Edward J. Calabrese. Muller’s Nobel Prize data: Getting the dose wrong and its significance. Environmental Research 176 (2019) 108528
This paper evaluates the significant historical paper of Muller and Mott-Smith (1930), which successfully disputed the proposal of Olson and Lewis (1928) that background ionizing radiation is the driving mechanism of evolution. While the present analysis supports the general conclusion that background radiation is not a quantifiable factor affecting evolution, the paper reveals methodological errors and questionable conclusions in the Muller and Mott-Smith (1930) paper, which may have impacted the acceptance of the linear non-threshold (LNT) model. Most importantly, this paper reveals that in Muller’s (1927) Nobel Prize research he used a treatment exposure (total dose) that was 95 million-fold greater than the average background exposure, a value far greater than the 200,000 fold reported by Muller and Mott-Smith (1930). Such a large exposure rate dis- crepancy may be historically important as it may have led to the over-reliance on Muller’s research in support of the derivation and use of the LNT single-hit model.
“… overpowering influence of low dose biostatistical modeling perspectives that swayed the quantitatively overwhelmed chemical toxicologists. This resulted in the LNT policy going forward, becoming broadly institutionalized across many governmental agencies and in multiple countries. The rest is history.”
Edward J. Calabresea, Robert J. Golden. Why toxicologists resisted and radiation geneticists supported EPA’S Tadoption of LNT for cancer risk assessment Chemico-Biological Interactions Volume 310, 1 September 2019, 108736
The linear non-threshold (LNT) dose response model for cancer risk assessment has been a controversial concept since its initial proposal during the 1930s. It was long advocated by the radiation genetics community in the 1950s, some two decades prior to being generally adopted within the chemical toxicology community. This paper explores possible reasons for such major differences in the acceptance of LNT for cancer risk assessment by these two key groups of scientists.
E.J. Calabrese. EPA adopts LNT: New historical perspectives. Chemic-Biological Interactions 308 (2019) 110-112
This paper provides an historical assessment of how the linear non-threshold (LNT) model became adopted as policy by the United States Environmental Protection Agency (US EPA) in 1975  and how prior United States National Academy of Sciences (US NAS) radiation advisory panels may have affected this EPA decision. The paper highlights a generally unrecognized set of recommendations of the 1960 Biological Effect of Atomic Radiation  Genetics and Medical/Pathology Panels that did not support LNT for cancer risk assessment due to their judgements of its scientific limitations and unacceptable uncertainties. These convergent, independent and high profile recommendations were not promoted by the sponsors (i.e., Rockefeller Foundation and the NAS), and were ignored by the media, Congress and the scientific community in contrast to the vast attention directed to the linearity recommendation for germ cell mutation by the BEAR Genetics Panel in 1956 [3,4]. The subsequent Biological Effects of Ionizing Radiation (BEIR) I Committee (1972)  report ignored these BEAR Panel (1960)  recommendations, only commenting on the BEAR 1956 linearity supporting recommendation [3,4]. These actions are documented and assessed for how they influenced why and how EPA adopted linearity for cancer risk assessment based on the BEIR I report.
“The linear no-threshold paradigm, which asserts there are no safe exposure levels, is the product of flawed and corrupted science.” click here
Edward J. Calabrese. The linear No-Threshold (LNT) dose response model: A comprehensive assessment of its historical and scientific foundations. Chemico-Biological Interactions. 301 (2019) 6–25. https://doi.org/10.1016/j.cbi.2018.11.020
The linear no-threshold (LNT) single-hit (SH) dose response model for cancer risk assessment is comprehensively assessed with respect to its historical foundations. This paper also examines how mistakes, ideological biases, and scientific misconduct by key scientists affected the acceptance, validity, and applications of the LNT model for cancer risk assessment. In addition, the analysis demonstrates that the LNT single-hit model was inappropriately adopted for governmental risk assessment, regulatory policy, practices, and for risk communication.
Clewell HH 3rd, Gentry PR, Hack CE, Greene T, Clewell RA. An evaluation of the USEPA Proposed Approaches for applying a biologically based dose-response model in a risk assessment for perchlorate in drinking water. Regulatory toxicology and pharmacology. 2019 Jan 29. pii: S0273-2300(19)30036-4. doi: 10.1016/j.yrtph.2019.01.028.
The United States Environmental Protection Agency’s (USEPA) 2017 report, “Draft Report: Proposed Approaches to Inform the Derivation of a Maximum Contaminant Level Goal for Perchlorate in Drinking Water”, proposes novel approaches for deriving a Maximum Contaminant Level Goal (MCLG) for perchlorate using a biologically-based dose-response (BBDR) model. The USEPA (2017) BBDR model extends previously peer-reviewed perchlorate models to describe the relationship between perchlorate exposure and thyroid hormone levels during early pregnancy. Our evaluation focuses on two key elements of the USEPA (2017) report: the plausibility of BBDR model revisions to describe control of thyroid hormone production in early pregnancy and the basis for linking BBDR model results to neurodevelopmental outcomes. While the USEPA (2017) BBDR model represents a valuable research tool, the lack of supporting data for many of the model assumptions and parameters calls into question the fitness of the extended BBDR model to support quantitative analyses for regulatory decisions on perchlorate in drinking water. Until more data can be developed to address uncertainties in the current BBDR model, USEPA should continue to rely on the RfD recommended by the NAS (USEPA, 2005) when considering further regulatory action.
Yuan T, Zhang H, Chen B, Zhang H, Tao S. Association between lung cancer risk and inorganic arsenic concentration in drinking water: a dose-response meta-analysis. Toxicol Res (Camb). 2018 Sep 18;7(6):1257-1266. doi: 10.1039/c8tx00177d.
High dose arsenic in drinking water (≥100 μg L-1) is known to induce lung cancer, but lung cancer risks at low to moderate arsenic levels and its dose-response relationship remains inconclusive. We conducted a systematic review of cohort and case-control studies that quantitatively reported the association between arsenic concentrations in drinking water and lung cancer risks by searching the PubMed database till June 14, 2018. Pooled relative risks (RRs) of lung cancer associated with full range (10 μg L-1-1000 μg L-1) and low to moderate range (<100 μg L-1) of water arsenic concentrations were calculated using random-effects models. A dose-response meta-analysis was performed to estimate the pooled associations between restricted cubic splines of log-transformed water arsenic and the lung cancer risks. Fifteen studies (9 case-control and 6 cohort studies) involving a total of 218 481 participants met the inclusion criteria. Meta-analysis identified significantly increased risks of lung cancer on exposure to both full range (RR = 1.21; 95% confidence interval [CI] = 1.05-1.37; heterogeneity I 2 = 54.3%) and low to moderate range (RR = 1.18; 95%CI = 1.00-1.35; I 2 = 56.3%) of arsenic-containing water. In the dose-response meta-analysis of eight case-control studies, we found no evidence of non-linearity, although statistical power was limited. The corresponding pooled RRs and their 95%CIs for exposure to 10 μg L-1, 50 μg L-1, and 100 μg L-1 water arsenic were 1.02 (1.00-1.03), 1.10 (1.04-1.15), and 1.20 (1.08-1.32), respectively. We provide evidence on the association between increased lung cancer risks and inorganic arsenic in drinking water across low, moderate and high levels. Minimizing arsenic levels in drinking water may be of public health importance.