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closely related to the initial lead compound, but being altered progressively by addition reaction chemistry along well-defined structure activity relationships. This has allowed companies to not only synthesize whole libraries of new compounds (> 10,000) in a few weeks, but also to test them for up to 20 different activities at the same time. This enormous number of potential new molecules must be reduced to a handful of candidate drugs that can be taken into the costly development scheme. In the past, some basic pharmacology in a few animals together with limited acute toxicity was normally sufficient, but today there is an increasing reliance on kinetics and metabolic data to predict both efficacy and safety in man, providing additional information to make such a choice. But how can useful ADME information on the likely kinetics and metabolism in man be obtained at such an early stage and for potentially so many compounds? |
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The amount of information that is now being generated in medical and pharmaceutical research is enormous. Keeping abreast of these data and trying to put them into some meaningful framework is becoming increasingly difficult. The complexities and interrelation of the physiological systems are too great for our minds to comprehend and we continue to myopically concentrate on one small area of interest without necessarily seeing how everything fits together. With the rapid advances in drug metabolism and kinetics the same problems are arising and we must turn to the new developing science of artificial intelligence and expert systems to progress more efficiently [5]. |
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1. Quantitative Structure Pharmacokinetic Relationships (QSPR) |
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For many years, it has been possible to relate the measurements of the physicochemical properties of a compound within a particular series with its potential activity using Hansch-type relationships [6]. Since passive diffusion and nonspecific binding to macromolecules throughout the body are related to the same physicochemical measures, it follows that a comparable form of analysis could be equally applied to biodisposition [7,8]. Certainly for simple kinetic parameters, such as the volume of distribution, which is directly related to lipid solubility, QSPR can be successfully used as a predictor, even for those compounds that are not congeners within the same series [9]. However, these methods have rarely been used for other parameters such as clearance. These processes are dependent on active enzymatic reactions that are more related to the 3-dimensional structure of the molecule. However, in any one homologous series that is being screened, differences in metabolism may be dependent on relatively simple structural differences. Thus, for the glucuronidation of phenols, alcohols and benzoic acids, optimal rates of metabolism by UDP-glu- |
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