Update of the Cancer Potency Database (CPDB) to enable derivations of Thresholds of Toxicological Concern (TTC) for cancer potency.

The purpose of this study was to update the existing Cancer Potency Database (CPDB) in order to support the development of a dataset of compounds, with associated points of departure (PoDs), to enable a review and update of currently applied values for the Threshold of Toxicological Concern (TTC) for cancer endpoints. This update of the current CPDB, last reviewed in 2012, includes the addition of new data (44 compounds and 158 studies leading to additional 359 dose-response curves). Strict inclusion criteria were established and applied to select compounds and studies with relevant cancer potency data. PoDs were calculated from dose-response modeling, including the benchmark dose (BMD) and the lower 90% confidence limits (BMDL) at a specified benchmark response (BMR) of 10%. The updated full CPDB database resulted in a total of 421 chemicals which had dose-response data that could be used to calculate PoDs. This candidate dataset for cancer TTC is provided in a transparent and adaptable format for further analysis of TTC to derive cancer potency thresholds.

High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge.

Read-across is an in silico method applied in chemical risk assessment for data-poor chemicals. The read-across outcomes for repeated-dose toxicity end points include the no-observed-adverse-effect level (NOAEL) and estimated uncertainty for a particular category of effects. We have previously developed a new paradigm for estimating NOAELs based on chemoinformatics analysis and experimental study qualities from selected analogues, not relying on quantitative structure-activity relationships (QSARs) or rule-based SAR systems, which are not well-suited to end points for which the underpinning data are weakly grounded in specific chemical-biological interactions. The central hypothesis of this approach is that similar compounds have similar toxicity profiles and, hence, similar NOAEL values. Analogue quality (AQ) quantifies the suitability of an analogue candidate for reading across to the target by considering similarity from structure, physicochemical, ADME (absorption, distribution, metabolism, excretion), and biological perspectives. Biological similarity is based on experimental data; assay vectors derived from aggregations of ToxCast/Tox21 data are used to derive machine learning (ML) hybrid rules that serve as biological fingerprints to capture target-analogue similarity relevant to specific effects of interest, for example, hormone receptors (ER/AR/THR). Once one or more analogues have been qualified for read-across, a decision theory approach is used to estimate confidence bounds for the NOAEL of the target. The confidence interval is dramatically narrowed when analogues are constrained to biologically related profiles. Although this read-across process works well for a single target with several analogues, it can become unmanageable when, for example, screening multiple targets (e.g., virtual screening library) or handling a parent compound having numerous metabolites. To this end, we have established a digitalized framework to enable the assessment of a large number of substances, while still allowing for human decisions for filtering and prioritization. This workflow was developed and validated through a use case of a large set of bisphenols and their metabolites.

Integration of evidence to evaluate the potential for neurobehavioral effects following exposure to USFDA-approved food colors.

California's Office of Environmental Health Hazard Assessment was tasked with conducting risk assessments for United States Food and Drug Administration-approved food dyes relative to neurobehavioral concerns. The purpose of this assessment was to evaluate the evidence for neurodevelopment effects based on three streams of evidence: 1) studies identified by OEHHA for consideration in a quantitative risk assessment; 2) studies relevant to understanding mechanisms of neurobehavioral effects; 3) an in silico assessment of the bioavailability of USFDA-approved food dyes. The results indicate a lack of adequate or consistent evidence of neurological effects, supported by a lack of bioavailability and brain penetration predicted by the in silico assessment. Further, the mechanistic evidence supports a lack of activity from in vitro neurotransmitter assays, and a lack of evidence to support molecular initiating events or key events in adverse outcome pathways associated with neurodevelopmental effects, supporting a lack of biological plausibility for neurobehavioral effects following food exposures to colors. These conclusions are consistent with other authoritative bodies, such as JECFA and EFSA, that have determined (i) other effects are more appropriate for estimating acceptable daily intakes and (ii) evidence from the neurobehavioral studies lack the strength to be relied upon for quantitative risk assessment.

