Population Pharmacokinetics Modeling and Simulation of Deutenzalutamide, A Novel Androgen Receptor Antagonist, in Patients With Metastatic Castration-Resistant Prostate Cancer.

Deutenzalutamide is a new molecular entity androgen receptor antagonist. The primary aim of this study was to develop a population pharmacokinetic model of deutenzalutamide and evaluate effects of intrinsic and extrinsic factors on pharmacokinetics. A nonlinear mixed-effects modeling approach was performed to develop the population pharmacokinetic of deutenzalutamide using data from 1 Phase I trial of deutenzalutamide. Goodness-of-fit plots, prediction-corrected visual predictive check, and bootstrap analysis were carried out to evaluate the final model. Simulation for the developed model was used to evaluate the covariate effects on the pharmacokinetics of deutenzalutamide. A 2-compartment model with first-order absorption and elimination from the central compartment was established for deutenzalutamide. The final covariate included body weight on peripheral compartment volume. This is the first research developing the population pharmacokinetic model of deutenzalutamide in patients with metastatic castration-resistant prostate cancer, and it is expected to support the future clinical administration of deutenzalutamide.

Modeling and simulation for the sustainable recovery of aromatics (BTX) from petrochemical industrial wastewater.

Petrochemical wastewater is a major industrial source of pollution that produces a variety of toxic organic and inorganic pollutants, naturally present or added during the process. These pollutants are a serious threat to the soil, water, environment, and human being due to their complex and hazardous nature. Glycols such as monoethylene glycol (MEG), diethylene glycol (DEG), triethylene glycol (TEG), and aromatics (BTX-benzene, toluene, and xylene) are the most common organic impurities present in petrochemical wastewater. The objective of this paper is to recover aromatics and water from petrochemical industrial wastewater. The reclamation process is used to remove inorganic impurities such as heavy metals Fe, Zn, Pb, Mn, Al, Ni, As, Cr, Cu, Cd, and K and salts. In the present work, 1% sodium bi-carbonate (NaHCO3) is used to precipitate the inorganic impurities present in the wastewater at 40 °C atmospherically. Aspen Hysys simulation software is used for modeling and simulation for the treatment process using NRTL (non-random-two-liquid) thermodynamic model. The process generated from Aspen Hysys is validated with lab experiments. To support global sustainable development, this study is focused on reducing, reusing, and recycling separation techniques such as centrifuge separation and vacuum distillation have been used. The characterization of regenerated water was performed using ICP-OES (inductively coupled plasma-optical emission spectroscopy) to determine the reduction in heavy metals. It was found that > 99.5% of heavy metals were removed. The regeneration of these aromatics is necessary for economic and environmental reasons so that it can be reused to avoid its disposal in and contamination of natural environments.

Appropriate use of triazolam in elderly patients considering a quantitative benefit-risk assessment based on the pharmacokinetic-pharmacodynamic modeling and simulation approach supported by real-world data.

BACKGROUND: Triazolam is a typical drug commonly used in the elderly; however, there have been concerns about its adverse events resulting from age-related changes in physiological function and drug interactions with concomitant drugs. Thus, updated information contributing to the appropriate use based on the latest pharmacokinetic and post-marketing surveillance methods is needed. In this study, we evaluated the appropriate use of triazolam in the elderly by integrating real-world data with a modeling and simulation approach. METHODS: The occurrence risk of adverse events in the elderly was evaluated using the spontaneous adverse event reporting regulatory databases from Japan and the United States. Information on drug concentrations and reactions was extracted from previous publications to estimate the threshold for plasma triazolam concentrations that cause adverse events. The pharmacokinetic/pharmacodynamic (PK/PD) model was then constructed, and the dose and administration were evaluated in various situations anticipated in medical practice. RESULTS: Among all prescriptions, 25.4% were prescribed to individuals aged 80 years or above, and 51.8% were for those aged 70 years or above. A majority of cases involved CYP3A-metabolized drug combinations, accounting for 85.6%. Elderly individuals were at a higher risk of developing delirium and fall-fracture. Based on the constructed PK/PD model, the risk of adverse events increased when the plasma concentration of triazolam exceeded the calculated threshold of 0.44 ng/mL at approximately 6 h after administration. Administering 0.125 mg of triazolam, is half the approved dose for the elderly in Japan was deemed appropriate. Moreover, there was a substantial risk of adverse events even at a dosage of 0.0625 mg in combination with a moderate or strong inhibitor of cytochrome P450 3 A. CONCLUSION: Analyzing large-scale databases and existing research publications on PK/PD can practically contribute to optimizing triazolam drug therapy for the elderly in the daily clinical setting.

