Threshold Dose-response Model Research Articles

Dose response and risk assessment: Evolutionary foundations

The present paper indicates that the origin of the LNT concept for ionizing radiation was based on insufficient understanding of evolution, which precluded the possibility of repair of gene mutation. The denial of such repair processes had important implications, leading to a belief in a linear dose response and thus in Hermann J Muller's proclamation of a Proportionality Rule for ionizing radiation. The paper documents how the lack of repair concept dominated the radiation geneticist community to the 1960s leading to the establishment of the linear no threshold dose response (LNT) model for radiation and chemical reproductive and cancer risk assessment. Research from the late 1950s onward would establish the occurrence, generality, and efficacy of genetic and cellular repair processes. While the assumption of a lack of gene mutation repair was wrong, Muller was correct that dose-response concepts need to be founded on mechanistic understandings of evolution. Such mechanisms require the integration of constitutive and inducible adaptive and repair mechanisms that operate in the low-dose zone. This perspective reflects the comment of Dobzhansky (1973) that “nothing makes sense in biology except in light of evolution.” While this is a powerful scientific dictum, it assumes a correct understanding of evolution, something that the origins of the LNT dose response lacked. Such modern mechanistic repair developments reveal that the historical foundations of LNT were flawed from the start. Nonetheless, they have been carried forward to the present time, principally by environmental health regulatory agencies that decoupled risk assessment policy from a sound evolutionary foundation.

Disinfectant-induced hormesis: An unknown environmental threat of the application of disinfectants to prevent SARS-CoV-2 infection during the COVID-19 pandemic?

Massive additional quantities of disinfectants have been applied during the COVID-19 pandemic as infection preventive and control measures. While the application of disinfectants plays a key role in preventing the spread of SARS-CoV-2 infection, the effects of disinfectants applied during the ongoing pandemic on non-target organisms remain unknown. Here we collated evidence from multiple studies showing that chemicals used for major disinfectant products can induce hormesis in various organisms, such as plants, animal cells, and microorganisms, when applied singly or in mixtures, suggesting potential ecological risks at sub-threshold doses that are normally considered safe. Among other effects, sub-threshold doses of disinfectant chemicals can enhance the proliferation and pathogenicity of pathogenic microbes, enhancing the development and spread of drug resistance. We opine that hormesis should be considered when evaluating the effects and risks of such disinfectants, especially since the linear-no-threshold (LNT) and threshold dose-response models cannot identify or predict their effects.

Evidence of a Dose-Rate Threshold for Life Span Reduction of Dogs Exposed Lifelong to γ-Radiation.

Our return to a study on dogs exposed lifelong to cobalt-60 γ-radiation was prompted by a comment that data in dog studies have large statistical errors due to the small number of dogs. We located an earlier article on the same study that had a better mortality curve for the dogs in each dose-rate group. The median life span of the dogs in each group was tabulated, and the standard error of each was calculated. No statistically significant shortening of median life span was observed for the lowest dose-rate group at any reasonable significance level (P value: .005-.05), whereas for dogs with higher irradiation rates, life span shortening was statistically significant at highest reasonable significance level (P value: .005). The results were entered on a graph of life span versus dose rate, assuming a threshold dose–response model. The fitted line indicates that the dose-rate threshold for γ-radiation induced life span reduction is about 600 mGy per year, which is close to the value we found previously. Making allowance for the calculated standard errors, we conclude that this threshold is in the range from 300 to 1100 mGy per year. This evidence is relevant for emergency measures actions (evacuation of residents) and for nuclear waste management.

Radiation dose response estimation with emphasis on low dose range using restricted cubic splines: application to all solid cancer mortality data, 1950-2003, in atomic bomb survivors.

Using the all solid cancer mortality data set of the Life Span Study (LSS) cohort from 1950 to 2003 (LSS Report 14) data among atomic bomb survivors, excess relative risk (ERR) statistical analyses were performed using the second degree polynomial and the threshold and restricted cubic spline (RCS) dose response models. For the RCS models with 3 to 7 knots of equally spaced percentiles with margins in the dose range greater than 50 mGy, the dose response was assumed to be linear at less than 70 to 90 mGy. Due to the skewed dose distribution of atomic bomb survivors, the current knot system for the RCS analysis results in a detailed depiction of the dose response as less than approximately 0.5 Gy. The 6 knot RCS models for the all-solid cancer mortality dose response of the whole dose or less than 2 Gy were selected with the AIC model selection criterion and fit significantly better (p < 0.05) than the linear (L) model. The usual RCS includes the L-global model but not the quadratic (Q) nor linear-quadratic (LQ) global models. The authors extended the RCS to include L or LQ global models by putting L or LQ constraints on the cubic spline in the lower and upper tails, and the best RCS model selected with AIC criterion was the usual RCS with L-constraints in both the lower and upper tails. The selected RCS had a linear dose-response model in the lower dose range (i.e., < 0.2-0.3 Gy) and was compatible with the linear no-threshold (LNT) model in this dose range. The proposed method is also useful in describing the dose response of a specific cancer or non-cancer disease incidence/mortality.

