Vancomycin population pharmacokinetics and dosing proposal for the initial treatment in obese adult patients.

Aim: The aim of this study was to develop a vancomycin population pharmacokinetic model in adult obese patients and propose covariate-based dosing individualization in order to maximize the achievement of the newly recommended PK/PD target, according to a revised consensus guideline from 2020. Methods: Therapeutic drug monitoring data from initial vancomycin therapy (first 3 days of treatment) in adult obese (BMI ≥ 30 kg/m2) patients from 2013 to 2022 were analyzed using a non-linear mixed-effects modeling method, and Monte Carlo simulations were then used to find the optimal dosage maximizing the PK/PD target attainment. Results: A total of 147 vancomycin serum levels obtained from 138 patients were included in the analysis. Based on the covariate model diagnosis among all tested variables, no reliable predictor of vancomycin volume of distribution (Vd) was identified, while clearance (CL) was positively correlated with eGFR and lean body mass. Creatinine-based eGFR predicted vancomycin CL better than cystatin C-based eGFR. The median (interquartile range) value from conditional modes of individual estimates of Vd, CL, and elimination half-life in our population was 74.0 (70.5-75.4) L, 6.65 (4.95-8.42) L/h, and 7.7 (6.0-10.0) h, respectively. Conclusion: We proposed dosing individualization based on the covariate found in order to maximize the achievement of the newly recommended PK/PD target of the AUC/MIC ratio of 400-600. Clinical pharmacy/pharmacology interventions may lead to an improvement in vancomycin dosing with a reflection in PK/PD target attainment.

Agreement Between a Colorimetric Assay and Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry for Quantifying Paracetamol Plasma Concentrations.

Special populations, like geriatric patients, experience altered paracetamol pharmacokinetics (PK), complicating pain management. More PK research is essential to optimize paracetamol (acetaminophen) dosing. Yet, the reference method ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) is not readily available. Therefore, we aimed to evaluate the agreement between UPLC-MS/MS and the more accessible colorimetric Roche acetaminophen (ACETA) assay in quantifying paracetamol plasma concentrations, to facilitate PK studies and therapeutic drug monitoring for pain management. Patient data and plasma samples were obtained from a prospective study including geriatric patients admitted to the geriatric wards. ACETA and UPLC-MS/MS assays were performed in two separate laboratories. Bland-Altman plot and Passing-Bablok regression were used to assess agreement. Accuracy was evaluated using the McNemar test for a threshold value of 10 mg/L. Population PK modeling was employed to bridge PK data obtained from both methods (NONMEM 7.5). A total of 242 plasma sample pairs were available from 40 geriatric patients (age range, 80-95 years). Paracetamol plasma concentrations from ACETA (median 9.8 [interquartile range 6.1-14.4] mg/L) and UPLC-MS/MS (9.5 [6.2-14.8] mg/L) did not differ significantly (P > 0.05). No significant proportional nor additive bias was observed between both assay methods. The classification accuracy (at threshold 10 mg/L) was 85% (P = 0.414). The conversion factor between ACETA and UPLC-MS/MS was estimated at 1.06 (relative standard error 5%), yet with a 13.4% (relative standard error 23%) interindividual variability. ACETA assay showed no systematic bias in comparison with the UPLC-MS/MS assay in determining paracetamol exposure in geriatric blood samples despite the imprecision.

Sex-specific responses in glucose-insulin homeostasis and lipoprotein-lipid components after high-dose supplementation with marine n-3 PUFAs in abdominal obesity: a randomized double-blind crossover study.

