Whole-body Model Research Articles

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.

Impact of route-dependent phase-II gut metabolism and enterohepatic circulation on the bioavailability and systemic disposition of resveratrol in rats and humans: A comprehensive whole body physiologically-based pharmacokinetic modeling

Resveratrol, a natural polyphenolic phytoalexin, is a dietary supplement that improves the outcomes of metabolic, cardiovascular, and other age-related diseases due to its diverse pharmacological activities. Although there have been several preclinical and clinical investigations of resveratrol, the contributions of gut phase-II metabolism and enterohepatic circulation to the oral bioavailability and pharmacokinetics of resveratrol remain unclear. Furthermore, a physiologically-based pharmacokinetic (PBPK) model that accurately describes and predicts the systemic exposure profiles of resveratrol in clinical settings has not been developed. Experimental data were acquired from several perspectives, including in vitro protein binding and blood distribution, in vitro tissue S9 metabolism, in situ intestinal perfusion, and in vivo pharmacokinetics and excretion studies. Using these datasets, an in-house whole-body PBPK model incorporating route-dependent phase-II (glucuronidation and sulfation) gut metabolism and enterohepatic circulation processes was constructed and optimized for chemical-specific parameters. The developed PBPK model aligned with the observed systemic exposure profiles of resveratrol in single and multiple dosing regimens with an acceptable accuracy of 0.538–0.999-fold errors. Furthermore, the model simulations elucidated the substantial contribution of gut first-pass metabolism to the oral bioavailability of resveratrol and suggested differential effects of enterohepatic circulation on the systemic exposure of resveratrol between rats and humans. After partial modification and verification, our proposed PBPK model would be valuable to optimize dosage regimens and predict food-drug interactions with resveratrol-based natural products in various clinical scenarios.

Mechanistic Modeling of Central Nervous System Pharmacokinetics and Target Engagement of HER2 Tyrosine Kinase Inhibitors to Inform Treatment of Breast Cancer Brain Metastases.

This study evaluated the central nervous system (CNS) pharmacokinetics and target engagement of lapatinib, neratinib, and tucatinib in patients with cancer, using a physiologically based pharmacokinetic (PBPK) modeling approach. Drug-specific parameters for in vitro metabolism, binding to plasma proteins and brain tissues, transcellular passive permeability, and interactions with efflux transporters were determined. Whole-body PBPK models integrated with a 4-compartment permeability-limited brain model was developed and verified for predicting plasma and CNS pharmacokinetics. Target engagement ratio (TER), defined as the ratio of the average steady-state unbound drug brain concentration (Css,ave,br) to in vitro IC50 for HER2 inhibition, was used as a predictor of intracranial efficacy. PBPK models predicted that following 1 cycle of standard dosing, tucatinib and lapatinib achieved similar Css,ave,br (14.5 vs. 16.8 nmol/L), while neratinib Css,ave,br (0.68 nmol/L) was 20-fold lower. Tucatinib and neratinib were equally potent for HER2 inhibition (IC50, 6.9 vs. 5.6 nmol/L), while lapatinib was less potent (IC50, 109 nmol/L). The model-predicted population mean TER in the human normal brain was 2.1 for tucatinib, but < 0.20 for lapatinib and neratinib. The PBPK modeling suggests that tucatinib induces sufficient HER2 inhibition (TER > 2.0) in not only brain metastases with a disrupted blood-brain barrier (BBB), but also micrometastases where the BBB largely remains intact. These findings, in line with available clinical pharmacokinetics and efficacy data, support the therapeutic value of tucatinib for treatment of brain metastases and warrant further clinical investigation for the prevention of brain metastases in patients with HER2-positive breast cancer.

A physiologically based pharmacokinetic model to predict pegylated liposomal doxorubicin disposition in rats and human.

