Search Results (122)

(1) Background: Overexpression of P-glycoprotein (P-gp) is one mediator of multidrug resistance in cancer. While many studies demonstrate the efficacy of modulating P-glycoprotein expression to increase drug response in cancer cells, the nature of the mathematical relationship between drug sensitivity and P-glycoprotein surface density is not yet characterized. (2) Methods: In this study, we employ siRNA to modulate P-gp expression in two model cell lines and evaluate their steady-state response to three common chemotherapeutics in vitro. Additionally, we model the kinetics of calcein-AM, a P-gp substrate, as a function of P-gp expression. (3) Results: For both cell lines, a robust linear relationship governs chemotherapeutic sensitivity as a function of P-gp expression, demonstrating that characterization of P-gp surface density is a strong indicator of drug response in drug-resistant cells. Furthermore, calcein accumulation and initial influx rate exhibit first-order kinetics with respect to P-gp density, further elucidating the nature of substrate interactions with P-gp-overexpressing cells. When transport kinetics are evaluated using a Michaelis–Menten model, V_max varies with P-gp density according to a first-order relationship. (4) Conclusions: These results establish the mathematical relationships between chemotherapeutic response and substrate influx as a function of P-gp expression and suggest that rational changes in P-gp expression could be used as a predictive measure of drug sensitivity in model cell lines. Full article

The non-monotonic behavior of amperometric enzyme-based biosensors under uncompetitive and noncompetitive (mixed) substrate inhibition is investigated computationally using a two-compartment model consisting of an enzyme layer and an outer diffusion layer. The model is based on a system of reaction–diffusion equations that includes a nonlinear term associated with non-Michaelis–Menten kinetics of the enzymatic reaction and accounts for the partitioning between layers. In addition to the known effect of substrate inhibition, where the maximum biosensor current differs from the steady-state output, it has been determined that external diffusion limitations can also cause the appearance of a local minimum in the current. At substrate concentrations greater than both the Michaelis–Menten constant and the uncompetitive substrate inhibition constant, and in the presence of external diffusion limitation, the transient response of the biosensor, after immersion in the substrate solution, may follow a five-phase pattern depending on the model parameter values: it starts from zero, reaches a global or local maximum, decreases to a local minimum, increases again, and finally decreases to a steady intermediate value. The biosensor performance is analyzed numerically using the finite difference method. Full article

Background: Glucuronide recycling in the gut and liver profoundly affects the systemic and/or local exposure of drugs and their glucuronide metabolites, impacting both clinical efficacy and toxicity. This recycling also alters drug exposure in the colon, making it critical to establish local concentration for drugs targeting colon (e.g., drugs for colon cancer and inflammatory bowel disease). Methods: In this study, a parent–metabolite middle-out physiologically based pharmacokinetic (PBPK) model was built for genistein and its glucuronide metabolite to estimate the systemic and local exposure of the glucuronide and its corresponding aglycone in rats by incorporating UDP-glucuronosyltransferase (UGT)-mediated metabolism and transporter-dependent glucuronide disposition in the liver and intestine, as well as gut microbial-mediated deglucuronidation that enables the recycling of the parent compound. Results: This parent–metabolite middle-out rat PBPK model utilized in vitro-to-in vivo extrapolated (IVIVE) metabolic and transporter clearance values based on in vitro kinetic parameters from surrogate species, the rat tissue abundance of relevant proteins, and saturable Michaelis–Menten mechanisms. Inter-system extrapolation factors (ISEFs) were used to account for transporter protein abundance differences between in vitro systems and tissues and between rats and surrogate species. Model performance was evaluated at multiple dose levels for genistein and its glucuronide. Model sensitivity analyses demonstrated the impact of key parameters on the plasma concentrations and local exposure of genistein and its glucuronide. Our model was applied to simulate the quantitative impact of glucuronide recycling on the pharmacokinetic profiles in both plasma and colonocytes. Conclusions: Our study underlines the importance of glucuronide recycling in determining local drug concentrations in the intestine and provides a preliminary modeling tool to assess the influence of transporter-mediated drug–drug interactions on glucuronide recycling and local drug exposure, which are often misrepresented by systemic plasma concentrations. Full article

