Framework For Development Of Models Research Articles

A 3D In-vitro model of the human dentine interface shows long-range osteoinduction from the dentine surface

Emerging regenerative cell therapies for alveolar bone loss have begun to explore the use of cell laden hydrogels for minimally invasive surgery to treat small and spatially complex maxilla-oral defects. However, the oral cavity presents a unique and challenging environment for in vivo bone tissue engineering, exhibiting both hard and soft periodontal tissue as well as acting as key biocenosis for many distinct microbial communities that interact with both the external environment and internal body systems, which will impact on cell fate and subsequent treatment efficacy. Herein, we design and bioprint a facile 3D in vitro model of a human dentine interface to probe the effect of the dentine surface on human mesenchymal stem cells (hMSCs) encapsulated in a microporous hydrogel bioink. We demonstrate that the dentine substrate induces osteogenic differentiation of encapsulated hMSCs, and that both dentine and β-tricalcium phosphate substrates stimulate extracellular matrix production and maturation at the gel-media interface, which is distal to the gel-substrate interface. Our findings demonstrate the potential for long-range effects on stem cells by mineralized surfaces during bone tissue engineering and provide a framework for the rapid development of 3D dentine-bone interface models.

A comprehensive AI model development framework for consistent Gleason grading

BackgroundArtificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability.MethodsWe present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI.ResultsOur model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance.ConclusionsThis pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.

Sustainable management of land use patterns and water allocation for coordinated multidimensional development

The sustainable and integrated management of resources is a crucial approach to achieving sustainable regional development in the face of limited land and increasingly scarce water resources. This paper proposes a multidimensional optimization model to jointly optimize land use patterns and water resource allocation under the conditions of maximizing economic benefits and minimizing social differences, ecological loads, environmental pollution, and carbon emissions. The optimization model is solved using the affiliation function method and then combined with random forest (RF) and cellular automata (CA) models to further obtain the optimal spatial layout of land use. This paper takes the Mid-Yangtze River City Cluster as an example and optimizes the land use pattern and water allocation in the region in 2020 and 2030. The results of the study show that the optimized water and land resource allocation scheme performs better than the status quo in all dimensions. The optimized economic benefits, social effects, ecological effects, environmental effects, and overall sustainability in 2020 are increased by 45.97%, 22.89%, 10.07%, 24.65%, and 42.73%, respectively. By 2030, overall sustainability reaches 0.69. The developed modeling framework helps to elucidate the internal linkages between land use and water allocation, and it provides decision-makers with diversified approaches to land and water resource management and adjustment for different cities in different periods, which promotes the sustainable development of urban agglomerations.

Application of transfer learning to overcome data imbalance and extrapolation for model predictive control: A real-life case

This paper proposes a transfer learning (TL)-based control-oriented model development framework. In particular, this study examines the transferability from virtual (source) to existing (target) buildings to overcome the data imbalance issue of the data-driven approach. The target system is a cooling system comprising two supply air fans and four condensing units. First, synthetic data rich enough to provide fundamental knowledge about the target system were generated using the EnergyPlus model. A data-driven model was subsequently developed to learn the underlying dynamics of the system. By adopting TL using an imbalanced dataset measured from the target system, the knowledge that the model learned from the virtual data was transferred to the target system of the existing building. The results showed that the transfer learning model could accurately describe the dynamic behavior of the target system and predict the supply air temperature with marginal errors (CVRMSE: 5.4%, MAE: 0.96 ℃). In other words, the TL from virtual to existing buildings can overcome the data imbalance issue for developing a reliable data-driven model.

Understanding the influence of surgical parameters on craniofacial surgery outcomes: a computational study.

