# Presentation: 112235

## Mode of Action and Dosimetry Considerations in Interspecies Extrapolation

- Melvin E. Andersen, Ph.D.
- CIIT Centers for Health Research
- Board of Scientific Counselors (BOSC)
- Risk Assessment Workshop
- February 2-3, 2005
- Washington, DC

## Outline:

- Interspecies extrapolation defaults
-  Dosimetry models in risk assessment and what we’ve learned.
- Mode of Action models and dose-response – where we are going
- Some Recommendations, especially about the idea of defining defaults

## Defaults:

- The process we follow in the absence of information
- Change default process as information becomes available
- It’s a bit backwards.
- What kind of information do we want to have to do a risk assessment and what do we do when it is lacking?

## Default Interspecies Scaling (Model independent):

- FDA – 1950’s: Two safety factors, 10-each “rules of fingers”: animal-to-human, among human populations;
**	****	****	****Safety factors**
- Cancer Risk Assessment (1970’s) – linear risk models
- 	 		Surface area adjustment **(bw1/bw2)****0.33**
- Reference Dose (1980’s) – non-linear risk models
- 			**Uncertainty factors**
- 		10-interspecies
- 		10-intraindividual
- 		others – data base, NOAEL/LOAEL/Duration
- 		Proliferation of **independent**, uncertainty factors

## What are we accounting for?

**Dosimetry** – for a given exposure in test animals and humans how do tissue doses vary between species
**Tissue Response** – for a specified tissue dose, how will the response vary between test animals and humans.

- In the context of some presumption about the **dose-response relationship**?

## What tools help us evaluate these relationships?

**Pharmacokinetics – **calculate the tissue dose of active forms of the toxic chemical for various doses, dose-routes, and animal species
**Pharmacodynamics** – calculate the degree of response for any level of tissue dose in different species

## Physiologically Based Pharmacokinetic (PBPK) Modeling

**Define Realistic Model**

**Collect Needed**
**Data**

**Refine Model Structure**

**Make **
**Predictions**

**Metabolic Constants**
**Tissue Solubility**
**Tissue Volumes**
**Blood and Air Flows**
**Experimental System**

**Model Equations**

**X**

**X**

**X**

**X**

**X**

**X**

**X**

**X**

**Tissue Concentration**

**Time**

**You can be wrong!**

**Liver**

**Fat**

**Body**

**Lung**

**Air**

## Using PBPK models - 1985

- Identify toxic effects in animals and people
- Evaluate available data on mode(s) of action, metabolism, chemistry of compound, metabolites and related chemicals
- Describe potential **mode(s) of action**
**Propose relation between response and tissue dose**
- Develop a PBPK model to calculate tissue doses
- Estimate tissue dose during toxic exposures with model
**Estimate risk in humans assuming similar tissue response for equivalent target tissue dose**

## Extrapolations supported by these models

- High doses to low doses
- Dose route – inhalation, oral, dermal
- Between species
- Across classes of chemicals
- in vitro to in vivo
- Dosing scenarios

## What we’ve  learned with these dosimetry models

- The process – how to do it.
- Broad utility, i.e., 1000 papers by 2002
- Mode of action specific extrapolation defaults
- Analysis tools – assess variability, evaluate model sensitivity, improve experimental design
- Assumptions in process made more explicit
-  Identify, quantify and reduce uncertainty** **

## RfD and RfC Calculations Distinguish PK and PD components of Interspecies Uncertainty Factor.

**Default – the PK and PD portions are regarded as having equal value (10)****1/2**
- May be changed with information about a specific compound, leading to data-derived uncertainty factors (Renwick and colleagues)

## Dose-Response Models

- Linear models – some risk probability at all doses
- Threshold ‘models’ – use independent, uncertainty factors.  **Why??**

## BBDR models for tissue level responses, MVK cancer model and carcinogenesis

## Exposures produce toxicity and DNA-protein cross-links.  With toxicity, there is increased cell proliferation, tumor promotion and cancer.

## Cellular responses

**Adaptive**
**State**

**Stressed**
**State**

- Molecular
- Biochemical
- Histologic
- Physiological

**Dosimetry**
- Inhaled Stressors

- Ventilation

- Tissue Phase Reactions
- Cl2                  HOCl + HCl
- CH2O           HCOOH

## In general, 

**Lack organized information about basic responses of cell signaling pathways to toxic compounds**
**High coverage/signal transduction studies, in a functional genomics context, are changing the situation**

## Common Signaling Themes. Science Nov. 2004.

## MAP-Kinase Families

**MAP-Kinase Families**

- And others................ (Johnson and Lapadat, Science, 2002).

## MAPKinase Cascades

**MAPKinase Cascades**

**MAPKKK             MAPKKK-PO****4****              MAPKKK + PO****4**

**MAPKK                                          MAPKK + PO****4**

**MAPK               MAPK-(PO****4****)****2****         MAPK + PO****4**
** **

**Function**

**Target Protein**
**Transcriptional Factor**

**phospahatases**

**MKKK**

**MKK**

**MAPK**

**MAPKK-(PO4)****2**

- Input

## Platelet Derived Growth Factor (PDGF)

- Negative feedback loop through MKP and PP2A

**Platelet Derived Growth Factor (PDGF)**

## UNCERTAINTY

**Exposure   **

**Tissue**
**Dose**

**Biologically**
**Effective Dose**

**Early**
**Responses**

**Late**
**Responses**

**Pathology**

**UNCERTAINTY**

- Physiologically Based
- Pharmacokinetic Models

- Mode of Action

- Tissue Dose
- Metric

- The fading paradigm

## Perturbations of biological function

**Systems**
**Inputs**

**Affected**
**Biological**
**Function**

**Exposure**
**Tissue Dose**
**Biological Interaction(s)**
**Perturbation**

- Molecular or Pathway
- Target

## Slide 23

## Use mode of action based defaults
Develop parameter data bases for human PBPK models
Encourage dosimetry based approaches for cumulative & aggregate risk assessments
Expand suite of ‘validated’ human PBPK models
Improve understanding of parameters important for understanding PK of lipophilic compounds 

- Develop parameter data bases for human PBPK models
- Encourage dosimetry based approaches for cumulative & aggregate risk assessments
- Expand suite of ‘validated’ human PBPK models
- Improve understanding of parameters important for understanding PK of lipophilic compounds** **

**Dosimetry Recommendations**

## Develop clear articulation of the underlying ‘models’ –linear and non-linear - used for risk assessment and the implicit structure/assumptions

Explain how data when available are to be used in these assessments

Discuss rationale for defaults used when data are unavailable

Develop mechanistic dose-response models for obligatory precursor cellular responses to toxic stressors, e.g., chloroform or chlorine, etc., or to altered signaling pathways from EACs 

- Explain how data when available are to be used in these assessments
- Discuss rationale for defaults used when data are unavailable
**Develop mechanistic dose-response models for obligatory precursor cellular responses to toxic stressors, e.g., chloroform or chlorine, etc., or to altered signaling pathways from EACs**

**More Fundamental Recommendations**

## What do we want to do with dose response models 

**The process – what do we think we are doing and how should we do it.**
**Find out if independent use of uncertainty factors is actually a rational approach **
**Analysis tools – assess variability, evaluate sensitivity, improve experimental design**
**Assumptions in extrapolation process made more explicit**
** ****Identify, quantify and reduce uncertainty **