Whole-body Model Research Articles

Integration of In Vitro and In Vivo Models to Predict Cellular and Tissue Dosimetry of Nanomaterials Using Physiologically Based Pharmacokinetic Modeling.

Nanomaterials (NMs) have been increasingly used in a number of areas, including consumer products and nanomedicine. Target tissue dosimetry is important in the evaluation of safety, efficacy, and potential toxicity of NMs. Current evaluation of NM efficacy and safety involves the time-consuming collection of pharmacokinetic and toxicity data in animals and is usually completed one material at a time. This traditional approach no longer meets the demand of the explosive growth of NM-based products. There is an emerging need to develop methods that can help design safe and effective NMs in an efficient manner. In this review article, we critically evaluate existing studies on in vivo pharmacokinetic properties, in vitro cellular uptake and release and kinetic modeling, and whole-body physiologically based pharmacokinetic (PBPK) modeling studies of different NMs. Methods on how to simulate in vitro cellular uptake and release kinetics and how to extrapolate cellular and tissue dosimetry of NMs from in vitro to in vivo via PBPK modeling are discussed. We also share our perspectives on the current challenges and future directions of in vivo pharmacokinetic studies, in vitro cellular uptake and kinetic modeling, and whole-body PBPK modeling studies for NMs. Finally, we propose a nanomaterial in vitro to in vivo extrapolation via physiologically based pharmacokinetic modeling (Nano-IVIVE-PBPK) framework for high-throughput screening of target cellular and tissue dosimetry as well as potential toxicity of different NMs in order to meet the demand of efficient evaluation of the safety, efficacy, and potential toxicity of a rapidly increasing number of NM-based products.

Prediction of Drug-Drug-Gene Interaction Scenarios of (E)-Clomiphene and Its Metabolites Using Physiologically Based Pharmacokinetic Modeling.

Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug-gene interactions (DGIs), drug-drug interactions (DDIs) and drug-drug-gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted Cmax and 80% of AUClast values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.

An Observer-Based Stiffness Estimation for Space Target Capture by Space Robot with Controllable Damping Mechanism

The space target capturing task using the spacecraft-manipulator system (SMS) has special significance in on-orbit servicing due to its theoretical challenges and practical value. The contact force between the end effector (gripper) and the target exerted by the tumbling motion of the space target destabilizes the spacecraft base. A full-dimensional controllable damping mechanism (FDCDM) with a cross-axis structure was designed to buffer the transient impact force on the end joint. The introduction of a damping mechanism gives the space robot a variable stiffness and damping system, and a stiffness estimation algorithm is proposed to calibrate the system stiffness, as stiffness cannot be measured directly. The full-dimensional controllable damping mechanism (FDCDM) with a cross-axis structure is equivalent to a four-DOF tandem joint, and the whole-body dynamic model of the SMS endowed with a full-dimensional controllable damping mechanism (FDCDM) was established using the Kane equation. Then, an unknown input observer (UIO)-based identification theory is proposed to precisely estimate the internal flexibility torque and the corresponding joint stiffness. A model-based neural learning algorithm is proposed to update the variable parameter matrix of the observer. The simulation experiment results demonstrate that the flexibility torque and joint stiffness could be accurately estimated within the expected error, illustrating the feasibility and effectiveness of the proposed method.

The Role of Decreased Levels of Neuronal Autophagy in Increased Susceptibility to Post-traumatic Epilepsy.

