Model-based Simulation Research Articles

Population pharmacokinetics of rilpivirine following oral administration and long-acting intramuscular injection in real-world people with HIV

BackgroundThe pharmacokinetics of long-acting rilpivirine has mostly been studied in clinical trials, which do not fully address the uncertainties that arise in routine clinical situations.Aims and methodsOur population analysis aims to establish percentile curves for rilpivirine concentrations in people with HIV (PWH) followed-up in a routine clinical setting, while identifying patient-related factors that may influence rilpivirine exposure. A total of 238 PWH enrolled in our nationwide multicenter observational study contributed to 1038 concentrations (186 and 852 concentrations after oral and intramuscular injection, respectively).ResultsRilpivirine pharmacokinetics were best described by a two-compartment model with an oral to intramuscular relative bioavailability factor. A simple zero-order absorption process was retained for oral administration while a parallel first-order absorption was used for intramuscular administration, with 27.6% of the dose released via a fast absorption pathway and the remaining fraction via a slow absorption pathway. Our model estimated that long-acting rilpivirine reaches steady-state after 2.5 years and has an elimination half-life of 18 weeks, consistent with published estimates. In females, a 45.6% reduction in the proportion of the dose absorbed via the rapid absorption pathway was observed. However, this resulted in no more than 15% difference in trough concentrations (Ctrough) compared to males, which was not considered to be clinically relevant.ConclusionOverall, our model-based simulations showed that only approximately 50% of long-acting rilpivirine Ctrough would be above the 50 ng/mL threshold associated with optimal therapeutic response, while approximately 85% of Ctrough would be above the first quartile of concentrations observed in Phase III trials (32 ng/mL).

The distinctive pharmacokinetic profile of rezafungin, a long-acting echinocandin developed in the era of modern pharmacometrics.

Echinocandin drugs are the current first-line therapy for fungal infections caused by Candida spp. Most patients require once-daily intravenous (IV) administration in a hospital or outpatient setting for treatment, which may negatively impact their quality of life and stress healthcare resources. Similar to other echinocandins, the novel FDA-, EMA-, and Medical and Healthcare Products Regulatory Agency-approved echinocandin, rezafungin (CD101), exhibited strong antifungal activity against several fungal pathogens and a low drug-drug interaction liability, which are important for medically complex patients. A pharmacometric-based approach has been adopted throughout the development of rezafungin, which contrasts with older echinocandins where dosing regimens were largely derived empirically, and only recently based on pharmacometric guidance. This state-of-the-art approach used model-based simulations incorporating pre-clinical and clinical data as it became available to optimize the dosing regimen for rezafungin. The enhanced stability of the molecular structure and the safety profile of rezafungin allow for the administration of once-weekly IV doses, compared to the daily dosing requirement for other echinocandin drugs, with this distinctive pharmacokinetic profile of rezafungin resulting in a front-loaded dosing regimen with high exposures early in therapy for enhanced fungal killing. The long shelf-life of rezafungin makes this echinocandin more flexible in terms of storage and manufacturing. Demonstrated across clinical development, rezafungin may provide patients with next-generation first-line antifungal treatment for the treatment of candidaemia and invasive candidiasis.

Pharmacometric Analysis to Describe Pharmacokinetics and Exposure-Efficacy Response of Ivermectin in Adolescents Infected with Trichuris trichiura.

