Associations between serum health biomarker concentrations and reproductive performance, accounting for milk yield, in pasture-based Holstein cows in southeastern Australia.

In this single cohort study, we investigated associations between the concentrations of a suite of serum biomarkers measured in the first 30 d of lactation and subsequent reproductive performance measured as mating start date to conception intervals, in pasture-based Holstein cows. A secondary objective was to examine associations between biomarker concentrations and 305-d milk yield to assess whether any positive associations between biomarker concentration and reproductive performance were explained by reduced milk production. The data used had been collected as part of an ongoing project from 2017 to 2020 to compile a data set from a large population of lactating dairy cows. Biomarkers measured were those associated with energy balance (ß-hydroxybutyrate [BHB] and nonesterified fatty acids [NEFA]), protein nutritional status (urea and albumin), immune status (globulin, albumin to globulin ratio and haptoglobin), and macromineral status (calcium and magnesium). Associations between biomarker concentrations and mating start date to conception interval were investigated using Cox proportional hazard models, using between 634 and 1,121 lactations (varying by biomarker) from 632 to 1,103 cows and 11 to 17 mating periods from 10 to 13 herds. Based on hazard ratio (HR) estimates and associated 95% confidence intervals (CI), hazard of conception on any particular day of the herds' mating periods was positively associated with the concentrations of albumin (HR = 1.09; 95% CI: 1.05-1.12), albumin to globulin ratio (HR = 2.82; 95% CI: 1.66-4.79), calcium (HR = 2.01; 95% CI: 1.18-3.43), and magnesium (HR = 2.17; 95% CI: 1.01-4.66), and negatively associated with globulin concentration (HR = 0.98; 95% CI: 0.97 to 1.00). There was also some evidence that NEFA concentration was negatively associated (HR = 0.76; 95% CI: 0.57 to 1.01), and urea concentration positively associated (HR = 1.05; 95% CI: 0.99 to 1.11), with reproductive performance, but no evidence that BHB and haptoglobin concentrations were associated with reproductive performance. Except for NEFA, presence and direction of the associations between the biomarker and milk yield were not discordant with that for reproductive performance. Also, except for NEFA, we found no substantial evidence of nonlinear relationships between biomarker concentration and either reproductive performance or milk yield. Correlations between biomarker concentrations were generally weak, indicating that multibiomarker panels may collectively predict reproductive performance better than any single biomarker. We noted substantial variation in the concentrations of all biomarkers within, and for some biomarkers, between herd-year groups. Collectively, these results indicate that there may be scope to improve biomarker concentrations through nutritional, management, and genetic interventions, and by association, reproductive performance and milk yield may also improve.

A novel approach to assess the health risk of aryl hydrocarbon receptor-bound contaminants via inhalation exposure using CYP1A1 expression as a biomarker.

Polycyclic aromatic hydrocarbons (PAHs) and dioxins are potential causes of multiple diseases by activating the aryl hydrocarbon receptor (AhR) pathway. Health risk assessment of chemicals primarily relies on the relative potency factor (RPF), although its accuracy may be limited when solely using EC50 values. The induction of cytochrome P4501A1 (CYP1A1) serves as a biomarker for AhR activation and is an integrator of dioxin-like toxicity. Here, we present a method for evaluating the risks associated with AhR activation using mathematical models of dose-CYP1A1 induction. The dose-effect curves for certain PAHs and dioxins, including Ant, BghiP, 1,2,3,4,7,8-HxCDD, and others, exhibited a non-classical S-shaped form. The toxic equivalent factor (TEF) profiles revealed a broad range of toxic equivalent factor values. The TEFs for PAHs ranged from approximately 0.01 to 6, with higher values being observed when the concentration was less than 10-10 M, with the exceptions of Ace, Phe, and BghiP. Most congeners of dioxins got the lowest TEF value at around 10-10 M, ranging from 0.04 to 1.00. The binding affinity of AhR to ligands did not display a strong correlation with the EC50 of CYP1A1 expression, suggesting that the AhR-mediated effects of PAHs and dioxins are not fixed but instead fluctuate with the dose. Air samples acquired from a parking area were used to compare the proficiency of RPF and our current approach. In the current method, naphthalene and chrysene were the primary contributors of PAHs to AhR-mediated risks in parking lots air samples, respectively. However, the contributions of naphthalene and chrysene could be disregarded in the RPF approach.