Post-traumatic epilepsy (PTE) caused by mild TBI (mild traumatic brain injury, mTBI) has a high incidence and poor prognosis, but its mechanisms are unclear. Herein, we investigated the role of reduced levels of neuronal autophagy during the latency period in the increased susceptibility to PTE. In the study, a gentle whole-body mechanical trauma rat model was prepared using Noble-Collip drums, and the extent of injury was observed by cranial CT and HE staining of hippocampal tissue. The incidence of epilepsy and its seizure form were observed 7-90days after mTBI, and electroencephalography (EEG) was recorded during seizures in rats. Subcortical injection of non-epileptogenic dose of ferrous chloride (FeCl2) was used to observe the changes of PTE incidence after mTBI. Western blot and Real-time PCR were used to detect the level of autophagy in hippocampal cells at different time points during the latency period of PTE, and its incidence was observed after up-regulation of autophagy after administration of autophagy agonist-rapamycin. The results showed that mTBI was prepared by Noble-Collip drum, which could better simulate the clinical mTBI process. There was no intracerebral hemorrhage and necrosis in rats, no early-onset seizures, and the incidence of PTE after mTBI was 26.7%. The incidence of PTE was 56.7% in rats injected cortically with FeCl2 at a dose lower than the epileptogenic dose 48h after mTBI, and the difference was significant compared with no FeCl2 injection, suggesting an increased susceptibility to PTE after mTBI. Further study of neuronal autophagy during PTE latency revealed that autophagy levels were reduced, and the incidence of PTE was significantly reduced after administration of rapamycin to upregulate autophagy. Taken together, the decreased level of neuronal autophagy during the latency period may be a possible mechanism for the increased susceptibility to PTE after mTBI.

A heuristic gait template planning and dynamic motion control for biped robots

AbstractBiped robots with dynamic motion control have shown strong robustness in complex environments. However, many motion planning methods rely on models, which have difficulty dynamically modifying the walking cycle, height, and other gait parameters to cope with environmental changes. In this study, a heuristic model-free gait template planning method with dynamic motion control is proposed. The gait trajectory can be generated by inputting the desired speed, walking cycle, and support height without a model. Then, the stable walking of the biped robot can be realized by foothold adjustment and whole-body dynamics model control. The gait template can be changed in real time to achieve gait flexibility of the biped robot. Finally, the effectiveness of the method is verified by simulations and experiments of the biped robot BHR-B2. The research presented here helps improve the gait transition ability of biped robots in dynamic locomotion.

Effect of whole-body vibration training on the recovery of lower limb function in people with stroke: a systematic review and meta-analysis

Purpose The purpose of this meta-analysis was to systematically evaluate the effects of whole-body vibration training (WBVT) on the recovery of lower limb function in people with stroke. Methods The literature search was made in the electronic databases PubMed, Web of Science, Scopus and Embase electronic databases. Only randomized controlled trials were included. Data extraction, quality assessment and meta-analysis were performed. The search was conducted on September 01, 2022. The data analysis software was RevMan 5.3. Results A total of 13 RCTs were included, including 687 patients. The results showed that compared with the control group, the overall difference in balance function was statistically significant [MD = 4.23, 95% CI 2.21 ∼ 6.26, p < 0.0001]. There was no significant difference in the evaluation indexes of lower limb motor function, including the TUG, 10MWT, 6MWT, and FMA - LE. The overall difference in lower limb muscle spasticity was statistically significant [MD = −0.53, 95% CI −0.81 ∼ 0.26, p = 0.0001]. Conclusions Compared with the control group, using WBVT treatment has a more obvious effect on the recovery of lower limb function and muscle spasticity, and there is no obvious advantage in motor function recovery. IMPLICATIONS FOR REHABILITATION This Systematic Review and meta-analysis of evidence suggest that whole-body vibration training is effective in the rehabilitation of lower limb function in patients with stroke. Whole body vibration training may be a better choice for improving balance and spasm in people with stroke. Currently it is not known which whole-body vibration training model with vibration intensity, stimulus type and duration is most effective and to design more targeted interventions.

