Mechanistic Model Research Articles

Formulation development, characterization, and mechanistic PBPK modeling of metoclopramide loaded halloysite nanotube (HNT) based drug-in-adhesive type transdermal drug delivery system

Formulation development, characterization, and mechanistic PBPK modeling of metoclopramide loaded halloysite nanotube (HNT) based drug-in-adhesive type transdermal drug delivery system

A Gaussian Process Regression and Wavelet Transform Time Series approaches to modeling Influenza A.

The global spread of Influenza A viruses is worsening economic and social challenges. Various mechanistic models have been developed to understand the virus's spread and evaluate intervention effectiveness. This study aimed to model the temporal dynamics of Influenza A using Gaussian Process Regression (GPR) and wavelet transform approaches. The study employed Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and Wavelet Power Spectrum to analyze time-series data from 2009 to 2023. The GPR model, known for its non-parametric Bayesian nature, effectively captured non-linear trends in the Influenza A data, while wavelet transforms provided insights into frequency and time-localized characteristics. The integration of GPR with DWT denoising techniques demonstrated superior performance in forecasting Influenza A cases compared to traditional models like Auto Regressive Integrated Moving Averages (ARIMA) and Exponential Smoothing (ETS) using Holt-Winter method. The study identified significant anomalies in Influenza A cases, corresponding to known pandemic events and seasonal variations. These findings highlight the effectiveness of combining wavelet transform analysis with GPR in understanding and predicting infectious disease patterns, offering valuable insights for public health planning and intervention strategies. The research recommends extending this approach to other respiratory viruses to assess its broader applicability.

Chromothripsis in cancer.

Chromothripsis is a mutational phenomenon in which a single catastrophic event generates extensive rearrangements of one or a few chromosomes. This extreme form of genome instability has been detected in 30-50% of cancers. Studies conducted in the past few years have uncovered insights into how chromothripsis arises and deciphered some of the cellular and molecular consequences of chromosome shattering. This Review discusses the defining features of chromothripsis and describes its prevalence across different cancer types as indicated by the manifestations of chromothripsis detected in human cancer samples. The different mechanistic models of chromothripsis, derived from in vitro systems that enable causal inference through experimental manipulation, are discussed in detail. The contribution of chromothripsis to cancer development, the selective advantages that cancer cells might gain from chromothripsis, the evolutionary trajectories of chromothriptic tumours, and the potential vulnerabilities and therapeutic opportunities presented by chromothriptic cells are also highlighted.

Artificial intelligence modeling of biomarker-based physiological age: Impact on phase 1 drug-metabolizing enzyme phenotypes.

Age and aging are important predictors of health status, disease progression, drug kinetics, and effects. The purpose was to develop ensemble learning-based physiological age (PA) models for evaluating drug metabolism. National Health and Nutrition Examination Survey (NHANES) data were modeled with ensemble learning to obtain two PA models, PA-M1 and PA-M2. PA-M1 included body composition, blood and urine biomarkers, and disease variables as predictors. PA-M2 had blood and urine-derived variables as predictors. Activity phenotypes for cytochrome-P450 (CYP) CYP2E1, CYP1A2, CYP2A6, xanthine oxidase (XO), and N-acetyltransferase-2 (NAT-2) and telomere attrition were assessed. Bayesian networks were used to obtain mechanistic systems pharmacology model structures for PA. The study included n = 22,307 NHANES participants (51.5% female, mean age 46.0 years, range: 18-79 years). The PA-M1 and PA-M2 distributions had greater dispersion across age strata with a right skew for younger age strata and a left skew for older age strata. There was no evidence of algorithmic bias based on sex or race/ethnicity. Klotho, lean body mass, glycohemoglobin, and systolic blood pressure were the top four predictors for PA-M1. Glycohemoglobin, serum creatinine, total cholesterol, and urine creatinine were the top four predictors for PA-M2. The models also performed satisfactorily in independent validation. Model-predicted PA was associated with CYP2E1, CYP1A2, CYP2A6, XO, and NAT-2 activity. Telomere attrition was associated with greater PA-M1 and PA-M2. Ensemble learning models provide robust assessments of PA from easily obtained blood and urine biomarkers. PA is associated with Phase I drug-metabolizing enzyme phenotypes.

