dataesr/scientific-paragraphs-categorization huggingface dataset:
2021; Pletz et al. 2020). Each of these relevant processes are simulated based on in vitro and in silico input data. On the other hand, generic PBK models generally apply only limited number of input parameters, particularly liver metabolism and distribution parameters like tissue:partition coefficients, to obtain first estimates of the kinetics of multiple chemicals  (Jongeneelen and Berge 2011; Peters 2008; Zhang et al. 2019). Different platforms are available that provide a userfriendly interface around bottom-up PBK model approaches, including commercial and open-source/freeware, such as GastroPlus®, Simcyp™, PK-Sim®, MEGEN/RVIS, and IndusChemFate. Bottom-up PBK tools are also available as function libraries for computer programming languages for example, httk, PLETHEM, and SimBiology  (Breen et al.
2021; Hack et al. 2020; Pearce et al. 2017b ). The societal request to reduce animal testing, and therefore the increased reliance on in vitro data for quantitative risk assessment demonstrate the necessity for good and reliable QIVIVE models. As indicated above, the PBK model stands at the very centre of QIVIVE modelling, taking into account the number of chemicals which will need to be assessed by New Approach Methodologies (NAMs) if animal testing is reduced significantly. PBK model development should become as efficient as possible. Therefore, it is essential that generic PBK models based on limited input data are available for such purposes. However, such models, efficient as they may be, will also need to be reliable and their applicability domain will need to be defined  (Honda et al.
This article summarises the outcome of the discussion based on the following questions: I. What are the commonalities and differences in these models? II. What are the critical input parameters to existing and new models? III. How can we make existing model domains reliable and acceptable to regulators? Commonalities and differences in structure of bottom-up PBK models for QIVIVE Over the last decade, an increasing number of platforms have become available to perform PBK model simulations. A summary of the different tools has been reported by  Hack et al. (2020). A survey amongst the PBK modelling community showed that amongst the different modelling platforms, Berkeley Madonna, R, and MATLAB are currently most frequently used for model building  (Hack et al. 2020; Paini et al. 2017 ). These are general-purpose differential equation solvers that require the definition of the model equations by the user.
2019). It depends on the platform, in which additional inputs need to be included. In addition, with general purpose differential equation solvers, each additional kinetic process needs to be manually added to the model code. Given that the physiology and differential equations of bottom-up PBK modelling approaches within different platforms are similar, differences in results are generally not due to the platform that is used, but due to the choices that users make and the input data that are included. Recently, Ahmad et al. (  2020 ) compared half-lives (t 1/2 ), maximum plasma concentrations (Cmax) and area under the plasma concentration-time curve (AUC) obtained with three different software packages (GiSim, GastroPlus and Simcyp) for a range of pharmaceutical compounds. They observed that the predicted AUC 0-tlast , Cmax, and t 1/2 of 62-75% of the 58 selected compounds were within threefold range of the observed values in humans.
In addition, they observed that the average predictive performance did not relate to the software package that was used  (Ahmad et al. 2020). Potential differences in model predictions between different PBK modelling platforms can also be seen in Fig. 1 , which shows literature reported Cmax and AUC predictions (normalised to the dose) for acetaminophen, caffeine, and coumarin, that are each obtained with different platforms. The simulations are generally within a narrow range. Although the Cmax and AUC predictions are overall close to each other, the results of Fig. 1  also reveal that, in case of coumarin, the Cmax and AUC predictions are substantially different when using the results from the httk package  (Pearce et al. 2017b ) compared with the other platforms. The httk package uses an intrinsic liver metabolic clearance estimate of 0 L/h for coumarin, based on in vitro measurements with human hepatocytes  (Wetmore et al.