Modulation of mouse laryngeal inflammatory and immune cell responses by low and high doses of mainstream cigarette smoke

Cigarette smoking is a major risk factor for laryngeal diseases. Despite well-documented cigarette smoke (CS) induced laryngeal histopathological changes, the underlying immunopathological mechanisms remain largely unexplored. The goal of this study was to evaluate inflammatory and immune cell responses in a CS-exposed larynx. Specifically, we used a 4-week subacute whole-body CS inhalation mouse model to assess these responses in the laryngeal mucosa upon exposure to low (LD; 1 h/day) and high dose (HD; 4 h/day) CS. Laryngeal tissues were harvested and evaluated using a 254-plex NanoString inflammation panel and neutrophil/macrophage/T-cell immunohistochemistry (IHC). NanoString global and differential gene expression analysis revealed a unique expression profile only in the HD group, with 26 significant differentially expressed genes (DEGs). StringDB KEGG pathway enrichment analysis revealed the involvement of these DEGs with pro-inflammatory pathways including TNF/TNFα and IL-17. Furthermore, inflammatory responses remained inhibited in conjunction with predicted activated states of anti-inflammatory regulators like PPARγ and NFE2L2 upon Ingenuity Pathway Analysis (IPA). Subglottic T-cell levels remained significantly inhibited as corroborated by IPA predictions. Overall, our key findings are consistent with HD exposures being anti-inflammatory and immunosuppressive. Furthermore, the identification of important regulatory genes and enriched pathways may help improve clinical interventions for CS-induced laryngeal diseases.

A three-dimensional whole-body model to predict human walking on level ground

Predictive simulation of human walking has great potential in clinical motion analysis and rehabilitation engineering assessment, but large computational cost and reliance on measurement data to provide initial guess have limited its wide use. We developed a computationally efficient model combining optimization and inverse dynamics to predict three-dimensional whole-body motions and forces during human walking without relying on measurement data. Using the model, we explored two different optimization objectives, mechanical energy expenditure and the time integral of normalized joint torque. Of the two criteria, the sum of the time integrals of the normalized joint torques produced a more realistic walking gait. The reason for this difference is that most of the mechanical energy expenditure is in the sagittal plane (based on measurement data) and this leads to difficulty in prediction in the other two planes. We conclude that mechanical energy may only account for part of the complex performance criteria driving human walking in three dimensions.

Coordinated Control after Grasping the Space Targets Using Controllable Damping Mechanism

Compliant capture of the space non-cooperative targets is a key technology in on-orbit services. A great challenge is that the multi-dimensional contact force generated by the tumbling space target can destabilize the spacecraft-manipulator system (SMS), which may eventually cause failure of the capture task. A full-dimensional controllable damping mechanism (FDCDM) with gyroscopic structure is introduced into the joint of the SMS to buffer the multi-dimensional contact forces during capture. The six-dimensional damping force outputs by the FDCDM can be equivalent to the actuator outputs in the end joint, which could form a coordinated control system with the torque of base flywheel and active joints. The whole-body dynamic model of SMS with FDCDM is established using the Kane method. Furthermore, a backstepping non-singular sliding mode control is proposed to optimize the momentum distribution and impact absorption. The characteristics of collision process for the above SMS-FDCDM system is analyzed in the ADAMS workspace, and the experiments performed in MATLAB demonstrate that the full-dimensional damping mechanism and coordinated control can greatly reduce the vibration caused by the impact force, and the attitude of SMS is quickly stabilized after capture, which proves the feasibility of its application in non-cooperative target capturing tasks.

Reinforcement Learning for Bipedal Gait with MAX-E2 Humanoid Robot

We consider the whole-body humanoid gait control model via reinforcement learning, where an initial predefined parametric gait policy is optimized by employing the augmented random search algorithm realizing most of the experiments in simulation and verifying the results onto the real robot. In our proposal, we take into account the reality gap problem, also known as Sim to real problem, for the development of the experiments and the policy guaranteeing functional results in the real robot employing a simplified custom simulator. We show that our proposed learning algorithm allows the humanoid robot MAX-E2 to walk efficiently in virtual and real environments.

