<p>Steroidna jedinjenja spadaju u grupu supstanci sa &scaron;irokim spektrom biolo&scaron;kog delovanja i predstavljaju dobru polaznu osnovu za sintezu mnogih derivata sa željenim biolo&scaron;kim potencijalom. Organskim sintezama se do&scaron;lo do velikog broja steroidnih derivata, od kojih su neki pokazali značajnu biolo&scaron;ku aktivnost, kao &scaron;to je citotoksičnost prema različitim ćelijskim linijama kancera. Karakterizacija novosintetisanih jedinjenja može se izvesti eksperimentalnim i računarskim (in silico) metodama. U ovoj doktorskoj disertaciji predstavljeno je eksperimentalno određivanje lipofilnosti 17&alpha;-pikolil i 17(E)-pikoliniliden androstanskih derivata primenom visokopritisne tečne hromatografije na obrnutim fazama, a potom hemometrijska analiza hromatografskog pona&scaron;anja (hromatografske lipofilnosti) QSRR pristupom. Hemijska struktura analiziranih derivata opisana je numerički, pomoću izračunatih molekulskih deskriptora. U drugom delu doktorske disertacija predstavljena je QSAR analiza citotoksične aktivnosti 17&alpha;-pikolil i 17(E)-pikoliniliden androstanskih derivata prema ćelijama androgen-receptor negativnog kancera prostate (AR-neg. PC-3). Odabir najkvalitetnijih QSRR i QSAR modela obavljen je na osnovu izračunatih statističkih parametara, a njihovo rangiranje izvedeno je primenom metode sume razlika rangova (SRD). Pored regresionih QSRR i QSAR hemometrijskih metoda, primenjene su i klaster analiza i analiza glavnih komponenata sa ciljem utvrđivanja sličnosti ili razlika između analiziranih derivata na<br />osnovu izračunatih molekulskih deskriptora.</p> / <p>Steroidal compounds belong to the group of substances with wide spectrum of biological activity and represent the basic material for synthesis of many derivatives<br />with preferred biological potential. A grate number of steroidal derivatives have been<br />obtained through organic syntheses, many of which have demonstrated significant<br />biological activity, such as cytotoxicity toward various cancer cell lines. Characterization of newly synthesized compounds can be achieved experimentally<br />and by computational approach (in silico). This doctoral dissertation describes<br />experimental determination of lipophilicity of 17&alpha;-picolyl and 17(E)-picolinylidene<br />androstane derivatives applying reversed-phase high pressure liquid chromatography followed by quantitative structure-retention relationship (QSRR)<br />chemometric analysis of chromatographic behaviour (chromatographic lipophilicity).<br />Chemical structure of the analyzed derivatives was described numerically by in silico<br />molecular descriptors. The second part of this dissertation describes quantitative<br />structure-activity relationship (QSAR) analysis of cytotoxic activity of 17&alpha;-picolyl and<br />17(E)-picolinylidene androstane derivatives toward androgen-receptor negative<br />prostate cancer cell line (AR-neg. PC-3). Selection of the best QSRR and QSAR<br />models was carried out based on their statistical parameters, and their ranking was<br />done by sum of ranking differences (SRD) method. Besides the regression QSRR and QSAR chemometric methods, cluster analysis and principal components analysis were conducted in order to reveal possible similarities and dissimilarities among the studied derivatives on the basis of calculated molecular descriptors.</p>

According to the REACH regulation chemicals produced or imported to the European Union need to be assessed to manage the risk of potential hazard to human health and the environment. An increasing number of chemicals in commerce prompts the need for utilizing faster and cheaper alternative methods for this assessment, such as quantitative structure-activity or property relationships (QSARs or QSPRs). QSARs and QSPRs are models that seek correlation between data on chemicals molecular structure and a specific activity or property, such as environmental fate characteristics and (eco)toxicological effects. The aim of this thesis was to evaluate and develop models for the hazard assessment of industrial chemicals and the exposure assessment of pharmaceuticals. In focus were the identification of chemicals potentially demonstrating carcinogenic (C), mutagenic (M), or reprotoxic (R) effects, and