Action-At-A-Distance in DNA Mismatch Repair: Mechanistic Insights and Models for How DNA and Repair Proteins Facilitate Long-Range Communication

Many DNA metabolic pathways, including DNA repair, require the transmission of signals across long stretches of DNA or between DNA molecules. Solutions to this signaling challenge involve various mechanisms: protein factors can travel between these sites, loop DNA between sites, or form oligomers that bridge the spatial gaps. This review provides an overview of how these paradigms have been used to explain DNA mismatch repair, which involves several steps that require action-at-a-distance. Here, we describe these models in detail and how current data fit into these descriptions. We also outline regulation steps that remain unanswered in how the action is communicated across long distances along a DNA contour in DNA mismatch repair.

Climate change and plant pathogens: Understanding dynamics, risks and mitigation strategies

AbstractClimate change is reshaping the interactions between plants and pathogens, exerting profound effects on global agricultural systems. Elevated tropospheric ozone levels due to climate change hinder plant photosynthesis and increase vulnerability to biotic invasion. The prevailing atmospheric conditions, including temperature and humidity, profoundly influence fungal pathogenesis, as each stage of a pathogen's life cycle is intricately linked to temperature variations. Likewise, climate change alters bacterial behaviour, fostering increased production of extracellular polysaccharides by plant‐pathogenic bacteria in warmer temperatures. Heat‐adapted bacteria, such as Burkholderia glumea and Ralstonia solanacearum, are emerging as significant global threats as temperature rise. Viruses, too, respond dynamically to climate shifts, with certain species favouring warmer climates for replication, resulting in expanded geographical ranges and modified transmission patterns. Nematodes, formidable constraints in crop production, exhibit temperature‐dependent life cycles and would have potentially accelerated proliferation and distribution as global warming progresses. Molecular‐level changes in pathogenesis, induced by temperature fluctuations, influence various pathogens, thereby impacting their virulence and interactions with host plants. Modelling studies predict changes in disease risks and distributions under future climate scenarios, highlighting the necessity of integrating climate data into crop disease models for accurate forecasts. Mechanistic and observational models illustrate pathogen behaviours amidst changing environmental conditions, providing crucial insights into future disease dynamics. In addition, controlled experiments study disease responses under simulated climate scenarios, underscoring the urgency for comprehensive research to devise effective resistance strategies against severe plant diseases.

Estimates of resource use in the public-sector health-care system and the effect of strengthening health-care services in Malawi during 2015-19: a modelling study (Thanzi La Onse).

