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Commentary: Novel strategies and new tools to curtail the health effects of pesticides

Abstract

Background

Flaws in the science supporting pesticide risk assessment and regulation stand in the way of progress in mitigating the human health impacts of pesticides. Critical problems include the scope of regulatory testing protocols, the near-total focus on pure active ingredients rather than formulated products, lack of publicly accessible information on co-formulants, excessive reliance on industry-supported studies coupled with reticence to incorporate published results in the risk assessment process, and failure to take advantage of new scientific opportunities and advances, e.g. biomonitoring and “omics” technologies.

Recommended Actions

Problems in pesticide risk assessment are identified and linked to study design, data, and methodological shortcomings. Steps and strategies are presented that have potential to deepen scientific knowledge of pesticide toxicity, exposures, and risks.

We propose four solutions:

(1) End near-sole reliance in regulatory decision-making on industry-supported studies by supporting and relying more heavily on independent science, especially for core toxicology studies. The cost of conducting core toxicology studies at labs not affiliated with or funded directly by pesticide registrants should be covered via fees paid by manufacturers to public agencies.

(2) Regulators should place more weight on mechanistic data and low-dose studies within the range of contemporary exposures.

(3) Regulators, public health agencies, and funders should increase the share of exposure-assessment resources that produce direct measures of concentrations in bodily fluids and tissues. Human biomonitoring is vital in order to quickly identify rising exposures among vulnerable populations including applicators, pregnant women, and children.

(4) Scientific tools across disciplines can accelerate progress in risk assessments if integrated more effectively. New genetic and metabolomic markers of adverse health impacts and heritable epigenetic impacts are emerging and should be included more routinely in risk assessment to effectively prevent disease.

Conclusions

Preventing adverse public health outcomes triggered or made worse by exposure to pesticides will require changes in policy and risk assessment procedures, more science free of industry influence, and innovative strategies that blend traditional methods with new tools and mechanistic insights.

Peer Review reports

Background

Today’s near-total dependence on pesticides for weed, insect, and pathogen control on conventionally managed farms is likely unsustainable. Steadily rising pesticide expenditures reduce net returns to farmers. Heavy pesticide use imposes collateral damage on farmers and workers, the environment, and public health that can become unacceptable as a result of direct costs; fines, penalties, or more costly insurance; regulation; social pressure or local controls. Examples of such damage include crop and tree losses due to dicamba and other herbicide drift [1, 2], reduced biodiversity [3,4,5,6], diminished ecosystem services [7, 8], pollinator decline [8, 9], acute poisonings [9], the emergence and spread of resistant weeds [10] and insects [11], loss of vital drugs needed to treat human infections [12], and water contamination [13, 14].

Although the human health impacts stemming from exposure to pesticides are not fully known or understood, an increasing number of studies describe adverse effects following acute occupational exposures [9], heightened risk of cancer [15,16,17,18], and residential proximity to farms heavily reliant on pesticides [19, 20].

In the US, European Union, and other developed countries, pesticides undergo an evaluation of their environmental and human health effects before they are placed on the market. However, current regulatory systems have often failed to detect and/or mitigate some of the health effects triggered by pesticides. Examples include damage to the neurological system and brain from chlorpyrifos and other organophosphates [19, 21], the impact of exposures to glyphosate-based herbicides on non-Hodgkin lymphoma [15, 22, 23], and adverse reproductive impacts of multiple pesticides [24, 25]. These failures to detect and prevent adverse health outcomes arise from systemic shortcomings in how regulators, industry and public health professionals assess pesticide risks and strive to mitigate them. Because of these shortcomings, problems can linger for years, as in the case of:

  • The role paraquat has played in the etiology of Parkinson’s disease [26,27,28],

  • Impacts of prenatal exposures to glyphosate-based herbicide on preterm births [24, 29], the gut microbiome [30], and non-alcoholic fatty liver disease [31, 32], and

  • Exposures leading to autism and autoimmune diseases [20, 33].

In some of the cases cited above, substantial evidence of toxicity and significant human risk had been published many years, or even decades before the EPA or other regulators acted to restrict use and exposures [34]. Other shortcomings stem from the limited set of tools regulators utilize to quantify and mitigate risks arising from how pesticides are actually used in the real world, in contrast to how pesticides are supposed to be used in accord with label directions [35].

The science supporting pesticide risk assessment and regulation has been reviewed critically in recent years [36, 37]. After providing an overview of the most consequential problems in how pesticides are used and risks are managed in the US and other countries, we propose strategies to:

  • Deepen scientific knowledge of adverse pesticide impacts,

  • Build trust in regulatory risk assessments, and

  • Broaden support for steps to reduce overall reliance on pesticides, and especially use of high-risk pesticides.

The paper focuses predominately on shortcomings and challenges confronting pesticide risk assessors and regulators in the US and the European Union. In general, these countries follow guidelines and testing principles from the Organization for Economic Co-operation and Development (OECD). Our recommendations are likely to apply to other OECD countries, and to developing countries with regulations that draw upon OECD standards, e.g. members of the Economic Community of West African States (ECOWAS) [38]. Progress in the sciences and policies governing pesticide use has potential to support reductions in the adverse public health impact of pesticide use in other parts of the world where the need for significant reductions in the frequency of high-exposure episodes is more acute.

Flaws in the science supporting pesticide risk assessment and regulation

Formulation chemistry matters

Pesticide formulation chemistry is a complex interplay of different ingredients at varying concentrations. Formulations directly impact product effectiveness, environmental fate, and risk profiles. Applications via specialized spray equipment, as part of regional pest management systems, or on particular soils can require different formulations for optimal efficacy [39]. Some formulations allow the mixing of pesticides with liquid fertilizers, so one pass through a field can accomplish multiple tasks.

So-called “inert ingredients” in formulated herbicides enhance adhesion and allow herbicides to move more quickly through the epidermis of weeds. They help reduce drift and prevent clogged nozzles. Some surfactants also markedly enhance toxicity to non-target organisms [40] and human-health risks [41]. Applicator exposure episodes leading to glyphosate doses high enough to trigger ocular damage [42], skin irritation [43], non-Hodgkin lymphoma [15], and gastrointestinal disorders [44, 45] were not due to glyphosate alone, but to the glyphosate plus surfactants in formulated commercial products (e.g. MON 0818 for Roundup MON 2139) [41, 46, 47].

But regulatory reviews largely ignore the impacts of co-formulants. In the US, nearly all pesticide risk assessments are based on studies done on the nearly pure “active ingredients” — “active” in the sense that they are responsible for the desired impact on weeds, insects, plant diseases, rodents, or other pests. But when applied in the field, nearly all pesticides are complex mixtures with multiple adjuvants and surfactants and an active ingredient. Many pesticide products contain more than one active ingredient, and some up to three, as in the case of Expert® herbicide that includes a mix of S-metolachlor, atrazine and glyphosate (EPA Reg. No. 100–1161) [48].

In the past decade, published data have confirmed what regulators and industry have known for years: substantial differences exist in the risk profile of pesticide active ingredients in contrast to the formulated products containing them. These differences alter environmental fate, metabolism, and excretion [41]. This is clearly the case with the world’s leading herbicide glyphosate in vivo in rats [47, 49] or in vitro in human cells [46], and for the most widely used family of insecticides (neonicotinoids) [50, 51].

Moreover, current US and EU law allows pesticide registrants to claim that the formula of commercial products are confidential business information. Hence, there is often no way to know what is in a given end-use pesticide. In the case of glyphosate, hundreds of products containing glyphosate are available in the US. These products have different names, although they can have the same, or very similar composition. They are often marketed under alternate names when sold in another country. Herbicide manufacturers use a trade name (e.g. Roundup) and unique formulation number (e.g. MON 2139) to accurately identify and link a product back to its confidential statement of formula. But the internal formulation number and its contents and concentrations are rarely disclosed [41]. Farmers and scientists cannot be expected to understand and manage efficacy issues and pesticide risks without detailed and specific information on what is in the products actually applied.

Occupational exposures are an afterthought

Applicators, farmworkers and those occupationally exposed, or living near treated fields, face heightened risks compared to the general public, as is apparent in most pesticide human health risk assessments completed by the EPA (e.g. glyphosate [52], paraquat [53], or chlorpyrifos [54]). There are many causes of this emerging environmental justice issue:

  • Much less rigorous focus on the exposure data available to quantify occupational exposures, especially in contrast to dietary exposures,

  • Highly questionable data supporting estimates of dermal absorption rates derived predominately from studies using pure active ingredients rather than formulated products,

  • The assumptions that: (a) all required personal protective equipment will be appropriate to achieve the hoped-for reduction in exposure, in good working order, and properly utilized, and (b) all requirements, precautions, and warnings will be adhered to, and

  • The generally higher risks deemed acceptable by regulators when farmworkers and applicators are exposed to pesticides, in contrast to the general public.

Applicator, farm worker, and other bystander and occupational exposures and risks are presumptively managed by changing use rates or application methods, by imposing incrementally more Personal Protective Equipment (PPE), via engineering controls, or other measures. Requiring more PPE may work on paper, but often falls short in practice [33]. Applicators of high-risk pesticides are often unable or not willing to comply with all existing PPE and use directions requirements, or do so inappropriately, undercutting the reductions in exposures projected by regulators [55].

The science supporting current applicator and mixer-loader exposure and risk assessments is plagued by poor data (e.g. exposure estimates from models estimating the impacts of various combinations of PPE, rather than direct measurements) and methodological flaws (incorrect dermal absorption rates, studies based on pure active ingredients rather than formulations). Actual exposures derived from field-level biomonitoring studies are often far higher than what the EPA has estimated [56]. This is especially true in the case of applicators spraying pesticides with handheld and backpack sprayers [57, 58]. A person applying one pound of pesticide active ingredient in a 6-h workday using handheld equipment will almost always experience tenfold to 100-fold higher exposure than the operator of a modern pesticide sprayer who sits in a glass-steel cab and sprays 1,000 lb of pesticide in a day [59].

