing variability and improve individualization of dosing regimens. However, significant covariates included varied across studies. The primary objective of this review was to summarize and discuss population pharmacokinetic models of methotrexate and covariates that influence pharmacokinetic variability in pediatric patients. METHODS: Systematic searches were conducted in the PubMed and EMBASE databases from inception to 7 July 2023. Reporting Quality was evaluated based on a checklist with 31 items. The characteristics of studies and information for model construction and validation were extracted, summarized, and discussed. RESULTS: Eighteen studies (four prospective studies and fourteen retrospective studies with sample sizes of 14 to 772 patients and 2.7 to 93.1 samples per patient) were included in this study. Two-compartment models were the commonly used structural models for methotrexate, and the clearance range of methotrexate ranged from 2.32 to 19.03 L/h (median: 6.86 L/h). Body size and renal function were found to significantly affect the clearance of methotrexate for pediatric patients. There were limited reports on the role of other covariates, such as gene polymorphisms and co-medications, in the pharmacokinetic parameters of methotrexate pediatric patients. Internal and external evaluations were used to assess the performance of the population pharmacokinetic models. CONCLUSION: A more rigorous external evaluation needs to be performed before routine clinical use to select the appropriate PopPK model. Further research is necessary to incorporate larger cohorts or pool analyses in specific susceptible pediatric populations to improve the understanding of predicted exposure profiles and covariate identification.

Wood volume is overestimated in the Brazilian Amazon: Why not use generic volume prediction methods in tropical forest management?

The Amazon has a range of species with high potential for sustainable timber harvesting, but for them to be utilized globally, the merchantable wood volume must be accurately quantified. However, since the 1950s, inadequate methods for estimating merchantable timber volumes have been employed in the Amazon, and Brazilian Government agencies still require some of them. The natural variability of the Amazon Forest provides an abundance of species of different sizes and shapes, conferring several peculiarities, which makes it necessary to use up-to-date and precise methods for timber quantification in Amazon Forest management. Given the employment of insufficient estimation methods for wood volume, this study scrutinizes the disparities between the actual harvested merchantable wood volume and the volume estimated by the forest inventory during the harvesting phase across five distinct public forest areas operating under sustainable forest management concessions. We used mixed-effect models to evaluate the relationships between inventory and harvested volume for genera and forest regions. We performed an equivalence test to assess the similarity between the volumes obtained during the pre-and post-harvest phases. We calculated root mean square error and percentage bias for merchantable volume as accuracy metrics. There was a strong tendency for the 100% forest inventory to overestimate merchantable wood volume, regardless of genus and managed area. There was a significant discrepancy between the volumes inventoried and harvested in different regions intended for sustainable forest management, in which only 22% of the groups evaluated were equivalent. The methods currently practiced by forest companies for determining pre-harvest merchantable volume are inaccurate enough to support sustainable forest management in the Amazon. They may even facilitate the region's illegal timber extraction and organized crime.

Revisiting methods for modeling longitudinal and survival data: Framingham Heart Study.

BACKGROUND: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. METHODS: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. RESULTS: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years. CONCLUSIONS: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Gender differences in pharmacokinetics of perfluoropentanoic acid using non-linear mixed-effect modeling in rats.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) have been used in various industrial applications for many years. Long-chain PFASs are ubiquitous in wildlife and are reported to have a long elimination half-life in biological systems. Moreover, significant gender difference exists in the elimination of PFASs, where less is eliminated in male than in female. Recently, PFASs manufacturers and agencies have tried to replace the use of long-chain PFASs with short-chain PFASs, since they are expected to exhibit less bioaccumulation potential. Nevertheless, the potential risk and the pharmacokinetic (PK) characteristics of the short-chain PFASs still remain unknown. This study aims to fill the knowledge gap on short-chain PFASs, perfluoropentanoic acid (PFPeA), in terms of its PK properties using non-linear mixed-effect modeling and to explore gender differences in rats. Animal studies were carried out following oral or intravenous administration of PFPeA in male and female rats at a dose of 0.5-10 mg/kg. Plasma, urine, feces and nine tissues were collected and analyzed using ultra-performance liquid chromatography-tandem mass spectrometry. PK findings revealed that the clearance and the inter-compartmental clearance were 1.75 and 3.12 times higher in female rats than in male rats, respectively. According to the result, PFPeA is eliminated more rapidly in female rats than in male rats. Also, the tissue distribution study confirmed that distribution characteristics exhibited gender difference. This study provides scientific evidence for conducting further investigation into short-chain PFASs, biomonitoring plans and decision making regarding human health risk assessment.