Balancing food security with fertilizer-driven climate impacts remains critical in intensive agriculture. While organic–inorganic substitution enhances soil fertility, its effects on nitrous oxide (N₂O) and nitric oxide (NO) emissions remain uncertain. This study evaluated N₂O/NO emissions, crop yields, and agronomic parameters in a subtropical wheat–maize rotation under four fertilization regimes: inorganic-only (NPK), manure-only (OM), and partial substitution with crop residues (CRNPK, 15%) or manure (OMNPK, 30%), all applied at 280 kg N ha⁻¹ yr⁻¹. Emissions aligned with the dual Arrhenius–Michaelis–Menten kinetics and revised “hole-in-the-pipe” model. Annual direct emission factors (EF_d) for N₂O and NO were 1.01% and 0.11%, respectively, with combined emissions (1.12%) exponentially correlated to soil nitrogen surplus (p < 0.01). CRNPK and OMNPK reduced annual N₂O+NO emissions by 15–154% and enhanced NUE by 10–45% compared with OM, though OMNPK emitted 1.7–2.0 times more N₂O/NO than CRNPK. Sole OM underperformed in yield, while partial substitution—particularly with crop residues—optimized productivity while minimizing environmental risks. By integrating emission modeling and agronomic performance, this study establishes CRNPK as a novel strategy for subtropical cereal systems, reconciling high yields with low greenhouse gas emissions. Full article

In groundwater quality analysis, nonlinear models are typically used, with domains spanning the entire real number line. In this study, alongside these models (Logistic, Gompertz and Richards), nonlinear models defined based on functions whose domain is only the positive part of the real number line are presented (Michaelis–Menten, Hill 1 and 2 and Rosin–Rammler 1 and 2). Two case studies were observed in the paper: (i) the dependence of nitrate concentration on the pumping rate in the Bartolovec wellfield, and (ii) the dependence of nitrate concentration on the distance from the source of pollution in the Varaždin wellfield. Both wellfields are located in the alluvial aquifer in northwestern Croatia. In this way, the curves obtained on the basis of the mentioned mathematical functions were fitted to the experimental data. The results show a good fit, so that the values of the coefficients of determination R² are greater than 0.82 for the case study (i) and greater than 0.96 for the case study (ii). Since the models differ in the number of parameters (e.g., three parameters for Michaelis–Menten and five parameters for Rosin–Rammler), the corrected Akaike information criterion (AIC_c) was used for their comparison. In this way, the best fit for the case study (i) was obtained for the Rosin–Rammler 1 model, while for the case study (ii), it was for the Hill 1 model. A t-test was performed for all models, and they can be considered reliable at a significance level of 0.05. However, t-values and p-values were also calculated for each parameter of each model. Based on these results, it is concluded that all model parameters can be considered reliable at a significance level of 0.05 only for the Hill 1 and Rosin–Rammler 1 models in both case studies. For this reason, these models can generally be considered the best fit to the experimental data. The study demonstrates the superiority of nonlinear models with domains restricted to positive real numbers (e.g., Hill 1, Rosin–Rammler 1) over traditional models (e.g., Logistic, Richards) in groundwater quality analysis. These findings offer practical tools for predicting contaminant extremes (e.g., maximum/minimum concentrations) and optimizing groundwater management strategies. Full article

Alternative models for the respiration rate (RR) of ready-to-eat strawberries were evaluated as a function of O₂ and CO₂ concentration and temperature. The effect of the gaseous atmosphere and temperature on RR was determined in a total factorial experiment where 45 treatments were applied by combining factors: oxygen (0–21%) and carbon dioxide (0–15%) concentration at three levels and temperature (4–26 °C) at five levels. Both phenomenological (Michaelis–Menten, Langmuir) and non-phenomenological (Generalized linear and Quadratic) approaches were used to fit RR data. The temperature effect was modeled by Arrhenius, exponential, and power models. Model selection was performed based on R2-adjusted, RMSE, and IAC indicators. Models with R² greater than 0.80, lower RMSE, and AIC were selected. The quadratic model and Michaelis–Menten Uncompetitive-with power model for temperature dependence were the best predictors of the experimental data. An integrated mathematical model based on strawberry respiration activity considering the influence of oxygen, carbon dioxide, and temperature was obtained, allowing its use for MAP modeling. Full article

In this paper, we present a kinetic–metabolic model describing adeno-associated virus (AAV) production via HEK293 cells that encompasses the main metabolic pathways, namely, glycolysis, tricarboxylic acid cycle (TCA), pyruvate fates, the pentose phosphate pathway, anaplerotic reaction, amino acid metabolism, nucleotides synthesis, biomass synthesis, and the metabolic pathways of protein synthesis of the AAV (capsid and Rep proteins). For the modeling, Michaelis–Menten kinetics was assumed to define the metabolic model. A dataset from bioreactor cultures containing metabolite profiles of adeno-associated virus 6 (AAV6) production via triple transient transfection in a low-cell-density culture, including the concentration profiles of glutamine, glutamic acid, glucose, lactate, and ammonium, was utilized for fitting and computing the model parameters. The model that resulted from the adjusted parameters defined the experimental data well. Subsequently, a Sobol-based global sensitivity analysis procedure was applied to determine the most sensitive parameters in the final model. Full article