Optimizing prediction of binge eating episodes: a comparison approach to test alternative conceptualizations of the affect regulation model.

BackgroundAlthough a wealth of studies have tested the link between negative mood states and likelihood of a subsequent binge eating episode, the assumption that this relationship follows a typical linear dose–response pattern (i.e., that risk of a binge episode increases in proportion to level of negative mood) has not been challenged. The present study demonstrates the applicability of an alternative, non-linear conceptualization of this relationship, in which the strength of association between negative mood and probability of a binge episode increases above a threshold value for the mood variable relative to the slope below this threshold value (threshold dose response model).MethodsA sample of 93 women aged 18 to 40 completed an online survey at random intervals seven times per day for a period of one week. Participants self-reported their current mood state and whether they had recently engaged in an eating episode symptomatic of a binge.ResultsAs hypothesized, the threshold approach was a better predictor than the linear dose–response modeling of likelihood of a binge episode. The superiority of the threshold approach was found even at low levels of negative mood (3 out of 10, with higher scores reflecting more negative mood). Additionally, severity of negative mood beyond this threshold value appears to be useful for predicting time to onset of a binge episode.ConclusionsPresent findings suggest that simple dose–response formulations for the association between negative mood and onset of binge episodes miss vital aspects of this relationship. Most notably, the impact of mood on binge eating appears to depend on whether a threshold value of negative mood has been breached, and elevation in mood beyond this point may be useful for clinicians and researchers to identify time to onset.

Optimizing prediction of binge eating episodes: a comparison approach to test alternative conceptualizations of the affect regulation model

BackgroundAlthough a wealth of studies have tested the link between negative mood states and likelihood of a subsequent binge eating episode, the assumption that this relationship follows a typical linear dose–response pattern (i.e., that risk of a binge episode increases in proportion to level of negative mood) has not been challenged. The present study demonstrates the applicability of an alternative, non-linear conceptualization of this relationship, in which the strength of association between negative mood and probability of a binge episode increases above a threshold value for the mood variable relative to the slope below this threshold value (threshold dose response model).MethodsA sample of 93 women aged 18 to 40 completed an online survey at random intervals seven times per day for a period of one week. Participants self-reported their current mood state and whether they had recently engaged in an eating episode symptomatic of a binge.ResultsAs hypothesized, the threshold approach was a better predictor than the linear dose–response modeling of likelihood of a binge episode. The superiority of the threshold approach was found even at low levels of negative mood (3 out of 10, with higher scores reflecting more negative mood). Additionally, severity of negative mood beyond this threshold value appears to be useful for predicting time to onset of a binge episode.ConclusionsPresent findings suggest that simple dose–response formulations for the association between negative mood and onset of binge episodes miss vital aspects of this relationship. Most notably, the impact of mood on binge eating appears to depend on whether a threshold value of negative mood has been breached, and elevation in mood beyond this point may be useful for clinicians and researchers to identify time to onset.

The Hormetic Dose-Response Model: The Future Default Dose-Response Model in the Human Health Risk Assessment Paradigm

Regulatory bodies such as the Environmental Protection Agency (EPA) use the dose-response relationship as part of the risk assessment process to a particular environmental exposure. The dose-response relationship is important in determining how a government implements environmental risk assessment methods for protecting human and animal beings form food, water, air, and soil threats. The default dose-response for carinogens is the linear no threshold (LNT) dose-response model. However, a growing number of research studies illustrate the complexity of biological processes and mechanisms involved in mutagenesis, which are not easily explained by the LNT model. Since the LNT model sees any dose to be potentially carcinogenic, there is a growing interst in nonlinear dose-response relationships to explain certain phenomenon, such as the beneficial effects induced by low doses of particular chemicals that are harmful at higher doses. Hormesis, in particular, is a nonlinear dose-relationship model that is increasingly considered as a possible replacement to the LNT model. This report addresses whether or not the hormesis model is a viable replacement to the LNT model.