Background: Clinical studies on effects of marine-derived omega-3 (n-3) polyunsaturated fatty acids (PUFAs), mainly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), and the plant-derived omega-6 (n-6) PUFA linoleic acid (LA) on lipoprotein-lipid components and glucose-insulin homeostasis have shown conflicting results, which may partly be explained by differential responses in females and males. However, we have lacked data on sexual dimorphism in the response of cardiometabolic risk markers following increased consumption of n-3 or n-6 PUFAs. Objective: To explore sex-specific responses after n-3 (EPA + DHA) or n-6 (LA) PUFA supplementation on circulating lipoprotein subfractions, standard lipids, apolipoproteins, fatty acids in red blood cell membranes, and markers of glycemic control/insulin sensitivity among people with abdominal obesity. Methods: This was a randomized double-blind crossover study with two 7-week intervention periods separated by a 9-week washout phase. Females (n = 16) were supplemented with 3 g/d of EPA + DHA (fish oil) or 15 g/d of LA (safflower oil), while males (n = 23) received a dose of 4 g/d of EPA + DHA or 20 g/d of LA. In fasting blood samples, we measured lipoprotein particle subclasses, standard lipids, apolipoproteins, fatty acid profiles, and markers of glycemic control/insulin sensitivity. Results: The between-sex difference in relative change scores was significant after n-3 for total high-density lipoproteins (females/males: -11%*/-3.3%, p = 0.036; *: significant within-sex change), high-density lipoprotein particle size (+2.1%*/-0.1%, p = 0.045), and arachidonic acid (-8.3%*/-12%*, p = 0.012), and after n-6 for total (+37%*/+2.1%, p = 0.041) and small very-low-density lipoproteins (+97%*/+14%, p = 0.021), and lipoprotein (a) (-16%*/+0.1%, p = 0.028). Circulating markers of glucose-insulin homeostasis differed significantly after n-3 for glucose (females/males: -2.1%/+3.9%*, p = 0.029), insulin (-31%*/+16%, p < 0.001), insulin C-peptide (-12%*/+13%*, p = 0.001), homeostasis model assessment of insulin resistance index 2 (-12%*/+14%*, p = 0.001) and insulin sensitivity index 2 (+14%*/-12%*, p = 0.001), and quantitative insulin sensitivity check index (+4.9%*/-3.4%*, p < 0.001). Conclusion: We found sex-specific responses after high-dose n-3 (but not n-6) supplementation in circulating markers of glycemic control/insulin sensitivity, which improved in females but worsened in males. This may partly be related to the sex differences we observed in several components of the lipoprotein-lipid profile following the n-3 intervention. Clinical trial registration: https://clinicaltrials.gov/, identifier [NCT02647333].

Optimizing study design in LPS challenge studies for quantifying drug induced inhibition of TNFα response: Did we miss the prime time?

In this work we evaluate the study design of LPS challenge experiments used for quantification of drug induced inhibition of TNFα response and provide general guidelines of how to improve the study design. Analysis of model simulated data, using a recently published TNFα turnover model, as well as the optimal design tool PopED have been used to find the optimal values of three key study design variables - time delay between drug and LPS administration, LPS dose, and sampling time points - that in turn could make the resulting TNFα response data more informative. Our findings suggest that the current rule of thumb for choosing the time delay should be reconsidered, and that the placement of the measurements after maximal TNFα response are crucial for the quality of the experiment. Furthermore, a literature study summarizing a wide range of published LPS challenge studies is provided, giving a broader perspective of how LPS challenge studies are usually conducted both in a preclinical and clinical setting.

Immunosuppressive drugs for nontransplant comorbidities are not associated with abdominal aortic aneurysm growth.

