General Concepts in QSAR for Using the QSAR Application Toolbox

Part 1 General Concepts in QSAR

Online Course Outline The need for predictive methods Basic terminology in QSAR development Selecting biological endpoints for modeling Using trends to define chemical categories Chemical categories for filling data gaps Overview of the QSAR Toolbox

Need for Predictive Methods Laboratory measurements of chemical toxicity must address many different hazards and responses (biological effects) under many exposure scenarios Regulatory risk (or safety) assessments rely heavily on the interpretation of bioassays, which are designed to reveal the spectrum of effects of a chemical Most assessments rely on batteries of bioassays intended to characterize important hazards such as short-term effects, carcinogenicity, mutagenicity, reproductive impairment and development deficits Screening-level assessments can cost from $1-5M, while comprehensive risk assessments can cost more than $60M in testing and analysis

Need for Predictive Methods Due to the high cost of animal tests, risk assessments based on such tests are limited to a small percentage of industrial chemicals Fewer than 10,000 chemicals have been tested for the major hazards; the majority of these chemicals have been tested for only a few hazards The world inventory of chemicals in commerce exceeds 160,000 chemicals and is growing by more than 3,000 new chemicals each year The collective capacity of all OECD member countries to conduct the SIDS initial hazard assessments was ~500 chemicals/year for the last decade

Need for Predictive Methods Alternative test methods that are more diagnostic and faster are one leading approach to fill the data gaps Non-testing alternative methods involve the use of chemical models to extrapolate the hazards of tested chemicals to similar untested chemicals The non-testing alternative method includes the use of quantitative structure-activity relationships (QSARs) that relate biological activity to structure Physical chemists, engineers and medicinal chemists have been reliably estimating the behavior of untested chemicals for more than 60 years

Basic Definitions in QSAR Chemistry is based on the simple premise that similar chemicals will behave similarly If two chemicals appear to be very similar but behave dramatically differently (e.g., stereoisomers), one’s perception of similarity is wrong Like most complex systems, the behavior of a chemical as a molecular system is largely derived from the electronic and steric properties of its structure Therefore, the field of QSAR research is concerned with methods for quantifying chemical similarity in order to improve ways of grouping similar chemicals Similarity is not an absolute, but must be determined within a specific context for a specific attribute or behavior

Basic Definitions in QSAR For toxicology, structure-activity relationships start with selecting a test endpoint such as lethality (LC50) or effect concentration (EC50) QSAR searches for relationships between chemical structure and activity so that the test endpoint can be predicted accurately from structure For example, industrial chemicals are classified as inhalation hazards when the 4-hour LC50 of a chemical for rats is less than 20 mg/l When LC50 values are compiled for 20 - 30 chemicals and chemical structure is represented by the vapor pressure (VP), a QSAR model can be formed In this example, the QSAR is log LC50 (rat, 4hr) = 0.69 log VP + 1.54, which allows the LC50 of untested similar chemicals to be estimated

Selecting Biological Endpoints QSARs can be used to estimate important toxicity endpoints for thousands of chemical structures in order to focus assessments on the greatest risks However, a single QSAR model for a toxicity endpoint like LC50 is only reliable for chemicals that are similar to the training set of chemicals In toxicology, similar chemicals are usually defined as those that cause toxic effects through the same toxicity mechanisms Therefore, QSAR models must first predict whether a chemical has the same toxicity mechanism for which a particular model was built If a chemical’s toxicity mechanisms differs from the one for which a particular model was built, it is, by definition, not similar and its effects cannot be estimated reliably with that same QSAR model

Steps to creating QSAR Models Choose a well-defined endpoint for biological activity that is relevant to the assessment Compile measured values of the biological endpoint using a consistent test method for similar chemicals (training set) –OR- Select a homologous series of relevant chemicals and systematically test all of them for the biological endpoint using a consistent method Identify the chemical attributes that are likely to be important in toxicity mechanisms and the endpoint, and then calculate for each chemical the “molecular descriptors” ( e.g., VP, Log P, pKa, etc.) that put those attributes in numerical terms Explore the statistical variances among the molecular descriptors and endpoint values, and identify relationships between the molecular descriptor and endpoint for the assessment

Simple Example for QSAR Compile data for lethality (LC50) in mice from 30-minute inhalation exposures from the literature In this example, restricting chemicals to simple aliphatic ethers increases the likelihood that the toxicity mechanism for lethality will be the same As shown on the next slide, estimate or measure the vapor pressure to be used as a molecular descriptor (selected from theory or by trial and error) Correlate LC50 values with the vapor pressure to get: log LC50 = 0.57 x log VP + 2.08 This regression equation is the QSAR for this endpoint and this class of chemicals even though the toxicity mechanism is not known

Chemical name CAS MW Table 1. LC50-30 min of aliphatic ethers in mice VP (mmHg) LC50 (mmol/m3) Diisobutyl ether 628-55-7 130.2 15 1200 Disec-butyl ether 6863-58-7 17 1000 Diethyl ether 60-29-7 74.1 537 6000 Diisopropyl ether 108-20-3 102.2 170 1500 Dimethyl ether 115-10-6 46.1 4450 37000 Dipropyl ether 111-43-3 60 1600 Divinyl ether 109-93-3 70.1 684 4700 Ethyl amyl ether 17952-11-3 116.2 18 Ethyl butyl ether 628-81-9 52 Ethyl cyclopropyl ether 5614-38-0 86.1 150 Ethyl isoamyl ether 628-04-6 30 Ethyl isobutyl ether 627-02-1 98 Ethyl isopropyl ether 625-54-7 88.1 250 2500 Ethyl propyl ether 628-32-0 185 Ethyl- sec-butyl ether 2679-87-0 1400 Ethyl tert-amyl ether 919-94-8 43 700 Ethyl tert-butyl ether 637-92-3 155 Ethyl vinyl ether 109-92-2 72.1 500 4500 Methyl amyl ether 628-80-8 55 1300 Methyl butyl ether 628-28-4 160 2000 Methyl cyclopropyl ether 540-47-6 410 1750 Methyl ethyl ether 540-67-0 60.1 1493 18000 Methyl isobutyl ether 625-44-5 210 Methyl isopropyl ether 598-53-8 550 5500 Methyl propyl ether 557-17-5 3500 Methyl sec-butyl ether 6795-87-5 230 Methyl tert-butyl ether 1634-04-4 244 Propyl isopropyl ether 627-08-7 85

Simple Example for QSAR Notice the dependence on VP (slope) is almost the same as the QSAR derived from the 4-hour exposure with rats shown earlier, suggesting the same structural attributes are controlling toxicity Notice the intercept is about 0.5 log units greater for the 30-minute test with mice versus the 4-hour test with rats If we assume the toxicity mechanism causing lethality is the same for chemicals in both sets, can you explain why the LC50 in mice is greater (lower toxicity) than the LC50 in rats? Statistical exploration of data compiled for different chemicals is one of several important methods for defining chemical similarity

Simple Example for QSAR This QSAR implies that the vapor pressure of a chemical is an important factor in determining that chemical’s potency in a lethality test Many other molecular descriptors would not correlate to toxicity, and the good correlation here points to structural attributes that influence VP Chemicals that cause lethality by other toxicity mechanisms, chemicals such as acrolein or phosgene, will appear as statistical outliers Therefore, in QSAR “outlier analysis” is often used to gain insight into chemical similarity as defined in terms of common mechanisms