Growth-mediated negative feedback shapes quantitative antibiotic response.

Dose-response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose-response curves. The shape of the dose-response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose-response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose-response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose-response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.

Effects of UV-C Disinfection on N95 and KN95 Filtering Facepiece Respirator Reuse.

The objective of this study was to evaluate the effectiveness of UV technology for virus disinfection to allow FFR reuse. UV is a proven decontamination tool for microbial pathogens, including the SARS-CoV-2 virus. Research findings suggest that the impacts of UV-C treatment on FFR material degradation should be confirmed using microbial surrogates in addition to the commonly performed abiotic particle testing. This study used the surrogates, E. coli and MS-2 bacteriophage, as they bracket the UV response of SARS-CoV-2. Lower log inactivation was observed on FFRs than predicted by aqueous-based UV dose-response data for MS-2 bacteriophage and E. coli. In addition, the dose-response curves did not follow the trends commonly observed with aqueous data for E. coli and MS-2. The dose-response curves for the respirators in this study had a semicircle shape, where the inactivation reached a peak and then decreased. This decrease in UV inactivation is thought to be due to the degradation of the fibers of the FFR and allows for more viral and bacterial cells to wash through the layers of the respirator. This degradation phenomenon was observed at UV doses at and above 2,000 mJ/cm2. Results have demonstrated that FFR materials yield various results in terms of effective disinfection in experiments conducted on KN95 and N95 face respirators. The highest inactivation for both surrogates was observed with the KN95 respirator made by Purism, yielding 3 and 2.75 log inactivation for E. coli and MS-2 at UV doses of 1,500 mJ/cm2. The KN95 made by Anboruo yielded the lowest inactivation for MS-2 at 0.75 log when exposed to 1,000 mJ/cm2. To further test the degradation theory, experiments used a collimated beam device to test the hypothesis further that degradation is occurring at and above UV doses of 1,500 mJ/cm2. The experiment aimed to determine the effect of "predosing" a respirator with UV before inoculating the respirator with MS-2. In this test, quantification of the penetrated irradiance value and the ability of each layer to retain MS-2 were quantified. The results of the experiments varied from the intact FFR degradation experiments but displayed some data to support the degradation theory. IMPORTANCE Research suggests degradation of FFR materials at high UV doses is important. There appears to be a peak inactivation dose at approximately 1,500 mJ/cm2. The subsequent dose increases appear to have the reverse effect on inactivation values; these trends have shown true with both the N95 and KN95-Purism respirators.

A meta-analysis of responses of C3 plants to atmospheric CO2 : dose-response curves for 85 traits ranging from the molecular to the whole-plant level.

Generalised dose-response curves are essential to understand how plants acclimate to atmospheric CO2 . We carried out a meta-analysis of 630 experiments in which C3 plants were experimentally grown at different [CO2 ] under relatively benign conditions, and derived dose-response curves for 85 phenotypic traits. These curves were characterised by form, plasticity, consistency and reliability. Considered over a range of 200-1200 µmol mol-1 CO2 , some traits more than doubled (e.g. area-based photosynthesis; intrinsic water-use efficiency), whereas others more than halved (area-based transpiration). At current atmospheric [CO2 ], 64% of the total stimulation in biomass over the 200-1200 µmol mol-1 range has already been realised. We also mapped the trait responses of plants to [CO2 ] against those we have quantified before for light intensity. For most traits, CO2 and light responses were of similar direction. However, some traits (such as reproductive effort) only responded to light, others (such as plant height) only to [CO2 ], and some traits (such as area-based transpiration) responded in opposite directions. This synthesis provides a comprehensive picture of plant responses to [CO2 ] at different integration levels and offers the quantitative dose-response curves that can be used to improve global change simulation models.