The efficacy of the combination therapy of albendazole and ivermectin against Trichuris trichiura infection is higher in Tanzania than in Côte d'Ivoire. This study therefore aimed to investigate the difference between the population pharmacokinetics (PK) at these study sites and to determine if an exposure-response analysis could explain the low efficacy of the combination therapy in Côte d'Ivoire. Twenty-four participants (aged 12-19 years) receiving single doses of ivermectin (200 µg/kg) and albendazole (400 mg) were included in the population PK modeling. A regression analysis was performed to investigate the relationship between the reduction of fecal whipworm eggs and different exposure metrics (peak concentration, area under the plasma drug concentration-time curve [AUC], and time above a certain threshold). The PK profile of ivermectin was best described by a one-compartment model, first-order absorption, and no delay in absorption, with the absorption rate constant estimated as 0.26 per h, an apparent volume of distribution of 162.43 L, and an apparent clearance of 7.82 L/h. In Tanzania, all patients showed a very high reduction in egg count independent of exposure. In Côte d'Ivoire, a relationship was found between higher ivermectin exposure and egg reduction, although not statistically significant. There was no significant difference between the PK profiles at both study sites, despite a difference in clinical outcome. Model-based simulations indicate that higher ivermectin doses such as 400 and 600 µg/kg may be associated with reduced egg count. Larger clinical studies are warranted to explore further the exposure-efficacy response relationship at 200 µg/kg and higher ivermectin doses in adults and children.

Dynamic modeling of post-combustion carbon capture process based on multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder network

Solvent-based post-combustion carbon capture (PCC) technology is a promising, near-term solution for decarbonizing power generation and industrial facilities. Model-based process simulation is crucial for the optimal design and operation of the PCC process. Recently, data-driven models have gained attention due to their adaptability, efficient computation and high accuracy. However, the nonlinearity, strong couplings and multi-time scale features of the PCC process pose significant challenges for model identification. To this end, this paper proposes a multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder (MMoE-DAED) network for dynamic modeling of the PCC process under wide operating conditions. An encoder-decoder composed of long short-term memory (LSTM) network is employed to extract features from the time-dependent input data and learn the complex dynamic interactions caused by the inertial and delay properties of the process. Dual-stage attention mechanism is incorporated into the encoder and decoder respectively to select the most relevant input features and their correlations within the time series data. To enhance multi-output prediction accuracy, multi-gate mixture-of-experts (MMoE) framework that considers correlations of multitask learning is implemented. Simulation results using operating data from a PCC experimental setup indicate that the proposed modeling approach accurately predicts the steady-state values and dynamic trends of the CO2 capture rate and stripper bottom temperature over a wide operating range. The RMSE, MAPE and R2 indices for the CO2 capture rate are 2.1592, 0.0295, 0.9641, respectively, and for the stripper bottom temperature are 0.1491, 0.0003, 0.9833, respectively. Validations on a PCC simulator further verify the accuracy and efficiency of the MMoE-DAED model, which enables an 80.87% reduction in computation time compared to the simulator. This paper points to a new direction for the data-driven dynamic modeling of complex energy conversion processes.

Longitudinal and time-to-event modeling for the survival of advanced pancreatic ductal adenocarcinoma patients.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers especially at advanced stage. In order to analyze the dynamics of potential prognostic biomarkers and further quantify their relationships with the overall survival (OS) of advanced PDAC patients, we herein developed a parametric time-to-event (TTE) model integrated with longitudinal submodels. Data from 104 patients receiving standard chemotherapies were retrospectively collected for model development, and other 54 patients were enrolled as external validation. The longitudinal submodels were developed with the time-course data of sum of longest diameters (SLD) of tumors, serum albumin (ALB) and body weight (BW) using nonlinear mixed effect models. The model-derived metrics including model parameters and individual predictions at different time points were further analyzed in the TTE model, together with other baseline information of patients. A linear growth-exponential shrinkage model was employed to describe the dynamics of SLD, while logistic models were used to fit the relationship of time prior to death with ALB and BW. The TTE model estimated the ALB and BW changes at the 9th week after chemotherapies as well as the baseline CA19-9 level that showed most significant impact on the OS, and the model-based simulations could provide individual survival rate predictions for patients with different prognostic factors. This study quantitatively demonstrates the importance of physical status and baseline disease for the OS of advanced PDAC patients, and highlights that timely nutrition support would be helpful to improve the prognosis.

Material-Sparing Approach to Predict Tablet Capping Under Processing Compression Conditions Based on Mechanical and Molecular Properties Derived from Compaction Simulation and Crystal Structural Analysis.

