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A Bayesian approach to dose-response assessment and synergy and its application to in vitro dose-response studies.

ABSTRACT: In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC(50)). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median-effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27-55), which ignore important sources of variability and uncertainty.

SUBMITTER: Hennessey VG

PROVIDER: S-EPMC3773943 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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A Bayesian approach to dose-response assessment and synergy and its application to in vitro dose-response studies.

Hennessey Violeta G VG Rosner Gary L GL Bast Robert C RC Chen Min-Yu MY

Biometrics 20101201 4

In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no d ...[more]

PMID: 20337630

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Project description:When assessing the human risks due to exposure to environmental chemicals, traditional dose-response analyses are not straightforward when there are numerous high-quality epidemiological studies of priority cancer and non-cancer health outcomes. Given this wealth of information, selecting a single "best" study on which to base dose-response analyses is difficult and would potentially ignore much of the available data. Therefore, systematic approaches are necessary for the analysis of these rich databases. Examples are meta-analysis (and further, meta-regression), which are well established methods that consider and incorporate information from multiple studies into the estimation of risks due to exposure to environmental contaminants. In this paper, we propose a hierarchical, Bayesian meta-analysis approach for the dose-response analysis of multiple epidemiological studies. This paper is the second of two papers detailing this approach; the first covered "pre-analysis" steps necessary to prepare the data for dose-response modeling. This paper focuses on the hierarchical Bayesian approach to dose-response modeling and extrapolation of risk to populations of interest using the association between bladder cancer and oral inorganic arsenic (iAs) exposure as an illustrative case study. In particular, this paper addresses the modeling of both case-control and cohort studies with a flexible, logistic model in a hierarchical Bayesian framework that estimates study-specific slopes, as well as a pooled slope across all studies. This approach is akin to a random effects model in which no assumption is made a priori that there is a single, common slope for all included studies. Further, this paper also details extrapolation of the estimates of logistic slope to extra risk in a target population using a lifetable analysis and basic assumptions about background iAs exposure levels. In this case, the target population was the general United States population and information on all-cause mortality and incidence and mortality from bladder cancer was used to perform the lifetable analysis. The methods herein were developed for general use in investigating the association between any pollutant and observed health-effects in epidemiological studies. In order to demonstrate these methods, inorganic arsenic was chosen as a case study given the large epidemiological database that exists for this contaminant.

Project description:BackgroundIn quantitative chemical risk assessment, a reference value is an estimate of an exposure to a chemical that is "likely to be without appreciable risk." Because current "deterministic" approaches do not quantitatively characterize the likelihood or severity of harm, the National Academies has recommended using reference values derived from a risk-specific dose that are treated as random variables, with probability distributions characterizing uncertainty and variability.ObjectivesIn order to build familiarity and address issues needed for routine and standardized derivation of probabilistic risk-specific dose distributions, a case example applying the unified probabilistic framework presented in Chiu and Slob (2015) is developed for acrolein. This case study is based on an updated systematic evidence map of literature (Keshava et al., 2020) identifying nasal lesions reported in Dorman et al. (2008) as the most appropriate endpoint and study for reference value derivation.MethodsThe probability distribution was calculated for the risk-specific dose, which in this implementation of the approach was calculated for the dose at which 1% of the human population is estimated to experience minimal lesions, and a probabilistic reference value was computed as the 5th percentile of this distribution. A deterministic reference value was also derived for comparison, and a sensitivity analysis of the probabilistic reference value was conducted investigating alternative assumptions for the point of departure type and exposure duration.ResultsThe probabilistic reference value of 6 × 10-4 mg/m3 was slightly lower than the deterministic reference value of 8 × 10-4 mg/m3, and the risk-specific dose distribution had an uncertainty spanning a factor of 137 (95th-5th percentile ratio). Sensitivity analysis yielded slightly higher probabilistic reference values ranging between 9 × 10-4 mg/m3 and 2 × 10-3 mg/m3.ConclusionsUsing a probabilistic approach for deriving a reference value allows quantitative characterization of the severity, incidence, and uncertainty of effects at a given dose. The results can be used to inform risk management decisions and improve risk communication.

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A Bayesian approach to dose-response assessment and synergy and its application to in vitro dose-response studies.

Publications

A Bayesian approach to dose-response assessment and synergy and its application to in vitro dose-response studies.

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