Physiologically based in vitro - In vivo correlation of modified release oral formulations with non-linear intestinal absorption: A case study using mirabegron.

Establishing an in vitro - in vivo correlation (IVIVC) for oral modified release (MR) formulations would make it possible to substitute an in vitro dissolution test for human bioequivalence (BE) studies when changing the formulation or manufacturing methods. However, the number of IVIVC applications and approvals are reportedly low. One of the main reasons for failure to obtain IVIVCs using conventional methodologies may be the lack of consideration of the dissolution and absorption mechanisms of drugs in the physiological environment. In particular, it is difficult to obtain IVIVC using conventional methodologies for drugs with non-linear absorption processes. Therefore, the aim of the present study was to develop a physiologically based biopharmaceutics model (PBBM) that enables Level A IVIVCs for mirabegron MR formulations with non-linear absorption characteristics. Using human pharmacokinetic (PK) data for immediate-release formulations of mirabegron, the luminal drug concentration-dependent membrane permeation coefficient was calculated through curve fitting. The membrane permeation coefficient data were then applied to the human PK data of the MR formulations to estimate the in vivo dissolution rate by curve fitting. It was assumed that in vivo dissolution could be described using a zero-order rate equation. Furthermore, a Levy plot was generated using the estimated in vivo dissolution rate and the in vitro dissolution rate obtained from the literature. Finally, the dissolution rate of the MR formulations from the Levy plot was applied to the PBBM to predict the oral PK of the mirabegron MR formulations. This PB-IVIVC approach successfully generated linear Levy plots with slopes of almost 1.0 for MR formulations with different dose strengths and dissolution rates. The Cmax values of the MR formulations were accurately predicted using this approach, whereas the prediction errors for AUC exceeded the Level A IVIVC criteria. This can be attributed to the incomplete description of colonic absorption in the current PBBM.

Pharmacodynamic Exposure-Response Analysis of Fracture Count Data Following Treatment with Burosumab in Patients with XLH.

X-linked hypophosphatemia (XLH) is a rare genetic disorder caused by excessive fibroblast growth factor 23 (FGF23), leading to low serum phosphate levels resulting in increased risk of fractures and pseudofractures. Burosumab is indicated for the treatment of XLH. In this work, we aimed to understand the quantitative relationship between burosumab-treatment-induced improvements in serum phosphate and reduction in fracture and pseudofracture counts in adults with XLH. Burosumab pharmacokinetic pharmacodynamic data from nine clinical studies were first utilized to update a prior population pharmacokinetic pharmacodynamic (PPKPD) model. The updated PPKPD model predictions for serum phosphate exposures along with other factors (i.e., time and treatment) were utilized to evaluate the relationship on fracture counts using Poisson model. The updated PPKPD model suggested that burosumab concentrations required for 50% of maximal effect decreased with increasing baseline serum phosphate levels. A Poisson model with time from baseline, average serum phosphate, and burosumab treatment described the time-varying fracture and pseudofracture count data appropriately. The model suggested a baseline rate of fracture and pseudofracture of 1.87 counts. The model predicted that fracture counts decrease by 1% each week, and by 23% with each unit increase (1.0 mg/dL) in average serum phosphate from lower limit of normal (2.5 mg/dL). An additional 1% decrease in fracture count each week was attributed to burosumab treatment that could not be explained by improvements in serum phosphate. Overall, the model quantified the relationship between burosumab-treatment-induced serum phosphate improvements and reduction in fracture and pseudofracture counts in patients with XLH over time.

Current status and usefulness of therapeutic drug monitoring implementation of theophylline in elderly patients based on a nationwide database study and modeling approach.

