Application of QSTRs in the selection of a surrogate toxicity value for a chemical of concern

Estimating the Potential Toxicity of Chemicals Associated with Hydraulic Fracturing Operations Using Quantitative Structure-Activity Relationship Modeling

The United States Environmental Protection Agency (EPA) identified 1173 chemicals associated with hydraulic fracturing fluids, flowback, or produced water, of which 1026 (87%) lack chronic oral toxicity values for human health assessments. To facilitate the ranking and prioritization of chemicals that lack toxicity values, it may be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) models. Here we describe an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides estimates of the rat chronic oral lowest-observed-adverse-effect level (LOAEL). Of the 1173 chemicals, TOPKAT was able to generate LOAEL estimates for 515 (44%). To address the uncertainty associated with these estimates, we assigned qualitative confidence scores (high, medium, or low) to each TOPKAT LOAEL estimate, and found 481 to be high-confidence. For 48 chemicals that had both a high-confidence TOPKAT LOAEL estimate and a chronic oral reference dose from EPA's Integrated Risk Information System (IRIS) database, Spearman rank correlation identified 68% agreement between the two values (permutation p-value =1 × 10(-11)). These results provide support for the use of TOPKAT LOAEL estimates in identifying and prioritizing potentially hazardous chemicals. High-confidence TOPKAT LOAEL estimates were available for 389 of 1026 hydraulic fracturing-related chemicals that lack chronic oral RfVs and OSFs from EPA-identified sources, including a subset of chemicals that are frequently used in hydraulic fracturing fluids.

A novel UV-MALDI-MS analytical approach for determination of halogenated phenyl-containing pesticides

The paper highlighted the capability of the UV-MALDI mass spectrometry, employing the Orbitrap analyzer for solid-state assay of halogenated phenyl-pesticides in mixtures. It is successfully applied for the analysis of eighteen (1)-(18) molecular objects of Fenarimol (1) type and their condensation products (P5)-(P12). The full method and technique validation is performed using the dried droplet sample preparation technique on embedded analytes in novel organic matrix crystals of N-(1H-benzoimidazol-2-yl)-guanidine (M4) and (E)-phenyl-2-pyridyl ketone oxime (M5), resulting to successful ionization of the analytes. Since the sampling technique in the UV-MALDI method is a key step in the overall process impacting significantly the metrology through the reproducibility of the data, the crystallization of M4, M5 and matrix-analyte in situ morphology of the samples is controlled by single crystal X-ray diffraction. The achieved promising metrology of LODs, of 0.46ngkg(-1) (1.53ngkg(-1) LOQs), is several orders of magnitude lower than the reported ecotoxicological effect values of studied pesticides. It is confirmed by the partial validated protocol based on ESI-MS. Inasmuch that LC-MS/MS is a method of choice for foodstuffs monitoring of organic contaminations, due to its routine quantitative analytical procedures, it is often characterized with the difficulties of the chromatographic separation of the closely structured analytes at a large scale of experimental conditions, complex multi-step sampling pretreatments, which inevitably alert the variables through the systematic and random errors. In this respect, notwithstanding the complex quantitative UV-MALDI-Orbitrap-MS procedure, conceptually different from the LC-MS/MS one, its high resolving power, capability for achieving meaningful analytical qualitative, quantitative and structural information of low-molecular weight analytes, its instrumental and sampling operation flexibility, applicable for a large scale of foodstuff matrices, and operating at the analyte concentrations of up to fgg(-1) make UV-MALDI-Orbitrap-MS a perspective method of choice for an extensive implementation in the foodstuffs monitoring practice for control of the most essential task related to the assessment of the human health risks from environmental and foodstuff contaminations.

Developmental toxicity prediction

Developmental toxicity may be estimated using commercial and noncommercial software that is already available in the market and/or literature, or models may be built from scratch using both commercial and noncommercial software packages. In this chapter, commonly available software programs that can predict the developmental toxicity of chemicals are described. In addition, a method for developing qualitative structure-activity relationship (SAR) models to predict the developmental toxicity of chemicals qualitatively (yes/no prediction) and quantitative structure-activity relationship (QSAR) models to predict quantitative estimates (e.g., LOAEL) of developmental toxicants is also described in this chapter. Additional information described in this chapter include methods to predict physicochemical properties of chemicals that can be used as descriptor variables in the model building process, statistical methods that be used to build QSAR models as well as methods to validate the models that are developed. Most of the methods described in this chapter can be used to develop models for health endpoints other than developmental toxicity as well.