Deficiencies and gaps in the data supporting applicator and occupational risk assessment give rise to systemic underestimation of worker exposures and risk. Estimates of typical rates of dermal absorption for many pesticides are too low [60, 61]. The pesticide industry prefers to use, and regulators have unfortunately accepted, non-viable skin-based penetration assays that underestimate actual rates [62]. Additionally for most pesticides, dermal absorption rates are derived from studies of pure active ingredient, while in the field, exposures always occur to formulated pesticides containing surfactants, many of which enhance dermal penetration [63]. Pesticide manufacturers are aware of this, but have generally not taken the actions needed to sharpen the accuracy of applicator and worker risk assessments. A common explanation from registrants is that they comply with all requirements imposed on them by regulators.

The EPA and EU regulators have begun asking registrants to carry out exposure-route specific toxicity tests to set Points of Departure (POD) for risk assessments. A POD is typically the Lowest Observed Adverse Effect Level (LOAEL) in an animal assay. Regulators then try to assure that exposure levels in a day will be at least 100-fold or more lower than the estimated POD. But when the EPA accepts a toxicity study done in an animal model that entails treatments with a pure active ingredient rather than the formulated products, unrealistically high PODs can result, further separating applicator and occupational risk assessments from real-world conditions. Similar sources of uncertainty arise in the way worker risks are evaluated in other countries. Estimates are often based solely or largely on data generated and submitted by registrants.

Evaluating real-life exposure scenarios

Most pesticide toxicology data requirements rely on outdated testing methods that either originated from or have been influenced by guidelines issued by the OECD [64]. The core toxicology tests required by the EPA, EU, and most other regulatory authorities focus almost exclusively on pure active ingredients. Most methods dose adult animals up to a Maximum Tolerated Dose (MTDs). This provides vital information for hazard classification. However, pesticide doses to which human populations are exposed in the environment (e.g. via diet) are much lower, with the possible exception of some applicator exposure episodes entailing use of handheld or backpack sprayers and little or no PPE.

Current risk assessment methods are based, for the most part, on the assumption that the severity of toxic effects at low environmental doses can be approximated using linear extrapolations from effects measured at higher doses. A large number of studies have now demonstrated that this is not true for hormonal effects [65, 66].

Likewise, conventional pesticide risk assessment does not adequately consider the impacts of exposures in early fetal development when the timing of exposure may be as important, or even more important, than dose [67]. Moreover, OECD guideline studies are not designed or able to detect some cancers with long latency periods nor neural decline, and other diseases that typically become evident later in life [68]. OECD testing protocols are especially limited in their ability to detect adverse health impacts stemming from low-level chronic exposures, and from periodic high-level exposures among people mixing, loading and applying pesticides, or working or living in or near heavily treated fields.

New strategies for assessing low-dose, non-linear and early developmental toxicity have been developed. These include the use of high-throughput technologies known as ‘omics’ methods (metabolomics, transcriptomics, genomics, metagenomics, or even epigenomics). These methods allow the monitoring of multiple biomarkers and can identify metabolic perturbations that typically remain undetected despite full compliance with current regulatory test requirements (Table 1). This was the case in a recent study of a mixture of six pesticides frequently detected in foodstuffs [69].

Table 1 Multiple Omic layers are available to serve both as an exposure and health outcome biomarkers. Other omics methods such as glycomics or ncRNomics have been used to understand human health but they have not been extensively used in toxicology

While standard histopathology and serum biochemistry measures revealed few effects, the combination of high-throughput omics revealed the activation of stress-response pathways. Metabolomics or RNA sequencing of sensitive tissues or in urine or blood can also be deployed to identify biomarkers and predict the risk of long-term health effects from pesticide exposures (Table 1). For instance, exposure to complex pesticide mixtures induces specific modifications of metabolism in pregnant women [80]. The profiling of small RNA in urine samples from individuals who ingested high doses of paraquat or glyphosate identified acute kidney injury [106]. In another study, small RNAs in urine were associated with organophosphate metabolites in farmworkers [107]. It was even hypothesized that small RNA could provide a link between exposure to pesticides and the development of Parkinson's disease [108]. Novel mechanistic assays have also been developed to allow the identification of key characteristics of human carcinogens [109].

Reliance in risk assessments on industry-sponsored studies versus published studies by scientist not funded by or affiliated with pesticide manufacturers

The majority of studies considered by pesticide regulators are undertaken in compliance with Good Laboratory Practice (GLP) guidelines [110]. GLPs are intended to ensure consistency and reliability in laboratory testing. Studies that do not adhere to stated GLP guidelines, but are otherwise sound, are typically not considered or are given little weight in “weight of the evidence” judgements. This bias against published, non-GLP studies excludes consideration of mechanistic insights from genomic analyses (see Table 1 for an overview of emerging genomic tools that can support pesticide risk assessment) [111, 112].

The EPA’s assessment of glyphosate oncogenicity is a good example of the pitfalls inherent in relying on industry-supported, GLP-compliant toxicology studies [59, 113]. The EPA determined in 2015 that glyphosate is “not likely to be carcinogenic to humans.” This finding contrasted with the “probably carcinogenic to humans” Group 2A classification issued by the International Agency for Research on Cancer (IARC), also in 2015 [23, 114]. Why the difference?

In the most recent cycle of reregistration of glyphosate-based herbicides in both the US and EU, regulators relied mostly on industry-conducted, GLP-compliant glyphosate genotoxicity assays, of which about 1% reported a positive result. Over one-half of these assays were bacterial mutation assays which are known to have a limited sensitivity [115]. The IARC glyphosate Working Group placed heavy weight on the nearly three-quarters of published glyphosate genotoxicity studies that reported one or more positive assays [59, 114].

Industry genotoxicity studies reporting no evidence of genotoxicity were mostly done in the 1980s and 1990s, while most positive published studies were done since 2000 and were conducted with generally more sensitive assay systems. Over 90% of the glyphosate genotoxicity assays conducted since completion of the IARC glyphosate monograph have reported one or more positive assays [116]. IARC placed heavy weight on in vivo studies with formulated glyphosate-based herbicides (GBHs), including three studies in human populations exposed as a result of aerial spraying that reported evidence of genotoxicity in exposed people compared to people living nearby who were not exposed.

Other examples of substantial gaps between the findings of industry-sponsored GLP studies and published studies conducted by university-based scientists include studies on the endocrine disrupting effects of endosulfan [117, 118], the reproductive impacts of the synthetic pyrethroid insecticides [39, 119], and the developmental toxicity of chlorpyrifos [68, 120].

Multiple concurrent exposures and vulnerable populations

Another shortcoming with traditional toxicology testing — which examines each pesticide in isolation — is that no one is exposed to just one pesticide, nor are people exposed only to pesticides [121]. Biomonitoring data generated in the US by the Centers for Disease Control and Prevention demonstrate that in recent years more than 90% of Americans have a few to several pesticides and/or pesticide metabolites in their body on any given day [122, 123], as well as several other chemicals, e.g. metals, furans, polycyclic aromatic hydrocarbons, plasticizers such as phthalates and bisphenols, or brominated and fluorinated chemicals [124]. Some can accumulate in a variety of human tissues, others can be found in breast milk [125] and pass via the placenta into the fetus [126].

Pesticide biomonitoring data has been published reporting the frequency and levels of pesticide analytes in human urine [124, 127,128,129]. Expanded EU investments in biomonitoring will produce additional data to support refined exposure and risk assessments and epidemiological analyses [130].

Further complications in pesticide risk assessment arise as a result of the well-known fact that not all members of exposed populations are equally vulnerable to pesticides. A variety of genetic polymorphisms, health conditions, and drug therapies render segments of the population more vulnerable following exposure to certain pesticides [131]. Since the enzyme paraoxonase 1 hydrolyzes most organophosphorus insecticides in plasma and the liver, its activity, or lack thereof in some people, modulates organophosphate toxicity by an order of magnitude, and for some people, up to two orders of magnitude [132, 133]. Low paraoxonase status increases susceptibility to disruption in children’s neurobehavioral development [134, 135]. Maternal levels of chlorpyrifos coupled with low maternal PON1 activity were associated with a significant reduction in head circumference in their offspring [136].

Dietary factors such as malnutrition and vitamin deficiency can enhance the toxic effects of some pesticides. A recent animal study suggested that chronic vitamin deficiency can influence the toxicity of a pesticide mixture [137]. Another study showed that neonicotinoids and nutritional stress can act synergistically to reduce survival of honey bees [138]. Epidemiological studies clearly indicate that agricultural workers exposed to pesticides are subject to damaging oxidative stress and this stress can activate antioxidant responses [139]. Further studies are needed to determine whether and to what extent a person’s nutritional status can influence the development of pathologies after pesticide exposures.

People living in farming areas, in buildings sprayed for insects, or near golf courses experience added exposures. Multiple pesticides with endocrine disrupting activity have been found in public playgrounds and schoolyards in agricultural areas [140]. People mixing and loading pesticides and applying them are by far the most heavily exposed [141], especially those using handheld, backpack, or ATV-mounted sprayers [37, 57, 116], yet no product labels have warnings addressed to more highly exposed occupational users, nor pregnant women or individuals currently receiving chemotherapy. In the US, many plaintiffs in the Roundup-non-Hodgkin litigation continued to spray Roundup post NHL diagnosis and between rounds of chemotherapy and/or stem cell transplants. When asked why by counsel, some testified that no one alerted them to the need to avoid future exposures to possibly cancer-causing chemicals.