Sagittal craniosynostosis (SC) is a congenital condition whereby the newborn skull develops abnormally owing to the premature ossification of the sagittal suture. Spring-assisted cranioplasty (SAC) is a minimally invasive surgical technique to treat SC, where metallic distractors are used to reshape the newborn's head. Although safe and effective, SAC outcomes remain uncertain owing to the limited understanding of skull-distractor interaction and the limited information provided by the analysis of single surgical cases. In this work, an SC population-averaged skull model was created and used to simulate spring insertion by means of the finite-element analysis using a previously developed modelling framework. Surgical parameters were varied to assess the effect of osteotomy and spring positioning, as well as distractor combinations, on the final skull dimensions. Simulation trends were compared with retrospective measurements from clinical imaging (X-ray and three-dimensional photogrammetry scans). It was found that the on-table post-implantation head shape change is more sensitive to spring stiffness than to the other surgical parameters. However, the overall end-of-treatment head shape is more sensitive to spring positioning and osteotomy size parameters. The results of this work suggest that SAC surgical planning should be performed in view of long-term results, rather than immediate on-table reshaping outcomes.

Artificial Intelligence (AI) Model Development Framework for the Protection of State Borders, with a Focus on Analyzing Behavioral Patterns

This study explores the development and implementation of an artificial intelligence (AI) model designed to predict illegal border crossing locations, thereby enhancing the effectiveness of national border security measures. By integrating and analyzing diverse data sources, -including satellite imagery, social media, and environmental factors, -this AI model aims to identify potential migration patterns and high-risk areas for illegal crossing. This research highlights the model's ability to provide real-time risk assessments, offering a novel approach to border security that surpasses traditional methods in terms of both efficiency and cost-effectiveness. The model's adaptability, continuous learning capabilities, and user-friendly interfaces ensure its relevance in addressing contemporary border-security challenges. This article contributes to the ongoing discourse on the application of AI in national security by proposing a solution that leverages technology for improved coordination among immigration services, government organizations, and international bodies.

Continuous-Time Graph Representation with Sequential Survival Process

Over the past two decades, there has been a tremendous increase in the growth of representation learning methods for graphs, with numerous applications across various fields, including bioinformatics, chemistry, and the social sciences. However, current dynamic network approaches focus on discrete-time networks or treat links in continuous-time networks as instantaneous events. Therefore, these approaches have limitations in capturing the persistence or absence of links that continuously emerge and disappear over time for particular durations. To address this, we propose a novel stochastic process relying on survival functions to model the durations of links and their absences over time. This forms a generic new likelihood specification explicitly accounting for intermittent edge-persistent networks, namely GraSSP: Graph Representation with Sequential Survival Process. We apply the developed framework to a recent continuous time dynamic latent distance model characterizing network dynamics in terms of a sequence of piecewise linear movements of nodes in latent space. We quantitatively assess the developed framework in various downstream tasks, such as link prediction and network completion, demonstrating that the developed modeling framework accounting for link persistence and absence well tracks the intrinsic trajectories of nodes in a latent space and captures the underlying characteristics of evolving network structure.

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data.

Although immune checkpoint inhibitors (ICIs) have improved outcomes in certain patients with cancer, they can also cause life-threatening immunotoxicities. Predicting immunotoxicity risks alongside response could provide a personalized risk-benefit profile, inform therapeutic decision making, and improve clinical trial cohort selection. We aimed to build a machine learning (ML) framework using routine electronic health record (EHR) data to predict hepatitis, colitis, pneumonitis, and 1-year overall survival. Real-world EHR data of more than 2,200 patients treated with ICI through December 31, 2018, were used to develop predictive models. Using a prediction time point of ICI initiation, a 1-year prediction time window was applied to create binary labels for the four outcomes for each patient. Feature engineering involved aggregating laboratory measurements over appropriate time windows (60-365 days). Patients were randomly partitioned into training (80%) and test (20%) sets. Random forest classifiers were developed using a rigorous model development framework. The patient cohort had a median age of 63 years and was 61.8% male. Patients predominantly had melanoma (37.8%), lung cancer (27.3%), or genitourinary cancer (16.4%). They were treated with PD-1 (60.4%), PD-L1 (9.0%), and CTLA-4 (19.7%) ICIs. Our models demonstrate reasonably strong performance, with AUCs of 0.739, 0.729, 0.755, and 0.752 for the pneumonitis, hepatitis, colitis, and 1-year overall survival models, respectively. Each model relies on an outcome-specific feature set, though some features are shared among models. To our knowledge, this is the first ML solution that assesses individual ICI risk-benefit profiles based predominantly on routine structured EHR data. As such, use of our ML solution will not require additional data collection or documentation in the clinic.

Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units, various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure with disbond and delamination type of damages, validated using data generated by finite element simulations and experiments performed at various temperatures in the range 0–90 °C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage. Despite the limited number of features, the lightweight model shows reasonably high accuracy, thereby enabling detection of small size defects with improved sensitivity on an edge device for online GW-SHM.

The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python

Abstract. Plankton community modeling is a critical tool for understanding the processes that shape marine ecosystems and their impacts on global biogeochemical cycles. These models can be of variable ecological, physiological, and physical complexity. Many published models are either not publicly available or implemented in static and inflexible code, thus hampering adoption, collaboration, and reproducibility of results. Here we present Phydra, an open-source library for plankton community modeling, and Xarray-simlab-ODE (XSO), a modular framework for efficient, flexible, and reproducible model development based on ordinary differential equations. Both tools are written in Python. Phydra provides pre-built models and model components that can be modified and assembled to develop plankton community models of various levels of ecological complexity. The components can be created, adapted, and modified using standard variable types provided by the XSO framework. XSO is embedded in the Python scientific ecosystem and is integrated with tools for data analysis and visualization. To demonstrate the range of applicability and how Phydra and XSO can be used to develop and execute models, we present three applications: (1) a highly simplified nutrient–phytoplankton (NP) model in a chemostat setting, (2) a nutrient–phytoplankton–zooplankton–detritus (NPZD) model in a zero-dimensional pelagic ocean setting, and (3) a size-structured plankton community model that resolves 50 phytoplankton and 50 zooplankton size classes with functional traits determined by allometric relationships. The applications presented here are available as interactive Jupyter notebooks and can be used by the scientific community to build, modify, and run plankton community models based on differential equations for a diverse range of scientific pursuits.

A Predictive Model of Vaccine Reactogenicity Using Data from an InVitro Human Innate Immunity Assay System.

A primary concern in vaccine development is safety, particularly avoiding an excessive immune reaction in an otherwise healthy individual. An accurate prediction of vaccine reactogenicity using invitro assays and computational models would facilitate screening and prioritization of novel candidates early in the vaccine development process. Using the modular invitro immune construct model of human innate immunity, PBMCs from 40 healthy donors were treated with 10 different vaccines of varying reactogenicity profiles and then cell culture supernatants were analyzed via flow cytometry and a multichemokine/cytokine assay. Differential response profiles of innate activity and cell viability were observed in the system. In parallel, an extensive adverse event (AE) dataset for the vaccines was assembled from clinical trial data. A novel reactogenicity scoring framework accounting for the frequency and severity of local and systemic AEs was applied to the clinical data, and a machine learning approach was employed to predict the incidence of clinical AEs from the invitro assay data. Biomarker analysis suggested that the relative levels of IL-1B, IL-6, IL-10, and CCL4 have higher predictive importance for AE risk. Predictive models were developed for local reactogenicity, systemic reactogenicity, and specific individual AEs. A forward-validation study was performed with a vaccine not used in model development, Trumenba (meningococcal group B vaccine). The clinically observed Trumenba local and systemic reactogenicity fell on the 26th and 93rd percentiles of the ranges predicted by the respective models. Models predicting specific AEs were less accurate. Our study presents a useful framework for the further development of vaccine reactogenicity predictive models.

A Computational Tool to Track Sewage Flow Discharge into Rivers Based on Coupled HEC-RAS and DREAM

Worldwide abatement of untreated sewage discharge into surface water is a challenging task. Sewage discharging into surface waters has a detrimental impact on water quality. This paper presents a MATLAB (R2018b) framework designed to identify sewage flow discharges into rivers from an inverse problem-solving perspective. The computational tool integrates a hydrodynamic model using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 5.0.0) and an open-source toolbox for Differential Evolution Adaptive Metropolis (DREAM) as the inverse problem method. The proposed framework can effectively infer discharge sources in scenarios of highly transient flow based on hydraulic data at pre-set monitoring sites. To validate its capabilities, one hypothetical case and two real cases of sewage flow discharges entering a river were used to test the developed modeling framework. The results based on three performance metrics showed that this mathematical tool can be extended to simulate complex hydrodynamic flow patterns. This accomplishment underscores its potential as a valuable asset for environmental monitoring and water quality restoration efforts.