Background: In the present study, we examined the association of immunosuppressant drug prescriptions with the growth of small abdominal aortic aneurysms (AAAs). Methods: Participants with an AAA measuring between 30 and 50 mm were recruited from four Australian centers. AAA growth was monitored by ultrasound. The immunosuppressant drugs included conventional disease-modifying antirheumatic drugs (eg, methotrexate, sulfasalazine, leflunomide), steroids, hydroxychloroquine, other immunosuppressant drugs (eg, cyclosporine, azacitidine), or a combination of these drugs. Linear mixed effects modeling was performed to examine the independent association of an immunosuppressant prescription with AAA growth. A subanalysis examined the association of steroids with AAA growth. Results: Of the 621 patients, 34 (5.3%) had been prescribed at least one (n = 26) or more (n = 8) immunosuppressant drug and had been followed up for a median period of 2.1 years (interquartile range, 1.1-3.5 years), with a median of three ultrasound scans (interquartile range, two to five ultrasound scans). No significant difference was found in AAA growth when stratified by a prescription of immunosuppressant drugs on either unadjusted (mean difference, 0.2 mm/y; 95% confidence interval [CI], -0.4 to 0.7; P = .589) or risk factor-adjusted (mean difference, 0.2 mm/y; 95% CI, -0.3 to 0.7; P = .369) analyses. The findings were similar for the unadjusted (mean difference, 0.0 mm/y; 95% CI, -0.7 to 0.7; P = .980) and risk factor-adjusted (mean difference, 0.1 mm/y; 95% CI, -0.6 to 0.7; P = .886) subanalyses focused on steroid use. Conclusions: The results from this study suggest that AAA growth is not affected by immunosuppressant drug prescription. Studies with larger sample sizes are needed before reliable conclusions can be drawn.

Gentamicin Population Pharmacokinetics in Pediatric Patients-A Prospective Study with Data Analysis Using the saemix Package in R.

The aminoglycoside gentamicin is used for the empirical treatment of pediatric infections. It has a narrow therapeutic window. In this prospective study at University Children's Hospital Zurich, Switzerland, we aimed to characterize the pharmacokinetics of gentamicin in pediatric patients and predict plasma concentrations at typical recommended doses. We recruited 109 patients aged from 1 day to 14 years, receiving gentamicin (7.5 mg/kg at age ≥ 7 d or 5 mg/kg). Plasma levels were determined 30 min, 4 h and 24 h after the infusion was stopped and then transferred, together with patient data, to the secure BioMedIT node Leonhard Med. Population pharmacokinetic modeling was performed with the open-source R package saemix on the SwissPKcdw platform in Leonhard Med. Data followed a two-compartment model. Bodyweight, plasma creatinine and urea were identified as covariates for clearance, with bodyweight as a covariate for central and peripheral volumes of distribution. Simulations with 7.5 mg/kg revealed a 95% CI of 13.0-21.2 mg/L plasma concentration at 30 min after the stopping of a 30-min infusion. At 24 h, 95% of simulated plasma levels were <1.8 mg/L. Our study revealed that the recommended dosing is appropriate. It showed that population pharmacokinetic modeling using R provides high flexibility in a secure environment.

Does One Size Fit All? Socioeconomic Moderators of Post-divorce Health and the Effects of a Post-divorce Digital Intervention / ¿Una talla única para todos? Los moderadores socioeconómicos de la salud tras el divorcio y los efectos de una intervención digital postdivorcio

Public health efforts to reduce divorce-induced health adversities are gaining momentum and positive interventional outcomes of the online Cooperation After Divorce (CAD) digital platform for divorcees have been documented by previous research. However, it is unknown whether socioeconomic characteristics previously associated with prolonged divorce recovery are also associated with post-divorce intervention efficacy, and if so, in what way. Multilevel modeling was employed using a sample of 1,856 recently divorced Danes, who participated in an RCT study of the CAD digital platform, to investigate whether educational and income level (1) predict post-divorce health, (2) moderate the time-induced trajectories of post-divorce health, and (3) moderate the intervention-induced trajectories on post-divorce health over the first 12-months following legal divorce. The findings indicated that lower education and lower income predicted worse post-divorce health over one year post-divorce. Furthermore, it was indicated that education moderated post-divorce anxiety so that lower-educated participants experienced a larger reduction in symptoms of anxiety over time. However, except for depression, no moderating effect of income and education on the intervention effect of CAD was found. Our results suggest a beneficial effect of the CAD digital platform across socioeconomic characteristics in the post-divorce period, bolstering claims of the scalability of post-divorce interventions. Moreover, the findings suggest that, theoretically, the intervention may work to compensate for the lack of educational resources in reducing the health gap in post-divorce recovery. (AU)

