Subject-specific computer models (male and female) of the human head were used to investigate the possible axonal deformation resulting from the primary phase blast-induced skull flexures. The corresponding axonal tractography was explicitly incorporated into these finite element models using a recently developed technique based on the embedded finite element method. These models were subjected to extensive verification against experimental studies which examined their pressure and displacement response under a wide range of loading conditions. Once verified, a parametric study was developed to investigate the axonal deformation for a wide range of loading overpressures and directions as well as varying cerebrospinal fluid (CSF) material models. This study focuses on early times during a blast event, just as the shock transverses the skull (< 5 milliseconds). Corresponding boundary conditions were applied to eliminate the rotation effects and the resulting axonal deformation. A total of 138 simulations were developed– 128 simulations for studying the different loading scenarios and 10 simulations for studying the effects of CSF material model variance–leading to a total of 10,702 simulation core hours. Extreme strains and strain rates along each of the fiber tracts in each of these scenarios were documented and presented here. The results suggest that the blast-induced skull flexures result in strain rates as high as 150–378 s^-1. These high-strain rates of the axonal fiber tracts, caused by flexural displacement of the skull, could lead to a rate dependent micro-structural axonal damage, as pointed by other researchers.

Computational models provide additional tools for studying the brain, however, many techniques are currently disconnected from each other. There is a need for new computational approaches that span the range of physics operating in the brain. In this review paper, we offer some new perspectives on how the embedded element method can fill this gap and has the potential to connect a myriad of modeling genre. The embedded element method is a mesh superposition technique used within finite element analysis. This method allows for the incorporation of axonal fiber tracts to be explicitly represented. Here, we explore the use of the approach beyond its original goal of predicting axonal strain in brain injury. We explore the potential application of the embedded element method in areas of electrophysiology, neurodegeneration, neuropharmacology and mechanobiology. We conclude that this method has the potential to provide us with an integrated computational framework that can assist in developing improved diagnostic tools and regeneration technologies.

Background/Objectives: Adapalene is a synthetic retinoid used as a treatment for acne vulgaris. In this study, we attempted to evaluate the dermal pharmacokinetics of adapalene utilizing experimental and in silico tools. Methods: We utilized three over the counter (OTC) adapalene gels to evaluate local dermal pharmacokinetics. A data-driven, robust, mechanistic dermal physiologically based pharmacokinetic (PBPK) model was developed by integrating the physicochemical properties of adapalene, the formulation attributes of the gels, and the biophysical aspects of dermal absorption. The dermal PBPK model was validated against experimental data using in vitro release studies and in vitro permeation studies with human cadaver skin. A clinical study was performed to evaluate the effects of adapalene from the three gel formulations. The impact of adapalene delivery from three gels on the stratum corneum (SC) thickness, pilosebaceous unit area, keratinocyte number, and epidermal thickness was captured using a non-invasive technique, line-field confocal optical coherence tomography (LC–OCT). These responses were evaluated using an Emax model. Results: The dermal PBPK model has successfully predicted adapalene penetration profiles across different gel formulations. The model accuracy, in predicting drug release and permeation characteristics, was confirmed using the experimental data. Clinical evaluation revealed formulation-dependent differences in adapalene’s effects on measured skin parameters, with distinct pharmacodynamic profiles observed for each gel formulation. Conclusions: The overall study gave us a detailed insight into potential effects of formulation on the dermal pharmacokinetics and pharmacodynamics of adapalene using three marketed gels.