Dating Sediments by EPR Using Al-h Centre: A Comparison between the Properties of Fine (4−11 µm) and Coarse (>63 µm) Quartz Grains

The possibility of EPR dating for sediments using Al-h signals of fine (4-11 µm) grains of quartz has not been previously discussed. Here, the Al-h and peroxy EPR spectra of fine (4-11 µm) and coarse (63-90, 125-180 µm) sedimentary quartz from thoroughly investigated loess sites in Eastern Europe were examined. By comparing experimental spectra with a simulated signal, we evaluated the overestimation observed when using the standard approach established by Toyoda and Falguères to measure Al-h intensity for different doses of radiation, up to 40,000 Gy. This overestimation, caused by the presence of peroxy signals, was much more pronounced for fine grains. Fine grains exhibited some additional dose-dependent signals, which, for some samples, caused a complete distortion of the Al-h spectra at high doses, making it impossible to measure the standard amplitude. We propose a new approach to measuring Al-h signal intensity, focusing on the peak-to-baseline amplitude of the part of the signal at g ≈ 2.0603, which is not affected by the peroxy signals and therefore has the potential of providing more accurate results. The shapes of dose response curves constructed for coarse and fine grains using the new approach show considerable similarity, suggesting that Al-h centre formation in fine and coarse grains upon artificial radiation at room temperature follows the same pattern.

Evaluation of disinfection byproducts for their ability to affect mitochondrial function.

In the race to deliver clean water to communities through potable water reuse, disinfection and water quality assessment are and will continue to be fundamental factors. There are over 700 disinfection byproducts (DBPs) in water; evaluating each compound is practically impossible and very time consuming. A bioanalytical approach could be an answer to this challenge. In this work, the response of four major classes of DBPs toward mitochondrial membrane potential (ΔΨm) and cytoplasmic adenosine triphosphate (C-ATP) was investigated with human carcinoma (HepG2) cells. Within 90 min of cell exposure, only the haloacetic acid (HAA) mixture caused a cytotoxic response as measured by C-ATP. All four groups (haloacetonitriles (HANs), trihalomethanes (THMs), nitrosamines (NOAs), and HAAs) responded well to ΔΨm, R2 > 0.70. Based on the half-maximum concentration that evoked a 50% response in ΔΨm, the response gradient was HANs >> HAAs ∼ THM > NOAs. The inhibition of the ΔΨm by HANs is driven by dibromoacetonitrile (DBAN), while dichloroacetonitrile (DCAN) did not cause a significant change in the ΔΨm at less than 2000 µM. A mixture of HANs exhibited an antagonistic behavior on the ΔΨm compared to individual compounds. If water samples are concentrated to increase HAN concentrations, especially DBAN, then ΔΨm could be used as a biomonitoring tool for DBP toxicity.

Distinct dose-dependent effects of methamphetamine on real-time dopamine transmission in the rat nucleus accumbens and behaviors.

Methamphetamine (METH) is a potent psychostimulant that exerts many of its physiological and psychomotor effects by increasing extracellular dopamine (DA) concentrations in limbic brain regions. While several studies have focused on how potent, neurotoxic doses of METH augment or attenuate DA transmission, the acute effects of lower and behaviorally activating doses of METH on modulating DA regulation (release and clearance) through DA D2 autoreceptors and transporters remain to be elucidated. In this study, we investigated how systemic administration of escalating, subneurotoxic doses of METH (0.5-5 mg/kg, IP) alter extracellular DA regulation in the nucleus accumbens (NAc), in both anesthetized and awake-behaving rats through the use of in vivo fast-scan cyclic voltammetry. Pharmacological, electrochemical, and behavioral evidence show that lower doses (≤2.0 mg/kg, IP) of METH enhance extracellular phasic DA concentrations and locomotion as well as stereotypies. In contrast, higher doses (≥5.0 mg/kg) further increase both phasic and baseline DA concentrations and stereotypies but decrease horizontal locomotion. Importantly, our results suggest that acute METH-induced enhancement of extracellular DA concentrations dose dependently activates D2 autoreceptors. Therefore, these different METH dose-dependent effects on mesolimbic DA transmission may distinctly impact METH-induced behavioral changes. This study provides valuable insights regarding how low METH doses alter DA transmission and paves the way for future clinical studies on the reinforcing effects of METH.