OBJECTIVES: Theophylline is used in the treatment of chronic obstructive pulmonary disease but readily causes symptoms of intoxication and exhibits high risks in elderly patients. However, there have only been a few recent reports on the significance of therapeutic drug monitoring (TDM) implementation, especially in elderly patients. To examine the usefulness of theophylline TDM, we evaluated the current status of prescriptions containing theophylline and its side effects and assessed the influence of aging, sex, drug formulation, and concurrent drugs use on theophylline exposure using data from various nationwide databases and a pharmacokinetic modeling approach. METHODS: We utilized sampling data from the National Database of Health Insurance Claims and Specific Health Checkups of Japan. Using the data of patients aged ≥80 years, we conducted an association analysis of theophylline and concurrent drugs. The transition in plasma theophylline concentration levels of elderly patients was estimated based on a previously reported physiological-based pharmacokinetic model. RESULTS: Altogether, 3973 patients using theophylline were registered in our dataset, and about 50% were over 70 years old and used theophylline. Therapeutic drug monitoring implementation was confirmed in only 1.13% of patients. The association analysis confirmed a frequent co-occurrence with allopurinol and famotidine, which increase theophylline exposure, in elderly patients aged ≥80 years. The physiologically based pharmacokinetic model indicated that theophylline trough concentrations were 1.65-fold higher in elderly patients aged 80 years compared to those aged 30 years and 1.35-fold higher in females compared to males. CONCLUSION: This study effectively combined information on the nationwide health care database and modeling approach, indicating the importance of proactive TDM and dose justification for female and elderly patients.

A Practical In Silico Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning.

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration-time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration-time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silico prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration-time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration-time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration-time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration-time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration-time profiles.

Toward trustworthy medical device in silico clinical trials: a hierarchical framework for establishing credibility and strategies for overcoming key challenges.

Computational models of patients and medical devices can be combined to perform an in silico clinical trial (ISCT) to investigate questions related to device safety and/or effectiveness across the total product life cycle. ISCTs can potentially accelerate product development by more quickly informing device design and testing or they could be used to refine, reduce, or in some cases to completely replace human subjects in a clinical trial. There are numerous potential benefits of ISCTs. An important caveat, however, is that an ISCT is a virtual representation of the real world that has to be shown to be credible before being relied upon to make decisions that have the potential to cause patient harm. There are many challenges to establishing ISCT credibility. ISCTs can integrate many different submodels that potentially use different modeling types (e.g., physics-based, data-driven, rule-based) that necessitate different strategies and approaches for generating credibility evidence. ISCT submodels can include those for the medical device, the patient, the interaction of the device and patient, generating virtual patients, clinical decision making and simulating an intervention (e.g., device implantation), and translating acute physics-based simulation outputs to health-related clinical outcomes (e.g., device safety and/or effectiveness endpoints). Establishing the credibility of each ISCT submodel is challenging, but is nonetheless important because inaccurate output from a single submodel could potentially compromise the credibility of the entire ISCT. The objective of this study is to begin addressing some of these challenges and to identify general strategies for establishing ISCT credibility. Most notably, we propose a hierarchical approach for assessing the credibility of an ISCT that involves systematically gathering credibility evidence for each ISCT submodel in isolation before demonstrating credibility of the full ISCT. Also, following FDA Guidance for assessing computational model credibility, we provide suggestions for ways to clearly describe each of the ISCT submodels and the full ISCT, discuss considerations for performing an ISCT model risk assessment, identify common challenges to demonstrating ISCT credibility, and present strategies for addressing these challenges using our proposed hierarchical approach. Finally, in the Appendix we illustrate the many concepts described here using a hypothetical ISCT example.

When to consider intra-target microdosing: physiologically based pharmacokinetic modeling approach to quantitatively identify key factors for observing target engagement.

Intra-Target Microdosing (ITM), integral to Phase 0 clinical studies, offers a novel approach in drug development, effectively bridging the gap between preclinical and clinical phases. This methodology is especially relevant in streamlining early drug development stages. Our research utilized a Physiologically Based Pharmacokinetic (PBPK) model and Monte Carlo simulations to examine factors influencing the effectiveness of ITM in achieving target engagement. The study revealed that ITM is capable of engaging targets at levels akin to systemically administered therapeutic doses for specific compounds. However, we also observed a notable decrease in the probability of success when the predicted therapeutic dose exceeds 10 mg. Additionally, our findings identified several critical factors affecting the success of ITM. These encompass both lower dissociation constants, higher systemic clearance and an optimum abundance of receptors in the target organ. Target tissues characterized by relatively low blood flow rates and high drug clearance capacities were deemed more conducive to successful ITM. These insights emphasize the necessity of taking into account each drug's unique pharmacokinetic and pharmacodynamic properties, along with the physiological characteristics of the target tissue, in determining the suitability of ITM.