Factors influencing lithium pharmacokinetics in patients with acute mania: A population pharmacokinetic analysis.

OBJECTIVE: The objective of this study was to conduct a population pharmacokinetic model of lithium in Thai patients with acute mania. METHODS: Lithium concentrations from 222 acute manic patients were included in this study. The population pharmacokinetic model was developed using NONMEM 7.3 software. Influences of potential covariates including body size, age, renal function, and gender were evaluated through a stepwise approach. Bootstrap analysis and an external validation approach were used to evaluate the robustness and predictability of the final model. RESULTS: A one-compartment model adequately described lithium pharmacokinetics. Body weight and age were significant predictors for lithium clearance, with the following relationship: CL/F (L/hr) = 1.43 * (WT/65)0.425  * (age/38)-0.242 . The population estimates of lithium clearance, volume of distribution, and absorption rate constant of the final model were 1.43 L/hr, 54 L (fixed), and 0.426 hr-1 (fixed), respectively. Model evaluation showed that the final model was predictive and robust. CONCLUSIONS: A population pharmacokinetic model of lithium with good performance was developed in Thai patients with acute mania. This model can be used to assist clinicians in individualized lithium therapy.

A model-based comparative meta-analysis of the efficacy of dolutegravir-based and efavirenz-based regimens in HIV-infected patients.

Currently, combinations of typical types of antiretroviral agents have been adopted as chemotherapy for human immunodeficiency virus (HIV) infection, comprising two nucleoside analogue reverse transcriptase inhibitors plus one of a non-nucleoside reverse transcriptase inhibitor, an integrase strand-transfer inhibitor, and a protease inhibitor. Although several meta-analyses have been conducted to determine first-line combination antiretroviral therapy, this has yet to be confirmed due to the technical limitation associated. In the present study, we applied a model-based meta-analysis (MBMA) approach, because it allows integration of information from clinical trials with varying dosing, duration, and sampling time points, resulting in enlargement of available data sources. We performed a bibliographic search to identify clinical trials involving dolutegravir (DTG)-based and efavirenz (EFV)-based regimens in HIV-infected, antiretroviral therapy-naïve adults, and then identified 30 independent trial data. The time course of drug effect was described by a consecutive first-order kinetic model and analyzed using the nonlinear mixed effect modeling approach. The developed model suggests that the DTG-based regimen provides a faster-acting and more sustainable drug effect than the EFV-based regimen. Moreover, the drug effect tends to appear more slowly and decay faster in severe patients having higher viral load or smaller baseline CD4 count.

Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses.

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B-cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case-control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01-15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor-2 (FGF-2 p = 7.2 × 10-4 ) and transforming growth factor alpha (TGF-α, p = 6.5 × 10-5 ) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF-2 (p = 7.8 × 10-7 ), TGF-α (p = 4.08 × 10-5 ), fractalkine (p = 1.12 × 10-3 ), monocyte chemotactic protein-3 (p = 1.36 × 10-4 ), macrophage inflammatory protein 1-alpha (p = 4.6 × 10-4 ) and vascular endothelial growth factor (p = 4.23 × 10-5 ). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case-only analyses showed that Granulocyte-macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth-factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.

Predicting time to castration resistance in hormone sensitive prostate cancer by a personalization algorithm based on a mechanistic model integrating patient data.