Las campañas de salud pública para disminuir las consecuencias negativas del divorcio para la salud están tomando impulso, y los resultados positivos de la intervención de la plataforma digital para divorciados, Cooperación tras el Divorcio (CTD), han sido documentados con investigaciones previas. No obstante, es incierto que las características socioeconómicas, antes asociadas a la recuperación prolongada del divorcio, también se asocien a la eficacia de la intervención tras el divorcio, y si es así, de qué modo. Se han utilizado modelos multinivel en una muestra de 1,856 daneses divorciados recientemente, los cuales participaron en un estudio RCT de la plataforma digital CTD con el fin de investigar si el nivel educativo y de ingresos (1) predecía la salud tras el divorcio, (2) moderaba las trayectorias de salud postdivorcio inducidas por el tiempo y (3) moderaba las trayectorias inducidas por la intervención en la salud postdivorcio durante los primeros 12 meses tras el divorcio legal. Los resultados indican que un menor nivel educativo y un un menor nivel de ingresos eran predictores de peor salud a lo largo de un año después del divorcio. Además se indicaba que el nivel educativo moderaba la ansiedad postdivorcio, de modo que los participantes de menor formación experimentaban una mayor reducción de los síntomas de ansiedad con el tiempo. No obstante, excepto para la depresión, no se encontró efecto moderador alguno de los ingresos y el nivel educativo en los efectos de la intervención de CTD. Los resultados indican que hay un efecto positivo de la plataforma digital CTD independientemente de las características socioeconómicas en el periodo postdivorcio, apoyando la escalabilidad de las intervenciones postdivorcio. Además, los resultados indican que teóricamente la intervención puede servir para compensar a las personas con un menor nivel educativo en la reducción de la brecha en salud que se produce en la recuperación postdivorcio. (AU)

Recent lessons learned from population pharmacokinetic studies of mycophenolic acid: physiological, genomic, and drug interactions leading to the prediction of drug effects.

INTRODUCTION: Mycophenolic acid (MPA) is a widely used immunosuppressant in transplantation and autoimmune disease. Highly variable pharmacokinetics have been observed with MPA, but the exact mechanisms remain largely unknown. AREAS COVERED: The current review provided a critical, comprehensive update of recently published population pharmacokinetic/dynamic models of MPA (n = 16 papers identified from PubMed and Embase, inclusive from January 2017 to August 2021), with specific emphases on the intrinsic and extrinsic factors influencing the pharmacology of MPA. The significance of the identified covariates, potential mechanisms, and comparisons to historical literature have been provided. EXPERT OPINION: While select covariates affecting the population pharmacokinetics of MPA are consistently observed and mechanistically supported (e.g. cyclosporine and post-transplant time on MPA clearance), some variables have not been regularly reported and/or lacked mechanistic explanation (e.g. diarrhea and several genetic polymorphisms). Very few pharmacodynamic models were available, pointing to the need to extrapolate pharmacokinetic findings. Ideal models of MPA should consist of: i) utilizing optimal sampling points to allow the characterizations of absorption, re-absorption, and elimination phases; ii) characterizing unbound/total MPA, MPA metabolites, plasma/urinary concentrations, and genetic polymorphisms to facilitate mechanistic interpretations; and iii) incorporating actual outcomes (e.g. rejection, leukopenia, infections) and pharmacodynamic data (e.g. inosine-5'-monophosphate dehydrogenase activities) to establish clinical relevance. We anticipate the field will continue to expand in the next 5 to 10 years.

Normal fat mass cannot be reliably estimated in typical pharmacokinetic studies.

