Pitfalls in the application of statistics to chemical data: the determination of the partition ratio $$K_{\mathrm{ow} }$$ K ow as a case in point

Retrieval, Selection, and Evaluation of Chemical Property Data for Assessments of Chemical Emissions, Fate, Hazard, Exposure, and Risks

“…Carefully evaluated and curated data from a single experimental study can be suited for chemical assessments, particularly when the measurements are not too challenging (e.g., do not involve the determination of K OW or S W for superhydrophobic compounds or K OA for low volatility compounds) and were made in compliance with Good Laboratory Practices using standard methodologies. When multiple experimentally derived data are available, they can be consolidated in literature-derived values (LDVs), with their central tendency described by corresponding means (e.g., geometric or arithmetic) …”

“…When multiple experimentally derived data are available, they can be consolidated in literature-derived values (LDVs), with their central tendency described by corresponding means (e.g., geometric or arithmetic). 44 For properties related to the air−water−octanol system, one needs to additionally check whether their LDVs are thermodynamically consistent. While the "three solubilities approach" 28 defines the relationship between partition ratio and solubilities in octanol, air, and water, such thermodynamic constraints are often violated in reality if the properties are sourced from different independent measurements.…”

“…7,8 In addition, Table S5 shows that arithmetic and harmonic means of log-transformed values show similar agreement with the FAVs, and the arithmetic means of nontransformed values perform less satisfactorily in reproducing the FAVs because they tend to be biased by high extreme values. The use of different types of means (e.g., geometric, arithmetic, or harmonic) in calculating the consensus values of different chemical properties also warrants considerations in the statistical distribution of in silico predictions, 164 the nature of the properties, 44,164 and the intended context of chemical assessments (e.g., whether conservativeness is desired). 165 Previously, Puzyn 149 found that for 12 organochlorines and hydrocarbons, the OECD POV and LRTP Screening Tool makes consistent predictions for their potential for persistence and long-range transport, regardless of whether the model was parametrized with experimentally derived or QSAR/QSPRpredicted partition ratios and half-lives.…”

“…While experimentally derived data are often recommended to be given the highest priority in chemical assessment practice, − this must be done with caution because not all laboratory measurements are of the same quality, and therefore, they are not equally credible or reliable. , Typical indications for low data quality include but are not limited to the use of nonstandard methodologies or noncompliance with Good Laboratory Practices inadequate documentation of the measurement procedure, test chemical, or biological material inconsistency between the chemicals used in the test and reported in the test results (such as pure chemical vs technical substance or isomeric mixture, neutral acid/base vs electronically charged salt, parent chemical vs metabolites, liquid vs solid states at the temperature of interest) , an insufficient number of independent experimental runs or a lack of replication the measured properties being outside of the range of applicability or suitability of an experimental design/technique or the instrumental analysis method errors in processing and reporting data, units, and references , a lack of appropriate documentation of uncertainty information, such as the absence of the standard deviation of measurements, and statistically insignificant differences between experimental and control groups. A value without information on its uncertainty can even be inferior to a value with high reported uncertainty. …”

“…Such a conclusion is important because the use of in silico predictions is often unavoidable for premanufactured chemicals or chemicals in design and for model-based high-throughput chemical screening and prioritization. , In addition, Table S5 shows that arithmetic and harmonic means of log-transformed values show similar agreement with the FAVs, and the arithmetic means of nontransformed values perform less satisfactorily in reproducing the FAVs because they tend to be biased by high extreme values. The use of different types of means (e.g., geometric, arithmetic, or harmonic) in calculating the consensus values of different chemical properties also warrants considerations in the statistical distribution of in silico predictions, the nature of the properties, , and the intended context of chemical assessments (e.g., whether conservativeness is desired) …”