Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

Mechanistic intracellular PK/PD modeling to inform development strategies for small interfering RNA therapeutics

Small interfering RNA (siRNA) therapeutics provide a targeted approach to silence disease-related genes, with notable success in liver-targeting applications. However, the quantitative effects of siRNA properties, such as stability and affinity, as well as biological factors like cell proliferation, mRNA turnover, and abundance, on gene silencing, particularly for extrahepatic targets, remain poorly understood. To identify determinants influencing gene knockdown extent and duration, we developed a mechanistic intracellular pharmacokinetic/pharmacodynamic (PK/PD) model for RNAiMAX-delivered siRNA, based on cytoplasmic siRNA disposition, RISC-loaded siRNA exposure, and mRNA knockdown across different targets in MCF7 and BT474 cells. The model highlighted the critical roles of cell proliferation in silencing duration and mRNA turnover rates on knockdown extent. In rapid-dividing cells, mRNA half-life drives knockdown profiles, whereas chemical siRNA stabilization extends silencing in slow-dividing cells. Targets with extremely low or high mRNA abundance pose silencing challenges. While sufficient RISC occupancy is essential, increasing RISC exposure has minimal impact on silencing extent; enhancing siRNA-mRNA target engagement is more effective. The model also defined a quantitative relationship for maximal mRNA knockdown, governed by cell proliferation, mRNA half-life, and RISC-mediated cleavage rates. This mechanistic PK/PD modeling provides insights into optimizing siRNA design and target selection in therapeutic development.

Entropy-based groundwater quality evaluation with multivariate analysis and Sobol sensitivity for non-carcinogenic health risks in mid-Gangetic plains, India

This study assessed the quality and pollution status of the groundwater in an agricultural and densely populated area of Mid-Gangetic Plain Utilizing Principal Component Analysis (PCA), Spearman's correlation analysis, and Entropy water quality index (EWQI) and evaluated the public health hazard resulting due to nitrate and fluoride exposure using USEPA-based Health risk model and Sobol sensitivity analysis (SSA) on the basis of collected groundwater samples. The analysis revealed that several water quality parameters surpassed the permissible levels established by the Bureau of Indian Standards (BIS). Based on the third quartile values the sequence of ionic dominance in the groundwater was observed as: HCO3- > Ca2+ > Mg2+ > Cl- > SO42- > NO3- > PO43- > F-. Approximately 10% of groundwater samples exceeded the desirable fluoride level of 1 mg/l, and 12% of samples surpassed the BIS permissible nitrate limit of 45 mg/l. Correlation analysis suggested key factors driving groundwater chemistry, including agricultural runoff, wastewater discharge, and geological activities. PCA reduced 12 variables to 4 significant components, explaining 68.074% of the variation, identifying both geogenic and anthropogenic interventions on the groundwater quality, and highlighting the complex interplay of these factors in the study area. Groundwater quality, measured by EWQI, ranged from 36.30 to 234 revealing about 85% of samples falling in excellent to fair quality, suitable for drinking. Notedly, there was some overlap in the distribution pattern of poor water quality samples and those with high nitrate, phosphate, and magnesium levels. Health risk assessment revealed that nitrate and fluoride pollution pose a significant non-carcinogenic threat. The total hazard index ranging 0.328-2.77 for children, 0.26-2.23 for females, and 0.22-1.89 for males, with 56.10% of samples exceeding the safe threshold for children, signifying a potential health risk for children than adults. SSA revealed that concentration and intake rate are the most influential variables of nitrate and fluoride exposure, which causes health risks to residents. To ensure public health and safety, the study advises residents to rely on treated water from underground sources. Additionally, it stresses the need for ongoing monitoring of groundwater resources to guide the development of effective pollution mitigation strategies and maintain a safe and reliable water supply.

Temporal evaluation of seawater intrusion vulnerability in Shabestar Plain using GALDIT and AI techniques

Groundwater contamination, with seawater intrusion (SWI) being the most widespread form particularly in coastal areas, has become a pressing global environmental challenge. Groundwater serves as a vital freshwater resource, particularly in arid and semi-arid regions, making its efficient management essential. In this study, the GALDIT method-an index-based approach that evaluates the vulnerability of aquifers by scoring six key parameters based on expert judgment (groundwater occurrence (G), aquifer hydraulic conductivity (A), groundwater elevation above sea level (L), distance from the shoreline (D), impact of existing seawater intrusion (I), and aquifer thickness (T))-was employed to assess the vulnerability of the Shabestar aquifer to SWI. The study employs the GALDIT method to map aquifer vulnerability for 2002, 2012, and 2022, enabling a temporal comparison of changes over time. The final GALDIT index map, categorized into low, moderate, and high vulnerability classes, revealed an increase in very high vulnerability areas from 10.9% in 2002 to 17.8% in 2022, alongside a decrease in moderate vulnerability areas from 56.4 to 37.3%, indicating a deteriorating condition of the aquifer. However, the reliance on expert judgment introduces potential subjectivity and bias in the vulnerability assessment. To mitigate these limitations, AI-based models, namely artificial neural networks (ANNs) and random forest (RF), were applied to enhance model performance. The GALDIT parameters served as input for the AI models, while observed electrical conductivity (EC), a key indicator of water salinity, and total dissolved solids (TDS), an indicator of drinking water quality, were used as output variables to estimate condition for the year 2022. Results demonstrated that the ANN model outperformed the RF model during verification, improving estimation accuracy by up to 10% and 9% in terms of the determination coefficient (DC), respectively. To further enhance model interpretability and identify the most influential parameters for EC and TDS estimation, a global, variance-based sensitivity analysis using the Sobol method was conducted. This analysis revealed that factors I and D were the most influential for EC, while factors I and T had the greatest impact on TDS in the study region.

A Framework for Quantitative Systems Pharmacology Model Execution

A mathematical model can be defined as a theoretical approximation of an observed pattern. The specific form of the model and the associated mathematical methods are typically dictated by the question(s) to be addressed by the model and the underlying data. In the context of research and development of new medicines, these questions often focus on the dose-exposure-response relationship.The general workflow for model development and application can be delineated in three major elements: defining the model, qualifying the model, and performing simulations. These elements may vary significantly depending on modeling objectives. Quantitative systems pharmacology (QSP) models address the formidable challenge of quantitatively and mechanistically characterizing human and animal biology, pathophysiology, and therapeutic intervention.QSP model development, by necessity, relies heavily on preexisting knowledge, requires a comprehensive understanding of current physiological concepts, and often makes use of heterogeneous and aggregated datasets from multiple sources. This reliance on diverse datasets presents an upfront challenge: the determination of an optimal model structure while balancing model complexity and uncertainty. Additionally, QSP model calibration is arduous due to data scarcity (particularly at the human subject level), which necessitates the use of a variety of parameter estimation approaches and sensitivity analyses, earlier in the modeling workflow as compared to, for example, population modeling. Finally, the interpretation of model-based predictions must be thoughtfully aligned with the data and the mathematical methods applied during model development.The purpose of this chapter is to provide readers with a high-level yet comprehensive overview of a QSP modeling workflow, with an emphasis on the various challenges encountered in this process. The workflow is centered around the construction of ordinary differential equation models and may be extended beyond this framework. It includes the fundamentals of systematic literature reviews, the selection of appropriate structural model equations, the analysis of system behavior, model qualification, and the application of various types of model-based simulations. The chapter concludes with details on existing software options suitable for implementing the described methodologies.This workflow may serve as a valuable resource to both newcomers and experienced QSP modelers, offering an introduction to the field as well as operating procedures and references for routine analyses.