Prostatic Pharmacokinetic and Pharmacodynamic Analysis of Ceftazidime: Dosing Strategy for Bacterial Prostatitis.

This study aimed to develop a prostatic pharmacokinetic model of ceftazidime and suggest more effective dosing strategy for the bacterial prostatitis, based on a site-specific pharmacokinetic and pharmacodynamic perspective. Subjects were prostatic hyperplasia patients prophylactically receiving a 0.5-h infusion of 1.0 g or 2.0 g ceftazidime before transurethral resection of the prostate. Plasma and prostate samples were premeditatedly collected after the administration and the concentrations were measured by high-performance liquid chromatography. The prostate tissue/plasma ratio in area under the drug concentration-time curve was approximately 0.476. The prostatic population pharmacokinetic model incorporated creatinine clearance (CLcr) into ceftazidime clearance was developed, and adequately predicted prostate tissue concentrations by diagnostic scatter plots and visual predictive checks. Aiming for a bactericidal target of 70% of time above minimum inhibitory concentration (T > MIC) in prostate tissue, 2.0 g twice daily achieved ≥90% expected probability against main pathogens like Escherichia coli and Proteus species in patients regardless of renal function (CLcr = 60 and 90 mL/min). However, since the expected probability of attaining the bactericidal target of 0.5-h infusion dosing regimen did not achieve 90% against Pseudomonas aeruginosa in patients with CLcr = 60 and 90 mL/min, 4-h infusion dosing regimen of 2.0 g three times daily (6 g/day) might be required for empirical treatment. Based on site-specific simulations, the present study provides more effective dosing strategy for bacterial prostatitis.

Soft sensor for substrate characterization through the reverse application of the ADM1 model for anaerobic digestion plant operations.

Accurately characterizing the substrate used in anaerobic digestion is crucial for predicting the biogas plant's performance. This issue makes particularly challenging the application of modeling in codigestion plants. In this work, a novel methodology called substrate prediction module (SPM) has been developed and tested, using virtual codigestion data. The SPM aims to estimate the inlet properties of the substrate based on the reverse application of the anaerobic digestion model n1 (ADM1). The results show that, while the SPM can estimate some properties of the substrate based on certain output parameters, there are limitations in accurately determining all required variables.

Drug-drug interactions and synergy: from pharmacological models to clinical application.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts such as concentration-response curves, additive effects, and predictive models are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. While various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected at bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. Significance Statement Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug interaction research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Mooney-Rivlin Parameter Determination Model as a Function of Temperature in Vulcanized Rubber Based on Molecular Dynamics Simulations.

The automotive industry is entering a digital revolution, driven by the need to develop new products in less time that are high-quality and environmentally friendly. A proper manufacturing process influences the performance of the door grommet during its lifetime. In this work, uniaxial tensile tests based on molecular dynamics simulations have been performed on an ethylene-propylene-diene monomer (EPDM) material to investigate the effect of the crosslink density and its variation with temperature. The Mooney-Rivlin (MR) model is used to fit the results of molecular dynamics (MD) simulations in this paper and an exponential-type model is proposed to calculate the parameters C1(T) and C2T. The experimental results, confirmed by hardness tests of the cured part according to ASTM 1415-88, show that the free volume fraction and the crosslink density have a significant effect on the stiffness of the EPDM material in a deformed state. The results of molecular dynamics superposition on the MR model agree reasonably well with the macroscopically observed mechanical behavior and tensile stress of the EPDM at the molecular level. This work allows the accurate characterization of the stress-strain behavior of rubber-like materials subjected to deformation and can provide valuable information for their widespread application in the injection molding industry.

Dynamic Simulation and Modeling of a Novel NeuRaiSya for Railway Monitoring System Using Petri Nets.