The use of nanoparticles (NPs) can support an enhancement of drug distribution, resulting in increased drug penetration into key tissues. Experimental in vitro data can be integrated into computational approaches to simulate NP absorption, distribution, metabolism and elimination (ADME) processes and provide quantitative pharmacokinetic predictions. The aim of this study is to develop a novel mechanistic and physiologically based pharmacokinetic (m-PBPK) model to predict the biodistribution of NPs focusing on Doxil. The main processes underpinning NPs ADME were represented considering molecular and cellular mechanisms such as stability in biological fluids, passive permeability and uptake activity by macrophages. A whole-body m-PBPK rat and human models were designed in Simbiology v. 9.6.0 (MATLAB R2019a). The m-PBPK models were successfully qualified across doxorubicin and Doxil® in both rat and human since all PK parameters AUC0-inf, Cmax, t1/2, Vd and Cl were within twofold, with an AUC0-inf absolute average-fold error (AAFE) value of 1.23 and 1.16 and 1.76 and 1.05 for Doxorubicin and Doxil® in rat and human, respectively. The time to maximum concentration in tissues for doxorubicin in both rat and human models was before 30min of administration, while for Doxil®, the tmax was after 24h of administration. The organs that accumulate most NP are the spleen, liver and lungs, in both models. The m-PBPK represents a predictive platform for the integration of in vitro and formulation parameters in a physiological context to quantitatively predict the NP biodistribution. Schematic diagram of the whole-body m-PBPK models developed for Doxil® in rat and human physiology.

Whole-body balance stability regions for multi-level momentum and stepping strategies

A unified framework is established for general balance stability criteria of biped systems. The stability regions of balanced and steppable states are partitions of the center-of-mass (COM) state space that is augmented for general biped systems and tasks. Their boundaries are the set of maximum allowable COM velocity perturbations, under which a specified contact can be maintained (balanced) or a desired step can be made (steppable). Whole-body models with full-order dynamics and system-specific kinematic and actuation limits are established to quantify the effects of momenta and stepping on the system's balance stability under contact interactions. The generality of the framework with respect to systems and tasks is verified with a humanoid robot and a human for walking and standing. Validity and implications of the computed stability regions are demonstrated in the sagittal plane through comparative analyses across strategies and against existing criteria (capturability and zero-moment point), reduced-order models, and simulations. Distinct stability characteristics of experimental walking for the robot (overly balanced) versus humans (unbalanced but steppable) are validated, and the walking principle is analyzed. The partitions also provide inter/intra-region analyses of multi-level momentum and stepping strategies for balancing. A partition-aware controller is implemented for the robot to monitor the stability of the current state and selectively exploit stabilizing actions during simulated standing push recovery.

Evaluating the pharmacokinetics of intrapulmonary administered ciprofloxacin solution for respiratory infections using in vivo and in silico PBPK rat model studies

Respiratory antibiotics have been proven clinically beneficial for the treatment of severe lung infections such as Pseudomonas aeruginosa. Maintaining a high local concentration of inhaled antibiotics for an extended time in the lung is crucial to ensure an adequate antimicrobial efficiency. In this study, we aim to investigate whether an extended exposure of ciprofloxacin (CIP), a model fluoroquinolone drug, in the lung epithelial lining fluid (ELF) could be achieved via a controlled-release formulation strategy. CIP solutions were intratracheally instilled to the rat lungs at 3 different rates, i.e. T0h (fast), T2h (medium), and T4h (slow), to mimic different release profiles of inhaled CIP formulations in the lung. Subsequently, the concentration-time profiles of CIP in the plasma and the lung ELF were obtained, respectively, to determine topical exposure index (ELF-Plasma AUC Ratio, EPR). The in silico PBPK model, validated based on the in vivo data, was used to identify the key factors that influence the disposition of CIP in the plasma and lungs. The medium and slow rates groups exhibited much higher EPR than that fast instillation group. The ELF AUC of the medium and slow instillation groups were about 200 times higher than their plasma AUC. In contrast, the ELF AUC of the fast instillation group was only about 20 times higher than the plasma AUC. The generated whole-body PBPK rat model, validated by comparison with the in vivo data, revealed that drug pulmonary absorption rate was the key factor that determined pulmonary absorption of CIP. This study suggests that controlled CIP release from inhaled formulations may extend the exposure of CIP in the ELF post pulmonary administration. It also demonstrates that combining the proposed intratracheal installation model and in silico PBPK model is a useful approach to identify the key factors that influence the absorption and disposition of inhaled medicine. In silico ciprofloxacin-specific physiologically based pharmacokinetic model was established and exploited to reveal the critical pharmacokinetic parameters influencing its systemic and local drug exposure via intratracheal instillation at different rates.