endocrine disruption, the importance of metabolism in hazard identification, and the understanding of adsorption of ionisable chemicals to sludge with implications to the fate of pharmaceuticals in waste water treatment plants (WWTPs). Also, issues related to QSARs including consensus modelling, applicability domain, and ionisation of input structures were addressed. The main findings presented herein are as follows: QSARs were successful in identifying almost all carcinogens and most mutagens but worse in predicting chemicals toxic to reproduction. Metabolic activation is a key event in the identification of potentially hazardous chemicals, particularly for chemicals demonstrating estrogen (E) and transthyretin (T) related alterations of the endocrine system, but also for mutagens. The accuracy of currently available metabolism simulators is rather low for industrial chemicals. However, when combined with QSARs, the tool was found useful in identifying chemicals that demonstrated E- and T- related effects in vivo. We recommend using a consensus approach in final judgement about a compound’s toxicity that is to combine QSAR derived data to reach a consensus prediction. That is particularly useful for models based on data of slightly different molecular events or species. QSAR models need to have well-defined applicability domains (AD) to ensure their reliability, which can be reached by e.g. the conformal prediction (CP) method. By providing confidence metrics CP allows a better control over predictive boundaries of QSAR models than other distance-based AD methods. Pharmaceuticals can interact with sewage sludge by different intermolecular forces for which also the ionisation state has an impact. Developed models showed that sorption of neutral and positively-charged pharmaceuticals was mainly hydrophobicity-driven but also impacted by Pi-Pi and dipole-dipole forces. In contrast, negatively-charged molecules predominantly interacted via covalent bonding and ion-ion, ion-dipole, and dipole-dipole forces. Using ionised structures in multivariate modelling of sorption to sludge did not improve the model performance for positively- and negatively charged species but we noted an improvement for neutral chemicals that may be due to a more correct description of zwitterions.   Overall, the results provided insights on the current weaknesses and strengths of QSAR approaches in hazard and exposure assessment of chemicals. QSARs have a great potential to serve as commonly used tools in hazard identification to predict various responses demanded in chemical safety assessment. In combination with other tools they can provide fundaments for integrated testing strategies that gather and generate information about compound’s toxicity and provide insights of its potential hazard. The obtained results also show that QSARs can be utilized for pattern recognition that facilitates a better understanding of phenomena related to fate of chemicals in WWTP. / Enligt kemikalielagstiftningen REACH måste kemikalier som produceras i eller importeras till Europeiska unionen riskbedömas avseende hälso- och miljöfara. Den ökande mängden kemikalier som används i samhället kräver snabbare och billigare alternativa riskbedömningsmetoder, såsom kvantitativa struktur-aktivitets- eller egenskapssamband (QSARs eller QSPRs). QSARs och QSPRs är datamodeller där samband söks korrelationer mellan data för kemikaliers struktur-relaterade egenskaper och t.ex. kemikaliers persistens eller (eko)toxiska effekter. Målet med den här avhandlingen var att utvärdera och utveckla modeller för riskbedömning av industri kemikalier och läkemedel för att studera hur QSARs/QSPRs kan förbättra riskbedömningsprocessen. Fokus i avhandlingen var utveckling av metoder för identifiering av potentiellt cancerframkallande (C), mutagena (M), eller reproduktionstoxiska (R) kemikalier, och endokrint aktiva kemikalier, att studera betydelsen av metabolism vid riskbedömning och att öka vår förståelse för joniserbara kemikaliers adsorption till avloppsslam. Avhandlingen behandlar även konsensusmodellering, beskrivning av modellers giltighet och betydelsen av jonisering för kemiska deskriptorer. De huvudsakliga resultaten som presenteras i avhandlingen är: QSAR-modeller identifierade nästan alla cancerframkallande