Selected aspects of avascular tumor growth reproduced by a hybrid model of cell dynamics and chemical kinetics

We here propose a hybrid computational framework to reproduce and analyze aspects of the avascular progression of a generic solid tumor. Our method first employs an individual-based approach to represent the population of tumor cells, which are distinguished in viable and necrotic agents. The active part of the disease is in turn differentiated according to a set of metabolic states. We then describe the spatio-temporal evolution of the concentration of oxygen and of tumor-secreted proteolytic enzymes using partial differential equations (PDEs). A differential equation finally governs the local degradation of the extracellular matrix (ECM) by the malignant mass. Numerical realizations of the model are run to reproduce tumor growth and invasion in a number scenarios that differ for cell properties (adhesiveness, duplication potential, proteolytic activity) and/or environmental conditions (level of tissue oxygenation and matrix density pattern). In particular, our simulations suggest that tumor aggressiveness, in terms of invasive depth and extension of necrotic tissue, can be reduced by (i) stable cell-cell contact interactions, (ii) poor tendency of malignant agents to chemotactically move upon oxygen gradients, and (iii) presence of an overdense matrix, if coupled by a disrupted proteolytic activity of the disease.

Assessing mutualistic metacommunity capacity by integrating spatial and interaction networks

We develop a spatially realistic model of mutualistic metacommunities that exploits the joint structure of spatial and interaction networks. Assuming that all species have the same colonisation and extinction parameters, this model exhibits a sharp transition between stable non-null equilibrium states and a global extinction state. This behaviour allows defining a threshold on colonisation/extinction parameters for the long-term metacommunity persistence. This threshold, the 'metacommunity capacity', extends the metapopulation capacity concept and can be calculated from the spatial and interaction networks without needing to simulate the whole dynamics. In several applications we illustrate how the joint structure of the spatial and the interaction networks affects metacommunity capacity. It results that a weakly modular spatial network and a power-law degree distribution of the interaction network provide the most favourable configuration for the long-term persistence of a mutualistic metacommunity. Our model that encodes several explicit ecological assumptions should pave the way for a larger exploration of spatially realistic metacommunity models involving multiple interaction types.

On a two-strain epidemic mathematical model with vaccination

In this paper, we study mathematically a two strains epidemic model taking into account non-monotonic incidence rates and vaccination strategy. The model contains seven ordinary differential equations that illustrate the interaction between the susceptible, the vaccinated, the exposed, the infected and the removed individuals. The model has four equilibrium points, namely, disease free equilibrium, endemic equilibrium with respect to the first strain, endemic equilibrium with respect to the second strain and the endemic equilibrium with respect to both strains. The global stability of the equilibria has been demonstrated using some suitable Lyapunov functions. The basic reproduction number is found depending on the first strain reproduction number R 0 1 and the second reproduction number R 0 2 . We have shown that the disease dies out when the basic reproduction number is less than unity. It was remarked that the global stability of the endemic equilibria depends, on the strain basic reproduction number and on the strain inhibitory effect reproduction number. We have also observed that the strain with high basic reproduction number will dominate the other strain. Finally, the numerical simulations are presented in the last part of this work to support our theoretical results. We notice that our suggested model has some limitations and does not predicting the long-term dynamics for some reproduction numbers cases.

Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model

AIMS

To develop a semi-mechanistic hepatic compartmental model to predict the effects of rifampicin, a known inducer of CYP3A4 enzyme, on the metabolism of five drugs, in the hope of informing dose adjustments to avoid potential drug-drug interactions.

METHODS

A search was conducted for DDI studies on the interactions between rifampicin and CYP substrates that met specific criteria, including the availability of plasma concentration-time profiles, physical and absorption parameters, pharmacokinetic parameters, and the use of healthy subjects at therapeutic doses. The semi-mechanistic model utilized in this study was improved from its predecessors, incorporating additional parameters such as population data (specifically for Chinese and Caucasians), virtual individuals, gender distribution, age range, dosing time points, and coefficients of variation.

RESULTS

Optimal parameters were identified for our semi-mechanistic model by validating it with clinical data, resulting in a maximum difference of approximately 2-fold between simulated and observed values. PK data of healthy subjects were used for most CYP3A4 substrates, except for gilteritinib, which showed no significant difference between patients and healthy subjects. Dose adjustment of gilteritinib co-administered with rifampicin required a 3-fold increase of the initial dose, while other substrates were further tuned to achieve the desired drug exposure.

CONCLUSIONS

The pharmacokinetic parameters AUCR and CmaxR of drugs metabolized by CYP3A4, when influenced by Rifampicin, were predicted by the semi-mechanistic model to be approximately twice the empirically observed values, which suggests that the semi-mechanistic model was able to reasonably simulate the effect. The doses of four drugs adjusted via simulation to reduce rifampicin interaction.

Performance of Eight Infliximab Population Pharmacokinetic Models in a Cohort of Dutch Children with Inflammatory Bowel Disease

BACKGROUND AND OBJECTIVE

Efficacy of infliximab in children with inflammatory bowel disease can be enhanced when serum concentrations are measured and further dosing is adjusted to achieve and maintain a target concentration. Use of a population pharmacokinetic model may help to predict an individual's infliximab dose requirement. The aim of this study was to evaluate the predictive performance of available infliximab population pharmacokinetic models in an independent cohort of Dutch children with inflammatory bowel disease.

METHODS

In this retrospective study, we used data of 70 children with inflammatory bowel disease (443 infliximab concentrations) to evaluate eight models that focused on infliximab pharmacokinetic models in individuals with inflammatory bowel disease, preferably aged ≤ 18 years. Predictive performance was evaluated with prior predictions (based solely on patient-specific covariates) and posterior predictions (based on covariates and infliximab trough concentrations). Model accuracy and precision were calculated with relative bias and relative root mean square error and we determined the classification accuracy at the trough concentration target of ≥ 5 mg/L.

RESULTS

The population pharmacokinetic model by Fasanmade was identified to be most appropriate for the total dataset (relative bias before/after therapeutic drug monitoring: -20.7%/11.2% and relative root mean square error before/after therapeutic drug monitoring: 84.1%/51.6%), although differences between models were small and several were deemed suitable for clinical use. For the Fasanmade model, sensitivity and specificity for maximum posterior predictions for the next infliximab trough concentration to be ≥ 5 mg/L were respectively 83.5% and 80% with an area under the receiver operating characteristic curve of 0.870.

CONCLUSIONS

In our paediatric cohort, various models provided acceptable predictive performance, with the Fasanmade model deemed most suitable for clinical use. Model-informed precision dosing can therefore be expected to help to maintain infliximab trough concentrations in the target range.

Collective behaviors of animal groups may stem from visual lateralization-Tending to obtain information through one eye

Animal groups exhibit various captivating movement patterns, which manifest as intricate interactions among group members. Several models have been proposed to elucidate collective behaviors in animal groups. These models achieve a certain degree of efficacy; however, inconsistent experimental findings suggest insufficient accuracy. Experiments have shown that some organisms employ a single information channel and visual lateralization to glean knowledge from other individuals in collective movements. In this study, we consider individuals' visual lateralization and a single information channel and develop a self-propelled particle model to describe the collective behavior of large groups. The results suggest that homogeneous visual lateralization gives the group a strong sense of cohesiveness, thereby enabling diverse collective behaviors. As the overlapping field grows, the cohesiveness gradually dissipates. Inconsistent visual lateralization among group members can reduce the cohesiveness of the group, and when there is a high degree of heterogeneity in visual lateralization, the group loses their cohesiveness. This study also examines the influence of visual lateralization heterogeneity on specific formations, and the results indicate that the directional migration formation is responsive to such heterogeneity. We propose an information network to portray the transmission of information within groups, which explains the cohesiveness of groups and the sensitivity of the directional migration formation.