For example, the preservation of expression and activity levels in in vitro renal cell models is poor, variable and often unknown, hampering their use for QIVIVE  (Scotcher et al. 2016). It should be noted that not all parameters depicted in Table  1  need to be included for every combination of   (Pearce et al. 2017b). The remaining PBK model results were obtained from different scientific publications. In case of acetaminophen, the data were obtained from  (Gaohua et al. 2016; Mian et al. 2020; Pearce et al. 2017b; Statelova et al. 2020). For caffeine, the PBK-predicted plasma levels were obtained from  (Gajewska et al. 2014; Gaohua et al. 2016; Mian et al. 2020; Pearce et al. 2017b ) and for coumarin from  (Gajewska et al. 2014; Moxon et al. 2020; Pearce et al. 2017b ). Both the Cmax and AUC values were normalised to the dose to be able to compare the results from the different publications.
2019; Wetmore et al. 2015 Wetmore et al. , 2013 Wetmore et al. , 2012)). These form a basis for a strategy for PBK model optimisation and selection for QIVIVE applications that covers the wide spectrum of chemical entities and is applicable to in vitro assays that represent all elements of chemical safety testing. Aside from kinetic input parameters to develop PBK models, the type of in vitro toxicity data as input to calculate external exposure levels that are safe/toxic to humans is critical for adequate risk assessments. In vitro toxicity readouts are biomarkers for molecular perturbations in toxicity pathways. Organism-level Adverse Outcome Pathway (AOP) models are required to extrapolate in vitro toxicity pathway perturbation to in vivo Points of Departure (POD). Organism-level Adverse Outcome Pathway (AOP) models are required to extrapolate in vitro toxicity pathway perturbation to in vivo Points of Departure (POD).
For QIVIVE researchers, the principle of parsimony can also guide research-if a kinetic process is a necessity but cannot be measured, then it may be addressed through either development of a new in vitro method or identification of a set of pre-existing data sufficient to allow inference for new chemicals. Of course, scientific dialogue between Fig. 2  Considerations influencing regulatory acceptance of PBK models regulators and model developers will always be extremely valuable to facilitate the overall understanding and adoption of a modelling approach. Default parameters, step 2 "Built-in" values for "default" point estimates of physiological and anatomical parameters may vary amongst platforms/implementations. This highlights the importance to harmonising physiological and anatomical parameters for a specific context of use and an organism.
It is important to realise that when QIVIVE becomes the standard approach for risk assessment, where animal testing would not be allowed anymore, evaluation of the PBK model against in vivo data specific to a given chemical will not be possible (that is, new chemical-specific data will not be collected). PBK models will need to be evaluated against pre-existing in vivo data and the observed performance extrapolated to new chemicals  (Cohen Hubal et al. 2019; Wambaugh et al. 2015). Although PBK-based predictions of in vivo blood concentration based on in vitro data have frequently been shown to be sufficiently adequate for a wide range of chemicals, poor model performance is often due to applications outside the original model domain  (Punt et al. 2022 (Punt et al. , 2021a;; Wambaugh et al.
2015). Read-across approach is proposed as an alternative to in vivo kinetic data to evaluate model predictive performance. The predictive ability on the analogues provides evidence for the applicability of the model to the chemical of interest. So, PBK models for data-poor chemicals may be useful if they are built using a database of structurally diverse data-rich analogues. The rational identification and selection of the analogues have been previously discussed  (Ellison 2018; Ellison and Wu 2020; OECD Environment 2021; Paini et al. 2021b ). However, this requires more initiatives for data sharing amongst different sectors. Along these lines, the European Partnership for Alternative Approaches to Animal Testing (EPAA) is currently working on assessing the chemical space coverage of existing PBK models  (Thompson et al.
Conclusion With the increasing application of in vitro non-animal NAMs for chemical risk assessment and the concomitant phasing out of animal testing, the use of in vitro assays for quantitative hazard characterisation becomes increasingly important. QIVIVE is a general process translating in vitro concentration-response relationships to in vivo dose-response relationships in humans and animals. PBK models provide a means for QIVIVE and have a substantial role in these extrapolations. Several PBK platforms are available, have a common general approach, and provide similar results, when harmonised inputs are used. Discrepancies in model output are generally due to the choices in model structure and the input data included. Although bottom-up PBK modelling is increasingly used by the scientific community, regulatory acceptance of model predictions in regulatory risk assessments remains a challenge.