EPA's Stage 2 Disinfection Byproducts Rules (DBPR) and Northern Kentucky Water: An Economic and Scientific Review.

Implementation of EPA's Stage 2 Disinfection Byproducts Rules (DBPR) in Northern Kentucky will cause a water rate increase of over 25%. Hence a review was undertaken, considering both economics and science in the context of President Obama's 2009 scientific integrity directive. The rules purport to avoid up to 0.49% of new bladder cancers by reducing the levels of DBPs in drinking water - a benefit so small that failure to implement will not cause unreasonable risk to health (URTH). It suggests at most one Northern Kentucky death avoided over 17 years for a cost of $136,000,000 ($1700 per household). Even this small benefit is probably overstated. EPA finds no "causal link" between DBPs and bladder cancer, and EPA acknowledges problems with the epidemiological data used in their calculation: the data appear contradictory and inconsistent, may be skewed toward "positive" results, and suggest different cancer sites than animal studies. Two similar international agencies disagree with EPA's conclusions. The science is based on the Linear No Threshold (LNT) dose response model for DBPs, but this may not be the correct model. 83% of EPA's epidemiological data show a statistical possibility that low levels of DBPs might be beneficial or have no effect.

Food safety and security and the dose-response

Getting the dose-response right is a critical component in the assessment of chemical food safety. Being able to validate dose-response model predictions in the low-dose zone would be a critical aspect of the assurances that correct decisions about food safety and security are made. The present paper examines the history of the threshold dose-response model and how it became incorporated into governmental regulatory and risk assessment activities, including those affecting chemical food safety. The present analysis reveals that the major risk assessment models used by international regulatory agencies, such as the threshold dose-response model, were never validated prior to their acceptance and use by international regulatory agencies. Furthermore, once they were tested both the threshold and linear dose-response models failed to make accurate predictions in the low-dose zone. The only model providing accurate predictions in the low dose zone was the hormetic dose-response, a model not used by regulatory agencies. The present analysis raises important new questions for the risk assessment process in general and that of chemical food security in particular.

Hormesis: Toxicological foundations and role in aging research

The field of toxicology adopted the threshold dose response in the early decades of the 20th century. The model was rapidly incorporated into governmental regulatory assessment procedures and became a central feature of chemical evaluation and assessment. The toxicological community never validated the capacity of this model to make accurate predictions throughout the remainder of the 20th century. A series of recent investigations have demonstrated that the threshold and linear dose response model failed to make accurate predictions in the low dose zone. Such findings demonstrate a profound failure by the toxicology community on the central pillar of its discipline and one with profound public health, medical and economic implications. Ironically, the hormetic dose response, which was rejected by the toxicology community during the early decades of the 20th century, accurately predicted responses in the low dose zone in the same three large-scale validation assessments. Within the past two decades hormetic dose responses have been frequently reported in the experimental biogerontology literature, associated with endpoints associated enhancing healthy aging and longevity. The low dose stimulatory response of the hormetic dose response model represents the quantification of enhanced biological performance in the experimental facilitation of aging quality via multiple endpoints and mechanisms and in the extension of lifespan in such animal models research.

Evidence for hormesis in mutagenicity dose–response relationships

This study assessed the occurrence of hormetic dose responses from three previously published data sets [1–3] with 825 chemicals in three Ames assay tester strains (i.e., TA97, TA98, TA100) with and without the S9 fraction, using a five dose protocol and semi-log dose spacing. Ninety-five (95) (11.5%) chemicals satisfied the multiple a priori entry criteria, with a total of 107 assays. Of the assays satisfying the entry criteria, 61 involved TA100, a strain that detects base-pair substitution mutations. 29.5% (18/61) satisfied the statistical evaluative criteria for hormesis, exceeding that predicted by chance by 4.0-fold (p<0.001). The remaining 46 assays involved TA97 and TA98, strains that detect frameshift mutations. Of these 46 assays, the overall responses for the lowest two doses closely approximated the control response (e.g., 101.77% of the control for TA98; 99.20% for TA97). Only 2.2% (1/46) of the assays satisfied the evaluative criteria for hormesis. In conclusion, these data support a hormetic model for TA100, whereas the responses for TA97 and TA98 are consistent with a threshold dose–response model.

