Biological Activity Data Research Articles

TD-DFT calculations, dipole moments, and solvatochromic properties of 2-aminochromone-3-carboxaldehyde and its hydrazone derivatives.

2-Aminochromone-3-carboxaldehyde (ACC) and its hydrazones (ACMHCA and ACMNPHTCA) with semicarbazide hydrochloride and N-phenylthiosemicarbazide were synthesized and characterized by elemental analysis and spectral studies. The solvatochromic behavior of the title compounds in various solvents showed distinct bathochromic shifts on going from nonpolar to polar solvents, suggesting intramolecular-charge-transfer (ICT) solute-solvent interactions. The ground and excited state dipole moments of ACC, ACMHCA, and ACMNPHTCA were determined experimentally by the solvatochromic shift method using the Bilot-Kawski, Lippert-Mataga, Bakhshiev, Kawski-Chamma-Viallet functions, and a microscopic Reichardt's solvent polarity parameter (ENT). All the investigated molecules showed a substantial increase in the dipole moment upon excitation to the emitting state. The experimental results were generally consistent with the values obtained by the TD-DFT, B3LYP/6-311G++(d,p) method. Molecular electrostatic potential (MEP) mapping and natural charge and natural bonding orbital (NBO) analysis were performed and the results were discussed. The 1H NMR chemical shifts of the prepared compounds were simulated by the gage independent atomic orbital (GIAO) method and compared with their experimental chemical shift values. The biological activity data were correlated with the frontier molecular orbitals. The photovoltaic behavior of the title compounds showed there was sufficient electron injection.

Binary Classification of the Endocrine Disrupting Chemicals by Artificial Neural Networks

We develop a machine learning framework that integrates high content/high throughput image analysis and artificial neural networks (ANNs) to model the separation between chemical compounds based on their estrogenic receptor activity. Natural and man-made chemicals have the potential to disrupt the endocrine system by interfering with hormone actions in people and wildlife. Although numerous studies have revealed new knowledge on the mechanism through which these compounds interfere with various hormone receptors, it is still a very challenging task to comprehensively evaluate the endocrine disrupting potential of all existing chemicals and their mixtures by pure in vitro or in vivo approaches. Machine learning offers a unique advantage in the rapid evaluation of chemical toxicity through learning the underlying patterns in the experimental biological activity data. Motivated by this, we train and test ANN classifiers for modeling the activity of estrogen receptor-α agonists and antagonists at the single-cell level by using high throughput/high content microscopy descriptors. Our framework preprocesses the experimental data by cleaning, scaling, and feature engineering where only the middle 50% of the values from each sample with detectable receptor-DNA binding is considered in the dataset. Principal component analysis is also used to minimize the effects of experimental noise in modeling where these projected features are used in classification model building. The results show that our ANN-based nonlinear data-driven framework classifies the benchmark agonist and antagonist chemicals with 98.41% accuracy.

3D QSAR based Virtual Screening of Flavonoids as Acetylcholinesterase Inhibitors.

In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found tobe the leadcompound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.

Quality Control of Chemogenomic Library Using LC-MS.

In chemical biology, using compounds with incorrect identity or insufficientpurity can lead to misleading biological activity data. Chemical quality control for confirmation of purity and compound identity is thus central to chemogenomics. We have established a medium-throughput LC-MS-based semi-automated quality control (QC) workflow with a minimal requirement for materials suitable for chemogenomics and other small molecule libraries. This rapid method can cover a broad chemical space ofsmall organic compounds with diverse physicochemical properties such as polarity or lipophilicity.

Novel 1,3,4-oxadiazole sulfonate/carboxylate flavonoid derivatives: synthesis and biological activity.