Do Similar Structures Have Similar No Observed Adverse Effect Level (NOAEL) Values? Exploring Chemoinformatics Approaches for Estimating NOAEL Bounds and Uncertainties.

Determination of the no observed adverse effect level (NOAEL) of a substance is an important step in safety and regulatory assessments. Application of conventional in silico strategies, for example, quantitative structure-activity relationship (QSAR) models, to predict NOAEL values is inherently problematic. Whereas QSAR models for well-defined toxicity endpoints such as Ames mutagenicity or skin sensitization can be developed from mechanistic knowledge of molecular initiating events and adverse outcome pathways, QSAR is not appropriate for predicting a NOAEL value, a concentration at which "no effect" is observed. This paper presents a chemoinformatics approach and explores how it can be further refined through the incorporation of toxicity endpoint-specific information to estimate confidence bounds for the NOAEL of a target substance, given experimentally determined NOAEL values for one or more suitable analogues. With a sufficiently large NOAEL database, we analyze how a difference in NOAEL values for pairs of structures depends on their pairwise similarity, where similarity takes both structural features and physicochemical properties into account. The width of the estimate NOAEL confidence interval is proportional to the uncertainty. Using the new threshold of toxicological concern (TTC) database enriched with antimicrobials, examples are presented to illustrate how uncertainty decreases with increasing analogue quality and also how NOAEL bounds estimation can be significantly improved by filtering the full database to include only substances that are in structure categories relevant to the target and analogue.

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment.

Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure-activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster-Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an in silico platform for systematic evaluation.

A new paradigm in threshold of toxicological concern based on chemoinformatics analysis of a highly curated database enriched with antimicrobials.

A new database of antimicrobial-enriched chemicals for the Threshold of Toxicological Concern (TTC) approach has been compiled, comprising 1357 chemicals with 276, 54, and 1027 substances in Cramer Classes I, II, and III, respectively. To enrich the chemical space of the No-/Lowest-Observed-Adverse Effect Level (NOAEL/LOAEL) database, a reference Antimicrobial (AM) Inventory (681) was established for chemical inclusion. To this database, the three existing TTC datasets were combined via robust data fusion process. From the final AM TTC Dataset, the fifth percentiles were derived to be 2.7, 0.43, and 0.12 mg/kg-bw/day for Cramer Classes I, II, and III, respectively. Considering the high percentage of AMs being Cramer Class III, the thresholds are remarkably stable across various TTC datasets. Based on the AM-enriched database, a set of AM categories stratified across potency were developed to classify AMs beyond the capability of the conventional Cramer Tree approach. Grouping the query chemical within the AM category, further distribution analyses were conducted to identify subclasses and differentiate potency. This study proposes a new framework for potential assessment of chronic toxicity made possible with the power of modern reliable databases and chemoinformatic methods.

Modeling the overall persistence and environmental mobility of sulfur-containing polychlorinated organic compounds.