Translational pharmacokinetics of a novel bispecific antibody against Ebola virus (MBS77E) from animal to human by PBPK modeling & simulation

The goal of this study was to construct a PBPK model to accelerate the translation of MBS77E, a humanized bispecific antibody against the Ebola virus. In-depth nonclinical pharmacokinetic studies in rats, monkeys, wild-type mice and transgenic mice were conducted. The pH-dependent affinities (KD) of MBS77E to recombinant FcRn of different species were determined by surface plasmon resonance analysis. A mechanistic whole-body PBPK model of MBS77E was developed and validated in the assessment of PK profiles and tissue distributions in preclinical models. This PBPK model was finally used to predict human PK behaviors of MBS77E. Simulations from the PBPK model with measured and fitted parameters were able to yield good predictions of the serum and tissue pharmacokinetic parameters of MBS77E within 2-fold errors. The predicted serum concentration in humans was able to maintain a sufficiently high level for more than 14 days after 50 mg/kg i.v. administrating. This achievement unlocks that PBPK modeling is a powerful tool to gain insights into the properties of antibody drugs. It guided experimental efforts to obtain necessary information before entry into humans.

Studies on glycogen storage disease type 1a animal models: a brief perspective.

Glycogen storage disease type 1 (GSD1) is a rare hereditary monogenic disease characterized by the disturbed glucose metabolism. The most widespread variant of GSD1 is GSD1a, which is a deficiency of glucose-6-phosphatase-ɑ. Glucose-6-phosphatase-ɑ is expressed only in liver, kidney, and intestine, and these organs are primarily affected by its deficiency, and long-term complications of GSD1a include hepatic tumors and chronic liver disease. This article is a brief overview of existing animal models for GSD1a, from the first mouse model of 1996 to modern CRISPR/Cas9-generated ones. First whole-body murine models demonstrated exact metabolic symptoms of GSD1a, but the animals did not survive weaning. The protocol for glucose treatment allowed prolonged survival of affected animals, but long-term complications, such as hepatic tumorigenesis, could not be investigated. Next, organ-specific knockout models were developed, and most of the metabolic research was performed on liver glucose-6-phosphate-deficient mice. Naturally occuring mutation was also discovered in dogs. All these models are widely used to study GSD1a from metabolic and physiological standpoints and to develop possible treatments involving gene therapy. Research performed using these models helped elucidate the role of glycogen and lipid accumulation, hypoxia, mitochondrial dysfunction, and autophagy impairment in long-term complications of GSD1a, including hepatic tumorigenesis. Recently, gene replacement therapy and genome editing were tested on described models, and some of the developed approaches have reached clinical trials.

Drosophila as a toolkit to tackle cancer and its metabolism.

Cancer is one of the most severe health problems worldwide accounting for the second leading cause of death. Studies have indicated that cancers utilize different metabolic systems as compared with normal cells to produce extra energy and substances required for their survival, which contributes to tumor formation and progression. Recently, the fruit fly Drosophila has been attracting significant attention as a whole-body model for elucidating the cancer mechanisms including metabolism. This tiny organism offers a valuable toolkit with various advantages such as high genetic conservation and similar drug response to mammals. In this review, we introduce flies modeling for cancer patient genotypes which have pinpointed novel therapeutic targets and drug candidates in the salivary gland, thyroid, colon, lung, and brain. Furthermore, we introduce fly models for metabolic diseases such as diabetes mellitus, obesity, and cachexia. Diabetes mellitus and obesity are widely acknowledged risk factors for cancer, while cachexia is a cancer-related metabolic condition. In addition, we specifically focus on two cancer metabolic alterations: the Warburg effect and redox metabolism. Indeed, flies proved useful to reveal the relationship between these metabolic changes and cancer. Such accumulating achievements indicate that Drosophila offers an efficient platform to clarify the mechanisms of cancer as a systemic disease.

Predicting cross-tissue hormone-gene relations using balanced word embeddings.

