Hormesis: from marginalization to mainstream: A case for hormesis as the default dose-response model in risk assessment

Advances in the technology of human cell and tissue culture and the increasing availability of human tissue for laboratory studies have led to the increased use of in vitro human tissue models in toxicology and pharmacodynamics studies and in quantitative modeling of metabolism, pharmacokinetic behavior, and transport. In recognition of the potential importance of such models in toxicological risk assessment, the Society of Toxicology sponsored a workshop to evaluate the current status of human cell and tissue models and to develop consensus recommendations on the use of such models to improve the scientific basis of risk assessment. This report summarizes the evaluation by invited experts and workshop attendees of the current status of such models for prediction of human metabolism and identification of drug-drug interactions, prediction of human toxicities, and quantitative modeling of pharmacokinetic and pharmaco-toxicodynamic behavior. Consensus recommendations for the application and improvement of current models are presented.

Limits to chemical hormesis as a dose–response model in health risk assessment

Biologically based dose-response (BBDR) models predict health outcomes (response) resulting from the presence of a toxicant at a biological target (dose). The benefits of BBDR models are many, and research programs are increasingly focusing on mechanistic research to support model development; however, progress has been slow. Impediments to progress include the complexity of dose response modeling, the need for a multidisciplinary team and consistent funding support, and difficulty in identifying and extracting the needed data. Of immediate concern is the lack of transparency of published models to the supporting data and literature, difficulty in accessing model code and simulation conditions sufficient to allow independent replication of results, and absence of well-defined quality criteria. Suggestions are presented to improve the development and use of BBDR models in risk assessment and to address the above limitations. Examples from BBDR models for methylmercury neurotoxicity and 5-fluorouracil embryotoxicity are presented to illustrate the suggestions including what kinds of databases are needed to support model development and transparency, quality assurance for modeling, and how the internet can advance database development and collaboration within the biological modeling community.

Development of physiologically based pharmacokinetic and physiologically based pharmacodymamic models for applications in toxicology and risk assessment

In his commentary in Environmental Health Perspectives, Calabrese (2009) offered a number of responses to my critique of hormesis methodology (Mushak 2009). Here I will provide a counterpoint to that effort.

BackgroundOccupational hazards such as solvents and noise in the electronics industry are serious. Although various occupational health risk assessment models have been applied in the electronics industry, they have only been used to assess the risks of individual job positions. Few existing studies have focused on the total risk level of critical risk factors in enterprises.MethodsTen electronics enterprises were selected for this study. Information, air samples and physical factor measurements were collected from the selected enterprises through on-site investigation, and then the data were collated and samples were tested according to the requirements of Chinese standards. The Occupational Health Risk Classification and Assessment Model (referred to as the Classification Model), the Occupational Health Risk Grading and Assessment Model (referred to as the Grading Model), and the Occupational Disease Hazard Evaluation Model were used to assess the risks of the enterprises. The correlations and differences between the three models were analyzed, and the results of the models were validated by the average risk level of all of the hazard factors.ResultsHazards with concentrations exceeding the Chinese occupational exposure limits (OELs) were methylene chloride, 1,2-dichloroethane, and noise. The exposure time of workers ranged from 1 to 11 h per day and the frequency of exposure ranged from 5 to 6 times per week. The risk ratios (RRs) of the Classification Model, the Grading Model and the Occupational Disease Hazard Evaluation Model were 0.70 ± 0.10, 0.34 ± 0.13, and 0.65 ± 0.21, respectively. The RRs for the three risk assessment models were statistically different (P < 0.001), and there were no correlations between them (P > 0.05). The average risk level of all of the hazard factors was 0.38 ± 0.18, which did not differ from the RRs of the Grading Model (P > 0.05).ConclusionsThe hazards of organic solvents and noise in the electronics industry are not negligible. The Grading Model offers a good reflection of the actual risk level of the electronics industry and has strong practicability.

