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Reverzno-faznom tečnom hromatografijom pod visokim pritiskom primenom dva sistemarastvarača ispitano je ponašanje i hromatografska lipofilnost prirodnih stiril laktona 7-(+)-goniofufurona, 7-epi-(+)-goniofufurona, krasalaktona B i C i dvadeset njihovihnovosintetizovanih derivata i analoga. U ranijim ispitivanjima pokazalo se da ova jedinjenjaimaju veliki biološki potencijal jer pokazuju zapaženu citotoksičnost prema više humanihtumorskih ćelijskih linija. Hromatografsko ponašanje jedinjenja uglavnom je u skladu sanjihovom strukturom. Ustanovljene su linearne veze između hromatografskih retencionihkonstanti i većine in silico parametara lipofilnosti. Primenom hemometrijske QSRR analizeutvrđeni su veoma dobri multi linearni regresioni prediktivni modeli kvantitativne zavisnostiizmeđu eksperimentalno dobijene hromatografske retencione konstante, koja definišeretenciju jedinjenja u čistoj vodi i in silico molekulskih deskriptora odnosno strukturejedinjenja. Lipofilnost jedinjenja ima najveći uticaj na njihove farmakokinetičke, tj. ADME(apsorpcija, distribucija, metabolizam, eliminacija) osobine. Definisani su i statističkipotvrđeni najbolji multi linearni regresioni modeli zavisnosti farmakokinetičkih parametarastiril laktona i od drugih molekulskih deskriptora. In vitro citotoksična aktivnost jedinjenjaevaluirana je prema četiri nove humane maligne ćelijske linije: kancer prostate (PC3), kancer debelog creva (HT-29), melanom (Hs294T), adenokancer pluća (A549). Najaktivnijenovosintetizovano jedinjenje je triciklični 4-fluorocinamatni analog, koji ispoljavananomolarnu aktivnost (IC50 2,1 nM) prema ćelijama melanoma i aktivniji je preko 2250 puta od komercijalnog antitumorskog agensa doksorubicina (DOX). SAR analizom utvrđena je zavisnost između strukture i biološke aktivnosti jedinjenja. Molekulskim dokingom ispitana je veza stiril laktona i ciljanog proteina značajnog za kancer prostate. Jedinjenja sa visokom inhibitornom aktivnošću prema ćelijama kancera prostate imaju visok doking skor i mogu graditi koordinativno-kovalentnu vezu sa Fe2+jonom prisutnim u aktivnom centru enzima. 3D-QSAR analizom, koja je izvedena metodama komparativnih polja CoMFA i CoMSIA, formiran je značajan prediktivni model između hemijske strukture i biološke aktivnosti stiril laktona., The behavior and the chromatographic lipophilicity natural styryl lactone 7-(+)-goniofufurone, 7-epi-(+)-goniofufurone, crassalactones B and C and twenty of their newlysynthesized derivatives and analogs were examined using reverse-phase high performance liquid chromatography in the two solvent systems. In previous studies it has been shown that these compounds have great biological potential toward several human tumor cell lines. Chromatographic behavior of the compounds is generally in accordance with their structure. The relationships between the chromatographic retention constants and the majority of their in silico lipophilicity parameters are linear. The application of chemometric QSRR analysis determined very good multiple linear regression predictive models of quantitative correlation between experimentally obtained chromatographic retention constant, which determines the retention of the compound in pure water and in silico molecular descriptors, i.e. the structure of the compound. The lipophilicity of the compounds has a major influence on their pharmacokinetics, i.e. ADME (absorption, distribution, metabolism, elimination) properties. The best multi-linear regression models depending on the pharmacokinetic parameters of styryl lactone and other molecular descriptors have been defined and statistically validated. In vitro cytotoxic activity of the compounds was evaluated according to four novel human malignant cell lines: prostate cancer (PC3), colon cancer (HT-29), melanoma (Hs294T), lung adenocarcinom (A549). The most active compound was tricyclic 4-fluorocinnamic analog, which exhibits a nanomolar activity (IC50 2,1 nM) toward melanoma cells. This compound is over 2250 times more active than commercial antitumor agent doxorubicin (DOX). SAR analysis has revealed a correlation between the structure and the biological activity of the compounds. Using the molecular docking the relationship of the styryl lactone and the target protein important for prostate cancer was examined. The compounds with high inhibitory activity against prostate cancer cells have a high docking score and are capable to form a coordinative-covalent bond with a Fe2+ ion present in the active centre of the enzyme. 3DQSAR analysis, which was performed by methods of comparative CoMFA and CoMSIA fields, has formed a good predictive model between chemical structure and biological activity of the styryl lactone.

