Approaches to risk assessment

Risk is a combination of the seriousness and likelihood of a harmful effect following a course of action. Risk assessment characterizes the amount of risk associated with an activity. It contributes to making decisions about whether to undertake an activity, such as the import, field testing, or cultivation of a specific GM crop. Some authors have raised concern (e.g., Craig et al. 2008) that the amount of data required for risk assessments of GM crops is increasing and becoming detrimental to decision-making in many countries.

Two possible approaches to risk assessment have been described. In the “bucket” approach (Raybould 2011), data on the properties of the GM crop are collected in an untargeted manner, often termed profiling. Profiling could comprise measurements of the crop’s gross phenotype, composition of key tissues, transcriptome, proteome, metabolome, and so on. By comparing these profiles with those of a suitable conventional crop, the risk assessor is supposed to be able to identify changes, which in turn indicate that there may be changes in the GM crop that are potentially harmful (see “Omics technologies” below).

There are several limitations to this approach. First, what to regard as harmful is defined by policy; it is not discovered in data (Sarevitz 2004; Sanvido et al. 2012). Second, even if harm is defined, profiling will collect data that do not predict the seriousness or probability of harm following use of the GM crop. These data are thus irrelevant for risk assessment and may impair decision-making because they distract from data that are relevant. Furthermore, it is difficult to interpret the effect of an altered metabolite in the absence of a hypothesis.

The second approach regards risk assessment as a hypothesis-testing exercise. The risk assessor identifies those effects that would be regarded as harmful if they were to occur, based on relevant legislation or regulations (Evans et al. 2006), and builds scenarios comprising a series of events leading from the proposed use of the particular GM crop to the identified harmful effects. These scenarios, or “pathways to harm”, allow the risk assessor to devise testable hypotheses about the likelihood, frequency, or magnitude of the events in the pathway. Data are collected to test these hypotheses and thereby characterize risk (Raybould 2011).

A concern raised about the latter approach is that it represents a biased approach to assessment. Effective risk assessment does involve bias in that representative protection goals must be selected from among all the possible effects of using a GM crop. Limited resources are then targeted to test hypotheses about the probability and consequences of those effects. These will be strong tests of clear hypotheses, which, if corroborated, provide high confidence in conclusions of low risk.

Problem formulation in environmental risk assessment

Environmental risk assessment (ERA) for GM crops deals almost exclusively with the phenotype and considers all plant traits that may have been altered by the transformation, whether intended or unintended. Of particular interest are any unintended changes in traits that may make the GM plant more persistent or invasive (“weedy”) in either agricultural or natural environments. These include changes in the properties of the seeds (such as developmental rates, number, release from the plant [shattering], dormancy, and germination rates) that are important in the “regeneration niche” of the plant’s establishment and spread, and in those traits that affect the plant’s competitiveness (such as seedling vigor, plant height, growth rates, and resistance to pests and disease).

The first stage in problem formulation in an ERA is to identify a set of environmental protection goals derived from local, national, or international policy. These may be broadly stated (e.g., the Cartagena Protocol) or more specific laws, statutes, or even guidelines, but collectively they enable a risk assessor to identify those aspects of the environment that must be protected. These can sometimes be formally defined in terms of assessment endpoints (e.g., “insect pollinator abundance”), which are whatever will be measured to ascertain whether protection has been achieved as intended (Paes de Andrade et al. 2014).

The second stage of problem formulation is to seek a link between the cultivation of the GM crop and the assessment endpoint that may result in harm (i.e., the pathway to harm). For example, insect pollinators are likely to be harmed if the plant presents a hazard (e.g., an insecticidal protein that negatively affects the insect) to which the insect may be exposed. Steps along the pathway to harm can be recast as risk hypotheses that can be validated or rejected from existing data, or by designing new experiments or trials where appropriate. For example, validation of the hypothesis “the insect is not harmed by the protein” or “the protein is not expressed in pollen” allows a confident risk assessment without further experimentation. Wolt et al. (2010) describe the process in detail and Raybould (2011) and earlier papers referred to therein give specific examples of formulating and testing risk hypotheses. Gray (2012) and Tepfer et al. (2013) give practical examples of the use of problem formulation in ERA for GM crops.

Evaluation of food and feed safety

As mentioned earlier, the approach to food and feed safety described by the Codex Alimentarius Commission (Codex 2003) is based on comparison of the GM crop to a conventional counterpart employing a weight-of-evidence approach. In this way, the assessment is focused on identification of potential new hazards or changes in hazard levels in the GM product.