Development and Validation of a Whole Human Body Finite Element Model with Detailed Lumbar Spine

Investigations showed that low back pain of occupational drivers might be closely related to the whole-body vibration. Restricted by ethical concerns, the finite element method had become a viable alternative to invasive human experiments. Many mechanical behaviors of the human spine inside of the human body were unclear; therefore, a human whole-body finite element model might be required to better understand the lumbar behavior under whole-body vibration. In this study, a human whole-body finite element model with a detailed lumbar spine segment was developed. Several validations were performed to ensure the correctness of this model. The results of anthropometry and geometry validation, static validation, and dynamic validation were presented in this study. The validation results showed that the whole human body model was reasonable and valid by comparing with published data. The model developed in this study could reflect the biomechanical response of the human lumbar spine under vibration and could be used in further vibration analysis and offer proposals for protecting human body under whole-body vibration environment.

First Order Approximation of Model Predictive Control Solutions for High Frequency Feedback

The lack of computational power on mobile robots is a well-known challenge when it comes to implementing a real-time MPC scheme to perform complex motions. Currently the best solvers are barely able to reach 100 Hz for computing the control of a whole-body legged model, while modern robots are expecting new torque references in less than 1 ms. This problem is usually tackled by using a handcrafted low-level tracking control whose inputs are the low-frequency trajectory computed by the MPC. We show that a linear state feedback controller naturally arises from the optimal control formulation and can be used directly in the low-level control loop along with other sensitivities of relevant time-varying parameters of the problem. When the optimal control problem is solved by DDP, this linear controller can be computed for cheap as a by-product of the backward pass, and corresponds in part to the classical Riccati gains. A side effect of our proposition is to show that Riccati gains are valuable assets that must be used to achieve an efficient control and that they are not stiffer than the optimal control scheme itself. We propose a complete implementation of this idea on a full-scale humanoid robot and demonstrate its importance with real experiments on the robot Talos.

Patient-specific deep learning model to enhance 4D-CBCT image for radiomics analysis

Objective. 4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic features. Previously we developed group-patient-trained deep learning methods to enhance the 4D-CBCT quality for radiomics analysis, which was not optimized for individual patients. In this study, a patient-specific model was developed to further improve the accuracy of 4D-CBCT based radiomics analysis for individual patients. Approach. This patient-specific model was trained with intra-patient data. Specifically, patient planning 4D-CT was augmented through image translation, rotation, and deformation to generate 305 CT volumes from 10 volumes to simulate possible patient positions during the onboard image acquisition. 72 projections were simulated from 4D-CT for each phase and were used to reconstruct 4D-CBCT using FDK back-projection algorithm. The patient-specific model was trained using these 305 paired sets of patient-specific 4D-CT and 4D-CBCT data to enhance the 4D-CBCT image to match with 4D-CT images as ground truth. For model testing, 4D-CBCT were simulated from a separate set of 4D-CT scan images acquired from the same patient and were then enhanced by this patient-specific model. Radiomics features were then extracted from the testing 4D-CT, 4D-CBCT, and enhanced 4D-CBCT image sets for comparison. The patient-specific model was tested using 4 lung-SBRT patients’ data and compared with the performance of the group-based model. The impact of model dimensionality, region of interest (ROI) selection, and loss function on the model accuracy was also investigated. Main results. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested. In addition, the patient-specific models with different dimensionality (2D versus 3D) or loss functions (L1 versus L1 + VGG + GAN) achieved comparable results for improving the radiomics accuracy. Using whole-body or whole-body+ROI L1 loss for the model achieved better results than using the ROI L1 loss alone as the loss function. Significance. This study demonstrated that the patient-specific model is more effective than the group-based model on improving the accuracy of the 4D-CBCT radiomic features analysis, which could potentially improve the precision for outcome prediction in radiotherapy.