Reverzno-faznom tečnom hromatografijom pod visokim pritiskom primenom dva sistemarastvarača ispitano je ponašanje i hromatografska lipofilnost prirodnih stiril laktona 7-(+)-goniofufurona, 7-epi-(+)-goniofufurona, krasalaktona B i C i dvadeset njihovihnovosintetizovanih derivata i analoga. U ranijim ispitivanjima pokazalo se da ova jedinjenjaimaju veliki biološki potencijal jer pokazuju zapaženu citotoksičnost prema više humanihtumorskih ćelijskih linija. Hromatografsko ponašanje jedinjenja uglavnom je u skladu sanjihovom strukturom. Ustanovljene su linearne veze između hromatografskih retencionihkonstanti i većine in silico parametara lipofilnosti. Primenom hemometrijske QSRR analizeutvrđeni su veoma dobri multi linearni regresioni prediktivni modeli kvantitativne zavisnostiizmeđu eksperimentalno dobijene hromatografske retencione konstante, koja definišeretenciju jedinjenja u čistoj vodi i in silico molekulskih deskriptora odnosno strukturejedinjenja. Lipofilnost jedinjenja ima najveći uticaj na njihove farmakokinetičke, tj. ADME(apsorpcija, distribucija, metabolizam, eliminacija) osobine. Definisani su i statističkipotvrđeni najbolji multi linearni regresioni modeli zavisnosti farmakokinetičkih parametarastiril laktona i od drugih molekulskih deskriptora. In vitro citotoksična aktivnost jedinjenjaevaluirana je prema četiri nove humane maligne ćelijske linije: kancer prostate (PC3), kancer debelog creva (HT-29), melanom (Hs294T), adenokancer pluća (A549). Najaktivnijenovosintetizovano jedinjenje je triciklični 4-fluorocinamatni analog, koji ispoljavananomolarnu aktivnost (IC50 2,1 nM) prema ćelijama melanoma i aktivniji je preko 2250 puta od komercijalnog antitumorskog agensa doksorubicina (DOX). SAR analizom utvrđena je zavisnost između strukture i biološke aktivnosti jedinjenja. Molekulskim dokingom ispitana je veza stiril laktona i ciljanog proteina značajnog za kancer prostate. Jedinjenja sa visokom inhibitornom aktivnošću prema ćelijama kancera prostate imaju visok doking skor i mogu graditi koordinativno-kovalentnu vezu sa Fe2+jonom prisutnim u aktivnom centru enzima. 3D-QSAR analizom, koja je izvedena metodama komparativnih polja CoMFA i CoMSIA, formiran je značajan prediktivni model između hemijske strukture i biološke aktivnosti stiril laktona., The behavior and the chromatographic lipophilicity natural styryl lactone 7-(+)-goniofufurone, 7-epi-(+)-goniofufurone, crassalactones B and C and twenty of their newlysynthesized derivatives and analogs were examined using reverse-phase high performance liquid chromatography in the two solvent systems. In previous studies it has been shown that these compounds have great biological potential toward several human tumor cell lines. Chromatographic behavior of the compounds is generally in accordance with their structure. The relationships between the chromatographic retention constants and the majority of their in silico lipophilicity parameters are linear. The application of chemometric QSRR analysis determined very good multiple linear regression predictive models of quantitative correlation between experimentally obtained chromatographic retention constant, which determines the retention of the compound in pure water and in silico molecular descriptors, i.e. the structure of the compound. The lipophilicity of the compounds has a major influence on their pharmacokinetics, i.e. ADME (absorption, distribution, metabolism, elimination) properties. The best multi-linear regression models depending on the pharmacokinetic parameters of styryl lactone and other molecular descriptors have been defined and statistically validated. In vitro cytotoxic activity of the compounds was evaluated according to four novel human malignant cell lines: prostate cancer (PC3), colon cancer (HT-29), melanoma (Hs294T), lung adenocarcinom (A549). The most active compound was tricyclic 4-fluorocinnamic analog, which exhibits a nanomolar activity (IC50 2,1 nM) toward melanoma cells. This compound is over 2250 times more active than commercial antitumor agent doxorubicin (DOX). SAR analysis has revealed a correlation between the structure and the biological activity of the compounds. Using the molecular docking the relationship of the styryl lactone and the target protein important for prostate cancer was examined. The compounds with high inhibitory activity against prostate cancer cells have a high docking score and are capable to form a coordinative-covalent bond with a Fe2+ ion present in the active centre of the enzyme. 3DQSAR analysis, which was performed by methods of comparative CoMFA and CoMSIA fields, has formed a good predictive model between chemical structure and biological activity of the styryl lactone.

In this study, a quantitative structure-pharmacokinetic relationship (QSPkR) model for the volume of distribution (Vd) values of 126 anti-infective drugs in humans was developed employing multiple linear regression (MLR), artificial neural network (ANN) and support vector regression (SVM) using theoretical molecular structural descriptors. A correlation-based feature selection (CFS) was employed to select the relevant descriptors for modeling. The model results show that the main factors governing Vd of anti-infective drugs are 3D molecular representations of atomic van der Waals volumes and Sanderson electronegativities, number of aliphatic and aromatic amino groups, number of beta-lactam rings and topological 2D shape of the molecule. Model predictivity was evaluated by external validation, using a variety of statistical tests and the SVM model demonstrated better performance compared to other models. The developed models can be used to predict the Vd values of anti-infective drugs., U radu je određen kvantitativni odnos strukture i farmakokinetičkih parametara (QSPkR) za volumen distribucije (Vd) 126 antiinfektivnih lijekova u ljudi koristeći višestruku linearnu regresiju (MLR), umjetne neuronske mreže (ANN), regresiju potpornim vektorima (SVM) i teorijske molekulske deskriptore. Selekcija na temelju korelacije (CFS) upotrjebljena je za izbor relevantnih deskriptora za modeliranje. Rezultati su pokazali da su glavni faktori koji utječu na Vd antiinfektivnih lijekova 3D molekulski prikaz van der Waalsovih volumena atoma i Sandersonove elektronegativnosti, broj alifatskih i aromatskih skupina, broj beta-laktamskih prstena i topološki 2D oblik molekule. Prediktivnost modela procijenjena je vanjskom validacijom, koristeći različite statističke testove. SVM model pokazao se boljim od ostalih modela. Razvijeni model može se upotrijebiti za predviđanje vrijednosti Vd antiinfektivnih lijekova.

Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.