ämnen och de flesta mutagener men var sämre på att identifiera reproduktionstoxiska kemikalier. Metabolisk aktivering är av stor betydelse vid identifikationen av potentiellt toxiska kemikalier, speciellt för kemikalier som påvisar östrogen- (E) och sköldkörtel-relaterade (T) förändringar av det endokrina systemet men även för mutagener. Träffsäkerheten för de tillgängliga metabolismsimulatorerna är ganska låg för industriella kemikalier men i kombination med QSARs så var verktyget användbart för identifikation av kemikalier som påvisade E- och T-relaterade effekter in vivo. Vi rekommenderar att använda konsensusmodellering vid in silico baserad bedömning av kemikaliers toxicitet, d.v.s. att skapa en sammanvägd förutsägelse baserat på flera QSAR-modeller. Det är speciellt användbart för modeller som baseras på data från delvis olika mekanismer eller arter. QSAR-modeller måste ha ett väldefinierat giltighetsområde (AD) för att garantera dess pålitlighet vilket kan uppnås med t.ex. conformal prediction (CP)-metoden. CP-metoden ger en bättre kontroll över prediktiva gränser hos QSAR-modeller än andra distansbaserade AD-metoder. Läkemedel kan interagera med avloppsslam genom olika intermolekylära krafter som även påverkas av joniseringstillståndet. Modellerna visade att adsorptionen av neutrala och positivt laddade läkemedel var huvudsakligen hydrofobicitetsdrivna men också påverkade av Pi-Pi- och dipol-dipol-krafter. Negativt laddade molekyler interagerade huvudsakligen med slam via kovalent bindning och jon-jon-, jon-dipol-, och dipol-dipol-krafter. Kemiska deskriptorer baserade på joniserade molekyler förbättrade inte prestandan för adsorptionsmodeller för positiva och negativa joner men vi noterade en förbättring av modeller för neutrala substanser som kan bero på en mer korrekt beskrivning av zwitterjoner. Sammanfattningsvis visade resultaten på QSAR-modellers styrkor och svagheter för användning som verkyg vid risk- och exponeringsbedömning av kemikalier. QSARs har stor potential för bred användning vid riskidentifiering och för att förutsäga en mängd olika responser som krävs vid riskbedömning av kemikalier. I kombination med andra verktyg kan QSARs förse oss med data för användning vid integrerade bedömningar där data sammanvägs från olika metoder. De erhållna resultaten visar också att QSARs kan användas för att bedöma och ge en bättre förståelse för kemikaliers öde i vattenreningsverk.

Dans un premier temps, nous avons modélisé la structure d’une famille d’ARN avec une grammaire de graphes afin d’identifier les séquences qui en font partie. Plusieurs autres méthodes de modélisation ont été développées, telles que des grammaires stochastiques hors-contexte, des modèles de covariance, des profils de structures secondaires et des réseaux de contraintes. Ces méthodes de modélisation se basent sur la structure secondaire classique comparativement à nos grammaires de graphes qui se basent sur les motifs cycliques de nucléotides. Pour exemplifier notre modèle, nous avons utilisé la boucle E du ribosome qui contient le motif Sarcin-Ricin qui a été largement étudié depuis sa découverte par cristallographie aux rayons X au début des années 90. Nous avons construit une grammaire de graphes pour la structure du motif Sarcin-Ricin et avons dérivé toutes les séquences qui peuvent s’y replier. La pertinence biologique de ces séquences a été confirmée par une comparaison des séquences d’un alignement de plus de 800 séquences ribosomiques bactériennes. Cette comparaison a soulevée des alignements alternatifs pour quelques unes des séquences que nous avons supportés par des prédictions de structures secondaires et tertiaires. Les motifs cycliques de nucléotides ont été observés par les membres de notre laboratoire dans l'ARN dont la structure tertiaire a été résolue expérimentalement. Une étude des séquences et des structures tertiaires de chaque cycle composant la structure du Sarcin-Ricin a révélé que l'espace des séquences dépend grandement des interactions entre tous les nucléotides à proximité dans l’espace tridimensionnel, c’est-à-dire pas uniquement entre deux paires de bases adjacentes. Le nombre de séquences générées par la grammaire de graphes est plus petit que ceux des méthodes basées sur la structure secondaire classique. Cela