Benefits and risks of the hormetic effects of dietary isothiocyanates on cancer prevention

The isothiocyanate (ITC) sulforaphane (SFN) was shown at low levels (1-5 µM) to promote cell proliferation to 120-143% of the controls in a number of human cell lines, whilst at high levels (10-40 µM) it inhibited such cell proliferation. Similar dose responses were observed for cell migration, i.e. SFN at 2.5 µM increased cell migration in bladder cancer T24 cells to 128% whilst high levels inhibited cell migration. This hormetic action was also found in an angiogenesis assay where SFN at 2.5 µM promoted endothelial tube formation (118% of the control), whereas at 10-20 µM it caused significant inhibition. The precise mechanism by which SFN influences promotion of cell growth and migration is not known, but probably involves activation of autophagy since an autophagy inhibitor, 3-methyladenine, abolished the effect of SFN on cell migration. Moreover, low doses of SFN offered a protective effect against free-radical mediated cell death, an effect that was enhanced by co-treatment with selenium. These results suggest that SFN may either prevent or promote tumour cell growth depending on the dose and the nature of the target cells. In normal cells, the promotion of cell growth may be of benefit, but in transformed or cancer cells it may be an undesirable risk factor. In summary, ITCs have a biphasic effect on cell growth and migration. The benefits and risks of ITCs are not only determined by the doses, but are affected by interactions with Se and the measured endpoint

Biphasic toxicodynamic features of some antimicrobial agents on microbial growth: a dynamic mathematical model and its implications on hormesis

<p>Abstract</p> <p>Background</p> <p>In the present work, we describe a group of anomalous dose-response (DR) profiles and develop a dynamic model that is able to explain them. Responses were obtained from conventional assays of three antimicrobial agents (nisin, pediocin and phenol) against two microorganisms (<it>Carnobacterium piscicola </it>and <it>Leuconostoc mesenteroides</it>).</p> <p>Results</p> <p>Some of these anomalous profiles show biphasic trends which are usually attributed to hormetic responses. But they can also be explained as the result of the time-course of the response from a microbial population with a bimodal distribution of sensitivity to an effector, and there is evidence suggesting this last origin. In light of interest in the hormetic phenomenology and the possibility of confusing it with other phenomena, especially in the bioassay of complex materials we try to define some criteria which allow us to distinguish between <it>sensu stricto </it>hormesis and biphasic responses due to other causes. Finally, we discuss some problems concerning the metric of the dose in connection with the exposure time, and we make a cautionary suggestion about the use of bacteriocins as antimicrobial agents.</p> <p>Conclusions</p> <p>The mathematical model proposed, which combines the basis of DR theory with microbial growth kinetics, can generate and explain all types of anomalous experimental profiles. These profiles could also be described in a simpler way by means of bisigmoidal equations. Such equations could be successfully used in a microbiology and toxicology context to discriminate between hormesis and other biphasic phenomena.</p

Resolving intertracer inconsistencies in soil ingestion estimation.

In this article we explore sources and magnitude of positive and negative error in soil ingestion estimates for children on a subject-week and trace element basis. Errors varied among trace elements. Yttrium and zirconium displayed predominantly negative error; titanium and vanadium usually displayed positive error. These factors lead to underestimation of soil ingestion estimates by yttrium and zirconium and a large overestimation by vanadium. The most reliable tracers for soil ingestion estimates were aluminum, silicon, and yttrium. However, the most reliable trace element for a specific subject-day (or week) would be the element with the least error during that time period. The present analysis replaces our previous recommendations that zirconium and titanium are the most reliable trace elements in estimating soil ingestion by children. This report identifies limitations in applying the biostatistical model based on data for adults to data for children. The adult-based model used data less susceptible to negative bias and more susceptible to source error (positive bias) for titanium and vanadium than the data for children. These factors contributed significantly to inconsistencies in model predictions of soil ingestion rates for children. Correction for error at the subject-day level provides a foundation for generation of subject-specific daily soil ingestion distributions and for linking behavior to soil ingestion

A Toxicological Basis to Derive a Generic Interspecies Uncertainty Factor.

A new method is proposed to derive the size of the interspecies uncertainty factor (UF) that is toxicologically and statistically based. The method involves quantifying interspecies variation in susceptibility to numerous toxic substances via the use of binary interspecies comparisons that are converted to a 95% UF. This interspecies UF represents an estimate of the upper 95% of the population of 95% prediction intervals (PI) for binary interspecies comparisons within four categories of phylogenetic relatedness (species within genus, genera within family, families within order, orders within class). The 95% interspecies UFs range from a low of 10 for species within genus up to 65 for orders within class. Most mammalian toxicology studies involving mice, rats, cats, dogs, gerbils, and rabbits are orders-within- class categories for human risk assessment and would be provided a 65-fold UF. Larger or smaller interspecies UF values could be selected on the level of protection desired

Dispelling urban myths about default uncertainty factors in chemical risk assessment - Sufficient protection against mixture effects?