This research introduces the NeuRaiSya (Neural Railway System Application), an innovative railway signaling system integrating deep learning for passenger analysis. The objectives of this research are to simulate the NeuRaiSya and evaluate its effectiveness using the GreatSPN tool (graphical editor for Petri nets). GreatSPN facilitates evaluations of system behavior, ensuring safety and efficiency. Five models were designed and simulated using the Petri nets model, including the Dynamics of Train Departure model, Train Operations with Passenger Counting model, Timestamp Data Collection model, Train Speed and Location model, and Train Related-Issues model. Through simulations and modeling using Petri nets, the study demonstrates the feasibility of the proposed NeuRaiSya system. The results highlight its potential in enhancing railway operations, ensuring passenger safety, and maintaining service quality amidst the evolving railway landscape in the Philippines.

Application of physiologically based pharmacokinetic modeling of novel drugs approved by the U.S. food and drug administration.

Physiologically based pharmacokinetic (PBPK) models which can leverage preclinical data to predict the pharmacokinetic properties of drugs rapidly became an essential tool to improve the efficiency and quality of novel drug development. In this review, by searching the Application Review Files in Drugs@FDA, we analyzed the current application of PBPK models in novel drugs approved by the U.S. Food and Drug Administration (FDA) in the past five years. According to the results, 243 novel drugs were approved by the FDA from 2019 to 2023. During this period, 74 Application Review Files of novel drugs approved by the FDA that used PBPK models. PBPK models were used in various areas, including drug-drug interactions (DDI), organ impairment (OI) patients, pediatrics, drug-gene interaction (DGI), disease impact, and food effects. DDI was the most widely used area of PBPK models for novel drugs, accounting for 74.2 % of the total. Software platforms with graphical user interfaces (GUI) have reduced the difficulty of PBPK modeling, and Simcyp was the most popular software platform among applicants, with a usage rate of 80.5 %. Despite its challenges, PBPK has demonstrated its potential in novel drug development, and a growing number of successful cases provide experience learned for researchers in the industry.

Marker Data Enhancement For Markerless Motion Capture.

Objective: Human pose estimation models can measure movement from videos at a large scale and low cost; however, open-source pose estimation models typically detect only sparse keypoints, which leads to inaccurate joint kinematics. OpenCap, a freely available service for researchers to measure movement from videos, addresses this issue using a deep learning model-the marker enhancer-that transforms sparse keypoints into dense anatomical markers. However, OpenCap performs poorly on movements not included in the training data. Here, we create a much larger and more diverse training dataset and develop a more accurate and generalizable marker enhancer. Methods: We compiled marker-based motion capture data from 1176 subjects and synthesized 1433 hours of keypoints and anatomical markers to train the marker enhancer. We evaluated its accuracy in computing kinematics using both benchmark movement videos and synthetic data representing unseen, diverse movements. Results: The marker enhancer improved kinematic accuracy on benchmark movements (mean error: 4.1°, max: 8.7°) compared to using video keypoints (mean: 9.6°, max: 43.1°) and OpenCap's original enhancer (mean: 5.3°, max: 11.5°). It also better generalized to unseen, diverse movements (mean: 4.1°, max: 6.7°) than OpenCap's original enhancer (mean: 40.4°, max: 252.0°). Conclusion: Our marker enhancer demonstrates both accuracy and generalizability across diverse movements. Significance: We integrated the marker enhancer into OpenCap, thereby offering its thousands of users more accurate measurements across a broader range of movements.

Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review.

Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other's weaknesses.

Optimizing inferior vena cava filter design: A computational fluid dynamics study on strut configuration for enhanced hemodynamic performance and thrombosis reduction.