Required concentration index quantifies effective drug combinations against hepatitis C virus infection.

Successful clinical drug development requires rational design of combination treatments based on preclinical data. Anti-hepatitis C virus (HCV) drugs exhibit significant diversity in antiviral effect. Dose-response assessments can be used to determine parameters profiling the diverse antiviral effect during combination treatment. In the current study, a combined experimental and mathematical approaches were used to compare and score different combinations of anti-HCV treatments. A "required concentration index" was generated and used to rank the antiviral profile of possible double- and triple-drug combinations against HCV genotype 1b and 2a. Rankings varied based on target HCV genotype. Interestingly, multidrug (double and triple) treatment not only augmented antiviral activity, but also reduced genotype-specific efficacy, suggesting another advantage of multidrug treatment. The current study provides a quantitative method for profiling drug combinations against viral genotypes, to better inform clinical drug development.

Colloidal structure of honey and its influence on antibacterial activity.

Honey colloidal structure emerges as a new trend in research on honey functions since it became recognized as a major factor altering bioactivity of honey compounds. In honey complex matrix, macromolecules self-associate to colloidal particles at the critical concentration, driven by honey viscosity. Sequestration of macromolecules into colloids changes their activities and affects honey antibacterial function. This review fills the 80-year-old gap in research on honey colloidal structure. It summarizes past and current status of the research on honey colloids and describes physicochemical properties and the mechanisms of colloid formation and their dissociation upon honey dilution. The experimental observations are explained in the context of theoretical background of colloidal science. The functional changes and bioactivity of honey macromolecules bound to colloidal particles are illustrated here by the production of H2 O2 by glucose oxidase and the effect they have on antibacterial activity of honey. The changes in the production of H2 O2 and antibacterial activity of honey were coordinated with the changes in the aggregation-dissociation states of honey colloidal particles upon dilution. In all cases, these changes were nonlinear, assuming an inverted U-shaped dose-response curve. At the curve maximum, the production of H2 O2 and antibacterial activity reached the peak. The curve maximum signaled the minimum honey concentration required for the phase separation. With phase transition from two-phase colloidal condense state to dilute state dispersion, the change to opposite effects of dilution on these honey's activities occurred. Thus, the colloidal structure strongly influences bioactivity of honey compounds and affects its antibacterial activity.

Impact of wind speed and air pollution on COVID-19 transmission in Pakistan.

This study investigated the effect of wind speed and air pollution on COVID-19 from March 10, 2020, to October 04, 2020, in Pakistan. Wind speed and COVID-19 had positive correlation in Pakistan and its provinces. The inverted U-shaped dose-response curve was found for wind speed and COVID-19 in Punjab. Initially, the dose-response curve showed a positive link between wind speed and COVID-19 in Pakistan, Sindh, Khyber Pakhtunkhwa, and Islamabad Capital Territory. Later, it becomes downward sloped in Sindh, Khyber Pakhtunkhwa, and Islamabad Capital Territory. The expected log count of COVID-19 was increased by 0.113 times (Pakistan), 0.074 times (Punjab), 0.042 times (Sindh), and 0.082 times (Khyber Pakhtunkhwa) for a 1 km/h increase in the wind speed. The correlation between particulate matter and COVID-19 was positive (Pakistan, Punjab, and Islamabad Capital Territory) and negative (Sindh). The dose-response curve for particulate matter and COVID-19 had inverted U-shaped (Pakistan, Punjab, and Khyber Pakhtunkhwa) positively sloped (Islamabad Capital Territory), and negatively sloped (Sindh). The inverted U-shaped association shows that the COVID-19 initially increased due to a rise in the particulate matter but reduced when the particulate matter was above the threshold level. The particulate matter was also responsible to wear face masks and restricted mobility. The expected log count of COVID-19 cases was reduced by 0.005 times in Sindh for 1 unit increase in particulate matter. It is recommended to reduce particulate matter to control respiratory problems. The government should use media (print, electronic, social) and educational syllabus to create awareness about precautionary measures.