Present study evaluates the usability of compaction simulation-based mechanical models as a material-sparing approach to predict tablet capping under processing compression conditions using Acetaminophen (APAP) and Ibuprofen (IBU). Measured mechanical properties were evaluated using principal component analysis (PCA) and principal component regression (PCR) models. PCR models were then utilized to predict the capping score (CS) from compression pressure (CP). APAP formulations displayed a quadratic correlation between CS and CP, with CS rank order following CP of 200MPa < 300MPa < 100MPa, indicating threshold compression pressure (TCP) limit between 200 and 300 MPa, resulting in higher CS at 300 than 200 MPa regardless of increased CP. IBU formulations displayed a linear correlation between CS and CP, with CS rank order following CP of 100MPa < 200MPa < 300MPa, indicating TCP limit between 100 and 200 MPa, resulting in higher CS at 200 and 300 than 100 MPa regardless of increased CP. Molecular models were developed as validation methods to predict capping from CP. Measured XRPD patterns of compressed tablets were linked with calculated Eatt and d-spacing of slip planes and analyzed using variable component least square methods to predict TCP triggering cleavage in slip planes and leading to capping. In APAP and IBU, TCP values were predicted at 245 and 175 MPa, meaning capped tablets above these TCP limits regardless of increased CP. A similar trend was observed in CS predictions from mechanical assessment, confirming that compaction simulation-based mechanical models can predict capping risk under desired compression conditions rapidly and accurately.

A model-informed clinical trial simulation tool with a graphical user interface for Duchenne muscular dystrophy.

Quantitative model-based clinical trial simulation tools play a critical role in informing study designs through simulation before actual execution. These tools help drug developers explore various trial scenarios in silico to select a clinical trial design to detect therapeutic effects more efficiently, therefore reducing time, expense, and participants' burden. To increase the usability of the tools, user-friendly and interactive platforms should be developed to navigate various simulation scenarios. However, developing such tools challenges researchers, requiring expertise in modeling and interface development. This tutorial aims to address this gap by guiding developers in creating tailored R Shiny apps, using an example of a model-based clinical trial simulation tool that we developed for Duchenne muscular dystrophy (DMD). In this tutorial, the structural framework, essential controllers, and visualization techniques for analysis are described, along with key code examples such as criteria selection and power calculation. A virtual population was created using a machine learning algorithm to enlarge the available sample size to simulate clinical trial scenarios in the presented tool. In addition, external validation of the simulated outputs was conducted using a placebo arm of a recently published DMD trial. This tutorial will be particularly useful for developing clinical trial simulation tools based on DMD progression models for other end points and biomarkers. The presented strategies can also be applied to other diseases.

Wettbewerbsfähigkeit der Industrie und Energiepreise

Abstract The rise in energy prices in 2021/22 put the competitiveness of Austrian manufacturing industry to the test. Model-based simulations and an enterprise survey were carried out to analyze the medium-term effects of high energy prices. The results show that the cost disadvantages in international competition induced by energy price increases will lead to a decline in industrial production and employment in the medium term and increase the risk of relocation of energy-intensive production.

Model-implied simulation-based power estimation for correctly specified and distributionally misspecified models: Applications to nonlinear and linear structural equation models.

Closed-form (asymptotic) analytical power estimation is only available for limited classes of models, requiring correct model specification for most applications. Simulation-based power estimation can be applied in almost all scenarios where data following the model can be estimated. However, a general framework for calculating the required sample sizes for given power rates is still lacking. We propose a new model-implied simulation-based power estimation (MSPE) method for the z-test that makes use of the asymptotic normality property of estimates of a wide class of estimators, the M-estimators, and give theoretical justification for the approach. M-estimators include maximum-likelihood, least squares estimates and limited information estimators, but also estimators used for misspecified models, hence, the new simulation-based power modeling method is widely applicable. The MSPE employs a parametric model to describe the relationship between power and sample size, which can then be used to determine the required sample size for a specified power rate. We highlight its performance in linear and nonlinear structural equation models (SEM) for correctly specified models and models under distributional misspecification. Simulation results suggest that the new power modeling method is unbiased and shows good performance with regard to root mean squared error and type I error rates for the predicted required sample sizes and predicted power rates, outperforming alternative approaches, such as the naïve approach of selecting a discrete selection of sample sizes with linear interpolation of power or simple logistic regression approaches. The MSPE appears to be a valuable tool to estimate power for models without an (asymptotic) analytical power estimation.