In all health-care systems, decisions need to be made regarding allocation of available resources. Evidence is needed for these decisions, especially in low-income countries. We aimed to estimate how health-care resources provided by the public sector were used in Malawi during 2015-19 and to estimate the effects of strengthening health-care services. For this modelling study, we used the Thanzi La Onse model, an individual-based simulation model. The scope of the model was health care provided by the public sector in Malawi during 2015-19. Health-care services were delivered during health-care system interaction (HSI) events, which we characterised as occurring at a particular facility level and requiring a particular number of appointments. We developed mechanistic models for the causes of death and disability that were estimated to account for approximately 81% of deaths and approximately 72% of disability-adjusted life-years (DALYs) in Malawi during 2015-19, according to the Global Burden of Disease (GBD) estimates; we computed DALYs incurred in the population as the sum of years of life lost and years lived with disability. The disease models could interact with one another and with the underlying properties of each person. Each person in the Thanzi La Onse model had specific properties (eg, sex, district of residence, wealth percentile, smoking status, and BMI, among others), for which we measured distribution and evolution over time using demographic and health survey data. We also estimated the effect of different types of health-care system improvement. We estimated that the public-sector health-care system in Malawi averted 41·2 million DALYs (95% UI 38·6-43·8) during 2015-19, approximately half of the 84·3 million DALYs (81·5-86·9) that the population would otherwise have incurred. DALYs averted were heavily skewed to children aged 0-4 years due to services averting DALYs that would be caused by acute lower respiratory tract infection, HIV or AIDS, malaria, or neonatal disorders. DALYs averted among adults were mostly attributed to HIV or AIDS and tuberculosis. Under a scenario whereby each appointment took the time expected and health-care workers did not work for longer than contracted, the health-care system in Malawi during 2015-19 would have averted only 19·1 million DALYs (95% UI 17·1-22·4), suggesting that approximately 21·3 million DALYS (20·0-23·6) of total effect were derived through overwork of health-care workers. If people becoming ill immediately accessed care, all referrals were successfully completed, diagnostic accuracy of health-care workers was as good as possible, and consumables (ie, medicines) were always available, 28·2% (95% UI 25·7-30·9) more DALYS (ie, 12·2 million DALYs [95% UI 10·9-13·8]) could be averted. The health-care system in Malawi provides substantial health gains with scarce resources. Strengthening interventions could potentially increase these gains, so should be a priority for investigation and investment. An individual-based simulation model of health-care service delivery is valuable for health-care system planning and strengthening. The Wellcome Trust, UK Research and Innovation, the UK Medical Research Council, and Community Jameel.

Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown.

Pattern‐moving‐based dynamic description and optimal control for non‐Newtonian mechanical systems with generalized cell mapping

AbstractThis article focuses on the problem of modelling and control for a non‐Newtonian mechanical system that may be subject to the statistical law instead of the traditional Newtonian principles mechanics. A discrete method, synthesizing the generalized cell mapping (GCM) together with dynamic programming (DP), with inverse search strategy, using pattern moving theory (PMT) framework, was proposed. The basic idea is to describe and control the system's dynamic peculiarity utilizing pattern class variable in ‘pattern moving space’. First, a few prior concepts were reviewed, including PMT, cell mapping, and optimal control for this kind system. Then, we articulated a cross‐mapping method to analyze the system dynamic behaviour, which takes into account the computational and statistical properties of pattern class variable simultaneously. For system optimal control, the improved GCM and cost function were performed to determine the discrete optimal control table (DOCT) in accordance with dynamic programming and inverse search. Finally, simulation results of two cases demonstrate the efficiency and practicality of the proposed approach. The study has resulted in a solution of describing and controlling based on pattern class variable for non‐Newtonian mechanical systems, and its main objective was to give a different perspective in term of research into non mechanistic principles modelling and application of nonlinear systems.

Abstract 4139141: Predicted efficacy of inclisiran on cardiovascular outcomes in lower extremity peripheral artery disease: results of the In Silico Sirius study

Introduction: Symptomatic lower extremity peripheral artery disease (PAD) patients have the highest risk of cardiovascular (CV) outcomes among patients with established atherosclerotic cardiovascular disease (ASCVD). Inclisiran, an siRNA targeting PCSK9 mRNA, reduces LDL-C levels. In SIRIUS in silico trial (NCT05974345), inclisiran was predicted to lower CV events in ASCVD patients. Research question/Hypothesis: SIRIUS in silico study aims to predict the efficacy of inclisiran on CV outcomes in subgroups of patients with or without PAD. Methods: The SIRIUS in silico trial was conducted using a knowledge-based mechanistic computational model of ASCVD applied to a virtual ASCVD population with LDL-C ≥ 70 mg/dL. Each virtual patient is its own control. This model was previously calibrated and validated before running the trial. SIRIUS compared the efficacy of inclisiran vs placebo on top of High Intensity (HI) statins with or without ezetimibe on 3-Point-MACE defined as a composite of time to first occurrence of CV death, nonfatal myocardial infarction (MI) or nonfatal ischemic stroke (IS) over 5 years. Occurrence of major acute limb events (MALE) was also individually assessed in time-to-first-event analyses. Results: Among 204, 691 virtual SIRIUS ASCVD patients, 28,072 (13.7%) had PAD. At 5 years, the mean predicted percentage reduction in LDL-C with inclisiran as compared to placebo was -49.3% and -49.8% in patients with or without PAD, respectively. The predicted rate of 3P-MACE in the inclisiran arm was consistently lower than in the placebo arm in patients with PAD (17.62% vs 22.88%; Hazard Ratio (HR): 0.75 medium uncertainty) and in patients without PAD (10.33% vs 13.64%; HR: 0.75 low uncertainty). Compared to placebo, inclisiran was also predicted to consistently reduce MALE in patients with or without PAD (2.71% vs 4.11%; HR: 0.65 medium uncertainty and 0.19% vs 0.29%; HR: 0.66 high uncertainty respectively). Conclusion: Pending the results of ORION-4 and VICTORION-2-P, this first In Silico trial in virtual PAD patients predicted a potential effect of inclisiran on 3P-MACE and MALE reductions over 5 years follow-up.