With the long-term use of traditional bactericides and antiviral agents, drug resistance has become increasingly prominent, resulting in impaired crop growth and yields. Based on this, the introduction of small molecular active groups into natural products has become the direction of research for green pesticides. In this study, novel 1,3,4-oxadiazole sulfonate/carboxylate flavonoid derivatives were explored. Among them, D4 exhibited good inhibitory effects on plant bacteria. It is worth mentioning that D4 (15 μg ml-1 ) exhibited an excellent median effective concentration (EC50 ) value against Xanthomonas oryzae pv. oryzae (Xoo), which was better than bismerthiazol (73 μg ml-1 ) and thiodiazole copper (100 μg ml-1 ). The EC50 for D4 was much lower than the two positive controls (bismerthiazol, thiodiazole copper), making D4 more potent in this assay of bacterial growth inhibition. In addition, mechanism research using scanning electron microscopy revealed that D4 could cause deformation or rupture of the cell membranes of Xoo and Pseudomonas syringae pv. actinidiae. Moreover, D4 exhibited the best EC50 for in vivo curative (132 μg ml-1 ) and protective (101 μg ml-1 ) activities against tobacco mosaic virus, which were more effective than ningnanmycin. Microscale thermophoresis data suggested that D4 [dissociation constant(Kd ) = 0.038 ± 0.011 μmol L-1 ] exhibited a stronger binding capacity than the control agent ningnanmycin (Kd = 4.707 ± 2.176 μmol L-1 ). The biological activity data and mode of action demonstrated that D4 had the best antibacterial and antiviral effects. Compound D4 discovered in the current work may be a very promising agricultural drug. © 2022 Society of Chemical Industry.

Poorly Investigated Ecuadorian Medicinal Plants.

Ecuador has, in proportion of its size, one of the richest floras of Latin America and the world; the country also has an immense cultural heritage due to the presence of different ethnic groups that have implemented the use of many wild and cultivated plants, mainly as medicinal remedies. In a recent publication, we have summarized the results of research activities recently carried out on about 120 plants native to Ecuador, which includes the structures of non-volatile isolated compounds, as well as the chemical composition of essential oils (EOs) and the in vitro tested biological activity data. For the sake of completeness, we have collected in this paper the main information obtained from recent ethnobotanical investigations on other important Ecuadorian medicinal plants for which phytochemical, pharmacological, and toxicological studies are, however, still largely lacking. Thus, one of the objectives of this paper is to preserve the traditional knowledge of Ecuadorian Indigenous communities which, being transmitted orally, is in danger of becoming lost. Moreover, it is our intention to stimulate more extensive studies on the rich medicinal flora of the country, which can provide economic and social benefits, especially to the people who traditionally cultivate or collect the plants.

Antimicrobial evaluation and QSAR studies of 3,6-disubstituted-11H-benzo[5,6][1,4]thiazino[3,4-a]isoindol-11-ones

The antimicrobial evaluation of a series of twenty 3,6-disubstituted-11H-benzo[5,6][1,4]thiazino[3,4-a]isoindol-11-ones 8a–t was done against two Gram-positive bacterial strains viz. Bacillus subtilis (MTCC 441) and Staphylococcus epidermidis (MTCC 6880), two Gram-negative bacterial strains viz. Escherichia coli (MTCC 1652) and Pseudomonas aeruginosa (MTCC 424), and two fungal strains viz. Candida albicans (MTCC 227) and Aspergillus niger (MTCC 8189) employing serial dilution method. Ciprofloxacin and Fluconazole were used as reference drugs for antibacterial and antifungal activities, respectively. The results of antimicrobial assay were reported in terms of Minimum Inhibitory Concentrations (MIC, μmol/mL). Structure Activity Relationships (SAR) were established for both antibacterial and antifungal inhibitory abilities of the compounds 8a–t under study. Further, Quantitative Structure Activity Relationships (QSAR) was accomplished to correlate the biological activity data with structural properties of compounds, using linear regression. The quality of models was determined by using parameters like R-squared (R2), root mean squared error (RMSE) and Pearson correlation coefficient (Pcor) along with scatter plot and residual plot. But, the best model was obtained for P. aeruginosa with excellent internal predictivity because of randomly distribution in the residual plot around the zero-error line. Thus, this model proved helpful for further synthesis of new derivatives of this series.

New 1,2,3‐Triazole‐Tethered Thiazolidinedione Derivatives: Synthesis, Bioevaluation and Molecular Docking Study

In search of new active molecules, a small focused library of 1,2,3-triazoles based 2,4-thiazolidinedione derivatives has been efficiently prepared via the click chemistry approach. Several derivatives were exhibited excellent anti-inflammatory activity compared to the standard drug. Further, the synthesized compounds were found to have potential antioxidant activity. Furthermore, to rationalize the observed biological activity data, the molecular docking study has also been carried out against the active site of inflammation enzyme PPARγ, which revealed a significant correlation between the binding score and biological activity for these compounds. The results of the in vitro and in silico study suggest that the triazole incorporated 2,4-thiazolidinedione derivatives may possess the ideal structural requirements for the further development of novel therapeutic agents.