Scientific dialogues between risk assessment bodies and modellers will help increase the understanding of a model and the value or limitations and facilitate gaining confidence in PBK model simulations, including alignment of terminologies and definitions within the PBK model community. -Harmonising physiological input parameters and their distribution (to assess inter-individual variability). Criteria for good-quality chemical-specific input parameters, especially in the absence of in vivo data, should be defined. -Using GMP and a stepwise approach to develop PBK models to achieve transparency. -Developing benchmark datasets and standardised metrics to evaluate QIVIVE predictions. -Carrying out case studies to gain confidence in PBK model predictions in a regulatory context and serve as critical inputs for developing guidelines.
Acknowledgements The ECETOC organizing committee and the authors would like to thank all experts who also contributed to this work:  David Bell  (ECHA),  Jean Lou Dorne (EFSA) ,  Eric Fabian (BASF) ,  Carsten Kneuer  and  Ralph Pirow  ( Federal Institute for Risk Assessment, Germany ),  George M. Loizou (Health and Safety Executive (HSE) Science and Research Center, UK) ,  Sheila Annie Peters  ( Merck KGaA, Darmstadt, Germany ),  Amin Rostami-Hodjegan  ( Centre for Applied Pharmacokinetic Research (CAPKR), University of Manchester ),  Alex Stevens (Syngenta) , and  Marco Zeilmaker  ( National Institute for Public Health and the Environment, Netherlands ). The views expressed in this publication are those of the authors and do not necessarily represent the views or policies of the  U. S. EPA.
Statistical analysis Data were subjected to analysis of variance (ANOVA) of SAS  [39] , with the diet as the main factor. All data were analyzed with the GLM procedure of SAS, according to a complete block design, with the pen being considered as the experimental unit. Before carrying out statistical analysis of the microbial counts, logarithmic conversion of the data was performed. In addition, the diarrhea rate was analyzed with nonparametric test. Differences among treatment means were determined using Tukey's multiple range tests with a P < 0. 05 indicating signi cance. P-value between 0. 05 and 0. 10 are shown and described as a trend. All data were examined for residual normality, linearity, homogeneity of variance, and outliers. Variables were analyzed according to the following mathematical model: Yij = μ + βi + ϵij in which Yij = observation j of experimental unit subjected to treatments i, µ = general constant, βi = dietary treatments, and εij = random error associated to each observation.
In view of the very large number of mutations that have been associated in vitro/in vivo/in silico with antibiotic resistance, it is time to set up not only retrospective, but also prospective validation of a database compiling the mutations with a real impact and that should be searched for before treatment, following the example of the WHO's data curation for Mycobacterium tuberculosis. Since, to our knowledge, no such database has yet been effectively compiled, we are proposing the validation criteria necessary to consider a newly discovered mutation as solidly proven. First, the discovery must be observed on an isolated bacterial strain rather than on a clinical sample, whether or not the patient is in therapeutic failure. If the mutation cannot be found, the authors should obtain the strain and perform the susceptibility test according to the current reference procedure.
Stool PCR globalizing subpopulations could help to detect minor variant subpopulations  [10, 11]. In conclusion, the increasing rate of antibiotic resistance is a major public health problem, especially in major bacterial pathogens such as Helicobacter pylori. In such infections, it is widely recognized that guided antibiotic therapy is crucial to optimize clinical management. In the context of difficult bacterial culture and given the democratization of molecular biology systems in medical laboratories, validated databases developed in appropriate models to guide the development of new antibiotic molecules or diagnostic procedures are crucial prior to further exploration in animal model examples. The proposed approach will open a new field of research on therapeutic development designed to render therapies more effective by improving the initial choice of antibiotics.
