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

2. Part 1 General Concepts in QSAR

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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 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 , which allows the LC50 of untested similar chemicals to be estimated

9. 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

10. 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

11. 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
This regression equation is the QSAR for this endpoint and this class of chemicals even though the toxicity mechanism is not known

12. Chemical name CAS MW Table 1. LC50-30 min of aliphatic ethers in miceVP
(mmHg)
LC50
(mmol/m3)
Diisobutyl ether
130.2 15
1200
Disec-butyl ether 17
1000
Diethyl ether
74.1
537
6000
Diisopropyl ether
102.2
170
1500
Dimethyl ether
46.1
4450
37000
Dipropyl ether 60
1600
Divinyl ether
70.1
684
4700
Ethyl amyl ether
116.2 18
Ethyl butyl ether 52
Ethyl cyclopropyl ether
86.1
150
Ethyl isoamyl ether 30
Ethyl isobutyl ether 98
Ethyl isopropyl ether
88.1
250
2500
Ethyl propyl ether
185
Ethyl- sec-butyl ether
1400
Ethyl tert-amyl ether 43
700
Ethyl tert-butyl ether
155
Ethyl vinyl ether
72.1
500
4500
Methyl amyl ether 55
1300
Methyl butyl ether
160
2000
Methyl cyclopropyl ether
410
1750
Methyl ethyl ether
60.1
1493
18000
Methyl isobutyl ether
210
Methyl isopropyl ether
550
5500
Methyl propyl ether
3500
Methyl sec-butyl ether
230
Methyl tert-butyl ether
244
Propyl isopropyl ether 85

13. 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

14. 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