Application Of Mathematical Modeling In Medicinal Chemistry And Metabolism In The Rat Brain

Nowadays, structure activity-property relationship (SAR/SPR) approach is avery popular method in many research groups. Over the past twenty years a largenumber of papers has been published every year and the number continues to rise. Theaims of the SAR/SPR method are very broad, including various physical-chemicalproperties of substances, biology activity, toxicology, bioavailability, etc, and its'research area is related to chemistry, biology, drugs and environmental chemistry.Therefore, the development of this approach will drive the advancement of the crossdiscipline.In chemiformatics, this method only utilizes the information of themolecular structures, and calculated multifarious physical-chemical parameters usingtheoretical computation approaches. Using these parameters and the selected trainingset, some mathematical methods, such as heuristic method, genetic algorithm, lineardiscriminant analysis, etc, are used to select the most important descriptors, and thenconstruct many different linear or non-linear models. Using these models, researcherscan successfully predict the properties and activities of the compounds. At last, thisapproach also provides some important information, which can be used to discuss thebasic theory of the activities and the influence factors of the properties on molecularlevel.In the first part of this dissertation, we discuss the application of SAR/SPRmethod in the physical-chemical properties of substrates and drug screen domain. Thefocus of this dissertation is on an improved new machine learning method: grid searchsupport vector machine (GS-SVM). Using this method, we build efficient, and stablequantitative structure-property relationship (QSPR) and classification structure- activity relationship (CSAR) models. At last, this dissertation also covers theapplication of mathematical modeling to the rat brain's metabolism, and in particularthe influence of nicotine in the rates of different rat brain regions. This dissertationconsists of four chapters:The first chapter discusses the machine learning method and the statisticallearning theory; then describes the basic theory of support vector machine algorithm,and also summarizes the other classification methods. At last, we describe the basictheory of QSAR methods, the main steps, the stability and reliability of the models.In the second chapter, we investigate the application of QSPR method in thedomain of prediction of the properties of substrates. It consists of two separate parts:(a) The QSPR method was developed to predict power rotation of 18 kinds ofnecessary amino acids. The heuristic method (HM) was utilized to select the mostimportant descriptors which were calculated from the molecular structures alone, andto build a linear regression model at the same time. The coefficient of determination(R~2) of this model is 0.918. In order to build a more reliable model, another descriptor-molecular chirality was added (+1 represent left hand, and -1 represent right hand)into the pool of former selected descriptors, and got much better results-R~2=0.970.The work provides a new and efficient way to investigate the power rotation of chiralcompounds. (b) The heuristic method and support vector machine were used toconstruct linear and non-linear regression models to predict 196 compounds' surfacetension. By comparing both of the models, the non-linear regression SVM model getsmuch better results than the linear one, and the coefficient of determination and factorof error were 0.9348 and 0.9097, 1.22 and 1.07 for the training and test set,respectively. This study provides a new method for the research of surface chemistry. The third chapter detailed introduce an improved support vector machinemethod - grid-search support vector machine (GS-SVM), and also discuss its'application in classification area. This chapter consists of three sections: (a) The GSSVMmethod was used for the classification of the anti-HIV activity of 141 kinds ofnucleosides derivatives. At first, the stepwise linear discriminant analysis method wasused to select the major descriptors which were significantly influence the anti-HIVactivity, and build a dual linear classification model. The predictability of this modelis 83.0% and 88.6% for the training set and test set separately. In order to arrive at amore accurate model, another non-linear classification model - GS-SVM - wasconstructed using the same selected descriptors, and got better results, 91.5% (trainingset), 91.4% (test set). This study provides a new approach to guide the research on theanti-HIV activity of nucleoside derivatives. (b) Using classification structure-activityrelationship (CSAR) method, the genotoxicity property of thiophene derivatives wasinvestigated. In this project, the stepwise LDA method was used to select the mostimportant descriptors, which correlated strongly with genotoxicity, and build a linearclassification model at the same time. Using the selected parameters and improvedsupport vector machine method (GS-SVM), another non-linear classification modelwas finish. By comparing the results of these two models, the GS-SVM methodprovides a more accurate predictions: 92.9% for the training set, and 92.6% for thetest set. At the same time, some important information was obtained by theinterpretation of the selected descriptors. (c) The LDA and GGS-SVM methods wereseparately used again to build a linear and non-linear classification model for 167kinds of drugs' bioavailability. Turner and his co-workers utilized regression methodsto research the bioavailability and got some results that were not promising. In thiswork, we used another way to study it, and got better results. By comparing the two generated models, the GS-SVM models give much better predicted results: 85.82%(training set), 84.85% (test set) and 85.63% (all data set). Thus this investigationprovides a new approach to investigate the bioavailability.The first three chapters were finish in Lanzhou University, under the supervisionof Prof. Zhide Hu, and the last chapter was finish in the school of Medicine, YaleUniversity, under the supervision of Dr. Graeme F. Mason. In this chapter, themathematical modeling method was used to research the total quantity of metabolitesand the metabolic rates in different regions of the rat brain. At first, the classical t-testmethod was used to analyze the effect of nicotine on the individual parameters(different metabolites and different regions) of total concentration of metabolites. Theresults indicated that the following parameters were significantly changed after a doseof subcutaneously injected nicotine: striatum (GABA, glutamate, and NAA), parietalcortex (creatine, glutamate and NAA), frontal cortex (NAA), temporal cortex (alanine,choline), medulla (aspartate, glutamate), and olfactory bulb (NAA). By comparing thesame compounds, in different regions, we found that NAA was significantlydecreased in every region. Later, the LDA method was used to separate the 38 ratsinto two different groups (saline and nicotine), using the parameters different regions(except olfactory bulb) multiply different compounds (except lactate). Thisclassification model only gave one wrong rat. The results indicated that nicotine hadeffect on the metabolism of the rat brain. At last, the metabolic rates in differentregions and the effect of nicotine were determined using the 13C labeled glutamateC4, glutamine C4 and GABA C2.