High Throughput Testing-The NRC Vision, The Challenge of Elucidating Real Changes in Biological Systems, and the Reality of Low Throughput Environmental.

High Throughput Testing-The NRC Vision, The Challenge of Elucidating Real

Changes in Biological Systems, and the Reality of Low Throughput Environmental

Health Decision-Making

Dale Hattis

George Perkins Marsh Institute

Clark University

Outline• The Older NRC Vision Based on High-Throughput

Testing and Safety-Type Risk Analysis

• One Goal of This Talk is to Illuminate Alternatives

• An Alternative Vision for Toxicology Based on – Quantitative Modeling of Homeostatic Biological Systems, and

Problems in Their Early Life Setup and Late Life Breakdown

– Interactions of Toxicant Actions and “Normal Background” Chronic Pathological Processes

– Variability/Uncertainty Analysis Transforming Current Risk Analyses Used for Standard Setting

The Older NRC Vision Based on High Throughput Testing Results

• Decades-long period for future development.

• Ensemble of high-throughput assays to represent a large number (100+) of toxicity pathways.

• Well adapted to rapid screening of new chemicals and old chemicals with no prior testing.

• Supports decisions on what concentrations of agents will sufficiently perturb specific pathways to be of concern.

• Relate concentrations causing those perturbations to in vivo concentrations using physiologically-based pharmacokinetic modeling.

• No quantitative assessment of health risks or benefits of exposure reductions for existing agents.

Traditional Toxicological Model Leading To General Expectations of Thresholds in Dose

Response Relationships for Toxicants

• Biological systems have layers on layers of homeostatic processes (think of a thermostat)

• Any perturbation automatically gives rise to offsetting processes that, up to a point, keep the system functioning without long term damage.

• After any perturbation that does not lead to serious effects, the system returns completely to the status quo before the perturbation.

Caveats--There might not be no-adverse-effect thresholds for:

• Tasks that challenge the maximum capacities of the organism to perform (e.g., 100 yard dash; perhaps learning to read)

• Circumstances where some pathological process has already used up all the reserve capacity of the organism to respond to an additional challenge without additional damage (e.g. infarction causes heart muscle cell death that may be marginally worsened by incremental exposure to carbon monoxide)

Other Caveats• The ground state of the system is not a stable equilibrium,

but a series of cyclic changes on different time scales (e.g. cell cycle; diurnal, monthly).

• Sometimes continuous vs pulsatile patterns of change carry important signaling information in biological systems (e.g. growth hormone signaling for the sex-dependent pattern of P450 expression) – Therefore there must be resonance systems that respond

to cyclic fluctuations of the right period. – (Think of the timing needed to push a child on a swing)– Therefore the effective “dose” may need to be modified

by the periodicity to model dose response relationships.

Potential Paradigm Change for Applications to Therapeutics

• Historical Paradigm--”The Magic Bullet”– Find a molecule that will kill the nasty bacteria

– Find a spot in the brain that, if electrically stimulated, will control Parkinson’s disease symptoms

• New Paradigm--Understand and exploit natural resonances to enhance or damp system oscillations– Potential for pulsatile systems for drug release

– Potential for sensor-based systems for drug release (e.g., smart pumps that release insulin in response to real time measurements of blood glucose)

– Potential for timed or sensor-based electrical stimulation of target tissues (e.g., heart pacemakers)

Key Idea for Transforming Risk Assessment for Traditional Toxic Effects

• Quantitatively characterize each uncertainty (including those currently represented by “uncertainty factors”) by reducing it to an observable variability among putatively analogous cases. – Human interindividual variability--kinetic and dynamic– Variation in sensitivity between humans and test species– Adjustment for short- vs. longer periods of dosing and observation– Adjustment for database deficiencies (e.g. missing

repro/developmental studies)

• This general approach is not without difficulty—need rules for making the analogies (defining the reference groups to derive uncertainty distributions for particular cases).

• However it does provide a way forward for health scientists to learn to reason quantitatively from available evidence relevant to specific uncertainties.

