Herbicidal activity correlations

Herbicidal Activity Correlations. Tables I and II give pre-emergence herbicidal activity and partition coefficient data gathered in the presence of 0.1% Tween 80 for the 15 TFMS compound evaluated in this study. For reasons discussed previously (6), in the correlations which follow, the Hammett sigma constant was assumed to be relatively unaffected by the presence of the surfactant, so that the o-values listed in Tables I and II could be used to correlate data obtained both in the presence and absence of surfactant. Pertinent herbicidal activity data for the TFMS compounds acting on Foxtail grass are presented in Table I. Similar data for the same compounds acting on the broadleaf Wild Mustard are tabulated in Table II. 

Comparing the inhibitory potency against plant PDHc with the herbicidal activity of tested compounds, it could be found that their herbicidal activity correlated well with their enzyme inhibition including selectivity and SAR. Based on the general stmcrnre of phosphonates lo, the relationship between enzyme inhibition and herbicidal activity is summarized as follows ... 

The work of several investigators (5, 5-7, 12) has indicated a general, although far from perfect, correlation between toxicity and percentage of sulfonatable residue of oil fractions. The sulfonatable hydrocarbons are the olefins and aromatics. The paraflSnic hydrocarbons do not react in the sulfonation treatment. These results suggest that the olefin and aromatic groups are largely responsible for the herbicidal activity of oils. 

Upchurch, R.P, F.L. Selman, D.D. Mason, and E.J. Kamprath (1966). The correlation of herbicidal activity with soil and climatic factors. Weeds, 14 42 -8. 

Aryloxyphenoxypropanoates and cyclohexanediones are two classes of herbicides that control many monocotyledoneous species. Although these herbicides are structurally very different (Fig. 1), there has been some conjecture that they have a similar mode of action because of their similarity in selectivity and symptomology. This paper describes the experiments that led to the discovery that aryloxyphenoxypropanoate and cyclohexanedione herbicides inhibit acetyl coenzyme A carboxylase (acetyl-coenzyme A bicarbonate ligase [ATP], EC 6.4.1.2) activity in susceptible species (1). In addition, evidence is presented indicating that the inhibition of acetyl coenzyme A carboxylase (ACCase) is well correlated to observed herbicidal activity. Similar, independent findings have recently been reported by two other research groups (2.3). 

Bipyridylium compounds are reduced similarly with one electron per nucleus,122-124 the semiquinone (121) having a considerable stability. An attempt has been made to correlate the electrochemical properties of quaternary bipyridylium salts with their herbicidal activity.125... 

Structure-Activity Correlations for Meta-and Para-Substituted Trifluoromethane-sulfonanilide Pre-Emergence Herbicides... 

Although the complex herbicidal activity and selectivity characteristics of the trifluoromethanesulfonanilides could not be correlated with single molecular properties of series members, we believed that an appro-... 

Therefore, it is difficult to define a reasonable basis for selecting any of the parent series previously examined by Fujita et al. (11) as a model for the TFMS series of the present study. Indeed, some initial Hansch analyses of our TFMS pre-emergence herbicidal activity data using Fujita s phenoxyacetic acid substituent n values produced very poor correlations. We thus deemed it prudent (if not essential) to determine experimentally w values for all substituents in the TFMS series. 

Hansch Correlations of TFMS Herbicidal Activity. Stepwise regression techniques were used to correlate the pre-emergence herbicidal activity data gathered for all three weed types with one or more of the following appropriate general forms of the Hansch equation (cf. Equations 2 and 6 and Table VI). 

For all regression analyses, herbicidal activity data were taken from the appropriate column in Table VII. Corresponding tt, tt or tt" values were selected from Table VI. Hammett sigma constants (a) were taken from the compilation of Jaffe (13) and correspond to those in Table IV. Since it was assumed throughout that <7 would be relatively unaffected by the presence of surfactant, the <7 values in Table IV were used to correlate data obtained in the presence and absence of Tween 80 for all three weed types. This assumption is reasonable since the surfactant was used at a low 0.1% level in all herbicidal and partitioning tests. Furthermore, surfactant effects would be expected to manifest themselves primarily in the partitioning behavior (tt values) of the TFMS compounds... 

Separation of TFMS Herbicides into Meta- and Para-Substituted Series. In our initial correlations, herbicidal activity data from Table VII for each weed type in the presence and absence of Tween 80 were fitted to the appropriate form of Equations 7-9. A typical result is illustrated by the stepwise regression obtained for Foxtail grass in the absence of surfactant ... 

Methylthio TFMS Derivatives. Data for the meta- and para-substituted methylthio TFMS derivatives (3-SCH3, 4-SCH3) were not included in Hansch structure—activity correlations for the several weed types. This omission was a result of our noting that when herbicidal data for the methylthio derivatives were included in fits, much poorer Hansch correlations were obtained. This was true whether or not the TFMS compounds were separated into meta- and para-derivatives for fitting purposes. It was also true for all weed types examined, in the presence and absence of surfactant. A typical example of the improvement in statistical parameters effected by omitting methylthio points from the data pool is illustrated by the following Hansch correlation of TFMS activity on Foxtail grass in the presence of surfactant. 

Optimum Correlation Equations. Table XI gives the final best-fit Hansch relationships which mathematically describe the pre-emergence herbicidal activity of the 3- and 4-substituted TFMS compounds under all conditions examined in this study. The results and conclusions reached... 

Besides providing mathematical relationships correlating structural changes to herbicidal activity variations among TFMS series members, Hansch analyses brought to light other facts which undoubtedly would have been overlooked had the TFMS herbicides not been examined by the Hansch method. In particular, the stepwise regression procedure demonstrated that ... 

Finally, Table XII showed that for both 3- and 4-substituted TFMS series, poorer correlations were obtained when herbicidal data obtained in the presence and absence of Tween 80 for a given series acting on a particular weed type were pooled and fitted simultaneously. Comparing the appropriate surface plots in Figures 2-3 for Foxtail and Wild Mustard, it is evident that poor correlations of the pooled data would have been obtained. The very different graphical curve shapes obtained for each TFMS series in the presence and absence of Tween 80 illustrate why correlations of the pooled data produced poor structure-activity correlations. 

Structure-activity correlations were carried out using least-squares regression analysis techniques on an IBM 360 computer. As in the accompanying publication (6), the data in Tables I and II were fitted to Equation 3 in stepwise fashion. Standard statistical tests were carried out at each stage of fitting to determine the over-all goodness of fit of the x and o- data to the various equational forms examined. As in our previous study (6), the most statistically significant correlations were always obtained when activity data for meta-substituted and para-sub-stituted TFMS herbicides were divided into two discrete series and fitted separately. 

Equation 6 is very useful for estimating changes in the partition coefficient as well as changes in the herbicidal activity of para-substituted TFMS compounds (see below) produced when 0.1% Tween 80 is added to the herbicidal formulations. An expression analogous to Equation 6 can be derived from Equation 5b for meta-substituted TFMS series members although the predictive utility of this latter equation will obviously be limited by the lesser degree of correlation between an(l X ... 

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