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THE BIOLOGICAL-ACTIVITY TESTING AND
MODELING LABORATORY

A PLACE WHERE BIOLOGY, CHEMISTRY, AND MATHEMATICS CONVERGE

General Information:

The Biological-Activity Testing and Modeling Laboratory is housed in the York Veterinary Medical Center at The University of Tennessee Institute of Agriculture.

The laboratory is coordinated by Prof. T. W. Schultz and includes post-doctoral trainees, technicians, graduate students, and collaborating scientists.

The mission of the laboratory includes the: 1) development, validation, and use of rapid and inexpensive assays for the evaluation of organic toxicants; 2) development, validation, and use of structure-activity models for predicting chemical reactivity and toxic potencies, and 3) advancement of the basic understanding of chemical-biological interactions.

The laboratory has academic ties to the Department of Comparative Medicine in the College of Veterinary Medicine and the Department of Ecology and Evolutionary Biology in the College of Arts and Sciences.

The laboratory has research ties to the Center for Environmental Biotechnology. In addition, the laboratory has cooperative arrangements with several foreign research groups.

Further Information:

  • Toxicity Test Systems

  • TETRATOX Database

  • In Chemico Reactivity Test Systems

  • Glutathione Reactivity Database

  • Structure-Toxicity Modeling

  • Current Projects

  • Recent Publications

How to Contact Us:

If you have questions, please contact Prof. Schultz.

Toxicity Test Systems

The Biological-Activity Testing and Modeling Laboratory is the world leader in experimental toxicological work with the freshwater ciliate Tetrahymena pyriformis. Other systems being developed or used in the Biological-Activity Testing and Modeling Laboratory include recombinant yeast (Saccharomyces cerevisiae) strains for assaying endocrine disruption and the Vibrio fischeri (MICROTOX ) systems.

TETRATOX Database

The TETRATOX database is a collection of toxic potency data for more than 2,600 industrial organic compounds, of which more than 1,800 have been published in the open literature. The TETRATOX assay is a short-term, static protocol using the common freshwater ciliate Tetrahymena pyriformis, with the 50% impairment growth concentration (IGC50) the recorded endpoint.

Cultures are reared in 50 ml of a semi-defined medium in 250 ml Erlenmeyer flasks. During the assay, a range-finding assay followed by three replicate definitive tests is performed on each test material. Definitive test replicates consist of a minimum of five different concentrations of each test material with duplicate flasks of each concentration. Thus, a minimum of 30 data points comprise each analysis. Duplicate controls, which have no test material but were inoculated with T. pyriformis, and a “blank” are used to provide a measure of the acceptability of the test by indicating the suitability of the medium and test conditions, and as a basis for interpreting data from other treatments. While subtle differences in the protocol (duration, temperature, medium composition, etc.) were used over the course of more than 25 years of testing, each was used with a test regime to allow for 8 to 9 cell cycles in controls. Duplicate flasks are inoculated with an initial density of 2,500 cells/ml with log-growth-phase ciliates. Following 40 hours of incubation at 27 1C, population density is measured spectrophotometrically and 50% effect levels determined.

The IGC50 (mg/L) and the 95% fiducial interval are determined for each test compound. The IGC50 is calculated by probit analysis using the percent control-normalized absorbance as the dependent variable and the toxicant concentration in mg/l as the independent variable. Both the slope and intercept of the probit regression equation are recorded as well as the Chi-squared value. The latter is an indicator of the fit of the data to the probit model. Normally the Chi-squared value is greater than 0.9.

In the majority (> 90%) of the more than 3,000 tests completed, it is possible to generate a statistically valid concentration-response curve. However, some chemicals (e.g., neutral organics with 1-octanol/water partition coefficients greater than 5.0) are not toxic at saturation; others do not attain the measured 50% effect endpoint at saturation, and still other chemicals (e.g., highly bioreactive toxicants) have a very narrow concentration-response range that precludes proper statistical analyses.

In Chemico Reactivity Test Systems

The Biological-Activity Testing and Modeling Laboratory is also a world leader in experimental work designed to qualify and quantify the ability of organic chemicals to react with selected nucleophiles. In deciding the nature of the biological hazard, whether for classification or other uses, several beliefs are typically applied. These beliefs include: 1) the toxicant and molecular target interaction obey the laws of chemistry, 2) the laws of chemistry determine the molecular mechanism of action, and 3) the molecular mechanism of action determines the biological mechanism of action and the molecular initiating event. It stands to reason that the effectiveness of identifying toxicity depends on how well characterized is the chemistry.