Examples of Data Bases Assembled/Analyzed

Type of Projection Parameters Original Authors

General HumanInterindividual

Pharmacokinetic andPharmacodynamic

Parameters

Hattis et al. 2002“Straw Man” Proposal

Adult/Child Classical Drug PK (T1/2,Clearance, Vd) 27-41

drugs; 366 data groups

Ginsberg et al., 2002;Hattis et al., 2003

Young Adult/Elderly Classical Drug PK (AUC,T1/2, Clearance, Vd) 17-44 drugs 215 data groups

Hattis and Russ 2004Report to EPA

Interspecies—AcuteToxicity

LD5010,160 Species-Pairs

Rhomberg and Wolff(1998)

Additional Data Bases Analyzed And/Or Assembled

Type of Projection Parameters Original Authors

Interspecies Sensitivity--Multi-Dose

and Carcinogenesis

Human Maximum Tolerated Dose and

Putative Animal Equivalents for 61 Anti-

Cancer Agents

Price et al., 2001; Hattis et al., 2002

Pharmacokinetics in Pregnancy--Parameters

Derived from PBPK Model Fits or Direct

Observations

Fetal Growth, Placental/Fetal Transfer,

Maternal Tissue Growth, Partition

Coefficients

Hattis, 2004 Report to EPA

Adult/Early Life Stage Carcinogenic Animal

Bioassay Sensitivity for 9 Mutagenic Agents +

Ionizing Radiation

Cancer Transformations Per PPM, per dose/

(body weight) 0.75 or per rem ionizing radiation

EPA (2005) cancer guidelines + Hattis et

al., (2004, 2005)

The “Straw Man” Quantitative Probabilistic Framework for Traditional “Individual Threshold” Modes of Action

• It is ultimately hopeless to try to fairly and accurately represent the compounding effects of multiple sources of uncertainty as a series of point estimate “uncertainty” factors.

• Distributional treatments are possible in part by creating reference data sets to represent the prior experience in evaluating each type of uncertainty.

Interpretation of Dose Response Information for Quantal Effects in Terms of a Lognormal Distribution of

Individual Threshold Doses

Shaded area represents the fraction of people with thresholds below a given dose, who therefore suffer the effect at that dose.

Number of People With Thresholds at a Given Log(Dose)

Log(Threshold Dose) or Z-Score (in units of standard deviations)

Log(ED50) Z = 0

Log(ED84) Z = +1

Log(ED16) Z = -1

Log (ED02.5) Z = -2

Log (ED97.5) Z = +2

Analytical Approach for Putative Individual Threshold-Type Effects

• Select Point of Departure (ED50), then define needed distributional adjustments:

• LOAEL to ED50 or NOAEL to ED50• Acute/chronic• Animal to human• Human variability, taking into account the organ/body

system affected and the severity of the response• Incompleteness of the data base

Elements of the “Straw Man” Proposal--Tentatively it is suggested that the RfD be the lower

(more restrictive) value of:• (A) The daily dose rate that is expected (with 95% confidence) to

produce less than 1/100,000 excess incidence over background of a minimally adverse response in a standard general population of mixed ages and genders, or

• (B) The daily dose rate that is expected (with 95% confidence) to produce less than a 1/1,000 excess incidence over background of a minimally adverse response in a definable sensitive subpopulation.

100001000100.1

1

10

100

ContinuousQuantal

Overall Uncertainty Factor

Fac

tor

Red

ucti

on N

eede

d to

Rea

ch t

he "

Stra

w

Man

" C

rite

rion

of

<1E

-5 R

isk

wit

h 95

% C

onfi

denc

e

Results of Application of the Straw Man Analysis to18 Randomly-Selected RfDs from IRIS

Recent Results from Application of the “Straw Man” Approach to “Value of Information Testing of the IPCS “Data-Derived Uncertainty Factor Formulae

• Split of PD/PK variability should be closer to 5:2, rather than 3.1:3.1 as implied by the IPCS proposal (if one wishes the product to multiply out to the traditional 10-fold uncertainty factor assigned for interindividual variability

• Approximately equal protectiveness would be achieved by substitution of the following values for the interindividual variability factor 10 for RfD’s with the following characteristics:

Quantal Endpoint Continuous EndpointOverall UF = 100 17 43

Overall UF = 1000 7.4 19

More Details of Our Analysis are Available in:• Hattis, D., Baird, S., and Goble, R. “A Straw Man

Proposal for a Quantitative Definition of the RfD,” Drug and Chemical Toxicology, 25: 403-436, (2002).