Optimizing charge transport simulation for hybrid pixel detectors

To enhance the spatial resolution of the MÖNCH 25 µm pitch hybrid pixel detector, deep learning models have been trained using both simulation and measurement data. Challenges arise when comparing simulation-based deep learning models to measurement-based models for electrons, as the spatial resolution achieved through simulations is notably inferior to that from measurements. Discrepancies are also observed when directly comparing X-ray simulations with measurements, particularly in the spectral output of single pixels. These observations collectively suggest that current simulations require optimization. To address this, the dynamics of charge carriers within the silicon sensor have been studied using Monte Carlo simulations, aiming to refine the charge transport modeling. The simulation encompasses the initial generation of the charge cloud, charge cloud drift, charge diffusion and repulsion, and electronic noise. The simulation results were validated with measurements from the MÖNCH detector for X-rays, and the agreement between measurements and simulations was significantly improved by accounting for the charge repulsion.

Contact-implicit Model Predictive Control: Controlling diverse quadruped motions without pre-planned contact modes or trajectories

This paper presents a contact-implicit model predictive control (MPC) framework for the real-time discovery of multi-contact motions, without predefined contact mode sequences or foothold positions. This approach utilizes the contact-implicit differential dynamic programming (DDP) framework, merging the hard contact model with a linear complementarity constraint. We propose the analytical gradient of the contact impulse based on relaxed complementarity constraints to further the exploration of a variety of contact modes. By leveraging a hard contact model-based simulation and computation of search direction through a smooth gradient, our methodology identifies dynamically feasible state trajectories, control inputs, and contact forces while simultaneously unveiling new contact mode sequences. However, the broadened scope of contact modes does not always ensure real-world applicability. Recognizing this, we implemented differentiable cost terms to guide foot trajectories and make gait patterns. Furthermore, to address the challenge of unstable initial roll-outs in an MPC setting, we employ the multiple shooting variant of DDP. The efficacy of the proposed framework is validated through simulations and real-world demonstrations using a 45 kg HOUND quadruped robot, performing various tasks in simulation and showcasing actual experiments involving a forward trot and a front-leg rearing motion.

Crossing the Chasm: How to Approach Translational Pharmacokinetic-Pharmacodynamic Modeling of Phage Dosing.

Effectively treating multidrug-resistant bacterial infections remains challenging due to the limited drug development pipeline and a scarcity of novel agents effective against these highly resistant pathogens. Bacteriophages (phages) are a potential addition to the antimicrobial treatment arsenal. Though, phages are currently being tested in clinical trials for antibiotic-resistant infections, phages lack a fundamental understanding of optimal dosing in humans. Rationally designed preclinical studies using in vitro and in vivo infection models, allow us to assess clinically relevant phage +/- antibiotic exposure (pharmacokinetics), the resulting treatment impact on the infecting pathogen (pharmacodynamics) and host immune response (immunodynamics). A mechanistic modeling framework allows us to integrate this knowledge gained from preclinical studies to develop predictive models. We reviewed recently published mathematical models based on in vitro and/or in vivo data that evaluate the effects of varying bacterial or phage densities, phage characteristics (burst size, adsorption rate), phage pharmacokinetics, phage-antibiotic combinations and host immune responses. In our review, we analyzed study designs and the data used to inform the development of these mechanistic models. Insights gained from model-based simulations were reviewed as they help identify crucial phage parameters for determining effective phage dosing. These efforts contribute to bridging the gap between phage therapy research and its clinical translation.