Financial integrity is fundamental to the stability and sustainability of global financial systems, requiring robust internal audit mechanisms, comprehensive risk assessment models, and effective governance frameworks. This paper explores the role of an advanced internal audit risk assessment and governance model in enhancing financial integrity, addressing key challenges in financial oversight, and mitigating risks associated with fraud, regulatory non-compliance, and unethical financial practices. It begins by examining the theoretical foundations of internal auditing and governance, highlighting key models and frameworks that shape financial oversight practices. The paper then delves into the evolution of risk assessment methodologies, emphasizing the integration of artificial intelligence, predictive analytics, and data-driven auditing techniques to enhance risk detection and mitigation. A critical aspect of this study is the development of a sophisticated internal audit risk assessment model that leverages technological advancements to strengthen financial oversight. The paper outlines essential components of an effective risk assessment framework, emphasizing the role of data analytics, automated compliance monitoring, and industry benchmarks in improving financial transparency. Additionally, it presents case studies and industry best practices that demonstrate the effectiveness of enhanced risk assessment models in preventing financial irregularities. The governance framework proposed in this paper underscores the importance of corporate transparency, ethical leadership, regulatory compliance, and strong internal controls in ensuring financial integrity. It evaluates the impact of regulatory policies, including the Sarbanes-Oxley Act, Basel Accords, and anti-money laundering frameworks, on corporate governance structures. Furthermore, the study highlights the role of cybersecurity risk management and blockchain-based audit mechanisms in strengthening financial accountability. Future research opportunities in audit risk assessment and governance are also discussed, with a focus on AI-driven audit systems, blockchain transparency solutions, behavioral governance models, and global regulatory harmonization. The paper concludes by providing strategic policy recommendations for financial institutions, regulatory bodies, and corporate entities, advocating for the integration of advanced analytics, enhanced whistle-blower protections, stronger cybersecurity governance, and cross-border regulatory cooperation. By implementing these measures, organizations can fortify financial integrity, mitigate systemic risks, and build a more transparent and resilient financial ecosystem.

Peripherally Inserted Central Catheters-Related Thrombosis (PICC-RT) is a challenging complication associated with PICC in cancer patients. Risk assessment models (RAMs) help identify patients at high risk of PICC-RT. This study compares the predictive performance of the Michigan RAM, the Caprini RAM, and the Maneval RAM in cancer patients with PICCs. The results could help recommend an optimal RAM for PICC-RT risk assessment in practice. A prospective observational study was conducted in a large cancer center in Guangzhou, China. 281 patients with cancer who underwent PICC insertions were enrolled from April 2023 to January 2024. The extracted data included basic information on patients and catheters, the scores of the Michigan, Caprini, and Maneval RAMs, and the occurrence of PICC-RT. The sensitivity, specificity, and area under the receiver operating characteristics (AUC) values were used to compare the predictive performance of these three RAMs. 275 participants were finally included for data analysis. Eighteen patients (6.5%) developed symptomatic PICC-RT. The average time between catheter insertion and PICC-RT diagnosis was 83.40 ± 34.63 days (range 28-143 days). The sensitivity/specificity of the Michigan, Caprini, and Maneval RAM were 0.06/0.97, 0.44/0.78, and 0.89/0.68, respectively. The AUC of the Maneval RAM (0.85, 95% CI: 0.78-0.91) was higher than that of Michigan (0.50, 95% CI: 0.37-0.64) and Caprini RAM (0.62, 95% CI: 0.47-0.76). Maneval RAM has a better predictive value for PICC-RT than Michigan and Caprini RAM. Maneval RAM is recommended for predicting the risk of PICC-RT in patients with cancer.

Risk assessment models are important tools in economic analysis of infectious disease. Risk assessment is the science of identifying and understanding hazards (unwanted events), of estimating the likelihood of these events occurring and of estimating the consequences if they do occur. Disease outcome tree models can organize medical data to estimate the probability of lifetime outcomes due to food-borne pathogens. Economists can then estimate public health costs for these outcomes. If the damage to society is significant, a cost-benefit analysis of public and private control options can be conducted. After the options for controlling pathogens in food production and distribution are determined, scenario models can be combined with probabilistic risk assessment (PRA) models to estimate quantitatively the impact of alternative risk-reducing options. Both individual companies seeking to control pathogens better and the public regulators setting priorities among food-borne hazards can use these risk assessment models. The ‘market failure’, due to limited information about the presence of pathogens in food, has a strong impact on economic incentives. Some remedies to the information problems are suggested. How economic incentives affect investment in both short- and long-term control options is also discussed. The various risk assessment models presented here are applicable to economic analysis of any infectious disease.