PURPOSE: An influential covariate for pharmacokinetics is (body) size. Recently, the method of estimation of normal fat mass (NFM) has been advocated. Here, the relative contribution of fat mass, estimated as a fraction fat (Ffat), is used to explain differences in pharmacokinetic parameters. This concept is more and more applied. However, it remains unclear whether NFM can be reliably estimated in these typical studies. METHODS: We performed an evaluation of the reliability of NFM estimation in a typical study size (n = 30), otherwise best-case scenario, by means of a pharmacokinetic simulation study. Several values of Ffat were investigated. RESULTS: In a typical pharmacokinetic study, high imprecision was observed for NFM parameter estimates over a range of scenarios. For example, in a scenario where the true value of Ffat on clearance was 0.5, we found a 95% confidence interval of - 0.1 to 2.1, demonstrating a low precision. The implications for practice are that one could conclude that fat-free mass best describes the relationship of the pharmacokinetics with body size, while the true relationship was between fat-free mass and total body weight. Consequently, this could lead to incorrect extrapolation of pharmacokinetics to extreme body sizes. CONCLUSION: In typical pharmacokinetic studies, NFM should be used with caution because the Ffat estimates have low precision. The estimation of Ffat should always be preceded by careful study design evaluation before planning a study, to ensure that the design and sample size is sufficient to apply this potentially useful methodology.

Model-Based Simulation of Maintenance Therapy of Childhood Acute Lymphoblastic Leukemia.

Acute lymphoblastic leukemia is the most common malignancy in childhood. Successful treatment requires initial high-intensity chemotherapy, followed by low-intensity oral maintenance therapy with oral 6-mercaptopurine (6MP) and methotrexate (MTX) until 2-3 years after disease onset. However, intra- and inter-individual variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of 6MP and MTX make it challenging to balance the desired antileukemic effects with undesired excessive myelosuppression during maintenance therapy. A model to simulate the dynamics of different cell types, especially neutrophils, would be a valuable contribution to improving treatment protocols (6MP and MTX dosing regimens) and a further step to understanding the heterogeneity in treatment efficacy and toxicity. We applied and modified a recently developed semi-mechanistic PK/PD model to neutrophils and analyzed their behavior using a non-linear mixed-effects modeling approach and clinical data obtained from 116 patients. The PK model of 6MP influenced the accuracy of absolute neutrophil count (ANC) predictions, whereas the PD effect of MTX did not. Predictions based on ANC were more accurate than those based on white blood cell counts. Using the new cross-validated mathematical model, simulations of different treatment protocols showed a linear dose-effect relationship and reduced ANC variability for constant dosages. Advanced modeling allows the identification of optimized control criteria and the weighting of specific influencing factors for protocol design and individually adapted therapy to exploit the optimal effect of maintenance therapy on survival.

Three-year changes of cortical 18F-FDG in amnestic vs. non-amnestic sporadic early-onset Alzheimer's disease.

PURPOSE: To examine and compare longitudinal changes of cortical glucose metabolism in amnestic and non-amnestic sporadic forms of early-onset Alzheimer's disease and assess potential associations with neuropsychological performance over a 3-year period time. METHODS: Eighty-two participants meeting criteria for early-onset (< 65 years) sporadic form of probable Alzheimer's disease and presenting with a variety of clinical phenotypes (47 amnestic and 35 non-amnestic forms) were included at baseline and followed up for 1.44 ± 1.23 years. All of the participants underwent a work-up at baseline and every year during the follow-up period, which includes clinical examination, neuropsychological testing, genotyping, cerebrospinal fluid biomarker assays, and structural MRI and 18F-FDG PET. Vertex-wise partial volume-corrected glucose metabolic maps across the entire cortical surface were generated and longitudinally assessed together with the neuropsychological scores using linear mixed-effects modeling as a function of amnestic and non-amnestic sporadic forms of early-onset Alzheimer's disease. RESULTS: Similar evolution patterns of glucose metabolic decline between amnestic and non-amnestic forms were observed in widespread neocortical cortices. However, only non-amnestic forms appeared to have a greater reduction of glucose metabolism in lateral orbitofrontal and bilateral medial temporal cortices associated with more severe declines of neuropsychological performance compared with amnestic forms. Furthermore, results suggest that glucose metabolic decline in amnestic forms would progress along an anterior-to-posterior axis, whereas glucose metabolic decline in non-amnestic forms would progress along a posterior-to-anterior axis. CONCLUSIONS: We found differences in spatial distribution and temporal trajectory of glucose metabolic decline between amnestic and non-amnestic early-onset Alzheimer's disease groups, suggesting that one might want to consider treating the two forms of the disease as two separate entities.

