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

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

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  • The simplest exercise in QSAR is canonical ordering which starts with choosing a group of chemicals, and a selected property or biological activity for each. In this slide, nine chemicals are listed with their boiling points. If we think we understand how chemical structure relates to boiling point, we would expect that we could choose those molecular descriptors which would place the chemicals in the same order as would a measured behaviour such as boiling point.
  • In this slide, the chemicals are sorted by increasing boiling point. Can we identify molecular descriptors that create the same order. If not, we do not understand the inter- and intramolecular forces that control boiling point. If QSAR can order them properly, the task is then to find chemicals that fit between these values an test the QSAR model. Through numerous iterations , theoretical explanations can be evaluated for relevance, and the important molecular descriptors are discovered. This came approach can be used for toxicity data.
  • In this example, I am illustrating that there are many toxicity mechanisms, and if all the chemicals having the same mechanism are compiled, it would not be unusual for the potency of those chemicals to range over 8-10 orders of magnitude. Even if the range were much less, the first challenge for QSAR would be to identify a molecular descriptor that places the chemicals in the same order as the potency measures (LC50). To illustrate, I am using aquatic lethality with fish just to move away from the rodent inhalation example, but keep in mind that a fish test is just an inhalation test with aquatic organisms.
  • For many mechanisms, uptake of the chemicals is controlled by passive transport and one would expect the octanol/water partition coefficient to covary with passive transport. When the entire range of potency values are plotted vesus Log Ko/w, the chemicals remain in the same order and quantitative relationship between LC50 and Ko/w can be derived exactly like that for the rodent inhalation data.

Transcript

  • 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