Background and objective: Inferior vena cava filters have been shown to be effective in preventing deep vein thrombosis and its secondary complication, pulmonary embolism, thereby reducing the high mortality rate. Although inferior vena cava filters have evolved, specific complications like inferior vena cava thrombosis-induced deep vein thrombosis worsening and recurrent pulmonary embolism continue to pose challenges. This study analyzes the effects of geometric parameter variations of inferior vena cava filters, which have a significant impact on the thrombus formation inside the filter, the capture, dissolution, and hemodynamic flow of thrombus, as well as the shear stress on the filter and vascular wall. Methods: This study used computational fluid dynamic simulations with the carreau model to investigate the impact of varying inferior vena cava filter design parameters (number of struts, strut arm length, and tilt angle) on hemodynamics. Results: Recirculation and stagnation areas due to flow velocity and pressure, along with wall shear stress values, were identified as key factors. It is important to find a balance between wall shear stress high enough to aid thrombolysis and low enough to prevent platelet activation. The results of this paper show that the risk of platelet activation and thrombus filtration may be lowest when the wall shear stress of the filter ranges from 0 to 4 [Pa], minimizing stress concentration within the filter. Conclusion: 16 arm struts with a length of 20 mm and a tilt angle of 0° provide the best balance between thrombus capture and minimization of hemodynamic disturbance. This configuration minimizes the size of the stagnation and recirculation zones while maintaining sufficient wall shear stress for thrombus dissolution.

A His bundle pacing protocol for suppressing ventricular arrhythmia maintenance and improving defibrillation efficacy.

BACKGROUND: The excitable gap (EG), defined as the excitable tissue between two subsequent wavefronts of depolarization, is critical for maintaining reentry that underlies deadly ventricular arrhythmias. EG in the His-Purkinje Network (HPN) plays an important role in the maintenance of electrical wave reentry that underlies these arrhythmias. OBJECTIVE: To determine if rapid His bundle pacing (HBP) during reentry reduces the amount of EG in the HPN and ventricular myocardium to suppress reentry maintenance and/or improve defibrillation efficacy. METHODS: In a virtual human biventricular model, reentry was initiated with rapid line pacing followed by HBP delivered for 3, 6, or 9 s at pacing cycle lengths (PCLs) ranging from 10 to 300 ms (n=30). EG was calculated independently for the HPN and myocardium over each PCL. Defibrillation efficacy was assessed for each PCL by stimulating myocardial surface EG with delays ranging from 0.25 to 9 s (increments of 0.25 s, n=36) after the start of HBP. Defibrillation was successful if reentry terminated within 1 s after EG stimulation. This defibrillation protocol was repeated without HBP. To test the approach under different pathological conditions, all protocols were repeated in the model with right (RBBB) or left (LBBB) bundle branch block. RESULTS: Compared to without pacing, HBP for >3 seconds reduced average EG in the HPN and myocardium across a broad range of PCLs for the default, RBBB, and LBBB models. HBP >6 seconds terminated reentrant arrhythmia by converting HPN activation to a sinus rhythm behavior in the default (6/30 PCLs) and RBBB (7/30 PCLs) models. Myocardial EG stimulation during HBP increased the number of successful defibrillation attempts by 3%-19% for 30/30 PCLs in the default model, 3%-6% for 14/30 PCLs in the RBBB model, and 3%-11% for 27/30 PCLs in the LBBB model. CONCLUSION: HBP can reduce the amount of excitable gap and suppress reentry maintenance in the HPN and myocardium. HBP can also improve the efficacy of low-energy defibrillation approaches targeting excitable myocardium. HBP during reentrant arrhythmias is a promising anti-arrhythmic and defibrillation strategy.

Beyond the basics: A deep dive into parameter estimation for advanced PBPK and QSP models.

Physiologically-based pharmacokinetic (PBPK) models and quantitative systems pharmacology (QSP) models have contributed to drug development strategies. The parameters of these models are commonly estimated by capturing observed values using the nonlinear least-squares method. Software packages for PBPK and QSP modeling provide a range of parameter estimation algorithms. To choose the most appropriate method, modelers need to understand the basic concept of each approach. This review provides a general introduction to the key points of parameter estimation with a focus on the PBPK and QSP models, and the respective parameter estimation algorithms. The latter part assesses the performance of five parameter estimation algorithms - the quasi-Newton method, Nelder-Mead method, genetic algorithm, particle swarm optimization, and Cluster Gauss-Newton method - using three examples of PBPK and QSP modeling. The assessment revealed that some parameter estimation results were significantly influenced by the initial values. Moreover, the choice of algorithms demonstrating good estimation results heavily depends on factors such as model structure and the parameters to be estimated. To obtain credible parameter estimation results, it is advisable to conduct multiple rounds of parameter estimation under different conditions, employing various estimation algorithms.