An integrative Bayesian approach to matrix-based analysis in neuroimaging.

Understanding the correlation structure associated with brain regions is a central goal in neuroscience, as it informs about interregional relationships and network organization. Correlation structure can be conveniently captured in a matrix that indicates the relationships among brain regions, which could involve electroencephalogram sensors, electrophysiology recordings, calcium imaging data, or functional magnetic resonance imaging (FMRI) data-We call this type of analysis matrix-based analysis, or MBA. Although different methods have been developed to summarize such matrices across subjects, including univariate general linear models (GLMs), the available modeling strategies tend to disregard the interrelationships among the regions, leading to "inefficient" statistical inference. Here, we develop a Bayesian multilevel (BML) modeling framework that simultaneously integrates the analyses of all regions, region pairs (RPs), and subjects. In this approach, the intricate relationships across regions as well as across RPs are quantitatively characterized. The adoption of the Bayesian framework allows us to achieve three goals: (a) dissolve the multiple testing issue typically associated with seeking evidence for the effect of each RP under the conventional univariate GLM; (b) make inferences on effects that would be treated as "random" under the conventional linear mixed-effects framework; and (c) estimate the effect of each brain region in a manner that indexes their relative "importance". We demonstrate the BML methodology with an FMRI dataset involving a cognitive-emotional task and compare it to the conventional GLM approach in terms of model efficiency, performance, and inferences. The associated program MBA is available as part of the AFNI suite for general use.

Covariates in Pharmacometric Repeated Time-to-Event Models: Old and New (Pre)Selection Tools.

During covariate modeling in pharmacometrics, computational time can be reduced by using a fast preselection tool to identify a subset of promising covariates that are to be tested with the more computationally demanding likelihood ratio test (LRT), which is considered to be the standard for covariate selection. There is however a lack of knowledge on best practices for covariate (pre)selection in pharmacometric repeated time-to-event (RTTE) models. Therefore, we aimed to systematically evaluate the performance of three covariate (pre)selection tools for RTTE models: the likelihood ratio test (LRT), the empirical Bayes estimates (EBE) test, and a novel Schoenfeld-like residual test. This was done in simulated datasets with and without a "true" time-constant covariate, and both in the presence and absence of high EBE shrinkage. In scenarios with a "true" covariate effect, all tools had comparable power to detect this effect. In scenarios without a "true" covariate effect, the false positive rates of the LRT and the Schoenfeld-like residual test were slightly inflated to 5.7% and 7.2% respectively, while the EBE test had no inflated false positive rate. The presence of high EBE shrinkage (> 40%) did not affect the performance of any of the covariate (pre)selection tools. We found the EBE test to be a fast and accurate tool for covariate preselection in RTTE models. The novel Schoenfeld-like residual test proposed here had a similar performance in the tested scenarios and might be applied more readily to time-varying covariates, such as drug concentration and dynamic biomarkers.

Intraclass correlation: Improved modeling approaches and applications for neuroimaging.

Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, and so on. The popular definitions and interpretations of ICC are usually framed statistically under the conventional ANOVA platform. Here, we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging, and we show that the standard ANOVA framework is often limited, rigid, and inflexible in modeling capabilities. These intrinsic limitations motivate several improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degenerative circumstance, and second, incorporating precision information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), regularized mixed-effects (RME), multilevel mixed-effects (MME), and regularized multilevel mixed-effects (RMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects and their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data and fixed effects for confounding variables. More importantly, we show that the MME and RMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or RMME is preferable when precision information (i.e., weights that more accurately allocate the variances in the data) is available for the effect estimate; when precision information is unavailable, ICC estimation through LME or the RME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole-brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.

Alpha-Band Brain Oscillations Shape the Processing of Perceptible as well as Imperceptible Somatosensory Stimuli during Selective Attention.