BACKGROUND: Prostate cancer (PCa) is a leading cause of cancer death of men worldwide. In hormone-sensitive prostate cancer (HSPC), androgen deprivation therapy (ADT) is widely used, but an eventual failure on ADT heralds the passage to the castration-resistant prostate cancer (CRPC) stage. Because predicting time to failure on ADT would allow improved planning of personal treatment strategy, we aimed to develop a predictive personalization algorithm for ADT efficacy in HSPC patients. METHODS: A mathematical mechanistic model for HSPC progression and treatment was developed based on the underlying disease dynamics (represented by prostate-specific antigen; PSA) as affected by ADT. Following fine-tuning by a dataset of ADT-treated HSPC patients, the model was embedded in an algorithm, which predicts the patient's time to biochemical failure (BF) based on clinical metrics obtained before or early in-treatment. RESULTS: The mechanistic model, including a tumor growth law with a dynamic power and an elaborate ADT-resistance mechanism, successfully retrieved individual time-courses of PSA (R(2) = 0.783). Using the personal Gleason score (GS) and PSA at diagnosis, as well as PSA dynamics from 6 months after ADT onset, and given the full ADT regimen, the personalization algorithm accurately predicted the individual time to BF of ADT in 90% of patients in the retrospective cohort (R(2) = 0.98). CONCLUSIONS: The algorithm we have developed, predicting biochemical failure based on routine clinical tests, could be especially useful for patients destined for short-lived ADT responses and quick progression to CRPC. Prospective studies must validate the utility of the algorithm for clinical decision-making.

Statistical Modeling of Occupational Exposure to Polycyclic Aromatic Hydrocarbons Using OSHA Data.

Polycyclic aromatic hydrocarbons (PAHs) are a group of pollutants with multiple variants classified as carcinogenic. The Occupational Safety and Health Administration (OSHA) provided access to two PAH exposure databanks of United States workplace compliance testing data collected between 1979 and 2010. Mixed-effects logistic models were used to predict the exceedance fraction (EF), i.e., the probability of exceeding OSHA's Permissible Exposure Limit (PEL = 0.2 mg/m3) for PAHs based on industry and occupation. Measurements of coal tar pitch volatiles were used as a surrogate for PAHs. Time, databank, occupation, and industry were included as fixed-effects while an identifier for the compliance inspection number was included as a random effect. Analyses involved 2,509 full-shift personal measurements. Results showed that the majority of industries had an estimated EF < 0.5, although several industries, including Standardized Industry Classification codes 1623 (Water, Sewer, Pipeline, and Communication and Powerline Construction), 1711 (Plumbing, Heating, and Air-Conditioning), 2824 (Manmade Organic Fibres), 3496 (Misc. Fabricated Wire products), and 5812 (Eating Places), and Major group's 13 (Oil and Gas Extraction) and 30 (Rubber and Miscellaneous Plastic Products), were estimated to have more than an 80% likelihood of exceeding the PEL. There was an inverse temporal trend of exceeding the PEL, with lower risk in most recent years, albeit not statistically significant. Similar results were shown when incorporating occupation, but varied depending on the occupation as the majority of industries predicted at the administrative level, e.g., managers, had an estimated EF < 0.5 while at the minimally skilled/laborer level there was a substantial increase in the estimated EF. These statistical models allow the prediction of PAH exposure risk through individual occupational histories and will be used to create a job-exposure matrix for use in a population-based case-control study exploring PAH exposure and breast cancer risk.

Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency.

Climate warming is linked to earlier onset and extended duration of cyanobacterial blooms in temperate rivers. This causes an unpredictable extent of harm to the functioning of the ecosystem and public health. We used Microcystis spp. cell density data monitored for seven years (2016-2022) in ten sites across four temperate rivers of the Republic of Korea to define the phenology of cyanobacterial blooms and elucidate the climatic effect on their pattern. The day of year marking the onset, peak, and end of Microcystis growth were estimated using a Weibull function, and linear mixed-effect models were employed to analyze their relationships with environmental variables. These models identified river-specific temperatures at the beginning and end dates of cyanobacterial blooms. Furthermore, the most realistic models were employed to project future Microcystis bloom phenology, considering downscaled and quantile-mapped regional air temperatures from a general circulation model. Daily minimum and daily maximum air temperatures (mintemp and maxtemp) primarily drove the timing of the beginning and end of the bloom, respectively. The models successfully captured the spatiotemporal variations of the beginning and end dates, with mintemp and maxtemp predicted to be 24℃ (R2 = 0.68) and 16℃ (R2 = 0.35), respectively. The beginning and end dates were projected to advance considerably in the future under the Representative Concentration Pathway 2.6, 4.5, and 8.5. The simulations suggested that the largest uncertainty lies in the timing of when the bloom ends, whereas the timing of when blooming begins has less variation. Our study highlights the dependency of cyanobacterial bloom phenology on temperatures and earlier and prolonged bloom development.