How ex ante policy evaluation supports circular city development: Amsterdam's mass timber construction policy

This article aimed to assess the potential impact of policy actions to support mass timber construction through an ex ante policy analysis in Amsterdam. Through a combination of policy coherence analysis and agent-based simulation, the study evaluates 130 policy actions, including 80 specific instruments, for the transition from traditional masonry to mass timber construction. The coherence analysis reveals a predominance of regulatory instruments (62%) and a lack of active economic measures (16%), which limits their impact on circular city development. The simulation tested three instruments - demolition notification, a mass timber subsidy proxy and a carbon tax proxy - to assess their individual and combined effectiveness. Isolated measures, such as material price adjustments, were found to be insufficient due to systemic inertia. However, the combination of subsidies and carbon taxes proves more effective, significantly increasing the uptake of mass timber construction as its cost is reduced and construction companies develop expertise. A key finding highlights the complementary role of recycled concrete in supporting mass timber construction, highlighting the need for integrated policies targeting both mass timber and secondary materials. Improving industry knowledge and expertise is identified as a transformative approach to reducing costs and overcoming barriers to adoption. This research is the first contribution to demonstrate the value of ex ante policy evaluation and agent-based simulation in formulating coherent and effective policies for circular city transitions. Policy makers in Amsterdam and other Dutch cities are advised to implement synergistic instruments, support local material reuse and invest in capacity building to achieve carbon neutrality and resource circularity in urban construction. The findings provide actionable guidance for Amsterdam and similar cities seeking to promote sustainable and circular urban environments.

Prediction of Pharmacokinetics for CYP3A4-Metabolized Drugs in Pediatrics and Geriatrics Using Dynamic Age-Dependent Physiologically Based Pharmacokinetic Models

Background/Objectives: The use of medicines in pediatrics and geriatrics is widespread. However, information on pharmacokinetics of therapeutic drugs mainly comes from healthy adults, and the pharmacokinetic parameters of therapeutic drugs in other age stages, including pediatrics and geriatrics, are limited. The aim of the study was to develop a dynamic age-dependent physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of drugs in humans at different ages. Method: The PBPK models characterizing dynamic age-dependence were developed in adults (20-59 years old) and 1000 virtual individuals were constructed. Four CYP3A substrates, namely midazolam, fentanyl, alfentanil and sufentanil, served as model drugs. Following validation using clinic observations in adult populations, the developed PBPK models were extrapolated to other age populations, such as pediatrics and geriatrics, via replacing their physiological parameters and pharmacokinetic parameters, such as organ volume, organ blood flow, clearance, fu,b and Kt:p. The simulations were compared with clinic observations in corresponding age populations. Midazolam served as an example, the dose transitions between adult pediatrics and adult geriatrics were visualized using the developed PBPK models. Results: Most of observed plasma concentrations fell within the 5th-95th percentile of the predicted values in the 1000 virtual individuals, and the predicted AUC0-t and Cmax were almost within between 0.5 and 2 times of the observations. The optimization of dosages in pediatrics and geriatrics were further documented. Conclusions: The developed PBPK model may be successfully used to predict the pharmacokinetics of CYP3A4-metabolized drugs in different age groups and to optimize their dosage regiments in pediatrics and geriatrics.

Exploring the swimming performance and the physical mechanisms ofTomopterislocomotion

Tomopterids are mesmerizing holopelagic swimmers. They use two modes of locomotion simultaneously: drag-based metachronal paddling and bodily undulation.Tomopterishas two rows of flexible, leg-like parapodia positioned on opposite sides of its body. Each row metachronally paddles out of phase to the other. Both paddling behaviors occur in concert with a lateral bodily undulation. However, when looked at independently, each mode appears in tension with the other. The direction of the undulatory wave is opposite of what one may expect for forward (FWD) swimming and appears to actively work act against the direction of swimming initiated by metachronal paddling. To investigate how these two modes of locomotion synergize to generate effective swimming, we created a self-propelled, fluid-structure interaction model of an idealizedTomopteris. We holistically explored swimming performance over a 3D mechanospace comprising parapodia length, paddling amplitude, and undulatory amplitude using a machine learning framework based on polynomial chaos expansions. Although undulatory amplitude minimally affected FWD swimming speeds, it helped mitigate the larger costs of transport that arise from either using more mechanically expensive (larger) paddling amplitudes and/or having longer parapodia.

Autoinjector optimization through cavitation response and severity minimization

Abrupt acceleration of the syringe of an autoinjector (AI) upon rod-plunger impact may induce undesired severe cavitation events and impose extraneous stresses upon the device, leading to device failure. Cavitation results from a rapid and significant pressure drop in a liquid, leading to the formation and growth of small vapor-filled cavities. Upon collapse, these cavities generate an intense shock wave that may lead to protein aggregation and device container damage and shatter. Since the maximum acceleration of the syringe depends upon the operating conditions of the AI, the severity of cavitation will likewise depend on the operating conditions of the AI. Likewise, injection time and ensuring proper needle displacement before drug release also depend on operating conditions, making optimization of the autoinjector a multiobjective optimization problem. Therefore, in this study, optimization of an autoinjector to limit cavitation severity is pursued via an experimentally validated computational model for cavitation in spring-driven autoinjectors. Our goal is to locate AI design configurations that balance maximizing device performance and patient comfort and minimizing the risks of device damage and severe cavitation upon actuation. Relevant parameters of interest are the drive spring force, air gap height, solution viscosity, friction between the rod and spring, frictional force on the plunger, rates of change of frictional force on the plunger, elasticity of plunger, viscosity of the plunger, and initial displacement between the plunger and the driving rod. The kinematics of the syringe barrel, needle displacement (travel distance) at the start of drug delivery, and injection time are gathered using an experimentally validated autoinjector kinematics model. At the same time, cavitation bubble dynamics are resolved using an experimentally validated cavitation model that takes the temporal displacement of the syringe and temporal air gap pressure as inputs. We use our experimentally validated models to explore the parameter space and understand the driving factors of our desired outcomes. Subsequently, we pose the design problem as a multi-objective optimization problem and develop a deep neural network surrogate model supplemented with iterative learning to speed up optimization. A variance-based sensitivity analysis was performed to determine the sensitivity and influence of design parameters on the outcomes, and the main contributors to the outcomes of interest were isolated. Using a multi-objective optimization framework, we located 300 + successful candidates and evaluated them through uncertainty analysis to identify three promising candidates that meet all criteria for drug viscosities of interest. Finally, we show that this methodology can be used to conduct hypothesis testing, leading to novel design configurations.

Spin parameter optimization for spin-polarized extended tight-binding methods

We present an optimization strategy for atom-specific spin-polarization constants within the spin-polarized GFN2-xTB framework, aiming to enhance the accuracy of molecular simulations. We compare a sequential and global optimization of spin parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine. Sensitivity analysis using Sobol indices guides the identification of the most influential parameters for a given reference dataset, allowing for a nuanced understanding of their impact on diverse molecular properties. In the case of the W4-11 dataset, substantial error reduction was achieved, demonstrating the potential of the optimization. Transferability of the optimized spin-polarization constants over different properties, however, is limited, as we demonstrate by applying the optimized parameters on a set of singlet-triplet gaps in carbenes. Further studies on ionization potentials and electron affinities highlight some inherent limitations of current extended tight-binding methods that can not be resolved by simple parameter optimization. We conclude that the significantly improved accuracy strongly encourages the present re-optimization of the spin-polarization constants, whereas the limited transferability motivates a property-specific optimization strategy.

Multiscale, mechanistic model of Rheumatoid Arthritis to enable decision making in late stage drug development

Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disease that affects about 0.1% to 2% of the population worldwide. Despite the development of several novel therapies, there is only limited benefit for many patients. Thus, there is room for new approaches to improve response to therapy, including designing better trials e.g., by identifying subpopulations that can benefit from specific classes of therapy and enabling reverse translation by analyzing completed clinical trials. We have developed an open-source, mechanistic multi-scale model of RA, which captures the interactions of key immune cells and mediators in an inflamed joint. The model consists of a treatment-naive Virtual Population (Vpop) that responds appropriately (i.e. as reported in clinical trials) to standard-of-care treatment options-Methotrexate (MTX) and Adalimumab (ADA, anti-TNF-α) and an MTX inadequate responder sub-population that responds appropriately to Tocilizumab (TCZ, anti-IL-6R) therapy. The clinical read-outs of interest are the American College of Rheumatology score (ACR score) and Disease Activity Score (DAS28-CRP), which is modeled to be dependent on the physiological variables in the model. Further, we have validated the Vpop by predicting the therapy response of TCZ on ADA Non-responders. This paper aims to share our approach, equations, and code to enable community evaluation and greater adoption of mechanistic models in drug development for autoimmune diseases.

