Part II. Trend Analysis and Filling Data Gaps in Hazard Assessment

1. General Concepts in QSAR for Using the QSAR Application Toolbox

2. Part IITrend Analysis and Filling Data Gaps in Hazard Assessment

3. Some Important Lessons We saw that vapor pressure correlates with rodent LC50s, but hundreds of other molecular descriptors may not The trend in the data will be evident only when the toxicity mechanism of all chemicals is the same Chemicals with other mechanisms (i.e. acrolein, phosgene) will appear as statistical outliers to the major trend because they do not produce lethality as do the aliphatic ethers. The QSAR approach uses trend analysis to evaluate mechanisms and to test chemical similarity in terms of common toxicity mechanisms or modes of action

4. QSAR and Trend Analysis QSAR research asks simple questions about why chemicals behave the way they do in all kinds of tests If we wanted to model the boiling point of chemicals, we might start with a list of chemicals and their boiling points In the following slide, chemicals were picked to give a wide range of boiling points

5. Let’s Look at the Order in Boiling Point Boiling Point (°C) Chemical Amyl amine Amyl chloride Dibromobenzene Ethyl bromide n-Heptanol Methacrolein Methyl-p-anisylketone n-Octane n-Nonane 103-4 98-9 219-2 38.4 192 68 267-9 126 151

6. QSAR and Trend Analysis If we reorder the chemicals to put the boiling points in ascending order, we have the next slide The goal of QSAR would be to calculate molecular descriptors for each chemical and look for those giving the same order If a molecular descriptor used in this canonical ordering produced an order different from that for measured boiling points, that descriptor would clearly not reflect the intra- and intermolecular forces controlling the boiling point

7. Look for Order in Molecular Descriptors Chemical Boiling Point (°C) Ethyl bromide Methacrolein Amyl chloride Amyl amine n-Octane n-Nonane n-Heptanol Dibromobenzene Methyl-p-anisylketone 38.4 68 98-9 103-4 126 151 192 219-2 267-9

8. QSAR and Trend Analysis In the next example, we might assume that we have compiled a set of toxicity values (LC50 in fish) for chemicals that are expected to be nonpolar narcotics The actual toxicity values (chemical potency) for a single mechanism can range as much as six orders of magnitude

9. 10+2 10 0 10_2 Acute Toxicity (Moles/L) It is not uncommon to find endpoint values spanning 6-10 orders for a single toxicity mechanism 10_4 10_6 10-8 1 2 3 4 5 Chemical Class or Mechanism Exploring Mechanisms with Simple Endpoints

10. QSAR and Trend Analysis As with boiling points, the QSAR approach evaluates molecular descriptors and identifies those descriptors that yield the same order as the LC50 values would provide In this example, Log Kow explains most of the variance, as a correlation (or trend) emerges between LC50 and Log Kow With a good trend between the structure and biological activity for one toxicity mechanism, the similarity of other chemicals can be judged from consistency with the trend As we shall see, this type of trend analysis is the centerpiece of defining and defending chemical categories

11. 10+2 10 0 Class #2 10_2 Acute Toxicity (moles/l) 10_4 10_6 10-8 0 2 4 6 8 LOG K o/w Exploring Mechanisms with Simple Endpoints

12. Nonpolar Narcotic Toxicants

13. Oxidative Phosphorylase Uncouplers LC50-96hr MATC-30 day

14. Reactive Chemicals (Electrophiles)

15. Current Limitations in QSAR The general QSAR approach is most reliable for chemicals where the parent chemical structure is the actual toxicant One limitation has been predicting the effects of chemicals that are metabolically converted to more active (potent) metabolites Predicting metabolic activation in many test species is a limitation being overcome with metabolic simulators (virtual livers, kidneys, skin, lung etc.) Once the metabolites are predicted, the same library of toxicity models can be used on parent and metabolites to identify the most toxic form of the chemical

16. Which Metabolite should we use in modeling interactions? O N H O O O O O N H N H N H O H H O O O H O O N H N H N H O O H O H O O H O O N H X N H O N H N H 2 O O H O O H O X = H , O H , O H H O O O N H N H N H O H O O O . . . . . . + N H O O H H O + + N H N H O O Simulated 2-Acetylaminofluorene Metabolism Activated metabolites

17. Current Limitations in QSAR The general QSAR approach works well for short-term bioassays where steady-state exposures are achieved Long-term toxic effects, particularly low-incident effects such as cancer, can result from a chemical perturbation of biological functions but are influenced by many other biological factors as well QSAR models for long-term effects will be limited until modeling both chemistry and disease progression over time can be integrated One promising approach to overcome this limitation is the use of adverse outcome pathways

18. Adverse Outcome Pathway For A Well-Defined Endpoint Molecular Initiating Event Speciation, Metabolism Reactivity etc. In Vitro and System Effects In Vivo Adverse Outcomes Parent Chemical Chemical Interactions Structure-Activity Relationships Biological Responses Effects at Different Levels of Organization

19. Molecular Initiating Event Macro -Molecular Interactions Toxicant Chemical Reactivity Profiles Receptor, DNA, Protein Interactions Biological Responses Mechanistic Profiling The Adverse Outcome Pathway

20. Molecular Initiating Event Biological Responses Macro -Molecular Interactions Cellular Toxicant Chemical Reactivity Profiles Gene Activation Protein Production Signal Alteration Receptor, DNA, Protein Interactions NRC Toxicological Pathway The Adverse Outcome Pathway

21. Molecular Initiating Event Biological Responses Macro -Molecular Interactions Cellular Organ Toxicant Chemical Reactivity Profiles Gene Activation Protein Production Signal Alteration Altered Function Altered Development Receptor, DNA, Protein Interactions Mechanistic Profiling In Vitro & HTP Screening The Adverse Outcome Pathway

22. Molecular Initiating Event Biological Responses Macro -Molecular Interactions Cellular Organ Toxicant Organism Population Chemical Reactivity Profiles Gene Activation Protein Production Signal Alteration Altered Function Altered Development Lethality Sensitization Birth Defect Reproductive Impairment Cancer Structure Extinction Receptor, DNA, Protein Interactions Mechanistic Profiling In Vitro & HTP Screening In Vivo Testing The Adverse Outcome Pathway

23. Major Pathways for Reactive Toxicity from Moderate Electrophiles Interaction Mechanisms Molecular Initiating Events In vivo Endpoints Exposed Surface Irritation Michael Addition Schiff base Formation SN2 Acylation Atom Centered Irreversible (Covalent) Binding Necrosis: Which Tissues? Pr-S Adducts GSH Oxidation GSH Depletion NH2 Adducts RN Adducts DNA Adducts Oxidative Stress Systemic Responses Skin Liver Lung Systemic Immune Responses Dose-Dependent Effects