Measurement Of Steady-state Concentrations Research Articles

Population pharmacokinetics and pharmacogenetics analyses of imatinib in Chinese patients with chronic myeloid leukemia in a real-world situation.

Imatinib is presently the first-line choice for the treatment of chronic myeloid leukemia. However, there are limited real-world data on Chinese patients to support individualized medicine. This work aims to characterize population pharmacokinetics in Chinese patients with chronic myeloid leukemia, investigate the effects of several covariates on imatinib exposure, and provide support for personalized medicine and dose reduction. A total of 230 patients with chronic myeloid leukemia were enrolled, and 424 steady-state concentration measurements were taken to perform the population pharmacokinetic analysis and Monte Carlo simulations with Phoenix NLME software. The effects of the demographic, biological, and pharmacogenetic (ten SNP corresponding to CYP3A4, CYP3A5, ABCB1, ABCG2, SCL22A1 and POR) covariates on clearance were evaluated. A one-compartmental model best-described imatinib pharmacokinetics. The hemoglobin and the estimated glomerular filtration rate (< 85mL⋅min-1⋅1.73 m2) were associated with imatinib clearance. The genetic polymorphisms related to pharmacokinetics were not found to have a significant effect on the clearance of imatinib. The final model estimates of parameters are: ka (h-1) = 0.329; Vd/F (L) = 270; CL/F (L⋅h-1) = 7.60. Key covariates in the study population accounting for variability in imatinib exposure are hemoglobin and the estimated glomerular filtration rate. There is some need for caution when treating patients with moderate-to-severe renal impairment and significant hemoglobin changes.

The Cutting Edge of Disease Modeling: Synergy of Induced Pluripotent Stem Cell Technology and Genetically Encoded Biosensors.

The development of cell models of human diseases based on induced pluripotent stem cells (iPSCs) and a cell therapy approach based on differentiated iPSC derivatives has provided a powerful stimulus in modern biomedical research development. Moreover, it led to the creation of personalized regenerative medicine. Due to this, in the last decade, the pathological mechanisms of many monogenic diseases at the cell level have been revealed, and clinical trials of various cell products derived from iPSCs have begun. However, it is necessary to reach a qualitatively new level of research with cell models of diseases based on iPSCs for more efficient searching and testing of drugs. Biosensor technology has a great application prospect together with iPSCs. Biosensors enable researchers to monitor ions, molecules, enzyme activities, and channel conformation in live cells and use them in live imaging and drug screening. These probes facilitate the measurement of steady-state concentrations or activity levels and the observation and quantification of in vivo flux and kinetics. Real-time monitoring of drug action in a specific cellular compartment, organ, or tissue type; the ability to screen at the single-cell resolution; and the elimination of the false-positive results caused by low drug bioavailability that is not detected by in vitro testing methods are a few of the benefits of using biosensors in drug screening. Here, we discuss the possibilities of using biosensor technology in combination with cell models based on human iPSCs and gene editing systems. Furthermore, we focus on the current achievements and problems of using these methods.

Validation of Individual Non-Linear Predictive Pharmacokinetic Parameters in a Rabbit Phenytoin Model

Purpose: To evaluate the predictive performance of phenytoin multiple dosing non-linear pharmacokinetic model in rabbits for possible application in therapy individualization in humans.Methods: Phenytoin was intravenously administered to 10 rabbits (2 – 3 kg). Plasma concentrations were measured by high pressure liquid chromatography (HPLC). Rabbits received 3 single phenytoin doses (11, 22 and 44 mg/kg) and plasma concentrations were fitted according to linear twocompartmental model. In all the rabbits, based on 3 different multiple doses (D1, D2, D3, range 9 – 15 mg/kg), 3 steady state plasma concentrations (Css1, Css2, Css3, range 20 - 56mg/l) were achieved. Formultiple dosage, the non-linear parameters, Km and Vm, were calculated according to the equations: Km = (D1-D2)/[(D2/Css2)-(D1/Css1)] and Vm = D2+KmD2/Css2, and individually used to calculate Css3 = D3Km/(Vm-D3). Predicted and measured Css3 values were compared.Results: The values for pharmacokinetic parameters after single doses were dose-dependent. The pronounced inter-individual variations in Km (extreme values 18 – 91 mg/l differed 5.5 times) and Vm (11 – 28 mg/kg/h) values were recorded. Significant correlation of predicted Css3 with the measured value for the same dose (D3) was found (r = 0.854, N = 10, p &lt; 0.01). There was no statistical difference between predicted and measured concentrations (t-dependent test = 1.074, p &lt; 0.05).Conclusion: Non-linear parameters, Km and Vm, obtained from only two steady-state concentration measurements can be successfully used to compute and achieve a particular steady-state plasma concentration and optimal dosage regimen.Keywords: Phenytoin, Rabbit, Pharmacokinetic model, Multiple dosing, Non-linear, Individualization

