Hepatitis C is a blood-borne disease affecting 130-170 million people worldwide. The causative agent, hepatitis C virus (HCV), infects the liver and is the major reason for chronic liver disease worldwide. The HCV NS3 protease, a key enzyme in the virus replication cycle, has been confirmed to be an important target for drug development. With the recent release of two HCV NS3 protease inhibitors onto the market and an arsenal of inhibitors in clinical trials, there are now hopes of finally combating the disease. However, the success of treatment relies heavily on the ability to overcome the emergence of drug-resistant forms of the protease. The main focus of this thesis was on designing and synthesizing novel inhibitors of the NS3 protease with a unique resistance profile. Efforts were also made to decrease the peptide character of the compounds, with the long-term goal of making them into more drug-like compounds. Special attention was devoted to developing inhibitors based on a phenylglycine in the P2 position, instead of the highly optimized and commonly used P2 proline. Around ninety acyclic and macrocyclic inhibitors have been synthesized and biochemically evaluated. P2 pyrimidinyloxy phenylglycine was successfully combined with an aromatic P1 moiety and alkenylic P1´ elongations, yielding a distinct class of HCV NS3 protease inhibitors. Macrocyclization was performed in several directions of the inhibitors via ring-closing metathesis. Only the macrocyclization between the P3-P1´ residues was successful in terms of inhibitory potency, which suggests that the elongated P1-P1´ residue is oriented towards the P3 side chain. The metathesis reaction was found to be significantly more dependent on the substrate than on the reaction conditions. It was also found that the P3 truncated inhibitors were able to retain good inhibitory potency, which initiated the synthesis and evaluation of a series of P2-P1´ inhibitors. The potential of the P3-P1´cyclized inhibitor and the smaller, acyclic P2-P1´ as new potential drug leads remains to be determined through pharmacokinetic profiling. Gratifyingly, all the inhibitors evaluated on A156T and D168V substituted enzyme variants were able to retain inhibitory potency towards these as compared to wild-type inhibition.

Research on self-assembling gemini surfactants and other amphiphiles for potential gene delivery applications in research as well as in clinical practice, and as alternatives to viral gene delivery vectors, is beginning to focus more on structureactivity relationships to address the current low gene delivery efficiencies of amphiphiles. Some underlying structureactivity relations are beginning to emerge. But, as a better understanding of the factors that govern the transfection abilities of amphiphile molecules emerges, development of improved non-viral vectors with clinical potential may also emerge.<p> The research conducted for this thesis was aimed at the design, synthesis and in vitro investigation of gemini surfactants as one of a family of novel amphiphiles being investigated for gene therapeutic applications. The properties of these compounds can be controlled as well as allowed to vary naturally. Gemini surfactant-based gene delivery systems were prepared and characterized for transfer of Luciferase plasmid (pMASIA.Luc) to both COS-7 and PAM 212 cells. Characterization was accomplished using microscopy, dynamic light scattering (DLS) and zeta (ζ) potential analysis. In vitro gene expression and toxicities were evaluated in COS-7 cell and PAM 212 keratinocyte cultures.<p> The level of in vitro transfection in general was found to correlate strongly with the structure of the gemini surfactants. Among the 12-spacer-12 surfactants, incorporation of a pH-sensitive aza (N-CH3) group, which is also steric hindrance-imposing, in the spacer chain yielded increased transfection, particularly for the 12-7N-12 surfactant. In comparison, the incorporation of the more pH-sensitive imino (N-H) group in the 12-7NH-12 surfactant yielded the highest increase in transfection among the 12-spacer-12 surfactants. The deleterious effect of steric hindrance due to the aza group is more evident when comparing the transfection efficiency of 12-5N-12 (1 × aza, higher) vs. 12-8N-12 (2 × aza, lower transfection). Another highlighted structural feature is provided by the fact that both the 12-7NH-12 and 12-7N-12 surfactants had higher transfection efficiencies than 12-5N-12 and 12-8N-12 surfactants; the first pair has trimethylene spacing, which constitutes an optimal separation between nitrogen centres, while the second pair has shorter dimethylene spacings.