Predicting removal of arsenic from groundwater by iron based filters using deep neural network models

Arsenic (As) contamination in drinking water has been highlighted for its environmental significance and potential health implications. Iron-based filters are cost-effective and sustainable solutions for As removal from contaminated water. Applying Machine Learning (ML) models to investigate and optimize As removal using iron-based filters is limited. The present study developed Deep Learning Neural Network (DLNN) models for predicting the removal of As and other contaminants by iron-based filters from groundwater. A small Original Dataset (ODS) consisting of 20 data points and 13 groundwater parameters was obtained from the field performances of 20 individual iron-amended ceramic filters. Cubic-spline interpolation (CSI) expanded the ODS, generating 1600 interpolated data points (IDPs) without duplication. The Bayesian optimization algorithm tuned the model hyper-parameters and IDPs in a Stratified fivefold Cross-Validation (CV) setup trained all the models. The models demonstrated reliable performances with the coefficient of determination (R2) 0.990-0.999 for As, 0.774-0.976 for Iron (Fe), 0.934-0.954 for Phosphorus (P), and 0.878-0.998 for predicting manganese (Mn) in the effluent. Sobol sensitivity analysis revealed that As (total order index (ST) = 0.563), P (ST = 0.441), Eh (ST = 0.712), and Temp (ST = 0.371) are the most sensitive parameters for the removal of As, Fe, P, and Mn. The comprehensive approach, from data expansion through DLNN model development, provides a valuable tool for estimating optimal As removal conditions from groundwater.

Analyzing parametric influences driving age-associated changes in absorption using a PBPK-GSA approach

The advanced age population may be susceptible to an increased risk of adverse effects due to increased drug exposure after oral dosing. Factors such as high-interindividual variability and lack of data has led to poor characterization of absorption's role in pharmacokinetic changes in this population. Physiologically based pharmacokinetic (PBPK) models are increasingly being used during the drug development process, as their unique qualities are advantageous in atypical scenarios such as drug-drug interactions or special populations such as older people. Along with relying on various sources of data, auxiliary tools including parameter estimation and sensitivity analysis techniques are employed to support model development and other applications. However, sensitivity analyses have mostly been limited to localized techniques in the majority of reported PBPK models using them. This is disadvantageous, since local sensitivity analyses are unsuitable for risk analysis, which require assessment of parametric interactions and proper coverage of the input space to better estimate and subsequently mitigate the effects of the phenomenon of interest. For this reason, this study seeks to integrate a global sensitivity analysis screening method with PBPK models based in PK-Sim® to characterize the consequences of potential changes in absorption that are often associated with advanced age. The Elementary Effects (Morris) method and visualization of the results are implemented in R and three model drugs representing Biopharmaceutical Classification System classes I-III that are expected to exhibit some sensitivity to three age-associated hypotheses were successfully tested.

A percolation-type criticality threshold controls immune protein coating of surfaces

When a material enters the body, it is immediately attacked by hundreds of proteins, organized into complex networks of binding interactions and reactions. How do such complex systems interact with a material, "deciding" whether to attack? We focus on the "complement" system of ∼40 blood proteins that bind microbes, nanoparticles, and medical devices, initiating inflammation. We show a sharp threshold for complement activation upon varying a fundamental material parameter, the surface density of potential complement attachment points. This sharp threshold manifests at scales spanning single nanoparticles to macroscale pathologies, shown here for diverse engineered and living materials. Computational models show these behaviors arise from a minimal subnetwork of complement, manifesting percolation-type critical transitions in the complement response. This criticality switch explains the "decision" of a complex signaling network to interact with a material, and elucidates the evolution and engineering of materials interacting with the body.

Modelling COVID-19 mutant dynamics: understanding the interplay between viral evolution and disease transmission dynamics

Understanding virus mutations is critical for shaping public health interventions. These mutations lead to complex multi-strain dynamics often under-represented in models. Aiming to understand the factors influencing variants' fitness and evolution, we explore several scenarios of virus spreading to gain qualitative insight into the factors dictating which variants ultimately predominate at the population level. To this end, we propose a two-strain stochastic model that accounts for asymptomatic transmission, mutations and the possibility of disease import. We find that variants with milder symptoms are likely to spread faster than those with severe symptoms. This is because severe variants can prompt affected individuals to seek medical help earlier, potentially leading to quicker identification and isolation of cases. However, milder or asymptomatic cases may spread more widely, making it harder to control the spread. Therefore, increased transmissibility of milder variants can still result in higher hospitalizations and fatalities due to widespread infection. The proposed model highlights the interplay between viral evolution and transmission dynamics. Offering a nuanced view of factors influencing variant spread, the model provides a foundation for further investigation into mitigating strategies and public health interventions.

Quantifying trade-offs between therapeutic efficacy and resistance dissemination for enrofloxacin dose regimens in cattle

The use of antimicrobial drugs in food-producing animals contributes to the selection pressure on pathogenic and commensal bacteria to become resistant. This study aims to evaluate the existence of trade-offs between treatment effectiveness, cost, and the dynamics of resistance in gut commensal bacteria. We developed a within-host ordinary differential equation model to track the dynamics of antimicrobial drug concentrations and bacterial populations in the site of infection (lung) and the gut. The model was parameterized to represent enrofloxacin treatment for bovine respiratory disease (BRD) caused by Pastereulla multocida in cattle. Three approved enrofloxacin dosing regimens were compared for their effects on resistance on P. multocida and commensal E. coli: 12.5 mg/kg and 7.5 mg/kg as a single dose, and 5 mg/kg as three doses. Additionally, we explored non-FDA-approved regimes. Our results indicated that both 12.5 mg/kg and 7.5 mg/kg as a single dose scenario increased the most the treatment costs and prevalence of P. multocida resistance in the lungs, while 5 mg/kg as three doses increased resistance in commensal E. coli bacteria in the gut the most out of the approved scenarios. A proposed non-FDA-approved scenario (7.5 mg/kg, two doses 24 h apart) showed low economic costs, minimal P. multocida, and moderate effects on resistant E. coli. Overall, the scenarios that decrease P. multocida, including resistant P. multocida did not coincide with those that decrease resistant E. coli the most, suggesting a trade-off between both outcomes. The sensitivity analysis suggests that bacterial populations were the most sensitive to drug conversion factors into plasma ( β ), elimination of the drug from the colon ( ϑ ), fifty percent sensitive bacteria (P. multocida) killing effect ( L s50 ), fifty percent of bacteria (E. coli) above ECOFF killing effect ( C r50 ), and net drug transfer rate in the lung ( γ ) parameters.

Real-World Application of a Quantitative Systems Pharmacology (QSP) Model to Predict Potassium Concentrations from Electronic Health Records: A Pilot Case towards Prescribing Monitoring of Spironolactone

Quantitative systems pharmacology (QSP) models are rarely applied prospectively for decision-making in clinical practice. We therefore aimed to operationalize a QSP model for potas-sium homeostasis to predict potassium trajectories based on spironolactone administrations. For this purpose, we proposed a general workflow that was applied to electronic health records (EHR) from patients treated in a German tertiary care hospital. The workflow steps included model exploration, local and global sensitivity analyses (SA), identifiability analysis (IA) of model parameters, and specification of their inter-individual variability (IIV). Patient covariates, selected parameters, and IIV then defined prior information for the Bayesian a posteriori prediction of individual potassium trajectories of the following day. Following these steps, the successfully operationalized QSP model was interactively explored via a Shiny app. SA and IA yielded five influential and estimable parameters (extracellular fluid volume, hyperaldosteronism, mineral corticoid receptor abundance, potassium intake, sodium intake) for Bayesian prediction. The operationalized model was validated in nine pilot patients and showed satisfactory performance based on the (absolute) average fold error. This provides proof-of-principle for a Prescribing Monitoring of potassium concentrations in a hospital system, which could suggest preemptive clinical measures and therefore potentially avoid dangerous hyperkalemia or hypokalemia.