MotivationInter-organ/inter-tissue communication is central to multi-cellular organisms including humans, and mapping inter-tissue interactions can advance system-level whole-body modeling efforts. Large volumes of biomedical literature have fostered studies that map within-tissue or tissue-agnostic interactions, but literature-mining studies that infer inter-tissue relations, such as between hormones and genes are solely missing.ResultsWe present a first study to predict from biomedical literature the hormone–gene associations mediating inter-tissue signaling in the human body. Our BioEmbedS* models use neural network-based Biomedical word Embeddings with a Support Vector Machine classifier to predict if a hormone–gene pair is associated or not, and whether an associated gene is involved in the hormone’s production or response. Model training relies on our unified dataset Hormone-Gene version 1 of ground-truth associations between genes and endocrine hormones, which we compiled and carefully balanced in the embedded space to handle data disparities, such as between poorly- versus well-studied hormones. Our BioEmbedS model recapitulates known gene mediators of tissue–tissue signaling with 70.4% accuracy; predicts novel inter-tissue communication genes in humans, which are enriched for hormone-related disorders; and generalizes well to mouse, thereby holding promise for its extension to other multi-cellular organisms as well.Availability and implementationFreely available at https://cross-tissue-signaling.herokuapp.com are our model predictions & datasets; https://github.com/BIRDSgroup/BioEmbedS has all relevant code.Supplementary information Supplementary data are available at Bioinformatics online.

Analysis of loading to the hip joint in fall using whole-body FE model

Hip fractures caused by falls are important health problems for the elderly. Many studies used finite element (FE) models of the femur and its surroundings to evaluate the hip fracture risk during the impact phase in a fall. In this study, the whole-body human FE model (THUMS) of a small female was applied from the descent to the impact phase in a fall to understand the effect of the whole body. Brosh’s material model was used for the soft tissue of the hip. A low-BMI and high-BMI model were developed based on THUMS (middle-BMI). For the middle-BMI model, the torso angle and the pelvis impact velocity were 45.2° and 2.62 m/s at the time of pelvis impact. The effective mass changed with time, and was 18.3 kg when the femoral neck force was maximum. The femoral neck force was 2.11 kN for the low-BMI model. The femoral neck forces when wearing a soft and a hard hip protector, and when falling on an energy-absorbing floor were compared for the FE models of human and a hip protector testing system. Though the force attenuation of the protective devices was 32.0–44.3 % in the testing system, the force attenuation in the middle-BMI was 0.1–22.2 %. In the low-BMI model, the attenuation of the soft protector was limited (4.2 %) because the hip protector protruded from the outer surface, so the contact force was concentrated at the hip region. This research suggests the importance of including the whole body to evaluate the hip fracture risk.

A Visible Human Data-Based Whole-Body Model for Investigating the Transmission Attenuation of Intrabody Communication

Intrabody communication (IBC) is a promising approach for realizing information transmission among various wearable or implantable devices within bodyNET. The existing IBC studies are limited to the local or abstract geometric channel model, which is limited to providing efficient channel characteristics for the various transmitting scenarios. The aim of this article is to reconstruct a whole-body model with anatomical features and combine the field-circuit model for investigating the human channel characteristics. A visible human data (VHD) is established by segmentation and 3-D reconstruction of layered tissues. The transmission attenuation was simulated and analyzed through the finite element method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In vivo</i> experiments and phantom experiments were implemented to validate the simulation results. For the on-body communication, the transmission attenuation of the short distance was similar to the low-pass filter’s amplitude-frequency characteristics. For the implantable communication, the consistency coefficient of the attenuation between in-body to on-body and on-body to in-body was higher than 98%, indicating that the body channel is bidirectional. This work provides a repeatable and verified VHD-based whole-body model for the human channel attenuation investigation. It is an essential step for the further development of IBC, such as the transceiver design and the multinodes’ protocol for bodyNET.

Body size, shape and ecology in tetrapods

Body size and shape play fundamental roles in organismal function and it is expected that animals may possess body proportions that are well-suited to their ecological niche. Tetrapods exhibit a diverse array of body shapes, but to date this diversity in body proportions and its relationship to ecology have not been systematically quantified. Using whole-body skeletal models of 410 extinct and extant tetrapods, we show that allometric relationships vary across individual body segments thereby yielding changes in overall body shape as size increases. However, we also find statistical support for quadratic relationships indicative of differential scaling in small-medium versus large animals. Comparisons of locomotor and dietary groups highlight key differences in body proportions that may mechanistically underlie occupation of major ecological niches. Our results emphasise the pivotal role of body proportions in the broad-scale ecological diversity of tetrapods.