Delay epidemic models determined by latency, infection, and immunity duration

We propose new single and two-strain epidemic models represented by systems of delay differential equations and based on the number of newly exposed individuals. Transitions between exposed, infectious, recovered, and back to susceptible compartments are determined by the corresponding time delays. Existence and positiveness of solutions are proved. Reduction of delay differential equations to integral equations allows the analysis of stationary solutions and their stability. In the case of two strains, they compete with each other, and the strain with a larger individual basic reproduction number dominates the other one. However, if the basic reproduction number exceeds some critical values, stationary solution loses its stability resulting in periodic time oscillations. In this case, both strains are present and their dynamics is not completely determined by the basic reproduction numbers but also by other parameters. The results of the work are illustrated by comparison with data on seasonal influenza.

Biomechanical Aspects in Bone Tumor Engineering

In the past decades, anticancer drug development brought the field of tumor engineering to a new level by the need of robust test systems. Simulating tumor microenvironment in vitro remains a challenge, and osteosarcoma-the most common primary bone cancer-is no exception. The growing evidence points to the inevitable connection between biomechanical stimuli and tumor chemosensitivity and aggressiveness, thus making this component of the microenvironment a mandatory requirement to the developed models. In this review, we addressed the question: is the "in vivo - in vitro" gap in osteosarcoma engineering bridged from the perspective of biomechanical stimuli? The most notable biomechanical cues in the tumor cell microenvironment are observed and compared in the contexts of in vivo conditions and engineered three-dimensional in vitro models. Impact statement The importance of biomechanical stimuli in three-dimensional in vitro models for drug testing is becoming more pronounced nowadays. This review might assist in understanding the key players of the biophysical environment of primary bone cancer and the current state of bone tumor engineering from this perspective.

Dynamic flux balance analysis of high cell density fed-batch culture of Escherichia coli BL21 (DE3) with mass spectrometry-based spent media analysis

Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome-scale metabolic models (GSMM) and by assuming instantaneous pseudo-steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry coupled with liquid and gas chromatography for a fed-batch, high-cell density cultivation of Escherichia coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in tri-carboxylic acid cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.

Heat and desiccation tolerances predict bee abundance under climate change

Climate change could pose an urgent threat to pollinators, with critical ecological and economic consequences. However, for most insect pollinator species, we lack the long-term data and mechanistic evidence that are necessary to identify climate-driven declines and predict future trends. Here we document 16 years of abundance patterns for a hyper-diverse bee assemblage1 in a warming and drying region2, link bee declines with experimentally determined heat and desiccation tolerances, and use climate sensitivity models to project bee communities into the future. Aridity strongly predicted bee abundance for 71% of 665 bee populations (species × ecosystem combinations). Bee taxa that best tolerated heat and desiccation increased the most over time. Models forecasted declines for 46% of species and predicted more homogeneous communities dominated by drought-tolerant taxa, even while total bee abundance may remain unchanged. Such community reordering could reduce pollination services, because diverse bee assemblages typically maximize pollination for plant communities3. Larger-bodied bees also dominated under intermediate to high aridity, identifying body size as a valuable trait for understanding how climate-driven shifts in bee communities influence pollination4. We provide evidence that climate change directly threatens bee diversity, indicating that bee conservation efforts should account for the stress of aridity on bee physiology.

Interlinked bi-stable switches govern the cell fate commitment of embryonic stem cells

The development of embryonic stem (ES) cells to extraembryonic trophectoderm and primitive endoderm lineages manifests distinct steady-state expression patterns of two key transcription factors-Oct4 and Nanog. How dynamically such kind of steady-state expressions are maintained remains elusive. Herein, we demonstrate that steady-state dynamics involving two bistable switches which are interlinked via a stepwise (Oct4) and a mushroom-like (Nanog) manner orchestrate the fate specification of ES cells. Our hypothesis qualitatively reconciles various experimental observations and elucidates how different feedback and feedforward motifs orchestrate the extraembryonic development and stemness maintenance of ES cells. Importantly, the model predicts strategies to optimize the dynamics of self-renewal and differentiation of embryonic stem cells that may have therapeutic relevance in the future.

Population pharmacokinetics of ciprofloxacin in newborns with early onset neonatal sepsis and suspected meningitis

BACKGROUND

Neonatal Sepsis accounts for significant proportion of neonatal mortality globally. Ciprofloxacin can be used as an effective antimicrobial against common causative agents of neonatal sepsis. However, there is only limited information about its pharmacokinetic distribution in plasma and Cerebrospinal fluid (CSF) of neonates.

METHODS

Plasma and CSF samples were taken using a sparse sampling technique from neonates who received at least one dose of intravenous ciprofloxacin. Ciprofloxacin levels were analysed using high-performance liquid chromatography (HPLC). Population pharmacokinetic analysis was conducted using a non-linear mixed-effects modelling using Pumas® (Pharmaceutical Modelling and Simulation) package (Version 2.0).

RESULTS

53 neonates were enroled in the study of whom; 9 (17%) had meningitis. The median concentration of ciprofloxacin in CSF was 1.4 (0.94-2.06) ug/ml and plasma was 2.94 (1.8-5.0) ug/ml. A one-compartment model with first-order elimination fitted the data. Body weight was found to be a significant covariate on volume of distribution (Vd). Simulations based on the final model suggest that dose of 10 mg/kg, intravenous b.d may not be able to achieve the desirable indices.

CONCLUSIONS

One compartment model with weight as a covariate explained the available data. Further studies with modified sampling strategy, larger sample size and variable dose levels are needed.

Physiologically-based pharmacokinetic modeling of prominent oral contraceptive agents and applications in drug-drug interactions

Considerable interest remains across the pharmaceutical industry and regulatory landscape in capabilities to model oral contraceptives (OCs), whether combined (COCs) with ethinyl estradiol (EE) or progestin-only pill. Acceptance of COC drug-drug interaction (DDI) assessment using physiologically-based pharmacokinetic (PBPK) is often limited to the estrogen component (EE), requiring further verification, with extrapolation from EE to progestins discouraged. There is a paucity of published progestin component PBPK models to support the regulatory DDI guidance for industry to evaluate a new chemical entity's (NCE's) DDI potential with COCs. Guidance recommends a clinical interaction study to be considered if an investigational drug is a weak or moderate inducer, or a moderate/strong inhibitor, of CYP3A4. Therefore, availability of validated OC PBPK models within one software platform, will be useful in predicting the DDI potential with NCEs earlier in the clinical development. Thus, this work was focused on developing and validating PBPK models for progestins, DNG, DRSP, LNG, and NET, within Simcyp, and assessing the DDI potential with known CYP3A4 inhibitors (e.g., ketoconazole) and inducers (e.g., rifampicin) with published clinical data. In addition, this work demonstrated confidence in the Simcyp EE model for regulatory and clinical applications by extensive verification in 70+ clinical PK and CYP3A4 interaction studies. The results provide greater capability to prospectively model clinical CYP3A4 DDI with COCs using Simcyp PBPK to interrogate the regulatory decision-tree to contextualize the potential interaction by known perpetrators and NCEs, enabling model-informed decision making, clinical study designs, and delivering potential alternative COC options for women of childbearing potential.

Dosing of Convalescent Plasma and Hyperimmune Anti-SARS-CoV-2 Immunoglobulins: A Phase I/II Dose-Finding Study

BACKGROUND AND OBJECTIVE

During the COVID-19 pandemic, trials on convalescent plasma (ConvP) were performed without preceding dose-finding studies. This study aimed to assess potential protective dosing regimens by constructing a population pharmacokinetic (popPK) model describing anti-SARS-CoV-2 antibody titers following the administration of ConvP or hyperimmune globulins (COVIg).