PubChem Protein, Gene, Pathway, and Taxonomy Data Collections: Bridging Biology and Chemistry through Target-Centric Views of PubChem Data

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database at the U.S. National Institutes of Health. Visited by millions of users every month, it plays a role as a key chemical information resource for biomedical research communities. Data in PubChem is from hundreds of contributors and organized into multiple collections by record type. Among these are the Protein, Gene, Pathway, and Taxonomy data collections. Records in these collections contain information on chemicals related to a given biological target (i.e., protein, gene, pathway, or taxon), helping users to analyze and interpret the biological activity data of molecules. In addition, annotations about the biological targets are collected from authoritative or curated data sources and integrated into the four collections. The content can be programmatically accessed through PubChem’s web service interfaces (including PUG View). A machine-readable representation of this content is also provided within PubChemRDF.

Synthesis, structural elucidation, and density functional theory investigation of new mononuclear Fe(III), Ni(II), and Cu(II) mixed‐ligand complexes: Biological and catalase mimicking activity exploration

AbstractThe present paper deals with synthesis new mononuclear 1:1:1 metal:ligand:co‐ligand complexes, FeLG, NiLG, and CuLG, where L = 1‐{(E)‐[(4‐methylphenyl)imino]methyl}‐2‐naphthol and G = Glycine. The synthesized complexes were characterized based on elemental analysis, Fourier transform infrared spectroscopy (FT‐IR), ultraviolet–visible (UV–vis), mass spectra, molar conductance, magnetic susceptibility, and thermal analysis (TGA) in addition to stoichiometry determination via molar ratio method. The isolated metal complexes showed interesting geometry variation as tetrahedral for CuLG and octahedral for both FeLG and NiLG. The molecular structures of the titled compounds were optimized theoretically using density functional theory (DFT) approach, and the quantum chemical descriptors were calculated. The disk diffusion method was used to investigate the growth inhibition of the titled compounds aganist selected pathogenic bacterial and fungal strains. The metal complexes exhibited higher enhancement as antimicrobial candidates with lower minimum inhibitory concentration (MIC) than the free ligands, and in some cases, the complexes were closed to the standard species. Molecular docking investigation was carried out to ascertain the inhibitory action of the studied compounds against 1HNJ protein, the target enzyme for the antimicrobial agents. The results indicated that the FeLG has the highest binding affinity in comparison with other compounds. Thus, these molecules could be promising antimicrobial candidates. Furthermore, catalase mimicking activity of the complexes has been investigated. The data revealed that CuLG complex catalyzes the decomposition of hydrogen peroxide most effectively. Finally, the quantum chemical parameters of the titled complexes and other different compounds were correlated with their practical biological activity data in a trial to build a SAR model.

Study on Quantitative Prediction of Anticancer Compound Activity Based on Neural Network Model

Breast cancer poses a serious threat to global women's health. Around this problem, a quantitative prediction model of biological activity based on neural network and a quantitative prediction model of multiple linear regression are established in this paper. 20 kinds of molecular descriptor data with the most significant effect on biological activity of 1974 compounds were selected as input layer and 1 biological activity data as output layer, and a quantitative prediction model of biological activity based on neural network was established. Based on the quantitative prediction model based on parameter setting, the single hidden layer BP neural network structure is used to test the model, and the fitting error R values of the training set and test set of the model are close to 1, and the trend in the scatter diagram is obvious, which shows that the prediction value of the model is more accurate.

