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Dose-Response Modeling: Past, Present, and Future. Rory B. Conolly, Sc.D. Center for Computational Systems Biology & Human Health Assessment CIIT Centers for Health Research (919) 558-1330 - voice [email protected] - e-mail

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Dose-Response Modeling: Past, Present, and Future

Rory B. Conolly, Sc.D.

Center for Computational Systems Biology

& Human Health Assessment

CIIT Centers for Health Research

(919) 558-1330 - voice

[email protected] - e-mail

SOT Risk Assessment Specialty Section, Wednesday, December 15, 2004


Outline

  • Why do we care about dose response?

  • Historical perspective

    • Brief, incomplete!

  • Formaldehyde

  • Future directions


Perspective

  • This talk mostly deals with issues of cancer risk assessment, but I see no reason for any formal separation of the methodologies for cancer and non cancer dose-response assessments

    • PK

    • Modes of action

    • Tumors, reproductive failure, organ tox, etc.


Dose

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

Response


Interspecies

Dose

Not much!

Response

?


Interspecies

Dose

Possibilities

Response


Interspecies

Dose

Possibilities

Response


Interspecies

Dose

Possibilities

Response


Interspecies

Dose

Possibilities

Response


Benzene Decision of 1980

  • U.S. Supreme Court says that exposure standards must be accompanied by a demonstration of “significant risk”

    • Impetus for modeling low-dose dose response


1984 Styrene PBPK model(TAP, 73:159-175, 1984)

A physiologically based description of the inhalation pharmacokinetics of styrene in rats and humans

John C. Ramseya and Melvin E. Andersenba Toxicology Research Laboratory, Dow Chemical USA, Midland, Michigan 48640, USAb Biochemical Toxicology Branch, Air Force Aerospace Medical Research Laboratory (AFAMRL/THB), Wright-Patterson Air Force Base, Ohio 45433, USA


Biologically motivated computational models(or)Biologically based computational models

  • Biology determines

    • The shape of the dose-response curve

    • The qualitative and quantitative aspects of interspecies extrapolation

  • Biological structure and associated behavior can be

    • described mathematically

    • encoded in computer programs

    • simulated


Risk

assessment

Experiments to understand

mechanisms of toxicity and

extrapolation issues

Biologically-based computational models: Natural bridges between research and risk assessment

Computational

models


Garbage in – garbage out

  • Computational modeling and laboratory experiments must go hand-in-hand


Interspecies

Dose

Refining the description with research on pharmacokinetics and pharmacodynamics (mode of action)

Response


Interspecies

Dose

Refining the description with research on pharmacokinetics and pharmacodynamics (mode of action)

Response


Interspecies

Dose

Refining the description with research on pharmacokinetics and pharmacodynamics (mode of action)

Response


Interspecies

Dose

Refining the description with research on pharmacokinetics and pharmacodynamics (mode of action)

Response


Formaldehyde nasal cancer in rats:A good example of extrapolations across doses and species


1980 - First report of formaldehyde-induced tumors

Swenberg JA, Kerns WD, Mitchell RI, Gralla EJ, Pavkov KLCancer Research, 40:3398-3402 (1980)Induction of squamous cell carcinomas of the rat nasal cavity by inhalation exposure to formaldehyde vapor.


60

Kerns et al., 1983

50

Monticello et al., 1990

40

30

(%)

Tumor Response

20

10

0

0

0.7

2

6

10

15

Exposure Concentration (ppm)

Formaldehyde bioassay results


Mechanistic Studies and Risk Assessments


What did we know in the early ’80’s?

  • Formaldehyde is a carcinogen in rats and mice

  • Human exposures roughly a factor of 10 of exposure levels that are carcinogenic to rodents.


1982 – Consumer Product Safety Commission (CPSC) voted to ban urea-formaldehyde foam insulation.


1983 - Formaldehyde cross-links DNA with proteins - “DPX”

Casanova-Schmitz M, Heck HDToxicol Appl Pharmacol 70:121-32 (1983)Effects of formaldehyde exposure on the extractability of DNA from proteins in the rat nasal mucosa.


DPX


1984 - Risk Assessment Implications

Starr TB, Buck RDFundam Appl Toxicol 4:740-53 (1984)The importance of delivered dose in estimating low-dose cancer risk from inhalation exposure to formaldehyde.


1985 – No effect on blood levels

Heck, Hd’A, Casanova-Schmitz, M, Dodd, PD, Schachter, EN, Witek, TJ, and Tosun, T

Am. Ind. Hyg. Assoc. J. 46:1. (1985)

Formaldehyde (C2HO) concentrations in the blood of humans and Fisher-344 rats exposed to C2HO under controlled conditions.