Data acquisition and processing The sensors used to record the force (5 kN load cell) and the displacement (mechanical traverse stroke) were those natively associated with the testing machine. The synchronized acquisition of the measurements was carried out using the TrapeziumX software (Shimadzu, Japan) with a sampling frequency of 10 Hz. Owing to the large number of cycles, any acquisition represents a dataset of 1. 620. 000 points of measurement stored in a 41. 5 Mo file per test. The data were processed with Matlab ® Release 2018 (The MathWorks, Inc. , Natick, MA) and Excel ® (Microsoft Corporation, Albuquerque, NM). Raw data were analyzed to extract several parameters from each test. Raw displacement data were filtered by applying a two-way average moving filter (window size: N = 500 over approximately 30 cycles) to extract the global behavior of each tested sample (mean filtered displacement curves).
The linear stiffnesses from this first mechanical traction corresponded to a preloading phase and were therefore not considered later on. Linear stiffnesses calculated at the 2nd, 10th, and 100,000th cycles were quite similar between samples (SD = 90, 70, and 73 N/mm at the 2nd, 10th, and 100,000th cycles, respectively). The displacement recorded at the end of the tests was within the range (0. 72-4 mm) (mean = 2. 2 mm; SD 1. 15 mm). Two samples (1G and 4G) out of the seven exceeded a displacement of 3 mm: this threshold could be chosen as the in situ functionality range limit for the fixation system according to previous studies  (Loutzenheiser et al. , 1995; Wüst et al. , 2006). The linear stiffnesses after the first cycle seem homogeneous throughout the datasets, showing a strong dynamic strength of interference screw-based fixations of the UHMWPE implant in the femoral and tibial bones.
, 2018; Merlin et al. , 2021). In the absence of information on precise location of French equines, these investigations are time consuming and delay both the identification of sick animals and implementation of control measures. This example shows that the traceability of equines in France needs to be improved in order to effectively take measures against diseases. Currently, only the movements of racehorses in training have to be traced for anti-doping tests. For all other equines, current French regulations only require registration of equine premises in the SIRE database, which is France's national identification system. They do not, however require the registration of the equines kept there. The spatial distribution of French equines is currently estimated from surveys of professional structures, and identification checks carried out by the French horse and riding institute (IFCE) (Ecus-Ifce).
However, the representation obtained is partial, as the majority of equines are kept in non-professional structures, often on the horse owners' premises. All around the world, surveillance plan strategies and outbreak management efforts in the equine sector are hampered by the lack of information on the precise location of equines and their movements. The recent enforcement in April 2021 of the European Union animal health law (EU's AHL) on transmissible animal diseases (Regulation (EU) 2016/429  (Anonyme, 2016) ) should improve the traceability of equines, imposing mandatory notification of equines' usual location. Nevertheless, the operational implementation of this measure should take many years, even in countries with near-complete equine identification and a centralized identification database.
The final result was a theoretical map of the British equine population. Adapting the approach of Lo  Lacono et al. (2013) , our objective was to estimate the spatial distribution of the equine population in metropolitan France at the commune scale (a commune being the smallest French administrative unit). In our case, for comparison purposes and to assess whether the location of dead equines can be representative of that of live equines, two samples of live equines and dead equines were used like training datasets. To assign a location to live equines registered in the central database from the SIRE database, two different assignment methods were realized. The first one assigned a single holding commune for all equines of each owner and the second method allowed one, two or three holding communes of owners, depending on the number of linked equines (one commune for one equine, two communes for two equines, or three communes for three or more equines).
Training datasets The model was calibrated using information from three datasets: -A survey sample dataset of 2353 precisely located live equine-owner pairs. This sample came from a 2019 national survey of owners and keepers registered in the SIRE database  (Farchati et al. , 2020). The main characteristics of this sample are available in Table  1. The sample was obtained by linking each owner with one or more communes of equine ownership, with as many pairs as different communes of ownership for each owner. Racehorses in training were excluded from this dataset because they were considered differently in this study. This dataset is referred to as the live equine sample in the following. -A sample of 13,595 dead equine-owner pairs extracted in April 2019 from the fallen stock data interchange (FSDI) database, which has been centralizing information on all removed (dead) animals-animal identification, commune of death, and information on the person who requested the removal (name, surname or company name, commune of residence)-since 2011.
