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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 l.jpg

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 l.jpg

Outline

  • Why do we care about dose response?

  • Historical perspective

    • Brief, incomplete!

  • Formaldehyde

  • Future directions


Perspective l.jpg

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.


Typical high dose rodent data what do they tell us l.jpg

Dose

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

Response


Not much l.jpg

Interspecies

Dose

Not much!

Response

?


Possibilities l.jpg

Interspecies

Dose

Possibilities

Response


Possibilities7 l.jpg

Interspecies

Dose

Possibilities

Response


Possibilities8 l.jpg

Interspecies

Dose

Possibilities

Response


Possibilities9 l.jpg

Interspecies

Dose

Possibilities

Response


Benzene decision of 1980 l.jpg

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 l.jpg

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 l.jpg

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


Slide13 l.jpg

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 l.jpg

Garbage in – garbage out

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


Refining the description with research on pharmacokinetics and pharmacodynamics mode of action l.jpg

Interspecies

Dose

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

Response


Refining the description with research on pharmacokinetics and pharmacodynamics mode of action16 l.jpg

Interspecies

Dose

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

Response


Refining the description with research on pharmacokinetics and pharmacodynamics mode of action17 l.jpg

Interspecies

Dose

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

Response


Refining the description with research on pharmacokinetics and pharmacodynamics mode of action18 l.jpg

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 l.jpg

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


Slide21 l.jpg

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.


Formaldehyde bioassay results l.jpg

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 l.jpg

Mechanistic Studies and Risk Assessments


What did we know in the early 80 s l.jpg

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 l.jpg

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


Slide26 l.jpg

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.


Slide27 l.jpg

DPX


Slide28 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

1991 – US EPA cancer risk assessment

  • Linearized multistage (LMS) model

    • Low dose linear

    • DPX used as measure of dose


Slide34 l.jpg

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 l.jpg

Normal respiratory epithelium in the rat nose


Formaldehyde exposed respiratory epithelium in the rat nose 10 ppm l.jpg

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


Dose response for cell division rate l.jpg

(Raw data)

ppm formaldehyde

Dose-response for cell division rate


Dpx submodel simulation of rhesus monkey data l.jpg

DPX submodel – simulation of rhesus monkey data


Summary dose response inputs to the clonal growth model l.jpg

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 l.jpg

CFD Simulation of Nasal Airflow(Kimbell et. al)


2 stage clonal growth model mvk model l.jpg

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)


Dose response for cell division rate42 l.jpg

(Hockey stick transformation)

(Raw data)

ppm formaldehyde

ppm formaldehyde

Dose-response for cell division rate


Simulation of tumor response in rats l.jpg

Simulation of tumor response in rats


Ciit clonal growth cancer risk assessment for formaldehyde late 90 s l.jpg

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


1987 u s epa l.jpg

Cancer model

(LMS)

Tumor response

1987 U.S. EPA

Inhaled ppm


1991 u s epa l.jpg

Tissue dose

(DPX)

Cancer model

(LMS)

Tumor response

1991 U.S. EPA

Inhaled ppm


1999 ciit l.jpg

CFD modeling

Cell proliferation

Cell killing

Tissue dose

Cancer model

(Clonal growth)

Mutagenicity

(DPX)

Tumor response

1999 CIIT

Inhaled ppm


Slide48 l.jpg

Formaldehyde: Computational fluid dynamics models of the nasal airways

F344 Rat

Rhesus Monkey

Human


Human assessment l.jpg

Human assessment


Baseline calibration against human lung cancer data l.jpg

Baseline calibration against human lung cancer data


Dpx and direct mutation l.jpg

DPX and direct mutation

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

  • Is KMU big or small?


Grid search l.jpg

Grid search


Optimal value of kmu is zero l.jpg

Optimal value of KMU is zero


Upper bound on kmu l.jpg

Upper bound on KMU


Calculation of the value of kmu l.jpg

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


Human risk modeling l.jpg

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 l.jpg

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

95% UCL on KMU


Slide58 l.jpg

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 l.jpg

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

95% UCL on KMU


Make conservative choices when faced with uncertainty l.jpg

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 l.jpg

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 assessment62 l.jpg

Summary: CIIT Clonal Growth Assessment

  • International acceptance

    • Health Canada

    • WHO

    • MAK Commission (Germany)

    • Australia

    • U.S. EPA (??)

  • Peer-review


Iarc 2004 l.jpg

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


Slide64 l.jpg

nasopharynx

Anterior

nose

Whole

nose


Iarc hazard characterization vs dose response assessment l.jpg

IARC: hazard characterization vs. dose-response assessment


Formaldehyde summary l.jpg

Formaldehyde summary

  • Nasal SCC in rats

  • Mechanistic studies

  • Risk Assessments

  • Implications of the data

  • IARC


The future l.jpg

The future


Outline68 l.jpg

Outline

  • Long-range goal

  • Systems in biological organization

  • Molecular pathways

  • Data

  • Example

    • Computational modeling

    • Modularity


Long range goal l.jpg

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


Levels of biological organization l.jpg

(systems)

(systems)

(systems)

(systems)

(systems)

Levels of biological organization

Populations

Organisms

Tissues

Cells

Organelles

Molecules

Mechanistic

Descriptive


Levels of biological organization71 l.jpg

Today

Levels of biological organization

Populations

Organisms

Tissues

Cells

Organelles

Molecules

(systems)


Molecular pathways l.jpg

Molecular pathways


Segment polarity genes in drosophila l.jpg

Segment polarity genes in Drosophila

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


Slide74 l.jpg

ATM curated

Pathway from

Pathway Assist®


Approach l.jpg

Approach

  • Initial pathway identification

    • Static map

      • Existing data

      • New data

  • Computational modeling

    • Dynamic behavior

    • Iterate with data collection


Initial pathway identification l.jpg

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 l.jpg

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 l.jpg

DNA damage and cell cycle checkpoints


P21 time course data and simulation l.jpg

Experimental data

p21 time-course data and simulation


Mutations dose response and model prediction l.jpg

(Redpath et al, 2001)

Mutations dose-response and model prediction

model calculated values

Mutation Fraction Rate

IR


Slide81 l.jpg

Data


Slide82 l.jpg

(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 l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

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


Slide87 l.jpg

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 l.jpg

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)


Slide89 l.jpg

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 k b module simulation l.jpg

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


Slide91 l.jpg

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 l.jpg

MAPK intracellular signaling cascades

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


Mapk time course and bifurcation after a short pulse of pdgf l.jpg

Input pulse

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


Slide95 l.jpg

IL-1 MAPK crosstalk and NFkB activation


Gain of function screen l.jpg

Gain-of-function screen


Slide97 l.jpg

Model prediction


Future directions l.jpg

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 l.jpg

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 l.jpg

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


Acknowledgements101 l.jpg

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


Slide102 l.jpg

End


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