One often obtains information about a reaction by measuring its rate and how it is altered under different conditions. While in chemico reactivity investigations can be used to determine the rate law for a reaction, more importantly, these studies also can be used to measure change in the reaction when a particular parameter is altered. The most common electro(nucleo)philic mechanisms are investigated by quantifying reactions with a single nucleophile but varying the structure of the electrophile and then comparing reactivity with the structure of the electrophile. Current efforts are focused on measuring the reactions of electrophiles with various protein-related nucleophiles, especially ones at the soft end of the electrophilic spectrum.

Glutathione in Chemico Reactivity Database

The glutathione (GSH) in chemico reactivity database is a collection of abiotic reactivity potency data for more than 250 industrial organic compounds, of which more than 100 have been published in the open literature. The assay is a short-term, static, concentration-response protocol with cysteine in the tripeptide glutathione as the model nucleophiles. The 50% reactive concentration, an RC50 value, which measures the concentration required to complete half the reaction within a fixed time, is reported.

The thiol group of GSH is used as a model nucleophile, and chemical reactivity assessments are conducted in an abiotic concentration-response scenario with free thiol being quantified spectrophotometrically at 412 nm after reacting with 5,5'-dithio-bis(2-nitrobenzoic acid) (DTNB). Briefly, GSH is prepared fresh by dissolving 0.042g of reduced glutathione into 100ml of phosphate buffer at pH 7.4. Stock solutions of the test chemicals are prepared by dissolving them in dimethyl sulfoxide (DMSO). Subsequently, phosphate buffer is added to the test chemical/DMSO solution so the concentration of DMSO in the final solution is always < 20% and typically < 5%. Initial range finding experiments are carried out; afterward, definitive experiments are conducted with the concentrations adjusted so there are at least three partial effects, with one partial effect on each side of the 50% effect concentration. These experiments are repeated at least twice with fresh GSH and toxicant stock solution. No decrease of GSH has been observed in controls that are spiked with DMSO only. Initially, negative control experiments, ones without GSH, are conducted to make sure the test substances did not interfere with the absorbance of DTNB-conjugates. Additionally, two vials of buffer are included in each assay, one with thiol, the control, and the other without thiol, the “blank”.

In each small glass vial, 1 mL GSH solution is added, followed by an aliquot of stock solution and then the appropriate amount of phosphate buffer to bring the final volume to 10 mL. This resulted in a final thiol concentration of 0.1375 mM. Vials are capped, shaken gently, and left to stand for 120 minutes prior to the addition of 200 mL of 50 mM DTNB. The 120-minute time point was selected to allow the greatest time for reacting while reducing the potential for oxidation of the GSH to GSSG, which would reduce the free thiol concentration by means other than the test chemical binding to the thiol. Absorbance is read at 412 nm and recorded with a spectrophotometer. The effect levels are determined from nominal toxicant concentrations, and the RC50 values are determined by Probit Analysis of SAS with toxicant concentration as the X variable and absorbance normalized to control as the Y variable.

 

Structure-Toxicity Modeling

Structure-toxicity modeling work in the Biological-Activity Testing and Modeling Laboratory focuses on aquatic toxicity and selected human health endpoints. Current efforts for aquatic effects include models for bacteria, protozoa, algae, daphnid, and fish endpoints. The modeling of relationships between acute and chronic endpoints are also being examined. Human health endpoints of particular interest include skin and respiratory sensitization, and mammalian inhalation toxicity.

The premise of structure-toxicity modeling is that changes in the structure of a chemical may influence the type and potency of its toxic action. This principle is a continuation of the concept that all chemical-toxicological effects are the result of an interaction between the chemical and one or more components of the living system. These interactions may be reversible or covalent in character. Components of living systems that are capable of reversible or covalent binding with chemicals are referred to as molecular sites of action.

The objectives of structure-toxicity modeling are two-fold. First, we seek to determine as accurately as possible the limits of variation in the structure of a chemical that are consistent with the production of a specific biological effect (i.e., can a chemical elicit a specific biological endpoint). Second, we want to define the ways in which alterations in structure and thereby the overall properties of a compound influence potency. If enough data related to a specific biological effect becomes available, a hypothesis can be developed regarding the molecular basis of interaction between the toxicant and its active site. This is indeed the case for molecules that are specifically covalent reacting. However, a non-covalent narcosis or anesthetic response, especially elicited by neutral organic molecules, represents a nonspecific effect, in which no moiety or molecular substructure requirement is implicated and toxic potency is totally dependent on the hydrophobicity of the entire molecule.

