Highlights
PBK models have helped to facilitate quantitative
in vitro
to
in vivo
extrapolation.
PBK modelling has the potential to play a significant role in reducing animal testing.
It is critical to assess the validity of PBK models built using non-animal data.
A framework is needed for communicating characteristics and results of PBK modelling.
Physiologically based kinetic (PBK) models are used widely throughout a number of working sectors, including academia and industry, to provide insight into the dosimetry related to observed adverse health effects in humans and other species. Use of these models has increased over the last several decades, especially in conjunction with emerging alternative methods to animal testing, such as in vitro studies and data-driven in silico quantitative-structure-activity-relationship (QSAR) predictions. Experimental information derived from these new approach methods can be used as input for model parameters and allows for increased confidence in models for chemicals that did not have in vivo data for model calibration. Despite significant advancements in good modelling practice (GMP) for model development and evaluation, there remains some reluctance among regulatory agencies to use such models during the risk assessment process. Here, the results of a survey disseminated to the modelling community are presented in order to inform the frequency of use and applications of PBK models in science and regulatory submission. Additionally, the survey was designed to identify a network of investigators involved in PBK modelling and knowledgeable of GMP so that they might be contacted in the future for peer review of PBK models, especially in regards to vetting the models to such a degree as to gain a greater acceptance for regulatory purposes.
Costs, scientific and ethical concerns related to animal tests for regulatory decision-making have stimulated the development of alternative methods. When applying alternative approaches, kinetics have been identified as a key element to consider. Membrane transporters affect the kinetic processes of absorption, distribution, metabolism and excretion (ADME) of various compounds, such as drugs or environmental chemicals. Therefore, pharmaceutical scientists have intensively studied transporters impacting drug efficacy and safety. Besides pharmacokinetics, transporters are considered as major determinant of toxicokinetics, potentially representing an essential piece of information in chemical risk assessment. To capture the applicability of transporter data for kinetic-based risk assessment in non-pharmaceutical sectors, the EU Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) created a survey with a view of identifying the improvements needed when using in vitro and in silico methods.Seventy-three participants, from different sectors and with various kinds of expertise, completed the survey. The results revealed that transporters are investigated mainly during drug development, but also for risk assessment purposes of food and feed contaminants, industrial chemicals, cosmetics, nanomaterials and in the context of environmental toxicology, by applying both in vitro and in silico tools. However, to rely only on alternative methods for chemical risk assessment, it is critical that the data generated by in vitro and in silico methods are scientific integer, reproducible and of high quality so that they are trusted by decision makers and used by industry. In line, the respondents identified various challenges related to the interpretation and use of transporter data from non-animal methods. Overall, it was determined that a combined mechanistically-anchored in vitro-in silico approach, validated against available human data, would gain confidence in using transporter data within an animal-free risk assessment paradigm. Finally, respondents involved primarily in fundamental research expressed lower confidence in non-animal studies to unravel complex transporter mechanisms.
The article presents the scalar calibration method that uses a neural network for the determination of parameters of the inverse model of the vector magnetometer. Utilization of the one layered, feed-forward neural network with the back propagation algorithm has suppressed the systematic errors of the vector magnetometers, namely the multiplicative, additive, orthogonality and linearity errors. Methodology shown in the article was designed and used for a pre-flight calibration of the magnetometer used in the first Slovak satellite skCUBE, where the magnetometer performs stabilization and navigation tasks. The experiment was performed in a 3D Helmholtz coil system, where the Earth magnetic field was suppressed and at the same time the stimulation field was created. Suppression of the Earth magnetic field was achieved by special positioning of the satellite. Honeywell HMC 5883L was used for the verification of the methodology.
The article describes a method for diagnosing the accuracy of the vehicle scale without using standard weights. The novel method defines the possibility to estimate whether the scale would pass the test for error of indication in the next verification or not, only by using the results from simple tests with load of estimated weight and appropriate classifier. The method is primarily developed for users of these scales. Created classifier is based on the neural network algorithm. The neural network was trained with data from verifications, which are provided by Slovak Legal Metrology. Well trained classifier can provide not only information whether the scale will potentially pass the mentioned test or not, but reliability which is associated with this result as well. In this way, the user has valuable information about the scale in the period between the verifications.
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