Eliciting judgements about dependent quantities of interest: The SHeffield ELicitation Framework extension and copula methods illustrated using an asthma case study.

Pharmaceutical companies regularly need to make decisions about drug development programs based on the limited knowledge from early stage clinical trials. In this situation, eliciting the judgements of experts is an attractive approach for synthesising evidence on the unknown quantities of interest. When calculating the probability of success for a drug development program, multiple quantities of interest-such as the effect of a drug on different endpoints-should not be treated as unrelated. We discuss two approaches for establishing a multivariate distribution for several related quantities within the SHeffield ELicitation Framework (SHELF). The first approach elicits experts' judgements about a quantity of interest conditional on knowledge about another one. For the second approach, we first elicit marginal distributions for each quantity of interest. Then, for each pair of quantities, we elicit the concordance probability that both lie on the same side of their respective elicited medians. This allows us to specify a copula to obtain the joint distribution of the quantities of interest. We show how these approaches were used in an elicitation workshop that was performed to assess the probability of success of the registrational program of an asthma drug. The judgements of the experts, which were obtained prior to completion of the pivotal studies, were well aligned with the final trial results.

Artificial Intelligence for chemical risk assessment.

As the basis for managing the risks of chemical exposure, the Chemical Risk Assessment (CRA) process can impact a substantial part of the economy, the health of hundreds of millions of people, and the condition of the environment. However, the number of properly assessed chemicals falls short of societal needs due to a lack of experts for evaluation, interference of third party interests, and the sheer volume of potentially relevant information on the chemicals from disparate sources. In order to explore ways in which computational methods may help overcome this discrepancy between the number of chemical risk assessments required on the one hand and the number and adequateness of assessments actually being conducted on the other, the European Commission's Joint Research Centre organised a workshop on Artificial Intelligence for Chemical Risk Assessment (AI4CRA). The workshop identified a number of areas where Artificial Intelligence could potentially increase the number and quality of regulatory risk management decisions based on CRA, involving process simulation, supporting evaluation, identifying problems, facilitating collaboration, finding experts, evidence gathering, systematic review, knowledge discovery, and building cognitive models. Although these are interconnected, they are organised and discussed under two main themes: scientific-technical process and social aspects and the decision making process.

Potential of ToxCast Data in the Safety Assessment of Food Chemicals.

Tox21 and ToxCast are high-throughput in vitro screening programs coordinated by the U.S. National Toxicology Program and the U.S. Environmental Protection Agency, respectively, with the goal of forecasting biological effects in vivo based on bioactivity profiling. The present study investigated whether mechanistic insights in the biological targets of food-relevant chemicals can be obtained from ToxCast results when the chemicals are grouped according to structural similarity. Starting from the 556 direct additives that have been identified in the ToxCast database by Karmaus et al. [Karmaus, A. L., Trautman, T. D., Krishan, M., Filer, D. L., and Fix, L. A. (2017). Curation of food-relevant chemicals in ToxCast. Food Chem. Toxicol. 103, 174-182.], the results showed that, despite the limited number of assays in which the chemical groups have been tested, sufficient results are available within so-called "DNA binding" and "nuclear receptor" target families to profile the biological activities of the defined chemical groups for these targets. The most obvious activity identified was the estrogen receptor-mediated actions of the chemical group containing parabens and structurally related gallates, as well the chemical group containing genistein and daidzein (the latter 2 being particularly active toward estrogen receptor ß as a potential health benefit). These group effects, as well as the biological activities of other chemical groups, were evaluated in a series of case studies. Overall, the results of the present study suggest that high-throughput screening data could add to the evidence considered for regulatory risk assessment of food chemicals and to the evaluation of desirable effects of nutrients and phytonutrients. The data will be particularly useful for providing mechanistic information and to fill data gaps with read-across.

Generation of TD50 values for carcinogenicity study data.

Carcinogenic potency is a key factor in the understanding of chemical risk assessment. Measures of carcinogenic potency, for example TD50, are instrumental in the determination of metrics such as the threshold of toxicological concern (TTC), acceptable intake (AI) and permitted daily exposure (PDE), which in turn impact on human exposure. The Carcinogenic Potency Data Base (CPDB) has provided a source of study information, complete with calculated TD50 values. However, this is no longer actively updated. An understanding of carcinogenic potency, which can be derived from dose-response data, can be used as part of human risk assessments to generate safety thresholds under which cancer risk is judged to be minimal. The aim of this paper is to produce a transparent methodology for calculating TD50 values from experimental data in a manner consistent with the CPDB. This was then applied across the same data as used in the CPDB and analysis done on the correlation with the CPDB TD50 values. While the two sets of values showed a high level of correlation overall, there were some significant discrepancies. These were predominantly due to a lack of clarity in the CPDB methodology and inappropriate use of a linear model in TD50 calculation where the data was not suitable for such an approach.

T lymphocyte phenotype of contact-allergic patients: experience with nickel and p-phenylenediamine.

BACKGROUND: There is considerable interest in understanding the immunological variables that have the greatest influence on the effectiveness of sensitization by contact allergens, particularly in the context of developing new paradigms for risk assessment of novel compounds. OBJECTIVES: To examine the relationship between patch test score for three different contact allergens and the characteristics of T cell responses. METHODS: A total of 192 patients with confirmed nickel, p-phenylenediamine (PPD) or methylisothiazolinone (MI) allergy were recruited from the Contact Dermatitis Investigation Unit at Salford Royal Hospital. Severity of allergy was scored by the use of patch testing, peripheral blood lymphocytes were characterized for T cell phenotype by flow cytometry, and proliferative activity was characterized by radiolabelled thymidine incorporation. Comparisons were drawn with buffy coat samples from healthy volunteers. RESULTS: Patch test positivity for nickel, PPD and MI was associated with changes in the phenotype of peripheral blood T cells: increases in naïve cells, decreases in regulatory T cell frequency and the CD4+ /CD8hi ratio, and increased expression of the skin-homing marker cutaneous lymphocyte antigen (CLA), particularly for those patients with a +++ patch test score. CONCLUSIONS: This increased understanding of the characteristics of the T cell responses to contact allergens may provide parameters with which to better measure health risks associated with skin sensitization.