Simulation-based assessment of model selection criteria during the application of benchmark dose method to quantal response data.

BACKGROUND: To employ the benchmark dose (BMD) method in toxicological risk assessment, it is critical to understand how the BMD lower bound for reference dose calculation is selected following statistical fitting procedures of multiple mathematical models. The purpose of this study was to compare the performances of various combinations of model exclusion and selection criteria for quantal response data. METHODS: Simulation-based evaluation of model exclusion and selection processes was conducted by comparing validity, reliability, and other model performance parameters. Three different empirical datasets for different chemical substances were analyzed for the assessment, each having different characteristics of the dose-response pattern (i.e. datasets with rich information in high or low response rates, or approximately linear dose-response patterns). RESULTS: The best performing criteria of model exclusion and selection were different across the different datasets. Model averaging over the three models with the lowest three AIC (Akaike information criteria) values (MA-3) did not produce the worst performance, and MA-3 without model exclusion produced the best results among the model averaging. Model exclusion including the use of the Kolmogorov-Smirnov test in advance of model selection did not necessarily improve the validity and reliability of the models. CONCLUSIONS: If a uniform methodological suggestion for the guideline is required to choose the best performing model for exclusion and selection, our results indicate that using MA-3 is the recommended option whenever applicable.

Handling deviating control values in concentration-response curves.

In cell biology, pharmacology and toxicology dose-response and concentration-response curves are frequently fitted to data with statistical methods. Such fits are used to derive quantitative measures (e.g. EC[Formula: see text] values) describing the relationship between the concentration of a compound or the strength of an intervention applied to cells and its effect on viability or function of these cells. Often, a reference, called negative control (or solvent control), is used to normalize the data. The negative control data sometimes deviate from the values measured for low (ineffective) test compound concentrations. In such cases, normalization of the data with respect to control values leads to biased estimates of the parameters of the concentration-response curve. Low quality estimates of effective concentrations can be the consequence. In a literature study, we found that this problem occurs in a large percentage of toxicological publications. We propose different strategies to tackle the problem, including complete omission of the controls. Data from a controlled simulation study indicate the best-suited problem solution for different data structure scenarios. This was further exemplified by a real concentration-response study. We provide the following recommendations how to handle deviating controls: (1) The log-logistic 4pLL model is a good default option. (2) When there are at least two concentrations in the no-effect range, low variances of the replicate measurements, and deviating controls, control values should be omitted before fitting the model. (3) When data are missing in the no-effect range, the Brain-Cousens model sometimes leads to better results than the default model.

Optimum concentration-response curve metrics for supervised selection of discriminative cellular phenotypic endpoints for chemical hazard assessment.

High-content imaging (HCI) provides quantitative and information-rich measurements of chemical effects on human in vitro cell models. Identification of discriminative phenotypic endpoints from cellular features obtained from HCI is required for accurate assessments of potential chemical hazards. However, the use of suboptimal metrics to quantify the concentration-response curves (CRC) of chemicals based on these features may obscure discriminative features, and lead to non-predictive endpoints and poor chemical classifications or hazard assessments. Here, we present a systematic and data-driven study on the performances of different CRC metrics in identifying image-based phenotypic features that can accurately classify the effects of reference chemicals with known in vivo toxicities. We studied four previous HCI in vitro nephro- or pulmono-toxicity datasets, which contain phenotypic feature measurements from different cell and feature types. Within a feature type, we found that efficacy metrics at higher chemical concentrations tend to give higher classification accuracy, whereas potency metrics do not have obvious trends across different response levels. Across different cell and feature types, efficacy metrics generally gave higher classification accuracy than potency metrics and area under the curve (AUC). Our results suggest that efficacy metrics, especially at higher concentrations, are more likely to help us to identify discriminative phenotypic endpoints. Therefore, HCI experiments for toxicological applications should include measurements at sufficiently high chemical concentrations, and efficacy metrics should always be analyzed. The identified features may be used as specific toxicity endpoints for further chemical hazard assessment.

