Reducing aquatic hazards of industrial chemicals: probabilistic assessment of sustainable molecular design guidelines.
Basic toxicological information is lacking for the majority of industrial chemicals. In addition to increasing empirical toxicity data through additional testing, prospective computational approaches to drug development aim to serve as a rational basis for the design of chemicals with reduced toxicity. Recent work has resulted in the derivation of a "rule of 2," wherein chemicals with an octanol-water partition coefficient (log P) less than 2 and a difference between the lowest unoccupied molecular orbital and the highest occupied molecular orbital (ΔE) greater than 9 (log P<2 and ΔE >9 eV) are predicted to be 4 to 5 times less likely to elicit acute or chronic toxicity to model aquatic organisms. The present study examines potential reduction of aquatic toxicity hazards from industrial chemicals if these 2 molecular design guidelines were employed. Probabilistic hazard assessment approaches were used to model the likelihood of encountering industrial chemicals exceeding toxicological categories of concern both with and without the rule of 2. Modeling predicted that utilization of these molecular design guidelines for log P and ΔE would appreciably decrease the number of chemicals that would be designated to be of "high" and "very high" concern for acute and chronic toxicity to standard model aquatic organisms and end points as defined by the US Environmental Protection Agency. For example, 14.5% of chemicals were categorized as having high and very high acute toxicity to the fathead minnow model, whereas only 3.3% of chemicals conforming to the design guidelines were predicted to be in these categories. Considerations of specific chemical classes (e.g., aldehydes), chemical attributes (e.g., ionization), and adverse outcome pathways in representative species (e.g., receptor-mediated responses) could be used to derive future property guidelines for broader classes of contaminants.
Department of Environmental Science, Baylor University, Waco, Texas, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, Texas, USA; Institute of Biomedical Studies, Baylor University, Waco, Texas, USA.
Kristin A
KA
Connors
Adelina M
AM
Voutchkova-Kostal
Jakub
J
Kostal
Paul
P
Anastas
Julie B
JB
Zimmerman
Bryan W
BW
Brooks
D000818:Animals; D059001:Aquatic Organisms / Q000187:drug effects*; D054750:Ecotoxicology; D007221:Industry; D015233:Models, Statistical; D000442:Octanols / Q000737:chemistry; D011336:Probability; D018570:Risk Assessment / Q000379:methods*; D014481:United States; D014867:Water / Q000737:chemistry; D014874:Water Pollutants, Chemical / Q000737:chemistry* / Q000633:toxicity*
D000442:Octanols; D014874:Water Pollutants, Chemical; D014867:Water
Computational toxicology; Ecotoxicology; Green chemistry; Hazard/risk assessment; Probabilistic hazard assessment