Current EPA and EU pesticide risk assessment protocols and label warnings sometimes touch upon such differences, but do so inadequately. The warnings and exposure-reduction provisions on pesticide labels do not generally take into account nor address the heightened vulnerability of certain population cohorts. Labels on paraquat products fail to warn of neurotoxicity and do not mention the possible link between exposures and Parkinson’s disease [35]. Nearly all labels also fail to alert applicators spraying pesticides dozens of times annually that some health risks rise with repeated exposures. Nor do labels emphasize, or even mention the need for discipline to avoid high-risk scenarios caused by weather conditions, a leaky hose, or spray patterns. Rectifying these shortcomings warrants a global response across regulatory authorities, better data from specific exposure-scenario research, and policy reforms.

Four Solutions to Curtail the Adverse Health Effects of Pesticides

Independent science in support of pesticide risk assessment and regulation is sorely needed

Toxicology studies that underpin pesticide risk assessment and standard setting should largely be undertaken and analyzed by independent scientists. Currently in the US and in most of the world, almost all of the data used to regulate pesticides are derived from unpublished studies conducted by pesticide registrants, or companies working under contract for pesticide manufacturers [142]. Their job is to produce study results that support — and defend — existing pesticide tolerances, uses and registrations, and for the most part that is what they do.

As long as existing pesticide testing protocols remain the basis for regulation and public health protection, little progress is likely in mitigating adverse public health impacts stemming from pesticide use. The gaps between the risks that regulators recognize and strive to mitigate, and risks in the real world are sizable and greatest among those who are heavily exposed and/or more highly vulnerable.

The primary toxicology data packages supporting initial registration of new active ingredients are typically carried out in the US or EU and submitted for evaluation to the EPA and European Food Standards Agency (EFSA). The same core studies are usually submitted to multiple regulatory authorities around the world. To diversify and enhance the quality of the science base supporting pesticide risk assessments, the US EPA and EFSA should agree upon a process whereby registrants must provide sufficient funding via registration-process fees for the EPA or EFSA to sponsor a set of core toxicology studies at a laboratory with appropriate research capability that is not affiliated with the industry. Another solution which could be implemented at essentially no cost is the requirement for industry-supported studies to be registered in advance in a publicly accessible database like clinical trials for drug safety, in order to prevent companies from halting and never disclosing studies that seem to be generating unwelcomed results.

Adoption of these new approaches to enhance the quality of core pesticide toxicology studies won’t materially change the cost of studies supporting pesticide risk assessment, unless a registrant decides to also fund and/or conduct comparable studies inhouse. In either case, this approach should, over time, enhance the quality and reliability of studies supporting key regulatory decisions, and overtime, enhance public trust in the science supporting regulatory decisions.

Invest in direct measures of exposure

To determine which pesticides warrant stricter regulation, more biomonitoring data are essential. For example in the Midwestern US, the spread of glyphosate-resistant weeds is driving upward the use of 2,4-D, dicamba, and glufosinate [143, 144]. Tracking the public health impacts of rising herbicide use in this region requires accurate measures of changes in exposure. Such measures can best be obtained via biomonitoring.

Efforts are underway in many countries to harmonize and aggregate human biomonitoring data to better identify vulnerable or highly exposed populations. The European Joint Program HBM4EU [128] shows promise in improving both the volume and quality of biomonitoring data across the EU. Biomonitoring human exposure to glyphosate co-formulants has been designated as a priority by HMB4EU [145]. A method to do so was recently created and applied to the study of the urinary excretion of Roundup MON 52,276 co-formulants in exposed rats [146]. In the US, the CDC’s National Health and Nutrition Examination Survey [122] has recently developed new methods to test the most heavily applied herbicide (glyphosate [147]) and the insecticides people are most frequently exposed to via diet (the neonicotinoids [148]).

Place more weight on mechanistic data

DNA damage is a worrisome consequence of pesticide exposure and can occur via multiple mechanisms including oxidative stress, chromosomal strand breaks, and formation of adducts. A pesticide’s ability to damage DNA directly or indirectly is correlated with its carcinogenicity [149], ability to trigger developmental anomalies [150], and impacts on endocrine system function [151].

Prevention of oxidative stress and DNA damage should be key objectives of pesticide regulation [152, 153]. Yet the toxicological studies currently supporting pesticide registrations are not always capable of identifying the full range of metabolic and genetic mechanisms leading to, causing, or exacerbating DNA damage [154]. New methods that can identify genotoxic compounds with high accuracy and characterize their oxidative stress related mode, or modes of action are already available [155]. While bacterial mutation assays (Ames tests) mentioned above were able to detect genotoxic potential in 46% of a set of test compounds, new assays did so with 95% accuracy [156]. This new generation of assays were recently used to compare genotoxicity profiles of glyphosate and three formulations [109]. This work revealed that two Roundup herbicides but not glyphosate activated oxidative stress and led to misfolded proteins, two key characteristics of human carcinogens [149].

Metabolomics is a cutting-edge technology that has much to offer in emerging pesticide risk assessment methods [157]. New findings suggest exposures to the pesticides atrazine, diazinon, glyphosate-based herbicides, and trichlorfon cause sex-specific shifts in gut microbiota, which have been linked to many adverse effects in children and young adults [158,159,160].

For over a decade, metabolomics has been considered sufficiently mature to provide critical information for use in clinical medicine [161]. Metabolomics has, moreover, contributed to real-time diagnostics and integrative patient modeling during surgeries [162]. But guidelines for best practice and GLPs, and reporting standards for metabolomic results in chemical and pesticide risk assessment have been proposed only recently [163]. More funding is needed to pick up the pace in applications of metabolomics in regulation in order to buttress the ability of pesticide risk assessments to identify — and prevent — adverse public health outcomes.

It is encouraging that molecular profiling tools are increasingly incorporated in novel experimental systems [30, 157, 164, 165]. Such systems can detect subtle impacts triggered by chemical exposures that start the progression to disease. In recent studies, metabolomics has provided useful insights into the mechanisms leading to toxic effects of individual pesticides, and mixtures of pesticides at doses far below those triggering observable, adverse effects using standard histopathology and biochemistry methods [166].

Knit scientific tools together to accelerate progress

Novel approaches are needed to integrate genetics and genomics, toxicology, clinical science and epidemiology. One promising strategy is applying metabolomics in search of markers of epigenetic disruption in gene expression patterns in blood, bone marrow, and gut microbiomes. Unlike typical high-dose regulatory studies in mice and rats, metabolomic assays are being carried out using dose ranges that overlap current exposure levels among exposed human populations [69, 157].

In vivo study designs need to evolve in order to assess different end-points and explore and apply new mechanistic insights (e.g. how oxidative stress can promote fatty liver disease [167] or reproductive anomalies [168]). Progress will allow reductions in the number and diversity of animal studies, with potential to cut costs and promote more rapid progress in linking specific exposures to identified endpoints of concern [169]. Such innovation will be complemented by in vitro [170] and computational approaches to predict adverse outcomes [171].

Novel integration of mechanistic insights and traditional toxicology are among the goals of the Global Glyphosate Study [172]. The pilot phase of the study suggested that a glyphosate-based herbicide at the US EPA chronic reference dose altered sexual development in a rat study, damaged DNA, and disrupted the intestinal microbiome [47, 49, 173]. A subchronic toxicity study in rats used shotgun metagenomics and metabolomics to explore the effects of glyphosate and a GBH on the gut microbiome and serum metabolome [30]. This study revealed for the first time that glyphosate and a GBH inhibit the shikimate pathway in gut microbiota, and that the blood metabolome in exposed animals was altered in ways consistent with oxidative stress.

Conclusions

Improvements in pesticide risk assessment and regulation are particularly vital in the case of applicator and worker exposures and risks. Occupational exposures always occur to formulated products, yet most of the toxicology data supporting occupational risk assessments are derived from studies on pure active ingredients. Laws and policies prohibiting the disclosure of the surfactants and other “inert ingredient” in formulated pesticides, and their concentrations, should be amended in the interest of public health.

The surest path to achieve significant and sustained reductions in the adverse impacts of pesticides is reducing their overall use. Doing so will require more investment in public and private research and technical support for farmers to accelerate the shift toward prevention-based, biointensive Integrated Pest Management [174,175,176].

New approaches and strategies are essential to take full advantage of emerging mechanistic insights in the design of in vivo experiments and in the analysis of in vivo results. The creative synthesis of sequencing-based technologies with clinical assessments and epidemiology has great potential. Such innovation is needed to better identify exposures that really matter so that real-world pesticide risks can be mitigated or eliminated.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon request.

Abbreviations

2,4-D:

2,4-Dichlorophenoxyacetic acid (herbicide active ingredient)

ATV:

All-Terrain Vehicle

CDC:

United States Centers for Disease Control

CSF:

Confidential Statement of Formula

EPA:

United States Environmental Protection Agency

GBH:

Glyphosate-Based Herbicide

GLP:

Good Laboratory Practices

HBM4EU:

Human Biomonitoring for the European Union

IARC:

International Agency for Research on Cancer

LOAEL:

Lowest Observed Adverse Effect Level

MTD:

Maximum Tolerated Dose

OECD:

Organisation for Economic and Cooperative Development

POD:

Points of Departure

PPE:

Personal Protective Equipment

References

  1. 1.

    Bomgardner MM. Widespread crop damage from dicamba herbicide fuels controversy. Chem Eng News. 2017;95(33).

  2. 2.

    Borges M, Silva DV, Souza MF, Silva TS, Teófilo T, da Silva CC, et al. Glyphosate effects on tree species natives from Cerrado and Caatinga Brazilian biome: Assessing sensitivity to two ways of contamination. Sci The Total Environ. 2021;769:144113.