Experimentally-driven mathematical model to understand the effects of matrix deprivation in breast cancer metastasis.

Normal epithelial cells receive proper signals for growth and survival from attachment to the underlying extracellular matrix (ECM). They perceive detachment from the ECM as a stress and die - a phenomenon termed as 'anoikis'. However, metastatic cancer cells acquire anoikis-resistance and circulate through the blood and lymphatics to seed metastasis. Under normal (adherent) growth conditions, the serine-threonine protein kinase Akt stimulates protein synthesis and cell growth, maintaining an anabolic state in the cancer cell. In contrast, previously we showed that the stress due to matrix deprivation is sensed by yet another serine-threonine kinase, AMP-activated protein kinase (AMPK), that inhibits anabolic pathways while promoting catabolic processes. We illustrated a switch from Akthigh/AMPKlow in adherent condition to AMPKhigh/Aktlow in matrix-detached condition, with consequent metabolic switching from an anabolic to a catabolic state, which aids cancer cell stress-survival. In this study, we utilized these experimental data and developed a deterministic ordinary differential equation (ODE)-based mechanistic mathematical model to mimic attachment-detachment signaling network. To do so, we used the framework of insulin-glucagon signaling with consequent metabolic shifts to capture the pathophysiology of matrix-deprived state in breast cancer cells. Using the developed metastatic breast cancer signaling (MBCS) model, we identified perturbation of several signaling proteins such as IRS, PI3K, PKC, GLUT1, IP3, DAG, PKA, cAMP, and PDE3 upon matrix deprivation. Further, in silico molecular perturbations revealed that several feedback/crosstalks like DAG to PKC, PKC to IRS, S6K1 to IRS, cAMP to PKA, and AMPK to Akt are essential for the metabolic switching in matrix-deprived cancer cells. AMPK knockdown simulations identified a crucial role for AMPK in maintaining these adaptive changes. Thus, this mathematical framework provides insights on attachment-detachment signaling with metabolic adaptations that promote cancer metastasis.

Abstract 4134364: Predicted Low-Density Lipoprotein Cholesterol and Cardiovascular Outcomes Lowering With Inclisiran in Patients With or Without stroke: Insights from SIRIUS in silico trial.