Modelling Analysis of MEA Dynamic Operation Under Real-World Automotive Driving Cycle

In last years, Proton Exchange Membrane Fuel Cells (PEMFC) have received an increasing interest as a candidate for light and heavy-duty vehicle application1. To guarantee an efficient and durable operation of PEMFC, the understanding of local conditions, experienced by MEA, during dynamic operation is highly important. Effective numerical modelling can help to gain insights on the local heterogeneity linked to water and thermal management of the cell.A 1 + 1D multiphase dynamic non isothermal PEMFC model, implemented into MATLAB Simulink environment, was used to investigate experimental data obtained on a segmented cell hardware 2. Particular attention was paid to properly model the mass transport and kinetic related phenomena in catalyst layer(CL), as the local oxygen transport resistance through the ionomer thin film3 and the platinum oxide formation/reduction reaction, and their mutual interaction. Exploiting experimental data, obtained from tests on the segmented cell hardware, it was possible to give a robust validation to the 1+1D model, both at global and local level, thanks to the spatially-resolved information available. By simulating real-world driving cycle (DLC) protocol, developed within the ID-FAST project4, drying-up periods of ionomer contained in catalyst layers and in membrane, as well as flooding periods of porous layers and channels, were identified and analyzed during low-load and high-load operation.The performance model was coupled with a catalyst durability model, such to investigate the effect of ageing on dynamic performance of PEMFC. An innovative semi-empirical electrochemical surface area (ECSA) loss model was developed, based on the platinum dissolution mechanism, and validated initially on accelerated stress tests on different operating conditions5. The simulated load profile and local operating conditions in CLs were processed by a proper algorithm, to convert a driving cycle into a combination of elementary steps and to associate a loss factor for the ECSA to each of the identified elements, thus to estimate the retained local and global ECSA. Exploiting experimental database made up of 1000 operating hours performing ID-FAST DLC, the effect of local operating conditions on PEMFC degradation and performance was furtherly comprehended, focusing mainly on the effect of reactant humidification. The developed model framework succeeded in estimating performance loss during driving cycle operation, as visible in Figure 1(a-b). Simulated and experimental ECSA decay over time is shown in Figure 1(c), demonstrating good model prediction capability. The obtained results showed the potentiality of the model to capture complex two-phase and non-isothermal transport phenomena both in the along channel and through-MEA direction, consistently with a large set of experimental data.Experimental data on single cell were collected under ID-FAST project (Grant Agreement No 779565, Joint Undertaking, EU Horizon 2020).References D. A. Cullen et al., Nat. Energy, 6, 462–474 (2021) https://www.nature.com/articles/s41560-021-00775-z.E. Colombo, A. Baricci, A. Bisello, L. Guetaz, and A. Casalegno, J. Power Sources, 553, 232246 (2023) https://doi.org/10.1016/j.jpowsour.2022.232246.T. Suzuki, H. Yamada, K. Tsusaka, and Y. Morimoto, J. Electrochem. Soc., 165, F166–F172 (2018) https://iopscience.iop.org/article/10.1149/2.0471803jes.F. Wilhelm et al., ID-FAST -D4.3 – Analysis of coupling between mechanisms and definition of combined ASTs, p. 49, (2021) https://www.id-fast.eu/uploads/media/ID-FAST_D4-3_Analysis_of_coupling_between_mechanisms_and_definition_of_combined_ASTs_OK.pdf.A. Kneer and N. Wagner, J. Electrochem. Soc., 166, F120–F127 (2019) https://iopscience.iop.org/article/10.1149/2.0641902jes. Figure 1

Coupled THMC modeling on chemical stimulation in fractured geothermal reservoirs

Chemical stimulation, as a soft stimulation method, has been increasingly used in carbonatite geothermal reservoirs, but selecting and designing an optimal scheme of chemical stimulation is still challenging due to lack of robust and reasonable modeling tools. In this study the coupled thermal-hydro-mechanical-chemical (THMC) modeling framework is further developed to simulate chemical stimulation process in carbonatite geothermal reservoirs containing a system of intersecting fractures by considering the nonlinear constitutive models of rock fractures, which reflect the changes in fracture aperture mediated by chemical and mechanical processes. Based on a pilot field test of multi-stage alternate hydraulic and chemical stimulations in the geothermal well D22, Xiong'an New Area, China, the developed modeling framework is verified. The results showed that multi-stage alternate hydraulic and chemical stimulations can efficiently stimulate the carbonatite geothermal reservoirs of low permeability. In addition, a robust optimization approach is proposed to determine the proper human-controlled parameters in chemical stimulation including acid concentration, injection rate and total volume of acid solution.