  3. 3.

    Pleasants JM, Oberhauser KS. Milkweed loss in agricultural fields because of herbicide use: effect on the monarch butterfly population. Insect Conserv Diversity. 2012;6(2):135–44.

    Article  Google Scholar 

  4. 4.

    Pisa LW, Amaral-Rogers V, Belzunces LP, Bonmatin JM, Downs CA, Goulson D, et al. Effects of neonicotinoids and fipronil on non-target invertebrates. Environ Scie Pollut Res Int. 2015;22(1):68–102.

    CAS  Article  Google Scholar 

  5. 5.

    Schlappi D, Kettler N, Straub L, Glauser G, Neumann P. Long-term effects of neonicotinoid insecticides on ants. Commun Biol. 2020;3(1):335.

    CAS  Article  Google Scholar 

  6. 6.

    Schulz R, Bub S, Petschick LL, Stehle S, Wolfram J. Applied pesticide toxicity shifts toward plants and invertebrates, even in GM crops. Science. 2021;372(6537):81.

    CAS  Article  Google Scholar 

  7. 7.

    Gaupp-Berghausen M, Hofer M, Rewald B, Zaller JG. Glyphosate-based herbicides reduce the activity and reproduction of earthworms and lead to increased soil nutrient concentrations. Sci Rep. 2015;5:1–9.

  8. 8.

    Zaller JG, Cantelmo C, Santos GD, Muther S, Gruber E, Pallua P, et al. Herbicides in vineyards reduce grapevine root mycorrhization and alter soil microorganisms and the nutrient composition in grapevine roots, leaves, xylem sap and grape juice. Environ Sci Pollut Res Int. 2018;25(23):23215–26.

    CAS  Article  Google Scholar 

  9. 9.

    Boedeker W, Watts M, Clausing P, Marquez E. The global distribution of acute unintentional pesticide poisoning: estimations based on a systematic review. BMC Public Health. 2020;20(1):1875.

    Article  Google Scholar 

  10. 10.

    Heap I, Duke SO. Overview of glyphosate-resistant weeds worldwide. Pest Manage Sci. 2018;74(5):1040–9.

    CAS  Article  Google Scholar 

  11. 11.

    Werren JH. Symbionts provide pesticide detoxification. Proc Natl Acad Sci U S A. 2012;109(22):8364–5.

    CAS  Article  Google Scholar 

  12. 12.

    Toda M, Beer KD, Kuivila KM, Chiller TM, Jackson BR. Trends in Agricultural Triazole Fungicide Use in the United States, 1992–2016 and Possible Implications for Antifungal-Resistant Fungi in Human Disease. Environ Health Perspect. 2021;129(5):055001-1-055001-12.

  13. 13.

    Covert SA, Shoda ME, Stackpoole SM, Stone WW. Pesticide mixtures show potential toxicity to aquatic life in U.S. streams, water years 2013–2017. Sci Total Environ. 2020;745:141285.

  14. 14.

    Basley K, Goulson D. Neonicotinoids thiamethoxam and clothianidin adversely affect the colonisation of invertebrate populations in aquatic microcosms. Environ Sci Pollut Res Int. 2018;25(10):9593–9.

    CAS  Article  Google Scholar 

  15. 15.

    Zhang L, Rana I, Shaffer RM, Taioli E, Sheppard L. Exposure to Glyphosate-Based Herbicides and Risk for Non-Hodgkin Lymphoma: A Meta-Analysis and Supporting Evidence. Mutation Res Rev Mutation Res. 2019;781:186–206.

  16. 16.

    Lombardi C, Thompson S, Ritz B, Cockburn M, Heck JE. Residential Proximity to Pesticide Application as a Risk Factor for Childhood Central Nervous System Tumors. Environ Res. 2021;197:111078.

  17. 17.

    Lerro CC, Hofmann JN, Andreotti G, Koutros S, Parks CG, Blair A, et al. Dicamba use and cancer incidence in the agricultural health study: an updated analysis. Int J Epidemiol. 2020;49(4):1326–37.

  18. 18.

    Chen M, Chang CH, Tao L, Lu C. Residential Exposure to Pesticide During Childhood and Childhood Cancers: A Meta-Analysis. Pediatrics. 2015;136(4):719–29.

    Article  Google Scholar 

  19. 19.

    Gunier RB, Bradman A, Harley KG, Kogut K, Eskenazi B. Prenatal Residential Proximity to Agricultural Pesticide Use and IQ in 7-Year-Old Children. Environ Health Perspect. 2017;125(5):057002.

  20. 20.

    von Ehrenstein OS, Ling C, Cui X, Cockburn M, Park AS, Yu F, et al. Prenatal and infant exposure to ambient pesticides and autism spectrum disorder in children: population based case-control study. BMJ. 2019;364:l962.

  21. 21.

    Bouchard MF, Chevrier J, Harley KG, Kogut K, Vedar M, Calderon N, et al. Prenatal exposure to organophosphate pesticides and IQ in 7-year-old children. Environ Health Perspect. 2011;119:1189–95.

    CAS  Article  Google Scholar 

  22. 22.

    Leon ME, Schinasi LH, Lebailly P, Beane Freeman LE, Nordby KC, Ferro G, et al. Pesticide use and risk of non-Hodgkin lymphoid malignancies in agricultural cohorts from France, Norway and the USA: a pooled analysis from the AGRICOH consortium. Int J Epidemiol. 2019;48(5):1519–35.

  23. 23.

    Guyton KZ, Loomis D, Grosse Y, El Ghissassi F, Benbrahim-Tallaa L, Guha N, et al. Carcinogenicity of tetrachlorvinphos, parathion, malathion, diazinon, and glyphosate. Lancet Oncol. 2015;16(5):490–1.

    Article  Google Scholar 

  24. 24.

    Silver MK, Fernandez J, Tang J, McDade A, Sabino J, Rosario Z, et al. Prenatal Exposure to Glyphosate and Its Environmental Degradate, Aminomethylphosphonic Acid (AMPA), and Preterm Birth: A Nested Case-Control Study in the PROTECT Cohort (Puerto Rico). Environ Health Perspect. 2021;129(5):57011.

    Article  Google Scholar 

  25. 25.

    Landrigan PJ. Pesticides and Human Reproduction. JAMA Intern Med. 2018;178(1):26–7.

    Article  Google Scholar 

  26. 26.

    Tanner CM, Kamel F, Ross GW, Hoppin JA, Goldman SM, Korell M, et al. Rotenone, paraquat, and Parkinson’s disease. Environ Health Perspect. 2011;119(6):866–72.

  27. 27.

    Costello S, Cockburn M, Bronstein J, Zhang X, Ritz B. Parkinson’s disease and residential exposure to maneb and paraquat from agricultural applications in the central valley of California. Am J Epidemiol. 2009;169(8):919–26.

  28. 28.

    Tanner CM, Ross GW, Jewell SA, Hauser RA, Jankovic J, Factor SA, et al. Occupation and risk of parkinsonism: a multicenter case-control study. Arch Neurol. 2009;66(9):1106–13.

  29. 29.

    Parvez S, Gerona RR, Proctor C, Friesen M, Ashby JL, Reiter JL, et al. Glyphosate exposure in pregnancy and shortened gestational length: a prospective Indiana birth cohort study. Environ Health. 2018;17(1):23.

    CAS  Article  Google Scholar 

  30. 30.

    Mesnage R, Teixeira M, Mandrioli D, Falcioni L, Ducarmon Q, Zwittink R, et al. Use of shotgun metagenomics and metabolomics to evaluate the impact of glyphosate or Roundup MON 52276 on the gut microbiota and serum metabolome of Sprague-Dawley rats. Environ Health Perspect. 2021 (in press).

  31. 31.

    Mesnage R, Renney G, Seralini G-E, Ward M, Antoniou MN. Multiomics reveal non-alcoholic fatty liver disease in rats following chronic exposure to an ultra-low dose of Roundup herbicide. Sci Rep. 2017;7:39328.

    CAS  Article  Google Scholar 

  32. 32.

    Mills PJ, Caussy C, Loomba R. Glyphosate Excretion is Associated With Steatohepatitis and Advanced Liver Fibrosis in Patients With Fatty Liver Disease. Clin Gastroenterol Hepatol. 2019;18(3):741–3.

  33. 33.

    Parks CG, Santos ASE, Lerro CC, DellaValle CT, Ward MH, Alavanja MC, et al. Lifetime Pesticide Use and Antinuclear Antibodies in Male Farmers From the Agricultural Health Study. Front Immunol. 2019;10:1476.

    CAS  Article  Google Scholar 

  34. 34.

    Loomis D, Guyton K, Grosse Y, El Ghissasi F, Bouvard V, Benbrahim-Tallaa L, et al. Carcinogenicity of lindane, DDT, and 2, 4-dichlorophenoxyacetic acid. Lancet. 2015;16:891–2.

    CAS  Article  Google Scholar 

  35. 35.

    Fortenberry GZ, Beckman J, Schwartz A, Prado JB, Graham LS, Higgins S, et al. Magnitude and characteristics of acute paraquat- and diquat-related illnesses in the US: 1998–2013. Environ Res. 2016;146:191–9.

    CAS  Article  Google Scholar 

  36. 36.

    Robinson C, Portier CJ, ČAvoŠKi A, Mesnage R, Roger A, Clausing P, et al. Achieving a High Level of Protection from Pesticides in Europe: Problems with the Current Risk Assessment Procedure and Solutions. Eur J Risk Regul. 2020;11(3):450–80.

  37. 37.

    Benbrook C. Shining a Light on Glyphosate-Based Herbicide Hazard, Exposures and Risk: Role of Non-Hodgkin Lymphoma Litigation in the USA. Eur J Risk Regul. 2020;11(3):498–519.