Introduction: Inclisiran, an siRNA, targeting PCSK9 mRNA, reduces LDL-c levels. In SIRIUS in silico trial (NCT05974345), inclisiran was predicted to lower cardiovascular (CV) events in virtual patients with atherosclerotic cardiovascular disease (ASCVD). Research question: This analysis predicted the potential efficacy of inclisiran on CV outcomes in virtual patients with or without prior ischemic stroke (IS). Methods: The SIRIUS trial was conducted using a calibrated and validated knowledge-based mechanistic computational model of ASCVD applied to a virtual population with LDL-C ≥ 70 mg/dL. Each virtual patient was its own control. SIRIUS compared the efficacy of inclisiran vs placebo on top of High Intensity (HI) statin with or without ezetimibe on 3-Point-MACE defined as a composite of time to first occurrence of CV death, nonfatal myocardial infarction (MI) or nonfatal IS over 5 years in patients with or without prior IS. Occurrence of fatal and non-fatal (IS) was also individually assessed in time-to-first-event analyses. Results: Among 204,691 virtual SIRIUS ASCVD patients, 39 371 (19%) had prior IS. At 5 years, the predicted mean percentage reduction in LDL-C with inclisiran as compared to placebo was –49.17% and –49.88% in patients with or without prior IS respectively. Patients with prior IS were at higher risk of 3P-MACE than patients without IS both with placebo and inclisiran (17.01% vs 14.41% with placebo and 13.44% vs 10.83% with inclisiran). However, the predicted rate of 3P-MACE in the inclisiran arm was consistently lower than in the placebo arm for both prior IS and no prior IS (HR 0.78 medium uncertainty and 0.74 low uncertainty respectively). Compared to placebo, inclisiran was also predicted to consistently reduce fatal and non-fatal IS in patients with or without prior IS (5.45% vs 7.22%; HR: 0.75 medium uncertainty and 1.87% vs 2.54%; HR: 0.73 medium uncertainty respectively). Conclusion: SIRIUS provides insights into the potential efficacy of inclisiran on CV events suggesting a substantial 3P-MACE and fatal and non-fatal IS reduction in ASCVD patients including those with prior IS, several years before the availability of results from ongoing outcomes trials (ORION-4, VICTORION-2P).

An empirical model of carbon-ion relative biological effectiveness based on the linear correlation between radiosensitivity to photons and carbon ions.

To develop an empirical model to predict carbon ion (C-ion) relative biological effectiveness (RBE). 

Approach. We used published cell survival data comprising 360 cell line/energy combinations to characterize the linear energy transfer (LET) dependence of cell radiosensitivity parameters describing the dose required to achieve a given survival level, e.g. 5% (D5%), which are linearly correlated between photon and C-ion radiations. Based on the LET response of the metrics D5% and D37%, we constructed a model containing four free parameters that predicts cells' linear quadratic model (LQM) survival curve parameters for C-ions, αCand βC, from the reference LQM parameters for photons, αXand βX, for a given C-ion LET value. We fit our model's free parameters to the training dataset and assessed its accuracy via leave-one out cross-validation. We further compared our model to the local effect model (LEM) and the microdosimetric kinetic model (MKM) by comparing its predictions against published predictions made with those models for clinically relevant LET values in the range of 23-107 keV/μm. 

Main Results. Our model predicted C-ion RBE within ±7%-15% depending on cell line and dose which was comparable to LEM and MKM for the same conditions. 

Significance. Our model offers comparable accuracy to the LEM or MKM but requires fewer input parameters and is less computationally expensive and whose implementation is so simple we provide it coded into a spreadsheet. Thus, our model can serve as a pragmatic alternative to these mechanistic models in cases where cell-specific input parameters cannot be obtained, the models cannot be implemented, or for which their computational efficiency is paramount.

Using unmanned aerial vehicles to estimate body volume at scale for ecological monitoring

