Experimental Determination of Octanol-Water Partition Coefficients of Selected Natural Toxins

Natural toxins are ubiquitously occurring highly diverse organic compounds produced by e.g., plants or fungi. In predictive environmental fate and risk assessment of organic chemicals for regulatory purposes, the octanol-water partition coefficient (K ow ) remains one of the key 14 parameters. However, experimental data for natural toxins is largely missing and current 15 estimation models for K ow show limited applicability for multifunctional, ionizable compounds. Thus, log K ow data was first experimentally derived for a diverse set of 45 largely ionizable 17 natural toxins and then compared to predicted values from three different models (KOWWIN, 18 ACD/Percepta, Chemicalize). Both approaches were critically evaluated with regards to their 19 applicability for multifunctional, ionizable compounds. The miniaturized shake-flask approach 20 allowed reliable quantification of pH dependent partitioning behavior for neutral, acidic and 21 basic ionizable natural toxins. All analyzed toxins are rather polar with an average log K ow < 1 22 and an observed maximum log K ow of 2.7. Furthermore, the comparison of experimental data 23 to those of commonly used prediction models showed that the latter match the former with only 24 minorly increased errors. The Chemicalize tool gave overall best predictions with a mean 25 absolute error of 0.49 and thus should be preferred in comparison to ACD/Percepta and 26 KOWWIN.

In predictive environmental fate and risk assessment of organic chemicals for regulatory purposes, the octanol-water partition coefficient (Kow) remains one of the key parameters. However, experimental data for natural toxins is largely missing and current estimation models for Kow show limited applicability for multifunctional, ionizable compounds. Thus, log Kow data was first experimentally derived for a diverse set of 45 largely ionizable natural toxins and then compared to predicted values from three different models (KOWWIN, ACD/Percepta, Chemicalize). Both approaches were critically evaluated with regards to their applicability for multifunctional, ionizable compounds. The miniaturized shake-flask approach allowed reliable quantification of pH dependent partitioning behavior for neutral, acidic and basic ionizable natural toxins. All analyzed toxins are rather polar with an average log Kow < 1 and an observed maximum log Kow of 2.7.
Furthermore, the comparison of experimental data to those of commonly used prediction models showed that the latter match the former with only minorly increased errors. The Chemicalize tool gave overall best predictions with a mean absolute error of 0.49 and thus should be preferred in comparison to ACD/Percepta and KOWWIN.
File list (2) download file view on ChemRxiv 20191129_preprint_log D paper.pdf (0.99 MiB) download file view on ChemRxiv 20191129_preprint_log D paper SI.pdf (1.18 MiB) Introduction estimate that allows to derive a tendency on hydrophobicity or lipophilicity of a compound. 16, 19 48 The Kow is only valid for a neutral chemical, while partitioning of ionizable chemicals is 49 described by the respective distribution coefficient (Dow), accounting for both neutral and 50 charged species of the chemical. In the environmentally relevant pH range of pH 4 to 9, Dow is 51 considered more relevant for the description of environmental behavior of ionizable 52 compounds. 15 53 Different well-established methods are available for the experimental determination of Kow/Dow 54 (e.g., OECD 107, 117). 20,21 The indirect analysis of partitioning behavior based on column 55 chromatography (i.e., OECD 117) 21 allows simple and fast analysis of larger groups of compounds by pooled injection. [22][23][24][25] However, it was shown that data reproducibility is rather 57 poor 15 and that charge as well as steric effects may outcompete hydrophobic effects in 58 retention analysis. 26 Thus, the shake-flask technique (OECD 107) 20 is currently preferred for 59 reliable determination of Kow/Dow, particularly when focusing on ionizable compounds with an 60 expected log Kow/Dow < 4. 5. 15, 27 The method can be applied to ionizable compounds by 61 performing experiments in dependence of pH. Miniaturization can speed up total measurement 62 time and considerably minimizes required amounts of analytes and laboratory consumables. 63 This was demonstrated by Rothwell et al. for dietary flavonoids. 19 In combination with a 64 modified in situ approach developed for application in drug analysis, 28, 29 the approach can be 65 seen as potential high-throughput alternative for analysis of ionizable organics. 30, 31 66 Experimental Kow/Dow data for natural toxins is extremely scarce. Hence, current risk 67 assessment often has to resort to in silico prediction tools. 32, 33 However, for compounds with 68 physicochemical and structural complexity due to large numbers of ionizable functional groups, 69 current estimation models for phase distribution coefficients show limited applicability. 2, 34-37 70 Thus, reliable experimental data is required to improve understanding of natural toxin behavior 71 in the environment. 32 In this study, we provide experimentally derived log Kow/Dow values for a 72 set of 45 mainly ionizable natural toxins from different compound classes prioritized as 73 potential aquatic micropollutants. 33 In addition, we critically evaluate the applicability of both 74 experimental (miniaturized shake-flask approach 20, 30 ) and predictive methods (EPiSuite 38 , 75 ACD/Percepta 39 , Chemicalize 40 ) for Kow/Dow determination. 76 77