    Article  Google Scholar 

  38. 38.

    Economic Community of West African States. On Harmonization of the Rules Governing Pesticides Registation in ECOWAS Region. 2008;Regulation number C/REG.3/05/2008. Available from: http://ecowas.akomantoso.com/_lang/en/doc/_iri/akn/ecowas/statement/regulation/2008-05-18/C_REG.3_05_2008/eng@/!main. Accessed 14 Dec 2020.

  39. 39.

    Perry MJ, Venners SA, Barr DB, Xu X. Environmental pyrethroid and organophosphorus insecticide exposures and sperm concentration. Reprod Toxicol. 2007;23(1):113–8.

    CAS  Article  Google Scholar 

  40. 40.

    Mullin CA, Frazier M, Frazier JL, Ashcraft S, Simonds R, Vanengelsdorp D, et al. High levels of miticides and agrochemicals in North American apiaries: implications for honey bee health. PLoS One. 2010;5(3):e9754.

  41. 41.

    Mesnage R, Benbrook C, Antoniou MN. Insight into the confusion over surfactant co-formulants in glyphosate-based herbicides. Food Chem Toxicol. 2019;128:137–45.

    CAS  Article  Google Scholar 

  42. 42.

    Acquavella JF, Weber JA, Cullen MR, Cruz OA, Martens MA, Holden LR, et al. Human ocular effects from self-reported exposures to Roundup herbicides. Hum Exp Toxicol. 1999;18(8):479–86.

    CAS  Article  Google Scholar 

  43. 43.

    Mariager TP, Madsen PV, Ebbehoj NE, Schmidt B, Juhl A. Severe adverse effects related to dermal exposure to a glyphosate-surfactant herbicide. Clin Toxicol (Phila). 2013;51(2):111–3.

    CAS  Article  Google Scholar 

  44. 44.

    Hao JY, Jiang T, Huang XJ. Glyphosate-induced Delayed Pyloric Obstruction, Ulcer and Scar Changes. J Coll Physicians Surg Pak. 2020;30(8):868–70.

    Article  Google Scholar 

  45. 45.

    Blondell J. Glyphosate Poisoning Statistics Summary. Environ Protect Agency. 1986; EAB #70724-26. Available from: https://archive.epa.gov/pesticides/chemicalsearch/chemical/foia/web/pdf/103601/103601-230.pdf. Accessed 14 Dec 2020.

  46. 46.

    Mesnage R, Bernay B, Seralini GE. Ethoxylated adjuvants of glyphosate-based herbicides are active principles of human cell toxicity. Toxicology. 2013;313(2–3):122–8.

    CAS  Article  Google Scholar 

  47. 47.

    Manservisi F, Lesseur C, Panzacchi S, Mandrioli D, Falcioni L, Bua L, et al. The Ramazzini Institute 13-week pilot study glyphosate-based herbicides administered at human-equivalent dose to Sprague Dawley rats: effects on development and endocrine system. Environ Health. 2019;18(1):1–16.

  48. 48.

    Syngenta. Expert® Herbicide Label. 2015; EPA Reg. No. 100-1161.

  49. 49.

    Panzacchi S, Mandrioli D, Manservisi F, Bua L, Falcioni L, Spinaci M, et al. The Ramazzini Institute 13-week study on glyphosate-based herbicides at human-equivalent dose in Sprague Dawley rats: study design and first in-life endpoints evaluation. Environ Health. 2018;17(1):52.

    Article  CAS  Google Scholar 

  50. 50.

    Mullin CA, Fine JD, Reynolds RD, Frazier MT. Toxicological Risks of Agrochemical Spray Adjuvants: Organosilicone Surfactants May Not Be Safe. Front Public Health. 2016;4:92.

    Article  Google Scholar 

  51. 51.

    Lee Chao S, Casida JE. Interaction of Imidacloprid Metabolites and Analogs with the Nicotinic Acetylcholine Receptor of Mouse Brain in Relation to Toxicity. Pesticide Biochem and Physiol. 1997;58(1):77–88.

    Article  Google Scholar 

  52. 52.

    EPA. Glyphosate. Draft Human Health Risk Assessment in Support of Registration Review. 2017. Available from: https://www.regulations.gov/document/EPA-HQ-OPP-2009-0361-0068. Accessed 14 Dec 2020.

  53. 53.

    EPA. Paraquat Dichloride: Draft Human Health Risk Assessment in Support of Registration Review. 2019. Available from: https://www.regulations.gov/document/EPA-HQ-OPP-2011-0855-0121. Accessed 14 Dec 2020.

  54. 54.

    EPA. Chlorpyrifos: Revised Human Health Risk Assessment for Registration Review. 2016. Available from: https://www.regulations.gov/document/EPA-HQ-OPP-2015-0653-0454. Accessed 14 Dec 2020.

  55. 55.

    Perry MJ, Marbella A, Layde PM. Compliance with required pesticide-specific protective equipment use. Am J Industrial Med. 2002;41(1):70–3.

    CAS  Article  Google Scholar 

  56. 56.

    EPA. Chlorpyrifos: Revised Human Health Risk Assessment for Registration Review. 2014. Available from: https://www.regulations.gov/document/EPA-HQ-OPP-2008-0850-0195. Accessed 14 Dec 2020.

  57. 57.

    Baldi I, Lebailly P, Jean S, Rougetet L, Dulaurent S, Marquet P. Pesticide contamination of workers in vineyards in France. J Exposure Sci Environ Epidemiol. 2006;16(2):115–24.

    CAS  Article  Google Scholar 

  58. 58.

    Mahaboonpeeti R, Kongtip P, Nankongnab N, Tipayamongkholgul M, Bunngamchairat A, Yoosook W, et al. Evaluation of Dermal Exposure to the Herbicide Alachlor Among Vegetable Farmers in Thailand. Ann Work Expo Health. 2018;62(9):1147–58.

    CAS  Article  Google Scholar 

  59. 59.

    Benbrook C. How did the US EPA and IARC reach diametrically opposed conclusions on the genotoxicity of glyphosate-based herbicides? Environ Sciences Europe. 2019;31(1):1–16.

  60. 60.

    Meuling WJ, Ravensberg LC, Roza L, van Hemmen JJ. Dermal absorption of chlorpyrifos in human volunteers. Int Arch Occup Environ Health. 2005;78(1):44–50.

    CAS  Article  Google Scholar 

  61. 61.

    Moore CA, Wilkinson SC, Blain PG, Dunn M, Aust GA, Williams FM. Percutaneous absorption and distribution of organophosphates (chlorpyrifos and dichlorvos) following dermal exposure and decontamination scenarios using in vitro human skin model. Toxicol Lett. 2014;229(1):66–72.

    CAS  Article  Google Scholar 

  62. 62.

    Geer LA, Cardello N, Dellarco MJ, Leighton TJ, Zendzian RP, Roberts JD, et al. Comparative analysis of passive dosimetry and biomonitoring for assessing chlorpyrifos exposure in pesticide workers. Annals of Occupational Hygeine. 2004;48(8):683–95.

    CAS  Google Scholar 

  63. 63.

    Benbrook C. Enhancements Needed in GE Crop and Food Regulation in the U.S. Front Public Health. 2016;4:59.

  64. 64.

    Organisation for Economic Co-operation and Development (OECD). Pesticides Testing and Assessment. Available from: https://www.oecd.org/env/ehs/pesticides-biocides/pesticides-testing-assessment.htm. Accessed 14 Dec 2020.

  65. 65.

    Bucher JR. Doses in rodent cancer studies: sorting fact from fiction. Drug Metab Rev. 2000;32(2):153–63.

    CAS  Article  Google Scholar 

  66. 66.

    Hill CE, Myers JP, Vandenberg LN. Nonmonotonic Dose-Response Curves Occur in Dose Ranges That Are Relevant to Regulatory Decision-Making. Dose Response. 2018;16(3):1559325818798282.

    Article  Google Scholar 

  67. 67.

    Vandenberg LN, Colborn T, Hayes TB, Heindel JJ, Jacobs DR, Lee DH, et al. Hormones and endocrine-disrupting chemicals: Low-dose effects and nonmonotonic dose responses. Endocrine Rev. 2012;33:378–455.

    CAS  Article  Google Scholar 

  68. 68.

    Grandjean P, Landrigan PJ. Neurobehavioural effects of developmental toxicity. The Lancet Neurol. 2014;13:330–8.

    CAS  Article  Google Scholar 

  69. 69.

    Mesnage R, Teixeira M, Mandrioli D, Falcioni L, Ibragim M, Ducarmon QR, et al. Multi-omics phenotyping of the gut-liver axis reveals metabolic perturbations from a low-dose pesticide mixture in rats. Commun Biol. 2021;4(1):471.

    CAS  Article  Google Scholar 

  70. 70.

    Yauk CL, Buick JK, Williams A, Swartz CD, Recio L, Li HH, et al. Application of the TGx-28.65 transcriptomic biomarker to classify genotoxic and non-genotoxic chemicals in human TK6 cells in the presence of rat liver S9. Environ Mol Mutagen. 2016;57(4):243–60.

  71. 71.

    Ryan N, Chorley B, Tice RR, Judson R, Corton JC. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor alpha Modulation in a Microarray Compendium. Toxicol Sci. 2016;151(1):88–103.

    CAS  Article  Google Scholar 

  72. 72.

    Johnson KJ, Auerbach SS, Costa E. A Rat Liver Transcriptomic Point of Departure Predicts a Prospective Liver or Non-liver Apical Point of Departure. Toxicol Sci. 2020;176(1):86–102.

    CAS  Article  Google Scholar 

  73. 73.