Recursive model for dose-time responses in pharmacological studies.

BACKGROUND: Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage. RESULTS: In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time responses with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. CONCLUSION: We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharmacological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds.

A meta-analysis of plant responses to light intensity for 70 traits ranging from molecules to whole plant performance.

By means of meta-analyses we determined how 70 traits related to plant anatomy, morphology, chemistry, physiology, growth and reproduction are affected by daily light integral (DLI; mol photons m-2  d-1 ). A large database including 500 experiments with 760 plant species enabled us to determine generalized dose-response curves. Many traits increase with DLI in a saturating fashion. Some showed a more than 10-fold increase over the DLI range of 1-50 mol m-2  d-1 , such as the number of seeds produced per plant and the actual rate of photosynthesis. Strong decreases with DLI (up to three-fold) were observed for leaf area ratio and leaf payback time. Plasticity differences among species groups were generally small compared with the overall responses to DLI. However, for a number of traits, including photosynthetic capacity and realized growth, we found woody and shade-tolerant species to have lower plasticity. We further conclude that the direction and degree of trait changes adheres with responses to plant density and to vertical light gradients within plant canopies. This synthesis provides a strong quantitative basis for understanding plant acclimation to light, from molecular to whole plant responses, but also identifies the variables that currently form weak spots in our knowledge, such as respiration and reproductive characteristics.

Modeling to capture bystander-killing effect by released payload in target positive tumor cells.

BACKGROUND: Antibody-drug conjugates (ADCs) are intended to bind to specific positive target antigens and eradicate only tumor cells from an intracellular released payload through the lysosomal protease. Payloads, such as MMAE, have the capacity to kill adjacent antigen-negative (Ag-) tumor cells, which is called the bystander-killing effect, as well as directly kill antigen-positive (Ag+) tumor cells. We propose that a dose-response curve should be independently considered to account for target antigen-positive/negative tumor cells. METHODS: A model was developed to account for the payload in Ag+/Ag- cells and the associated parameters were applied. A tumor growth inhibition (TGI) effect was explored based on an ordinary differential equation (ODE) after substituting the payload concentration in Ag+/Ag- cells into an Emax model, which accounts for the dose-response curve. To observe the bystander-killing effects based on the amount of Ag+/Ag- cells, the Emax model is used independently. TGI models based on ODE are unsuitable for describing the initial delay through a tumor-drug interaction. This was solved using an age-structured model based on the stochastic process. RESULTS: ß∈(0,1] is a fraction parameter that determines the proportion of cells that consist of Ag+/Ag- cells. The payload concentration decreases when the ratio of efflux to influx increases. The bystander-killing effect differs with varying amounts of Ag+ cells. The larger ß is, the less bystander-killing effect. The decrease of the bystander-killing effect becomes stronger as Ag+ cells become larger than the Ag- cells. Overall, the ratio of efflux to influx, the amount of released payload, and the proportion of Ag+ cells determine the efficacy of the ADC. The tumor inhibition delay through a payload-tumor interaction, which goes through several stages, may be solved using an age-structured model. CONCLUSIONS: The bystander-killing effect, one of the most important topics of ADCs, has been explored in several studies without the use of modeling. We propose that the bystander-killing effect can be captured through a mathematical model when considering the Ag+ and Ag- cells. In addition, the TGI model based on the age-structure can capture the initial delay through a drug interaction as well as the bystander-killing effect.

An Invitation to Pharmacostatics.