Secure messaging telehealth billing in the digital age: moving beyond time-based metrics.

OBJECTIVE: We proposed adopting billing models for secure messaging (SM) telehealth services that move beyond time-based metrics, focusing on the complexity and clinical expertise involved in patient care. MATERIALS AND METHODS: We trained 8 classification machine learning (ML) models using providers' electronic health record (EHR) audit log data for patient-initiated non-urgent messages. Mixed effect modeling (MEM) analyzed significance. RESULTS: Accuracy and area under the receiver operating characteristics curve scores generally exceeded 0.85, demonstrating robust performance. MEM showed that knowledge domains significantly influenced SM billing, explaining nearly 40% of the variance. DISCUSSION: This study demonstrates that ML models using EHR audit log data can improve and predict billing in SM telehealth services, supporting billing models that reflect clinical complexity and expertise rather than time-based metrics. CONCLUSION: Our research highlights the need for SM billing models beyond time-based metrics, using EHR audit log data to capture the true value of clinical work.

Insights into the population pharmacokinetics and pharmacodynamics of quetiapine: a systematic review.

INTRODUCTION: Quetiapine exhibits notable pharmacokinetic and pharmacodynamic (PK/PD) variability, the origins of which are poorly understood. This systematic review summarizes published population PK/PD studies and identifies significant covariates accounting for this variability to inform precision dosing. METHODS: We systematically searched the PubMed, Web of Science, and Embase databases and compared study characteristics, model parameters, and covariate effects. Visual predictive distributions were used to compare different models. Forest plots and Monte Carlo simulations were used to assess the influence of covariates. RESULTS: Six population PK and three population PK/PD studies were included. The median apparent clearance in adults was 87.7 L/h. Strong and moderate cytochrome P450 3A4 inducers increased the apparent clearance approximately fourfold, while strong cytochrome P450 3A4 inhibitors reduced it by 93%. The half-maximum effect concentrations were 82.8 ng/mL for the Brief Psychiatric Rating Scale and 583 ng/mL for dopamine D2 receptor occupancy. Both treatment duration and quetiapine exposure were associated with weight gain. CONCLUSIONS: Concurrent administration of potent or moderate CYP3A4 inducers and inhibitors need to be avoided in quetiapine-treated patients. When co-medication is required, it is recommended to adjust the dosage based on therapeutic drug monitoring. Additional research is warranted to delineate the dose-exposure-response relationships of quetiapine and active metabolite norquetiapine in pediatrics, geriatrics, hepatically-impaired patients, and women using contraceptives or are pregnant or menopausal. PROSPERO REGISTRATION: CRD42023446654.

PK Modeling of L-4-Boronophenylalanine and Development of Bayesian Predictive Platform for L-4-Boronophenylalanine PKs for Boron Neutron Capture Therapy.

L-4-[(10B)]Boronophenylalanine (BPA) is an amino acid analogue with a boron-10 moiety. It is most widely used as a boron carrier in boron neutron capture therapy. In this study, a Bayesian predictive platform of blood boron concentration based on a BPA pharmacokinetic (PK) model was developed. This platform is user-friendly and can predict the individual boron PK and optimal time window for boron neutron capture therapy in a simple way. The present study aimed to establish a PK model of L-4-boronophenylalanine and develop a Bayesian predictive platform for blood boron PKs for user-friendly estimation of boron concentration during neutron irradiation of neutron capture therapy. Whole blood boron concentrations from seven previous reports were graphically extracted and analyzed using the nonlinear mixed-effects modeling (NONMEM) approach. Model robustness was assessed using nonparametric bootstrap and visual predictive check approaches. The visual predictive check indicated that the final PK model is able to adequately predict observed concentrations. The Shiny package was used to input real-time blood boron concentration data, and during the following irradiation session blood boron was estimated with an acceptably short calculation time for the determination of irradiation time. Finally, a user-friendly Bayesian estimation platform for BPA PKs was developed to optimize individualized therapy for patients undergoing BNCT.

