20.500.12556/RUL-189992 Napovedovanje biofarmacevtskih lastnosti bisfenolov in razvoj fizioloških farmakokinetičnih modelov Prediction of biopharmaceutical properties of bisphenols and development of physiologically-based pharmacokinetic models Pri razvoju novih zdravil moramo testirati več tisoč kandidatnih spojin ter oceniti njihove biofarmacevtske lastnosti in njihovo primernost za nadaljnji razvoj. Za oceno primernosti molekule za zdravilo so pomembni tudi procesi absorpcije, porazdelitve, presnove in izločanja, ki skupaj predstavljajo biofarmacevtske lastnosti spojine. Čeprav eksperimentalni podatki predstavljajo zlati standard za ocenjevanje teh lastnosti molekul, je določanje procesov in vitro pogosto dolgotrajno in drago, pri čemer je prenos rezultatov v nadaljnje in vivo okolje pogosto povezan z določeno stopnjo negotovosti. Mnogo učinkovin je zato v razvoju neuspešnih. Za premagovanje teh ovir lahko uporabljamo računalniške metode, ki nadomeščajo klasične eksperimentalne pristope in vitro, s hitrejšimi in učinkovitejšimi računalniškimi pristopi in silico. Ti s pomočjo strojnega ali globokega učenja ocenijo biofarmacevtski profil molekul ter omogočajo optimizacijo kandidatnih spojin. Na podlagi pridobljenega profila lahko nato razvijemo fiziološko osnovane farmakokinetične modele, s katerimi simuliramo procese absorpcije, porazdeljevanja, presnove in izločanja in natančneje ocenjujemo in napovedujemo farmakokinetične profile. Cilj magistrske naloge je bil napovedati biofarmacevtske lastnosti za bisfenol A in skupino njegovih analogov s pomočjo trinajstih prostodostopnih spletnih aplikacij. Ovrednotili smo napovedno zmogljivost, skladnost napovedi ter uporabnost teh orodij. V ta namen, smo razvili in validirali poenostavljena fiziološka farmakokinetična modela za bisfenol A in bisfenol S za intravensko aplikacijo pri podgani. Za razvoj fizioloških farmakokinetičnih modelov smo uporabili dva različna pristopa in programsko orodje PK-Sim. Razvite modele smo validirali z literaturnimi rezultati izbranih in vivo študij pri podganah. Pri tem smo ugotavljali vpliv različnih farmakokinetičnih lastnosti na napovedno moč modela ter ovrednotili njihovo točnost in natančnost pri napovedi farmakokinetičnega profila.Ocenili smo uporabnost izbranih pristopov uporabe spletnih aplikacij za razvoj modelov. Ugotovili smo, da aplikacija ADMETlab verzija 3.0 omogoča najširši obseg napovednih parametrov in vsesplošno zanesljivost napovedi. Točne napovedi sta dajali tudi DeepPK in PreADMET. Potrdili smo, da je za rešetanje spojin potrebno uporabiti več strežnikov. Najvišjo skladnost napovedi smo ugotovili za parameter lipofilnost, medtem ko je bila najbolj variabilna napoved proste frakcije učinkovine v plazmi. Uspešno smo razvili in validirali modele za intravensko dajanje, vendar so dosegali nizko točnost zaradi poenostavitve modelov ter neupoštevanja enterohepatičnega kroženja spojine pri podganah. Vsi razviti modeli so napovedali farmakokinetične parametre znotraj 2-kratnika eksperimentalnih vrednosti. Pri razvoju modelov v okolju PK-Sim smo ugotovili, da uporaba mediane ocen biofarmacevtskih lastnosti s spletnimi aplikacijami zagotavlja najvišjo natančnost. During drug development, several thousand candidate compounds must be screened and evaluated for their biopharmaceutical properties to determine their suitability as drug candidates. In assessing their drug-likeness, particular importance is attributed to the processes of absorption, distribution, metabolism, excretion, and toxicity, together referred to as biopharmaceutical drug properties. Although experimental in vitro determination of these properties represents the gold standard, it is often time-consuming and costly, while the obtained results do not always translate into successful in vivo outcomes. Consequently, most drug candidates fail during development. To overcome these limitations, computational methods have been developed as faster and more efficient option. These platforms use machine learning and deep learning algorithms to predict biopharmaceutical profile and support compound optimization. The predicted parameters can further be used for the development of physiologically based pharmacokinetic (PBPK) models to simulate systemic drug distribution and a more accurate evaluation of pharmacokinetic profiles. The aim of this master’s thesis was to predict the biopharmaceutical properties of bisphenol A and several bisphenol analogues using 13 free web-based applications. The predictive performance, concordance and applicability of these tools were evaluated. For this purpose, simplified physiologically based pharmacokinetic models for bisphenol A and bisphenol S were developed for intravenous administration in rats. Physiologically based pharmacokinetic models were developed using two different approaches and the PK-Sim software. The developed models were validated against data from selected in vivo studies in rats reported in the literature. The influence of different pharmacokinetic parameters on model prediction performance was investigated, and the developed models were evaluated in terms of accuracy, precision of predicted pharmacokinetic profiles. Applicability of the selected approach of using web-based applications in model development was assessed. . ADMETlab version 3.0 was found to provide the broadest prediction coverage and the highest overall reliability. Accurate predictions were also generated by DeepPK and PreADMET. The results confirmed that multiple prediction platforms should be used for compound evaluation. The highest agreement among predictions was observed for lipophilicity, whereas the greatest variability was observed for plasma unbound fraction predictions. Simplified models for intravenous administration were successfully developed and validated; however, their predictive accuracy remained limited due to model simplifications and the exclusion of enterohepatic recirculation in rats. Nevertheless, all developed models predicted pharmacokinetic parameters within a two-fold range of experimental values. The highest precision in PK-Sim was achieved when median biopharmaceutical values obtained from web-based platforms were applied. biofarmacevtske lastnosti bisfenol A bisfenol S prostodostopne spletne platforme fiziološki farmakokinetični modeli biopharmaceutical properties bisphenol A bisphenol S free web-based prediction platforms physiologically based pharmacokinetic model true false false [E. Turk] Slovenski jezik Angleški jezik Magistrsko delo/naloga 2026-10-20 08:45:07 2026-10-20 08:45:19 2026-10-21 04:19:33 0000-00-00 00:00:00 2026 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1970-01-01 615.015(043.2) 133403 301675779 36111.pdf 36111.pdf 1 321EA586B95D2146B75B4DE36F018746 6ee5d620871e34a53cb9aa2f82dfba0990bb47f4bd4e8a42c11fca8fbc6b62bc a31d84be-cc51-11f1-8bc5-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=250953 Fakulteta za farmacijo 0 0 0