Analytical calculation of protein production distributions in models of clustered protein expression

A theoretical framework is presented, which derives chemical master equations for the number of protein molecules produced in a given time window, It is applied to derive analytical solutions that describe protein production distributions for the random bursting model (with an exponential or geometric burst-size distribution) and the clustering model. This distribution is experimentally observable using recently developed, single-molecule gene expression experiments. Furthermore, intrinsic stochasticity in a gene's expression can be calculated from protein production distributions using a new, time-dependent noise curve analysis. Different models of gene expression are compared with respect to their protein production distributions and intrinsic stochasticity, revealing the effects of molecular memory and burstlike expression on fluctuations in gene expression. It is distinct from and provides major advantages over measurements of steady-state concentrations.

Chapter 4 - A Framework for Modeling the Relationship Between Cellular Steady-state and Stimulus-responsiveness

In cell signaling systems, the abundances of signaling molecules are generally thought to determine the response to stimulation. However, the kinetics of molecular processes, for example receptor trafficking and protein turnover, may also play an important role. Few studies have systematically examined this relationship between the resting state and stimulus-responsiveness. Fewer still have investigated the relative contribution of steady-state concentrations and reaction kinetics. Here we describe a mathematical framework for modeling the resting state of signaling systems. Among other things, this framework allows steady-state concentration measurements to be used in parameterizing kinetic models, and enables comprehensive characterization of the relationship between the resting state and the cellular response to stimulation.

Interpolation of Steady‐State Concentration Data by Inverse Modeling

In most groundwater applications, measurements of concentration are limited in number and sparsely distributed within the domain of interest. Therefore, interpolation techniques are needed to obtain most likely values of concentration at locations where no measurements are available. For further processing, for example, in environmental risk analysis, interpolated values should be given with uncertainty bounds, so that a geostatistical framework is preferable. Linear interpolation of steady-state concentration measurements is problematic because the dependence of concentration on the primary uncertain material property, the hydraulic conductivity field, is highly nonlinear, suggesting that the statistical interrelationship between concentration values at different points is also nonlinear. We suggest interpolating steady-state concentration measurements by conditioning an ensemble of the underlying log-conductivity field on the available hydrological data in a conditional Monte Carlo approach. Flow and transport simulations for each conditional conductivity field must meet the measurements within their given uncertainty. The ensemble of transport simulations based on the conditional log-conductivity fields yields conditional statistical distributions of concentration at points between observation points. This method implicitly meets physical bounds of concentration values and non-Gaussianity of their statistical distributions and obeys the nonlinearity of the underlying processes. We validate our method by artificial test cases and compare the results to kriging estimates assuming different conditional statistical distributions of concentration. Assuming a beta distribution in kriging leads to estimates of concentration with zero probability of concentrations below zero or above the maximal possible value; however, the concentrations are not forced to meet the advection-dispersion equation.

Use of steady-state concentration measurements in geostatistical inversion

In geostatistical inverse modeling, hydrogeological parameters, such as hydraulic conductivity, are estimated as spatial fields. Upon discretization this results in several thousand (log-)hydraulic conductivity values to be estimated. Common inversion schemes rely on gradient-based parameter estimation methods which require the sensitivity of all measurements with respect to all parameters. Point-like measurements of steady-state concentration in aquifers are generally not well suited for gradient-based methods, because typical plumes exhibit only a very narrow fringe at which the concentration decreases from a maximal value to zero. Only here the sensitivity of concentration with respect to hydraulic conductivity significantly differs from zero. Thus, if point-like measurements of steady-state concentration do not lie in this narrow fringe, their sensitivity with respect to hydraulic conductivity is zero. Observations of concentrations averaged over a larger control volume, by contrast, show a more regular sensitivity pattern. We thus suggest artificially increasing the sampling volume of steady-state concentration measurements for the evaluation of sensitivities in early stages of an iterative parameter estimation scheme. We present criteria for the extent of artificially increasing the sampling volume and for decreasing it when the simulation results converge to the measurements. By this procedure, we achieve high stability in geostatistical inversion of steady-state concentration measurements. The uncertainty of the estimated parameter fields is evaluated by generating conditional realizations.