suggère l’importance du contexte pour la relation entre la séquence et la structure, d’où l’utilisation d’une grammaire de graphes contextuelle plus expressive que les grammaires hors-contexte. Les grammaires de graphes que nous avons développées ne tiennent compte que de la structure tertiaire et négligent les interactions de groupes chimiques spécifiques avec des éléments extra-moléculaires, comme d’autres macromolécules ou ligands. Dans un deuxième temps et pour tenir compte de ces interactions, nous avons développé un modèle qui tient compte de la position des groupes chimiques à la surface des structures tertiaires. L’hypothèse étant que les groupes chimiques à des positions conservées dans des séquences prédéterminées actives, qui sont déplacés dans des séquences inactives pour une fonction précise, ont de plus grandes chances d’être impliqués dans des interactions avec des facteurs. En poursuivant avec l’exemple de la boucle E, nous avons cherché les groupes de cette boucle qui pourraient être impliqués dans des interactions avec des facteurs d'élongation. Une fois les groupes identifiés, on peut prédire par modélisation tridimensionnelle les séquences qui positionnent correctement ces groupes dans leurs structures tertiaires. Il existe quelques modèles pour adresser ce problème, telles que des descripteurs de molécules, des matrices d’adjacences de nucléotides et ceux basé sur la thermodynamique. Cependant, tous ces modèles utilisent une représentation trop simplifiée de la structure d’ARN, ce qui limite leur applicabilité. Nous avons appliqué notre modèle sur les structures tertiaires d’un ensemble de variants d’une séquence d’une instance du Sarcin-Ricin d’un ribosome bactérien. L’équipe de Wool à l’université de Chicago a déjà étudié cette instance expérimentalement en testant la viabilité de 12 variants. Ils ont déterminé 4 variants viables et 8 létaux. Nous avons utilisé cet ensemble de 12 séquences pour l’entraînement de notre modèle et nous avons déterminé un ensemble de propriétés essentielles à leur fonction biologique. Pour chaque variant de l’ensemble d’entraînement nous avons construit des modèles de structures tertiaires. Nous avons ensuite mesuré les charges partielles des atomes exposés sur la surface et encodé cette information dans des vecteurs. Nous avons utilisé l’analyse des composantes principales pour transformer les vecteurs en un ensemble de variables non corrélées, qu’on appelle les composantes principales. En utilisant la distance Euclidienne pondérée et l’algorithme du plus proche voisin, nous avons appliqué la technique du « Leave-One-Out Cross-Validation » pour choisir les meilleurs paramètres pour prédire l’activité d’une nouvelle séquence en la faisant correspondre à ces composantes principales. Finalement, nous avons confirmé le pouvoir prédictif du modèle à l’aide d’un nouvel ensemble de 8 variants dont la viabilité à été vérifiée expérimentalement dans notre laboratoire. En conclusion, les grammaires de graphes permettent de modéliser la relation entre la séquence et la structure d’un élément structural d’ARN, comme la boucle E contenant le motif Sarcin-Ricin du ribosome. Les applications vont de la correction à l’aide à l'alignement de séquences jusqu’au design de séquences ayant une structure prédéterminée. Nous avons également développé un modèle pour tenir compte des interactions spécifiques liées à une fonction biologique donnée, soit avec des facteurs environnants. Notre modèle est basé sur la conservation de l'exposition des groupes chimiques qui sont impliqués dans ces interactions. Ce modèle nous a permis de prédire l’activité biologique d’un ensemble de variants de la boucle E du ribosome qui se lie à des facteurs d'élongation. / Initially, we modeled the structure of an RNA family with a graph grammar to identify sequences that correspond to it. Several other modeling approaches have been developed to derive sequences, such as stochastic context-free grammars, covariance models, secondary structures profiles and constraint networks. These modeling methods are based on secondary structure compared to our graph grammars which are based on the nucleotide cyclic motifs. To exemplify our graph grammar model, we used the loop E of the ribosome that contains the Sarcin-Ricin motif