Finite element models are frequently used to simulate traumatic brain injuries. However, current models are unable to capture the progressive damage caused by repeated head trauma. In this work, we propose a method for computing the history-dependent mechanical damage of axonal fiber bundle tracts in the brain. Through the introduction of multiple damage models, we provide the ability to link consecutive head impact simulations, so that potential injury to the brain can be tracked over time. In addition, internal damage variables are used to degrade the mechanical response of each axonal fiber bundle element. As a result, the stiffness of the aggregate tissue decreases as damage evolves. To counteract this degenerative process, we have also introduced a preliminary healing model that reverses the accumulated damage, based on a user-specified healing duration. Using two detailed examples, we demonstrate that damage produces a significant decrease in fiber stress, which ultimately propagates to the tissue level and produces a measurable decrease in overall stiffness. These results suggest that damage modeling has the potential to enhance current brain simulation techniques and lead to new insights, especially in the study of repetitive head injuries.

The administration of therapeutic drugs through dermal routes, such as creams and ointments, has emerged as an increasingly popular alternative to traditional delivery methods, such as tablets and injections. In the context of drug development, it is crucial to identify the optimal doses and delivery routes that ensure successful outcomes. Physiologically based pharmacokinetic (PBPK) models have been proposed to simulate drug delivery and optimize drug formulations, but the calibration of these models is challenging due to the multitude of variables involved and limited experimental data. One significant research gap that this article addresses is the need for more efficient and accurate methods for calibrating PBPK models for dermal drug delivery. This manuscript presents a novel approach and an integrated dermal drug delivery model to address this gap that leverages virtual in vitro release (IVRT) and permeation (IVPT) testing data to optimize mechanistic models. The proposed approach was demonstrated through a study involving Desoximetasone cream and ointment formulations, where the release kinetics and permeation profiles of Desoximetasone were determined experimentally, and a computational model was created to simulate the results. The experimental studies showed that, even though the cumulative permeation of Desoximetasone at the end of the permeation study was comparable, there was a significant difference seen in the lag time in the permeation of Desoximetasone between the cream and ointment. Additionally, there was a significant difference seen in the amount of Desoximetasone permeated through human cadaver skin at early time points when the cream and ointment were compared. The computational model was optimized and validated, suggesting that this approach has the potential to bridge the existing research gap by improving the accuracy and efficiency of drug development processes. The model results show a good fit between the experimental data and model predictions. During the model optimization process, it became evident that there was variability in both the permeability and the partition coefficient within the stratum corneum. This variability had a significant and noteworthy influence on the overall performance of the model, especially when it came to its capacity to differentiate between cream and ointment formulations. Leveraging virtual models significantly aids the comprehension of drug release and permeation, mitigating the demanding data requirements. The use of virtual IVRT and IVPT data can accelerate the calibration of PBPK models, streamline the selection of the appropriate doses, and optimize drug delivery. Moreover, this novel approach could potentially reduce the time and resources involved in drug development, thus making it more cost-effective and efficient.

Military personnel involved in weapon training are subjected to repeated low-level blasts. The prevailing method of estimating blast loads involves wearable blast gauges. However, using wearable sensor data, blast loads to the head or other organs cannot be accurately estimated without knowledge of the service member's body posture. An image/video-augmented complementary experimental-computational platform for conducting safer weapon training was developed. This study describes the protocol for the automated generation of weapon training scenes from video data for blast exposure simulations. The blast scene extracted from the video data at the instant of weapon firing involves the service member body avatars, weapons, ground, and other structures. The computational protocol is used to reconstruct service members' positions and postures using this data. Image or video data extracted from service member body silhouettes are used to generate an anatomical skeleton and the key anthropometric data. These data are used to generate the 3D body surface avatars segmented into individual body parts and geometrically transformed to match extracted service member postures. The final virtual weapon training scene is used for 3D computational simulations of weapon blast wave loading on service members. The weapon training scene generator has been used to construct 3D anatomical avatars of individual service member bodies from images or videos in various orientations and postures. Results of the generation of a training scene from shoulder-mounted assault weapon system and mortar weapon system image data are presented. The Blast Overpressure (BOP) tool uses the virtual weapon training scene for 3D simulations of blast wave loading on the service member avatar bodies. This paper presents 3D computational simulations of blast wave propagation from weapon firing and corresponding blast loads on service members in training.