METHODS

Immunocompromised patients, testing negative for anti-SARS-CoV-2 spike antibodies despite vaccination, received a range of anti-SARS-CoV-2 antibodies in the form of COVIg or ConvP infusion. The popPK analysis was performed using NONMEM v7.4. Monte Carlo simulations were performed to assess potential COVIg and ConvP dosing regimens for prevention of COVID-19.

RESULTS

Forty-four patients were enrolled, and data from 42 were used for constructing the popPK model. A two-compartment elimination model with mixed residual error best described the Nab-titers after administration. Inter-individual variation was associated to CL (44.3%), V1 (27.3%), and V2 (29.2%). Lean body weight and type of treatment (ConvP/COVIg) were associated with V1 and V2, respectively. Median elimination half-life was 20 days (interquartile range: 17-25 days). Simulations demonstrated that even monthly infusions of 600 mL of the ConvP or COVIg used in this trial would not achieve potentially protective serum antibody titers for > 90% of the time. However, as a result of hybrid immunity and/or repeated vaccination, plasma donors with extremely high antibody titers are now readily available, and a > 90% target attainment should be possible.

CONCLUSION

The results of this study may inform future intervention studies on the prophylactic and therapeutic use of antiviral antibodies in the form of ConvP or COVIg.

CLINICAL TRIAL REGISTRATION NUMBER

NL9379 (The Netherlands Trial Register).

Scale resolving simulations of the effect of glottis motion and the laryngeal jet on flow dynamics during respiration

BACKGROUND AND OBJECTIVE

The movement of the respiratory walls has a significant impact on airflow through the respiratory tract. The majority of computational fluid dynamics (CFD) studies assume a static geometry which may not provide a realistic flow field. Furthermore, many studies use Reynolds Averaged Navier-Stokes (RANS) turbulence models that do not resolve turbulence structure. Combining the application of advanced scale-resolving turbulence models with moving respiratory walls using CFD will provide detailed insights into respiratory flow structures.

METHODS

This study simulated a complete breathing cycle involving inhalation and exhalation in a nasal cavity to trachea geometry that incorporated moving glottis walls. A second breathing cycle was simulated with static glottis walls for comparison. A recently developed hybrid RANS-LES turbulence model, the Stress-Blended Eddy Simulation (SBES), was incorporated to resolve turbulent flow structures in fine detail for both transient simulations. Transient results were compared with steady-state RANS simulations for the same respiratory geometry.

RESULTS

Glottis motion caused substantial effects on flow structure through the complete breathing cycle. Significant flow structure and velocity variations were observed due to glottal motion, primarily in the larynx and trachea. Resolved turbulence structures using SBES showed an intense mixing section in the glottis region during inhalation and in the nasopharynx during expiration, which was not present in the RANS simulations.

CONCLUSION

Transient simulations of a realistic breathing cycle uncovered flow structures absent in simulations with a constant flow rate. Furthermore, the incorporation of glottis motion impacted airflow characteristics that suggest rigid respiratory walls do not accurately describe respiratory flow. Future research in respiratory airflow should be conducted using transient scale-resolving models in conjunction with moving respiratory walls to capture flow structures in detail.

Evaluating inter-individual variability captured by the Eleveld pharmacokinetics model

Inter-individual variability in Pharmacokinetic (PK) and Pharmacodynamic (PD) models significantly affects the accuracy of Target Controlled Infusion and closed-loop control of anesthesia. We hypothesize that the novel Eleveld PK model captures more inter-individual variability relevant to both open-loop and closed-loop control design, resulting in reduced variability in PD models identified using the Eleveld PK model's plasma prediction compared to the Schuttler or Schnider PK model. We used a dataset of propofol infusion rates and Depth of Hypnosis measurements across three demographic groups: elderly, obese, and adult. PD models are identified based on plasma concentration prediction using three PK models (Schuttler, Schnider, and Eleveld). Validation methods are presented to confirm acceptable predictive performance and comparable PK-PD model variability within each demographic group. To test our hypothesis, we compared coefficient variations in step responses for open-loop control and multiplicative uncertainty of PD model sets for closed-loop control. Validated PKPD models using the Schuttler and Schnider PK model showed no significant differences in predictive response and multiplicative uncertainty compared to the Eleveld PK model. The coefficient variations in step responses of PD model sets and the frequency ranges, corresponding to uncertainty below one, were comparable for all three PK models. The comparison of the accumulated coefficient of variation in the step-response and the uncertainty of the PD model sets indicated that the Eleveld PK model does not offer any advantage for the design of open-loop or closed-loop control of anesthesia.

The three-species problem: Incorporating competitive asymmetry and intransitivity in modern coexistence theory

While natural communities can contain hundreds of species, modern coexistence theory focuses primarily on species pairs. Alternatively, the structural stability approach considers the feasibility of equilibria, gaining scalability to larger communities but sacrificing information about dynamic stability. Three-species competitive communities are a bridge to more-diverse communities. They display novel phenomena while remaining amenable to mathematical analysis, but remain incompletely understood. Here, we combine these approaches to identify the key quantities that determine three-species competition outcomes. We show that pairwise niche overlap and fitness differences are insufficient to completely characterize competitive outcomes, which requires a strictly triplet-wise quantity: cyclic asymmetry, which underlies intransitivity. Low pairwise niche overlap stabilizes the triplet, while high fitness differences promote competitive exclusion. The effect of cyclic asymmetry on stability is complex and depends on pairwise niche overlap. In summary, we elucidate how pairwise niche overlap, fitness differences and cyclic asymmetry determine three-species competition outcomes.

Review of non-penetrating ballistic testing techniques for protection assessment: From biological data to numerical and physical surrogates

Human surrogates have long been employed to simulate human behaviour, beginning in the automotive industry and now widely used throughout the safety framework to estimate human injury during and after accidents and impacts. In the specific context of blunt ballistics, various methods have been developed to investigate wound injuries, including tissue simulants such as clays or gelatine ballistic, physical dummies and numerical models. However, all of these surrogate entities must be biofidelic, meaning they must accurately represent the biological properties of the human body. This paper provides an overview of physical and numerical surrogates developed specifically for blunt ballistic impacts, including their properties, use and applications. The focus is on their ability to accurately represent the human body in the context of blunt ballistic impact.

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

Evaluation of the drug-drug interaction potential of brigatinib using a physiologically-based pharmacokinetic modeling approach

Brigatinib is an oral anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive metastatic non-small cell lung cancer. In vitro studies indicated that brigatinib is primarily metabolized by CYP2C8 and CYP3A4 and inhibits P-gp, BCRP, OCT1, MATE1, and MATE2K. Clinical drug-drug interaction (DDI) studies with the strong CYP3A inhibitor itraconazole or the strong CYP3A inducer rifampin demonstrated that CYP3A-mediated metabolism was the primary contributor to overall brigatinib clearance in humans. A physiologically-based pharmacokinetic (PBPK) model for brigatinib was developed to predict potential DDIs, including the effect of moderate CYP3A inhibitors or inducers on brigatinib pharmacokinetics (PK) and the effect of brigatinib on the PK of transporter substrates. The developed model was able to predict clinical DDIs with itraconazole (area under the plasma concentration-time curve from time 0 to infinity [AUC∞] ratio [with/without itraconazole]: predicted 1.86; observed 2.01) and rifampin (AUC∞ ratio [with/without rifampin]: predicted 0.16; observed 0.20). Simulations using the developed model predicted that moderate CYP3A inhibitors (e.g., verapamil and diltiazem) may increase brigatinib AUC∞ by ~40%, whereas moderate CYP3A inducers (e.g., efavirenz) may decrease brigatinib AUC∞ by ~50%. Simulations of potential transporter-mediated DDIs predicted that brigatinib may increase systemic exposures (AUC∞) of P-gp substrates (e.g., digoxin and dabigatran) by 15%-43% and MATE1 substrates (e.g., metformin) by up to 29%; however, negligible effects were predicted on BCRP-mediated efflux and OCT1-mediated uptake. The PBPK analysis results informed dosing recommendations for patients receiving moderate CYP3A inhibitors (40% brigatinib dose reduction) or inducers (up to 100% increase in brigatinib dose) during treatment, as reflected in the brigatinib prescribing information.