© 2013 Martin et al.; licensee BioMed Central LtdThis article has been made available through the Brunel Open Access Publishing Fund.Assessing the detrimental health effects of chemicals requires the extrapolation of experimental data in animals to human populations. This is achieved by applying a default uncertainty factor of 100 to doses not found to be associated with observable effects in laboratory animals. It is commonly assumed that the toxicokinetic and toxicodynamic sub-components of this default uncertainty factor represent worst-case scenarios and that the multiplication of those components yields conservative estimates of safe levels for humans. It is sometimes claimed that this conservatism also offers adequate protection from mixture effects. By analysing the evolution of uncertainty factors from a historical perspective, we expose that the default factor and its sub-components are intended to represent adequate rather than worst-case scenarios. The intention of using assessment factors for mixture effects was abandoned thirty years ago. It is also often ignored that the conservatism (or otherwise) of uncertainty factors can only be considered in relation to a defined level of protection. A protection equivalent to an effect magnitude of 0.001-0.0001% over background incidence is generally considered acceptable. However, it is impossible to say whether this level of protection is in fact realised with the tolerable doses that are derived by employing uncertainty factors. Accordingly, it is difficult to assess whether uncertainty factors overestimate or underestimate the sensitivity differences in human populations. It is also often not appreciated that the outcome of probabilistic approaches to the multiplication of sub-factors is dependent on the choice of probability distributions. Therefore, the idea that default uncertainty factors are overly conservative worst-case scenarios which can account both for the lack of statistical power in animal experiments and protect against potential mixture effects is ill-founded. We contend that precautionary regulation should provide an incentive to generate better data and recommend adopting a pragmatic, but scientifically better founded approach to mixture risk assessment. © 2013 Martin et al.; licensee BioMed Central Ltd.Oak Foundatio

The Importance of Hormesis to Public Health

BACKGROUND: Hormesis is a specific type of nonmonotonic dose response whose occurrence has been documented across a broad range of biological models, diverse types of exposure, and a variety of outcomes. The effects that occur at various points along this curve can be interpreted as beneficial or detrimental, depending on the biological or ecologic context in which they occur. OBJECTIVE: Because hormesis appears to be a relatively common phenomenon that has not yet been incorporated into regulatory practice, the objective of this commentary is to explore some of its more obvious public health and risk assessment implications, with particular reference to issues raised recently within this journal by other authors. DISCUSSION: Hormesis appears to be more common than dose–response curves that are currently used in the risk assessment process [e.g., linear no-threshold (LNT)]. Although a number of mechanisms have been identified that explain many hormetic dose–response relationships, better understanding of this phenomenon will likely lead to different strategies not only for the prevention and treatment of disease but also for the promotion of improved public health as it relates to both specific and more holistic health outcomes. CONCLUSIONS: We believe that ignoring hormesis is poor policy because it ignores knowledge that could be used to improve public health

Design considerations and analysis planning of a phase 2a proof of concept study in rheumatoid arthritis in the presence of possible non-monotonicity

BACKGROUND: It is important to quantify the dose response for a drug in phase 2a clinical trials so the optimal doses can then be selected for subsequent late phase trials. In a phase 2a clinical trial of new lead drug being developed for the treatment of rheumatoid arthritis (RA), a U-shaped dose response curve was observed. In the light of this result further research was undertaken to design an efficient phase 2a proof of concept (PoC) trial for a follow-on compound using the lessons learnt from the lead compound. METHODS: The planned analysis for the Phase 2a trial for GSK123456 was a Bayesian Emax model which assumes the dose-response relationship follows a monotonic sigmoid "S" shaped curve. This model was found to be suboptimal to model the U-shaped dose response observed in the data from this trial and alternatives approaches were needed to be considered for the next compound for which a Normal dynamic linear model (NDLM) is proposed. This paper compares the statistical properties of the Bayesian Emax model and NDLM model and both models are evaluated using simulation in the context of adaptive Phase 2a PoC design under a variety of assumed dose response curves: linear, Emax model, U-shaped model, and flat response. RESULTS: It is shown that the NDLM method is flexible and can handle a wide variety of dose-responses, including monotonic and non-monotonic relationships. In comparison to the NDLM model the Emax model excelled with higher probability of selecting ED90 and smaller average sample size, when the true dose response followed Emax like curve. In addition, the type I error, probability of incorrectly concluding a drug may work when it does not, is inflated with the Bayesian NDLM model in all scenarios which would represent a development risk to pharmaceutical company. The bias, which is the difference between the estimated effect from the Emax and NDLM models and the simulated value, is comparable if the true dose response follows a placebo like curve, an Emax like curve, or log linear shape curve under fixed dose allocation, no adaptive allocation, half adaptive and adaptive scenarios. The bias though is significantly increased for the Emax model if the true dose response follows a U-shaped curve. CONCLUSIONS: In most cases the Bayesian Emax model works effectively and efficiently, with low bias and good probability of success in case of monotonic dose response. However, if there is a belief that the dose response could be non-monotonic then the NDLM is the superior model to assess the dose response