Assessing Chemicals Using Partial Order Ranking Methodology

This review summarizes the use of selected partial order ranking (POR) techniques for the assessment of chemicals. Simple partial order ranking may advantageously be applied to give the single chemicals investigated an identity in relation to other substances. Thus, it constitutes an effective tool for the prioritization of chemicals, e.g., based on their PBT (Persistence, Bioaccumulating, Toxicity) characteristics. In more elaborate cases where a larger number of descriptors are taken into account, e.g., comprising physico-chemical characteristics, atmospheric parameters, geospecific factors and possibly socio-economic factors hierarchical partial order ranking (HPOR) may be applied. Thus, in a first ordering step a series of meta-descriptors are generated that later subsequently being used as descriptors in a subsequent ordering. HPOR allows a sensible ranking model even if a relative high number of descriptors are included. Assessing chemicals can, taken the actual situation into account, be based on a variety of parameters/descriptors. Thus, it might be valuable information to know the mutual importance of the applied descriptor. This information may appropriately be retrieved by a sensitivity study applying a designed module in the PyHasse software package. Finally accumulation partial order ranking (APOR) is illustrated. Accumulating partial APOR is a technique where data from a series of individual tests of various characteristics are aggregated, however, maintaining the basics of the partial order ranking methodology. APOR offers prioritization based on mutual probabilities derived from the aggregated data. Alternatively prioritization may be achieved based on average ranks derived from the APOR. The application APOR is demonstrated by an assessment of a series of potential PBT substances. In contrast to simple ranking techniques, partial order ranking does not automatically lead to an absolute rank of the single substances being studied. However, a weak linear order may in all cases be achieved based on the average ranks of the single substances. Additionally ranking probabilities can be derived based on Monte Carlo simulations.

Keywords: Partial order ranking techniques, hierarchical partial order ranking, accumulation partial order ranking, quantitative structure-activity relationships, assessment of chemicals, prioritization of chemicals, decision support tool, PBT substances.