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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
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
Outline
  • Why do we care about dose response?
  • Historical perspective
    • Brief, incomplete!
  • Formaldehyde
  • Future directions
perspective
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.
benzene decision of 1980
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
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
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

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
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

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

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

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

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
Formaldehyde nasal cancer in rats:A good example of extrapolations across doses and species
slide21

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

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
what did we know in the early 80 s
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.
slide26

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.

slide28

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
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
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
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
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
1991 – US EPA cancer risk assessment
  • Linearized multistage (LMS) model
    • Low dose linear
    • DPX used as measure of dose
slide34

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.

summary dose response inputs to the clonal growth model
Summary: Dose-response inputs to the clonal growth model
  • Cell replication
    • J-shaped
  • DPX
    • Low dose linear
2 stage clonal growth model mvk model

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)
ciit clonal growth cancer risk assessment for formaldehyde late 90 s
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

Cancer model

(LMS)

Tumor response

1987 U.S. EPA

Inhaled ppm

1991 u s epa

Tissue dose

(DPX)

Cancer model

(LMS)

Tumor response

1991 U.S. EPA

Inhaled ppm

1999 ciit

CFD modeling

Cell proliferation

Cell killing

Tissue dose

Cancer model

(Clonal growth)

Mutagenicity

(DPX)

Tumor response

1999 CIIT

Inhaled ppm

slide48

Formaldehyde: Computational fluid dynamics models of the nasal airways

F344 Rat

Rhesus Monkey

Human

dpx and direct mutation
DPX and direct mutation
  • Direct mutation is assumed to be proportional to the amount of DPX:
  • Is KMU big or small?
calculation of the value of 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
human risk modeling

Division

(aN)

(aI)

Mutation

(mI)

Mutation

(mN)

Normal

cells (N)

Cancer

cell

Initiated

cells (I)

(delay)

Death/

differentiation

(bN)

(bI)

Tumor

Human risk modeling
slide58
Predicted human cancer risks(hockey stick-shaped dose-response for cell replication; optimal value for KMU)

Optimal value of KMU

KMU = 0.

make conservative choices when faced with uncertainty
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
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
Summary: CIIT Clonal Growth Assessment
  • International acceptance
    • Health Canada
    • WHO
    • MAK Commission (Germany)
    • Australia
    • U.S. EPA (??)
  • Peer-review
iarc 2004
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

nasopharynx

Anterior

nose

Whole

nose

formaldehyde summary
Formaldehyde summary
  • Nasal SCC in rats
  • Mechanistic studies
  • Risk Assessments
  • Implications of the data
  • IARC
outline68
Outline
  • Long-range goal
  • Systems in biological organization
  • Molecular pathways
  • Data
  • Example
    • Computational modeling
    • Modularity
long range goal
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

(systems)

(systems)

(systems)

(systems)

(systems)

Levels of biological organization

Populations

Organisms

Tissues

Cells

Organelles

Molecules

Mechanistic

Descriptive

levels of biological organization71

Today

Levels of biological organization

Populations

Organisms

Tissues

Cells

Organelles

Molecules

(systems)

segment polarity genes in drosophila
Segment polarity genes in Drosophila

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

slide74

ATM curated

Pathway from

Pathway Assist®

approach
Approach
  • Initial pathway identification
    • Static map
      • Existing data
      • New data
  • Computational modeling
    • Dynamic behavior
    • Iterate with data collection
initial pathway identification
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
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
mutations dose response and model prediction

(Redpath et al, 2001)

Mutations dose-response and model prediction

model calculated values

Mutation Fraction Rate

IR

slide82

(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
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
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
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
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

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
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

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
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

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
MAPK intracellular signaling cascades

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

future directions
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
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
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
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
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