Influence of geospatial resolution on sociodemographic predictors of COVID-19 in Massachusetts.

PURPOSE: When studying health risks across a large geographic region such as a state or province, researchers often assume that finer-resolution data on health outcomes and risk factors will improve inferences by avoiding ecological bias and other issues associated with geographic aggregation. However, coarser-resolution data (e.g., at the town or county-level) are more commonly publicly available and packaged for easier access, allowing for rapid analyses. The advantages and limitations of using finer-resolution data, which may improve precision at the cost of time spent gaining access and processing data, have not been considered in detail to date. METHODS: We systematically examine the implications of conducting town-level mixed-effect regression analyses versus census-tract-level analyses to study sociodemographic predictors of COVID-19 in Massachusetts. In a series of negative binomial regressions, we vary the spatial resolution of the outcome, the resolution of variable selection, and the resolution of the random effect to allow for more direct comparison across models. RESULTS: We find stability in some estimates across scenarios, changes in magnitude, direction, and significance in others, and tighter confidence intervals on the census-tract level. Conclusions regarding sociodemographic predictors are robust when regions of high concentration remain consistent across town and census-tract resolutions. CONCLUSIONS: Inferences about high-risk populations may be misleading if derived from town- or county-resolution data, especially for covariates that capture small subgroups (e.g., small racial minority populations) or are geographically concentrated or skewed (e.g., % college students). Our analysis can help inform more rapid and efficient use of public health data by identifying when finer-resolution data are truly most informative, or when coarser-resolution data may be misleading.

Population pharmacokinetics of everolimus in patients with seizures associated with focal cortical dysplasia.

Background: Everolimus is an inhibitor of mammalian target of rapamycin complex 1. As mutations in TSC1 and TSC2, which cause partial-onset seizures associated with TSC, were found in focal cortical dysplasia type Ⅱ (FCD Ⅱ) patients, a clinical trial has been performed to explore the efficacy and safety of everolimus in FCD patients. However, no dosage regimen was determined to treat FCD II. To recommend an optimal dose regimen for FCD patients, a population pharmacokinetic model of everolimus in FCD patients was developed. Methods: The data of everolimus were collected from September 2017 to May 2020 in a tertiary-level hospital in Korea. The model was developed using NONMEM® software version 7.4.1 (Icon Development Solutions, Ellicott City, MD, United States). Results: The population pharmacokinetics of everolimus was described as the one-compartment model with first-order absorption, with the effect of BSA on clearance. The final model was built as follows: TVCL = 12.5 + 9.71 × (BSA/1.5), TVV = 293, and TVKA = 0.585. As a result of simulation, a dose higher than 7 mg/m2 is needed in patients with BSA 0.5 m2, and a dose higher than 6 mg/m2 is needed in patients with BSA 0.7 m2. A dose of 4.5 mg/m2 is enough in the population with BSA higher than 1.5 m2 to meet the target trough range of 5-15 ng/mL. Conclusion: Based on the developed pharmacokinetics model, the optimal dose of everolimus in practice was recommended by considering the available strengths of Afinitor disperz®, 2 mg, 3 mg, and 5 mg.

Parametric population pharmacokinetics of isoniazid: a systematic review.