The development of a structure-toxicity model requires three components: 1) A data set is required that provides toxicity for a group of chemicals. This group is defined typically by some selection criteria. 2) Also required of this group of chemicals are property data (i.e., descriptors). 3) These two data arrays then must be related usually via a statistical analysis method. Subsequent to initial model development, additional data are used as a means to define the scope and limitations of the preliminary model and to develop an improved and more robust predictive model. Collectively, these latter steps often are referred to as model validation.

Structure-toxicity models provide a rational, rapid, and inexpensive means of predicting toxic effects. There are two types of structure-toxicity models. The first type is qualitative. A qualitative relationship is a general rule type of model that provides either yes/no, or at best, A > B > C information. It can be developed with lower quality non-continuous-type data. The second type is quantitative. It can be developed only using continuous valued data and provides a mathematical model that describes toxic potency based on descriptors of the chemicals.

Chemical descriptor(s) embody empirical, quantum chemical, or non-empirical parameters. Empirical descriptors may be measured or estimated and include physicochemical properties. Physicochemical properties include hydrophobic, electronic, and steric descriptors. Non-empirical descriptors are typically structural properties based on topological or graph theory; as such they are 2-D indices. Quantum chemical descriptors are based on an optimized 3-D structure of molecules.

Properties of compounds are related to their molecular structure. While chemicals are normally thought of in a 2-D structure, toxicity is a manifestation of the 3-D structure of a molecule. Properties also are typically manifestations of 3-D structure. Thus, we generally prefer property-based models. Chemical descriptors may be based on the atom, substituent, or whole molecule. While atom-type descriptors and substituent constants have been used in modeling toxicity, we prefer using the more global, whole-molecule descriptors when possible.

Methods used in the development of structure-toxicity modeling are of two sorts; correlative and pattern recognition. The most common correlative method is regression analysis. Regression analysis is quantitative in character and is a simple approach that leads to a result that is easy to understand and often provides a mechanistic basis for the modeled toxicity. For this reason, we prefer quantitative models in toxicology that are derived using regression analysis. In contrast, pattern recognition techniques are qualitative in character. They are a complex approach, the results of which are often difficult to interpret.

Over the past two decades, schemes for structure-toxicity modeling have changed. The simple congeneric series approach was replaced by a chemical class-based approach. The latter approach was replaced by the mechanism of toxic action approach. More recently more complex statistical approaches have been explored. Regardless of the approach, the limiting factor in the development of toxicological models has been and still remains the availability of high quality experimental toxicity data. For this reason the overriding goal of the Biological-Activity Testing and Modeling Laboratory has been the production of high quality toxicity data.

Current Projects

On going projects in the Biological-Activity Testing and Modeling Laboratory include:

  • Continued development of the TETRATOX database Continued development of the glutathione in chemico reactivity database

  • Development of an amine-based in chemico reactivity assay Determination of applicability domains for chemical categories

  • Development of knowledge-based expert systems to segregate chemicals into toxicologically meaningful groups

  • Development and validation of structure-toxicity models for mammalian inhalation toxicity

  • Development and validation of structure-toxicity models for sensitization

  • Flow cytometry and toxicogenomics effects of environmental pollutants on immune-related cells

 

 

Recent Publications

Peer-reviewed publications authored by members of the Biological-Activity Testing and Modeling Laboratory number over 250. These include manuscripts on: 1) development, standardization, and validation of methods for toxic hazard assessment; 2) structure-activity relationships for toxic endpoints and industrial organic chemicals, and 3) elucidating mechanisms of toxic action. Recent publications include:

Dimitrov, S., Koleva, Y., Schultz, T.W., Walker, J.D. and Mekenyan, O. 2004. Interspecies QSAR model for aldehydes: Aquatic toxicity. Environmental Toxicology and Chemistry 23: 463-470.

Schultz, T.W., Seward-Nagel, J., Foster, K.A. and Tucker, V.A. 2004. Structure-activity relationships for aliphatic alcohols and aquatic toxicity to Tetrahymena. Environmental Toxicology 19: 1-10.

Schultz, T.W. and Yarbrough, J.W. 2004. Trends in structure-toxicity for carbonyl-containing a,b-unsaturated compounds. SAR QSAR in Environmental Research 15: 139-146.

Ren, S., Schultz, T.W. and Frymier, P.D. 2004. Evaluation of the Shk1 activated sludge bacterial luminescence inhibition assay: Narcotic chemicals. Bulletin of Environmental Contamination and Toxicology 72: 1187-1194.

Schultz, T.W., Netzeva, T.I. and Cronin, M.T.D. 2004. Ecotoxicity QSARs: A method for assigning quality and confidence. SAR QSAR in Environmental Research 15: 385-397.