Dose–response and operational thresholds/NOAELs for in vitro mutagenic effects from DNA-reactive mutagens, MMS and MNU

The dose–response relationships for in vitro mutagenicity induced by methylmethanesulfonate (MMS) or methylnitrosourea (MNU) in L5178Y mouse lymphoma (ML) cells were examined. DNA adducts (N7-methylguanine, N7MeG and O 6-methylguanine, O 6MeG) were quantified as biomarkers of exposure. Both endpoints were assessed using 5 replicates/dose (4-h treatment) with MMS or MNU (0.0069–50 μM), or vehicle (1% DMSO). Mutant frequency (MF) (thymidine kinase ( TK) locus) was determined using the soft agar cloning methodology and a 2-day expression period; in addition, microwell and Sequester–Express–Select (SES) methods were used for MMS. Isolated DNA was acid-hydrolyzed, and adducts quantified by LC/ESI-MS/MS, using authentic and internal standards. MF dose–responses were analyzed using several statistical approaches, all of which confirmed that a threshold dose–response model provided the best fit. NOAELs for MF were 10 μM MMS and 0.69 μM MNU, based on ANOVA and Dunnett's test ( p < 0.05). N7MeG adducts were present in all cell samples, including solvent-control cells, and were increased over control levels in cells treated with ≥10 μM MMS or 3.45 μM MNU. O 6MeG levels were only quantifiable at ≥10 μM MNU; O 6MeG was not quantifiable in control or MMS-treated cells at current detection limits. Thus, (1) cells treated with ≤0.69 μM MNU or ≤10 μM MMS did not demonstrate increases in TK − MF, but did demonstrate quantifiable levels of N7MeG adducts; and (2) the levels of N7MeG adducts did not correlate with induced MF, as MNU-treated cells had fewer N7MeG adducts but higher MF compared with MMS-treated cells, for quasi-equimolar doses. Taken together, these results demonstrate operational thresholds, defined as the highest dose for which the response is not significantly (statistically or biologically) distinguishable from the control/background values, for induction of mutations and N7MeG adducts in ML cells treated with MMS or MNU, and a lack of correlation between induced MF and levels of N7MeG adducts.

Quantification of Hormesis in Anticancer-Agent Dose-Responses

Quantitative features of dose responses were analyzed for 2,189 candidate anticancer agents in 13 strains of yeast (Saccharomyces cerevisiae). The agents represent a diverse class of chemical compounds including mustards, other alkylating agents, and antimetabolites, inter alia. Previous analyses have shown that the responses below the toxic threshold were stimulatory and poorly predicted by a threshold dose-response model, while better explained by a hormetic dose-response model. We determined the quantitative features of the hormetic concentration-responses (n = 4,548) using previously published entry and evaluative criteria. The quantitative features that are described are: (1) the width of the concentration range showing stimulation above 10% of the control (mean of 5-fold), (2) the maximum stimulation of the concentration-responses (mean of 27% above the control), and (3) the width from the maximum stimulation to the toxicological threshold (mean of 3.7-fold). These results show that 52.5% of the 2,189 chemicals evaluated display hormetic concentration-responses in at least one of the 13 yeast strains. Many chemicals showed hormesis in multiple strains, and 24 agents showed hormesis in all 13 strains. The data are compared to previously reported quantitative features of hormesis based on published literature.

Summary of Dose-Response Modeling for Developmental Toxicity Studies

Developmental toxicity studies are an important area in the field of toxicology. Endpoints measured on fetuses include weight and indicators of death and malformation. Binary indicator measures are typically summed over the litter and a discrete distribution is assumed to model the number of adversely affected fetuses. Additionally, there is noticeable variation in the litter responses within dose groups that should be taken into account when modeling. Finally, the dose-response pattern in these studies exhibits a threshold effect. The threshold dose-response model is the default model for non-carcinogenic risk assessment, according to the USEPA, and is encouraged by the agency for the use in the risk assessment process. Two statistical models are proposed to estimate dose-response pattern of data from the developmental toxicity study: the threshold model and the spline model. The models were applied to two data sets. The advantages and disadvantages of these models, potential other models, and future research possibilities will be summarized.