Redundancy Resolution and Disturbance Rejection via Torque Optimization in Hybrid Cable-Driven Robots

This article presents redundancy resolution and disturbance rejection via torque optimization in hybrid cable-driven robots (HCDRs). To begin with, we present a redundant HCDR for nonlinear whole-body system modeling and model reduction. Based on the reduced dynamic model, two new methods are proposed to solve the redundancy resolution problem: 1) joint-space torque optimization for actuated joints (TOAJ) and 2) joint-space torque optimization for actuated and unactuated joints (TOAUJ), and they can be extended to other HCDRs. Compared to the existing approaches, this article provides the first solution (TOAUJ-based method) for HCDRs that can solve the redundancy resolution problem as well as disturbance rejection. Additionally, this article develops detailed algorithms targeting TOAJ and TOAUJ implementation. A simple yet effective controller is designed for analysis and validation. Case studies are conducted to evaluate the performance of TOAJ and TOAUJ. The results show the effectiveness of the aforementioned approaches.

Running-Induced Fatigue Changes the Structure of Motor Variability in Novice Runners.

Understanding the effects of fatigue is a central issue in the context of endurance sports. Given the popularity of running, there are numerous novices among runners. Therefore, understanding the effects of fatigue in novice runners is an important issue. Various studies have drawn conclusions about the control of certain variables by analyzing motor variability. One variable that plays a crucial role during running is the center of mass (CoM), as it reflects the movement of the whole body in a simplified way. Therefore, the aim of this study was to analyze the effects of fatigue on the motor variability structure that stabilizes the CoM trajectory in novice runners. To do so, the uncontrolled manifold approach was applied to a 3D whole-body model using the CoM as the result variable. It was found that motor variability increased with fatigue (UCMꓕ). However, the UCMRatio did not change. This indicates that the control of the CoM decreased, whereas the stability was not affected. The decreases in control were correlated with the degree of exhaustion, as indicated by the Borg scale (during breaking and flight phase). It can be summarized that running-induced fatigue increases the step-to-step variability in novice runners and affects the control of their CoM.

Renal Transporter-Mediated Drug-Biomarker Interactions of the Endogenous Substrates Creatinine and N1 -Methylnicotinamide: A PBPK Modeling Approach.

Endogenous biomarkers for transporter-mediated drug-drug interaction (DDI) predictions represent a promising approach to facilitate and improve conventional DDI investigations in clinical studies. This approach requires high sensitivity and specificity of biomarkers for the targets of interest (e.g., transport proteins), as well as rigorous characterization of their kinetics, which can be accomplished utilizing physiologically-based pharmacokinetic (PBPK) modeling. Therefore, the objective of this study was to develop PBPK models of the endogenous organic cation transporter (OCT)2 and multidrug and toxin extrusion protein (MATE)1 substrates creatinine and N1 -methylnicotinamide (NMN). Additionally, this study aimed to predict kinetic changes of the biomarkers during administration of the OCT2 and MATE1 perpetrator drugs trimethoprim, pyrimethamine, and cimetidine. Whole-body PBPK models of creatinine and NMN were developed utilizing studies investigating creatinine or NMN exogenous administration and endogenous synthesis. The newly developed models accurately describe and predict observed plasma concentration-time profiles and urinary excretion of both biomarkers. Subsequently, models were coupled to the previously built and evaluated perpetrator models of trimethoprim, pyrimethamine, and cimetidine for interaction predictions. Increased creatinine plasma concentrations and decreased urinary excretion during the drug-biomarker interactions with trimethoprim, pyrimethamine, and cimetidine were well-described. An additional inhibition of NMN synthesis by trimethoprim and pyrimethamine was hypothesized, improving NMN plasma and urine interaction predictions. To summarize, whole-body PBPK models of creatinine and NMN were built and evaluated to better assess creatinine and NMN kinetics while uncovering knowledge gaps for future research. The models can support investigations of renal transporter-mediated DDIs during drug development.