Modeling transmission of avian influenza viruses at the human-animal-environment interface in Cuba

Introduction

The increasing geographical spread of highly pathogenic avian influenza viruses (HPAIVs) is of global concern due to the underlying zoonotic and pandemic potential of the virus and its economic impact. An integrated One Health model was developed to estimate the likelihood of Avian Influenza (AI) introduction and transmission in Cuba, which will help inform and strengthen risk-based surveillance activities.

Materials and methods

The spatial resolution used for the model was the smallest administrative district ("Consejo Popular"). The model was parameterised for transmission from wild birds to poultry and pigs (commercial and backyard) and then to humans. The model includes parameters such as risk factors for the introduction and transmission of AI into Cuba, animal and human population densities; contact intensity and a transmission parameter (β).

Results

Areas with a higher risk of AI transmission were identified for each species and type of production system. Some variability was observed in the distribution of areas estimated to have a higher probability of AI introduction and transmission. In particular, the south-western and eastern regions of Cuba were highlighted as areas with the highest risk of transmission.

Discussion

These results are potentially useful for refining existing criteria for the selection of farms for active surveillance, which could improve the ability to detect positive cases. The model results could contribute to the design of an integrated One Health risk-based surveillance system for AI in Cuba. In addition, the model identified geographical regions of particular importance where resources could be targeted to strengthen biosecurity and early warning surveillance.

Determining tissue distribution of the oral antileishmanial agent miltefosine: a physiologically-based pharmacokinetic modeling approach

Miltefosine (MTS) is the only approved oral drug for treating leishmaniasis caused by intracellular Leishmania parasites that localize in macrophages of the liver, spleen, skin, bone marrow, and lymph nodes. MTS is extensively distributed in tissues and has prolonged elimination half-lives due to its high plasma protein binding, slow metabolic clearance, and minimal urinary excretion. Thus, understanding and predicting the tissue distribution of MTS help assess therapeutic and toxicologic outcomes of MTS, especially in special populations, e.g., pediatrics. In this study, a whole-body physiologically-based pharmacokinetic (PBPK) model of MTS was built on mice and extrapolated to rats and humans. MTS plasma and tissue concentration data obtained by intravenous and oral administration to mice were fitted simultaneously to estimate model parameters. The resulting high tissue-to-plasma partition coefficient values corroborate extensive distribution in all major organs except the bone marrow. Sensitivity analysis suggests that plasma exposure is most susceptible to changes in fraction unbound in plasma. The murine oral-PBPK model was further validated by assessing overlay of simulations with plasma and tissue profiles obtained from an independent study. Subsequently, the murine PBPK model was extrapolated to rats and humans based on species-specific physiological and drug-related parameters, as well as allometrically scaled parameters. Fold errors for pharmacokinetic parameters were within acceptable range in both extrapolated models, except for a slight underprediction in the human plasma exposure. These animal and human PBPK models are expected to provide reliable estimates of MTS tissue distribution and assist dose regimen optimization in special populations.

Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

Implementation of mechanistic modeling and global sensitivity analysis (GSA) for design, optimization, and scale-up of a roller compaction process

The pharmaceutical industry has been shifting towards the application of mechanistic modeling to improve process robustness, enable scale-up, and reduce time to market. Modeling approaches have been well-developed for processes such as roller compaction, a continuous dry granulation process. Several mechanistic models/approaches have been documented with limited application to high drug-loaded formulations. In this study, the Johanson model was employed to optimize roller compaction processing and guide its scale-up for a high drug loaded formulation. The model was calibrated using a pilot-scale Minipactor and was validated for a commercial-scale Macropactor. Global sensitivity analysis (GSA) was implemented to determine the impact of process parameter variations (roll force, gap, speed) on a quality attribute [solid fraction (SF)]. The throughput method, which estimates SF values of ribbons using granule production rate, was also studied. The model predicted SF values for all 14 Macropactor batches within ± 0.04 SF. The throughput method estimated SF with ± 0.06 SF for 7 out of 11 batches. GSA confirmed that roll force had the largest impact on SF. This case study represents a process modeling approach to build quality into the products/processes and expands the use of mechanistic modeling during drug product development.

Quantitative systems pharmacology modeling of HER2-positive metastatic breast cancer for translational efficacy evaluation and combination assessment across therapeutic modalities

HER2-positive (HER2+) metastatic breast cancer (mBC) is highly aggressive and a major threat to human health. Despite the significant improvement in patients' prognosis given the drug development efforts during the past several decades, many clinical questions still remain to be addressed such as efficacy when combining different therapeutic modalities, best treatment sequences, interindividual variability as well as resistance and potential coping strategies. To better answer these questions, we developed a mechanistic quantitative systems pharmacology model of the pathophysiology of HER2+ mBC that was extensively calibrated and validated against multiscale data to quantitatively predict and characterize the signal transduction and preclinical tumor growth kinetics under different therapeutic interventions. Focusing on the second-line treatment for HER2+ mBC, e.g., antibody-drug conjugates (ADC), small molecule inhibitors/TKI and chemotherapy, the model accurately predicted the efficacy of various drug combinations and dosing regimens at the in vitro and in vivo levels. Sensitivity analyses and subsequent heterogeneous phenotype simulations revealed important insights into the design of new drug combinations to effectively overcome various resistance scenarios in HER2+ mBC treatments. In addition, the model predicted a better efficacy of the new TKI plus ADC combination which can potentially reduce drug dosage and toxicity, while it also shed light on the optimal treatment ordering of ADC versus TKI plus capecitabine regimens, and these findings were validated by new in vivo experiments. Our model is the first that mechanistically integrates multiple key drug modalities in HER2+ mBC research and it can serve as a high-throughput computational platform to guide future model-informed drug development and clinical translation.

In silico model development and optimization of in vitro lung cell population growth

Tissue engineering predominantly relies on trial and error in vitro and ex vivo experiments to develop protocols and bioreactors to generate functional tissues. As an alternative, in silico methods have the potential to significantly reduce the timelines and costs of experimental programs for tissue engineering. In this paper, we propose a methodology to formulate, select, calibrate, and test mathematical models to predict cell population growth as a function of the biochemical environment and to design optimal experimental protocols for model inference of in silico model parameters. We systematically combine methods from the experimental design, mathematical statistics, and optimization literature to develop unique and explainable mathematical models for cell population dynamics. The proposed methodology is applied to the development of this first published model for a population of the airway-relevant bronchio-alveolar epithelial (BEAS-2B) cell line as a function of the concentration of metabolic-related biochemical substrates. The resulting model is a system of ordinary differential equations that predict the temporal dynamics of BEAS-2B cell populations as a function of the initial seeded cell population and the glucose, oxygen, and lactate concentrations in the growth media, using seven parameters rigorously inferred from optimally designed in vitro experiments.