Quantitative imaging for discovery and assembly of the metabo‐regulome

Little is known about regulatory networks that control metabolic flux in plant cells. Detailed understanding of regulation is crucial for synthetic biology. The difficulty of measuring metabolites with cellular and subcellular precision is a major roadblock. New tools have been developed for monitoring extracellular, cytosolic, organellar and vacuolar ion and metabolite concentrations with a time resolution of milliseconds to hours. Genetically encoded sensors allow quantitative measurement of steady-state concentrations of ions, signaling molecules and metabolites and their respective changes over time. Fluorescence resonance energy transfer (FRET) sensors exploit conformational changes in polypeptides as a proxy for analyte concentrations. Subtle effects of analyte binding on the conformation of the recognition element are translated into a FRET change between two fused green fluorescent protein (GFP) variants, enabling simple monitoring of analyte concentrations using fluorimetry or fluorescence microscopy. Fluorimetry provides information averaged over cell populations, while microscopy detects differences between cells or populations of cells. The genetically encoded sensors can be targeted to subcellular compartments or the cell surface. Confocal microscopy ultimately permits observation of gradients or local differences within a compartment. The FRET assays can be adapted to high-throughput analysis to screen mutant populations in order to systematically identify signaling networks that control individual steps in metabolic flux.

Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy

One of the fundamental problems of cell biology is the understanding of complex regulatory networks. Such networks are ubiquitous in cells and knowledge of their properties is essential for the understanding of cellular behavior. In earlier work (Kholodenko et al. (PNAS 99: 12841), it was shown how the structure of biological networks can be quantified from experimental measurements of steady-state concentrations of key intermediates as a result of perturbations using a simple algorithm called “unravelling”. Here, we study the effect of experimental uncertainty on the accuracy of the inferred structure (i.e. whether interactions are excitatory or inhibitory) of the networks determined using the unravelling algorithm. We show that the accuracy of the network structure depends not only on the noise level but on the strength of the interactions within the network. In particular, both very small and very large values of the connection strengths lead to large uncertainty in the inferred network. We describe a powerful geometric tool for the intuitive understanding of the effect of experimental error on the qualitative accuracy of the inferred network. In addition, we show that the use of additional data beyond that needed to minimally constrain the network not only improves the accuracy of the inferred network, but also may allow the detection of situations in which the initial assumptions of unravelling with respect to the network and the perturbations have been violated. Our ideas are illustrated using the mitogen-activated protein kinase (MAPK) signaling network as an example.

Growth hormone and amino acid supply interact synergistically to control insulin-like growth factor-I production and gene expression in cultured ovine hepatocytes.

Many of the anabolic effects of growth hormone (GH) are indirect, occurring through GH-stimulated production of insulin-like growth factor-I (IGF-I) by the liver. As well as being regulated by GH, plasma IGF-I concentrations have been demonstrated to depend upon the level of dietary protein intake, with low protein diets being associated with reduced circulatory IGF-I levels. This inhibitory effect cannot be reversed by GH injection, suggesting that liver sensitivity to GH becomes impaired.To investigate the mechanisms through which protein supply affects GH sensitivity, primary cultures of ovine hepatocytes were grown in defined media, containing various proportions (0.2, 1.0 and 5.0) of jugular amino acid concentrations in fed sheep. Production of IGF-I by these cells was measured after 24 and 48 h in culture by radioimmunoassay. In the first 24-h period basal IGF-I production was the same in all defined media, and GH caused an approximately 2-fold increase in IGF-I release in cells grown in 1.0xor 5.0xamino acid media (P<0.01). Although GH appeared to increase IGF-I release in this period for cells grown in 0.2xamino acid media, this effect was not statistically significant. In the period from 24-48 h in defined media, both basal and GH-stimulated IGF-I production was dependent on amino acid availability (P<0.05 and P<0.001 respectively). Factorial analysis of variance demonstrated a strong positive interaction (P<0.001) between the effects of amino acid availability and GH, such that GH increased IGF-I production by more than 2-fold in cells grown in 5.0xamino acid media (P<0.01) but had no effect on production by cells grown in 1.0xor 0.2xamino acid media. Measurement of steady state concentrations of exon 1-derived IGF-I mRNAs using an RNase protection assay demonstrated that the observed effects on IGF-I peptide secretion were strongly associated with parallel effects at the RNA level. Incorporation of (35)S-methionine into cellular proteins over a 4-h period starting 20 h after transfer to defined culture media was not significantly reduced in 1.0xcompared with 5.0x amino acid media, although rates under both of these conditions were significantly higher than those seen in 0.2xamino acid media (P<0.01). The lack of correspondence between the dose-dependent effects of amino acid supply on cellular protein synthesis and those on basal and GH-stimulated IGF-I production, suggests that amino acid supply modulates IGF-I production through selective mechanisms. Steady state levels of the CCAAT/enhancer-binding protein beta (C/EBPbeta) isoforms, liver-enriched activating protein (LAP) and liver-enriched inhibitory protein (LIP) were determined by Western blotting. When levels of LAP were expressed relative to LIP levels in the same extracts, a significant decrease in the LAP:LIP ratio was observed in response to amino acid limitation (P<0.05). These data strengthen earlier arguments that synergistic interaction between the effects of amino acids and GH on hepatic IGF-I gene expression underlie nutrition-dependent changes in circulating IGF-I titres. The association between these effects and altered levels of C/EBPbeta isoforms suggests that CCAAT/enhancer mediated control of IGF-I gene expression may be involved in this phenomenon.