    Huan T, Joehanes R, Schurmann C, Schramm K, Pilling LC, Peters MJ, et al. A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking. Hum Mol Genet. 2016;25(21):4611–23.

    CAS  Google Scholar 

  74. 74.

    Ustinova M, Ansone L, Silamikelis I, Rovite V, Elbere I, Silamikele L, et al. Whole-blood transcriptome profiling reveals signatures of metformin and its therapeutic response. PLoS One. 2020;15(8):e0237400.

  75. 75.

    Suppli MP, Rigbolt KTG, Veidal SS, Heeboll S, Eriksen PL, Demant M, et al. Hepatic transcriptome signatures in patients with varying degrees of nonalcoholic fatty liver disease compared with healthy normal-weight individuals. Am J Physiol Gastrointest Liver Physiol. 2019;316(4):G462–72.

    CAS  Article  Google Scholar 

  76. 76.

    Tsai M, Lo S, Audeh W, Qamar R, Budway R, Levine E, et al. Association of 70-Gene Signature Assay Findings With Physicians' Treatment Guidance for Patients With Early Breast Cancer Classified as Intermediate Risk by the 21-Gene Assay. JAMA Oncol. 2018;4(1):e173470–e.

  77. 77.

    Xu Q, Chen J, Ni S, Tan C, Xu M, Dong L, et al. Pan-cancer transcriptome analysis reveals a gene expression signature for the identification of tumor tissue origin. Mod Pathol. 2016;29(6):546–56.

    Article  CAS  Google Scholar 

  78. 78.

    van Ravenzwaay B, Sperber S, Lemke O, Fabian E, Faulhammer F, Kamp H, et al. Metabolomics as read-across tool: A case study with phenoxy herbicides. Regul Toxicol Pharmacol. 2016;81:288–304.

    Article  CAS  Google Scholar 

  79. 79.

    Ramirez T, Strigun A, Verlohner A, Huener HA, Peter E, Herold M, et al. Prediction of liver toxicity and mode of action using metabolomics in vitro in HepG2 cells. Arch Toxicol. 2018;92(2):893–906.

    CAS  Article  Google Scholar 

  80. 80.

    Bonvallot N, Tremblay-Franco M, Chevrier C, Canlet C, Warembourg C, Cravedi JP, et al. Metabolomics tools for describing complex pesticide exposure in pregnant women in Brittany (France). PLoS One. 2013;8(5):e64433.

  81. 81.

    Saberi Hosnijeh F, Pechlivanis A, Keun HC, Portengen L, Bueno-de-Mesquita HB, Heederik D, et al. Serum metabolomic pertubations among workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Environ Mol Mutagen. 2013;54(7):558–65.

    CAS  Article  Google Scholar 

  82. 82.

    Salihovic S, Ganna A, Fall T, Broeckling CD, Prenni JE, van Bavel B, et al. The metabolic fingerprint of p, p’-DDE and HCB exposure in humans. Environ Int. 2016;88:60–6.

  83. 83.

    Deelen J, Kettunen J, Fischer K, van der Spek A, Trompet S, Kastenmüller G, et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat Commun. 2019;10(1):3346.

    Article  CAS  Google Scholar 

  84. 84.

    Park J, Shin Y, Kim TH, Kim DH, Lee A. Plasma metabolites as possible biomarkers for diagnosis of breast cancer. PLoS One. 2019;14(12):e0225129.

  85. 85.

    Mapstone M, Cheema AK, Fiandaca MS, Zhong X, Mhyre TR, MacArthur LH, et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med. 2014;20(4):415–8.

    CAS  Article  Google Scholar 

  86. 86.

    Balik-Meisner M, Truong L, Scholl EH, La Du JK, Tanguay RL, Reif DM. Elucidating Gene-by-Environment Interactions Associated with Differential Susceptibility to Chemical Exposure. Environ Health Perspect. 2018;126(6):067010.

  87. 87.

    Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–24.

    CAS  Article  Google Scholar 

  88. 88.

    Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, Goodman AL. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature. 2019;570(7762):462–7.

    CAS  Article  Google Scholar 

  89. 89.

    Zheng X, Zhao A, Xie G, Chi Y, Zhao L, Li H, et al. Melamine-induced renal toxicity is mediated by the gut microbiota. Sci Transl Med. 2013;5(172):172ra22.

  90. 90.

    Leshem A, Segal E, Elinav E. The Gut Microbiome and Individual-Specific Responses to Diet. mSystems. 2020;5(5):1–12.

  91. 91.

    Richardson JB, Dancy BCR, Horton CL, Lee YS, Madejczyk MS, Xu ZZ, et al. Exposure to toxic metals triggers unique responses from the rat gut microbiota. Sci Rep. 2018;8(1):6578.

    Article  CAS  Google Scholar 

  92. 92.

    Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med. 2021;27(2):321–32.

    CAS  Article  Google Scholar 

  93. 93.

    Oh TG, Kim SM, Caussy C, Fu T, Guo J, Bassirian S, et al. A Universal Gut-Microbiome-Derived Signature Predicts Cirrhosis. Cell Metab. 2020;32(5):901.

    CAS  Article  Google Scholar 

  94. 94.

    Chamorro-Garcia R, Diaz-Castillo C, Shoucri BM, Kach H, Leavitt R, Shioda T, et al. Ancestral perinatal obesogen exposure results in a transgenerational thrifty phenotype in mice. Nat Commun. 2017;8(1):2012.

    Article  CAS  Google Scholar 

  95. 95.

    Chappell G, Kobets T, O’Brien B, Tretyakova N, Sangaraju D, Kosyk O, et al. Epigenetic events determine tissue-specific toxicity of inhalational exposure to the genotoxic chemical 1,3-butadiene in male C57BL/6J mice. Toxicol Sci. 2014;142(2):375–84.

  96. 96.

    Joubert BR, Felix JF, Yousefi P, Bakulski KM, Just AC, Breton C, et al. DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis. Am J Hum Genet. 2016;98(4):680–96.

    CAS  Article  Google Scholar 

  97. 97.

    Breton CV, Yao J, Millstein J, Gao L, Siegmund KD, Mack W, et al. Prenatal Air Pollution Exposures, DNA Methyl Transferase Genotypes, and Associations with Newborn LINE1 and Alu Methylation and Childhood Blood Pressure and Carotid Intima-Media Thickness in the Children’s Health Study. Environ Health Perspect. 2016;124(12):1905–12.

  98. 98.

    Colicino E, Marioni R, Ward-Caviness C, Gondalia R, Guan W, Chen B, et al. Blood DNA methylation sites predict death risk in a longitudinal study of 12, 300 individuals. Aging (Albany NY). 2020;12(14):14092–124.

    CAS  Article  Google Scholar 

  99. 99.

    Onwuka JU, Li D, Liu Y, Huang H, Xu J, Liu Y, et al. A panel of DNA methylation signature from peripheral blood may predict colorectal cancer susceptibility. BMC Cancer. 2020;20(1):692.

    CAS  Article  Google Scholar 

  100. 100.

    Rietjens I, Vervoort J, Maslowska-Gornicz A, Van den Brink N, Beekmann K. Use of proteomics to detect sex-related differences in effects of toxicants: implications for using proteomics in toxicology. Crit Rev Toxicol. 2018;48(8):666–81.

    CAS  Article  Google Scholar 

  101. 101.

    Meneses-Lorente G, Guest PC, Lawrence J, Muniappa N, Knowles MR, Skynner HA, et al. A proteomic investigation of drug-induced steatosis in rat liver. Chem Res Toxicol. 2004;17(5):605–12.

    CAS  Article  Google Scholar 

  102. 102.

    Guipaud O. Serum and plasma proteomics and its possible use as detector and predictor of radiation diseases. Adv Exp Med Biol. 2013;990:61–86.

    CAS  Article  Google Scholar 

  103. 103.

    Kossowska B, Dudka I, Bugla-Płoskońska G, Szymańska-Chabowska A, Doroszkiewicz W, Gancarz R, et al. Proteomic analysis of serum of workers occupationally exposed to arsenic, cadmium, and lead for biomarker research: a preliminary study. Sci Total Environ. 2010;408(22):5317–24.

    CAS  Article  Google Scholar 

  104. 104.

    Tanaka T, Basisty N, Fantoni G, Candia J, Moore AZ, Biancotto A, et al. Plasma proteomic biomarker signature of age predicts health and life span. Elife. 2020;9:1–12.

  105. 105.

    Zhang MZ, Sun ZC, Fu XR, Nan FF, Fan QX, Wu XA, et al. Analysis of serum proteome profiles of non-Hodgkin lymphoma for biomarker identification. J Proteomics. 2009;72(6):952–9.

    CAS  Article  Google Scholar 

  106. 106.

    Shihana F, Wong WKM, Joglekar MV, Mohamed F, Gawarammana IB, Isbister GK, et al. Urinary microRNAs as non-invasive biomarkers for toxic acute kidney injury in humans. Sci Rep. 2021;11(1):9165.

    CAS  Article  Google Scholar 

  107. 107.

    Weldon BA, Shubin SP, Smith MN, Workman T, Artemenko A, Griffith WC, et al. Urinary microRNAs as potential biomarkers of pesticide exposure. Toxicol Appl Pharmacol. 2016;312:19–25.

    CAS  Article  Google Scholar 

  108. 108.

    Aloizou AM, Siokas V, Sapouni EM, Sita N, Liampas I, Brotis AG, et al. Parkinson's disease and pesticides: Are microRNAs the missing link? Sci Total Environ. 2020;744:140591.

  109. 109.