Materials and Methods 78
Materials 79 The chemicals used in this study were purchased from the following companies: HPLC grade 80 n-octanol and methanol as well as sodium salts used for preparation of buffer solutions from 81 Sigma-Aldrich (Buchs, Switzerland); investigated natural toxins from PhytoLab 82 (Vestenbergsreuth, Germany), Sigma-Aldrich (Buchs, Switzerland), or Fermentek (Jerusalem, All natural toxin stock solutions were prepared in methanol. Details on analytes (CAS, formula, 85 molecular structure), suppliers and stock solution concentrations are given in the supporting 86 information (SI , Table SI-1 Table 1) covered a molecular weight range of 150 to 650 g mol -1 88 and contained representatives of neutral, charged, acidic as well as basic ionizable 89 compounds. 90

Experimental Approach 91
Octanol-water partition coefficients were quantified using a miniaturized shake-flask approach 92 based on OECD 107 ( Figure SI-2). 19,20,30 To investigate analytes in either fully ionized or 93 neutral state as well as their pH dependent partitioning behavior, measurements were 94 performed using sodium based buffer solutions at a constant ionic strength of 0.1 M covering 95 three different pH: pH 4.0 (citrate buffer), pH 7.3 (phosphate buffer) and pH 10.2 (carbonate 96 buffer). All buffer solutions were saturated with octanol prior to analysis and vice versa. Stock 97 solutions of natural toxins were first diluted 1:100 in defined volumes of buffer solution and 98 added to standard 1.5 mL HPLC vials. Secondly, octanol was added to all but one of the 99 dilutions which served as reference stock dilution. Varying ratios of octanol (0.01-1 mL) and 100 aqueous buffer solutions (0.5-1.5 mL) were chosen based on the predicted Kow, with the goal 101 that between 30 -80 % of the toxin would remain in the aqueous phase after partitioning. Thus, 102 the theoretical operational range of the method was set from log Dow of -1 to 2.5 and was limited 103 by: 1) detection limits of the DAD, 2) minimum aqueous phase volumes of 0.5 mL required for 104 phase separation and HPLC injection. Further details on preparation of buffer solutions and 105 octanol-water phase ratios for individual natural toxins are presented in Section II of the 106 SI (Table SI-3 and 4). 107 All octanol-water mixtures were first vortexed for 1 min and then shaken for 2 h (125 rpm) at 108 room temperature (25 ± 3°C) to reach equilibrium and phase distribution. Kinetic 109 measurements were performed to confirm equilibrium conditions in the setup (data not shown). 110 in Table SI-4). Applying the water plug technique, 41 50 µl of the aqueous phase were injected. 113 In brief, first 5 µl of blank buffer solution were aspirated, followed by the aqueous phase of the 114 respective sample. To avoid any carryover of the analyte containing octanol phase, the outside 115 of the syringe was subsequently washed in methanol before injection. Analysis was done with 116 a Macherey Nagel Nucleoshell RP 18plus column (length 50 mm, ID 2 mm, particle size 2.7 117 µm) and without any column for comparison. Runs were performed at 40°C in isocratic mode 118 using a methanol/water mix (40/60, v/v) at a flow rate of 0.33 mL min -1 . As resulting partition 119 coefficients did not significantly differ between analysis with and without column, final results 120 were obtained from measurements without a column. This led to a substantial decrease in 121 analysis time and prevented potential memory effects due to accumulation of octanol on the 122 stationary phase of the column. However, obtained peak areas for analytes had to be corrected 123 with peak areas of the aqueous (octanol-saturated) buffer solution blanks at the respective 124 measurement wavelength. 125