Abstract Demographic data are essential to construct mechanistic models to understand how populations change over time and in response to global threats like climate change. Existing demographic data are either lacking or insufficient for many species, particularly those that are challenging to obtain direct measurements from that can be used to estimate demographic rates, like marine mammals. A method for collecting accurate demographic data to construct robust demographic models at scale would fill this knowledge gap for difficult‐to‐access species. We introduce a novel, non‐invasive method to estimate the 3D body size (volume) of pinnipeds (seals, sea lions and walruses) that will allow monitoring at high spatial and temporal scales. Our method integrates 3D structure‐from‐motion photogrammetry data collected via planned flight missions using off‐the‐shelf, multirotor unmanned aerial vehicles (UAVs). We apply and validate this method on the grey seal Halichoerus grypus, a pinniped species that spends much of its time at sea but is predictably observable during its annual breeding season. We investigate the optimal ground sampling distance (GSD) for surveys by calculating the success rates and accuracy of volume estimates of individuals at different altitudes. Based on current technology, we establish an optimal GSD of at least 0.8 cm px−1 for animals similar in size to UK grey seals (~1.2–2.5 m length), making our method reproducible and applicable to other species. We found volume estimates were accurate and could be successfully estimated for up to 68% of hauled‐out seals in study areas. Our method accurately estimates individual body volume of pinnipeds in a time‐ and cost‐effective manner while minimising disturbance. While the approach is applied to pinnipeds here, the method could be adapted to further taxa that are otherwise challenging to obtain direct measurements from. Our proposed approach therefore has the potential to fill demographic research gaps, which will improve our ability to protect and conserve species into the future.

Data-Driven Prediction of COF in Wet Friction Components: A Model Development and Interpretability Analysis

Abstract Predicting the coefficient of friction (COF) is essential for enhancing the efficiency and reliability of mechanical systems. Nevertheless, traditional mechanistic models relying on fixed values or fitted curves fail to accurately capture this complexity. To address this issue, this paper proposes a model for predicting the COF of wet friction components using an extreme gradient boosting (XGBoost) algorithm optimized by the sparrow search algorithm (SSA). This model effectively captures the nonlinear relationships among relative speed, pressure, temperature, and COF. As a result, the proposed SSA-XGBoost model exhibits excellent predictive performance with an RMSE of only 0.063, and 88.3% of the COF predictions have a relative error of less than 1%, significantly outperforming other deep learning algorithms. Additionally, to enhance the understanding of the COF prediction results for wet friction components, SHAP interpretability analysis is used to explore the influence of relative speed, pressure, and temperature on the predicted COF values.

Research on temperature prediction model of molten steel of tundish in continuous casting

To achieve the desired superheat of molten steel during the continuous casting process, optimization of process parameters such as molten steel temperature in ladle furnace, casting speed, and baking temperature is necessary. Therefore, obtaining the superheat corresponding to these process parameters in advance is particularly important. To address this issue, a model for predicting the temperature of molten steel in the tundish during continuous casting is designed. The model adopts a combined modeling approach of mechanistic model and data model. To address the issue of the mechanism model’s inability to capture the variation of the lining’s thermal parameters, this article improves the traditional physics-informed neural network (PINN) algorithm. It combines the constraints from both the forward and inverse problems, allowing for obtaining solutions to the equations while capturing the variation of equation parameters. Actual data from multiple casting sequences at a steel plant are collected to validate the accuracy and interpretability of the model. The results show that the error of the model is about 2.1k which has better accuracy compared to pure mechanistic model and pure data model. Additionally, it can capture the variation patterns of tundish lining thermal parameters under different operating conditions. Therefore, the model designed in this article can provide both profound physical interpretation ability and more practical predictions of molten steel temperature.

IMPROVING METHODS FOR PREDICTING THE STRUCTURE OF GAS-LIQUID FLOW IN HORIZONTAL PIPES

Analysis of existing research on the hydrodynamics of two-phase mixtures allows us to trace the main trends in the development of engineering calculation methods, as well as identify the most promising approaches for developing algorithms for predicting the patterns of gas-liquid flows in horizontal pipes. Predicting the patterns of two-phase flows is necessary to solve a number of applied problems related to the design of field pipeline systems, monitoring of operational parameters during the transportation of well products, calculation of hydraulic pressure drop in pipes, etc. Conventionally, in creating methods for predicting flow regimes in horizontal pipes, there are stages based on empirical approaches, mechanistic modeling and unification of hydrodynamic criteria for assessing the processes of patterns transformations of two-phase flows. Approaches based on the unification of hydrodynamic models of gas-liquid flows are noted as promising directions in the development of hydrodynamic forecasting criteria. The principles of unification consist in bringing to uniformity a mathematical apparatus capable of predicting all possible patterns of gas-liquid flow in vertical, inclined and horizontal pipes based on known volumetric flow rates of liquid and gas. The article proposes the improvement of known unification approaches in the development of criteria for predicting the annular and dispersed-bubble patterns of gas-liquid flow in field pipes. The verification of the obtained algorithms for pattern prediction of annular and dispersed-bubble modes of horizontal gas-liquid flow was carried out.