The pairs included were those for which i) the equine identification number was traceable in the SIRE database (n = 97,327, i. e. 33% of 298,733 initial records in the extraction of FSDI), ii) there was a high probability that the applicant was the owner of the dead equine (n = 44,771 among the 97,327) and iii) the applicant had filled out the information on the cadaver online (n = 13,595 among the 44,771) (through ATM 1 services managed by the IFCE). The selection was made by crossreferencing the FSDI and SIRE databases, and comparing the identification and location information for owners, keepers and removal applicants. The intention in limiting the sample to owners who used the online system was to increase the likelihood that the owners' home communes as registered in the SIRE database were up to date (as the recording of information at ATM allows it to update SIRE).
This sample of dead equine-owner pairs is referred to as the dead equine sample in the following and is described in Table  1. -Data extracted from the CORINE Land Cover (CLC)  (Cover)  geographic database, to calculate the proportion of urban coverage for each commune in metropolitan France. CLC is a biophysical inventory of European land use and its changes according to a nomenclature of 44 items. To take into account the urban/rural land use aspect for equines, we calculated the proportion of continuous and discontinuous urban coverage relative to the total surface area of each commune. 1 ATM-équidés ANGEE (ATM) is an association created in 2009, following privatization of the public rendering service, to organize cadaver removal. It offers owners negotiated prices for cadaver removal in order to limit illegal burial and provide rendering companies with a guaranteed payment for removals.
Mapping datasets In order to obtain realistic equine density maps, we assigned to each equine presumed to be living in metropolitan France a "holding" commune (where it was kept) out of the most probable ones, based on the model results. An initial identification of the equine presumed to be living in France was thus necessary. The SIRE database, managed since 1976 by the IFCE, centralizes declarative information on almost all of the equines living in France (equine identifying number, sex, breed, date of birth, date of death declaration (if relevant)), along with their owners (owner identifying number, commune for the owner's residence, date on which the equine owner became its owner, and date on which information was last updated) and in parallel information of keepers and equine premises (keeper identifying number, commune for the keeper's residence, commune of the equine premises and the date of the premises were opened and closed (if applicable)) without specific traceable link between keepers and the equines they keep, and therefore a lack of centralized equine location information in France.
This database has also difficulty in identifying with certainty whether an equine is still alive or dead (it has been estimated that nearly 30-40% of owners have never reported the death of their dead equine(s) to the IFCE, or reported it long after the death (IFCE, personal communication)). The living equine population is consequently not well known. Four data sources were cross-checked to constitute the set of equine-owner pairs for which the equine and its owner were presumed to be living in Metropolitan France: -Data were extracted from the SIRE database in late March 2019 as a basis for selecting appropriate data. This dataset included 3,031,446 equines owned by 790,051 owners located in 35,359 communes. -A dataset for 2015-2019 extracted from Europe's Trade Control and Expert System (TRACES) (TRACE) was used to identify equines that were no longer living in France because they had been exported.
TRACES is a computerized system for international trade in live animals, animal products and certain non-animal products. It records, among other things, the SIRE identifier, transponder or international number and the date of departure of exported equines. The dataset extracted concerned 31,336 equines. -The FSDI database was used to identify the dead equines collected by rendering companies that had not been declared dead in the SIRE database. The whole database since its creation was used i. e. , 298,733 removals. -A dataset of racehorses with the exact location of the horse corresponding to the commune in which it was being trained. This set included 37,776 trotters recorded as being in training at least once between August 2018 and August 2019, and 13,290 gallopers recorded as being in training at least once between July 2020 and October 2020.