that has been widely studied since its discovery by X-ray crystallography in the early 90s. We built a graph grammar for the structure of the Sarcin-Ricin motif and derived the sequences that correspond to it. The biological relevance of these sequences is supported by an alignment of 800 bacterial ribosomal sequences. This comparison raised alternative alignments for some of the sequences that we supported by predictions of secondary and tertiary structures. According to a new tertiary structure, those alternative alignments accommodate the new derived sequences. The nucleotide cyclic motifs used in the grammar were observed by members of our laboratory in RNA tertiary structures that were solved experimentally. We study the sequences and tertiary structures of the nucleotide cyclic motifs of the Sarcin-Ricin motif. This study suggests that the space of sequences depends heavily on interactions between all nucleotides in the nearby three-dimensional space and not only between two adjacent base pairs. We compare the number of sequences generated by the graph grammar with non contextual methods and our graph grammar generates less sequences. This suggests the importance of context for the relationship between sequence and structure, hence the use of a contextual graph grammar is more expressive than context-free grammars. The graph grammars we used include the tertiary structure but neglect the interactions with extra-molecular factors, such as other macromolecules or ligands. In a second stage and to take into account these interactions, we developed a model incorporating the positioning of chemical groups on the surface of the tertiary structures. The assumption being that the chemical groups that are conserved on the surface of the RNA in active sequences are more likely to be involved in interactions with extra-molecular factors. Continuing with the example of the loop E, we searched the groups that could be involved its interactions with elongation factors. Knowledge of the groups involved in the important interactions serves to predict by three-dimensional modeling new sequences that have potentials to realize these interactions and thus the same function. There are few models that have been developed to address this problem: molecular descriptors, nucleotide adjacency matrices and others based on thermodynamics. These models use an oversimplified representation of the RNA structure, which limits their applicability. We applied our model to the tertiary structures of a set of variants of a sequence of one instance of the Sarcin-Ricin motif from a bacterial ribosome. Wool and coworkers at the University of Chicago studied this proceeding experimentally by testing the viability of twelve variants. They identified four viable variants and eight lethal. We used this set of twelve sequences for training our model and we identified a set of essential properties to their biological function. For each variant of the training set we built models of tertiary structures. We then measured the partial charges of exposed atoms on the surface and we encoded this information into vectors. We used principal component analysis to transform the vectors into a set of uncorrelated variables, called principal components. Using the weighted Euclidean distance and a nearest neighbor algorithm, we applied the technique of "Leave-One-Out Cross-Validation" to choose the best parameters to predict the activity of a new sequence to match these principal components. Finally, we validated the predictive model using a new set of eight variants whose viability has been verified experimentally in our laboratory. In conclusion, graph grammars are used to model the relationship between sequence and structure of an RNA structural element, such as the ribosomal loop E containing the Sarcin-Ricin motif. Applications range from the correction of sequence alignment to sequence design with a predetermined structure. We also developed a model to take into account the specific interactions related to a specific biological function. Our model is based on the retention of the exposure of chemical groups that are involved in these interactions. This model has allowed us to predict the biological activity of a set of variants of the loop E that binds to elongation factors.