Multi-strain modeling of influenza vaccine effectiveness in older adults and its dependence on antigenic distance

Influenza vaccine effectiveness (VE) varies seasonally due to host, virus and vaccine characteristics. To investigate how antigenic matching and dosage impact VE, we developed a mechanistic knowledge-based mathematical model. Immunization with a split vaccine is modeled for exposure to A/H1N1 or A/H3N2 virus strains. The model accounts for cross-reactivity of immune cells elicited during previous immunizations with new antigens. We simulated vaccine effectiveness (sVE) of high dose (HD) versus standard dose (SD) vaccines in the older population, from 2011 to 2022. We find that sVE is highly dependent on antigenic matching and that higher dosage improves immunogenicity, activation and memory formation of immune cells. In alignment with clinical observations, the HD vaccine performs better than the SD vaccine in all simulations, supporting the use of the HD vaccine in the older population. This model could be adapted to predict the impact of alternative virus strain selection on clinical outcomes in future influenza seasons.

Dual function of overexpressing plasma membrane H+-ATPase in balancing carbon-water use.

Stomata respond slowly to changes in light when compared with photosynthesis, undermining plant water-use efficiency (WUE). We know much about stomatal mechanics, yet efforts to accelerate stomatal responsiveness have been limited despite the breadth of potential targets for manipulation. Here, we use mechanistic modeling to establish a hierarchy of putative targets affecting stomatal kinetics. Counterintuitively, modeling predicted that overexpressing plasma membrane H+-ATPases could speed stomata and enhance WUE under fluctuating light, even though overexpressed H+-ATPases is known to promote stomatal opening and reduce WUE in the steady state. Experiments validated the prediction, implicating an unexpected role of the H+-ATPases in improving WUE under fluctuating light. It suggests that H+-ATPases have a dual function, acting as a facilitator of carbon assimilation and water use, depending on the light conditions. These findings highlight the importance of integrating in silico modeling with experiments in future efforts toward enhancing stomatal function.

Quantitative pharmacology of dual-targeted bicistronic CAR-T-cell therapy using multiscale mechanistic modeling.

Despite the initial success of single-targeted chimeric-antigen receptor (CAR) T-cell therapy in hematological malignancies, its long-term effectiveness is often hindered by antigen heterogeneity and escape. As a result, there is a growing interest in cell therapies targeting multiple antigens (≥2). However, the dose-exposure-response relationship and specific factors influencing the pharmacology of dual-targeted CAR-T-cell therapy remain unclear. In this study, we have developed a multiscale cellular kinetic-pharmacodynamic (CK-PD) model using case studies from CD19/CD22 and GPRC5D/BCMA autologous CAR-Ts. Initially, an invitro tumor-killing model characterized the impact of individual binder affinities and their contribution to overall potency across varying (1) effector: target (ET) ratios and (2) tumor-associated antigen (TAA) expressing cell lines. Subsequently, an integrated CK-PD model was developed in pediatric acute lymphoblastic leukemia (ALL) patients, which accounted for CAR-T-cell product composition and relative antigen abundance in patients' tumor burden to characterize patient-level multiphasic cellular kinetics using multiple bioanalytical assays (e.g., flow and qPCR-based readouts). Global sensitivity analysis highlighted relative antigen expression, maximum killing rate constant, and CAR-T expansion rate constant as major determinants for observed exposure of dual-targeted CAR-T-cell therapy. This modeling framework could facilitate dose-optimization and construct refinement for dual-targeted bicistronic CAR-T-cell therapies, serving as a valuable tool for both forward and reverse translation in drug development.