INTRODUCTION: Isoniazid (INH) plays an important role in prevention and treatment of tuberculosis (TB). However, large pharmacokinetic (PK) variations are observed in patients receiving standard INH dosages. Considering the influence of PK variations on INH efficacy or adverse reactions, we reviewed the population PK studies of INH and explored significant covariates that influence INH PK. METHODS: The PubMed and Embase databases were systematically searched from their inception to 30 January 2023. PPK studies on INH using a parametric nonlinear mixed-effect approach were included in this review. The characteristics and identified significant covariates of the included studies were summarized. RESULTS: Twenty-one studies conducted in adults, and seven in pediatrics were included. A two-compartment model with first-order absorption and elimination was the frequently used structural model for INH. NAT2 genotype, body size, and age were identified as significant covariates affecting INH PK variation. The median clearance (CL) value in the fast metabolizers was 2.55-fold higher than that in the slow metabolizers. Infants and children had higher CL per weight values than adults with the same metabolic phenotype. In pediatric patients, CL value increased with postnatal age. CONCLUSIONS: Compared with slow metabolizers, the daily dose of INH should be increased by 200-600 mg in fast metabolizers. To achieve effective treatment, pediatric patients need a higher dose per kilogram than adults. Further PPK studies of anti-tuberculosis drugs are needed to comprehensively understand the covariates that affect their PK characteristics and to achieve accurate dose adjustments.

Population pharmacokinetics of mycophenolate mofetil in pediatric patients early after liver transplantation.

Objective: To investigate the factors influencing the pharmacokinetics of mycophenolate mofetil (MMF) in pediatric patients after liver transplantation, and to establish a population pharmacokinetics model, which can provide a reference for clinical dosage adjustment. Methods: A prospective study in a single center was performed on pediatric patients who were administrated with mycophenolate mofetil dispersible tablets (MMFdt) for at least 4 days after liver transplantation continuously. Blood samples were collected in ethylene diamine tetraacetic acid anticoagulant tubes before dosing and 0.5, 1, 2, 4, 8, and 12 h after the morning intake of MMFdt. The concentrations of mycophenolic acid (MPA) in plasma were assayed with a validated reverse-phase high-performance liquid chromatography method. UGT1A8 518C > G, UGT1A9 -275T > A, UGT1A9 -2152C > T, UGT2B7 211G > T, SLC O 1B1 521T > C polymorphism were determined by Sanger sequencing. Nonlinear mixed effects modeling was used to establish the population pharmacokinetics (PPK) model. The predictability and stability of the model were internally evaluated by the goodness of fit plots, visual prediction check, normalized prediction errors, and bootstraps. Results: A two-compartment model with first-order absorption and first-order elimination was established with 115 MPA concentrations from 20 pediatric patients. The final model were: CL/F (L/h) = 14.8×(WT/7.5)0.75×(DOSE/11.16)0.452×е0.06, Ka (h-1) = 2.02×(WT/7.5)-0.25, Vc/F (L) = 6.01×(WT/7.5), Vp/F (L) = 269 (fixed), Q/F (L/h) = 15.4×(WT/7.5)0.75×е1.39. Where CL/F was the apparent clearance rate, Ka was the absorption rate constant, Vc/F was the apparent distribution volume of the central compartment, Vp/F was the apparent distribution volume of the peripheral compartment, Q/F was the atrioventricular clearance rate, WT was the body weight of the subject, and DOSE was the MMFdt administered dose. The model indicated there was large inter-individual variability in CL/F and Q/F after multiple dosing of MMFdt. Internal evaluation results showed that the final model had good stability and prediction performance. Conclusion: A stable and predictive population pharmacokinetic model of MMFdt in pediatric patients after the early stage of liver transplantation was established. The pediatric patient's weight and the dose of MMFdt can be a reference to adjust the MMFdt dose.

Quantitative effects of sodium-glucose cotransporter-2 inhibitors dapagliflozin and empagliflozin on quality of life in heart failure patients.