Foxtail millet (Setaria italica), vital in northern China, has its quality and taste influenced by starch and protein. Existing models do not simulate the accumulation of these components during growth. To address this, we enhanced the DSSAT-CERES-Millet model (referred to as DSSAT) by integrating two newly developed modules: the protein simulation module and the starch simulation module. The protein simulation module uses a nitrogen-to-protein conversion coefficient to determine grain protein accumulation based on grain nitrogen accumulation simulated by the DSSAT model. In the starch simulation module, the carbon source supply (carbohydrates) received by millet grains is calculated based on the simulated aboveground and vegetative dry matter by the DSSAT model, and starch synthesis is modeled using the Michaelis–Menten equation to convert carbohydrates into starch within the grains. The integrated model demonstrates good performance in simulating grain protein and starch accumulation, with NRMSE (normalized root mean square error) values of 3.06–26.22% and 4.06–26.88%, respectively. It also accurately simulates grain amylopectin and amylose accumulation at maturity, achieving an NRMSE of less than 14%. The enhanced DSSAT-CERES-Millet model can provide guidance for optimizing irrigation and nitrogen management to enhance the protein and starch quality of millet grains. Full article

The utility of four mineral adsorbents as potential feed additives to bind the boar taint compounds, androstenone and skatole, was assessed with an in vitro system. The adsorbents were bentonite (BNT), diatomaceous earth (DE), spent filter aid (SFA) and hydrated sodium–calcium aluminosilicate (HSCAS), with activated charcoal (AC) as a positive control. The binding capacity (B_max) and binding affinity (K) of androstenone (AND), estrone (E1), estrone sulfate (E1S), and skatole were estimated using the modified Michaelis–Menten kinetics. The Langmuir and Freundlich isotherm models were also used to assess the adsorption behaviour. The B_max values with AND were 77.7 ± 1.12%, 71.9 ± 1.93%, 55.0 ± 7.85%, and 69.5 ± 1.39% for BNT, DE, SFA, and HSCAS, respectively, with no differences in the binding affinity K (p > 0.05). All the mineral adsorbents had very low binding with E1S. SFA bound skatole with a B_max of 89.9 ± 1.09%, while the B_max values for skatole binding by BNT, DE and HCAS were approximately 15%. Most adsorbent–adsorbate complexes fit best with the Freundlich isotherm model. We conclude that all four mineral adsorbents bound androstenone, but not E1S, and only SFA effectively bound skatole. This suggests that SFA may act as a selective dietary binding agent to control boar taint, but further research using animal models is needed to explore the utility and selectivity of these adsorbents as feed additives to control boar taint. Full article

Queueing delays are one very important performance measure for most engineering network systems. Providing low-delay systems is a major goal of service providers, as it is a leading concern for users/customers. These network systems and their performance measures are typically analyzed using queueing-based models. Even though there are several available strong and precise mathematical models for analyzing queueing systems, their applications are limited to simple and small-scale systems due to their lack of scalability in real-life systems. Researchers have spent a good portion of their efforts toward perfecting the analysis of such systems. Precise and accurate results are available for single-node systems with standard operations. However, for analyzing multi-node systems with complex operations, one has to resort to approximations or simulations. Some of these approximations usually give an oversimplified view of such systems; these approximations remain quite limited. In this paper, we present a machine learning tool that can potentially be used to analyze most finite buffer queues to obtain reasonable approximations for the mean number of items in such systems. The machine learning tool we develop is based on supervised learning using the Michaelis–Menten non-linear model used in biochemistry and the results are simple to obtain. It is fast and very scalable; these characteristics represent the main features of this approach compared to existing systems. The coefficient of determination $R^2$ for all the examples presented are all higher than $90\%$, with some as high as $99.6\%$. Full article

This paper investigates the dynamical behavior of a discrete fractional-order modified Leslie–Gower model with a Michaelis–Menten-type harvesting mechanism and a Holling-II functional response. We analyze the existence and stability of the nonnegative equilibrium points. For the interior equilibrium points, we study the conditions for period-doubling and Neimark–Sacker bifurcations using the center manifold theorem and bifurcation theory. To control the chaos arising from these bifurcations, two chaos control strategies are proposed. Numerical simulations are performed to validate the theoretical results. The findings provide valuable insights into the sustainable management and conservation of ecological systems. Full article

