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Two significant factors were identi fied. These two principal components explained about 70% of the variance in pharmacological activity of a se ries of oligopeptides. Another multivariate approach to the analysis of chromatographic data of chemically and closely related sol utes resulted in their classification according to their diversified phar macological activity (31). Large sets of polycratic LC capacity factors were determined by using various station ary phases, pH, and mobile-phase compositions, and the data were sub jected to PCA. The first principal c o m p o n e n t ( P C I ) a c c o u n t e d for 60.5% a n d t h e second (PC2) for 18.9% of the variance in the capacity factors considered. Figure 6 shows the positions of the drugs on the plane spanned by the two principal component axes. Be cause of their chromatographic be havior, the solutes can be grouped into three clusters: a, b , and c. Phar macology t e x t b o o k s classify t h e agents belonging to cluster a as se lective agonists of oc2 adrenoceptor, whereas those belonging to cluster c are considered ctj agonists. Imidazo lines belonging to cluster b possess affinity to both subtypes of α adreno ceptors. When applying chromatog raphy to bioactivity prediction, it ap pears to be more productive to collect a representative set of diverse reten tion parameters than to try to deter mine a universal chromatographic measure of hydrophobicity. Future trends QSRR studies are important for un derstanding the phenomena that de termine the physicochemical proper-
log P= 1.49 log k'+ 2.36
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ties of compounds (i.e., their direct interactions with their environment). These studies also have practical ap plications such as predicting reten tion and other physicochemical pa r a m e t e r s ; however, a good QSRR study t h a t is valid for structurally di verse solutes is still needed. The QSRR approach has been use ful with retention data obtained by GC on nonpolar stationary phases and by reversed-phase LC. The mod eling of solute b e h a v i o r in more s t r u c t u r a l l y selective c h r o m a t o graphic systems is much more diffi cult and has been reported only occa sionally. The main problem is the inadequacy of the available descrip tors in representing the structural features that determine retention. It is anticipated t h a t increased ac cess to modern molecular mechanics and quantum chemical software will lead to identification of easily deter mined s t r u c t u r a l p a r a m e t e r s t h a t better account for physicochemical a n d biological p r o p e r t i e s . W h e n structurally dependent retention data can be readily obtained, QSRR studies are preferable for testing new descriptors, and they may be helpful in discerning new means of repre senting chemical structures that ac count not only for reactivity but also for properties. In addition, QSRR equations describing retention on enantioselective, protein-based col u m n s and on the immobilized en-
PC1
log k'
Figure 5. Relationship between logarithms of capacity factors (log k') and logarithms of octanol-water partition coefficients (log P) for a diverse set of nonionized basic, acidic, and neutral solutes. Retention data were determined using a deactivated, chemically stable reversed-phase material.
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630 A · ANALYTICAL CHEMISTRY, VOL. 64, NO. 11, JUNE 1, 1992
Figure 6. Pharmacologically consistent distribution of imidazoline circulatory drugs on the plane determined by two first principal components extracted from a large set of diversified retention data. The drugs are identified as follows: 1, Medetomidine; 2, Detomidine; 3, Xylazine; 4, Moxonidine; 5, UK 14 304 ; 6, Lofexidine; 7, Clonidine; 8, Cirazoline; 9, Tolazine; 10, Tiamenidine; 11, Tetryzoline; 12, Phentolamine; 13, Naphazoline; 14, Antazoline; 15, Tymazoline; 16, Oxymetazoline; 17, Tramazoline; 18, Xylometazoline. (Adapted with permission from Reference 31.)