• Hattis, D. and Lynch, M. K. “Empirically Observed Distributions of Pharmacokinetic and Pharmacodynamic Variability in Humans—Implications for the Derivation of Single Point Component Uncertainty Factors Providing Equivalent Protection as Existing RfDs.” In Toxicokinetics in Risk Assessment, J. C. Lipscomb and E. V. Ohanian, eds., Informa Healthcare USA, Inc., 2007, pp. 69-93.

• Detailed Data Bases and Distributional Analysis Spreadsheets:http://www2.clarku/edu/faculty/dhattis

Implications for Information Inputs to Risk Management Decision-Making

• Increasingly cases such as airborne particles, ozone, and lead are forcing the recognition that even for non-cancer effects, some finite rates of adverse effects will remain after implementation of reasonably feasible control measures.

• Societal reverence for life and health means “doing the very best we can” with available resources to reduce these effects.

• This means that responsible social decision-making requires estimates of how many people are likely to get how much risk (for effects of specific degrees of severity) with what degree of confidence—in cases where highly resource-intensive protective measures are among the policy options.

• The traditional multiple-single-point uncertainty factor system cannot yield estimates of health protection benefits that can be juxtaposed with the costs of health protection measures.

Alternative Vision#2--Multiple Directions for Improvement for Toxicology and Risk

Assessment

• New Pharmacodynamic Taxonomies and Approaches to Quantification– Taxonomy based on what the agent is doing to the

organism– Taxonomy based on what the organism is trying to

accomplish and how agents can help screw it up

• Quantitative Probabilistic Framework for Traditional “Individual Threshold” Modes of Action

Taxonomy Based on What Organisms Need to Accomplish to Develop and Maintain Functioning, and What Can Go Wrong

• Establishment and Maintenance of Homeostatic Systems at Different Scales of Distance, Time, Biological Functions, Involving– Sensors of Key Parameters to Be Controlled– Criteria (E.g. “Set Points”) for Evaluating Desirability of Current

State– Effector Systems That Act to Restore Desirable State With Graded

Responses to Departures Detected by the Sensors

• Some Examples of Perturbations:– Hormonal “Imprinting” by Early-Life Exposure to Hormone

Agonists– The “Tax” Theory of General Toxicant Influences on Fetal

Growth, and Possible Consequences

Key Challenges for Biology in the 21st Century

• How exactly are the set points set?• How does the system determine how, and how vigorously

to respond to various degrees of departure from specific set points?

• Could all this possibly be directly coded in the genome?• Or, more interestingly, does the genome somehow bring

about a learning procedure where, during development, the system “learns” what values of set points and modes/degrees of response work best using some set of internal scoring system?

• How exactly are the set points, etc., adjusted to meet the challenges of different circumstances (“allostasis” states--see Shulkin 2003, “Rethinking Homeostasis”).

5000400030002000100001

10

100

1000

Relationship Between Weight at Birth (in 500 Gram Increments) and Infant Mortality

Upper Ends of 500 G Ranges of Birth Weights

Infa

nt

Mor

talit

y/10

00 B

abie

sin

Eac

h B

irth

Wei

ght

Ran

ge

Conventional Cutoff Defining"Low Birth Weight"

353025201510503100

3200

3300

3400

7

8

9

10

11

12

13

14

Average BirthweightInfant Mortality/1000 Babies

Relationships Between Reported Cigarettes/DaySmoked, Average Birthweight, and Infant Mortality--U. S. National Center for Health Statistics 1990 Data

Reported No. Cigarettes/day

Ave

rage

Bir

thw

eigh

t (g

)

Infa

nt

Mor

talit

y/10

00 B

abie

s

50040030020010000.6

0.7

0.8

0.9

1.0

1.1

Results of Regresssion Analysis of theFraction of Control Fetal Weight Responsein Grouped Categores of TCA Equivalents--Interpretation with a Linear Model

TCA equiv mg/kg

Fra

ct C

ontr

ol F

etal

Wt y = 0.9925 - 7.40e-4x R^2 = 0.961

Error bars represent ± 1 standard error

50040030020010000.6

0.7

0.8

0.9

1.0

1.1

Results of Regresssion Analysis of theFraction of Control Fetal Weight Responsein Grouped Categores of TCA Equivalents--Interpretation With a Quadratic Model

