Chemical-class-related interactions

A part of the chemical consequences of the cyclic orbital interactions in the cyclic conjngation is well known as the Hueckel rule for aromaticity and the Woodward-Hoffmann rule for the stereoselection of organic reactions [14]. In this section, we describe the basis for the rnles very briefly and other rules derived from or related to the orbital phase theory. The rules include kinetic stability (electron-donating and accepting abilities) of cyclic conjugate molecules (Sect. 2.2.2) and discontinnity of cyclic conjngation or inapplicability of the Hueckel rule to a certain class of conjngate molecnles (Sect. 2.2.3). Further applications are described in Sect. 4. 

From the examination of structure-activity relationships, it has been concluded that a phenyl moiety at C-6 as well as a 4-hydroxypiperidine side-chain attached to C-3 of the pyridazine system is essential for anticonvulsant activity in this class of compounds [184], Compounds (54) and (55) have been found to have similar anticonvulsant profiles in animals (mice, rats and baboons) [165, and literature cited therein] and to represent potent broad-spectrum antiepileptic drugs. Their potency with regard to antagonizing seizures (induced by electro-shock or various chemicals) has been compared with standard anticonvulsants like carbamazepine and phenobarbitone [185, 186], A quantitative electroencephalographic analysis of (55) has been published [187]. From in vitro studies it has been concluded that the anticonvulsant activities of these compounds are not mediated by an enhancement of GABAergic transmission or by an interaction with benzodiazepine receptor sites [ 165,186,187], On the other hand, in vivo experiments showed that (54), at anticonvulsant doses, increases the affinity of flunitrazepam for its central receptor site [ 186], Investigations of (54) and (55) in a behavioural test predictive of antianxiety activity revealed a marked difference in the pharmacological profiles of these structurally closely related compounds the dichloro compound SR 41378 (55) has also been found to possess anxiolytic (anticonflict) properties [165],... 

Whether the prediction scheme is a simple chart, a formula, or a complex numerical procedure, there are three basic elements that must be considered meteorology, source emissions, and atmospheric chemical interactions. Despite the diversity of methodologies available for relating emissions to ambient air quality, there are two basic types of models. Those based on a fundamental description of the physics and chemistry occurring in the atmosphere are classified as a priori approaches. Such methods normally incorporate a mathematical treatment of the meteorological and chemical processes and, in addition, utilize information about the distribution of source emissions. Another class of methods involves the use of a posteriori models in which empirical relationships are deduced from laboratory or atmospheric measurements. These models are usually quite simple and typically bear a close relationship to the actual data upon which they are based. The latter feature is a basic weakness. Because the models do not explicitly quantify the causal phenomena, they cannot be reliably extrapolated beyond the bounds of the data from which they were derived. As a result, a posteriori models are not ideally suited to the task of predicting the impacts of substantial changes in emissions. 

While the relations of chemical size and solubility are gratifying to recognize, we still notice that each compound class exhibits its own behavior (Fig. 5.2). Hence, we may wonder if there is any means to account for variations from compound class to compound class. Based on our visualizations of organic solute intermolecular interactions, it is not surprising to learn that parameters that quantify the importance of interactions like hydrogen bonding can be used to adjust for differences between compound classes. 

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