Optimal starting dosing regimen of intravenous oxytocin for labor induction based on the population kinetic-pharmacodynamic model of uterine contraction frequency

BACKGROUND

Intravenous oxytocin is commonly used for labor induction. However, a consensus on the initial dosing regimen is lac with conflicting research findings and varying guidelines. This study aimed to develop a population kinetic-pharmacodynamic (K-PD) model for oxytocin-induced uterine contractions considering real-world data and relevant influencing factors to establish an optimal starting dosing regimen for intravenous oxytocin.

METHODS

This retrospective study included pregnant women who underwent labor induction with intravenous oxytocin at Peking University Third Hospital in 2020. A  population K-PD model was developed to depict the time course of uterine contraction frequency (UCF), and covariate screening identified significant factors affecting the pharmacokinetics and pharmacodynamics of oxytocin. Model-based simulations were used to optimize the current starting regimen based on specific guidelines.

RESULTS

Data from 77 pregnant women with 1095 UCF observations were described well by the K-PD model. Parity, cervical dilation, and membrane integrity are significant factors influencing the effectiveness of oxytocin. Based on the model-based simulations, the current regimens showed prolonged onset times and high infusion rates. This study proposed a revised approach, beginning with a rapid infusion followed by a reduced infusion rate, enabling most women to achieve the target UCF within approximately 30 min with the lowest possible infusion rate.

CONCLUSION

The K-PD model of oxytocin effectively described the changes in UCF during labor induction. Furthermore, it revealed that parity, cervical dilation, and membrane integrity are key factors that influence the effectiveness of oxytocin. The optimal starting dosing regimens obtained through model simulations provide valuable clinical references for oxytocin treatment.

Computational modelling of articular joints with biphasic cartilage: recent advances, challenges and opportunities

Biphasic models have been widely used to simulate the time-dependent biomechanical response of soft tissues. Modelling techniques of joints with biphasic weight-bearing soft tissues have been markedly improved over the last decade, enhancing our understanding of the function, degenerative mechanism and outcomes of interventions of joints. This paper reviews the recent advances, challenges and opportunities in computational models of joints with biphasic weight-bearing soft tissues. The review begins with an introduction of the function and degeneration of joints from a biomechanical aspect. Different constitutive models of articular cartilage, in particular biphasic materials, are illustrated in the context of the study of contact mechanics in joints. Approaches, advances and major findings of biphasic models of the hip and knee are presented, followed by a discussion of the challenges awaiting to be addressed, including the convergence issue, high computational cost and inadequate validation. Finally, opportunities and clinical insights in the areas of subject-specific modeling and tissue engineering are provided and discussed.

The need for speed - Does the force-velocity property significantly alter strain distributions within skeletal muscle?

Skeletal muscles are complex structures with nonlinear constitutive properties. This complexity often requires finite element (FE) modeling to better understand muscle behavior and response to activation, especially the fiber strain distributions that can be difficult to measure in vivo. However, many FE muscle models designed to study fiber strain do not include force-velocity behavior. To investigate force-velocity property impact on strain distributions within skeletal muscle, we modified a muscle constitutive model with active and passive force-length properties to include force-velocity properties. We implemented the new constitutive model as a plugin for the FE software FEBio and applied it to four geometries: 1) a single element, 2) a multiple-element model representing a single fiber, 3) a model of tapering fibers, and 4) a model representing the bicep femoris long head (BFLH) morphology. Maximum fiber velocity and boundary conditions of the finite element models were varied to test their influence on fiber strain distribution. We found that force-velocity properties in the constitutive model behaved as expected for the single element and multi-element conditions. In the tapered fiber models, fiber strain distributions were impacted by changes in maximum fiber velocity; the range of strains increased with maximum fiber velocity, which was most noted in isometric contraction simulations. In the BFLH model, maximum fiber velocity had minimal impact on strain distributions, even in the context of sprinting. Taken together, the combination of muscle model geometry, activation, and displacement parameters play a critical part in determining the magnitude of impact of force-velocity on strain distribution.

Non-Fourier Bioheat Transfer Analysis in Brain Tissue During Interstitial Laser Ablation: Analysis of Multiple Influential Factors

This work presents the dual-phase lag-based non-Fourier bioheat transfer model of brain tissue subjected to interstitial laser ablation. The finite element method has been utilized to predict the brain tissue's temperature distributions and ablation volumes. A sensitivity analysis has been conducted to quantify the effect of variations in the input laser power, treatment time, laser fiber diameter, laser wavelength, and non-Fourier phase lags. Notably, in this work, the temperature-dependent thermal properties of brain tissue have been considered. The developed model has been validated by comparing the temperature obtained from the numerical and ex vivo brain tissue during interstitial laser ablation. The ex vivo brain model has been further extended to in vivo settings by incorporating the blood perfusion effects. The results of the systematic analysis highlight the importance of considering temperature-dependent thermal properties of the brain tissue, non-Fourier behavior, and microvascular perfusion effects in the computational models for accurate predictions of the treatment outcomes during interstitial laser ablation, thereby minimizing the damage to surrounding healthy tissue. The developed model and parametric analysis reported in this study would assist in a more accurate and precise prediction of the temperature distribution, thus allowing to optimize the thermal dosage during laser therapy in the brain.

Primary study of the relative and compound biological effectiveness model for boron neutron capture therapy based on nanodosimetry

BACKGROUND

The current radiobiological model employed for boron neutron capture therapy (BNCT) treatment planning, which relies on microdosimetry, fails to provide an accurate representation the biological effects of BNCT. The precision in calculating the relative biological effectiveness (RBE) and compound biological effectiveness (CBE) plays a pivotal role in determining the therapeutic efficacy of BNCT. Therefore, this study focuses on how to improve the accuracy of the biological effects of BNCT.

PURPOSE

The purpose of this study is to propose new radiation biology models based on nanodosimetry to accurately assess RBE and CBE for BNCT.

METHODS

Nanodosimetry, rooted in ionization cluster size distributions (ICSD), introduces a novel approach to characterize radiation quality by effectively delineating RBE through the ion track structure at the nanoscale. In the context of prior research, this study presents a computational model for the nanoscale assessment of RBE and CBE. We establish a simplified model of DNA chromatin fiber using the Monte Carlo code TOPAS-nBio to evaluate the applicability of ICSD to BNCT and compute nanodosimetric parameters.

RESULTS

Our investigation reveals that both homogeneous and heterogeneous nanodosimetric parameters, as well as the corresponding biological model coefficients α and β, along with RBE values, exhibit variations in response to varying intracellular 10B concentrations. Notably, the nanodosimetric parameterM 1 C 2 $M_1^{{{\mathrm{C}}}_2}$ effectively captures the fluctuations in model coefficients α and RBE.

CONCLUSION

Our model facilitates a nanoscale analysis of BNCT, enabling predictions of nanodosimetric quantities for secondary ions as well as RBE, CBE, and other essential biological metrics related to the distribution of boron. This contribution significantly enhances the precision of RBE calculations and holds substantial promise for future applications in treatment planning.