Quantifying trade-offs between therapeutic efficacy and resistance dissemination for enrofloxacin dose regimens in cattle

The use of antimicrobial drugs in food-producing animals increases the selection pressure on pathogenic and commensal bacteria to become resistant. This study aims to evaluate the existence of trade-offs between treatment effectiveness, cost, and the dissemination of resistance in gut commensal bacteria. We developed a within-host ordinary differential equation model to track the dynamics of antimicrobial drug concentrations and bacterial populations in the site of infection (lung) and the gut. The model was parameterized to represent enrofloxacin treatment for bovine respiratory disease (BRD) caused by Pastereulla multocida in cattle. Three approved enrofloxacin dosing regimens were compared for their effects on resistance on P. multocida and commensal E. coli: 12.5 mg/kg and 7.5 mg/kg as a single dose, and 5 mg/kg as three doses. Additionally, we explored non-approved regimes. Our results indicated that both 12.5 mg/kg and 7.5 mg/kg as a single dose scenario increased the most the treatment costs and prevalence of P. multocida resistance in the lungs, while 5 mg/kg as three doses increased resistance in commensal E. coli bacteria in the gut the most out of the approved scenarios. A proposed scenario (7.5 mg/kg, two doses 24 hours apart) showed low economic costs, minimal P. multocida, and moderate effects on resistant E. coli. Overall, the scenarios that decrease P. multocida, including resistant P. multocida did not coincide with the scenarios that decrease resistant E. coli the most, suggesting a trade-off between both outcomes. The sensitivity analysis indicates that bacterial populations were the most sensitive to drug conversion factors into plasma (β), elimination of the drug from the colon (υ), fifty percent sensitive bacteria (P. multocida) killing effect (Ls50), fifty percent of bacteria (E. coli) above ECOFF killing effect (Cr50), and net drug transfer rate in the lung (γ) parameters.

Nanobody-mediated neutralization of candidalysin prevents epithelial damage and inflammatory responses that drive vulvovaginal candidiasis pathogenesis

Candida albicans can cause mucosal infections in humans. This includes oropharyngeal candidiasis, which is commonly observed in human immunodeficiency virus infected patients, and vulvovaginal candidiasis (VVC), which is the most frequent manifestation of candidiasis. Epithelial cell invasion by C. albicans hyphae is accompanied by the secretion of candidalysin, a peptide toxin that causes epithelial cell cytotoxicity. During vaginal infections, candidalysin-driven tissue damage triggers epithelial signaling pathways, leading to hyperinflammatory responses and immunopathology, a hallmark of VVC. Therefore, we proposed blocking candidalysin activity using nanobodies to reduce epithelial damage and inflammation as a therapeutic strategy for VVC. Anti-candidalysin nanobodies were confirmed to localize around epithelial-invading C. albicans hyphae, even within the invasion pocket where candidalysin is secreted. The nanobodies reduced candidalysin-induced damage to epithelial cells and downstream proinflammatory responses. Accordingly, the nanobodies also decreased neutrophil activation and recruitment. In silico mathematical modeling enabled the quantification of epithelial damage caused by candidalysin under various nanobody dosing strategies. Thus, nanobody-mediated neutralization of candidalysin offers a novel therapeutic approach to block immunopathogenic events during VVC and alleviate symptoms.IMPORTANCEWorldwide, vaginal infections caused by Candida albicans (VVC) annually affect millions of women, with symptoms significantly impacting quality of life. Current treatments are based on anti-fungals and probiotics that target the fungus. However, in some cases, infections are recurrent, called recurrent VVC, which often fails to respond to treatment. Vaginal mucosal tissue damage caused by the C. albicans peptide toxin candidalysin is a key driver in the induction of hyperinflammatory responses that fail to clear the infection and contribute to immunopathology and disease severity. In this pre-clinical evaluation, we show that nanobody-mediated candidalysin neutralization reduces tissue damage and thereby limits inflammation. Implementation of candidalysin-neutralizing nanobodies may prove an attractive strategy to alleviate symptoms in complicated VVC cases.

Machine learning analysis of socioeconomic drivers in urban ozone pollution in Chinese cities

The escalation of ground-level ozone (O3) pollution presents a significant challenge to the sustainable growth of Chinese cities. This study utilizes advanced machine learning algorithms to investigate the intricate interplay between urban socioeconomic growth and O3 levels. Surpassing traditional environmental chemistry, it assesses the effectiveness of these algorithms in interpreting socioeconomic and environmental data, while elucidating urban development's environmental impacts from a novel socioeconomic perspective. Key findings indicate that factors such as urban infrastructure, industrial activities, and demographic dynamics significantly influence O3 pollution. The study highlights the particular sensitivity of urban public transportation and population density, each exerting a unique and substantial effect on O3 levels. Additionally, the research identifies nuanced interactions among these factors, indicating a complex web of influences on urban O3 pollution. These interactions suggest that the impact of individual socioeconomic elements on O3 pollution is interdependent, being either amplified or mitigated by other factors. The study emphasizes the crucial need to integrate socioeconomic variables into urban O3 pollution strategies, advocating for policies tailored to each city's distinct characteristics, informed by the detailed analysis provided by machine learning. This approach is essential for developing effective and nuanced urban pollution management strategies.

Improving the interpretability of deep reinforcement learning in urban drainage system operation

Deep reinforcement learning (DRL) has been increasingly used as an adaptive and efficient solution for real-time control (RTC) of the urban drainage system (UDS). Despite the promising potential of DRL, it is a black-box model whose control logic and control consequences are difficult to be understood and evaluated. This leads to issues of interpretability and poses risks in practical applications. This study develops an evaluation framework to analyze and improve the interpretability of DRL-based UDS operation. The framework includes three analysis methods: Sobol sensitivity analysis, tree-based surrogate modelling, and conditional probability analysis. It is validated using two different DRL approaches, i.e., deep Q-learning network (DQN) and proximal policy optimization (PPO), which are trained to reduce combined sewer overflow (CSO) discharges and flooding in a real-world UDS. According to the results, the two DRLs have been shown to perform better than a rule-based control system that is currently being used. Sobol sensitivity analysis indicates that DQN is particularly sensitive to the flow of links and rainfall, while PPO is sensitive to all the states. Tree-based surrogate models effectively reveal the control logic behind the DRLs and indicate that PPO is more comprehensible but DQN is more forward-looking. Conditional probability analysis demonstrates the potential control consequences of the DRLs and identifies three situations where the DRLs are ineffective: a) the storage of UDS is fully utilized; b) peak flows have already passed through actuators; c) a substantial amount of water enters one location simultaneously. The proposed evaluation framework enhances the interpretability of DRL in UDS operations, fostering trust and confidence from operators, stakeholders, and regulators.

A compartment model for subcutaneous injection of monoclonal antibodies

Despite the growing popularity of subcutaneous (SC) administration for monoclonal antibodies (mAbs), there remains a limited understanding of the significance of mAb transport rate constants within the interstitial space and the lymphatic system on their pharmacokinetics. To bridge this knowledge gap, we introduce a compartmental model for subcutaneously administered mAbs. Our model differentiates FcRn-expressing cells across various sites, and the model predictions agree with experimental data from both human and rat studies. Our findings indicate that the time to reach the maximum mAb concentration in the plasma, denoted by Tmax, displays a weak positive correlation with mAb half-life and a negligible correlation with bioavailability. In contrast, the half-life of mAbs exhibits a strong positive correlation with bioavailability. Moreover, the rate of mAb transport from lymph to plasma significantly affects the mAb half-life. Increasing the transport rates of mAbs from the injection site to the lymph or from lymph to plasma enhances bioavailability. These insights, combined with our compartmental model, contribute to a deeper understanding of the pharmacokinetics of subcutaneously administered mAbs.