1987 – U.S. EPA cancer risk assessment

  • Linearized multistage (LMS) model

    • Low dose linear

    • Dose input was inhaled ppm

    • U.S. EPA declined to use DPX data


Summary: 1980’s

  • Research

    • DPX – delivered dose

    • Breathing rate protects the mouse (Barrow)

    • Blood levels unchanged

  • Regulatory actions

    • CPSC ban

    • US EPA risk assessment


Key events during the ’90s

  • Greater regulatory acceptance of mechanistic data for risk assessment (U.S. EPA)

  • Cell replication dose-response

  • Better understanding of DPX (Casanova & Heck)

  • Dose-response modeling of DPX (Conolly, Schlosser)

  • Sophisticated nasal dosimetry modeling (Kimbell)

  • Clonal growth models for cancer risk assessment (Moolgavkar)


1991 – US EPA cancer risk assessment

  • Linearized multistage (LMS) model

    • Low dose linear

    • DPX used as measure of dose


1991, 1996 - regenerative cellular proliferation

Monticello TM, Miller FJ, Morgan KT Toxicol Appl Pharmacol 111:409-21 (1991)Regional increases in rat nasal epithelial cell proliferation following acute and subchronic inhalation of formaldehyde.


Normal respiratory epithelium in the rat nose


Formaldehyde-exposed respiratory epitheliumin the rat nose (10+ ppm)


(Raw data)

ppm formaldehyde

Dose-response for cell division rate


DPX submodel – simulation of rhesus monkey data


Summary: Dose-response inputs to the clonal growth model

  • Cell replication

    • J-shaped

  • DPX

    • Low dose linear


CFD Simulation of Nasal Airflow(Kimbell et. al)


Division

(aN)

(aI)

Mutation

(mI)

Mutation

(mN)

Normal

cells (N)

Cancer

cell

Initiated

cells (I)

(delay)

Death/

differentiation

(bN)

(bI)

Tumor

2-Stage clonal growth model(MVK model)


(Hockey stick transformation)

(Raw data)

ppm formaldehyde

ppm formaldehyde

Dose-response for cell division rate


Simulation of tumor response in rats


CIIT clonal growth cancer risk assessment for formaldehyde(late ’90’s)

  • Risk assessment goal

    • Combine effects of cytotoxicity and mutagenicity to predict the tumor response


Cancer model

(LMS)

Tumor response

1987 U.S. EPA

Inhaled ppm


Tissue dose

(DPX)

Cancer model

(LMS)

Tumor response

1991 U.S. EPA

Inhaled ppm


CFD modeling

Cell proliferation

Cell killing

Tissue dose

Cancer model

(Clonal growth)

Mutagenicity

(DPX)

Tumor response

1999 CIIT

Inhaled ppm


Formaldehyde: Computational fluid dynamics models of the nasal airways

F344 Rat

Rhesus Monkey

Human


Human assessment


Baseline calibration against human lung cancer data


DPX and direct mutation

  • Direct mutation is assumed to be proportional to the amount of DPX:

  • Is KMU big or small?


Grid search


Optimal value of KMU is zero


Upper bound on KMU


Calculation of the value of KMU

  • Grid search

  • Optimal value of KMU was zero

    • Modeling implies that direct mutation is not a significant action of formaldehyde

  • 95% upper confidence limit on KMU was estimated


Division

(aN)

(aI)

Mutation

(mI)

Mutation

(mN)

Normal

cells (N)

Cancer

cell

Initiated

cells (I)

(delay)

Death/

differentiation

(bN)

(bI)

Tumor

Human risk modeling


Final model: Hockey stick and 95% upper confidence limit on value of KMU

95% UCL on KMU


Predicted human cancer risks(hockey stick-shaped dose-response for cell replication; optimal value for KMU)

Optimal value of KMU

KMU = 0.


“Negative risk” using raw dose-response for cell replication

95% UCL on KMU


Make conservative choices when faced with uncertainty

  • Use hockey stick-shaped cell replication

  • Use a 95% upper bound on the dose-response for the directly mutagenic mode of action

    • Statistically optimal model has 0 (zero) slope

  • Risk model predicts low-dose linear risk.

  • Optimal, data based model predicts negative risk at low doses


Summary: CIIT Clonal Growth Assessment

  • Either no additional risk or a much smaller level of risk than previous assessments

  • Consistent with mechanistic database

    • Direct mutagenicity

    • Cell replication


Summary: CIIT Clonal Growth Assessment

  • International acceptance

    • Health Canada

    • WHO

    • MAK Commission (Germany)

    • Australia

    • U.S. EPA (??)

  • Peer-review


IARC 2004

  • Classified 1A based on nasopharyngeal cancer

  • Myeloid leukemia data suggestive but not sufficient

    • Concern about mechanism

    • British study negative

  • Reclassification driven by epidemiology

  • In my opinion inadequate consideration of regional dosimetry


nasopharynx

Anterior

nose

Whole

nose


IARC: hazard characterization vs. dose-response assessment


Formaldehyde summary

  • Nasal SCC in rats

  • Mechanistic studies

  • Risk Assessments

  • Implications of the data

  • IARC


The future


Outline

  • Long-range goal

  • Systems in biological organization

  • Molecular pathways

  • Data

  • Example

    • Computational modeling

    • Modularity


Long-range goal

  • A molecular-level understanding of dose- and time-response behaviors in laboratory animals and people.