Food-limited plant-herbivore model: Bifurcations, persistence, and stability

This research paper delves into the two-dimensional discrete plant-herbivore model. In this model, herbivores are food-limited and affect the plants' density in their environment. Our analysis reveals that this system has equilibrium points of extinction, exclusion, and coexistence. We analyze the behavior of solutions near these points and prove that the extinction and exclusion equilibrium points are globally asymptotically stable in certain parameter regions. At the boundary equilibrium, we prove the existence of transcritical and period-doubling bifurcations with stable two-cycle. Transcritical bifurcation occurs when the plant's maximum growth rate or food-limited parameter reaches a specific boundary. This boundary serves as an invasion boundary for populations of plants or herbivores. At the interior equilibrium, we prove the occurrence of transcritical, Neimark-Sacker, and period-doubling bifurcations with an unstable two-cycle. Our research also establishes that the system is persistent in certain regions of the first quadrant. We demonstrate that the local asymptotic stability of the interior equilibrium does not guarantee the system's persistence. Bistability exists between boundary attractors (logistic dynamics) and interior equilibrium for specific parameters' regions. We conclude that changes to the food-limitation parameter can significantly alter the system's dynamic behavior. To validate our theoretical findings, we conduct numerical simulations.

Nonlocal damage evaluation of a sigmoid-based damage model for fibrous biological soft tissues

The comprehension and modeling of the mechanical behavior of soft biological tissues are essential due to their clinical applications. This knowledge is essential for predicting tissue responses accurately and enhancing our ability to compute the behavior of biological structures and bio-prosthetic devices under specific loading conditions. The current research is centered on modeling the initiation and progression of soft tissues damage, which typically exhibit intricate anisotropic and nonlinear elastic characteristics. For this purpose, the following study presents a comparative analysis of the computational performance of two distinct damage modeling techniques. The first technique employs a well-established damage model, based on a piece-wise exponential damage function as proposed by Calvo et al. (Int J Numer Methods Eng 69:2036-2057, 2007. https://doi.org/10.1002/nme.1825 ). The second approach adopts a sigmoid function, as proposed by López-Campos et al. (Comput Methods Biomech Biomed Eng 23(6):213-223. https://doi.org/10.1080/10255842.2019.1710742 ). The aim of this study is to verify the validity of the López-Campos sigmoid-based damage model to be used in finite element simulation, the implementation of which is unknown. For this proposal, both models were implemented within a commercial Finite Element software package, and their responses to local and non-local damage algorithms were assessed in depth through two standard benchmark tests: a plate with a hole and a ball burst. The results of this study indicate that, for a wide range of cases, such as in-plane stresses, out-plane stresses, stress concentration and contact, all over large displacement conditions, the López-Campos damage model shows a good response to non-local algorithms achieving mesh independence and convergence in all these cases. The results obtained are in line with those obtained for the Calvo's damage model, showing, in addition, larger deformations under in-plane stress and stress concentration conditions and a lower number of iterations under out-plane stress and contact conditions. Consequently, the López-Campos' damage model emerges as a valuable and useful tool in the field of mechanical damage research in biological systems.

Low-dimensional neural ODEs and their application in pharmacokinetics

Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.

Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

Artificial Neural Network Prediction of COVID-19 Daily Infection Count

This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.

A thermoregulation model based on the physical and physiological characteristics of Chinese elderly

Given the increasing aging population and rising living standards in China, developing an accurate and straightforward thermoregulation model for the elderly has become increasingly essential. To address this need, an existing one-segment four-node thermoregulation model for the young was selected as the base model. This study developed the base model considering age-related physical and physiological changes to predict mean skin temperatures of the elderly. Measured data for model optimization were collected from 24 representative healthy Chinese elderly individuals (average age: 67 years). The subjects underwent temperature step changes between neutral and warm conditions with a temperature range of 25-34 °C. The model's demographic representation was first validated by comparing the subjects' physical characteristics with Chinese census data. Secondly, sensitivity analysis was performed to investigate the influences of passive system parameters on skin and core temperatures, and adjustments were implemented using measurement or literature data specific to the Chinese elderly. Thirdly, the active system was modified by resetting the body temperature set points. The active parameters to control thermoregulation activities were further optimized using the TPE (Tree-structured Parzen Estimator) hyperparameter tuning method. The model's accuracy was further verified using independent experimental data for a temperature range of 18-34 °C for Chinese elderly. By comprehensively considering age-induced thermal response changes, the proposed model has potential applications in designing and optimizing thermal management systems in buildings, as well as informing energy-efficient strategies tailored to the specific needs of the Chinese elderly population.

In Vitro Human Gastrointestinal Tract Simulation Systems: A Panoramic Review

Simulated human gastrointestinal (GI) tract systems are important for their applications in the fields of probiotics, nutrition and health. To date, various in vitro gut systems have been available to study GI tract dynamics and its association with health. In contrast to in vivo investigations, which are constrained by ethical considerations, in vitro models have several benefits despite the challenges involved in mimicking the GI environment. These in vitro models can be used for a range of research, from simple to dynamic, with one compartment to several compartments. In this review, we present a panoramic development of in vitro GI models for the first time through an evolutionary timeline. We tried to provide insight on designing an in vitro gut model, especially for novices. Latest developments and scope for improvement based on the limitations of the existing models were highlighted. In conclusion, designing an in vitro GI model suitable for a particular application is a multifaceted task. The bio-mimicking of the GI tract specific to geometrical, anatomical and mechanical features remains a challenge for the development of effective in vitro GI models. Advances in computer technology, artificial intelligence and nanotechnology are going to be revolutionary for further development. Besides this, in silico high-throughput technologies and miniaturisation are key players in the success of making in vitro modelling cost-effective and reducing the burden of in vivo studies.

Population Pharmacokinetics of Ozoralizumab in Patients with Rheumatoid Arthritis

Ozoralizumab is a bispecific NANOBODY compound that binds tumor necrosis factor alpha (TNFα) and human serum albumin. Ozoralizumab inhibits the TNFα physiological activity while maintaining long-term plasma retention owing to its human serum albumin-binding ability. A population pharmacokinetic (PK) model was developed using data from 494 Japanese patients with rheumatoid arthritis in Phase II/III and Phase III trials to assess the effects of potential PK covariates. The ozoralizumab PK after subcutaneous administration was described using a 1-compartment model with first-order absorption and first-order elimination processes. A proportional error model was used for inter- and intra-individual variabilities, with covariance set between inter-individual variabilities of the apparent clearance and apparent distribution volume. Body weight, sex, antidrug antibody status, estimated glomerular filtration rate, and concomitant methotrexate use were identified as covariates for apparent clearance, while body weight and sex were covariates for apparent distribution volume in the final model. Body weight had the greatest effect on the PK of ozoralizumab, while the other covariates had minor effects. When administered at 30 mg every 4 weeks, the predicted steady-state plasma trough concentration in a patient weighing 83.2 kg exceeded the trough concentration required to maintain efficacy of ozoralizumab, and the estimated exposure in a patient weighing 42.5 kg did not exceed the mean exposure at 80 mg, a well-tolerated dose, throughout 52 weeks. We developed a population PK model that adequately described the ozoralizumab PK in Japanese patients with rheumatoid arthritis, and none of the evaluated covariates showed clinically relevant effects on the PK of ozoralizumab.

Population Pharmacodynamic Dose-Response Analysis of Serum Potassium Following Dosing with Sodium Zirconium Cyclosilicate

BACKGROUND

Sodium zirconium cyclosilicate (SZC) is an approved oral treatment for hyperkalemia that selectively binds potassium (K+) in the gastrointestinal tract and removes K+ from the body through increased fecal excretion. Here, we describe the population pharmacodynamic (PopPD) response of serum K+ concentration in patients with hyperkalemia who are treated with SZC, estimate the impact of patients' intrinsic and extrinsic factors, and compare predicted serum K+ responses between 5 g alternate daily (QOD) and 2.5 g once daily (QD) maintenance doses.