Synergistic cotreatment of cooling tower blowdown and produced waters: Techno-economic, sustainability, and optimization systems analyses

A blowdown and produced water cotreatment train design was preliminarily simulated to explore water-energy nexus synergy. This process model was able to provide results concerning water compositions, chemical demand, and energy intensity. The computational expense associated with this simulation made it intractable to use within optimization algorithms for further informative, comprehensive systems analyses. This work details the efforts of developing a framework suitable for the optimization of computationally demanding wastewater treatment train simulations, specifically demonstrated for the cotreatment train’s process, economic, and sustainability models. The unique challenges of the cotreatment train model motivated a modified surrogate optimization methodology to be developed. Using this framework, worst and best case economic scenarios were optimized to minimize the levelized cost of water (LCOW) employed in a reduced-order model-based simulation. Applying the optimal designs obtained from the surrogate optimization into the original comprehensive simulation resulted in an expected range of LCOW from $2.01 to $4.43m-3. Other established techno-economic and sustainability indicators further inform on process implications. The process was compared to current management practices of blowdown water treatment and produced water injection to discuss the advantages and disadvantages of the proposed cotreatment train designed for water reuse. The cotreatment process was estimated to have lower levelized treatment costs but require more chemicals and energy to manage a higher salinity wastewater than pure blowdown water alone. If sustainable management goals motivate produced water remediation, the cotreatment process can be beneficial for the broader scope of water management.

Population pharmacokinetic analysis of zastaprazan (JP-1366), a novel potassium-competitive acid blocker, in patients and healthy volunteers.

Zastaprazan (JP-1366) is a novel potassium-competitive acid blocker for the treatment of acid-related disorders. We aimed to establish a population pharmacokinetic (PK) model of zastaprazan, thereby characterizing the PK of zastaprazan in patients with gastroesophageal reflux disease (GERD) as well as evaluating the impact of various covariates, including CYP2C19 phenotypes, on zastaprazan PK. This population PK analysis included zastaprazan plasma concentration-time data from 92 patients with erosive GERD and 68 healthy volunteers without any gastrointestinal disorders and was performed using nonlinear mixed-effect modeling. Simulations were conducted to predict zastaprazan PK under various dosing regimens in patients with GERD. The plasma PK of zastaprazan was adequately described by a two-compartment model with Erlang-type absorption (six sequential compartments) and first-order elimination. CYP2C19 phenotypes had no significant effect on zastaprazan PK. The disease status was identified as a significant covariate on apparent clearance of zastaprazan, showing lower values in patients with GERD compared to healthy volunteers. However, the model-based simulation indicated that the impact of disease status on zastaprazan exposure was not clinically meaningful. Overall, the current population PK model successfully characterized the observed zastaprazan PK in both patients with GERD and healthy volunteers.

Improving efficiency in the emergency department Monte Carlo simulation for determining the optimal number of staff

This paper examines the application of Monte Carlo simulation to determine optimal staffing levels at reception desks in the Emergency Department. The study utilizes data collected from a hospital in Bogotá, with all data anonymized to maintain the confidentiality of both the institution and its patients. By leveraging programming tools, the study randomizes the data and models various scenarios to assess the staffing requirements accurately. The primary goal is to enhance the efficiency and quality of service by aligning staffing levels with patient demand. The use of historical data, combined with the simulation of hypothetical scenarios, provides a robust basis for predicting future needs and making informed staffing decisions. The study's findings offer valuable insights into human resources management, enabling the Emergency Department to strategically allocate personnel, minimize wait times, and improve overall patient care. This approach demonstrates the potential for simulation-based models to optimize resource allocation in critical healthcare environments.