AC Electrothermal Effect Promotes Enhanced Solute Mixing in a Wavy Microchannel.

For liquids used in biological applications, a smaller diffusion coefficient results in a longer mixing time. We discuss, in this endeavor, the promising potential of the AC electrothermal (ACET) effect toward modulating enhanced mixing of electrolytic liquids with higher convective strength in a novel wavy micromixer. To this end, we develop a modeling framework and numerically solve the pertinent transport equations in a three-dimensional (3D) configuration numerically. By benchmarking the developed modeling framework with the experimental results available in this paradigm, we aptly demonstrate the maximum temperature rise, flow topology, species concentration field, and mixing efficiency in the proposed configuration for a set of parameters pertinent to this analysis. We find that the maximum temperature increase in the wavy micromixer, owing to the electrothermal effect, is less than 10 K even for the higher strength of the applied voltage, implying nondegradation of biological substances within the liquid sample. We report that the formation of significant lateral flow closer to the electrodes leads to a highly three-dimensional ACET flow field, which has a significant impact on the mixing efficiency for the range of diffusive Peclet numbers considered. We also report that the wave amplitude of the mixer, when intervening with the diffusive Peclet number, strongly impacts the mixing efficiency. As witnessed in this endeavor, for the smaller diffusive Peclet number, the mixing efficiency increases with amplitude, while the effect becomes the opposite for the higher Peclet number. The results of this study seem to provide an adequate basis for the design of a novel micromixer intended for enhanced solute mixing in realistic microfluidic applications.

Implementation of Developing English Learning Methods Through Audio Visual Technology for Student of Elementary School in Bangkalan Subdistrict

The primary goal of this research is to create educational videos with both auditory and visual elements to improve the overall learning experience, focusing on English lessons for 3rd-grade elementary students. By utilizing learning media, the teaching and learning process becomes easier and more engaging, enabling students to grasp the subject matter effortlessly. Additionally, it enhances learning efficiency as it aligns with the predetermined learning objectives. This research follows the Research and Development (R&D) methodology with the ADDIE model development framework. Based on the questionnaire results, a score of 80% was obtained, leading to the conclusion that this audio-visual media is 'very good' for enhancing the quality of English education in the Student of Elementary School in Bangkalan Subdistrict.

Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation.

Electronic health records are a valuable source of patient information that must be properly deidentified before being shared with researchers. This process requires expertise and time. In addition, synthetic data have considerably reduced the restrictions on the use and sharing of real data, allowing researchers to access it more rapidly with far fewer privacy constraints. Therefore, there has been a growing interest in establishing a method to generate synthetic data that protects patients' privacy while properly reflecting the data. This study aims to develop and validate a model that generates valuable synthetic longitudinal health data while protecting the privacy of the patients whose data are collected. We investigated the best model for generating synthetic health data, with a focus on longitudinal observations. We developed a generative model that relies on the generalized canonical polyadic (GCP) tensor decomposition. This model also involves sampling from a latent factor matrix of GCP decomposition, which contains patient factors, using sequential decision trees, copula, and Hamiltonian Monte Carlo methods. We applied the proposed model to samples from the MIMIC-III (version 1.4) data set. Numerous analyses and experiments were conducted with different data structures and scenarios. We assessed the similarity between our synthetic data and the real data by conducting utility assessments. These assessments evaluate the structure and general patterns present in the data, such as dependency structure, descriptive statistics, and marginal distributions. Regarding privacy disclosure, our model preserves privacy by preventing the direct sharing of patient information and eliminating the one-to-one link between the observed and model tensor records. This was achieved by simulating and modeling a latent factor matrix of GCP decomposition associated with patients. The findings show that our model is a promising method for generating synthetic longitudinal health data that is similar enough to real data. It can preserve the utility and privacy of the original data while also handling various data structures and scenarios. In certain experiments, all simulation methods used in the model produced the same high level of performance. Our model is also capable of addressing the challenge of sampling patients from electronic health records. This means that we can simulate a variety of patients in the synthetic data set, which may differ in number from the patients in the original data. We have presented a generative model for producing synthetic longitudinal health data. The model is formulated by applying the GCP tensor decomposition. We have provided 3 approaches for the synthesis and simulation of a latent factor matrix following the process of factorization. In brief, we have reduced the challenge of synthesizing massive longitudinal health data to synthesizing a nonlongitudinal and significantly smaller data set.