METHODS

PopPD analysis was based on pooled data from seven phase II and III clinical trials for SZC. A semi-mechanistic longitudinal mixed-effects (base) model was used to characterize serum K+ concentration after SZC dosing. Indirect-response, virtual pharmacokinetics-pharmacodynamics (PK-PD) modeling was used to mimic the drug exposure compartment. Full covariate modeling was used to assess covariate impact on the half-maximal effective concentration of drug (EC50), placebo response, and Kout. Models were evaluated using goodness-of-fit plots, relative standard errors, and visual predictive checks, and data were stratified to optimize model performance across subgroups. Covariate effects were evaluated based on the magnitude of change in serum K+ between baseline and end of correction phase dosing (48 h, SZC 10 g three times a day) and maintenance phase dosing (28 days, SZC 10 g QD) using a reference subject.

RESULTS

The analysis data set included 2369 patients and 25,764 serum K+ observations. The mean (standard deviation) patient age was 66.0 (12) years, 61% were male, 68% were White, 34% had congestive heart failure, and 62% had diabetes. Mean (standard deviation) serum K+ at baseline was 5.49 (0.43) mmol/L. Both the base and full covariance models adequately described observed data. In the final model, there was a sigmoid exposure response on Kin, with EC50 of 32.8 g and a Hill coefficient of 1.36. The predicted placebo-adjusted dose-responses of serum K+ change appeared nearly linear in the correction and maintenance phases. No clinically meaningful difference in placebo-adjusted serum K+ change from baseline at 28 days was observed between maintenance regimens of SZC 5 g QOD and 2.5 g QD. A greater SZC treatment response was associated with high serum K+ at baseline, advanced age, lower body weight, lower estimated glomerular filtration rate, and Black/African American and Asian race, compared with the reference patient. The impact of heart failure status and diabetes status was only minor.

CONCLUSIONS

The PopPD model of SZC adequately described changes in serum K+ concentration during correction and maintenance phase dosing. A greater treatment response was associated with various covariates, but the impact of each was modest. Overall, these findings suggest that no adjustment in SZC dose is needed for any of the covariates evaluated.

Multi-frequency spatial frequency domain imaging: a depth-resolved optical scattering model to isolate scattering contrast in thin layers of skin

Significance

Current methods for wound healing assessment rely on visual inspection, which gives qualitative information. Optical methods allow for quantitative non-invasive measurements of optical properties relevant to wound healing.

Aim

Spatial frequency domain imaging (SFDI) measures the absorption and reduced scattering coefficients of tissue. Typically, SFDI assumes homogeneous tissue; however, layered structures are present in skin. We evaluate a multi-frequency approach to process SFDI data that estimates depth-specific scattering over differing penetration depths.

Approach

Multi-layer phantoms were manufactured to mimic wound healing scattering contrast in depth. An SFDI device imaged these phantoms and data were processed according to our multi-frequency approach. The depth sensitive data were then compared with a two-layer scattering model based on light fluence.

Results

The measured scattering from the phantoms changed with spatial frequency as our two-layer model predicted. The performance of two δ - P 1 models solutions for SFDI was consistently better than the standard diffusion approximation.

Conclusions

We presented an approach to process SFDI data that returns depth-resolved scattering contrast. This method allows for the implementation of layered optical models that more accurately represent physiologic parameters in thin tissue structures as in wound healing.

Demonstrating Bioequivalence for Two Dose Strengths of Niraparib and Abiraterone Acetate Dual-Action Tablets Versus Single Agents: Utility of Clinical Study Data Supplemented with Modeling and Simulation

BACKGROUND AND OBJECTIVE

The combination of niraparib and abiraterone acetate (AA) plus prednisone is under investigation for the treatment of patients with metastatic castration-resistant prostate cancer (mCRPC) and metastatic castration-sensitive prostate cancer (mCSPC). Regular-strength (RS) and lower-strength (LS) dual-action tablets (DATs), comprising niraparib 100 mg/AA 500 mg and niraparib 50 mg/AA 500 mg, respectively, were developed to reduce pill burden and improve patient experience. A bioequivalence (BE)/bioavailability (BA) study was conducted under modified fasting conditions in patients with mCRPC to support approval of the DATs.

METHODS

This open-label randomized BA/BE study (NCT04577833) was conducted at 14 sites in the USA and Europe. The study had a sequential design, including a 21-day screening phase, a pharmacokinetic (PK) assessment phase comprising three periods [namely (1) single-dose with up to 1-week run-in, (2) daily dose on days 1-11, and (3) daily dose on days 12-22], an extension where both niraparib and AA as single-agent combination (SAC; reference) or AA alone was continued from day 23 until discontinuation, and a 30-day follow-up phase. Patients were randomly assigned in a parallel-group design (four-sequence randomization) to receive a single oral dose of niraparib 100 mg/AA 1000 mg as a LS-DAT or SAC in period 1, and patients continued as randomized into a two-way crossover design during periods 2 and 3 where they received niraparib 200 mg/AA 1000 mg once daily as a RS-DAT or SAC. The design was powered on the basis of crossover assessment of RS-DAT versus SAC. During repeated dosing (periods 2 and 3, and extension phase), all patients also received prednisone/prednisolone 5 mg twice daily. Plasma samples were collected for measurement of niraparib and abiraterone plasma concentrations. Statistical assessment of the RS-DAT and LS-DAT versus SAC was performed on log-transformed pharmacokinetic parameters data from periods 2 and 3 (crossover) and from period 1 (parallel), respectively. Additional paired analyses and model-based bioequivalence assessments were conducted to evaluate the similarity between the LS-DAT and SAC.

RESULTS

For the RS-DAT versus SAC, the 90% confidence intervals (CI) of geometric mean ratios (GMR) for maximum concentration at a steady state (Cmax,ss) and area under the plasma concentration-time curve from 0-24 h at a steady state (AUC 0-24h,ss) were respectively 99.18-106.12% and 97.91-104.31% for niraparib and 87.59-106.69 and 86.91-100.23% for abiraterone. For the LS-DAT vs SAC, the 90% CI of GMR for AUC0-72h of niraparib was 80.31-101.12% in primary analysis, the 90% CI of GMR for Cmax,ss and AUC 0-24h,ss of abiraterone was 85.41-118.34% and 86.51-121.64% respectively, and 96.4% of simulated LS-DAT versus SAC BE trials met the BE criteria for both niraparib and abiraterone.

CONCLUSIONS

The RS-DAT met BE criteria (range 80%-125%) versus SAC based on 90% CI of GMR for Cmax,ss and AUC 0-24h,ss. The LS-DAT was considered BE to SAC on the basis of the niraparib component meeting the BE criteria in the primary analysis for AUC 0-72h; abiraterone meeting the BE criteria in additional paired analyses based on Cmax,ss and AUC 0-24h,ss; and the percentage of simulated LS-DAT versus SAC BE trials meeting the BE criteria for both.

CLINICALTRIALS

GOV IDENTIFIER

NCT04577833.