<p> After expanding the structure of surfactants, transfection efficiencies were found to increase in response to increase in hydrocarbon tail length, but were much lower for surfactants with no amino functional groups, those that lacked the optimal trimethylene spacing, or those having both of these limitations in the gemini surfactant spacer. The 18-7NH-18 surfactant had the highest overall transfection in both COS-7 and PAM 212 cells. Gemini surfactant-based gene delivery systems capable of adopting both polymorphic structural phases and which could undergo pH-induced structural transition demonstrated high transfection efficiencies. Gemini surfactants with both characteristics (e.g., 12-7NH-12-based complexes are both polymorphic and pH-sensitive) had higher transfection than gemini surfactants with only one (e.g., 12-3-12-based complexes are only polymorphic).<p> Overall, the m-7NH-m surfactants, the most efficient surfactants studied, had transfection efficiencies similar to that of the commercial Lipofectamine Plus reagent and imposed no higher toxicity on cells relative to the less efficient surfactants. Thus, the design of the m-7NH-m surfactants to enhance their transfection abilities also ensured that their toxicity to cells were kept minimal. Overall, the design, synthesis and in vitro transfection screening of gemini surfactant candidates has revealed that the m-7NH-m surfactants have the highest transfection efficiencies; they have emerged as suitable candidates for non-viral gene delivery in vivo or at higher levels. Gene delivery investigations for six of the gemini surfactant candidates are being reported for the first time.

In an attempt to identify agents that specifically target neuroblastoma (NB) tumour-initiating cells (TIC) we performed drug screens using libraries of bioactive compounds. Cardiac glycosides (CGs) were the largest class of drugs identified with antitumour activity. At high CG doses inhibitory effects on the Na+/K+-ATPase induce cardiotoxicity; therefore, CG analogues were designed in an attempt to separate the effects on NB cells from cardiotoxicity. We identified RIDK34 as our lead compound from a structure-activity-relationship analysis (IC50 8 nM). RIDK34 contains a unique oxime group and shows increasing potency against NB TICs. The Na+/K+-ATPase is a target for the apoptotic activity of digoxin and RIDK34, whereby a signaling cascade involving Src and ERK may induce apoptosis. Furthermore, we predict that signaling activation does not require inactivation of the Na+/K+-ATPase and subsequent deregulation of [Na+]i and [K+]I gradients. Thus CGs and particularly RIDK34 may be expected to display diminished cardiotoxicity and greater therapeutic potential.

In an attempt to identify agents that specifically target neuroblastoma (NB) tumour-initiating cells (TIC) we performed drug screens using libraries of bioactive compounds. Cardiac glycosides (CGs) were the largest class of drugs identified with antitumour activity. At high CG doses inhibitory effects on the Na+/K+-ATPase induce cardiotoxicity; therefore, CG analogues were designed in an attempt to separate the effects on NB cells from cardiotoxicity. We identified RIDK34 as our lead compound from a structure-activity-relationship analysis (IC50 8 nM). RIDK34 contains a unique oxime group and shows increasing potency against NB TICs. The Na+/K+-ATPase is a target for the apoptotic activity of digoxin and RIDK34, whereby a signaling cascade involving Src and ERK may induce apoptosis. Furthermore, we predict that signaling activation does not require inactivation of the Na+/K+-ATPase and subsequent deregulation of [Na+]i and [K+]I gradients. Thus CGs and particularly RIDK34 may be expected to display diminished cardiotoxicity and greater therapeutic potential.