TCA equiv mg/kg

Fra

ct C

ontr

ol F

etal

Wt y = 1.0069 - 1.114e-3x + 8.62e-7x^2 R^2 = 0.983

3.73.63.53.43.33.24

5

6

7

8

9

10

11

12

Plot of the Incidence of Type 2 Diabetesin Relation to Log(Mean Birth Weight)--Data of Forsen et al., 2000

Log(Mean Birth Wt g)

% T

ype

2 D

iabe

tes

y = 47.05 - 11.46x R^2 = 0.960

Taxonomy Built from the Fundamental Ways that Chemicals Act to Perturb Biological Systems

• For preclinical stages or subclinical levels of effect, is the basic action reversible or irreversible, given a subsequent period of no exposure?– Reversible (enzyme inhibition; receptor activation or

inactivation--Traditional Acute or Chronic Toxicity--traditional toxicology/homeostatic system overwhelming framework (individual thresholds for response)

– Irreversible (DNA mutation; destruction of alveolar septa; destruction of most neurons)

Subcategories for Nontraditional (Based on

Irreversible Changes) Modes of Action • How many irreversible steps are needed to

produce clinically recognizable disease?– (few--up to a dozen or so) molecular biological

diseases--mutations, cancer via mutagenic mechanisms– (many--generally thousands+) chronic cumulative

diseases (emphysema and other chronic lung diseases cause by cumulative loss of lung structures or scarring, Parkinson’s and other chronic neurological diseases produced by cumulative losses of neurons)

Special Features of Chronic Cumulative Disease Processes• Clinical consequences depend on the number of irreversible

steps that have occurred in different people (often little detectable change until a large number of steps have occurred).

• Effects occur as shifts in population distributions of function.• Thresholds for the causation of individual damage steps must be

low enough that the disease progresses with time in the absence of exposures that cause acute symptoms.

• Different kinds of biomarkers needed for – Accumulated amount of damage/dysfunction (e.g. FEV1)– (most powerful for epidemiology based on associations with short

term measurements of exposure) Today’s addition to the cumulative store of damage (e.g.

• excretion of breakdown products for lung structural proteins; • blood or urine levels of tissue-specific proteins usually found only

inside specific types of cells such as heart-specific creatinine kinase for measurement of heart cell loss due to infarctions)

Toward Risk Assessment Models for Chronic Cumulative Pathological Processes

• Describe the fundamental mechanism(s) that causes the accumulation of the individual damage events (especially the quantitative significance of various contributory factors). Key aid--biomarkers of the daily progress of damage (e.g. key enzyme released from a dying neuron of the specific type involved in the disease)

• Quantitatively elucidate the ways in which specific environmental agents enhance the production of or prevent the repair of individual damage events

• Describe the relationships between the numbers, types, and physical distribution of individual damage events and the loss of biological function or clinical illness. Key aid--biomarkers for the accumulation of past damage, e.g. FEV1.

Motivation to Move On• Younger generation of analysts will ultimately not tolerate older procedures

that fail to provide a coherent way to use distributional information that is clearly relevant to the factual and policy issues.

• Younger generation of analysts will have greater mathematical and computational facility, particularly as biology becomes quantitative “systems biology” with quantitative feedback modeling--increasingly an applied engineering-like discipline.

• Legal processes will ultimately demand use of the “best science” as this becomes recognized in the technical community.

• Newer information/communication tools will foster increasing habits and demands for democratic accountability; experts worldwide will increasingly be required to expose the bases of their policy recommendations—leaving less

room for behind-the-scenes exercise of “old boy” safety factor judgments.

Contributions of High-Throughput Testing in Different Decision Contexts

• Preliminary evaluation of large numbers of new, and old but untested, environmental agents--good promise to be helpful.

• Evaluation of contaminated sites (e.g. superfund)--support for decision-making based on complex mixtures is highly dubious.

• Assistance to epidemiological research to locate contributors for human disease (e.g. asthma)--also doubtful.

• Assessment of relative risks of different industrial processes--fanciful because of the need to guess the relative weights to be assigned to numerous dissimilar findings on short term tests without established quantitative connections to adverse health effects.

• High profile choices of degrees of control/exposure reduction to be mandated for specific agents (“slow throughput” decision-making)--Likely to muddy the waters by raising difficult mode of action questions that can only be resolved by expensive and slow in vivo testing--e.g. using “knockout” mice. This is in fact the most likely near term contribution.