Toxicokinetics is the study of kinetics of absorption, distribution, metabolism, and excretion of a xenobiotic under the conditions of toxicity evaluation. Conventional toxicokinetics uses the hypothetical compartments, and the model is composed of rate equations that describe the time course of drug and chemical disposition. The utility of toxicokinetics in toxicity evaluation and interpretation of animal toxicology data is emerging as an important tool in product discovery and development. With implementation of the International Conference on Harmonization (ICH) guidelines on systemic exposure and dose selection, toxicokinetics have been integrated in routine toxicity evaluations. Although traditional compartmental/noncompartmental models are generally adequate for assessing systemic exposure, they are unable to the predict time course of drug disposition in target tissues and often fail to relate systemic drug levels to a biological response. Physiologically based toxicokinetic (PB-TK) models address this deficiency of traditional compartmental models. PB-TK models are the kinetic models of the uptake and disposition of chemicals based on rates of biochemical reactions, physiological and anatomical characteristics. These models, when developed appropriately, can predict target organ drug distribution in different species under variety of conditions. This minireview discusses the basic principles, and applications of traditional compartmental toxicokinetic and physiologically based toxicokinetics (PB-TK) models in drug development and risk assessment. Special emphasis will be placed on discussion related to interpretation of the ICH guidelines related to toxicokinetics and the utility of toxicokinetics data in dose selection for toxicity and carcinogenicity studies. The utility of PB-TK models in risk assessment of methylene chloride, vinyl chloride, retinoic acid, dioxin, and inhaled organic esters is discussed.

Listeria monocytogenes in Retail Delicatessens: An Interagency Risk Assessment—Model and Baseline Results

Study on risk assessment models for the aggregation of vehicles transporting hazardous chemicals

Transgenic animal models are powerful tools for developing a more detailed understanding on the roles of specific genes in biological pathways and systems. Applications of these models have been made within the field of toxicology, most notably for the screening of mutagenic and carcinogenic potential and for the characterization of toxic mechanisms of action. It has long been a goal of research toxicologists to use the data from these models to refine hazard identification and characterization to better inform human health risk assessments. This review provides an overview on the applications of transgenic animal models in the assessment of mutagenicity and carcinogenicity, their use as reporter systems, and as tools for understanding the roles of xenobiotic-metabolizing enzymes and biological receptors in the etiology of chemical toxicity. Perspectives are also shared on the future outlook for these models in toxicology and risk assessment and how transgenic technologies are likely to be an integral tool for toxicity testing in the 21st century.

Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: The first steps

Absorption modeling is an excellent strategic fit to perform a risk assessment for relative bioavailability (RBA) studies as it provides direct input into the question that is at the core of the RBA decision, namely, how does the absorption of the test drug product compare to the reference and is it likely to be different enough to justify an RBA study. The main limitation to absorption modeling in risk assessment is the inherent uncertainty associated with modeling. The extent to which the absorption modeling is integrated into the risk assessment should depend on the level of confidence in the modeling. It is difficult, however, to quantify the level of confidence on a case by case basis. The effective application of absorption modeling for RBA risk assessment therefore requires a general understanding of when modeling is expected to be reliable and also how to build reliability directly into the modeling. This paper describes a framework for effective modeling in RBA risk assessment that is based on four fundamental building blocks: (1) relate severity of drug product change and API properties to reliability of modeling, (2) use critical model variables to express the critical differences in the drug products, (3) generate a fraction-absorbed response surface expressed in terms of the critical model variables to evaluate the relative performance of the drug products, and (4) tie the first three building blocks together by following good model building practices that assure the highest quality model is built. The building blocks are demonstrated by a simple but common example of a change in solid state from free base to HCl salt.