In drug discovery, thousands of compounds are assayed to detect activity against a biological target. The goal of drug discovery is to identify compounds that are active against the target (e.g. inhibit a virus). Statistical learning in drug discovery seeks to build a model that uses descriptors characterizing molecular structure to predict biological activity. However, the characteristics of drug discovery data can make it difficult to model the relationship between molecular descriptors and biological activity. Among these characteristics are the rarity of active compounds, the large volume of compounds tested by high-throughput screening, and the complexity of molecular structure and its relationship to activity. This thesis focuses on the design of statistical learning algorithms/models and their applications to drug discovery. The two main parts of the thesis are: an algorithm-based statistical method and a more formal model-based approach. Both approaches can facilitate and accelerate the process of developing new drugs. A unifying theme is the use of unsupervised methods as components of supervised learning algorithms/models. In the first part of the thesis, we explore a sequential screening approach, Cluster Structure-Activity Relationship Analysis (CSARA). Sequential screening integrates High Throughput Screening with mathematical modeling to sequentially select the best compounds. CSARA is a cluster-based and algorithm driven method. To gain further insight into this method, we use three carefully designed experiments to compare predictive accuracy with Recursive Partitioning, a popular structureactivity relationship analysis method. The experiments show that CSARA outperforms Recursive Partitioning. Comparisons include problems with many descriptor sets and situations in which many descriptors are not important for activity. In the second part of the thesis, we propose and develop constrained mixture discriminant analysis (CMDA), a model-based method. The main idea of CMDA is to model the distribution of the observations given the class label (e.g. active or inactive class) as a constrained mixture distribution, and then use Bayes’ rule to predict the probability of being active for each observation in the testing set. Constraints are used to deal with the otherwise explosive growth of the number of parameters with increasing dimensionality. CMDA is designed to solve several challenges in modeling drug data sets, such as multiple mechanisms, the rare target problem (i.e. imbalanced classes), and the identification of relevant subspaces of descriptors (i.e. variable selection). We focus on the CMDA1 model, in which univariate densities form the building blocks of the mixture components. Due to the unboundedness of the CMDA1 log likelihood function, it is easy for the EM algorithm to converge to degenerate solutions. A special Multi-Step EM algorithm is therefore developed and explored via several experimental comparisons. Using the multi-step EM algorithm, the CMDA1 model is compared to model-based clustering discriminant analysis (MclustDA). The CMDA1 model is either superior to or competitive with the MclustDA model, depending on which model generates the data. The CMDA1 model has better performance than the MclustDA model when the data are high-dimensional and unbalanced, an essential feature of the drug discovery problem! An alternate approach to the problem of degeneracy is penalized estimation. By introducing a group of simple penalty functions, we consider penalized maximum likelihood estimation of the CMDA1 and CMDA2 models. This strategy improves the convergence of the conventional EM algorithm, and helps avoid degenerate solutions. Extending techniques from Chen et al. (2007), we prove that the PMLE’s of the two-dimensional CMDA1 model can be asymptotically consistent.

In drug discovery, thousands of compounds are assayed to detect activity against a biological target. The goal of drug discovery is to identify compounds that are active against the target (e.g. inhibit a virus). Statistical learning in drug discovery seeks to build a model that uses descriptors characterizing molecular structure to predict biological activity. However, the characteristics of drug discovery data can make it difficult to model the relationship between molecular descriptors and biological activity. Among these characteristics are the rarity of active compounds, the large volume of compounds tested by high-throughput screening, and the complexity of molecular structure and its relationship to activity. This thesis focuses on the design of statistical learning algorithms/models and their applications to drug discovery. The two main parts of the thesis are: an algorithm-based statistical method and a more formal model-based approach. Both approaches can facilitate and accelerate the process of developing new drugs. A unifying theme is the use of unsupervised methods as components of supervised learning algorithms/models. In the first part of the thesis, we explore a sequential screening approach, Cluster Structure-Activity Relationship Analysis (CSARA). Sequential screening integrates High Throughput Screening with mathematical modeling to sequentially select the best compounds. CSARA is a cluster-based and algorithm driven method. To gain further insight into this method, we use three carefully designed experiments to compare predictive accuracy with Recursive Partitioning, a popular structureactivity relationship analysis method. The experiments show that CSARA outperforms Recursive Partitioning. Comparisons include problems with many descriptor sets and situations in which many descriptors are not important for activity. In the second part of the thesis, we propose and develop constrained mixture discriminant analysis (CMDA), a model-based method. The main idea of CMDA is to