Synthesis, characterization and biological activity of new mixed-ligand divalent metal complexes derived from 1,3,4-thiadiazole Schiff base and 2,2′‐bipyridine

New mixed-ligand complexes of Cu(II), Ni(II), Co(II), Zn(II), Pt(II) and Pd(II) transition metals were synthesized from a Schiff base 5-((Z)-((E)-3-phenylallylidene)amino)-1,3,4-thiadiazole-2-thiol obtained by the condensation reaction between cinnamaldehyde and 5-amino-1, 3, 4-thiadiazol-2-thiol as a primary ligand (LH1) and the heterocyclic base 2,2′‐bipyridine (2,2′-bipy) as a secondary ligand (L2). The prepared ligand and their metal complexes have been characterized by elemental analysis, molar conductivity measurement, magnetic susceptibility, infrared (FT-IR), proton nuclear magnetic resonance (1H-NMR), electronic (UV-Vis), mass spectrometry (MS), thermal gravimetric analysis (TGA), and screened in vitro for their potential as biological activity by the agar well diffusion method against Gram-negative Escherichia coli (E. coli) and Gram-Positive Staphylococcus aureus (S. aureus) strain. The mixed-ligand complexes were found to have the following formulas [M (L1)(L2)]Y ⋅H2O L1 = Schiff base, L2 = 2,2′-bipy, (M = Cu(II), Ni(II), Co(II), Zn(II), Y=NO3), (M=pd(II), Y=Cl2) and (M= pt(II), Y=Cl) for metallic complexes. L1 acts as a bidentate ligand and coordinates to a metallic ion center through imine nitrogen and a deprotonated sulfur atom. Similarly, L2 acts as a neutral bidentate ligand coordinating to the metallic ions via two nitrogen atoms. The resultant data have shown that the mixed-ligand complexes have an electrolytic nature and Co(II), Pt(II), and Pd(II) metal complexes have square planer structure, as well as tetrahedral geometry, has been proposed for the complexes of Cu(II), Ni(II) and Zn(II). Thus, the biological activity data indicated that the Cu(II), Co(II), Zn(II), and Pd(II) complexes have a much higher biological activity than the Schiff base. It was found that the Zn(II) complex was more effective than the other metal complexes used against both types of the bacteria tested.

Metabolities from Marine Sponges of the Genus Callyspongia: Occurrence, Biological Activity, and NMR Data.

The genus Callyspongia (Callyspongiidae) encompasses a group of demosponges including 261 described species, of which approximately 180 have been accepted after taxonomic reviews. The marine organisms of Callyspongia are distributed in tropical ecosystems, especially in the central and western Pacific, but also in the regions of the Indian, the West Atlantic, and the East Pacific Oceans. The reason for the interest in the genus Callyspongia is related to its potential production of bioactive compounds. In this review, we group the chemical information about the metabolites isolated from the genus Callyspongia, as well as studies of the biological activity of these compounds. Through NMR data, 212 metabolites were identified from genus Callyspongia (15 species and Callyspongia sp.), belonging to classes such as polyacetylenes, terpenoids, steroids, alkaloids, polyketides, simple phenols, phenylpropanoids, nucleosides, cyclic peptides, and cyclic depsipeptides. A total of 109 molecules have been reported with bioactive activity, mainly cytotoxic and antimicrobial (antibacterial and antifungal) action. Thus, we conclude that polyacetylenes, terpenoids and steroids correspond to the largest classes of compounds of the genus, and that future research involving the anticancer action of the species’ bioactive metabolites may become relevant.

Identifying the link between chemical exposures and breast cancer in African American women via integrated in vitro and exposure biomarker data

Among women, breast cancer is the most prevalent form of cancer worldwide and has the second highest mortality rate of any cancer in the United States. The breast cancer related death rate is 40 % higher in non-Hispanic Black women compared to non-Hispanic White women. The incidence of triple negative breast cancer (TNBC), an aggressive subtype of breast cancer for which there is no targeted therapy, is also approximately three times higher for Black, relative to, White women. The drivers of these differences are poorly understood. Here, we aimed to identify chemical exposures which play a role in breast cancer disparities. Using chemical biomonitoring data from the National Health and Nutrition Examination Survey (NHANES) and biological activity data from the EPA’s ToxCast program, we assessed the toxicological profiles of chemicals to which US Black women are disproportionately exposed. We conducted a literature search to identify breast cancer targets in ToxCast to analyze the response of chemicals with exposure disparities in these assays. Forty-three chemical biomarkers are significantly higher in Black women. Investigation of these chemicals in ToxCast resulted in 32,683 assays for analysis, 5172 of which contained nonzero values for the concentration at which the dose-response fitted model reaches the cutoff considered “active”. Of these chemicals BPA, PFOS, and thiram are most comprehensively assayed. 2,5-dichlorophenol, 1,4-dichlorobenzene, and methyl and propyl parabens had higher biomarker concentrations in Black women and moderate testing and activity in ToxCast. The distribution of active concentrations for these chemicals in ToxCast assays are comparable to biomarker concentrations in Black women NHANES participants. Through this integrated analysis, we identify that multiple chemicals, including thiram, propylparaben, and p,p’ DDE, have disproportionate exposures in Black women and have breast cancer associated biological activity at human exposure relevant doses.