Data Evaluation 126
Distribution coefficients for all analytes were derived based on the difference of the blank 127 corrected peak areas of the toxin in both the reference stock dilution (areaStd) and the aqueous 128 phase of the partition samples containing octanol (areaw), multiplied with the volumetric ratio 129 of water (Vwater) and octanol (Voctanol) of the respective sample (given in Table SI Commonly used QSPR models evaluated with regards to predictability of natural toxin log Kow 143 were the free of charge KOWWIN, integrated in the Estimation Program Interface (EPI) Suite 144 from the U.S. EPA 38 , as well as the commercially available ACD/Percepta 39 and Chemicalize 40 145 tools. Predicted log Kow data for all compounds and models is given in the SI (Table SI- where n is the number of observations and Kow,exp and Kow,model the experimentally derived and 149 QSPR predicted partition coefficients, respectively. 150 151

Octanol-Water Partition Coefficients and Applicability of the Experimental Approach 153
Distribution coefficients (log Dow) for all investigated natural toxins are given in Table 1  strength was kept as narrow as possible (Table SI- Figure 1). Thus, the miniaturized shake-flask approach provides robust 191 data for natural toxin Kow/Dow. In the following, the data is further evaluated regarding influences 192 of pH changes on partitioning and serves as a basis for a critical assessment of the predictive 193 power of different QSPR models. 194

Influence of pH on Octanol-Water Partitioning 195
As a consequence of proton transfer reactions and formation of corresponding charged 196 species, partitioning of ionizable compounds is pH dependent. For seven of the ionizable 197 toxins, data for both the neutral as well as fully ionized species were acquired. An additional 198 16 compounds were measured only as neutral, nine as fully ionized species. All other 199 compounds were evaluated independent of their speciation (Table 1). In the environmentally 200 relevant range of pH 4 to 9 they would also never occur in their fully ionized or neutral form. 201 The medium pH would need to be more than one unit above (basic ionizable) or below (acidic 202 ionizable) the compound's pKa to achieve a system with only one species of the compound 203 present. 15, 20 Additionally, measurement limitations such as limited variability of octanol-water ratios and high pH values (pH > 10) that are not advisable for common HPLC systems did not 205 allow deriving those values in this study. 206 Generally, neutral species showed higher affinity for the octanol phase that their respective 207 (partly) ionized counterparts (Table 1). However, in the case of the acidic ionizable isoprenoids 208 (daidzein, 8-prenylnaringenin, cucurbitacin E) as well as some mycotoxins (β-zearalenol, 209 sterigmatocystin, zearalenone) relatively high partitioning (log Dow > 1.4) was observed, 210 although a substantial fraction of the analyte was ionized (> 39%). This illustrates that 211 partitioning of the ionized species cannot always be neglected. 212 The data generated for those toxins where both single species systems (fully ionized or neutral) 213 were evaluated can be used for modeling of partitioning behavior over the whole pH range for 214 ionizable compounds (   Table 1 For the evaluation of environmental mobility and sorption behavior of polar, ionizable 299 compounds, Kow/Dow has been criticized several times and other intrinsic compound properties such as the organic carbon-water partition coefficient (Koc) were suggested to be a better, more 301 reliable indicator of aquatic mobility. 46, 47 Firstly, Kow is not always proportional to e.g., Koc for 302 polar molecules, particularly for those that show additional specific (polar) and/or nonspecific 303 (apolar) interactions. 48      13

Preparation Buffer Solutions
All buffers were prepared from sodium salts (all Sigma-Aldrich) as displayed in Table SI -3. Salts were dissolved in ultrapure water and pH checked prior and after saturation with noctanol. Saturation was done by adding n-octanol (Sigma-Aldrich) to the buffer solutions, shaking the obtained solutions heavily and then leaving them to rest for at least 24 h to ensure complete separation of the two phases.

Regression Analysis -Model Evaluation
Figure SI -4: Results of regression analysis between KOWWIN 1) predicted and experimental log Kow data for acids, bases, neutral and all compounds combined into one set. Evaluation was based on slope, y-intercept as well as R 2 of the regression lines.

Figure SI -5: Results of regression analysis between ACD/Percepta 2) predicted and experimental log Kow data for acids, bases, neutral and all compounds combined into one set. Evaluation was based on slope, y-intercept as well as R 2 of the regression lines.
Figure SI -6: Results of regression analysis between Chemicalize 3) predicted and experimental log Kow data for acids, bases, neutral and all compounds combined into one set. Evaluation was based on slope, y-intercept as well as R 2 of the regression lines.