Attention filters and weights sensory information according to behavioral demands. Stimulus-related neural responses are increased for the attended stimulus. Does alpha-band activity mediate this effect and is it restricted to conscious sensory events (suprathreshold), or does it also extend to unconscious stimuli (subthreshold)? To address these questions, we recorded EEG in healthy male and female volunteers undergoing subthreshold and suprathreshold somatosensory electrical stimulation to the left or right index finger. The task was to detect stimulation at the randomly alternated cued index finger. Under attention, amplitudes of somatosensory evoked potentials increased 50-60 ms after stimulation (P1) for both suprathreshold and subthreshold events. Prestimulus amplitude of peri-Rolandic alpha, that is mu, showed an inverse relationship to P1 amplitude during attention compared to when the finger was unattended. Interestingly, intermediate and high amplitudes of mu rhythm were associated with the highest P1 amplitudes during attention and smallest P1 during lack of attention, that is, these levels of alpha rhythm seemed to optimally support the behavioral goal ("detect" stimuli at the cued finger while ignoring the other finger). Our results show that attention enhances neural processing for both suprathreshold and subthreshold stimuli and they highlight a rather complex interaction between attention, Rolandic alpha activity, and their effects on stimulus processing.SIGNIFICANCE STATEMENT Attention is crucial in prioritizing processing of relevant perceptible (suprathreshold) stimuli: it filters and weights sensory input. The present study investigates the controversially discussed question whether this attention effect extends to imperceptible (subthreshold) stimuli as well. We found noninvasive EEG signatures for attentional modulation of neural events following perceptible and imperceptible somatosensory stimulation in human participants. Specifically, stimulus processing for both kinds of stimulation, subthreshold and suprathreshold, is enhanced by attention. Interestingly, Rolandic alpha rhythm strength and its influence on stimulus processing are strikingly altered by attention most likely to optimally achieve the behavioral goal.

A big data approach to the development of mixed-effects models for seizure count data.

OBJECTIVE: Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. METHODS: Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS: For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day. SIGNIFICANCE: The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study.

BACKGROUND & NEW METHOD: The widely used framework of voxel-based morphometry for analyzing neuroimages is extended here to model longitudinal imaging data by exchanging the linear model with a linear mixed-effects model. The new approach is employed for analyzing a large longitudinal sample of 756 diffusion-weighted images acquired in 177 subjects of the Alzheimer's Disease Neuroimaging initiative (ADNI). RESULTS AND COMPARISON WITH EXISTING METHODS: While sample- and group-level results from both approaches are equivalent, the mixed-effect model yields information at the single subject level. Interestingly, the neurobiological relevance of the relevant parameter at the individual level describes specific differences associated with aging. In addition, our approach highlights white matter areas that reliably discriminate between patients with Alzheimer's disease and healthy controls with a predictive power of 0.99 and include the hippocampal alveus, the para-hippocampal white matter, the white matter of the posterior cingulate, and optic tracts. In this context, notably the classifier includes a sub-population of patients with minimal cognitive impairment into the pathological domain. CONCLUSION: Our classifier offers promising features for an accessible biomarker that predicts the risk of conversion to Alzheimer's disease. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ADNI Acknowledgement List.pdf. Significance statement This study assesses neuro-degenerative processes in the brain's white matter as revealed by diffusion-weighted imaging, in order to discriminate healthy from pathological aging in a large sample of elderly subjects. The analysis of time-series examinations in a linear mixed effects model allowed the discrimination of population-based aging processes from individual determinants. We demonstrate that a simple classifier based on white matter imaging data is able to predict the conversion to Alzheimer's disease with a high predictive power.

A generic viral dynamic model to systematically characterize the interaction between oncolytic virus kinetics and tumor growth.