Gagliardi, S.R. and Schultz, T.W. 2005. Regression comparisons of aquatic toxicity of benzene derivatives: Tetrahymena pyriformis and Rana japonica. Bulletin of Environmental Contamination and Toxicology 74: 256-262.

Schultz, T.W., Netzeva, T.I., Roberts, D.W. and Cronin, M.T.D. 2005. Structure-toxicity relationships for carbonyl-containing a,b-unsaturated aliphatic chemicals evaluated with Tetrahymena pyriformis. Chemical Research in Toxicology 18: 330-341.

Netzeva, T.I., Worth, A.P., Aldenberg, T. Benigni, R. Cronin, M.T.D., Gramatica, P., Jaworska, J.S., Klopman, G. Marchant, C.A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G.Y., Perkins, R. Roberts, D.W., Schultz, T.W., Stanton, D.T., van de Sandt, J.J.M., Tong, W. Veith, G.D. and Yang, C. 2005. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships: The report and recommendations of ECVAM workshop 52. Alternatives to Laboratory Animals 33: 155-173.

Aptula, A.O., Jeliazkova, N.G., Schultz, T.W. and Cronin, M.T.D. 2005. The better predictive model: High q2 for the training set or low root mean square error of prediction for the test set? QSAR and Combinatorial Sciences 24: 385-396.

Sanseverino, J., Saidak, L., Gupta, R.K., Layton, A.C., Patterson, S.S., Ripp, S., Simpson, M.L., Schultz, T.W. and Sayler, G.S. 2005. Saccharomyces cerevisiae BLYES expressing bacterial bioluminescence for rapid, sensitive detection of estrogenic compounds. Applied and Environmental Microbiology 71: 4455-4460.

Aptula, A.O., Roberts, D.W., Cronin, M.T.D. and Schultz, T.W. 2005. Chemistry-toxicity relationships for the effects of di- and tri-hydroxybenzenes to Tetrahymena pyriformis. Chemical Research in Toxicology 18: 844-854.

Hansen, L,. Machela, M., Fischer, L. James, M. Henning, J.B., Glauert, H., Narbonne, J.-F., van Bree, L., Schultz, T.W., Grevatt, P. Suk, W., Holoubek, I., Robertson, L. 2005. Research needs identified at the Second PCB Workshop in Brno, Czech Republic, May 7–11, 2002. Toxicological & Environmental Chemistry 87: 261–265.

Schultz, T.W., Yarbrough, J.W. and Johnson, E.L. 2005. Structure-activity relationships for glutathione reactivity of carbonyl-containing compounds. SAR QSAR in Environmental Research 16: 313-322.

Schultz, T.W., Yarbrough, J.W. and Woldemeskel, M. 2005. Toxicity to Tetrahymena and abiotic thiol reactivity of aromatic isothiocyanates. Cell Biology and Toxicology 21: 181-189.

Netzeva, T.I., and Schultz, T.W. 2005. QSARs for the aquatic toxicity of aromatic aldehydes from Tetrahymena data. Chemosphere 61: 1632-1643.

Aptula, A.O., Roberts, D.W., Patlewicz, G. and Schultz, T.W. 2006. Non-enzymatic glutathione reactivity and in vitro toxicity: A non-animal approach to skin sensitization. Toxicology in Vitro 20: 239-247.

Wan, B., Fleming, J.T., Schultz, T.W. and Sayler, G.S. 2006. In vitro immune toxicity of depleted uranium: Effects on murine macrophages, CD4+ T-cells and gene expression profiles. Environmental Health Perspectives 114: 85-91.

Schultz, T.W., Yarbrough, J.W. and Koss, S.K. 2006. Identification of reactive toxicants: Structure-activity relationships for amides. Cell Biology and Toxicology 22: 339-349.

Dawson, D.A., Pch, G. and Schultz, T.W. 2006. Chemical mixture toxicity testing with Vibrio fischeri: Combined effects of binary mixtures of ten soft electrophiles. Ecotoxicology Environmental Safety 65: 171-180.

Schultz, T.W., Carlson. R.E., Cronin, M.T.D., Hermens, J.L.M., Johnson, R., O'Brien, P.J., Roberts, D.W., Siraki, A., Wallace, K.D. and Veith, G.D. 2006. A conceptual framework for predicting toxicity of reactive chemicals: Models for soft electrophilicity. SAR QSAR in Environmental Research 17: 413-428.

Wan, B. Sayler, G.S. and Schultz, T.W. 2006. Structure-activity relationships for flow cytometric data of smaller polycyclic aromatic hydrocarbons. SAR QSAR in Environmental Research 17: 597-605.


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