The Effect of Mouse Size on Dose from an X-Rad320 Irradiator.

Irradiation protocols for murine experiments often use standardized dose rate estimates for calculating dose delivered, regardless of physical variations between mouse subjects. This work sought to determine the significance of mouse size on absorbed dose. Five mouse-like phantoms of various sizes based on the mouse whole-body (MOBY) model were 3D printed. The phantoms were placed in an X-Rad320 biological irradiator and a standard irradiation protocol was used to deliver dose. Dose was measured using thermoluminescent dosimeter (TLD) microcubes inside each phantom, and the relative readings were used to calculate output factors (OFs), normalized to the phantom of median volume. Additionally, the OF for each mouse was simulated in Monte Carlo N-Particle (MCNP) code. For both the TLD measurements and MCNP simulations, the OF for each mouse was determined by both experiments and calculations to be unity within the relative standard uncertainties (k = 1). This work supports comparing results across various studies using the X-Rad320 irradiator without need for corrections based on mouse size.

Prediction of lung exposure to anti-tubercular drugs using plasma pharmacokinetic data: Implications for dose selection

The development of novel candidate molecules for tuberculosis remains challenging, as drug distribution into the target tissue is not fully characterised in preclinical models of infection. Often antitubercular human dose selection is derived from pharmacokinetic data in plasma. Here, we explore whether whole-body physiologically-based pharmacokinetic (PBPK) modelling enables the prediction of lung exposure to anti-tubercular drugs in humans. Whole-body PBPK models were developed for rifampicin, isoniazid, pyrazinamide, and ethambutol using plasma data in mice as basis for the prediction of lung exposure. Model parameters were subsequently used to extrapolate disposition properties from mouse and determine lung:plasma ratio in humans. Model predictions were compared to biopsy data from patients. Predictions were deemed adequate if they fell within two-fold range of the observations. The concentration vs time profiles in lung were adequately predicted in mice. Isoniazid and pyrazinamide lung exposures were predicted to be comparable to plasma levels, whereas ethambutol lung exposure was predicted to be higher than in plasma. Lung:plasma ratio in humans could be reasonably predicted from preclinical data, but was highly dependent on the distribution model. This analysis showed that plasma pharmacokinetics may be used in conjunction with PBPK modelling to derive lung tissue exposure in mice and humans during early lead optimisation phase. However, the impact of uncertainty in predicted tissue exposure due to distribution should be always investigated through a sensitivity analysis when only plasma data is available. Despite these limitations, insight into lung tissue distribution represents a critical step for the dose rationale in tuberculosis patients.

Assessment of Twin Fetal Exposure to Environmental Magnetic and Electromagnetic Fields.

Fetal development is vital in the human lifespan. Therefore, it is essential to characterize exposure by a series of typical environmental magnetic and electromagnetic fields. In particular, there has recently been a sharp increase in the twin birth rate. However, lack of appropriate models has prohibited dosimetric evaluation, restricting characterization of the impact of these environmental factors on twins. The present study developed two whole-body pregnant models of 31 and 32 weeks of gestation with twin fetuses and explored several typical exposure scenarios, including 50-Hz uniform magnetic field exposure, local 125-kHz magnetic field (MF), and 13.56-MHz electromagnetic field exposure, as well as wideband planewave radiofrequency (RF) exposure from 20 to 6000 MHz. Finally, dosimetric results were derived. Compared to the singleton pregnancy with similar weeks of gestation, twin fetuses were overexposed at 50-Hz uniform MF, but they were probably underexposed in the RF scenarios with frequencies for wireless communications. Furthermore, the twin fetuses manifested large dosimetric variability compared to the singleton, which was attributed to the incident direction and fetal position. Based on the analysis, the dosimetric results over the entire gestation period were estimated. The results can be helpful to estimate the risk of twin-fetal exposure to electromagnetic fields and examine the conservativeness of the international guidelines.© 2022 Bioelectromagnetics Society.