Background: The aim of this study is to determine the impact of species and tissue differences on the glucuronidation of diclofenac in vitro. Method: Microsomes from different species (rat, monkey, mouse, dog, and human) and rat and human tissues (liver, intestine, and kidney) were used to assess the rate of glucuronidation reaction of diclofenac. The metabolites were quantified using ultra high-performance liquid chromatography (UHPLC) and fitted into a Michaelis–Menten model to determine the metabolic kinetic parameters. Results: The results showed higher rates of metabolism in the liver as compared to that of the intestine and kidney by both human and rat tissues microsomes. There were also differences in the rate of metabolism in the liver across the tested species, with mouse liver microsome having the highest maximum reaction rate (V_max) at 7.22 nmol/min/mg followed by human liver microsome at 6.66 ± 0.33 nmol/min/mg, dog liver microsome at 5.05 ± 0.42 nmol/min/mg, monkey liver microsome at 3.88 ± 0.15 nmol/min/mg, and rat liver microsome at 0.83 ± 0.04 nmol/min/mg. Conclusions: This study demonstrated that the liver is the major organ for the glucuronidation of diclofenac. In addition, glucuronidation of diclofenac was different across the tested species; therefore, the influence of species should be taken into consideration in the pharmacological, pharmaceutical, and toxicological study of diclofenac. Full article

Background: Peclinical and clinical studies have revealed that ezetimibe, an approved cholesterol-absorption inhibitor, is rapidly and extensively metabolized to a more potent metabolite, ezetimibe glucuronide. Since different species are commonly used in the pharmacokinetic and pharmacodynamic studies of ezetimibe, it is essential to determine the species difference in glucuronidation of ezetimibe in order to accurately evaluate ezetimibe’s pharmacokinetics and pharmacodynamics. The purpose of the study was to compare species differences in ezetimibe glucuronidation rates using intestinal microsomes from humans, rats, mice, monkeys, and dogs. Method: Intestinal microsomes from different species were used to assess the ezetimibe glucuronidation rates. Multiple substrate concentrations at 0.5, 2, 5, 10, 20, 30, 40, and 50 µM were tested and fitted into the Michaelis–Menten model to determine the enzyme kinetic parameters. Results: The results showed that the glucuronidation rates with these tested species were significantly different. Kinetic studies revealed that the maximum metabolic rate (V_max) was higher in monkeys (3.87 ± 0.22 nmol/mg/min) than that in rat (2.40 ± 0.148 nmol/mg/min) and mouse (2.23 ± 0.10 nmol/mg/min), and then human (1.90 ± 0.08 nmol/mg/min) and dog (1.19 ± 0.06 nmol/mg/min). The CLint was an 8.17-fold difference among these species, following the order of mouse > dog > human > rat = monkey. Conclusions: The study revealed that the rate of ezetimibe glucuronidation in the intestine was different in different species and has an impact on ezetimibe glucuronidation in the intestine. When analyzing the pharmacodynamics, pharmacokinetics, or toxicology of ezetimibe using different models, these species differences must be taken into consideration. Full article

Continuous flow technologies have become increasingly important for biocatalytic processes. In this study, we present the application and modelling of covalently immobilised N-acetylglucosamine 2-epimerase and N-acetylneuraminic acid lyase in packed bed reactors for the synthesis of N-acetylneuraminic acid. The immobilised enzymes were stable under continuous flow process conditions with half-life times of >28 d (epimerase immobilised on hexamethylamino methacrylate HA403/M) or 58 d (lyase immobilised on dimenthylamino methacrylate ECR8309M), suitable for continuous flow applications. Kinetic studies revealed Michaelis–Menten kinetic behaviour for both enzymes. The kinetic parameters and the inhibitions were analysed under continuous flow conditions and were integrated into a process model using Python. The model was validated by varying flow rates, the mass of immobilised enzymes and the reactor dimensions and shows a low error compared to the measured data. An error accuracy of 6% (epimerase) or 9% (lyase) was achieved. The product concentrations of the enzyme cascade at the end of the packed bed reactor can be predicted with an accuracy of 9% for the calculation of a large column (84.5 mL) or of 24% if several small columns (2.5 mL, 0.8 mL) are connected in series. The developed model has proved to be valid and will be used to optimise the process with respect to substrate concentrations, reactor dimensions and flow rate. Full article

A nonlinear log-Birnbaum–Saunders regression model with additive errors is introduced. It is assumed that the error term follows a flexible sinh-normal distribution, and therefore it can be used to describe a variety of asymmetric, unimodal, and bimodal situations. This is a novelty since there are few papers dealing with nonlinear models with asymmetric errors and, even more, there are few able to fit a bimodal behavior. Influence diagnostics and martingale-type residuals are proposed to assess the effect of minor perturbations on the parameter estimates, check the fitted model, and detect possible outliers. A simulation study for the Michaelis–Menten model is carried out, covering a wide range of situations for the parameters. Two real applications are included, where the use of influence diagnostics and residual analysis is illustrated. Full article