Nitrate concentrations and health risks in cow milk from Iran: Insights from deterministic, probabilistic, and AI modeling

Excessive nitrate consumption has been linked to potential health risks in humans. Thus, understanding nitrate levels in staple foods such as cow milk can provide insights into their health implications. This study meticulously examined nitrate concentrations in 70 cow milk samples from traditional and industrialized cattle farming systems in Fars province, Iran. A combination of deterministic modeling, a probabilistic approach, and six artificial intelligence algorithms was employed to determine health risk assessments. The data disclosed average nitrate concentrations of 32.63 mg/L in traditional farming and 34.95 mg/L in industrialized systems, presenting no statistically significant difference (p > 0.05). The Hazard Quotient (HQ) was deployed to gauge potential health threats, underscoring heightened vulnerability in children, who exhibited HQ values ranging from 0.05 to 0.58 (mean = 0.19) in contrast to adults, whose values spanned 0.01 to 0.16 (mean = 0.05). Monte Carlo simulations enriched the risk assessment, demarcating the 5th and 95th percentile nitrate concentrations for children at 0.07 and 0.39, respectively. In children, pivotal interactions that influenced HQ encompassed those between nitrate concentration and consumption rate, as well as nitrate concentration and body weight. The interplay between nitrate concentration and consumption rate was most consequential for the adult cohort. Among the algorithms assessed for HQ prediction, Gaussian Naive Bayes (GNB) was optimal for children and eXtreme Gradient Boosting (XGB) for adults, with nitrate concentration being a key determinant. The results underscore the imperative for rigorous oversight of milk nitrate concentrations, highlighting the enhanced susceptibility of children and emphasizing the need for preventive strategies and enlightened consumption.

Minimally sufficient experimental design using identifiability analysis

Mathematical models are increasingly being developed and calibrated in tandem with data collection, empowering scientists to intervene in real time based on quantitative model predictions. Well-designed experiments can help augment the predictive power of a mathematical model but the question of when to collect data to maximize its utility for a model is non-trivial. Here we define data as model-informative if it results in a unique parametrization, assessed through the lens of practical identifiability. The framework we propose identifies an optimal experimental design (how much data to collect and when to collect it) that ensures parameter identifiability (permitting confidence in model predictions), while minimizing experimental time and costs. We demonstrate the power of the method by applying it to a modified version of a classic site-of-action pharmacokinetic/pharmacodynamic model that describes distribution of a drug into the tumor microenvironment (TME), where its efficacy is dependent on the level of target occupancy in the TME. In this context, we identify a minimal set of time points when data needs to be collected that robustly ensures practical identifiability of model parameters. The proposed methodology can be applied broadly to any mathematical model, allowing for the identification of a minimally sufficient experimental design that collects the most informative data.

PBPK modeling to predict the pharmacokinetics of pantoprazole in different CYP2C19 genotypes

Pantoprazole is used to treat gastroesophageal reflux disease (GERD), maintain healing of erosive esophagitis (EE), and control symptoms related to Zollinger-Ellison syndrome (ZES). Pantoprazole is mainly metabolized by cytochrome P450 (CYP) 2C19, converting to 4'-demethyl pantoprazole. CYP2C19 is a genetically polymorphic enzyme, and the genetic polymorphism affects the pharmacokinetics and/or pharmacodynamics of pantoprazole. In this study, we aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of pantoprazole in populations with various CYP2C19 metabolic activities. A comprehensive investigation of previous reports and drug databases was conducted to collect the clinical pharmacogenomic data, physicochemical data, and disposition properties of pantoprazole, and the collected data were used for model establishment. The model was evaluated by comparing the predicted plasma concentration-time profiles and/or pharmacokinetic parameters (AUC and Cmax) with the clinical observation results. The predicted plasma concentration-time profiles in different CYP2C19 phenotypes properly captured the observed profiles. All fold error values for AUC and Cmax were included in the two-fold range. Consequently, the minimal PBPK model for pantoprazole related to CYP2C19 genetic polymorphism was properly established and it can predict the pharmacokinetics of pantoprazole in different CYP2C19 phenotypes. The present model can broaden the insight into the individualized pharmacotherapy for pantoprazole.

A discrete-time nutrients-phytoplankton-oysters mathematical model of a bay ecosystem

Populations are generally censused daily, weekly, monthly or annually. In this paper, we introduce a discrete-time nutrients-phytoplankton-oysters (NPO) model that describes the interactions of nutrients, phytoplankton and oysters in a bay ecosystem. We compute the threshold parameter R N for persistence of phytoplankton with or without oysters. When R N < 1 , then both phytoplankton and oysters populations go extinct. However, when R N > 1 , we show that the model may exhibit two scenarios: (1) a locally asymptotically stable equilibrium with positive values of nutrients and phytoplankton with oysters missing, and (2) a locally asymptotically stable interior equilibrium with positive values of nutrients, phytoplankton and oysters. We use sensitivity analysis to study the impact of human and environmental factors on the model. We use examples to illustrate that some human activities and environmental factors can force the interior equilibrium to undergo a Neimark-Sacker bifurcation which generates phytoplankton blooms with oscillations in oysters population and nutrients level.

Polynomial chaos expansion based sensitivity analysis of predicted knee reactions-assessing the influence of the primary ligaments in distraction based models

Computational knee models have shown that predicted condylar reactions are sensitive to the utilized ligament mechanical parameters. These models, however, are computationally expensive with multiple sources of uncertainty. Traditional uncertainty analysis using Monte-Carlo (MC) inspired methods are costly to perform. The purpose of this study was to use two example calibrated knee models to compare quasi-MC versus polynomial chaos expansion (PCE) sensitivity analyses of predicted condylar reactions that included uncertainty in the mechanical parameters of the ligaments. PCE was practically identical versus quasi-MC with 95% and 98% reductions in model evaluations for analyses with 10 and 6 uncertain variables, respectively.

Reconstruction of exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol using computational fluid dynamics, physiologically based toxicokinetics and statistical modeling

OBJECTIVES

This study employed computational fluid dynamics (CFD), physiologically based toxicokinetics (PBTK), and statistical modeling to reconstruct exposure to methylene diphenyl-4,4'-diisocyanate (MDI) aerosol. By utilizing a validated CFD model, human respiratory deposition of MDI aerosol in different workload conditions was investigated, while a PBTK model was calibrated using experimental rat data. Biomonitoring data and Markov Chain Monte Carlo (MCMC) simulation were utilized for exposure assessment.

RESULTS

Deposition fraction of MDI in the respiratory tract at the light, moderate, and heavy activity were 0.038, 0.079, and 0.153, respectively. Converged MCMC results as the posterior means and prior values were obtained for several PBTK model parameters. In our study, we calibrated a rat model to investigate the transport, absorption, and elimination of 4,4'-MDI via inhalation exposure. The calibration process successfully captured experimental data in the lungs, liver, blood, and kidneys, allowing for a reasonable representation of MDI distribution within the rat model. Our calibrated model also represents MDI dynamics in the bloodstream, facilitating the assessment of bioavailability. For human exposure, we validated the model for recent and long-term MDI exposure using data from relevant studies.

CONCLUSION

Our computational models provide reasonable insights into MDI exposure, contributing to informed risk assessment and the development of effective exposure reduction strategies.

Physiologically based pharmacokinetic (PBPK) modelling of oral drug absorption in older adults - an AGePOP review

The older population consisting of persons aged 65 years or older is the fastest-growing population group and also the major consumer of pharmaceutical products. Due to the heterogenous ageing process, this age group shows high interindividual variability in the dose-exposure-response relationship and, thus, a prediction of drug safety and efficacy is challenging. Although physiologically based pharmacokinetic (PBPK) modelling is a well-established tool to inform and confirm drug dosing strategies during drug development for special population groups, age-related changes in absorption are poorly accounted for in current PBPK models. The purpose of this review is to summarise the current state-of-knowledge in terms of physiological changes with increasing age that can influence the oral absorption of dosage forms. The capacity of common PBPK platforms to incorporate these changes and describe the older population is also discussed, as well as the implications of extrinsic factors such as drug-drug interactions associated with polypharmacy on the model development process. The future potential of this field will rely on addressing the gaps identified in this article, which can subsequently supplement in-vitro and in-vivo data for more robust decision-making on the adequacy of the formulation for use in older adults and inform pharmacotherapy.

In silico clinical trial evaluating lisdexamfetamine's and methylphenidate's mechanism of action computational models in an attention-deficit/hyperactivity disorder virtual patients' population

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is an impairing psychiatric condition with the stimulants, lisdexamfetamine (LDX), and methylphenidate (MPH), as the first lines pharmacological treatment.