Delineating the stepwise millisecond allosteric activation mechanism of the class C GPCR dimer mGlu5

Two-thirds of signaling hormones and one-third of approved drugs exert their effects by binding and modulating the G protein-coupled receptors (GPCRs) activation. While the activation mechanism for monomeric GPCRs has been well-established, little is known about GPCRs in dimeric form. Here, by combining transition pathway generation, extensive atomistic simulation-based Markov state models, and experimental signaling assays, we reveal an asymmetric, stepwise millisecond allosteric activation mechanism for the metabotropic glutamate receptor subtype 5 receptor (mGlu5), an obligate dimeric class C GPCR. The dynamic picture is presented that agonist binding induces dimeric ectodomains compaction, amplified by the precise association of the cysteine-rich domains, ultimately loosely bringing the intracellular 7-transmembrane (7TM) domains into proximity and establishing an asymmetric TM6-TM6 interface. The active inter-domain interface enhances their intra-domain flexibility, triggering the activation of micro-switches crucial for downstream signal transduction. Furthermore, we show that the positive allosteric modulator stabilizes both the active inter-domain 7TM interface and an open, extended intra-domain ICL2 conformation. This stabilization leads to the formation of a pseudo-cavity composed of the ICL2, ICL3, TM3, and C-terminus, which facilitates G protein coordination. Our strategy may be generalizable for characterizing millisecond events in other allosteric systems.

Enhancing resilience: model-based simulations

Enhancing resilience: model-based simulations

Prediction through Simulation-Based Corrective Models of Contra-Directional Couplers’ Experimental Results

Lithography variation presents one of the biggest challenges for photonic component optimization, especially for fabless designers. Lithography prediction models are a crucial tool for minimizing the necessary number of fabrication iterations for a device’s optimization. This paper presents one of these models specifically adapted for the contra-directional coupler structure. Through the experimental characterization of devices with a specific range of design parameters, it was possible to observe how the lithography process impacts their performance. A correction model based on effective refractive index variation and its impact on the Bragg condition of the structure was developed to predict the performance variation of a device based on the expected design variation induced by fabrication. The contra-directional couplers fabricated at CORNERSTONE foundry show a tendency to be redshifted as the gap decreases, due to an increase in waveguide width as a result of a diffraction-limited lithography process. Based on these and other findings, it was possible to correlate the design parameters to the posterior fabricated structure and ultimately predict the expected experimental response.

Model-driven engineering for digital twins: a graph model-based patient simulation application.

Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector. This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. A short case study is presented to demonstrate the viability of the proposed simulation architecture. The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making.

Model-informed Evidence for Clinical Non-inferiority of Every-2-Weeks Versus Standard Weekly Dosing Schedule of Cetuximab in Metastatic Colorectal Cancer.

Cetuximab was initially developed and approved as a first-line treatment in patients with unresectable metastatic colorectal cancer (mCRC) for weekly administration (250 mg/m2 Q1W with 400 mg/m2 loading dose). An every-2-weeks schedule (500 mg/m2 Q2W) was approved recently by several health authorities. Being synchronized with chemotherapy, Q2W administration should improve patients' convenience and healthcare resource utilization. Herein, we present evidence of non-inferiority of Q2W cetuximab, compared with Q1W dosing using pharmacometrics modeling and clinical trial simulation (CTS). Pooled data from five phase I-III clinical trials in 852 patients with KRAS wild-type mCRC treated with Q1W or Q2W cetuximab were modeled using a population exposure-tumor size (TS) model linked to overall survival (OS); exposure was derived from a previously established population pharmacokinetic model. A semi-mechanistic TS model adapted from the Claret model incorporated killing rate proportional to cetuximab area under the concentration-time curve over 2 weeks (AUC) with Eastern Cooperative Oncology Group (ECOG) status as covariate on baseline TS. The OS was modeled with Weibull hazard using ECOG, baseline TS, primary tumor location, and predicted percent change in TS at 8 weeks as covariates. Model-based simulations revealed indistinguishable early tumor shrinkage and survival between Q2W vs. Q1W cetuximab. CTS evaluated OS non-inferiority (predefined margin of 1.25) in 1,000 trials, each with 2,000 virtual patients receiving Q2W or Q1W cetuximab (1:1), and demonstrated non-inferiority in 94% of cases. Taken together, these analyses provide model-based evidence for clinical non-inferiority of Q2W vs. Q1W cetuximab in mCRC with potential benefits to patients and healthcare providers.