Population pharmacokinetics and pharmacodynamics of efmarodocokin alfa (IL-22Fc)

Efmarodocokin alfa (IL-22Fc) is a fusion protein of human IL-22 linked to the crystallizable fragment (Fc) of human IgG4. It has been tested in multiple indications including inflammatory bowel disease (IBD). The purposes of the present analyses were to describe the population pharmacokinetics (PK) of efmarodocokin alfa and perform pharmacodynamic (PD) analysis on the longitudinal changes of the PD biomarker REG3A after efmarodocokin alfa treatment as well as identify covariates that affect efmarodocokin alfa PK and REG3A PD. The data used for this analysis included 182 subjects treated with efmarodocokin alfa in two clinical studies. The population PK and PD analyses were conducted sequentially. Efmarodocokin alfa concentration-time data were analyzed using a nonlinear mixed-effects modeling approach, and an indirect response model was adopted to describe the REG3A PD data with efmarodocokin alfa serum concentration linked to the increase in REG3A. The analysis software used were NONMEM and R. A 3-compartment model with linear elimination best described the PK of efmarodocokin alfa. The estimated population-typical value for clearance (CL) was 1.12 L/day, and volume of central compartment was 6.15 L. Efmarodocokin alfa CL increased with higher baseline body weight, C-reactive protein, and CL was 27.6% higher in IBD patients compared to healthy subjects. The indirect response PD model adequately described the longitudinal changes of REG3A after efmarodocokin alfa treatment. A popPK and PD model for efmarodocokin alfa and REG3A was developed and covariates affecting the PK and PD were identified.

A taxonomy of multiple stable states in complex ecological communities

Natural systems are built from multiple interconnected units, making their dynamics, functioning and fragility notoriously hard to predict. A fragility scenario of particular relevance concerns so-called regime shifts: abrupt transitions from healthy to degraded ecosystem states. An explanation for these shifts is that they arise as transitions between alternative stable states, a process that is well-understood in few-species models. However, how multistability upscales with system complexity remains a debated question. Here, we identify that four different multistability regimes generically emerge in models of species-rich communities and other archetypical complex biological systems assuming random interactions. Across the studied models, each regime consistently emerges under a specific interaction scheme and leaves a distinct set of fingerprints in terms of the number of observed states, their species richness and their response to perturbations. Our results help clarify the conditions and types of multistability that can be expected to occur in complex ecological communities.

Unraveling power-law scaling through exponential cell division dynamics

A primary objective of biology is the development of universal laws that define how organic form develops and how it evolves as a function of size, both ontogenetically and across evolutionary time. Scaling theory has been essential in reaching this goal by giving a complete perspective point, particularly in illuminating the fundamental biological features produced within scaling exponents defining families of equations. Nonetheless, the theoretical basis of the allometric equation within scaling theory are inadequately explained, particularly when it comes to establishing links between micro-level processes at the cellular level and macro-level phenomena. We proposed an unlimited cell bipartition, resulting in an exponential growth in cell numbers during an individual's lifespan, to bridge this conceptual gap between cellular processes and allometric scaling. The power-law scaling between body mass and organ weight was produced by the synchronous exponential increments and the allometric exponent is rate of logarithmic cell proliferation rate. Substituting organ weight for erythrocyte weight aided in the development of a power-law scaling relationship between body mass and metabolic rate. Furthermore, it is critical to understand how cell size affects the exponent in power-law scaling. We find that a bigger exponent will result from an increase in the average weight of organ cells or a decrease in the average weight of all cells. Furthermore, cell proliferation dynamics showed a complex exponential scaling between body mass and longevity, defying the previously reported power-law scaling. We discovered a quadratic link between longevity and logarithmic body mass. Notably, all of the parameters included in these relationships are explained by indices linked to cell division and embryonic development. This research adds to our understanding of the complex interaction between cellular processes and overarching scaling phenomena in biology.

Maximum likelihood estimation of renal transporter ontogeny profiles for pediatric PBPK modeling

Optimal treatment of infants with many renally cleared drugs must account for maturational differences in renal transporter (RT) activity. Pediatric physiologically-based pharmacokinetic (PBPK) models may incorporate RT activity, but this requires ontogeny profiles for RT activity in children, especially neonates, to predict drug disposition. Therefore, RT expression measurements from human kidney postmortem cortical tissue samples were normalized to represent a fraction of mature RT activity. Using these data, maximum likelihood estimated the distributions of RT activity across the pediatric age spectrum, including preterm and term neonates. PBPK models of four RT substrates (acyclovir, ciprofloxacin, furosemide, and meropenem) were evaluated with and without ontogeny profiles using average fold error (AFE), absolute average fold error (AAFE), and proportion of observations within the 5-95% prediction interval. Novel maximum likelihood profiles estimated ontogeny distributions for the following RT: OAT1, OAT3, OCT2, P-gp, URAT1, BCRP, MATE1, MRP2, MRP4, and MATE-2 K. Profiles for OAT3, P-gp, and MATE1 improved infant furosemide and neonate meropenem PBPK model AFE from 0.08 to 0.70 and 0.53 to 1.34 and model AAFE from 12.08 to 1.44 and 2.09 to 1.36, respectively, and improved the percent of data within the 5-95% prediction interval from 48% to 98% for neonatal ciprofloxacin simulations, respectively. Even after accounting for other critical population-specific maturational differences, novel RT ontogeny profiles substantially improved neonatal PBPK model performance, providing validated estimates of maturational differences in RT activity for optimal dosing in children.

Joint extension speed dictates bio-inspired morphing trajectories for optimal longitudinal flight dynamics

Avian wing morphing allows dynamic, active control of complex flight manoeuvres. Previous linear time-invariant (LTI) models have quantified the effect of varying fixed wing configurations but the time-dependent effects of morphing between different configurations is not well understood. To fill this gap, I implemented a linear parameter-varying (LPV) model for morphing wing gull flight. This approach models the wing joint angles as scheduled parameters and accounts for nonlinear kinematic and gravitational effects while interpolating between LTI models at discrete trim points. With the resulting model, I investigated the longitudinal response associated with various joint extension trajectories. By optimizing the extension trajectory for four independent objectives (speed and pitch angle overshoot, speed rise time and pitch angle settling time), I found that the extension trajectory inherent to the gull wing does not guarantee an optimal response but may provide a sufficient response with a simpler mechanical implementation. Furthermore, the results indicated that gulls likely require extension speed feedback. This morphing LPV model provides insights into underlying control mechanisms, which may allow for avian-like flight in future highly manoeuvrable uncrewed aerial vehicles.

Microsphere-Enabled Modular Formation of Miniaturized In Vitro Breast Cancer Models

In search of effective therapeutics for breast cancers, establishing physiologically relevant in vitro models is of great benefit to facilitate the clinical translation. Despite extensive progresses, it remains to develop the tumor models maximally recapturing the key pathophysiological attributes of their native counterparts. Therefore, the current study aimed to develop a microsphere-enabled modular approach toward the formation of in vitro breast tumor models with the capability of incorporating various selected cells while retaining spatial organization. Poly (lactic-co-glycolic acid) microspheres (150-200 mm) with tailorable pore size and surface topography are fabricated and used as carriers to respectively lade with breast tumor-associated cells. Culture of cell-laden microspheres assembled within a customized microfluidic chamber allowed to form 3D tumor models with spatially controlled cell distribution. The introduction of endothelial cell-laden microspheres into cancer-cell laden microspheres at different ratios would induce angiogenesis within the culture to yield vascularized tumor. Evaluation of anticancer drugs such as doxorubicin and Cediranib on the tumor models do demonstrate corresponding physiological responses. Clearly, with the ability to modulate microsphere morphology, cell composition and spatial distribution, microsphere-enabled 3D tumor tissue formation offers a high flexibility to satisfy the needs for pathophysiological study, anticancer drug screening or design of personalized treatment.