Methods

Herein, we applied a novel in silico method to evaluate virtual LDX (vLDX) and vMPH as treatments for ADHD applying quantitative systems pharmacology (QSP) models. The objectives were to evaluate the model's output, considering the model characteristics and the information used to build them, to compare both virtual drugs' efficacy mechanisms, and to assess how demographic (age, body mass index, and sex) and clinical characteristics may affect vLDX's and vMPH's relative efficacies.

Results and Discussion

We molecularly characterized the drugs and pathologies based on a bibliographic search, and generated virtual populations of adults and children-adolescents totaling 2,600 individuals. For each virtual patient and virtual drug, we created physiologically based pharmacokinetic and QSP models applying the systems biology-based Therapeutic Performance Mapping System technology. The resulting models' predicted protein activity indicated that both virtual drugs modulated ADHD through similar mechanisms, albeit with some differences. vMPH induced several general synaptic, neurotransmitter, and nerve impulse-related processes, whereas vLDX seemed to modulate neural processes more specific to ADHD, such as GABAergic inhibitory synapses and regulation of the reward system. While both drugs' models were linked to an effect over neuroinflammation and altered neural viability, vLDX had a significant impact on neurotransmitter imbalance and vMPH on circadian system deregulation. Among demographic characteristics, age and body mass index affected the efficacy of both virtual treatments, although the effect was more marked for vLDX. Regarding comorbidities, only depression negatively impacted both virtual drugs' efficacy mechanisms and, while that of vLDX were more affected by the co-treatment of tic disorders, the efficacy mechanisms of vMPH were disturbed by wide-spectrum psychiatric drugs. Our in silico results suggested that both drugs could have similar efficacy mechanisms as ADHD treatment in adult and pediatric populations and allowed raising hypotheses for their differential impact in specific patient groups, although these results require prospective validation for clinical translatability.

Risk of Legionellosis in residential areas around farms irrigating with municipal wastewater

The conservation of freshwater is of both global and national importance, and in the United States, agriculture is one of the largest consumers of this resource. Reduction of the strain farming puts on local surface or groundwater is vital for ensuring resilience in the face of climate change, and one possible option is to irrigate with a combination of freshwater and reclaimed water from municipal wastewater treatment facilities. However, this wastewater can contain pathogens that are harmful to human health, such as Legionella pneumophila, which is a bacterium that can survive aerosolization and airborne transportation and cause severe pneumonia when inhaled. To assess an individual adult's risk of infection with L. pneumophila from a single exposure to agricultural spray irrigation, a quantitative microbial risk assessment was conducted for a scenario of spray irrigation in central Illinois, for the growing seasons in 2017, 2018, and 2019. The assessment found that the mean risk of infection for a single exposure exceeded the safety threshold of 10^-6 infections/exposure up to 1 km from a low-pressure irrigator and up to 2 km from a high-pressure irrigator, although no median risk exceeded the threshold for any distance or irrigator pressure. These findings suggest that spray irrigation with treated municipal wastewater could be a viable option for reducing freshwater consumption in Midwest farming, as long as irrigation on windy days is avoided and close proximity to the active irrigator is limited.

Global Sensitivity Analysis in Life-Cycle Assessment of Early-Stage Technology using Detailed Process Simulation: Application to Dialkylimidazolium Ionic Liquid Production

The ability to assess the environmental performance of early-stage technologies at production scale is critical for sustainable process development. This paper presents a systematic methodology for uncertainty quantification in life-cycle assessment (LCA) of such technologies using global sensitivity analysis (GSA) coupled with a detailed process simulator and LCA database. This methodology accounts for uncertainty in both the background and foreground life-cycle inventories, and is enabled by lumping multiple background flows, either downstream or upstream of the foreground processes, in order to reduce the number of factors in the sensitivity analysis. A case study comparing the life-cycle impacts of two dialkylimidazolium ionic liquids is conducted to illustrate the methodology. Failure to account for the foreground process uncertainty alongside the background uncertainty is shown to underestimate the predicted variance of the end-point environmental impacts by a factor of two. Variance-based GSA furthermore reveals that only few foreground and background uncertain parameters contribute significantly to the total variance in the end-point environmental impacts. As well as emphasizing the need to account for foreground uncertainties in LCA of early-stage technologies, these results illustrate how GSA can empower more reliable decision-making in LCA.

Gaunt T. Assessing the effects of hyperparameters on knowledge graph embedding quality

Embedding knowledge graphs into low-dimensional spaces is a popular method for applying approaches, such as link prediction or node classification, to these databases. This embedding process is very costly in terms of both computational time and space. Part of the reason for this is the optimisation of hyperparameters, which involves repeatedly sampling, by random, guided, or brute-force selection, from a large hyperparameter space and testing the resulting embeddings for their quality. However, not all hyperparameters in this search space will be equally important. In fact, with prior knowledge of the relative importance of the hyperparameters, some could be eliminated from the search altogether without significantly impacting the overall quality of the outputted embeddings. To this end, we ran a Sobol sensitivity analysis to evaluate the effects of tuning different hyperparameters on the variance of embedding quality. This was achieved by performing thousands of embedding trials, each time measuring the quality of embeddings produced by different hyperparameter configurations. We regressed the embedding quality on those hyperparameter configurations, using this model to generate Sobol sensitivity indices for each of the hyperparameters. By evaluating the correlation between Sobol indices, we find substantial variability in the hyperparameter sensitivities between knowledge graphs with differing dataset characteristics as the probable cause of these inconsistencies. As an additional contribution of this work we identify several relations in the UMLS knowledge graph that may cause data leakage via inverse relations, and derive and present UMLS-43, a leakage-robust variant of that graph.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00732-5.

Feasibility of neural network metamodels for emulation and sensitivity analysis of radionuclide transport models

In this paper we compare the outputs of neural network metamodels with numerical solutions of differential equation models in modeling cesium-137 transportation in sand. Convolutional neural networks (CNNs) were trained with differential equation simulation results. Training sets of various sizes (from 5120 to 163,840) were used. First order and total order Sobol methods were applied to both models in order to test the feasibility of neural network metamodels for sensitivity analysis of a radionuclide transport model. Convolutional neural networks were found to be capable of emulating the differential equation models with high accuracy when the training set size was 40,960 or higher. Neural network metamodels also gave similar results compared with the numerical solutions of the partial differential equation model in sensitivity analysis.

Optimal vaccine allocation for the control of sexually transmitted infections

The burden of sexually transmitted infections (STIs) poses a challenge due to its large negative impact on sexual and reproductive health worldwide. Besides simple prevention measures and available treatment efforts, prophylactic vaccination is a powerful tool for controlling some viral STIs and their associated diseases. Here, we investigate how prophylactic vaccines are best distributed to prevent and control STIs. We consider sex-specific differences in susceptibility to infection, as well as disease severity outcomes. Different vaccination strategies are compared assuming distinct budget constraints that mimic a scarce vaccine stockpile. Vaccination strategies are obtained as solutions to an optimal control problem subject to a two-sex Kermack-McKendrick-type model, where the control variables are the daily vaccination rates for females and males. One important aspect of our approach relies on conceptualizing a limited but specific vaccine stockpile via an isoperimetric constraint. We solve the optimal control problem via Pontryagin's Maximum Principle and obtain a numerical approximation for the solution using a modified version of the forward-backward sweep method that handles the isoperimetric budget constraint in our formulation. The results suggest that for a limited vaccine supply ([Formula: see text]-[Formula: see text] vaccination coverage), one-sex vaccination, prioritizing females, appears to be more beneficial than the inclusion of both sexes into the vaccination program. Whereas, if the vaccine supply is relatively large (enough to reach at least [Formula: see text] coverage), vaccinating both sexes, with a slightly higher rate for females, is optimal and provides an effective and faster approach to reducing the prevalence of the infection.

