Bioanalytical tools can be used for assessment of the chemical quality of drinking water and its sources. For water managers it is important to know the probability that a bioassay response above an established health-based 'effect-based trigger value' (EBT) indeed implies a harmful chemical (mixture) concentration. This study presents and applies a framework, based on Bayes' theorem, to derive such risk probabilities for bioassay responses. These were evaluated under varying (in silico) chemical mixture concentrations relevant to drinking water (sources), with toxicity data for six in vitro assays from the ToxCast database. For single chemicals and in silico mixtures, the negative predictive value (NPV) was 100 % for all assays. For water managers, this means that when a bioassay response is below the EBT, a chemical risk is reliably absent, and no further action is required. The positive predictive value (PPV) increased with increasing chemical concentrations (2 µg/L) up to 40-80 %, depending on the assay. For in silico mixtures of increasing numbers of chemicals, the PPV did not increase until higher sum concentrations (>2-10 µg/L). Hence, the ability to accurately signal a harmful chemical (mixture) using bioassays will be lowest for highly diverse, low-concentration chemical mixtures. For water managers, this means in practice that further investigations after an EBT exceedance will, in many cases, not reveal chemicals at harmful concentrations. A solution offered is to increase the trigger value for positive responses to achieve a higher PPV and maintain the EBT for negative responses to ensure an optimal NPV.
Keywords: Effect-based methods; In vitro; Predictive value; Risk assessment; Water quality.
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