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ARA Dose-Response Framework >

Problem Formulation
 > Qualitative


 

 






  • Use available data to identify adverse effects, focusing on those of concern for exposed populations
  • Consider strengths and uncertainties in data

 









  • What are expected targets, based on chemical structure, available data, and related chemicals?
  • What is known about MOA for related chemicals?

 

 









  • Assessment
  • Use available data to assist in the risk management
    decision














  • Use available data to assist in the risk management decision






 


 

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Problem Formulation > Quantitative > Dose-Response Selection

 

 


Note:  In general, the methods used here apply substantially health-protective assumptions to avoid type II errors*


 

 

Method Case Studies

expand Tiered Approach Case Study (includes threshold of concern approach )
  • Deriving Health-Protective Values for Evaluation of Acute Inhalation Exposures for Chemicals with Limited Toxicity Data Using a Tiered Screening Approach 
    Grant R.L., Phillips T., Ethridge S.

expand Screening Tools for the Interpretation of Chemical Biomonitoring Data
expand The Human Relevant Potency Threshold: Reducing Uncertainty by Human Calibration of Cumulative Risk Assessments
  • The Human Relevant Potency Threshold: Reducing Uncertainty by Human Calibration of Cumulative Risk Assessments
    Chris Borgert, Applied Pharmacology Toxicology Inc.
expand Low Dose Extrapolation from the BMD(L)
  • Implications of Linear Low-Dose Extrapolation from Benchmark Dose for Noncancer Risk Assessment 
    Kroner O., Haber L.
    Advisor: Dourson M.

expand Threshold of Toxicological Concern
  • Deriving Health-Protective Values for Evaluation of Acute Inhalation Exposures for Chemicals with Limited Toxicity Data Using a Tiered Screening Approach 
    Grant R.L., Phillips T., Ethridge S.

expand Threshold of regulation
  • Case Study
  • Case Study Summary
  • Presentation Slides
expand Class Based Exposure Level – (CBEL)
  • Case Study
  • Case Study Summary
  • Presentation Slides
expand Screening-level safe dose
  • Case Study
  • Case Study Summary
  • Presentation Slides
expand Structure-activity relationship (SAR) and read-across
  • Case Study
  • Case Study Summary
  • Presentation Slides
expand Provisionally Peer Reviewed Toxicity Values (PPRTV)
  • Case Study
  • Case Study Summary
  • Presentation Slides
expand Quantitative SAR
  • Case Study
  • Case Study Summary
  • Presentation Slides
 

 

 

 

   

*In statistical hypothesis testing, there are two types of errors that can be made or incorrect conclusions that can be drawn. If a null hypothesis is incorrectly rejected when it is in fact true, this is called a Type I error (also known as a false positive).  For example, when a dose is really a No Observed Adverse Effect Level (NOAEL), but is judged to be a Lowest Observed Adverse Effect Level (LOAEL).   A Type II error (also known as a false negative) occurs when a null hypothesis is not rejected despite being false.  For example, when a dose is accepted as a No Observed Adverse Effect Level (NOAEL), when it really should be considered as a Lowest Observed Adverse Effect Level (LOAEL).

 

 

Questions? Suggestions? Please send your feedback to framework@tera.org.