Development of 18F-labeled radiotracers for neuroreceptor imaging with positron emission tomography

Positron emission tomography (PET) is an in vivo molecular imaging tool which is widely used in nuclear medicine for early diagnosis and treatment follow-up of many brain diseases. PET uses biomolecules as probes which are labeled with radionuclides of short half-lives, synthesized prior to the imaging studies. These probes are called radiotracers. Fluorine-18 is a radionuclide routinely used in the radiolabeling of neuroreceptor ligands for PET because of its favorable half-life of 109.8 min. The delivery of such radiotracers into the brain provides images of transport, metabolic, and neurotransmission processes on the molecular level. After a short introduction into the principles of PET, this review mainly focuses on the strategy of radiotracer development bridging from basic science to biomedical application. Successful radiotracer design as described here provides molecular probes which not only are useful for imaging of human brain diseases, but also allow molecular neuroreceptor imaging studies in various small-animal models of disease, including genetically-engineered animals. Furthermore, they provide a powerful tool for in vivo pharmacology during the process of pre-clinical drug development to identify new drug targets, to investigate pathophysiology, to discover potential drug candidates, and to evaluate the pharmacokinetics and pharmacodynamics of drugs in vivo.     

Modeling longitudinal changes in hippocampal subfields and relations with memory from early- to mid-childhood

The hippocampus has been suggested to show protracted postnatal developmental growth across childhood. Most previous studies during this developmental period have been cross-sectional in nature and have focused on age-related differences in either hippocampal subregions or subfields, but not both, potentially missing localized changes. This study capitalized on a latent structural equation modeling approach to examine the longitudinal development of hippocampal subfields (cornu ammonis (CA) 2-4/dentate gyrus (DG), CA1, subiculum) in both the head and the body of the hippocampus, separately, in 165 typically developing 4- to 8-year-old children. Our findings document differential development of subfields within hippocampal head and body. Specifically, within hippocampal head, CA1 volume increased between 4−5 years and within hippocampal body, CA2-4/DG and subiculum volume increased between 5−6 years. Additionally, changes in CA1 volume in the head and changes in subiculum in the body between 4−5 years related to improvements in memory between 4−5 years. These findings demonstrate the protracted development of subfields in vivo during early- to mid-childhood, illustrate the importance of considering subfields separately in the head and body of the hippocampus, document co-occurring development of brain and behavior, and highlight the strength of longitudinal data and latent modeling when examining brain development.     

Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation

BackgroundSubthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson’s disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated.ObjectiveEvaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings.MethodsPathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified.ResultsGood model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort.ConclusionsOur findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.     

Dose proportionality and pharmacokinetics of dronedarone following intravenous and oral administration to rat

1. The aim of this study was to investigate the pharmacokinetic properties of dronedarone by using noncompartmental analysis and modeling approaches after intravenous and oral administration of dronedarone to rats.

2. Twenty-eight male Sprague-Dawley rats were randomly divided into four groups, and dronedarone was administered intravenously (1?mg/kg) and orally (5, 10 and 40?mg/kg) based on a parallel design. Blood samples were collected before and 0.083 (intravenous administration only), 0.25, 0.5, 0.75, 1, 2, 4, 6, 8, 12 and 24?h after drug administration. The plasma concentration of dronedarone was determined by using LC-MS/MS.

3. The oral bioavailability of dronedarone was evaluated as approximately 16% in rats, similar to that in humans. The assessment of dose proportionality by using the power model showed that AUC_inf increased in a dose-proportional manner, whereas AUC_24h and C_max exhibited a lack of dose proportionality over the dose range between 5 and 40?mg/kg. The two-compartment model, with first-order absorption and elimination rate constants, was sufficient to explain the pharmacokinetics of dronedarone with biexponential decay.

4. These findings will help to understand the pharmacology of dronedarone to develop the new formulation and therapeutics optimization linked to pharmacokinetic/pharmacodynamic study.

     

Immunoinformatics: an overview of computational tools and techniques for understanding immune function

In recent years, there has been a rapid expansion in the application of information technology to biological data. Although the use of information science techniques is less common for the discipline of immunology, this field has seen great strides in recent years. This review addresses why in silico modeling is needed in immunology research, highlights some of the major areas of research and suggests what may be important for the future of immunoinformatics.     

Adequate sample size for developing prediction models is not simply related to events per variable

ObjectivesThe choice of an adequate sample size for a Cox regression analysis is generally based on the rule of thumb derived from simulation studies of a minimum of 10 events per variable (EPV). One simulation study suggested scenarios in which the 10 EPV rule can be relaxed. The effect of a range of binary predictors with varying prevalence, reflecting clinical practice, has not yet been fully investigated.Study Design and SettingWe conducted an extended resampling study using a large general-practice data set, comprising over 2 million anonymized patient records, to examine the EPV requirements for prediction models with low-prevalence binary predictors developed using Cox regression. The performance of the models was then evaluated using an independent external validation data set. We investigated both fully specified models and models derived using variable selection.ResultsOur results indicated that an EPV rule of thumb should be data driven and that EPV ≥ 20 ​ generally eliminates bias in regression coefficients when many low-prevalence predictors are included in a Cox model.ConclusionHigher EPV is needed when low-prevalence predictors are present in a model to eliminate bias in regression coefficients and improve predictive accuracy.     

Identification of the neural component of torque during manually-applied spasticity assessments in children with cerebral palsy

Clinical assessment of spasticity is compromised by the difficulty to distinguish neural from non-neural components of increased joint torque. Quantifying the contributions of each of these components is crucial to optimize the selection of anti-spasticity treatments such as botulinum toxin (BTX). The aim of this study was to compare different biomechanical parameters that quantify the neural contribution to ankle joint torque measured during manually-applied passive stretches to the gastrocsoleus in children with spastic cerebral palsy (CP). The gastrocsoleus of 53 children with CP (10.9 ± 3.7 y; females n = 14; bilateral/unilateral involvement n = 28/25; Gross Motor Functional Classification Score I–IV) and 10 age-matched typically developing (TD) children were assessed using a manually-applied, instrumented spasticity assessment. Joint angle characteristics, root mean square electromyography and joint torque were simultaneously recorded during passive stretches at increasing velocities. From the CP cohort, 10 muscles were re-assessed for between-session reliability and 19 muscles were re-assessed 6 weeks post-BTX. A parameter related to mechanical work, containing both neural and non-neural components, was compared to newly developed parameters that were based on the modeling of passive stiffness and viscosity. The difference between modeled and measured response provided a quantification of the neural component. Both types of parameters were reliable (ICC > 0.95) and distinguished TD from spastic muscles (p < 0.001). However, only the newly developed parameters significantly decreased post-BTX (p = 0.012). Identifying the neural and non-neural contributions to increased joint torque allows for the development of individually tailored tone management.