Measurement and Verification Building Energy Prediction (MVBEP): An interpretable data-driven model development and analysis framework

Measurement and Verification Building Energy Prediction (MVBEP): An interpretable data-driven model development and analysis framework

Development of Fruit and Vegetable Consumption Promotion Model in a High-Risk Population for Cholangiocarcinoma in Thailand: An Action Research.

This study aimed to develop a model for promoting fruit and vegetable consumption in Thailand's high-risk population for cholangiocarcinoma (CCA). Action research was used as a guiding framework for model development. Participants were divided into groups for process development and evaluation. Group discussions and practice notes were utilized as tools for process development. Data were collected through questionnaires. Qualitative data were categorized and analyzed using content analysis. Descriptive and inferential statistics were applied to analyze quantitative data. The model development process, following the PAOR framework (Planning, Action, Observation, Reflection), involved fruit and vegetable consumption promotion. The model, named the "NONGBO NO-CCA Model" included various components: active involvement of villagers in planning, objective sharing of experiences and brainstorming to identify CCA prevention strategies, establishment of networks to support community healthcare, enhancement of community self-reliance through utilization of local resources, and encouragement of chemical-free and environmentally friendly fruit and vegetable cultivation. Following model development, at-risk individuals demonstrated a statistically significant improvement in knowledge, attitude, and practice (p<0.001). The findings indicate that at-risk individuals exhibited improvements in knowledge, attitude, and practice. Knowledge gains may be attributed to educational training activities, improved attitudes may result from networking processes, and modifications in practice behaviors may be influenced by community participation. Therefore, active involvement in community development can serve as a guiding principle for effective proactive CCA prevention.

Assessment of ibrutinib scheduling on leukocyte, lymph node size and blood pressure dynamics in chronic lymphocytic leukemia through pharmacokinetic-pharmacodynamic models.

Ibrutinib is a Bruton tyrosine kinase (Btk) inhibitor for treating chronic lymphocytic leukemia (CLL). It has also been associated with hypertension. The optimal dosing schedule for mitigating this adverse effect is currently under discussion. A quantification of relationships between systemic ibrutinib exposure and efficacy (i.e., leukocyte count and sum of the product of perpendicular diameters [SPD] of lymph nodes) and hypertension toxicity (i.e., blood pressure), and their association with overall survival is needed. Here, we present a semi-mechanistic pharmacokinetic-pharmacodynamic modeling framework to characterize such relationships and facilitate dose optimization. Data from a phase Ib/II study were used, including ibrutinib plasma concentrations to derive daily 0-24-h area under the concentration-time curve, leukocyte count, SPD, survival, and blood pressure measurements. A nonlinear mixed effects modeling approach was applied, considering ibrutinib's pharmacological action and CLL cell dynamics. The final framework included (i) an integrated model for SPD and leukocytes consisting of four CLL cell subpopulations with ibrutinib inhibiting phosphorylated Btk production, (ii) a turnover model in which ibrutinib stimulates an increase in blood pressure, and (iii) a competing risk model for dropout and death. Simulations predicted that the approved dosing schedule had a slightly higher efficacy (24-month, progression-free survival [PFS] 98%) than de-escalation schedules (24-month, average PFS ≈ 97%); the latter had, on average, ≈20% lower proportions of patients with hypertension. The developed modeling framework offers an improved understanding of the relationships among ibrutinib exposure, efficacy and toxicity biomarkers. This framework can serve as a platform to assess dosing schedules in a biologically plausible manner.