A multi-zone model evaluation of the efficacy of upper-room air ultraviolet germicidal irradiation.

Engineering controls can be used to reduce the spread of airborne infectious disease, particularly tuberculosis (TB), in high-risk settings. This article evaluates published data on the efficacy of upper-room air ultraviolet germicidal irradiation (UVGI). A three-zone representation of a TB patient room equipped with a germicidal UV lamp is developed. The lamp irradiates the upper-room zone and inactivates airborne mycobacteria; the unirradiated lower-room zone also contains a near-field zone surrounding the TB patient. Infectious particles are generated in the near-field zone and transported throughout the room by air flow between zones. Each zone is independently well-mixed; the whole room, however, is not well-mixed. The three-zone model is applied to a previously published study of UVGI against airborne mycobacteria in a test room. Based on the estimated slopes of the semi-log concentration decay curves for viable mycobacteria, and on the assumption that the test room was essentially well-mixed, the published study reported that UVGI provided 10 to 25 equivalent air changes per hour. However, when the same decay curve slopes are interpreted in the context of the three-zone model, UVGI is seen to be far less effective in reducing exposure intensity near the TB patient. Near-field exposure intensity is relevant because health care workers are usually in close proximity to the TB patients they attend. In general, the interpretation of concentration decay data depends on the specific model of room air mixing that is assumed appropriate. It is recommended that tests of the efficacy of UVGI and other control devices against airborne microorganisms be based on steady-state concentration measurements rather than concentration decay measurements, because the former measurements do not require inferences based on a particular model.

Maternal and fetal production rates of progesterone in rhesus macaques: placental transfer and conversion to cortisol.

Maternal and fetal progesterone (P4) metabolism and placental transfer were examined in vivo. Via a femoral vein, 3.2 microCi[14C]P4 were infused at a constant rate for 2 h into five rhesus macaques on days 131-137 of gestation. Simultaneously, 12 microCi[3H]P4 were infused into the fetuses via a placental bridging vein. Measurement of steady state concentrations of [14C]- and [3H]P4 in the maternal and fetal circulations permitted calculation of the MCRs, production rates (PRs), and transfer rates (Vs) of P4. The maternal MCR (533 liters/day) was higher than the fetal MCR (93 liters/day), whereas the maternal PR did not differ significantly from the fetal PR (2.3 and 1.0 mg/day, respectively). Placental transfer of P4 from the fetal to maternal circulation (VFM) was greater than that from the maternal to fetal circulation (VMF). Values were 0.23 and 0.07 mg/day, respectively. The utilization of circulating P4 as a substrate for fetal cortisol (F) production was examined in three additional monkeys for whom the amount of isotopically labeled P4 infusate was increased 5-fold. By determining the ratio of specific activities of [3H]F and [3H]P4 in the fetal circulation, we found the maximum contribution of circulating fetal P4 as a precursor of fetal F to be less than 1%. Our results indicate that: 1) higher fetal than maternal plasma P4 concentrations (11.3 and 4.3 ng/ml, respectively) are most likely the result of 5-fold lower fetal MCR, since the PRs are similar in the fetal and maternal compartments, and the VFM is greater than the VMF; and 2) fetal F production using circulating P4 as a substrate is minimal.