Predictive machine learning approaches for the microstructural behavior of multiphase zirconium alloys

Zirconium alloys are widely used in harsh environments characterized by high temperatures, corrosivity, and radiation exposure. These alloys, which have a hexagonal closed packed (h.c.p.) structure thermo-mechanically degrade, when exposed to severe operating environments due to hydride formation. These hydrides have a different crystalline structure, than the matrix, which results in a multiphase alloy. To accurately model these materials at the relevant physical scale, it is necessary to fully characterize them based on a microstructural fingerprint, which is defined here as a combination of features that include hydride geometry, parent and hydride texture and crystalline structure of these multiphase alloys. Hence, this investigation will develop a reduced order modeling approach, where this microstructural fingerprint is used to predict critical fracture stress levels that are physically consistent with microstructural deformation and fracture modes. Machine Learning (ML) methodologies based on Gaussian Process Regression, random forests, and multilayer perceptrons (MLP) were used to predict material fracture critical stress states. MLPs, or neural networks, had the highest accuracy on held-out test sets across three predetermined strain levels of interest. Hydride orientation, grain orientation or texture, and hydride volume fraction had the greatest effect on critical fracture stress levels and had partial dependencies that were highly significant, and in comparison hydride length and hydride spacing have less effects on fracture stresses. Furthermore, these models were also used accurately predicted material response to nominal applied strains as a function of the microstructural fingerprint.

Bayesian PBPK modeling using R/Stan/Torsten and Julia/SciML/Turing.Jl

Physiologically-based pharmacokinetic (PBPK) models are mechanistic models that are built based on an investigator's prior knowledge of the in vivo system of interest. Bayesian inference incorporates an investigator's prior knowledge of parameters while using the data to update this knowledge. As such, Bayesian tools are well-suited to infer PBPK model parameters using the strong prior knowledge available while quantifying the uncertainty on these parameters. This tutorial demonstrates a full population Bayesian PBPK analysis framework using R/Stan/Torsten and Julia/SciML/Turing.jl.

Cell state-directed therapy - epigenetic modulation of gene transcription demonstrated with a quantitative systems pharmacology model of temozolomide

Cancer therapy continues to be plagued by modest therapeutic advances. This is particularly evident in glioblastoma multiforme (GBM) wherein treatment failures are attributed to intratumoral heterogeneity (ITH), a dynamic process of cell state transitions or plasticity. To address ITH, we introduce the concept of cell state-directed (CSD) therapy through a quantitative systems pharmacology model of temozolomide (TMZ), a cornerstone of GBM drug therapy. The model consisting of multiple modules incorporated an epigenetic-based gene transcription-translation module that enabled CSD therapy. Numerous model simulations were conducted to demonstrate the potential impact of CSD therapy on TMZ activity. The simulations included those based on global sensitivity analyses to identify fragile nodes - MDM2 and XIAP - in the network, and also how an epigenetic modifier (birabresib) could overcome a mechanism of TMZ resistance. The positive results of CSD therapy on TMZ activity supports continued efforts to develop CSD therapy as a new anticancer approach.

Mucin and Agitation Shape Predation of Escherichia coli by Lytic Coliphage

The ability of bacteriophage (phage), abundant within the gastrointestinal microbiome, to regulate bacterial populations within the same micro-environment offers prophylactic and therapeutic opportunities. Bacteria and phage have both been shown to interact intimately with mucin, and these interactions invariably effect the outcomes of phage predation within the intestine. To better understand the influence of the gastrointestinal micro-environment on phage predation, we employed enclosed, in vitro systems to investigate the roles of mucin concentration and agitation as a function of phage type and number on bacterial killing. Using two lytic coliphage, T4 and PhiX174, bacterial viability was quantified following exposure to phages at different multiplicities of infection (MOI) within increasing, physiological levels of mucin (0-4%) with and without agitation. Comparison of bacterial viability outcomes demonstrated that at low MOI, agitation in combination with higher mucin concentration (>2%) inhibited phage predation by both phages. However, when MOI was increased, PhiX predation was recovered regardless of mucin concentration or agitation. In contrast, only constant agitation of samples containing a high MOI of T4 demonstrated phage predation; briefly agitated samples remained hindered. Our results demonstrate that each phage-bacteria pairing is uniquely influenced by environmental factors, and these should be considered when determining the potential efficacy of phage predation under homeostatic or therapeutic circumstances.

Design of a temperature-feedback controlled automated magnetic hyperthermia therapy device

Introduction

Magnetic hyperthermia therapy (MHT) is a minimally invasive adjuvant therapy capable of damaging tumors using magnetic nanoparticles exposed radiofrequency alternating magnetic fields. One of the challenges of MHT is thermal dose control and excessive heating in superficial tissues from off target eddy current heating.

Methods

We report the development of a control system to maintain target temperature during MHT with an automatic safety shutoff feature in adherence to FDA Design Control Guidance. A proportional-integral-derivative (PID) control algorithm was designed and implemented in NI LabVIEW^®. A standard reference material copper wire was used as the heat source to verify the controller performance in gel phantom experiments. Coupled electromagnetic thermal finite element analysis simulations were used to identify the initial controller gains.

Results

Results showed that the PID controller successfully achieved the target temperature control despite significant perturbations.

Discussion and Conclusion

Feasibility of PID control algorithm to improve efficacy and safety of MHT was demonstrated.

Search and rescue system-of-systems influence degree evaluation of aviation equipment based on simulation

Search and rescue (SAR) is an important part of joint operations, and also one of the key supports for ensuring combat effectiveness. Aviation equipment is a major component of SAR action. Therefore, the SAR capability of aviation equipment has become the key to affecting the overall SAR action. This paper proposes the concept of the system of systems influence degree (SoSID) and conducts a scientific quantitative evaluation to quantitatively measure the effect of aviation equipment used in SAR. First, according to the characteristics of SAR action in threat environments, the SAR capability of aviation equipment is analyzed, and an indicator decomposition hierarchy model based on this SAR capability is proposed. Second, based on the above model, the DECIDE (destroy, execute, cost, implement, defend, evade) SoSID evaluation model is proposed. Third, a comparative test is designed and a sensitivity analysis is conducted based on the sobol power sensitivity (SPS) analysis method to obtain the index sensitivity of the SAR capability. The sensitivity is then ranked to obtain key indicators. Finally, we build a simulation test environment to obtain multiple test plans for comparison and verify the rationality of the index decomposition hierarchy model and the SoSID evaluation model as well as the effectiveness of the SPS analysis method through analysis of the simulation results.

Predicting the systemic exposure and lung concentration of nafamostat using physiologically-based pharmacokinetic modeling

Nafamostat has been actively studied for its neuroprotective activity and effect on various indications, such as coronavirus disease 2019 (COVID-19). Nafamostat has low water solubility at a specific pH and is rapidly metabolized in the blood. Therefore, it is administered only intravenously, and its distribution is not well known. The main purposes of this study are to predict and evaluate the pharmacokinetic (PK) profiles of nafamostat in a virtual healthy population under various dosing regimens. The most important parameters were assessed using a physiologically based pharmacokinetic (PBPK) approach and global sensitivity analysis with the Sobol sensitivity analysis. A PBPK model was constructed using the SimCYP^® simulator. Data regarding the in vitro metabolism and clinical studies were extracted from the literature to assess the predicted results. The model was verified using the arithmetic mean maximum concentration (C_max), the area under the curve from 0 to the last time point (AUC_0-t), and AUC from 0 to infinity (AUC_0-∞) ratio (predicted/observed), which were included in the 2-fold range. The simulation results suggested that the 2 dosing regimens for the treatment of COVID-19 used in the case reports could maintain the proposed effective concentration for inhibiting severe acute respiratory syndrome coronavirus 2 entry into the plasma and lung tissue. Global sensitivity analysis indicated that hematocrit, plasma half-life, and microsomal protein levels significantly influenced the systematic exposure prediction of nafamostat. Therefore, the PBPK modeling approach is valuable in predicting the PK profile and designing an appropriate dosage regimen.