Focus on plausible outcomes

For food and feed safety risk assessment of GM crops, it is necessary to exclude hypothetical, extreme, or scientifically implausible circumstances. Instead, the purpose is to (1) identify practical, biologically plausible outcomes based on the extensive data now available, and (2) to define the nature of the at-risk group(s) to be addressed (population or individual health risks), the type of hazard(s) of concern (toxicological, dietary, immunological), and the risk time metric (acute, sub-chronic, or chronic).

It is unlikely that systemically toxic proteins that are unrelated to the parent plant variety or to the function of the transgene will be produced de novo in a GM plant (Weber et al. 2012). The reason is that systemic toxicity of an ingested protein requires at least three highly specific structural characteristics: (1) resistance to digestion, (2) ligand specificity for the gut uptake transporters, and (3) ligand/receptor specificity for site- and species-specific receptor-mediated toxicity (Hammond et al. 2013). A change in any one of these three characteristics is an implausible outcome from either conventional plant breeding or gene transfer; thus, the probability of having all three occur in the same plant is vanishingly small. Similarly, the potential for random genome effects to modify existing non-toxic proteins to create a toxic protein is also essentially zero (Weber et al. 2012). This prediction is evidenced by the current knowledge of conventional corn and other food crop varieties that have millions of single-nucleotide polymorphisms (SNPs) across genotypes (Tenaillon et al. 2001), but have never resulted in a novel toxic protein in a food crop (Steiner et al. 2013).

The de novo generation of the machinery necessary to produce a toxic secondary metabolite is also very unlikely. Such an event has not been observed in the extensive range of varieties produced by genetic manipulation in conventional and GM crop breeding over the past century. The reactivation of dormant pathways (i.e., pathways present in an ancestor of the crop that are inactive in the modern variety) has also never been observed and is implausible due to the accumulation of mutations in non-functional DNA, progressively degrading any residual potential functionality (Steiner et al. 2013).

As in conventional plant breeding, there are natural variations in the levels of compounds, including those of toxicological relevance, in crops developed through modern biotechnology. Examples of plausible mechanisms include the up (and down) regulation of pre-existing endogenous plant toxins, increased/decreased uptake of heavy metals from the soil or water (e.g., Cd, As, Se), altered levels of nutrients or antinutrients associated with population health outcomes, altered production of pesticide metabolites, altered levels of toxic substrates (precursors) due to blocking of an enzyme pathway, and altered release or availability of endogenous toxins.

Assessment for changes in levels of existing allergens

According to the European Union perspective on the assessment of the overall allergenicity of whole GM plants, in cases where the recipient species of a genetic modification is a known allergen, (e.g., soybean) the qualitative and quantitative composition of endogenous allergens in the GM crop and its conventional counterpart should be compared (Metcalfe et al. 1996). The concentration of endogenous allergens within a plant species is highly variable, and the comparative analysis should consider the influence of the cultivars and of the conditions of cultivation, harvest, storage, and processing on the expression of allergens (see “Examples of natural variation in allergens” below). The European Food Safety Authority (EFSA) guidance and the European Commission (EC) regulation have recommended including “key” endogenous allergens (i.e., such as those listed in the OECD consensus documents) in the comparative compositional analysis of plant materials collected from controlled field trials. This aims to assess whether the GM plant is more allergenic than its conventional counterpart (EFSA 2011; EC 2013). As described by the EC, “key allergens” are well-characterized allergens that are relevant for public health because of their allergenic potency and abundance. They are generally well-conserved proteins with important metabolic and physiological functions in the plant, such as enzymes, defense proteins, or storage proteins. Any significant change in key allergen levels could thus be directly related to the specific allergy risk and could also indicate the possible occurrence of other types of unintended effects.

Non-targeted analyses, such as proteomic approaches using mass spectrometry (e.g., matrix-assisted laser desorption/ionization (MALDI) or electrospray ionization time-of-flight mass spectrometry (ESI-TOF MS) in combination with different separation methods such as 2-dimensional gel electrophoresis or liquid chromatography), have been rapidly developed (Goodman et al. 2013) and can help to identify significant changes in endogenous allergen expression. They may not require human sera and have proven to be efficient (alternative) tools for the identification and quantification of known allergens in plants. However, such tests may sometimes be considered as complex and insufficiently standardized and needing further developments and validation before they can be routinely used for safety assessment (Fernandez et al. 2013).