Identification of transcript regulatory patterns in cell differentiation.

MOTIVATION: Studying transcript regulatory patterns in cell differentiation is critical in understanding its complex nature of the formation and function of different cell types. This is done usually by measuring gene expression at different stages of the cell differentiation. However, if the gene expression data available are only from the mature cells, we have some challenges in identifying transcript regulatory patterns that govern the cell differentiation. RESULTS: We propose to exploit the information of the lineage of cell differentiation in terms of correlation structure between cell types. We assume that two different cell types that are close in the lineage will exhibit many common genes that are co-expressed relative to those that are far in the lineage. Current analysis methods tend to ignore this correlation by testing for differential expression assuming some sort of independence between cell types. We employ a Bayesian approach to estimate the posterior distribution of the mean of expression in each cell type, by taking into account the cell formation path in the lineage. This enables us to infer genes that are specific in each cell type, indicating the genes are involved in directing the cell differentiation to that particular cell type. We illustrate the method using gene expression data from a study of haematopoiesis. AVAILABILITY AND IMPLEMENTATION: R codes to perform the analysis are available in http://www1.maths.leeds.ac.uk/∼arief/R/CellDiff/. CONTACT: a.gusnanto@leeds.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of the effect of formulation on the toxicity of chemicals.

Two approaches for the prediction of which of two vehicles will result in lower toxicity for anticancer agents are presented. Machine-learning models are developed using decision tree, random forest and partial least squares methodologies and statistical evidence is presented to demonstrate that they represent valid models. Separately, a clustering method is presented that allows the ordering of vehicles by the toxicity they show for chemically-related compounds.

Continuous Effector CD8(+) T Cell Production in a Controlled Persistent Infection Is Sustained by a Proliferative Intermediate Population.

Highly functional CD8(+) effector T (Teff) cells can persist in large numbers during controlled persistent infections, as exemplified by rare HIV-infected individuals who control the virus. Here we examined the cellular mechanisms that maintain ongoing T effector responses using a mouse model for persistent Toxoplasma gondii infection. In mice expressing the protective MHC-I molecule, H-2L(d), a dominant T effector response against a single parasite antigen was maintained without a contraction phase, correlating with ongoing presentation of the dominant antigen. Large numbers of short-lived Teff cells were continuously produced via a proliferative, antigen-dependent intermediate (Tint) population with a memory-effector hybrid phenotype. During an acute, resolved infection, decreasing antigen load correlated with a sharp drop in the Tint cell population and subsequent loss of the ongoing effector response. Vaccination approaches aimed at the development of Tint populations might prove effective against pathogens that lead to chronic infection.

Modelling consumer intakes of vegetable oils and fats.

Vegetable oils and fats make up a significant part of the energy intake in typical European diets. However, their use as ingredients in a diverse range of different foods means that their consumption is often hidden, especially when oils and fats are used for cooking. As a result, there are no reliable estimates of the consumption of different vegetable oils and fats in the diet of European consumers for use in, for example, nutritional assessments or chemical risk assessments. We have developed an innovative model to estimate the consumption of vegetable oils and fats by European Union consumers using the European Union consumption databases and elements of probabilistic modelling. A key feature of the approach is the assessment of uncertainty in the modelling assumptions that can be used to build user confidence and to guide future development.

Interpretation of the margin of exposure for genotoxic carcinogens - elicitation of expert knowledge about the form of the dose response curve at human relevant exposures.

The general approach to risk assessment of genotoxic carcinogens has been to advise reduction of exposure to "as low as reasonably achievable/practicable" (ALARA/P). However, whilst this remains the preferred risk management option, it does not provide guidance on the urgency or extent of risk management actions necessary. To address this, the "Margin of Exposure" (MOE) approach has been proposed. The MOE is the ratio between the point of departure for carcinogenesis and estimated human exposure. However, interpretation of the MOE requires implicit or explicit consideration of the shape of the dose-response curve at human relevant exposures. In a structured elicitation exercise, we captured expert opinion on available scientific evidence for low dose-response relationships for genotoxic carcinogens. This allowed assessment of: available evidence for the nature of dose-response relationships at human relevant exposures; the generality of judgments about such dose-response relationships; uncertainties affecting judgments on the nature of such dose-response relationships; and whether this last should differ for different classes of genotoxic carcinogens. Elicitation results reflected the variability in experts' views on the form of the dose-response curve for low dose exposure and major sources of uncertainty affecting the assumption of a linear relationship.

Quantifying experts' uncertainty about the future cost of exotic diseases.

Since the foot-and-mouth disease outbreak of 2001 in the United Kingdom, there has been debate about the sharing, between government and industry, both the costs of livestock disease outbreaks and responsibility for the decisions that give rise to them. As part of a consultation into the formation of a new body to manage livestock diseases, government veterinarians and economists produced estimates of the average annual costs for a number of exotic infectious diseases. In this article, we demonstrate how the government experts were helped to quantify their uncertainties about the cost estimates using formal expert elicitation techniques. This has enabled the decisionmakers to have a greater appreciation of government experts' uncertainty in this policy area.