Pharmacology, the study of interactions between biological processes and therapeutic agents, is traditionally presented as consisting of two subdisciplines: pharmacokinetics, which is about the distribution and metabolism of drugs in organisms, and pharmacodynamics, which is about the organisms' response to drugs. In discovery-stage pharmacology however, one primary concern is what we call pharmacostatics, the characterization of equilibrium parameters and states of core interactions of physiologic and therapeutic interest. This usually takes the form of studying dose-response curves, without consideration for the relevant qualitative properties of the underlying reaction networks, e.g., the existence, multiplicity and asymptotic stability of steady states. Furthermore, steady-state calculations customarily employ manually derived closed-form expressions based on approximating assumptions. While these formulas may seem adequate most of the time, the assumptions need not apply, and there are genuine though seemingly uncommon cases where this approach is not feasible and/or fails to explain non-monotone dose-response curves. It is this paper's aim to stimulate interest in mathematical problems arising in pharmacostatics. We specifically pose two problems about a particular relevant class of networks of reversible binding reactions. The first problem is to exploit a certain fixed-point formulation of the equilibrium equation to devise an algorithmic method that would be compellingly preferable to current practice in the pharmacostatics context. The second problem is to explicitly anticipate the possibility of non-monotone dose-response curves from network topology. Addressing these problems would positively impact biopharmaceutical research, and they have inherent mathematical interest.

Randomized controlled trials define shape of dose response for Pollinex Quattro Birch allergoid immunotherapy.

BACKGROUND: The Birch Allergoid, Tyrosine Adsorbate, Monophosphoryl Lipid A (POLLINEX® Quattro Plus 1.0 ml Birch 100%) is an effective, well-tolerated short course subcutaneous immunotherapy. We performed 2 phase II studies to determine its optimal cumulative dose. METHODS: The studies were conducted in Germany, Austria and Poland (EudraCT numbers: 2012-004336-28 PQBirch203 and 2015-000984-15 PQBirch204) using a wide range of cumulative doses. In both studies, subjects were administered 6 therapy injections weekly outside the pollen season. Conjunctival Provocation Tests were performed at screening, baseline and 3-4 weeks after completing treatment, to quantify the reduction in Total Symptom Scores (as the primary endpoint) with each cumulative dose. Multiple Comparison Procedure and Modeling analysis was used to test for the dose response, shape of the curve and estimation of the median effective dose (ED50 ), a measure of potency. RESULTS: Statistically significant dose responses (P < .01 & .001) were seen, respectively. The highest cumulative dose in PQBirch204 (27 300 standardized units [SU]) approached a plateau. Potency of the PQBirch was demonstrated by an ED50 2723 SU, just over half the current dose. Prevalence of treatment-emergent adverse events was similar for active doses, most being short-lived and mild. Compliance was over 85% in all groups. CONCLUSION: Increasing the cumulative dose of PQBirch 5.5-fold from 5100 to 27 300 SU achieved an absolute point difference from placebo of 1.91, a relative difference 32.3% and an increase in efficacy of 50%, without compromising safety. The cumulative dose response was confirmed to be curvilinear in shape.

Systematic optimization of L-tryptophan riboswitches for efficient monitoring of the metabolite in Escherichia coli.

Riboswitches form a class of genetically encoded sensor-regulators and are considered as promising tools for monitoring various metabolites. Functional parameters of a riboswitch, like dynamic or operational range, should be optimized before the riboswitch is implemented in a specific application for monitoring the target molecule efficiently. However, optimization of a riboswitch was not straightforward and required detailed studies owing to its complex sequence-function relationship. Here, we present three approaches for tuning and optimization of functional parameters of a riboswitch using an artificial L-tryptophan riboswitch as an example. First, the constitutive expression level was adjusted to control the dynamic range of an L-tryptophan riboswitch. The dynamic range increased as the constitutive expression level increased. Then, the function of a riboswitch-encoded protein was utilized to connect the regulatory response of the riboswitch to another outcome for amplifying the dynamic range. Riboswitch-mediated control of the host cell growth enabled the amplification of the riboswitch response. Finally, L-tryptophan aptamers with different dissociation constants were employed to alter the operational range of the riboswitch. The dose-response curve was shifted towards higher L-tryptophan concentrations when an aptamer with higher dissociation constant was employed. All strategies were effective in modifying the distinct functional parameters of the L-tryptophan riboswitch, and they could be easily applied to optimization of other riboswitches owing to their simplicity.