ERA vs ECOLOGICAL RESEARCH

The importance

of

a good problem formulation

Dr Joe Smith

DIVERSE CROPS / TRAITS

Drought tolerance Insect resistance

Salt tolerance Water use efficiency

Altered starch Nitrogen use efficiency

Enhanced nutrition Disease resistance

Altered oil content Fungal resistance

Improved fibre Sugar content

Herbicide tolerance

OUTLINE

ERA Vs ecological research

Problem formulation What is it?

Key factors

Role in risk assessment process

What data do we really need to know? Sufficient, relevant and credible

ERA ≠ ECOLOGICAL RESEARCH

Both address problems and test hypotheses

BUT

Different problem selection

Different types of hypotheses

Different testing of hypotheses

Confusion ineffective ERA & inadequate research

[Raybould 2008, 2010]

≠

ERA ≠ ECOLOGICAL RESEARCH

Research

• Problem - objective (from analysis of prior problems)

• Hypotheses - interesting (make precise predictions)

• Testing - aims to falsify hypothesis (corroborated by presence of

phenomena in field studies)

ERA

• Problem - subjective (from definitions of harm)

• Hypotheses - useful (predict no harm)

• Testing - aims to falsify hypothesis (corroborated by absence of phenomena in lab studies)

[Raybould 2008, 2010]

Essential to have clearly defined problem so that risk assessors can

conclude with some certainty that particular harms will not eventuate

PROBLEM FORMULATION WHAT IS IT?

‘…the first step in ERA where policy goals, scope, assessment endpoints, and methodology are distilled to an explicitly stated problem and approach for analysis’

• Improved consistency and focus of ERAs

• Enhances relevance and utility for regulatory decision-making

[Wolt et al, 2010]

PROBLEM FORMULATION KEY FACTORS

Legislation / regulations

Risk analysis process

Risk context

Protection goals – specific harms

Identify risks that genuinely need further analysis

Remove hypothetical or negligible risks

‘The formulation of a problem is often more important than its solution’ (Albert Einstein)

LEGISLATION AND REGULATIONS

The government mandate

Reflects community values, expectations

Responsibilities for decisions

Scope, definitions

High level regulatory criteria

RISK ANALYSIS PROCESS INCORPORATING PROBLEM FORMULATION

[Wolt et al, 2010]

RISK CONTEXT

Critical factor in framing the risk assessment, problem formulation and determining what we need to know

• Canola parent (B napus) – exotic, annual/biennial, natural toxicants, weed in agricultural but not undisturbed habitats

• Activities – as for commercial non-GM canola; widespread cultivation for oil, meal production

• Traits - herbicide tolerance

increased competitiveness in presence of herbicides

• Receiving environment – biotic and abiotic factors,

agricultural practices, related plants, related proteins

• Previous releases – field trials, commercial, overseas

CONTEXT FOR HT CANOLA [Commercial release]

Source: OGTR

• Expect few identified risks because of proposed limits and controls

• Measures to limit and control release:

Separate trial sites – pollen traps or isolation zones

Harvest trial plants separately

Clean equipment, destroy unused plant material

Monitor site, destroy volunteer plants

Regulator’s Guidelines for transport

No use for human or animal feed

CONTEXT FOR FIELD TRIALS

Source: OGTR

• Define assessment endpoints

• Identify characteristics of GM plants with potential to cause adverse effects

• Postulate exposure pathways for these adverse effects to occur

• Outline specific risk hypotheses to guide data generation and evaluation

• Develop conceptual model (pathways to harm) and analysis plan

FORMULATING THE

PROBLEM

RISK CONTEXT

Activities with

GM crop

Harm to people

or environment

Risk Scenario = plausible pathway to harm

Source: OGTR

EXAMPLE FOR COTTON

ACTIVITY GM cotton containing Bt gene

HARM Reduced plant biodiversity in protected area

Risk Scenario = plausible pathway to harm

Loss of GM seed during transport

Establishment of GM cotton near native cotton

Gene flow from GM to native cotton

Increased weediness of native cotton

Source: OGTR

DATA QUALITY, SUFFICIENCY & RELEVANCE

What is the quality of evidence ?

Is the data available relevant and sufficient?

What uncertainty is there with the estimate of risk ?

Is uncertainty relevant or important ?

CONCLUSIONS

• Regulatory risk analysis is not research – it is a structured mechanism for making effective and timely decisions using available information and taking account of uncertainty

• Problem formulation is critical in ensuring the ERA considers only

relevant and credible risks and is useful for decision-making

• Potential harms, assessment endpoints and credible pathways to the harms need to be clearly identified at the beginning of the ERA

• Problem formulation helps ensure that data used in ERA of GM crops is relevant to the questions that need to be answered

• Credibility, quality and relevance of data is essential

ACKNOWLEDGEMENT

The views expressed in this presentation are my personal views alone and not those of any government agency. They are derived in large part from my experiences as Australia’s Gene Technology Regulator and I remain indebted to the outstanding staff of the Office of the Gene Technology Regulator.