Interlitter Response Variability in a Threshold Dose-Response Model

In this article, we extend a previously formulated threshold dose-response model with random litter effects that was applied to a data set from a developmental toxicity study. The dose-response pattern of the data indicates that a threshold dose level may exist. Additionally, there is noticeable variation between the responses across the dose levels. With threshold estimation being critical, the assumed variability structure should adequately model the variation while not taking away from the estimation of the threshold as well as the other parameters directly involved in the dose-response relationship. In the prior formulation, the random effect was modeled assuming identical variation in the interlitter response probabilities across all dose levels, that is, the model had a single parameter to account for the interlitter variability. In this new model, the random effect is modeled as having different response variability across dose levels, that is, multiple interlitter variability parameters. We performed the likelihood ratio test (LRT) to compare our extended model to the previous model. We conducted a simulation study to compare the bias of each model when fit to data generated with the underlying parametric structure of the opposing model. The extended threshold dose-response model with multiple response variation was less biased.

Threshold dose—response model—RIP: 1911 to 2006

This essay represents a serious but fictional obituary of a scientific concept called the Threshold Dose-Response Model, which has long dominated the fields of toxicology and the broader biomedical sciences. Recent evidence indicates that the Threshold Dose-Response Model has long outlived its utility to predict low-dose effects. In fact, so poorly does this model predict low-dose responses that the idea arose that it should receive a symbolic burial recounting its achievements and failings, hence this obituary.

Testing Threshold and Hormesis in a Random Effects Dose-Response Model Applied to Developmental Toxicity Data

Here we describe a random effects threshold dose-response model for clustered binary-response data from developmental toxicity studies. For our model we assume that a hormetic effect occurs in addition to a threshold effect. Therefore, the dose-response curve is based on two components: relationships below the threshold (hormetic u-shaped model) and those above the threshold (logistic model). In the absence of hormesis and threshold effects, the estimation procedure is straightforward. We introduce score tests that are derived from a random effects hormetic-threshold dose-response model. The model and tests are applied to clustered binary data from developmental toxicity studies of animals to test for hormesis and threshold effects. We also compare the score test and likelihood ratio test to test for hormesis and threshold effects in a simulated study.

A new threshold dose-response model including random effects for data from developmental toxicity studies

Usually, in teratological dose finding studies, there are not only threshold effects but also extra variations that cannot be accounted for by the beta-binomial model alone. The beta-binomial model assumes correlation between fetuses in the same litter. The general random effect threshold (RE) model allows the additional variability that arises due to correlation and between litter variability to be modeled, in combination with threshold in the model. The goal of this research was to investigate a threshold dose-response model with random effects (RE) to model the variability that exists between litters of animals in studies of toxic agents. Data from a developmental toxicity study of a toxic agent were analysed, using the proposed RE threshold dose-response model, which is an extension of logit in form. Also, an approximate likelihood function was used to derive parameter estimates from this model, and tests were performed to determine the significance of the model parameters, in particular, the RE parameter. A simulation study was conducted to assess the performance of the RE threshold model in estimating the model parameters.

Regression splines for threshold selection with application to a random-effects logistic dose-response model

A flexible parametric method is proposed for smoothing the dose effect in a general, threshold dose-response model with random effects. This method uses a combination of linear B-splines in which an interior knot, regarded as a free parameter for a piece-wise linear spline, indicates a break point in the dose-response curve. The knot will be estimated and may be interpreted as a threshold value. The proposed methodology is applied to data from a developmental toxicity study conducted by the National Toxicology Program.

The hormetic dose-response model is more common than the threshold model in toxicology.

The threshold dose-response model is widely viewed as the most dominant model in toxicology. The present study was designed to test the validity of the threshold model by assessing the responses of doses below the toxicological NOAEL (no observed adverse effect level) in relationship to the control response (i.e., unexposed group). Nearly 1,800 doses below the NOAEL, from 664 dose-response relationships derived from a previously published database that satisfied a priori entry criteria, were evaluated. While the threshold model predicts a 1:1 ratio of responses "greater than" to "less than" the control response (i.e., a random distribution), a 2.5:1 ratio (i.e., 1171:464) was observed, reflecting 31% more responses above the control value than expected (p < 0.0001). The mean response (calculated as % control response) of doses below the NOAEL was 115.0% +/- 1.5 standard error of the mean (SEM). These findings challenge the long-standing belief in the primacy of the threshold model in toxicology (and other areas of biology involving dose-response relationships) and provide strong support for the hormetic-like biphasic dose-response model characterized by a low-dose stimulation and a high-dose inhibition. These findings may affect numerous aspects of toxicological and biological/biomedical research related to dose-response relationships, including study design, risk assessment, as well as chemotherapeutic strategies.