    Mesnage R, Ibragim M, Mandrioli D, Falcioni L, Belpoggi F, Brandsma I, et al. In-depth comparative toxicogenomics of glyphosate and Roundup herbicides: histopathology, transcriptome and epigenome signatures, and DNA damage. 2021. In Press.

  110. 110.

    EPA. Good Laboratory Practices Standards Compliance Monitoring Program. Available from: https://www.epa.gov/compliance/good-laboratory-practices-standards-compliance-monitoring-program. Accessed 14 Dec 2020.

  111. 111.

    Zoeller RT, Vandenberg LN. Assessing dose-response relationships for endocrine disrupting chemicals (EDCs): a focus on non-monotonicity. Environ Health. 2015;14:42.

    Article  CAS  Google Scholar 

  112. 112.

    Mandrioli D, Silbergeld EK. Evidence from Toxicology: The Most Essential Science for Prevention. Environ Health Perspect. 2016;124(1):6–11.

    Article  Google Scholar 

  113. 113.

    EPA. Glyphosate Issue Paper: Evaluation of Carcinogenic Potential. 2016. Available from: https://www.epa.gov/sites/default/files/2016-09/documents/glyphosate_issue_paper_evaluation_of_carcincogenic_potential.pdf. Accessed 14 Dec 2020.

  114. 114.

    IARC. IARC Monographs on the evaluation of carcinogenic risks to humans - Volume 112: Some organophosphate insecticides and herbicides. 2017. Available from: https://monographs.iarc.who.int/wp-content/uploads/2018/07/mono112.pdf. Accessed 14 Dec 2020.

  115. 115.

    Walmsley RM, Billinton N. How accurate is in vitro prediction of carcinogenicity? Br J Pharmacol. 2011;162(6):1250–8.

    CAS  Article  Google Scholar 

  116. 116.

    Benbrook CM. How did the US EPA and IARC reach diametrically opposed conclusions on the genotoxicity of glyphosate-based herbicides? Environ Sci Eur. 2019;31(1):2.

    Article  CAS  Google Scholar 

  117. 117.

    Sanchez MC, Alvarez Sedo C, Chaufan GR, Romanato M, Da Cuna R, Lo Nostro F, et al. In vitro effects of endosulfan-based insecticides on mammalian sperm. Toxicol Res. 2018;7(1):117–26.

    CAS  Article  Google Scholar 

  118. 118.

    Ingaramo PI, Guerrero Schimpf M, Milesi MM, Luque EH, Varayoud J. Acute uterine effects and long-term reproductive alterations in postnatally exposed female rats to a mixture of commercial formulations of endosulfan and glyphosate. Food Chem Toxicol. 2019;134:110832.

  119. 119.

    Perry MJ, Venners SA, Chen X, Liu X, Tang G, Xing H, et al. Organophosphorous pesticide exposures and sperm quality. Reprod Toxicol. 2011;31(1):75–9.

    CAS  Article  Google Scholar 

  120. 120.

    Rauh Va, Garfinkel R, Perera FP, Andrews HF, Hoepner L, Barr DB, et al. Impact of prenatal chlorpyrifos exposure on neurodevelopment in the first 3 years of life among inner-city children. Pediatrics. 2006;118:e1845–e59.

  121. 121.

    Kortenkamp A, Faust M. Regulate to reduce chemical mixture risk. Science. 2018;361(6399):224–6.

    CAS  Article  Google Scholar 

  122. 122.

    CDC. Fourth National Report on Human Exposure to Environmental Chemicals Updated Tables, January 2019, Volume One. 2019. Available at: https://www.cdc.gov/exposurereport/pdf/FourthReport_UpdatedTables_Volume1_Jan2019-508.pdf. Accessed 14 Dec 2020.

  123. 123.

    CDC. Fourth National Report on Human Exposure to Environmental Chemicals: Updated Tables, January 2019, Volume Two. 2019. Available at: https://www.cdc.gov/exposurereport/pdf/FourthReport_UpdatedTables_Volume2_Jan2019-508.pdf. Accessed 14 Dec 2020.

  124. 124.

    Calafat AM. The U.S. National Health and Nutrition Examination Survey and human exposure to environmental chemicals. Int J Hygiene Environ Health. 2012;215(2):99–101.

  125. 125.

    Nickerson K. Environmental Contaminants in Breast Milk. J Midwifery Womens Health. 2006;51(1):26–34.

    Article  Google Scholar 

  126. 126.

    O’Leary JA, Davies JE, Edmundson WF, Reich GA. Transplacental passage of pesticides. Am J Obstetrics Gynecol. 1970;107(1):65–8.

  127. 127.

    Li AJ, Kannan K. Urinary concentrations and profiles of organophosphate and pyrethroid pesticide metabolites and phenoxyacid herbicides in populations in eight countries. Environ Int. 2018;121(Pt 2):1148–54.

    CAS  Article  Google Scholar 

  128. 128.

    Apel P, Rousselle C, Lange R, Sissoko F, Kolossa-Gehring M, Ougier E. Human biomonitoring initiative (HBM4EU) - Strategy to derive human biomonitoring guidance values (HBM-GVs) for health risk assessment. Int J Hygiene Environ Health. 2020;230:113622.

  129. 129.

    Gillezeau C, Lieberman-Cribbin W, Taioli E. Update on human exposure to glyphosate, with a complete review of exposure in children. Environ Health. 2020;19(1):115.

    CAS  Article  Google Scholar 

  130. 130.

    Bopp S, Martin-Olmedo P. Data for characterizing human exposure to chemicals and their contribution to human diseases: IPCHEM, HBM4EU. Eur J Public Health. 2020;30(Supplement_5).

  131. 131.

    Curl CL, Spivak M, Phinney R, Montrose L. Synthetic Pesticides and Health in Vulnerable Populations: Agricultural Workers. Curr Environ Health Rep. 2020;7(1):13–29.

    CAS  Article  Google Scholar 

  132. 132.

    Costa LG, Cole TB, Furlong CE. Gene-Environment Interactions: Paraoxonase (PON1) and Sensitivity to Organophosphate Toxicity. Lab Med. 2006;37(2):109–13.

    Article  Google Scholar 

  133. 133.

    Costa LG, Richter RJ, Li WF, Cole T, Guizzetti M, Furlong CE. Paraoxonase (PON 1) as a biomarker of susceptibility for organophosphate toxicity. Biomarkers. 2003;8(1):1–12.

    CAS  Article  Google Scholar 

  134. 134.

    Eskenazi B, Huen K, Marks A, Harley KG, Bradman A, Barr DB, et al. PON1 and neurodevelopment in children from the chamacos study exposed to organophosphate pesticides in utero. Environ Health Perspect. 2010;118:1775–81.

    CAS  Article  Google Scholar 

  135. 135.

    Engel SM, Wetmur J, Chen J, Zhu C, Barr DB, Canfield RL, et al. Prenatal exposure to organophosphates, paraoxonase 1, and cognitive development in childhood. Environ Health Perspect. 2011;119:1182–8.

    Article  Google Scholar 

  136. 136.

    Berkowitz GS, Wetmur JG, Birman-Deych E, Obel J, Lapinski RH, Godbold JH, et al. In utero pesticide exposure, maternal paraoxonase activity, and head circumference. Environ Health Perspect. 2004;112(3):388–91.

    Article  Google Scholar 

  137. 137.

    Tsatsakis A, Tyshko NV, Goumenou M, Shestakova SI, Sadykova EvO, Zhminchenko VM, et al. Detrimental effects of 6 months exposure to very low doses of a mixture of six pesticides associated with chronic vitamin deficiency on rats. Food Chem Toxicol. 2021;152:112188.

  138. 138.

    Tosi S, Nieh JC, Sgolastra F, Cabbri R, Medrzycki P. Neonicotinoid pesticides and nutritional stress synergistically reduce survival in honey bees. Proc Biol Sci. 2017;284(1869):1–9.

  139. 139.

    Ledda C, Cannizzaro E, Cina D, Filetti V, Vitale E, Paravizzini G, et al. Oxidative stress and DNA damage in agricultural workers after exposure to pesticides. J Occup Med Toxicol. 2021;16(1):1.

    CAS  Article  Google Scholar 

  140. 140.

    Linhart C, Panzacchi S, Belpoggi F, Clausing P, Zaller JG, Hertoge K. Year-round pesticide contamination of public sites near intensively managed agricultural areas in South Tyrol. Environ Sci Eur. 2021;33(1).

  141. 141.

    Rutz R, Krieger RI. Exposure to pesticide mixer/loaders and applicators in California. Rev Environ Contam Toxicol. 1992;129:121–39.

    CAS  Google Scholar 

  142. 142.

    Boone MD, Bishop CA, Boswell LA, Brodman RD, Burger J, Davidson C, et al. Pesticide Regulation amid the Influence of Industry. Bioscience. 2014;64(10):917–22.

    Article  Google Scholar 

  143. 143.

    Benbrook C. Trends in glyphosate herbicide use in the United States and globally. Environ Sci Eur. 2016;28(1):3.

    Article  CAS  Google Scholar 

  144. 144.

    Givens WA, Shaw DR, Johnson WG, Weller SC, Young BG, Wilson RG, et al. A Grower Survey of Herbicide Use Patterns in Glyphosate-Resistant Cropping Systems. Weed Technol. 2017;23(1):156–61.

    Article  CAS  Google Scholar 

  145. 145.

    HBM4EU. Second list of HBM4EU priority substances and Chemical Substance Group Leaders for 2019–2021. 2018. Report No.: D 4.5. Available at: https://www.hbm4eu.eu/wp-content/uploads/2018/09/Deliverable-4.5-Second-list-of-HBM4EU-priority-substances-and-Chemical-Substance-Group-Leaders-for-2019-2021.pdf. Accessed 14 Dec 2020.

  146. 146.

