QSAR s by deV61o

VIEWS: 1 PAGES: 30

									QSARs

Quantitative Structure-Activity
Relationships and their Applications



                                Robin Hughes
QSARs

 What Are They?
 Why Do We Care?
 How Are They Used?
 Limitations
 Conclusions / What’s Next?
What Are They?
  Mathematical Models

  Chemical Structure Biological Activity


  Hydrophobic
  Electronic
  Steric
Why Do We Care?

    Based on Parameters
    Time
    Cost
    Predictive
How Are They Used?
    Recent (Past 10yrs)
    Pharmaceutical, Agricultural
    Establishing Priorities
    Estimation
    Risk Assessment
    International Decision-Making
Studies
  “Correlation between Hydrophobicity of Short-Chain
   Aliphatic Alcohols and Their Ability to Alter Plasma
   Membrane Integrity” McKarns et al. (1996)

  “Classifying Class I and Class II Compounds By
   Hydrophobicity and Hydrogen Bonding Descriptors”
   S. Ren (2002)

  “Structure- and Property- Activity Relationship Models
   of Microbial Toxicity of Organic Chemicals to Activated
   Sludge” N. Nirmalakhandan, E.Egemen, C. Treviso,
   and S. Xu (1997)
Correlation between Hydrophobicity of Short-
Chain Aliphatic Alcohols and Their Ability to
Alter Plasma Membrane Integrity McKarns et al. (1996)


   Lactate dehydrogenase (LDH)

   Log P (= log Kow)
Study Design

  2 Models
    LDH50
    EC50

    Almost Identical
Findings

  LDH50 and EC50 are positively correlated
   with Hydrophobicity

  Models are good predictors

  Predict effects of untested chemicals
Classifying Class I and Class II Compounds
By Hydrophobicity and Hydrogen Bonding
Descriptors                       S. Ren (2002)


   Class I: Nonpolar Narcotic
      Baseline Toxicity


   Class II: Polar Narcotic
      Above Baseline Toxicity
Study Design

  Nonlinear model
  Discriminant Analysis
  Descriptors Used
    Log Kow
    ELUMO, EHOMO
    Q+, Q-
Findings
  Applied to 190 compounds
  8 Misclassifications

  All 5 variables significant
Structure- and Property- Activity
Relationship Models of Microbial Toxicity
of Organic Chemicals to Activated Sludge
             N. Nirmalakhandan, E.Egemen, C. Treviso, and S. Xu (1997)

  Concern:
 Industrialization  SOCs in wastewater

  Microorganisms used in treatment

  What are threshold levels?
Study Design

  4 Models
  IC50

  Experimental Data: 16 chemicals
Models
 QSAR Models
 LSER Model

    VI, Instrinsic molar volume
    π*, Polarity/Polarizability
    αm, Hydrogen bond donor acidity
    βm, Basicity
Models
 QSAR Models
 MCI Model
  Structural and Atomic Information

  Express physical, chemical, and
   biological properties
Models
 QPAR Models
 Octanol-Water Partition Model

  log P

  Used calculated values for consistency
Models
 QPAR Models
 Aqueous Solubility Model
  log S correlated with other indicators

  Hypothesis: toxicity may be directly
   correlated with S

  Used experimental values
Findings
 LSER Model
  High statistical validity, but not the best toxicity
   predictor

 Log S
  No statistically significant correlation found

       Both models above acceptable FE for
             50% of tested chemicals
Findings

 log P and MCI Models
  “reliable and convenient”

  Parameters easily available

  Further testing of utility suggested
Findings

           Predicted vs
             Experimental

            LC50
            All 4 models
Findings

           Factor of error

            FE=predictive/measured

            If FE<1; 1/FE was used
Limitations
    Activity-Specific
    Experimental Error
    Too Many Parameters
    Validation
    Projecting Beyond Sample Space
Conclusions

    Similar Parameters
    Still Secondary
    Low-Cost (both time and $)
    Mechanism of Action
    Still in development
What’s Next?

    Databases
    Predictive Capabilities
    Prevention
    Remediation?
Works Cited
    Cronin, M. et al. (2003). Use of Quantitative Structure-Activity
     Relationships in International Decision-Making Frameworks to Predict
     Health Effects of Chemical Substances. In Journal of the National Institute
     of Environmental Health Sciences. (www.ehponline.org).
    McKarns, S., et al. (1997). Correlation between Hydrophobicity of Short-
     Chain Aliphatic Alcohols and Their Ability to Alter Plasma Membrane
     Integrity. In Fundamental and Applied Toxicology, 36: 62-70.
    Nirmalakhandan, N.,.Egemen, E, Treviso, C., and Xu, S. (1998).
     Structure- and Property- Activity Relationship Models of Microbial Toxicity
     of Organic Chemicals to Activated Sludge. IN Ecotoxicology and
     Enironmental Safety, 39:112-119.
    Rand, G.M., Ed. Fundamentals of Aquatic Toxicology, 2nd Ed. Taylor
     and Francis. Philadelphia, PA, 1995.
    Ren, S. (2002). Classifying Class I and Class II Compounds By
     Hydrophobicity and Hydrogen Bonding Descriptors . In Environmental
     Toxicology, 17: 415-423.
   QSAR and Modeling Society:




http://www.ndsu.nodak.edu/qsar_soc/aboutsoc/applicat.pdf

								
To top