Joint toxicity of alkoxyethanol mixtures: contribution of in silico applications

Exposure to chemicals occurs often as mixtures. Presented in this paper is information on alkoxyethanols and the impact they might have on human health in combination with some commonly found aliphatic and aromatic compounds. Our studies to evaluate the joint toxicity of these chemicals among themselves and in combination with other chemicals reveal a variety of possible outcomes depending on the exposure scenario. The interactions are predominantly based on metabolic pathways and are common among several solvents and organic compounds. Quantitative structure activity relationship (QSAR) analysis can be used with high confidence to identify chemicals that will interact to influence overall joint toxicity. Potential human exposure to a combination of alkoxyethanol, toluene and substituted benzenes may increase reproductive and developmental disease conditions. Inheritable gene alterations result in changes in the enzyme function in different subpopulations causing variations in quantity and/or quality of particular isoenzymes. These changes are responsible for differential metabolism of chemicals in species, genders, and life stages and are often the basis of a population's susceptibility. Unique genotypes introduced as a function of migration can alter the genetic makeup of any given population. Hence special consideration should be given to susceptible populations while conducting chemical health risk assessments.

A comprehensive statistical analysis of predicting in vivo hazard using high-throughput in vitro screening

Over the past 5 years, increased attention has been focused on using high-throughput in vitro screening for identifying chemical hazards and prioritizing chemicals for additional in vivo testing. The U.S. Environmental Protection Agency's ToxCast program has generated a significant amount of high-throughput screening data allowing a broad-based assessment of the utility of these assays for predicting in vivo responses. In this study, a comprehensive cross-validation model comparison was performed to evaluate the predictive performance of the more than 600 in vitro assays from the ToxCast phase I screening effort across 60 in vivo endpoints using 84 different statistical classification methods. The predictive performance of the in vitro assays was compared and combined with that from chemical structure descriptors. With the exception of chronic in vivo cholinesterase inhibition, the overall predictive power of both the in vitro assays and the chemical descriptors was relatively low. The predictive power of the in vitro assays was not significantly different from that of the chemical descriptors and aggregating the assays based on genes reduced predictive performance. Prefiltering the in vitro assay data outside the cross-validation loop, as done in some previous studies, significantly biased estimates of model performance. The results suggest that the current ToxCast phase I assays and chemicals have limited applicability for predicting in vivo chemical hazards using standard statistical classification methods. However, if viewed as a survey of potential molecular initiating events and interpreted as risk factors for toxicity, the assays may still be useful for chemical prioritization.

3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy

Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work ( González-Díaz, H. , et al. Chem. Res. Toxicol. 2003, 16, 1318- 1327 ), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic's spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity ( I i ) signals with m/ z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCH-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.

Assessment of the oral rat chronic lowest observed adverse effect level model in TOPKAT, a QSAR software package for toxicity prediction

The performance of the rat chronic lowest observed adverse effect level (LOAEL, the lowest exposure level at which there are biologically significant increases in the severity of adverse effects) model in Toxicity Prediction by Komputer Assisted Technology (TOPKAT), a commercial quantitative structure-activity relationship software package, was tested on a database of chemicals that are of interest to the U.S. EPA's Office of Pesticide Programs. The testing was repeated on a database of chemicals from three U.S. EPA sources that report peer-reviewed LOAELs. The results of this study were also contrasted with the results of the testing performed during TOPKAT's model-building process.

Contaminants as viral cofactors: assessing indirect population effects

Current toxicological methods often miss contaminant effects, particularly when immune suppression is involved. The failure to recognize and evaluate indirect and sublethal effects severely limits the applicability of those methods at the population level. In this study, the Vitality model is used to evaluate the population level effects of a contaminant exerting only indirect, sublethal effects at the individual level. Juvenile rainbow trout (Oncorhynchus mykiss) were injected with 2.5 or 10.0 mg/kg doses of the model CYP1A inducer, beta-naphthoflavone (BNF) as a pre-stressor, then exposed to a challenge dose of 10(2) or 10(4) pfu/fish of infectious hematopoietic necrosis virus (IHNV), an important viral pathogen of salmonids in North America. At the end of the 28-d challenge, the mortality data were processed according to the Vitality model which indicated that the correlation between the average rate of vitality loss and the pre-stressor dose was strong: R2=0.9944. Average time to death and cumulative mortality were dependent on the BNF dose, while no significant difference between the two viral dosages was shown, implying that the history of the organism at the time of stressor exposure is an important factor in determining the virulence or toxicity of the stressor. The conceptual framework of this model permits a smoother transfer of results to a more complex stratum, namely the population level, which allows the immunosuppressive results generated by doses of a CYP1A inducer that more accurately represent the effects elicited by environmentally-relevant contaminant concentrations to be extrapolated to target populations. The indirect effects of other environmental contaminants with similar biotransformation pathways, such as polycyclic aromatic hydrocarbons (PAH), could be assessed and quantified with this model and the results applied to a more complex biological hierarchy.