The aim of the present study is to investigate the quantitative effects of sodium-glucose cotransporter-2 (SGLT-2) inhibitors on the quality of life in heart failure (HF) patients. A total of 14,674 HF patients from two dapagliflozin and three empagliflozin studies is included for analysis via the nonlinear mixed-effect modeling (NONMEM) software, among which the change rate of the Kansas City Cardiomyopathy Questionnaire (KCCQ) score is used as the evaluation index. There is no significant difference in the pharmacodynamics influencing the quality of life in HF patients between the SGLT-2 inhibitors: 10 mg/day dapagliflozin and 10 mg/day empagliflozin. For the clinical summary score (CSS), total symptom score (TSS), and overall summary score (OSS), the Emax of the SGLT-2 inhibitors on the quality of life in HF patients is 3.74%, 4.43%, and 4.84%, respectively, and ET50 is 2.23, 4.37, and 7.15 weeks, respectively. In addition, the time duration of achieving 25%, 50%, 75%, and 80% Emax is 0.75, 2.23, 6.69, and 8.92 weeks for the CSS; 1.46, 4.37, 13.11, and 17.48 weeks for the TSS; and 2.39, 7.15, 21.45, and 28.6 weeks for the OSS, respectively. Therefore, to reach the plateau period (80% of Emax) of SGLT-2 inhibitors on the CSS, TSS, and OSS, 10 mg/day dapagliflozin (or 10 mg/day empagliflozin) is required to be taken for 8.92 weeks, 17.48 weeks, and 28.6 weeks, respectively. This is the first time that the quantitative effects of SGLT-2 inhibitors on the quality of life in HF patients are being explored.

Tumor Growth Inhibition-Overall Survival (TGI-OS) Model for Subgroup Analysis Based on Post-Randomization Factors: Application for Anti-drug Antibody (ADA) Subgroup Analysis of Atezolizumab in the IMpower150 Study.

Longitudinal changes of tumor size or tumor-associated biomarkers have been receiving growing attention as early markers of treatment benefits. Tumor growth inhibition-overall survival (TGI-OS) models represent mathematical frameworks used to establish a link from tumor size trajectory to survival outcome with the aim of predicting survival benefit with tumor data from a small number of subjects with a short follow-up time. In the present study, we applied the TGI-OS model to assess treatment benefit in the IMpower150 study for patients who exhibited development of anti-drug antibodies (ADA). Direct comparison between subgroups of the active arm [ADA positive (ADA +) and negative (ADA -) groups] to the entire control group is not appropriate, due to potential imbalances of baseline prognostic factors between ADA + and ADA - patients. Thus, the TGI-OS modeling framework was employed to adjust for differences in prognostic factors between the ADA subgroups to more accurately estimate the treatment benefits. After adjustment, the TGI-OS model predicted comparable hazard ratios (HRs) of OS between ADA + and ADA - subgroups, suggesting that the development of ADA does not have a clinically significant impact on the treatment benefit of atezolizumab.

Population Pharmacokinetics of Intravenous Acyclovir in Oncologic Pediatric Patients.

Background: Acyclovir represents the first-line prophylaxis and therapy for herpes virus infections. However, its pharmacokinetics in children exposes them to the risk of ineffective or toxic concentrations. The study was aimed at investigating the population pharmacokinetics (POP/PK) of intravenous (IV) acyclovir in oncologic children. Methods: Patients (age, 8.6 ± 5.0 years, 73 males and 47 females) received IV acyclovir for prophylaxis (n = 94) and therapy (n = 26) under a therapeutic drug monitoring (i.e., minimum and maximal plasma concentrations, >0.5 and <25 mg/L, respectively). Plasma concentrations were fitted by nonlinear mixed effect modeling and a simulation of dosing regimens was performed. Findings were stratified according to an estimated glomerular filtration rate (eGFR) threshold of 250 ml/min/1.73 m2. Results: The final 1-compartment POP/PK model showed that eGFR had a significant effect on drug clearance, while allometric body weight influenced both clearance and volume of distribution. The population clearance (14.0 ± 5.5 L/h) was consistent across occasions. Simulation of standard 1-h IV infusion showed that a 10-mg/kg dose every 6 h achieved target concentrations in children with normal eGFR (i.e., ≤250 ml/min/1.73 m2). Increased eGFR values required higher doses that led to an augmented risk of toxic peak concentrations. On the contrary, simulated prolonged (i.e., 2 and 3-h) or continuous IV infusions at lower doses increased the probability of target attainment while reducing the risk of toxicities. Conclusion: Due to the variable pharmacokinetics of acyclovir, standard dosing regimens may not be effective in some patients. Prospective trials should confirm the therapeutic advantage of prolonged and continuous IV infusions.