model the distribution of the observations given the class label (e.g. active or inactive class) as a constrained mixture distribution, and then use Bayes’ rule to predict the probability of being active for each observation in the testing set. Constraints are used to deal with the otherwise explosive growth of the number of parameters with increasing dimensionality. CMDA is designed to solve several challenges in modeling drug data sets, such as multiple mechanisms, the rare target problem (i.e. imbalanced classes), and the identification of relevant subspaces of descriptors (i.e. variable selection). We focus on the CMDA1 model, in which univariate densities form the building blocks of the mixture components. Due to the unboundedness of the CMDA1 log likelihood function, it is easy for the EM algorithm to converge to degenerate solutions. A special Multi-Step EM algorithm is therefore developed and explored via several experimental comparisons. Using the multi-step EM algorithm, the CMDA1 model is compared to model-based clustering discriminant analysis (MclustDA). The CMDA1 model is either superior to or competitive with the MclustDA model, depending on which model generates the data. The CMDA1 model has better performance than the MclustDA model when the data are high-dimensional and unbalanced, an essential feature of the drug discovery problem! An alternate approach to the problem of degeneracy is penalized estimation. By introducing a group of simple penalty functions, we consider penalized maximum likelihood estimation of the CMDA1 and CMDA2 models. This strategy improves the convergence of the conventional EM algorithm, and helps avoid degenerate solutions. Extending techniques from Chen et al. (2007), we prove that the PMLE’s of the two-dimensional CMDA1 model can be asymptotically consistent.

Research on self-assembling gemini surfactants and other amphiphiles for potential gene delivery applications in research as well as in clinical practice, and as alternatives to viral gene delivery vectors, is beginning to focus more on structureactivity relationships to address the current low gene delivery efficiencies of amphiphiles. Some underlying structureactivity relations are beginning to emerge. But, as a better understanding of the factors that govern the transfection abilities of amphiphile molecules emerges, development of improved non-viral vectors with clinical potential may also emerge.<p> The research conducted for this thesis was aimed at the design, synthesis and in vitro investigation of gemini surfactants as one of a family of novel amphiphiles being investigated for gene therapeutic applications. The properties of these compounds can be controlled as well as allowed to vary naturally. Gemini surfactant-based gene delivery systems were prepared and characterized for transfer of Luciferase plasmid (pMASIA.Luc) to both COS-7 and PAM 212 cells. Characterization was accomplished using microscopy, dynamic light scattering (DLS) and zeta (ζ) potential analysis. In vitro gene expression and toxicities were evaluated in COS-7 cell and PAM 212 keratinocyte cultures.<p> The level of in vitro transfection in general was found to correlate strongly with the structure of the gemini surfactants. Among the 12-spacer-12 surfactants, incorporation of a pH-sensitive aza (N-CH3) group, which is also steric hindrance-imposing, in the spacer chain yielded increased transfection, particularly for the 12-7N-12 surfactant. In comparison, the incorporation of the more pH-sensitive imino (N-H) group in the 12-7NH-12 surfactant yielded the highest increase in transfection among the 12-spacer-12 surfactants. The deleterious effect of steric hindrance due to the aza group is more evident when comparing the transfection efficiency of 12-5N-12 (1 × aza, higher) vs. 12-8N-12 (2 × aza, lower transfection). Another highlighted structural feature is provided by the fact that both the 12-7NH-12 and 12-7N-12 surfactants had higher transfection efficiencies than 12-5N-12 and 12-8N-12 surfactants; the first pair has trimethylene spacing, which constitutes an optimal separation between nitrogen centres, while the second pair has shorter dimethylene spacings.<p> After expanding the structure of surfactants, transfection efficiencies were found to increase in response to increase in hydrocarbon tail length, but were much lower for surfactants with no amino functional groups, those that lacked the optimal trimethylene spacing, or those having both of these limitations in the gemini surfactant spacer. The 18-7NH-18 surfactant had the highest overall transfection in both COS-7 and PAM 212 cells. Gemini surfactant-based gene delivery systems capable of adopting both polymorphic structural phases and which could undergo pH-induced structural transition demonstrated high transfection efficiencies. Gemini surfactants with both characteristics (e.g., 12-7NH-12-based complexes are both polymorphic and pH-sensitive) had higher transfection than gemini surfactants with only one (e.g., 12-3-12-based complexes are only polymorphic).<p> Overall, the m-7NH-m surfactants, the most efficient surfactants studied, had transfection efficiencies similar to that of the commercial Lipofectamine Plus reagent and imposed no higher toxicity on cells relative to the less efficient surfactants. Thus, the design of the m-7NH-m surfactants to enhance their transfection abilities also ensured that their toxicity to cells were kept minimal. Overall, the design, synthesis and in vitro transfection screening of gemini surfactant candidates has revealed that the m-7NH-m surfactants have the highest transfection efficiencies; they have emerged as suitable candidates for non-viral gene delivery in vivo or at higher levels. Gene delivery investigations for six of the gemini surfactant candidates are being reported for the first time.