PBPK Modelling of Dexamethasone in Patients With COVID-19 and Liver Disease

The aim of the study was to apply Physiologically-Based Pharmacokinetic (PBPK) modelling to predict the effect of liver disease (LD) on the pharmacokinetics (PK) of dexamethasone (DEX) in the treatment of COVID-19. A whole-body PBPK model was created to simulate 100 adult individuals aged 18–60 years. Physiological changes (e.g., plasma protein concentration, liver size, CP450 expression, hepatic blood flow) and portal vein shunt were incorporated into the LD model. The changes were implemented by using the Child-Pugh (CP) classification system. DEX was qualified using clinical data in healthy adults for both oral (PO) and intravenous (IV) administrations and similarly propranolol (PRO) and midazolam (MDZ) were qualified with PO and IV clinical data in healthy and LD adults. The qualified model was subsequently used to simulate a 6 mg PO and 20 mg IV dose of DEX in patients with varying degrees of LD, with and without shunting. The PBPK model was successfully qualified across DEX, MDZ and PRO. In contrast to healthy adults, the simulated systemic clearance of DEX decreased (35%–60%) and the plasma concentrations increased (170%–400%) in patients with LD. Moreover, at higher doses of DEX, the AUC ratio between healthy/LD individuals remained comparable to lower doses. The exposure of DEX in different stages of LD was predicted through PBPK modelling, providing a rational framework to predict PK in complex clinical scenarios related to COVID-19. Model simulations suggest dose adjustments of DEX in LD patients are not necessary considering the low dose administered in the COVID-19 protocol.

A whole-body multi-scale mathematical model for dynamic simulation of the metabolism in man

We propose a whole-body model of the metabolism in man as well as a generalized approach for modeling metabolic networks. Using this approach, we are able to write a large metabolic network in a systematic and compact way. We demonstrate the approach using a whole-body model of the metabolism of the three macronutrients, carbohydrates, proteins and lipids. The model contains 7 organs, 16 metabolites and 31 enzymatic reactions. All reaction rates are described by Michaelis-Menten kinetics with an addition of a hormonal regulator based on the two hormones insulin and glucagon. We incorporate ingestion of food in order to simulate metabolite concentrations during the feed-fast cycle. The model can simulate several days due to the inclusion of storage forms (glycogen, muscle protein and lipid droplets), that can be depleted if food is not ingested regularly. A physiological model incorporating complex cellular metabolism and whole-body mass dynamics can be used in virtual clinical trials. Such trials can be used to improve the development of medicine, treatment strategies such as control algorithms, and increase the likelihood of a successful clinical trial.

A food web bioaccumulation model for the accumulation of per- and polyfluoroalkyl substances (PFAS) in fish: how important is renal elimination?

Per- and polyfluoroalkyl substances (PFAS) are a large class of highly fluorinated anthropogenic chemicals. Some PFAS bioaccumulate in aquatic food webs, thereby posing risks for seafood consumers. Existing models for persistent organic pollutants (POPs) perform poorly for ionizable PFAS. Here we adapt a well-established food web bioaccumulation model for neutral POPs to predict the bioaccumulation behavior of six perfluoroalkyl acids (PFAAs) and two perfluoroalkyl ether acids (HFPO-DA, 9-Cl-PF3ONS) produced as PFAA replacements. The new model includes sorption to blood plasma proteins and phospholipids, empirically parameterized membrane transport, and renal elimination for PFAAs. Improved performance relative to prior models without these updates is shown by comparing simulations to field and lab measurements. PFAS with eight or more perfluorinated carbons (ηpfc ≥ 8, i.e., C8 perfluorosulfonic acid, C10–C11 perfluorocarboxylic acid, 9-Cl-PF3ONS) are often the most abundant in aquatic food webs. The new model reproduces their observed bioaccumulation potential within a factor of two for >80% of fish species, indicating its readiness to support development of fish consumption advisories for these compounds. Results suggest bioaccumulation of ηpfc ≥ 8 PFAS is primarily driven by phospholipid partitioning, and that renal elimination is negligible for these compounds. However, specific protein binding mechanisms are important for reproducing the observed tissue concentrations of many shorter-chain PFAAs, including protein transporter-mediated renal elimination. Additional data on protein-binding and membrane transport mechanisms for PFAS are needed to better understand the biological behavior of shorter-chain PFAAs and their alternatives.

