RIFM Publishes Ground-breaking Paper on Fragrance Ingredient Clustering and Read-across
5.28.20
Computational Chemist Mihir Date, PhD, likens chemical clustering—the process of organizing chemicals into structurally similar groups—to “arranging books in a library.”
Dr. Date heads the clustering and read-across processes for evaluating fragrance safety at the Research Institute for Fragrance Materials (RIFM). He and half a dozen fellow scientist-authors recently published a paper outlining RIFM’s clustering and read-across strategies.
“Read-across is an internationally-recognized alternative to animal testing,” Dr. Date explains, “wherein we use a chemical for which we have lots of data to help predict the toxicological profile of a structurally similar chemical for which little to no data exists.”
“But, as with so much science, the devil is in the details,” Dr. Date cautions. “Clustering the full inventory of fragrance compounds is just a first step. Comparing the whole structure of one chemical to another is not reliable; you need to compare chemicals at the sub-structural level to understand how chemical reactivity is linked to toxicity. In this new study, we present a novel way of clustering a chemical inventory and define a tiered approach to identify read-across analogs for filling data gaps.”
How it works
RIFM’s clustering and read-across process begins with organizing chemicals by organic functional groups (e.g., alcohols in one group, aldehydes in another, and so on). It then drills down to consider their structural similarity and that of their extended fragments, how they are metabolized, and what these variables might tell us about how a chemical will behave via different routes of exposure—for instance, via skin absorption or inhalation.
RIFM’s Safety Assessment Manager, Danielle Botelho, PhD, is one of the study’s co-authors. “This process makes use of the enormous amount of data that we have accumulated in the RIFM Database to create a common language between chemists, like Dr. Date, and toxicologists,” she explains. “This has made the process of identifying viable read-across options much clearer to everyone, in addition to being more accurate.”