Thirty-three 1,4-dihydropyridine diastereomeric pairs were synthesized and the structure-activity relationship studied in a Plasmodium falciparum in vitro model. Twenty-nine of these derivatives contained a 6-position oxygen, with 2.31, 2.32, 2.52 and 2.53 having single and double digit nanomolar activities. This SAR study revealed some insightful information about the 1,4-dihydropyridine substitution pattern. Substitution at the 7-position other than 3,4-dimethoxy severely reduced the activity. 4-phenyl substitution with 2- or 4- halo or methyl formed active compounds while substitution at the 3-position or with methoxy or conjugated aryl systems resulted in inactive compounds. The 2-position was found to majorly affect the activity, with groups larger than methyl being the most active. The other four derivatives contained a 6-position methylene, with 2.1, 2.59 and 2.60 having single nanomolar activities. Lastly, stereochemistry was revealed to play an important role in the activity of 2.1. One stereoisomer, (+)-trans-2.1, had subnanomolar activity in two assays. Another stereoisomer, (4S,7S)-2.1, had nanomolar activity. The other two stereoisomers were inactive. A series of N2,N4-disubstituted quinazoline-2,4-diamines has been synthesized and tested against Leishmania donovani and Leishmania amazonensis intracellular amastigotes. A structure-activity and structure-property relationship study was conducted in part using the Topliss operational scheme to identify new lead compounds. This study led to the identification of quinazolines with EC50s in the single digit micromolar or high nanomolar range in addition to favorable physicochemical properties. Quinazoline 3.23 also displayed efficacy in a murine model of visceral leishmaniasis, reducing liver parasitemia by 37% when given by the intraperitoneal route at 15 mg/kg/day for five consecutive days. Their antileishmanial efficacy, ease of synthesis, and favorable physicochemical properties make the N2,N4-disubstituted quinazoline-2,4-diamine compound series a suitable platform for future development of antileishmanial agents. A similar series of N2,N4-disubstituted quinazoline-2,4-diamines has been synthesized and tested against methicilin-resistant Staphylococcus aureus (MRSA) and multi-drug resistant strains of Acinetobacter baumannii. Quinazolines with MICs in the single digit micromolar or high nanomolar range were identified via SAR. In a murine model of MRSA infection, 1x the MIC for quinazoline 4.47 allowed for the survival of all tested mice at the end of a one week study. An in vivo model of A. baumannii was also undertaken using a Galleria mellonella model of infection. Quinazolines 4.74-4.76 afforded an increased protection of 87.5% when compared to the control experiments, with 70% of the wax worms surviving until day three. The observed potencies of frontrunner compounds in in vivo assays and their ease of synthesis make N2,N4-disubstituted quinazoline-2,4-diamines a suitable platform for the future development of anti-bacterial agents.

The melanocortin system regulates various physiological processes including feeding behavior, sexual function, skin pigmentation and photoprotection via five G-protein coupled receptors and several endogenous ligands. There is a need for selective and potent ligands to the human melanocortin receptors (hMCRs) that can chemically resolve these various functions. This thesis presents three studies aimed at refining the understanding of the structural differences between binding pockets of the hMCR subtypes. In the first study α-N-methylated analogues of the non-selective agonist, MT-II, are evaluated for their in vitro function. This study produced the most potent hMC1R selective agonist to date. The following two studies examine the effects of mutations on the biological activity of melanocortin receptor subtypes 1 and 4. Much of the mutation study data is preliminary and requires a demonstration of reproducibility.