BACKGROUND, AIM, AND SCOPE: Experimental data on partition coefficients and environmental half-lives of sulfur analogs of polychlorinated organic compounds are scarce. Consequently, little is known about their overall persistence and long-range transport potential, which are the most vital measures in the environmental exposure assessment. We performed Multimedia Modeling of environmental fate and transport to complement this paucity of scientific data. The main aim of our study was to investigate whether the sulfur analogs of polychlorinated dibenzo-p-dioxins, -dibenzofurans, and -diphenylethers are as environmentally persistent and/or mobile as their oxygen counterparts and to propose the environmental exposure-related classification of the examined sulfur compounds. MATERIALS AND METHODS: Our study included all possible congeners of the sulfur analogs generated in a combinatorial approach. We predicted (1) lacking data on partition coefficients (log K OW, log K OA and log K AW) for oxygen and sulfur analogs using Quantitative Structure-Property Relationship (QSPR) modeling and (2) their half-lives in air, water, and soil using US EPA tool 'The PBT Profiler, v. 1.203 2006'. Subsequently, we introduced these results into multimedia mass balance model 'The OECD POV and LRTP Screening Tool, v. 2.2'. RESULTS: Our study revealed that log K OW and log K OA are increasing by constant values of 0.60 and 1.07, respectively, and the values of log K AW are decreasing by 0.90, whenever one oxygen atom in the carbon skeleton is replaced by sulfur. The persistence ranking performed by the PBT Profiler showed that PCDDs, PCDFs, PCDEs, and their sulfur analogs belong to one half-life class. DISCUSSION: The Multimedia Modeling by the means of 'The OECD POV and LRTP Screening Tool, v. 2.2' suggested that the long-range transport potential depends on the presence/absence of oxygen/sulfur atoms in particular molecules, their substitution pattern and the parent carbon skeleton. Sulfur analogs are generally less mobile than their oxygen analogs, but have similar overall persistence and much higher bioaccumulation potential. Thus, according to the classification of chemicals proposed by Klasmeier et al. (Environ Sci Technol 40:53-60, 2006), some of them show POP-like POV and LRTP characteristics while the rest shows POP-like P OV characteristics. CONCLUSIONS: The sulfur analogs of PCDDs, PCDFs, or PCDEs bring environmental mobility comparable with the risk related to the oxygen ones; they belong to the pollutants of 'highest' or 'intermediate' priority. RECOMMENDATIONS AND PERSPECTIVES: Further studies that would verify the necessity to include the studied sulfur molecules in the international lists of high-priority environmental pollutants are recommended.

Predicting water solubility of congeners: chloronaphthalenes--a case study.

Since the important physicochemical data for chloronaphtalenes (PCNs) are still scarce, we have predicted water solubility (logS) of all 75 congeners with the Quantitative Structure-Property Relationship (QSPR) scheme. The values of logS, predicted by the most efficient model, varied from 0.01 to 1660 microg dm(-3) (2.85 x 10(-11)-1.02 x 10(-5) mol dm(-3)), depending on the number of chlorine atoms present in the molecule and the substitution pattern. We found that the main factor determining relative differences in solubility between the congeners is the solvent accessible volume related to the cavitation process occurring in the solvent. The results are presented as a case study of QSPR modeling for those Persistent Organic Pollutants (POPs) that exist as families of congeners. By investigating the impact of (i) the way of the molecular descriptors' calculation, (ii) the size of applied database and (iii) chemometric method of modeling (Multiple Linear Regression, MLR, and/or Partial Least Squares regression, PLS) on the quality of the models we proposed general recommendations for dealing with congeners. We found that the combination of the B3LYP functional with 6-311++G(d,p) basis set was the most optimal technique of the molecular descriptors' calculation for congeners when comparing with semi-empirical PM3, ab initio Hartee-Fock (HF), and Møller-Pleset 2 (MP2) method carried out with different-size basis sets. Moreover, the model developed with a larger and more general database that includes chloronaphthalenes, polychlorinated dibezno-p-dioxins, furans and biphenyls predicted the values of logS for PCNs noticeable worse than the model calibrated only on PCNs. In the later case it was possible to obtain satisfactory results by employing even the simplest MLR method and only one molecular descriptor. The values of logS were also calculated with the WSKOWIN and COSMO-RS models as the reference techniques and then compared to our results.

[Perfluorinated chemicals in the environment, food and human body]. / Zwiazki perfluorowane w srodowisku przyrodniczym, zywnosci i organizmie czlowieka.

Some data on production, toxicity, properties, uses, analytics as well as an environmental occurrence of PFCs in Poland are reviewed. In total 16 fluorochemicals were detected in surface water (Radunia River and Gulf of Gdansk), beaver's liver (Warmia and Mazury region), cod and eider duck blood (Gulf of Gdansk), young cattle blood (County of Stezyca) and human blood (Gdansk Coast; donors which declared elevated Baltic fish intake) in Poland. In blood of the Gdansk Coast inhabitants PFHxS, PFOS, PFOSA, PFHxA, PFHpA, PFOA, PFNA, PFDA, PFUnDA and PFDo-DA were found. In surface water for the first time were found fluorochemicals such as PFBuS, PFOcDA, PFBA and PFPeA, while in beavers' liver also PFTeA and N-Ethyl FOSA.