General Concepts in QSAR for Using the QSAR Application Toolbox Part 2

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GENERAL CONCEPTS IN QSAR FOR USING THE QSAR APPLICATION TOOLBOX

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

Transcript of General Concepts in QSAR for Using the QSAR Application Toolbox Part 2

  • 1.General Concepts in QSAR for Using theQSAR 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. Lets 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 uncommontofind endpoint
values spanning 6-10 ordersfor a
singletoxicity mechanism
10_4
10_6
10-8
1
2
3
4
5
Chemical Class orMechanism
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 approachto 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
DNAAdducts
Oxidative
Stress
Systemic
Responses
Skin
Liver
Lung
Systemic
Immune
Responses
Dose-Dependent Effects