Oncolytic viruses (OV) represent an encouraging new therapeutic concept for treatment of human cancers. OVs specifically replicate in tumor cells and initiate cell lysis whilst tumor cells act as endogenous bioreactors for virus amplification. This complex bidirectional interaction between tumor and oncolytic virus hampers the establishment of a straight dose-concentration-effect relation. We aimed to develop a generic mathematical pharmacokinetic/pharmacodynamics (PK/PD) model to characterize the relationship between tumor cell growth and kinetics of different OVs. U87 glioblastoma cell growth and titer of Newcastle disease virus (NDV), reovirus (RV) and parvovirus (PV) were systematically determined in vitro. PK/PD analyses were performed using non-linear mixed effects modeling. A viral dynamic model (VDM) with a common structure for the three different OVs was developed which simultaneously described tumor growth and virus replication. Virus specific parameters enabled a comparison of the kinetics and tumor killing efficacy of each OV. The long-term interactions of tumor cells with NDV and RV were simulated to predict tumor reoccurrence. Various treatment scenarios (single and multiple dosing with same OV, co-infection with different OVs and combination with hypothetical cytotoxic compounds) were simulated and ranked for efficacy using a newly developed treatment rating score. The developed VDM serves as flexible tool for the systematic cross-characterization of tumor-virus relationships and supports preselection of the most promising treatment regimens for follow-up in vivo analyses.

A Model-Based Approach for Joint Analysis of Pain Intensity and Opioid Consumption in Postoperative Pain.

Joint analysis of pain intensity and opioid consumption is encouraged in trials of postoperative pain. However, previous approaches have not appropriately addressed the complexity of their interrelation in time. In this study, we applied a non-linear mixed effects model to simultaneously study pain intensity and opioid consumption in a 4-h postoperative period for 44 patients undergoing percutaneous kidney stone surgery. Analysis was based on 748 Numerical Rating Scale (NRS) scores of pain intensity and 51 observed morphine and oxycodone dosing events. A joint model was developed to describe the recurrent pattern of four key phases determining the development of pain intensity and opioid consumption in time; (A) Distribution of pain intensity scores which followed a truncated Poisson distribution with time-dependent mean score ranging from 0.93 to 2.45; (B) Probability of transition to threshold pain levels (NRS ≥ 3) which was strongly dependent on previous pain levels ranging from 2.8-15.2% after NRS of 0-2; (C) Probability of requesting opioid when allowed (NRS ≥ 3) which was strongly correlated with the number of previous doses, ranging from 89.8% for requesting the first dose to 26.1% after three previous doses; (D) Reduction in pain scores after opioid dosing which was significantly related to the pain intensity at time of opioid request (P < 0.001). This study highlights the importance of analyzing pain intensity and opioid consumption in an integrated manner. Non-linear mixed effects modeling proved a valuable tool for analysis of interventions that affect pain intensity, probability of rescue dosing or the effect of opioids in the postoperative pain period.

A Pharmacokinetic-Pharmacodynamic Model of Morphine Exposure and Subsequent Morphine Consumption in Postoperative Pain.

PURPOSE: To characterize the pharmacokinetic-pharmacodynamic (PK-PD) relationship between exposure of morphine and subsequent morphine consumption and to develop simulation tools for model validation. METHODS: Dose, formulation and time of morphine administration was available from a published study in 63 patients receiving intravenous, oral immediate release or oral controlled release morphine on request after hip surgery. The PK-PD relationship between predicted exposure of morphine and morphine consumption was modeled using repeated time to event (RTTE) modeling in NONMEM. To validate the RTTE model, a visual predictive check method was developed with simulated morphine consumption given the exposure of preceding morphine administration. RESULTS: The probability of requesting morphine was found to be significantly related to the exposure of morphine as well as night/day. Oral controlled release morphine was more effective than intravenous and oral immediate release formulations at equivalent average concentrations. Maximum effect was obtained for 8 h by oral controlled release doses ≥ 15 mg, where probability of requesting a new dose was reduced to 20% for a typical patient. CONCLUSION: This study demonstrates the first quantitative link between exposure of morphine and subsequent morphine consumption and introduces an efficient visual predictive check approach with simulation of adaptive dosing.