Salvinorin A is a non-nitrogenous, selective kappa opioid receptor agonist with potent hallucinogenic properties. Because Salvinorin A has no basic nitrogen, it does not readily adhere to the “message-address” concept of selectivity for the opioid receptors. Therefore, a better understanding of how salvinorin A and its analogs interact with the kappa opioid receptor may shed some light on how salvinorin A obtains its potency and selectivity. The structure-affinity relationships (SAFIR) of salvinorin A and its analogs along with a discussion of the selectivity of the opioid receptors, is presented. A fragment of salvinorin A, methyl-3-acetoxy-4-oxocyclohexanecarboxylate, was synthesized to determine if the B, C and D rings are or are not necessary for binding to the opioid receptors. The fragment was found not to bind to the kappa, delta or mu receptor which reinforces the importance of the B, C and D rings in the binding of salvinorin A to the kappa opioid receptor. Homology models of the kappa, delta and mu opioid receptors were constructed based on inactive bovine rhodopsin, light-activated bovine rhodopsin and the human beta-2 adrenergic receptors. The program MODELLER was also used to construct the kappa opioid receptor. Two comparative molecular field analysis (CoMFA) studies are then presented which compared three different types of alignment methods. The alignment methods employed included a receptor-docked alignment in which the salvinorin A analogs were docked into a model of the kappa opioid receptor using the program GOLD. The docked poses for this alignment were chosen based on their similarity to our postulated model of salvinorin A in the kappa opioid receptor. In our model the furan oxygen forms hydrogen bonds with Q115(2.60) and Y320(7.43), the methoxy oxygen of the C-4 position ester group may form a hydrogen bond with Y312(7.35) and the methyl group of the C-2 position acetoxy moiety forms a hydrophobic interaction with Y313(7.36). These interactions are consistent with mutagenesis studies. The other alignment methods employed were a FlexS alignment and a realignment of the receptor-docked poses using the Fit Atoms function within SYBYL. Only the receptor-docked alignment method resulted in robust and predictive CoMFA models which indicates that the analogs may bind to the kappa opioid receptor in a similar but non-identical way. In addition, information from the CoMFA models based on the receptor-docked alignment led to a postulated binding mode for a set of amine analogs of salvinorin A which were not part of the original data set. Docking studies have the positively charged C-2 position amine group interacting with E209(XL2.49) while the furan oxygen and C-4 position ester group interacts with the same residues as in our model of salvinorin A in the kappa opioid receptor. The studies presented here not only support our postulated model of salvinorin A binding to the kappa opioid receptor but may also explain the trend of the beta epimers of the amine analogs to have a higher affinity than the corresponding alpha epimers. Site-directed mutagenesis studies could provide data to support or refute the postulated models of the amines docked in the kappa opioid receptor presented here.

The Dukat laboratory developed 2-amino-6-chloro-3,4-dihydroquinazoline (A6CDQ) as a 5-HT3 receptor ligand. A6CDQ and one of its positional isomers, the 7-chloro analog A7CDQ, produced antidepressant-like effects in the mouse tail suspension test (TST). We investigated and systematically ruled out a solely 5-HT3 receptor or hSERT mediated mechanism of antidepressant-like effect for both A6CDQ and A7CDQ. The role of organic cation transporter 3 (OCT3) as an alternative mechanism in the regulation of neurotransmitters including serotonin (5-HT) and the therapeutic potential of targeting hOCT3 to achieve antidepressant effects has been established. By virtue of possessing protonatable nitrogen atoms, 2-aminodihyroquinazolines could potentially exhibit activity at OCT3. A major goal of our present study was to explore the non-serotonergic mechanism of antidepressant-like effects and to study the as yet unexplored structure-activity-relationships (SARs) at OCT3. We examined the role of i) the chloro group, ii) the methylene bridge and iii) electronic/lipophilic effects at the 6-position. We developed the first 3-D homology models of both the human and mouse orthologs of OCT3, conducted docking studies and HINT analysis, and identified critical amino acid residues interacting with 2-aminodihydroquinazoline analogs at hOCT3 and mOCT3. Retention of antidepressant-like activity in the mouse and potential locomotor stimulant effects for TST-active doses were thoroughly investigated. We have successfully investigated initial SAR of 2-aminodihydroquinazolines at hOCT3 and generated the first 3-D homology models of hOCT3 and mOCT3. Highly potent and selective compounds could potentially be developed as radioligands to probe the binding site of OCT3 and as a mechanistically novel class of antidepressants.

Selective Mu Opioid Receptor (MOR) antagonists possess immense potential in the treatment of opioid abuse/addiction. Utilizing the “message-address” concept, our laboratory reported a novel, reversible, non-peptide MOR selective antagonist 17-cyclopropylmethyl-3,14β-dihydroxy-4,5α-epoxy-6β-[(4՛-pyridyl)carboxamido]morphinan (NAP). Molecular modeling studies revealed that the selectivity of NAP for the MOR is because of a π-π stacking interaction of its pyridine ring with the Trp318residue in theMOR. Pharmacological characterization showed that NAP is a P-glycoprotein substrate, thereby limiting its use in the treatment of opioid abuse/addiction. Thus, to modify NAP, we replaced the pyridine ring with its isosteric counterpart thiophene. Isosteric replacement could lead to development of compounds with different pharmacologic properties. Additionally, exploring other ring systems would diversify and enrich our library of compounds and aid in establishing a comprehensive structure-activity relationship. Therefore, newly synthesized compounds included thiophene derivatives of 6α/β-naltrexamine with potential to be used in the treatment of opioid abuse/addiction. Preliminary in vivo screening revealed that compounds 8 and 11 could be acting as antagonists. To aid in the design and synthesis of newer generation of MOR selective analogs, a 3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) Comparative Molecular Field Analysis (CoMFA) on 6β-N-heterocyclic substituted naltrexamine derivatives was conducted. After rigorous optimizations, the best CoMFA model possessed low predictive power. Results obtained suggested that small structural changes could lead to significant change in binding modes of these ligands. To further validate this observation, molecular docking studies were performed which revealed that these ligands indeed possessed multiple distinct binding modes thereby offering rationale for the CoMFA results. Thus, overall this study furnished useful information about the complexity of protein-ligand interactions which will aid in designing more potent and selective MOR ligands.

<p>Ten years ago, the first protease inhibitor targeting the human immunodeficiency virus (HIV) was approved for clinical use. Highly active antiretroviral therapy (HAART), which combined protease and reverse transcriptase inhibitors, quickly became the standard therapy for treating patients infected with HIV and Acquired Immune Deficiency Syndrome (AIDS). Nevertheless, last year the AIDS pandemic reached its highest level ever. Many infected patients, mainly in the developing countries, are still without treatment. Among those patients who receive treatment, an increase in drug resistance and new-infection with drug-resistant strains are seen. To come to terms with these problems, new drugs that are efficient against resistant strains and can be produced at low cost are needed.</p><p>In this study, we have focused our research efforts on cyclic sulfamides active as HIV-1 protease inhibitors. Distinctive to this compound class, as compared to the inhibitors so far approved for clinical use, was the incorporation of a water mimic that displaces the structural water (W301) observed in the X-ray crystal co-complexes. The first part of the study was aimed at understanding the rationale behind the nonsymmetric binding mode that the inhibitor adopted when bound to the enzyme. Symmetric and nonsymmetric inhibitors were synthesized and the structure-activity relationships and preferable binding modes were rationalized with the help of Comparative Molecular Field Analysis (CoMFA).</p><p>In the second part of the study, an attempt was made to reduce the size of these inhibitors. As a result, the traditional P1/P1' substituents were removed, while the P2/P2' substituents were elongated in an attempt to reach between the binding sites. The design hypothesis was shown to be successful and inhibitors possessing nanomolar activity were identified.</p>

<p>Rational conversion of biologically active peptides to nonpeptide compounds with retained activity is an appealing approach in drug development. One important objective of the work presented in this thesis was to use computational modeling to aid in such a conversion of the peptide angiotensin II (Ang II, Asp-Arg-Val-Tyr-Ile-His-Pro-Phe). An equally important objective was to gain an understanding of the requirements for ligand binding to the Ang II receptors, with a focus on interactions with the AT₂ receptor.