    • Environmental risk assessment

    • Drug development

    • Public health


(systems)

(systems)

(systems)

(systems)

(systems)

Levels of biological organization

Populations

Organisms

Tissues

Cells

Organelles

Molecules

Mechanistic

Descriptive


Today

Levels of biological organization

Populations

Organisms

Tissues

Cells

Organelles

Molecules

(systems)


Molecular pathways


Segment polarity genes in Drosophila

Albert & Othmer, J. Theor Biol. 223, 1 – 18, 2003


ATM curated

Pathway from

Pathway Assist®


Approach

  • Initial pathway identification

    • Static map

      • Existing data

      • New data

  • Computational modeling

    • Dynamic behavior

    • Iterate with data collection


Initial pathway identification

  • Use commercial software that can integrate data from a variety of sources (Pathway Assist)

    • Scan Pub Med abstracts to identify “facts”

    • Create pathway maps

    • Incorporate other, unpublished data

  • Quality control

    • Curate pathways


Computational modeling

  • To study the dynamic behavior of the pathway

  • Analyze data

    • Are model predictions consistent with existing data?

  • Make predictions

    • Suggest new experiments

    • Ability to predict data before it is collected is a good test of the model


DNA damage and cell cycle checkpoints


Experimental data

p21 time-course data and simulation


(Redpath et al, 2001)

Mutations dose-response and model prediction

model calculated values

Mutation Fraction Rate

IR


Data


(Fat)

Venous

blood

Air-blood

interface

Liver

Rest of Body

Tissue dosimetry is the “front end” to a molecular pathway model


Gain-of-function and loss-of-function screens to study network structure

  • Selectively alter behavior of the network

    • Loss-of-function

      • SiRNA

    • Gain-of-function

      • full-length genes

  • Look for concordance between lab studies and the behavior of the computational model

    • Mimic gain-of-function and loss-of-function changes in the computer


Example

  • Skin irritation

  • MAPK, IL-1a, and NF-kB computational “modules”

  • High throughput overexpression data to characterize IL-1a – MAPK interaction with respect to NF-kB


Skin Irritation

Chemical

Dead cells

  • Study on the dose response of the skin cells to inflammatory cytokines contributes to quantitative assessment of skin irritation

Tissue damage

Epidermis

(keratinocytes)

Tissue damage

Dermis

Nerve Endings

A cascade of inflammatory responses (cytokines)

(fibroblasts)

Blood vessels


Modular Composition of IL-1 Signaling

IL-1

Extracellular

IL-1R

Intracellular

IL-1 specific top module

Secondary messenger

Constitutive downstream NF-kB module

MAPK

Others

NF-kB

IL-6, etc.

Transcriptional factors


TRAF6

IkK

IkK

IRAK

IRAK

IRAK

IRAK

P

P

P

P

P

Self-limiting mechanism

IRAK gene

Top IL-1 Signaling Module

IL-1

IL-1R

TAB2

TAK1

TAB1

MyD88

TRAF6

NF-kB module

Degraded

Cytoplasm

Nucleus


Top Module Simulation

  • IL-1 receptor number and ligand binding parameters from human keratinocytes

  • Other parameters constrained by reasonable ranges of similar reactions/molecules, and tuned to fit data

Increasing IRAKp degradation

IRAKp

TAK1*

Time (hrs)

Time (hrs)


Constitutive NF-kB Signaling Module

Input signal

IkK

IkK

IB

P

P

P

B

NF

IB

Degraded

B

B

B

B

B

NF

NF

NF

NF

NF

Negativefeedback

IB

IB

IB

Cytoplasm

IL-6 gene

IB gene

Nucleus


NF-kB Module Simulation

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

IkB

IL-6

_

+

NF-kB

Smoothened oscillations

Concentration (mM)

Concentration (mM)

Time (hrs)

Add constant input signal

Time (hrs)

Longer delay


The IB–NF-B Signaling Module: Temporal Control and Selective Gene Activation

Alexander Hoffmann, Andre Levchenko, Martin L. Scott, David Baltimore

Science 298:1241 – 1245, 2002

6 hr


MAPK intracellular signaling cascades

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


Input pulse

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


IL-1 MAPK crosstalk and NFkB activation


Gain-of-function screen


Model prediction


Future directions

  • Computational modeling and data collection at higher levels of biological organization

    • Cells

      • Intercellular communication

    • Tissues

    • Organisms

  • NIH initiatives

  • Environmental health risk, drugs ==> in vivo


Summary

  • Biological organization and systems

  • Molecular pathways

    • identification

    • Computational modeling

  • Data

    • Gain-of-function

    • Loss-of-function

  • Skin irritation example

    • 3 modules

    • Crosstalk

    • Targeted data collection


Acknowledgements

  • Colleagues who worked on the clonal growth risk assessment

    • Fred Miller, Julian Preston, Paul Schlosser, Julie Kimbell, Betsy Gross, Suresh Moolgavkar, Georg Luebeck, Derek Janszen, Mercedes Casanova, Henry Heck, John Overton, Steve Seilkop


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


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