Theoretical-experimental study on protein-ligand interactions based on thermodynamics methods, molecular docking and perturbation models

The current doctoral thesis focuses on understanding the thermodynamic events of protein-ligand interactions which have been of paramount importance from traditional Medicinal Chemistry to Nanobiotechnology. Particular attention has been made on the application of state-of-the-art methodologies to address thermodynamic studies of the protein-ligand interactions by integrating structure-based molecular docking techniques, classical fractal approaches to solve protein-ligand complementarity problems, perturbation models to study allosteric signal propagation, predictive nano-quantitative structure-toxicity relationship models coupled with powerful experimental validation techniques. The contributions provided by this work could open an unlimited horizon to the fields of Drug-Discovery, Materials Sciences, Molecular Diagnosis, and Environmental Health Sciences

Penitsilliinide jääkide määramine piimas läbivoolulise biosensorsüsteemi abil

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Penitsilliinid on beetalaktaamide hulka kuuluvad antibiootikumid, mida kasutatakse peamiselt Gram-positiivsete bakterite poolt tekitatud haiguste raviks. Penitsilliini ja teiste antibiootikumide jäägid piimas tekitavad inimestel allergilisi reaktsioone ning soodustavad resistentsete mikroobitüvede teket, mistõttu on antibiootikumide jääkide lubatud sisaldus toidus rangelt reguleeritud. Tavaliselt kasutatakse antibiootikumide jääkide määramiseks piimas mitmesuguseid kromatograafial põhinevaid meetodeid ning erinevaid mikroobse inhibeerimise ja immuunoretseptor teste. Kuid tihtipeale on need meetodid kallid ning aeganõudvad. Üheks võimalikuks alternatiiviks traditsioonilistele analüüsimeetoditele on biosensorite kasutamine. Biosensorite eeliseks on nende lihtsus, suhteline odavus ning kiirus, mis võimaldab nende kasutamist kiireteks analüüsideks reaalajas. Käesoleva doktoritöö eesmärgiks oli välja töötada ning testida biosensorsüsteem penitsilliinide jääkide kiireks määramiseks toorpiimas. Selleks, et kiirendada eksperimentaalsete andmete analüüsi, me pakkusime välja matemaatiline mudel biosensori kalibratsiooniparameetrite arvutamiseks ning uurisime võimalusi biosensorites toimuvate äratundmisreaktsioonide kiirendamiseks. Töö praktilises osas testisime biosensori kasutamist penitsilliinide jääkide kiireks määramiseks toorpiimas. Töö tulemusena leidsime, et glükoosi tase piimas on heaks indikaatoriks penitsilliini jääkide määramiseks toorpiimas. Seega, kasutatud biosensorsüsteem on rakendatav kiireks penitsillinide jääkide määramiseks toorpiimas piimafarmides. Kiire lüpstava piima analüüs võimaldaks mittekvaliteetse piima õigeaegset eraldamist kvaliteetsest toodangust ning kogu toodetava piima kvaliteedi tõstmist. Kasutatud biosensorsüsteemi on erinevate antibiotikumide jääkide määramiseks toorpiimas võimalik tulevikus modifitseerida täiendavate biosensorite lisamisega.The use of antibiotics for the treatment of food-producing animals generates the risk to human health due to the transmission of the residues and metabolites of these compounds into food chain. In addition, scientists and health experts also fear that wide application of antimicrobial agents, including the first discovered penicillin antibiotics, is contributing to the rise and spread of antibiotic-resistant bacteria. At present, strict regulations have been established for the levels of antibiotic residues and metabolites in food of animal origin. Antibiotic residues in food are commonly determined with the help of chromatography and special tests. The application of biosensors for the detection of antibiotic residues in milk is a good alternative to traditional methods. The benefits of biosensors are their low cost, simplicity and possibility for rapid real-time analysis. The main goal of the present work was to propose a rapid method for real-time detection of penicillins’ residues in milk, to propose a simple but sufficiently accurate model to describe the quick response of biosensor and to test this biosensorsystem. The application of the model allowed to predict optimal biosensor parameters for obtaining maximal sensitivity and high stability on one hand, and to obtain fast results from the initial phase of the reaction on the other hand. The biosensor was applied to detect the penicillins in the milk of cows with mastitis. Glucose concentration in their milk decreased significantly compared to glucose levels in high quality milk, enabling to use glucose concentration as an indicator of the presence of penicillin residues in milk. The studied biosensor set-up has high potential to serve as a system for real-time automatic control of the quality of raw milk in the milk production farms. The application of this system allows the separation of substandard milk from the milk flow prior to milk collection tank. The system can be further modified by attaching additional biosensors to build up a more robust biosensor array, where the signals of individual biosensors form a typical pattern of milk sample, which changes in the presence of different antibiotics

The Use of Pseudo-Equilibrium Constant Affords Improved QSAR Models of Human Plasma Protein Binding

To develop accurate in silico predictors of Plasma Protein Binding (PPB)

An updated review on drug-induced cholestasis: Mechanisms and investigation of physicochemical properties and pharmacokinetic parameters

Drug-induced cholestasis is an important form of acquired liver disease and is associated with significant morbidity and mortality. Bile acids are key signaling molecules, but they can exert toxic responses when they accumulate in hepatocytes. This review focuses on the physiological mechanisms of drug-induced cholestasis associated with altered bile acid homeostasis due to direct (e.g. bile acid transporter inhibition) or indirect (e.g. activation of nuclear receptors, altered function/expression of bile acid transporters) processes. Mechanistic information about the effects of a drug on bile acid homeostasis is important when evaluating the cholestatic potential of a compound, but experimental data often are not available. The relationship between physicochemical properties, pharmacokinetic parameters, and inhibition of the bile salt export pump (BSEP) among seventy-seven cholestatic drugs with different pathophysiological mechanisms of cholestasis (i.e. impaired formation of bile vs. physical obstruction of bile flow) was investigated. The utility of in silico models to obtain mechanistic information about the impact of compounds on bile acid homeostasis to aid in predicting the cholestatic potential of drugs is highlighted

Review of QSAR Models and Software Tools for predicting Biokinetic Properties

In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog