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Hazard for aquatic organisms

Hazard for air

Hazard for terrestrial organisms

Hazard for predators

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Comprehensive CAS number search in internal databases as well as external databases, e.g. Toxbio, Embase, XToxline, RTECS, HSDB

Z CAS substance Title (Ti) Test sytem
(Organism - ORGN)
(Route - RTE)
test concentration
(Dose - DOSE)
(Effect - EFF)
reference (so) Summary (AB)
1 78-88-6 2,3-Dichloropropene The use of a biological early warning system to minimize risks associated with drinking water sources and wastewater discharges Fisch (Pimephales promelas für Laborstudien und Oncorhynchus mykiss bzw. Lepomis macrochirus für Feldstudien)       Gruber, David; Rasnake, William J. - Hazardous and Industrial Wastes (1997), 29th, 253-262 CODEN: HIWAEB; ISSN: 1044-0631 For online monitoring, chemical sensors are often used, but continuous, automated biomonitors can rapidly generate reliable, sensitive information concerning water quality. Sensitivity and reliability of an automated biomonitor to operate as a credible screening tool was tested, and performance in the laboratory and operating online at raw water intakes of drinking water plants was evaluated. The biomonitoring system used, Bio-Sensor Model 6012X, incorporated fish (Pimephales promelas for laboratory studies and Oncorhynchus mykiss and/or Lepomis macrochirus for field studies), electronics, and computer technologies to automatically, continuously assess water quality. Cu as copper sulfate was used as toxicant in laboratory studies of dechlorinated tap water and artificial wastewater. Sensitivity to malathion, formaldehyde, Cr, and diesel fuel, and organic compds. was tested in on-site studies. Bio-Sensor reliably detected sub-acute Cu concns. in both dechlorinated tap water and artificial wastewater within hours of exposure. Laboratory studies also demonstrated toxicity is closely related to other chemical and biol. parameters: dissolved organic C, total dissolved solids, BOD, hardness, alkalinity, and chelating or complexing of free ionic metal to reduce net toxicity of high total metal content. Field study results compared well with published literature values. It was concluded that the biomonitoring system was at least as sensitive as current toxicity tests are to detect most of the 25 compds. tested. Fish response is species specific relative to sensitivity to toxic material; therefore, selection of a valid indicator organism in biomonitoring systems should consider pollutants which might be encountered.
2 78-88-6 2,3-Dichloropropene Quantitative correlation studies between the acute lethal toxicity of 15 organic halides to the guppy (Poecilia reticulata) and chemical reactivity towards 4-nitrobenzylpyridine Fish guppy (Poecilia reticulata)   LC50; 14-day   Hermens, Joop; Busser, Frans; Leeuwanch, Peter; Musch, Aalt - Toxicological and Environmental Chemistry (1985), 9(3), 219-36 CODEN: TECSDY; ISSN: 0277-2248 The acute mortality [14-day median lethal concentration (LC50)] to the guppy of reactive organic halides (e.g., 2,3-dichloro-1-propene [78-88-6]) was determined These compds. were significantly more toxic than the nonreactive organic chems. with unspecific toxicity. The 14-day LC50 values of the compds. were correlated with hydrophobicity and a chemical reactivity parameter (reaction rate consts. with 4-nitrobenzylpyridine [49796-87-4]). The quality of the QSAR with chemical reactivity as parameter was much better than that of a QSAR with hydrophobicity.
3 78-88-6 2,3-Dichloropropene Discriminating toxicant classes by mode of action. 1. (Eco) toxicity profiles         Nendza, Monika; Wenzel, Andrea - Environmental Science and Pollution Research International (2006), 13(3), 192-203 CODEN: ESPLEC; ISSN: 0944-1344 Predictive toxicol., particularly quant. structure-activity relationships (QSARs), require classification of chems. by mode of action (MOA). MOA is, however, not a constant property of a compound but it varies between species and may change with concentration and duration of exposure. A battery of MOA-specific in-vitro and low-complexity assays, featuring biomol. targets for major classes of environmental pollutants, provides characteristic responses for (1) classification of chems. by MOA, (2) identification of (eco)toxicity profiles of chems., (3) identification of chems. with specific MOAs, (4) indication of most sensitive species, (5) identification of chems. that are outliers in QSARs and (6) selection of appropriate QSARs for predictions. Chems. covering nine distinct modes of toxic action (non-polar non-specific toxicants (n=14), polar non-specific toxicants (n=18), uncouplers of oxidative phosphorylation (n=25), inhibitors of photosynthesis (n=15), inhibitors of acetylcholinesterase (n=14), inhibitors of respiration (n=3), thiol-alkylating agents (n=9), reactives (irritants) (n=8), estrogen receptor agonists (n=9)) were tested for cytotoxicity in the neutral red assay, oxygen consumption in isolated mitochondria, oxygen production in algae, inhibition of AChE, reaction with GSH and activity in the yeast estrogen receptor assay. Data on in-vivo aquatic toxicity (LC50, EC50) towards fish, daphnids, algae and bacteria were collected from the literature for reasons of comparison and reference scaling. In the MOA-specific in-vitro test battery, most test chems. are specifically active at low concns., though multiple effects do occur. Graphical and statistical evaluation of the individual classes vs. MOA 1 (non-polar non-specific toxicants) identifies interactions related to predominant MOA. Discriminant analyses (DA) on subsets of the data revealed correct classifications between 70% (in-vivo data) and >90% (in-vitro data). Functional similarity of chemical substances is defined in terms of their (eco)toxicity profiles. Within each MOA class, the compds. share some properties related to the rate-limiting interactions, e.g., steric fit to the target site and/or reactivity with target biomols., revealing a specific pattern (fingerprint) of characteristic effects. The successful discrimination of toxicant classes by MOA is based on comprehensive characterization of test chems.' properties related to interactions with target sites. The suite of aquatic in-vivo tests using fish, daphnids, algae and bacteria covers most acute effects, while long-term (latent) impacts are generally neglected. With the MOA-specific in-vitro test battery such distinctions are futile, because it focuses on isolated targets, i.e. it indicates the possible targets of a chemical regardless of the timescale of effects. The data anal. indicates that the in-vitro battery covers most effects in vivo and moreover provides addnl. aspects of the compds.' MOA. Translating in-vitro effects to in-vivo toxicity requires combining physiol. and chemical knowledge about underlying processes. Comparison of the specific in-vitro effects of a compound with the resp. sensitivities of aquatic organisms indicates particularly sensitive species. Classifications of toxicants by MOA based on physico-chemical descriptors provides insight to interactions and directs to mechanistic QSARs.

Conclusion on classification