Background: Multidrug resistance (MDR) is resistance of cancer cells to multiple classes of chemotherapeutic drugs that can be structurally unrelated. MDR involves altered membrane transport that results in a lower cell concentration of cytotoxic drugs which plays an important role during cancer treatment. P-glycoprotein (Pgp) is localised at the apical surface of epithelial cell in the intestine and it functions as a biological barrier by extruding toxic substances and xenobiotics out of cells (Lin, 2003:54). The ATP-binding-cassette superfamily is a rapidly growing group of membrane transport proteins and are involved in diverse physiological processes which include antigen presentation, drug efflux from cancer cells, bacterial nutrient uptake and cystic fibrosis (Germann, 1996:928; Kerr, 2002:47). A number of drugs have been identified which are able to reverse the effects of Pgp, multidrug resistance protein (MRPI) and their associated proteins on multidrug resistance. The first MDR modulators discovered and studied during clinical trials were associated with definite pharmacological actions, but the doses required to overcome MDR were associated with the occurrence of unacceptable side effects. As a consequence, more attention has been given to the development of modulators with proper potency, selectivity and pharmacokinetic characteristics that it can be used at a lower dose. Several novel MDR reversing agents (also known as chemosensitisers) are currently undergoing clinical evaluation for the treatment of resistant tumours (Teodori et al., 2002:385). Aim: The aim of this study was to investigate the effect of selected flavonoids (morin, galangin, kaempferol and quercetin) at two different concentrations (10 μM and 20 μM) on the transport of a known Pgp substrate, Rhodamine 123 (Rho 123) across rat intestine (jejunum) and to investigate structure activity relationships (SAR) of the selected flavonoids with reference to the inhibition of Pgp. Methods: Morin, galangin, kaempferol and quercetin were evaluated as potential modulators of Rho 123 transport, each at a concentration of 10 μM and 20 μM across rat jejunum using Sweetana-Grass diffusion cells. This study was done bidirectionally, with two cells measuring transport in the apical to basolateral direction (AP-BL) and two cells measuring transport in the basolateral to apical direction (BL-AP). The rate of transport was expressed as the apparent permeability coefficient (Pap,) and the extent of active transport was expressed by calculating the ratio of BL-AP to AP-BL. Results: The BL-AP to AP-BL ratio calculated for Rho 123 with no modulators added was 3.29. Morin decreased the BL-AP to AP-BL ratio to 1.88 at a concentration of 10 μM and to 1.49 at a concentration of 20 μM. Galangin decreased the BL-AP to AP-BL ratio to 1.60 at a concentration of 20 μM. These two flavonoids showed statistically significant results and inhibition of active transport were clearly demonstrated. However, the other flavonoids inhibited active transport of Rho 123 but according to statistical analysis, the results were not significantly different. The two different concentrations (10 μM and 20 μM) indicated that galangin, kaempferol and quercetin showed practically significant differences according to the effect sizes. Morin, however, did not show any practically significant differences at the different concentrations. Regarding .the SAR, it was shown by Boumendjel and co-workers (2002:512) that the presence of a 5-hydroxyl group and a 3-hydroxyl group as well as the C2-C3 double bond are required for high potency binding to the nucleotide binding domain (NBD) of Pgp. All the flavonoids tested had the above-mentioned characteristics. Conclusion: All the selected flavonoids showed inhibition of active transport of Rho 123 and should have an effect on the bioavailability of the substrates of Pgp and other active transporters. This study described the inhibitory interaction of selected flavonoids on Pgp activity. Practical significant differences between the same modulator at different concentrations were also observed. Structure activity relationships were identified describing the inhibitory potency of the flavonoids based on hydroxyl group positioning / Thesis (M.Sc. (Pharmaceutics))--North-West University, Potchefstroom Campus, 2005.