3D QSAR analysis on quinoxaline derivatives as anti-malarial using K-nearest neighbour molecular field analysis

In the present article, k nearest neighbour molecular field analysis (kNN-MFA) method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. In this study 37 derivatives of quinoxaline having antimalarial activity were used. Sphere exclusion (SE) algorithm was used to create the biological activity data set in to into training and test set. For model generation kNN-MFA method has coupled with stepwise, simulated annealing and genetic algorithm this method provides various models, in which the most significant model developed by stepwise backward-forward method with predictive internal q2=0.7589 and external predictivity (pred_r2 = 0.4752). In the presented model electrostatic descriptors play crucial role for activity. Electrostatic descriptor (E_137) indicates regions in which electron withdrawing groups are favourable and descriptor (E_939) represents electron rich or electron donating groups are advantageous in particular region. The counter map/ plot of this model further helps to understand the relationship of structural feature of derivative of quinoxaline and its biological activity this would be applied for designing of new potent antimalarial containing quinoxaline as lead.

Tuning the activity of known drugs via the introduction of halogen atoms, a case study of SERT ligands - Fluoxetine and fluvoxamine.

The selective serotonin reuptake inhibitors (SSRIs), acting at the serotonin transporter (SERT), are one of the most widely prescribed antidepressant medications. All five approved SSRIs possess either fluorine or chlorine atoms, and there is a limited number of reports describing their analogs with heavier halogens, i.e., bromine and iodine. To elucidate the role of halogen atoms in the binding of SSRIs to SERT, we designed a series of 22 fluoxetine and fluvoxamine analogs substituted with fluorine, chlorine, bromine, and iodine atoms, differently arranged on the phenyl ring. The obtained biological activity data, supported by a thorough in silico binding mode analysis, allowed the identification of two partners for halogen bond interactions: the backbone carbonyl oxygen atoms of E493 and T497. Additionally, compounds with heavier halogen atoms were found to bind with the SERT via a distinctly different binding mode, a result not presented elsewhere. The subsequent analysis of the prepared XSAR sets showed that E493 and T497 participated in the largest number of formed halogen bonds. The XSAR library analysis led to the synthesis of two of the most active compounds (3,4-diCl-fluoxetine 42, SERT Ki=5nM and 3,4-diCl-fluvoxamine 46, SERT Ki=9nM, fluoxetine SERT Ki=31nM, fluvoxamine SERT Ki=458nM). We present an example of the successful use of a rational methodology to analyze binding and design more active compounds by halogen atom introduction. 'XSAR library analysis', a new tool in medicinal chemistry, was instrumental in identifying optimal halogen atom substitution.

Balancing Data on Deep Learning-Based Proteochemometric Activity Classification.

In silico analysis of biological activity data has become an essential technique in pharmaceutical development. Specifically, the so-called proteochemometric models aim to share information between targets in machine learning ligand–target activity prediction models. However, bioactivity data sets used in proteochemometric modeling are usually imbalanced, which could potentially affect the performance of the models. In this work, we explored the effect of different balancing strategies in deep learning proteochemometric target–compound activity classification models while controlling for the compound series bias through clustering. These strategies were (1) no_resampling, (2) resampling_after_clustering, (3) resampling_before_clustering, and (4) semi_resampling. These schemas were evaluated in kinases, GPCRs, nuclear receptors, and proteases from BindingDB. We observed that the predicted proportion of positives was driven by the actual data balance in the test set. Additionally, it was confirmed that data balance had an impact on the performance estimates of the proteochemometric model. We recommend a combination of data augmentation and clustering in the training set (semi_resampling) to mitigate the data imbalance effect in a realistic scenario. The code of this analysis is publicly available at https://github.com/b2slab/imbalance_pcm_benchmark.