Analysis of Loading to the Hip Joint in Fall Using Whole-Body Fe Model

Analysis of Loading to the Hip Joint in Fall Using Whole-Body Fe Model

Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication

We aimed at investigating host-virus co-metabolism during SARS-CoV-2 infection. Therefore, we extended comprehensive sex-specific, whole-body organ resolved models of human metabolism with the necessary reactions to replicate SARS-CoV-2 in the lung as well as selected peripheral organs. Using this comprehensive host-virus model, we obtained the following key results: 1. The predicted maximal possible virus shedding rate was limited by isoleucine availability. 2. The supported initial viral load depended on the increase in CD4+ T-cells, consistent with the literature. 3. During viral infection, the whole-body metabolism changed including the blood metabolome, which agreed well with metabolomic studies from COVID-19 patients and healthy controls. 4. The virus shedding rate could be reduced by either inhibition of the guanylate kinase 1 or availability of amino acids, e.g., in the diet. 5. The virus variants differed in their maximal possible virus shedding rates, which could be inversely linked to isoleucine occurrences in the sequences. Taken together, this study presents the metabolic crosstalk between host and virus and emphasises the role of amino acid metabolism during SARS-CoV-2 infection, in particular of isoleucine. As such, it provides an example of how computational modelling can complement more canonical approaches to gain insight into host-virus crosstalk and to identify potential therapeutic strategies.

Dosimetric Modeling of Lymphopenia in Patients With Metastatic Cancer Receiving Palliative Radiation and PD-1 Immune Checkpoint Inhibitors

PurposeRadiation therapy (RT)–associated lymphopenia may adversely affect treatment outcomes, particularly in the era of immunotherapy. We sought to determine dosimetric factors correlated with lymphopenia after palliative RT in a cohort of patients with advanced cancer treated with anti-PD-1 immune checkpoint inhibitors. Methods and MaterialsWe included patients with metastatic lung cancer, melanoma, or renal cell carcinoma who were treated with either pembrolizumab or nivolumab and received palliative RT to an extracranial site. Baseline and nadir absolute lymphocyte counts (ALCs) within 6 weeks of RT were recorded. Dosimetric factors were extracted from the corresponding dose-volume histograms and also used to model the dose to circulating lymphocytes via a whole-body blood flow model that simulates the spatiotemporal distribution of blood particles in major organs during RT. ResultsWe analyzed 55 patients who underwent 80 total courses of palliative RT; most (94%) were treated with 3-dimensional conformal RT. Doses to the whole body, bone, and large blood vessels (LBVs) were negatively correlated with the ALC nadir, with the strongest correlations seen at V15 (rs, –0.38, –0.43, and –0.37, and P = .0004, .0001, and .0008, respectively). Doses to other organs were not significantly correlated with the ALC nadir. The modeled dose to circulating lymphocytes was also negatively correlated with the ALC nadir and percent ALC change (for D2%, rs, –0.31 and –0.44, and P = .005 and .0001, respectively). Grade ≥3 lymphopenia was associated with LBV V15 (odds ratio [OR], 1.16; 95% CI, 1.07-1.26; P < .001), bone V15 (OR, 1.04; 95% CI, 1.01-1.08; P = .03), body V15 (OR, 1.003; 95% CI, 1.001-1.006; P = .008), and modeled lymphocyte dose (OR, 1.45; 95% CI, 1.16-1.82; P < .001). ConclusionsThe RT dose to the whole body, bone, and LBVs and the modeled dose to circulating lymphocytes were correlated with lymphopenia in patients treated with palliative RT and anti-PD-1 immune checkpoint inhibitors. These findings may inform future radiation planning in this setting.