Combinatorial Cooperativity in miR200-Zeb Feedback Network can Control Epithelial-Mesenchymal Transition

Carcinomas often utilize epithelial-mesenchymal transition (EMT) programs for cancer progression and metastasis. Numerous studies report SNAIL-induced miR200/Zeb feedback circuit as crucial in regulating EMT by placing cancer cells in at least three phenotypic states, viz. epithelial (E), hybrid (h-E/M), mesenchymal (M), along the E-M phenotypic spectrum. However, a coherent molecular-level understanding of how such a tiny circuit controls carcinoma cell entrance into and residence in various states is lacking. Here, we use molecular binding data and mathematical modeling to report that the miR200/Zeb circuit can essentially utilize combinatorial cooperativity to control E-M phenotypic plasticity. We identify minimal combinatorial cooperativities that give rise to E, h-E/M, and M phenotypes. We show that disrupting a specific number of miR200 binding sites on Zeb as well as Zeb binding sites on miR200 can have phenotypic consequences-the circuit can dynamically switch between two (E, M) and three (E, h-E/M, M) phenotypes. Further, we report that in both SNAIL-induced and SNAIL knock-out miR200/Zeb circuits, cooperative transcriptional feedback on Zeb as well as Zeb translation inhibition due to miR200 are essential for the occurrence of intermediate h-E/M phenotype. Finally, we demonstrate that SNAIL can be dispensable for EMT, and in the absence of SNAIL, the transcriptional feedback can control cell state transition from E to h-E/M, to M state. Our results thus highlight molecular-level regulation of EMT in miR200/Zeb circuit and we expect these findings to be crucial to future efforts aiming to prevent EMT-facilitated dissemination of carcinomas.

Final epidemic size of a two-community SIR model with asymmetric coupling

Communities are commonly not isolated but interact asymmetrically with each other, allowing the propagation of infectious diseases within the same community and between different communities. To reveal the impact of asymmetrical interactions and contact heterogeneity on disease transmission, we formulate a two-community SIR epidemic model, in which each community has its contact structure while communication between communities occurs through temporary commuters. We derive an explicit formula for the basic reproduction number R 0 , give an implicit equation for the final epidemic size z, and analyze the relationship between them. Unlike the typical positive correlation between R 0 and z in the classic SIR model, we find a negatively correlated relationship between counterparts of our model deviating from homogeneous populations. Moreover, we investigate the impact of asymmetric coupling mechanisms on R 0 . The results suggest that, in scenarios with restricted movement of susceptible individuals within a community, R 0 does not follow a simple monotonous relationship, indicating that an unbending decrease in the movement of susceptible individuals may increase R 0 . We further demonstrate that network contacts within communities have a greater effect on R 0 than casual contacts between communities. Finally, we develop an epidemic model without restriction on the movement of susceptible individuals, and the numerical simulations suggest that the increase in human flow between communities leads to a larger R 0 .

Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning

Alzheimer's disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrated that DeepHM accurately infers parameter distributions of the conductance-based model on several test cases using synthetic data generated with complex underlying parameter structures. We then used DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimer's mouse models compared to their wildtype controls at 12 and 24 months. We found that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively.

Geographic differences in body size distributions underlie food web connectance of tropical forest mammals

Understanding variation in food web structure over large spatial scales is an emerging research agenda in food web ecology. The density of predator-prey links in a food web (i.e., connectance) is a key measure of network complexity that describes the mean proportional dietary breadth of species within a food web. Connectance is a critical component of food web robustness to species loss: food webs with lower connectance have been shown to be more susceptible to secondary extinctions. Identifying geographic variation in food web connectance and its drivers may provide insight into community robustness to species loss. We investigated the food web connectance of ground-dwelling tropical forest mammal communities in multiple biogeographic regions to test for differences among regions in food web connectance and to test three potential drivers: primary productivity, contemporary anthropogenic pressure, and variation in mammal body mass distributions reflective of historical extinctions. Mammal communities from fifteen protected forests throughout the Neo-, Afro-, and Asian tropics were identified from systematic camera trap arrays. Predator-prey interaction data were collected from published literature, and we calculated connectance for each community as the number of observed predator-prey links relative to the number of possible predator-prey links. We used generalized linear models to test for differences among regions and to identify the site level characteristics that best predicted connectance. We found that mammal food web connectance varied significantly among continents and that body size range was the only significant predictor. More possible predator-prey links were observed in communities with smaller ranges in body size and therefore sites with smaller body size ranges had higher mean proportional dietary breadth. Specifically, mammal communities in the Neotropics and in Madagascar had significantly higher connectance than mammal communities in Africa. This geographic variation in contemporary mammalian food web structure may be the product of historical extinctions in the Late Quaternary, which led to greater losses of large-bodied species in the Neotropics and Madagascar thus contributing to higher average proportional dietary breadth among the remaining smaller bodied species in these regions.

Dynamical order and many-body correlations in zebrafish show that three is a crowd

Zebrafish constitute a convenient laboratory-based biological system for studying collective behavior. It is possible to interpret a group of zebrafish as a system of interacting agents and to apply methods developed for the analysis of systems of active and even passive particles. Here, we consider the effect of group size. We focus on two- and many-body spatial correlations and dynamical order parameters to investigate the multistate behavior. For geometric reasons, the smallest group of fish which can exhibit this multistate behavior consisting of schooling, milling and swarming is three. We find that states exhibited by groups of three fish are similar to those of much larger groups, indicating that there is nothing more than a gradual change in weighting between the different states as the system size changes. Remarkably, when we consider small groups of fish sampled from a larger group, we find very little difference in the occupancy of the state with respect to isolated groups, nor is there much change in the spatial correlations between the fish. This indicates that fish interact predominantly with their nearest neighbors, perceiving the rest of the group as a fluctuating background. Therefore, the behavior of a crowd of fish is already apparent in groups of three fish.

Enumeration of Rooted Binary Unlabeled Galled Trees

Rooted binary galled trees generalize rooted binary trees to allow a restricted class of cycles, known as galls. We build upon the Wedderburn-Etherington enumeration of rooted binary unlabeled trees with n leaves to enumerate rooted binary unlabeled galled trees with n leaves, also enumerating rooted binary unlabeled galled trees with n leaves and g galls, 0 ⩽ g ⩽ ⌊ n - 1 2 ⌋ . The enumerations rely on a recursive decomposition that considers subtrees descended from the nodes of a gall, adopting a restriction on galls that amounts to considering only the rooted binary normal unlabeled galled trees in our enumeration. We write an implicit expression for the generating function encoding the numbers of trees for all n. We show that the number of rooted binary unlabeled galled trees grows with 0.0779 ( 4 . 8230 n ) n - 3 2 , exceeding the growth 0.3188 ( 2 . 4833 n ) n - 3 2 of the number of rooted binary unlabeled trees without galls. However, the growth of the number of galled trees with only one gall has the same exponential order 2.4833 as the number with no galls, exceeding it only in the subexponential term, 0.3910 n 1 2 compared to 0.3188 n - 3 2 . For a fixed number of leaves n, the number of galls g that produces the largest number of rooted binary unlabeled galled trees lies intermediate between the minimum of g = 0 and the maximum of g = ⌊ n - 1 2 ⌋ . We discuss implications in mathematical phylogenetics.

Mathematical modeling of adipocyte size distributions: Identifiability and parameter estimation from rat data

Fat cells, called adipocytes, are designed to regulate energy homeostasis by storing energy in the form of lipids. Adipocyte size distribution is assumed to play a role in the development of obesity-related diseases. These cells that do not have a characteristic size, indeed a bimodal size distribution is observed in adipose tissue. We propose a model based on a partial differential equation to describe adipocyte size distribution. The model includes a description of the lipid fluxes and the cell size fluctuations and using a formulation of a stationary solution fast computation of bimodal distribution is achieved. We investigate the parameter identifiability and estimate parameter values with CMA-ES algorithm. We first validate the procedure on synthetic data, then we estimate parameter values with experimental data of 32 rats. We discuss the estimated parameter values and their variability within the population, as well as the relation between estimated values and their biological significance. Finally, a sensitivity analysis is performed to specify the influence of parameters on cell size distribution and explain the differences between the model and the measurements. The proposed framework enables the characterization of adipocyte size distribution with four parameters and can be easily adapted to measurements of cell size distribution in different health conditions.