</p><p>The bioactive conformation of a peptide can provide important guidance in peptidomimetic design. By designing and introducing well-defined secondary structure mimetics into Ang II the bioactive conformation can be addressed. In this work, both γ- and β-turn mimetic scaffolds have been designed and characterized for incorporation into Ang II. Using conformational analysis and the pharmacophore recognition method DISCO, a model was derived of the binding mode of the pseudopeptide Ang II analogues. This model indicated that the positioning of the Arg side chain was important for AT₂ receptor binding, which was also supported when the structure–activity relationship of Ang II was investigated by performing a glycine scan.</p><p>To further examine ligand binding, a 3D model of the AT₂ receptor was constructed employing homology modeling. Using this receptor model in a docking study of the ligands, binding modes were identified that were in agreement with data from point-mutation studies of the AT₂ receptor.</p><p>By investigating truncated Ang II analogues, small pseudopeptides were developed that were structurally similar to nonpeptide AT₂ receptor ligands. For further guidance in ligand design of nonpeptide compounds, three-dimensional quantitative structure–activity relationship models for AT₁ and AT₂ receptor affinity as well as selectivity were derived. </p>

Rational conversion of biologically active peptides to nonpeptide compounds with retained activity is an appealing approach in drug development. One important objective of the work presented in this thesis was to use computational modeling to aid in such a conversion of the peptide angiotensin II (Ang II, Asp-Arg-Val-Tyr-Ile-His-Pro-Phe). An equally important objective was to gain an understanding of the requirements for ligand binding to the Ang II receptors, with a focus on interactions with the AT2 receptor. The bioactive conformation of a peptide can provide important guidance in peptidomimetic design. By designing and introducing well-defined secondary structure mimetics into Ang II the bioactive conformation can be addressed. In this work, both γ- and β-turn mimetic scaffolds have been designed and characterized for incorporation into Ang II. Using conformational analysis and the pharmacophore recognition method DISCO, a model was derived of the binding mode of the pseudopeptide Ang II analogues. This model indicated that the positioning of the Arg side chain was important for AT2 receptor binding, which was also supported when the structure–activity relationship of Ang II was investigated by performing a glycine scan. To further examine ligand binding, a 3D model of the AT2 receptor was constructed employing homology modeling. Using this receptor model in a docking study of the ligands, binding modes were identified that were in agreement with data from point-mutation studies of the AT2 receptor. By investigating truncated Ang II analogues, small pseudopeptides were developed that were structurally similar to nonpeptide AT2 receptor ligands. For further guidance in ligand design of nonpeptide compounds, three-dimensional quantitative structure–activity relationship models for AT1 and AT2 receptor affinity as well as selectivity were derived.

Ten years ago, the first protease inhibitor targeting the human immunodeficiency virus (HIV) was approved for clinical use. Highly active antiretroviral therapy (HAART), which combined protease and reverse transcriptase inhibitors, quickly became the standard therapy for treating patients infected with HIV and Acquired Immune Deficiency Syndrome (AIDS). Nevertheless, last year the AIDS pandemic reached its highest level ever. Many infected patients, mainly in the developing countries, are still without treatment. Among those patients who receive treatment, an increase in drug resistance and new-infection with drug-resistant strains are seen. To come to terms with these problems, new drugs that are efficient against resistant strains and can be produced at low cost are needed. In this study, we have focused our research efforts on cyclic sulfamides active as HIV-1 protease inhibitors. Distinctive to this compound class, as compared to the inhibitors so far approved for clinical use, was the incorporation of a water mimic that displaces the structural water (W301) observed in the X-ray crystal co-complexes. The first part of the study was aimed at understanding the rationale behind the nonsymmetric binding mode that the inhibitor adopted when bound to the enzyme. Symmetric and nonsymmetric inhibitors were synthesized and the structure-activity relationships and preferable binding modes were rationalized with the help of Comparative Molecular Field Analysis (CoMFA). In the second part of the study, an attempt was made to reduce the size of these inhibitors. As a result, the traditional P1/P1' substituents were removed, while the P2/P2' substituents were elongated in an attempt to reach between the binding sites. The design hypothesis was shown to be successful and inhibitors possessing nanomolar activity were identified.