    Mesnage R, Mazzacuva F, Caldwell A, Halket J, Antoniou MN. Urinary excretion of herbicide co-formulants after oral exposure to roundup MON 52276 in rats. Environ Res. 2021;197:111103.

  147. 147.

    Schütze A, Morales P, Vidal M, Calafat AM, Ospina M. Quantification of Glyphosate and Other Organophosphorus Compounds in Human Urine via Ion Chromatography Isotope Dilution Tandem Mass Spectrometry. Chemosphere. 2021.

  148. 148.

    Baker SE, Serafim AB, Morales-Agudelo P, Vidal M, Calafat AM, Ospina M. Quantification of DEET and neonicotinoid pesticide biomarkers in human urine by online solid-phase extraction high-performance liquid chromatography-tandem mass spectrometry. Anal Bioanalytical Chem. 2019;411(3):669–78.

    CAS  Article  Google Scholar 

  149. 149.

    Hanahan D, Weinberg RA. The Hallmarks of Cancer. Cell. 2000;100:57–70.

    CAS  Article  Google Scholar 

  150. 150.

    Middelkamp S, van Tol HTA, Spierings DCJ, Boymans S, Guryev V, Roelen BAJ, et al. Sperm DNA damage causes genomic instability in early embryonic development. Sci Adv. 2020;6(16):eaaz7602.

  151. 151.

    Mnif W, Hassine AI, Bouaziz A, Bartegi A, Thomas O, Roig B. Effect of endocrine disruptor pesticides: a review. Int J Environ Res Public Health. 2011;8(6):2265–303.

    CAS  Article  Google Scholar 

  152. 152.

    Lee KM, Park SY, Lee K, Oh SS, Ko SB. Pesticide metabolite and oxidative stress in male farmers exposed to pesticide. Ann Occup Environ Med. 2017;29:5.

    Article  Google Scholar 

  153. 153.

    Ndonwi EN, Atogho-Tiedeu B, Lontchi-Yimagou E, Shinkafi TS, Nanfa D, Balti EV, et al. Gestational Exposure to Pesticides Induces Oxidative Stress and Lipid Peroxidation in Offspring that Persist at Adult Age in an Animal Model. Toxicol Res. 2019;35(3):241–8.

    CAS  Article  Google Scholar 

  154. 154.

    Kirkland D, Aardema M, Henderson L, Muller L. Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens I. Sensitivity, specificity and relative predictivity. Mutat Res. 2005;584(1–2):1–256.

  155. 155.

    Brandsma I, Moelijker N, Derr R, Hendriks G. Aneugen Versus Clastogen Evaluation and Oxidative Stress-Related Mode-of-Action Assessment of Genotoxic Compounds Using the ToxTracker Reporter Assay. Toxicol Sci. 2020;177(1):202–13.

    CAS  Article  Google Scholar 

  156. 156.

    Hendriks G, Derr RS, Misovic B, Morolli B, Calleja FM, Vrieling H. The Extended ToxTracker Assay Discriminates Between Induction of DNA Damage, Oxidative Stress, and Protein Misfolding. Toxicol Sci. 2016;150(1):190–203.

    CAS  Article  Google Scholar 

  157. 157.

    Lukowicz C, Ellero-Simatos S, Regnier M, Polizzi A, Lasserre F, Montagner A, et al. Metabolic Effects of a Chronic Dietary Exposure to a Low-Dose Pesticide Cocktail in Mice: Sexual Dimorphism and Role of the Constitutive Androstane Receptor. Environ Health Perspect. 2018;126(6):067007.

  158. 158.

    Rueda-Ruzafa L, Cruz F, Roman P, Cardona D. Gut microbiota and neurological effects of glyphosate. Neurotoxicology. 2019;75:1–8.

    CAS  Article  Google Scholar 

  159. 159.

    Tang J, Wang W, Jiang Y, Chu W. Diazinon exposure produces histological damage, oxidative stress, immune disorders and gut microbiota dysbiosis in crucian carp (Carassius auratus gibelio). Environ Pollut. 2021;269:116129.

  160. 160.

    Lozano VL, Defarge N, Rocque LM, Mesnage R, Hennequin D, Cassier R, et al. Sex-dependent impact of Roundup on the rat gut microbiome. Toxicol Rep. 2018;5:96–107.

    CAS  Article  Google Scholar 

  161. 161.

    Nicholson JK, Holmes E, Kinross JM, Darzi AW, Takats Z, Lindon JC. Metabolic phenotyping in clinical and surgical environments. Nature. 2012;491(7424):384–92.

    CAS  Article  Google Scholar 

  162. 162.

    Kinross JM, Holmes E, Darzi AW, Nicholson JK. Metabolic phenotyping for monitoring surgical patients. Lancet. 2011;377(9780):1817–9.

    Article  Google Scholar 

  163. 163.

    Viant MR, Ebbels TMD, Beger RD, Ekman DR, Epps DJT, Kamp H, et al. Use cases, best practice and reporting standards for metabolomics in regulatory toxicology. Nat Commun. 2019;10(1):3041.

    Article  CAS  Google Scholar 

  164. 164.

    Gao B, Chi L, Tu P, Gao N, Lu K. The carbamate aldicarb altered the gut microbiome, metabolome and lipidome of C57BL/6J mice. Chem Res Toxicol. 2018;32(1):67–79.

  165. 165.

    Zhao Y, Zhang Y, Wang G, Han R, Xie X. Effects of chlorpyrifos on the gut microbiome and urine metabolome in mouse (Mus musculus). Chemosphere. 2016;153:287–93.

    CAS  Article  Google Scholar 

  166. 166.

    Mesnage R, Teixeira M, Mandrioli D, Falcioni L, Ducarmon QR, Zwittink RD, et al. Multi-omics phenotyping of the gut-liver axis allows health risk predictability from in vivo subchronic toxicity tests of a low-dose pesticide mixture. bioRxiv. 2020:2020.08.25.266528.

  167. 167.

    Masarone M, Rosato V, Dallio M, Gravina AG, Aglitti A, Loguercio C, et al. Role of Oxidative Stress in Pathophysiology of Nonalcoholic Fatty Liver Disease. Oxid Med Cell Longev. 2018;2018:9547613.

    Article  CAS  Google Scholar 

  168. 168.

    Duhig K, Chappell LC, Shennan AH. Oxidative stress in pregnancy and reproduction. Obstet Med. 2016;9(3):113–6.

    Article  Google Scholar 

  169. 169.

    Manservisi F, Marquillas CB, Buscaroli A, Huff J, Lauriola M, Mandrioli D, et al. An Integrated Experimental Design for the Assessment of Multiple Toxicological End Points in Rat Bioassays. Environ Health Perspect. 2017;125(3):289–95.

    CAS  Article  Google Scholar 

  170. 170.

    Angrish MM, Kaiser JP, McQueen CA, Chorley BN. Tipping the Balance: Hepatotoxicity and the 4 Apical Key Events of Hepatic Steatosis. Toxicol Sci. 2016;150(2):261–8.

    CAS  Article  Google Scholar 

  171. 171.

    Luechtefeld T, Marsh D, Rowlands C, Hartung T. Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility. Toxicol Sci. 2018;165(1):198–212.

    CAS  Article  Google Scholar 

  172. 172.

    Landrigan PJ, Belpoggi F. The need for independent research on the health effects of glyphosate-based herbicides. Environ Health. 2018;17(1):51.

    Article  Google Scholar 

  173. 173.

    Mao Q, Manservisi F, Panzacchi S, Mandrioli D, Menghetti I, Vornoli A, et al. The Ramazzini Institute 13-week pilot study on glyphosate and Roundup administered at human-equivalent dose to Sprague Dawley rats: effects on the microbiome. Environ Health. 2018;17(1):50.

    Article  CAS  Google Scholar 

  174. 174.

    Benbrook C, Groth E III, Halloran J, Hansen M, Marquartd S. Pest Management at the Crossroads. Am J Alternative Agriculture. 1997;12(1):45–6.

    Google Scholar 

  175. 175.

    Liebman M, Gallandt E. Many Little Hammers: Ecological Management of Crop-Weed Interactions. In: Jackson LE, editor. Ecology and Agriculture: Academic Press; 1997. p. 291–343.

  176. 176.

    Epstein L, Zhang M. The Impact of Integrated Pest Management Programs on Pesticide Use in California, USA. In: Peshin R, Pimentel D, editors. Integrated Pest Management: Springer 2014. p. 173–200.

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Acknowledgements

The authors thank Rachel Benbrook, Operations Manager, Heartland Health Research Alliance for assistance with references and preparing the manuscript for submission. We thank and acknowledge the many people who have identified ways to enhance pesticide risk assessment and regulation. The reviewers provided appropriate and helpful suggestions and we appreciate their contributions to the improvement of this paper.

Funding

Support for the writing of this manuscript to CMB and RM was provided by the Heartland Health Research Alliance (www.hh-ra.org).

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CMB and RM produced the outline and early drafts. MP, PW, PL, DM, FB, MA wrote and/or revised sections. All co-authors contributed to the selection of topics and needed actions. All authors read and approved the final manuscript.

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Correspondence to Charles Benbrook.

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Competing interests

CMB has served as an expert witness in litigation involving pesticides. He has also participated in litigation involving the labeling of genetically engineered foods. RM has served as a consultant on pesticide risk-assessment issues as part of litigation in the US. MJ Perry is serving as an expert witness in pesticide litigation. FB, PJL, MP, DM, MNA, and PW declare no competing interests.

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Benbrook, C., Perry, M.J., Belpoggi, F. et al. Commentary: Novel strategies and new tools to curtail the health effects of pesticides. Environ Health 20, 87 (2021). https://doi.org/10.1186/s12940-021-00773-4

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