Examples of natural variation in allergens

Allergies to fruits and vegetables affect up to ~4 % of the population in Europe (Zuidmeer et al. 2008). Carrot (Daucus carota) and apple (Malus domestica) are among the most prevalent elicitors of allergic reactions to foods in northern and central Europe. In apple, variation in patient reaction and/or allergen levels has been observed among cultivars (Bolhaar et al. 2005), stored vs. unstored fruit (Sancho et al. 2006a, b), and patient geographical areas. Similarly, the carrot isoallergens (related allergens from the same species) Dau c 1.01 and Dau c 1.02 were quantified using ELISA (Foetisch et al. 2011) in two cultivars, ‘Rodelica’ and ‘Nerac’, in a two-year study. Initial evaluation of the field data suggests a large influence of the year of cultivation and an apparent difference between the two cultivars (unpublished data, research project BÖL 03OE349 granted by the German Federal Ministry of Food, Agriculture and Consumer Protection). Furthermore, some isoallergens might be more relevant than others for clinical reactivity, and the level of allergens can increase or decrease depending on genetic and environmental factors. Studies on the allergenicity of apple and carrot have focused on non-transgenic cultivars; however, they can be considered model foods to study the influence of genetic and environmental factors on the composition of panallergenic structures (functionally related allergenic molecules found in different species) and the isoallergen distribution in fruits and vegetables. Understanding the biochemical pathways of allergen synthesis and the range of natural variability may support hypothesis-driven studies on unintended effects in GM plants intended for human consumption.

Environmental risk assessment of GM crop plants

Example of hypothesis-driven testing: drought-tolerant maize

The evaluation of Monsanto’s recently introduced DroughtGard® maize hybrids (event MON 87460) was used as a case study to illustrate how hypothesis-driven testing can be used for safety assessment. This product expresses a bacterial cold shock domain protein B (Bacillus subtilis CSPB), which imparts reduced yield loss under water-limited conditions compared with conventional corn (Castiglioni et al. 2008). CSPB is a member of the cold shock domain-containing (CSD-containing) protein family. Under environmental stress, CSD-containing proteins moderate stress responses in bacteria and plants, primarily through stabilization of RNA and improved cellular function (Cristofari and Darlix 2002; Chaikam and Karlson 2008). Like endogenous CSD proteins found in bacteria and plants, the CSPB protein in MON 87460 interacts with RNA and accumulates and localizes to rapidly growing tissues and in developing reproductive organs, thereby helping to maintain cellular function in those tissues during stress (Nemali et al. 2014). Under water-limited conditions, there is a trend toward improved ear growth rate for MON 87460 compared with the control plants, while the common mechanisms of plant response to drought stress are not altered in transgenic CSPB-expressing maize plants (Castiglioni et al. 2008; Nemali et al. 2014). When plants were grown under well-watered conditions, no appreciable differences between CSPB-expressing lines and the control were detected (Castiglioni et al. 2008).

Based on the understanding of the CSPB mode of action, the ERA for MON 87460 included six hypothesis-driven studies that answered specific questions relevant to the nature of the trait, in addition to the standard phenotypic and agronomic field trials in the presence and absence of the trait (Sammons et al. 2014). The studies included assessments for persistence outside of cultivation; root growth and development; and drought, cold, heat, and salt tolerance (Sammons et al. 2014). No additional abiotic stress tolerances were identified and no differences in season-long water consumption or root growth and development were observed. These studies did not reveal any potential for adverse environmental impacts.

Example of hypothesis-driven testing: assessment of potential for weediness

In Australia, previous experience on assessment of weediness has been used to assess transgenic crops. Identification of characteristics that are relevant to weediness/invasiveness has been based on practical experience with more than 1200 major environmental and agricultural weeds in diverse landscapes (Randall 2012). Weed scientists have produced a robust and simple weed risk assessment protocol that can be readily applied to any plant (Keese et al. 2014). In addition, the large datasets available from weed risk assessments include plants across the whole risk spectrum and allow rigorous validation tests to be conducted (Virtue et al. 2008; Stone and Byrne 2011).