Draft genome sequences of two Streptomyces albidoflavus strains DEF1AK and DEF147AK with plant growth-promoting and biocontrol potential

PurposeBacteria belonging to the Streptomyces genus can be exploited in environmentally friendly approaches to food safety. Genome information can help to characterize bioactive strains opening the possibility to decipher their mechanisms of action.MethodsThe biocontrol and plant growth-promoting activity of two Streptomyces spp. strains, DEF1AK and DEF147AK, were assessed in vitro and in planta. The genome sequences were determined using the Illumina NextSeq sequencing system and were assembled using EvoCAT (Evogene Clustering and Assembly Toolbox).ResultStreptomyces spp. DEF1AK and DEF147AK were able to improve seed germination and early plant development of maize, wheat, and tomato and inhibited the mycelium growth of diverse fungal plant pathogens in vitro. The genome sequence analysis identified both strains as S. albidoflavus (99% sequence identity). Both genomes were of 7.1-Mb length with an average GC content of 73.45%. AntiSMASH and MIBiG analyses revealed strain-specific sets of secondary metabolite gene clusters in the two strains as well as differences in the number and type of duplicated genes.ConclusionThe combination of the biological activity and genomic data is the basis for in-depth studies aimed at the identification of secondary metabolites involved in plant growth-promoting and biocontrol activity of Streptomyces spp. The comparison of unique genomic features of the two strains will help to explain their diverse biocontrol and plant growth-promoting activities and warrant targeted functional genomics approaches to verify their mechanisms of action.

Characterization and biological activities of synthesized citrus pectin-MgO nanocomposite

Pectin, a biopolymer was extracted from the peels of orange. In extraction of the pectin from orange peel, acid hydrolysis of the dry orange fruit peel was carried out and continued precipitation using ethanol. The percentage yield of the extracted pectin is 12.25%. Equivalent weight, degree of esterification, methoxy content, AUA% and molecular weight for pectin were determined. Further by co-precipitation method the citrus pectin-MgO nanocomposite was prepared. Extracted pectin and citrus pectin-MgO nanocomposite were characterized by Powdered X-Ray Diffractometer (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electronic microscope (SEM). The antibacterial activity of pectin and pectin-MgO nanocomposite was extensively studied on clinical pathogens Bacillus subtilis and Lactobacillus. Antifungal activity was also studied against pathogenic organisms; Microsporum gypseum and Trichophyton mentagrophytes. They were also screened for antioxidant DPPH radical scavenging assay. The biological activity data showed that the citrus pectin-MgO nanocomposite is more potent than the pectin.

Development of a Web-Based Toolbox to Support Quantitative In-Vitro-to-In-Vivo Extrapolations (QIVIVE) within Nonanimal Testing Strategies.

The goal of the present study was to develop an online web-based toolbox that contains generic physiologically based kinetic (PBK) models for rats and humans, including underlying calculation tools to predict plasma protein binding and tissue:plasma distribution, to be used for quantitative in-vitro-to-in-vivo extrapolations (QIVIVE). The PBK models within the toolbox allow first estimations of internal plasma and tissue concentrations of chemicals to be made, based on the logP and pKa of the chemicals and values for intestinal uptake and intrinsic hepatic clearance. As a case study, the toolbox was used to predict oral equivalent doses of in vitro ToxCast bioactivity data for the food additives methylparaben, propyl gallate, octyl gallate, and dodecyl gallate. These oral equivalent doses were subsequently compared with human exposure estimates, as a low tier assessment allowing prioritization for further assessment. The results revealed that daily intake levels of especially propyl gallate can lead to internal plasma concentrations that are close to in vitro biological effect concentrations, particularly with respect to the inhibition of human thyroid peroxidase (TPO). Estrogenic effects were not considered likely to be induced by the food additives, as daily exposure levels of the different compounds remained 2 orders of magnitude below the oral equivalent doses for in vitro estrogen receptor activation. Overall, the results of the study show how the toolbox, which is freely accessible through www.qivivetools.wur.nl, can be used to obtain initial internal dose estimates of chemicals and to prioritize chemicals for further assessment, based on the comparison of oral equivalent doses of in vitro biological activity data with human exposure levels.