ArticleToxics2026
In Silico Prediction of Chronic Oral Reference Doses for PIANO Target Analytes.
Article in Toxics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterizing the human health risk posed by constituents in drinking water is often challenging due to a lack of published toxicity values. The PIANO (Paraffin, Isoparaffin, Aromatic, Naphthene, and Olefin) analytical method measures nearly 300 compounds in JP-5 jet fuel, 43 of which have published oral reference doses (RfDs). The remaining compounds are typically assigned surrogate toxicity values. We predict RfDs for 290 PIANO compounds using Quantitative Structure-Activity Relationship (QSAR) models based on stepwise linear regression of 2-dimensional molecular descriptors (MDs) and published toxicity values. Five training groups, created by randomly selecting 80% of the non-PIANO compounds and 50% of the 43 PIANO compounds that have RfDs within a master dataset of 1113 compounds, were used to develop five QSAR models. We used the geometric means of four QSAR model results of sufficient quality to predict RfDs for compounds lacking toxicological information. For compounds with known RfDs, 884 (79%) were within 8-fold of published RfDs, well within the acknowledged uncertainty inherent in published RfDs. Our approach has applicability beyond PIANO compounds and represents a new alternative methodology (NAM) that may be used to reduce uncertainty in human health risk assessment and guide regulatory decisions.
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Registered trials
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