The most advanced method for weed risk assessment is based on the post-border weed risk management protocol (Auld 2012), which was developed as a means of prioritizing existing weeds for control. It can be adapted to risk assessment of GM plants (Keese et al. 2014) by comparing the weed risk of the GM plant to that of the conventional counterpart. This approach is used to identify significant changes based on three factors: the risk context (i.e., the environment where the GM crop might be present), the ability of the GM plant to spread and persist, and the potential negative impacts on biodiversity, non-target organisms, soil nutrients, etc.

The weed risk approach specifies relevant characteristics of GM plants that affect spread and persistence (invasiveness) and those that potentially give rise to negative impacts on human or animal health, or the environment. These characteristics capture changes due to either intended or unintended effects. Changes that have no or negligible effect on weed risk need not be explored. The post-border weed risk assessment approach therefore provides guidance on the data requirements, for both intended and unintended traits, that are considered relevant for the ERA of a GM plant.

Unintended effects on non-target organisms

A common concern associated with the growing of GM crops is over their potential to have adverse impacts on non-target organisms. Arthropods in particular form a major part of the biodiversity in agricultural landscapes, and many are valued because they provide important ecosystem services, including biological control, pollination, and decomposition, or cultural services, including human enjoyment and education (Sanvido et al. 2012; Garcia-Alonso and Raybould 2014). Therefore, potential impacts that GM plants may have on valued non-target arthropods (NTAs) are addressed in ERA.

Both the intended change (e.g., production of a Bt Cry [crystal] protein for target insect control) and any unintended changes could cause unintended effects on valued non-target organisms. Since consequences of the intended change can be anticipated, it is possible to construct conceptual models (pathways to harm) of how growing of the GM plant could harm valued NTAs and to formulate risk hypotheses that can subsequently be tested (Raybould 2011). A common hypothesis is that the stressor (i.e., the Cry toxin) does not reduce the abundance and ecological functions of NTAs under field conditions. This hypothesis is typically tested within a tiered framework that moves from laboratory or early-tier tests using species that are readily available, amenable to testing, and able to detect potential hazards, to more complex (higher-tier) experiments that evaluate the risks under more realistic exposure conditions such as field studies (Romeis et al. 2008). Laboratory studies (termed tier 1 tests) are particularly powerful for testing the risk hypothesis; in cases where no adverse effects are detected under these highly controlled laboratory and worst-case exposure conditions, a “no effect” conclusion can be drawn with high confidence (Raybould et al. 2007; Romeis et al. 2011).

In the case of unintended, plant-transformation-related effects, the assessment typically follows a weight-of-evidence approach taking into account information from the molecular characterization of the particular GM event and from comparisons of composition and agronomic and phenotypic characteristics of the GM plant with its conventional counterpart (Garcia-Alonso and Raybould 2014). If differences are detected, their likely biological relevance will be assessed by considering the range of values known for the conventional crop varieties that have a history of safe use. The aim of this assessment is to identify potentially harmful unintended changes that, if found, would trigger a more detailed assessment (Romeis et al. 2008). This approach is considered sufficiently conservative given the fact that more than 99 % of all transformation events are eliminated during prior agronomic and phenotypic analyses (e.g., Phillips McDougall 2011).

It is sometimes argued that risk assessments should include experiments to study the impact of unintended, transformation-related effects on non-target organisms. For example, the EFSA requests non-target studies using GM plant material as a test substance to “… give indications on possible interactions between plant compounds and reflect realistic exposure conditions through bioavailability” (EFSA 2010). The justification for these additional data is that the compositional analyses do not necessarily target specific metabolites known to be involved in non-target-organism–plant relationships. This approach has many limitations. For example, it is usually unknown which metabolites are involved in these interactions, and different metabolites are likely to affect different non-target species differently. Furthermore it is more likely to detect differences in plant tissue composition among different plant varieties or even plant batches than between the tissue from GM plants and their non-transformed control (e.g., Meissle et al. 2014). Such experiments and their results may thus add confusion rather than certainty to the ERA. The published literature on the non-target impact of Bt maize, for example, provides a number of examples where studies using GM plant tissue as a test substance have resulted in inconclusive results (Romeis et al. 2013).

As a solution, the unintended, transformation-related effects that might adversely affect NTAs should be identified during the problem formulation phase of the ERA taking into account the results from the molecular, phenotypic, and agronomic characterization and the compositional analyses. If such characteristics are identified as stressors of concern, pathways to harm can be constructed and testable risk hypotheses can be formulated. This is a precondition to design and execute meaningful studies that provide data to support the ERA.