# Presentation: 598632

## Biologically-motivated approaches to extrapolation from high to low doses and the advent of systems biology:  The road to toxicological safety assessment

- Rory B. Conolly, Sc.D.
- Center for Computational Systems Biology
- & Human Health Assessment
- CIIT Centers for Health Research
- rconolly@ciit.org
- U.S. EPA Board of Scientific Counselors Risk Assessment Workshop, National Academy of Sciences Auditorium, Washington, DC, February 2-3, 2005.

## Outline

- Toxicity and dose-response
- Priorities:  Toxicity or safety?
- Toxicological safety assessment
- Summary and conclusions

## Typical high dose rodent data – what do they tell us?

- Response

## Not much!

- Response

- ?

## Possibilities

- Response

## Possibilities

- Response

## Possibilities

- Response

## Possibilities

*Possibilities*

- Response

## Historical perspective

- In the distant past...
- (1970’s and earlier)
- A level of comfort with the use of high dose data to imply human health risk
- Expedient
- Limited knowledge of mechanisms

## Historical perspective (II)

- Then,
- 1970’s, Gehring et al.,
- vinyl chloride
- 1980’s, Andersen et al.,
- PBPK modeling, styrene, methylene chloride
- U.S. EPA cancer guidelines
- Etc., etc.

## Today

- Widespread recognition that chemical-specific high-to-low dose extrapolation may differ from default approaches.
- Concern that some high dose mechanisms may not be relevant.
- Need to protect the public health.
- Need to avoid unnecessary loss of access to useful materials.
- Need to do *good science* in support of human health risk assessment.

## What are our priorities?

- Study of poisons?
- Effects and mechanisms regardless of dose
- Evaluation of chemical safety?
- Dose-response in the region of actual or predicted exposure levels.
- Given limited resources, where do we focus our efforts?

## Partition the problem into manageable parts

## Is safety assessment a practicable alternative?

- Three key elements
- Exposure
- Need data or predictions
- Biologically-based dosimetry models
- PBPK, CFD
- Systems biology
- Opportunity to understand, at the molecular level, the transition from normal biology to frank toxicity

## Tissue dosimetry is the “front end” to a pharmacodynamic model

*Tissue dosimetry is the “front end” to a pharmacodynamic model*

## CFD simulation of nasal airflow(Kimbell et. al)

## Systems biology

## Levels of biological organization

- Populations
- Organisms
- Tissues
- Cells
- Organelles
- Molecules

_**Descriptive**_

_**Mechanistic**_

## Levels of biological organization

- Populations
- Organisms
- Tissues
- Cells
- Organelles
- Molecules

*(systems)*

## Approach

- Initial pathway identification
- Static map
- Existing data
- New data
- Computational modeling
- Dynamic behavior
- Iterate with data collection

## Data

## “Omics”

- Genomics
- Proteomics
- Metabonomics
- “parts list of the biological machine”

## Robots

## Slide 24

## ATM curated

- Pathway from
- Pathway Assist®

## Computational systems biology

- Organize and integrate data
- Study dynamic behavior
- Analysis
- Are model predictions consistent with existing data?
- Predictions
- Suggest new experiments
- Ability to predict data before it is collected is a good test of the model

## Example

- Skin irritation
- MAPK, IL-1, and NF-B computational “modules”
- High throughput overexpression data to characterize IL-1 – MAPK interaction with respect to NF-B

## Skin Irritation

- Epidermis

- Dermis

- Dead cells

- Blood vessels

- (keratinocytes)

- Nerve Endings

- (fibroblasts)

- Chemical

- Tissue damage

- Tissue damage

- A cascade of inflammatory responses (cytokines)

- _Study on the dose response of the skin cells to inflammatory cytokines contributes to quantitative assessment of skin irritation_

## Modular Composition of IL-1 Signaling

- IL-1R

- IL-1

- Secondary messenger

- NF-B

- MAPK

- Others

- Extracellular

- Intracellular

- IL-6, etc.

- Transcriptional factors

- IL-1 specific top module

- Constitutive downstream NF-B module

## Slide 30

## Slide 31

## NF-B Module Simulation

- Parameters from existing NF-B model (Hoffmann et al., 2002) and refined to fit experimental data in literature

- Time (hrs)

- Concentration (M)

- IB

- NF-B

- +

- _

- Add constant input signal

## 6 hr

- Alexander Hoffmann, Andre Levchenko, Martin L. Scott, David Baltimore
- Science 298:1241 – 1245, 2002

## MAPK intracellular signaling cascades

*http://www.weizmann.ac.il/Biology/open_day/book/rony_seger.pdf*

## Slide 35

## MAPK time-course and bifurcation after a short pulse of PDGF

*MAPK time-course and bifurcation after a short pulse of PDGF*

## IL-1 MAPK crosstalk and NFkB activation

*IL-1 MAPK crosstalk and NFkB activation*

## Gain-of-function screen

## Model prediction

*Model prediction*

## Normal variation  frank toxicity

- Populations
- Organisms
- Tissues
- Cells
- Organelles
- Molecules

*Transient perturbations*

*Stable perturbations*

*Changes in *
*network topology*

*Structural changes*

*Functional changes*

## Paybacks

- Relevant toxicology
- Better understanding of what we mean by “toxic”
- Perspective on “chemical trespass”
- Resources used efficiently
- Science-based dose-response assessment

## Summary

- Toxicity or safety?
- Toxicological safety assessment
- Exposure
- Delivered dose
- Systems biology
- Paybacks

## Acknowledgements

*CIIT Centers for Health Research*
- Rusty Thomas
- Maggie Zhao
- Qiang Zhang
- Mel Andersen
*Purdue*
- Yanan Zheng
*Wright State University*
- Jim McDougal
*Funding*
- DOE
- ACC

## End