Thesis advisor: Marc A.T. Muskavitch / Malaria is a life-threatening infectious disease caused by inoculation of the apicomplexan Plasmodium parasite into vertebrate hosts. Transmission of the parasite is mediated by the Anopheles mosquito, which has the capacity to efficiently transmit the parasite from host to host, as the disease vector. There are many factors that make anopheline mosquitoes competent vectors for disease transmission. The hematophagous (blood-feeding) behavior of the female mosquito is one of most fundamental factors in physical transmission of parasites, because the ingestion of blood from an infected host allows parasite entry into the mosquito and the completion of parasite sexual reproduction. In addition to this blood-feeding behavior, there are a host of biological (i.e., parasite replication) and behavioral factors (i.e., mosquito chemosensation, host preference) that contribute to the high vectorial capacity of these vector species. There are over four hundred Anopheles species worldwide, approximately forty of which are considered epidemiologically critical human malaria vectors. Anopheles gambiae, the primary vector in malaria-endemic sub-Saharan Africa, is responsible for the largest number of malaria cases in the world and is therefore one of the most important vectors to study and target with control measures. Currently, vector-targeted control strategies remain our most effective tools for reduction of malaria transmission and incidence. Although control efforts based on the deployment of insecticides have proven successful in the past and are still widely used, the threat and continuing increases of insecticide resistance motivate the discovery of novel insecticides. In this thesis, I provide evidence that G protein-coupled receptors (GPCRs) may serve as “druggable” targets for the development of new insecticides, through the modulation of developmental and sensory processes. In Chapter II, “A critical role for the Drosophila dopamine 1-like receptor Dop1R2 at the onset of metamorphosis,” I provide evidence supporting an essential role for this receptor in Drosophila melanogaster metamorphosis via transgenic RNA interference and pharmacological methods. In An. gambiae, we find that the receptor encoded by the mosquito ortholog GPRDOP2 can be inhibited in vitro using pharmacological antagonists, and that in vivo inhibition with such antagonists produces pre-adult lethality. These findings support the inference that this An. gambiae dopamine receptor may serve as a novel target for the development of vector-targeted larvicides. In Chapter III, “RNAi trigger delivery into Anopheles gambiae pupae,” I describe the development of a method for injection directly into the hemolymph of double strand RNA (dsRNA) during the pupal stage, and I demonstrate that knockdown of the translational product of the SRPN2 gene occurs efficiently, based on reductions in the levels of SRPN2 protein and formation of melanized pseudo-tumors, in SRPN2 knockdown mosquitoes. This method was developed for rapid knockdown of target genes, using a dye-labeled injection technique that allows for easy visualization of injection quality. This technique is further utilized in Chapter IV, “Uncovering the Role of an Anopheles gambiae G Protein-Coupled Receptor, GPRGR2, in the Detection of Noxious Compounds,” where the role for GPRGR2 in the detection of multiple noxious compounds is elucidated. We find that pupal stage knockdown of this receptor decreases the ability of adult Anopheles gambiae to identify multiple noxious compounds. While these findings provide a strong link between GPRGR2 and a very interesting mosquito behavior, they may also provide opportunities to develop better field-based strategies (i.e., insecticides baited traps) for vector control. The goal of this thesis is to understand the functional roles of selected mosquito GPCRs that may serve as targets for the development of new vector-targeted control strategies. Exploiting these GPCRs genetically and pharmacologically may provide insights into novel vector control targets that can be manipulated so as to decrease the vectorial capacity of An. gambiae and other malaria vectors in the field, and thereby decrease the burden of human malaria. / Thesis (PhD) — Boston College, 2015. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

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This thesis explores two approaches to fragment-based drug discovery. First, protein target CK2 was chosen due to its importance in the cancer phenotype. A literature fragment, NMR154L, proved to be a promising compound for fragment development, due to its binding at the interface site of the protein rather than the highly conserved ATP pocket. Analogues were synthesised of this fragment leading to a candidate with a better IC50. Additionally, computer modelling of the interface site suggested that a series of spirocyclic compounds would inhibit this protein. These were synthesised and tested in vitro. Results from these tests were analysed and informed the synthesis of new inhibitors with the aid of crystal structures and computer modelling. Secondly, to address the lack of spirocyclic scaffolds in fragment screening libraries a number of diversity-orientated synthetic campaigns were undertaken. The first of these utilised glycine as starting material. Two terminal alkenes were installed. The alkenes were linked and the amino and acidic residues cyclised. This allowed for the formation of a diverse range of spirocyclic scaffolds from this one starting material. Having established chemistry for linking amino and acidic residues a campaign with dehydroalanine was under taken. This would allow for the installation of the second ring by pericyclic chemistry as well as using chemistry previously established. This pericyclic chemistry was also applied to synthesising spirocycles from rings with exocyclic double bonds. These being readily installed from Wittig chemistry, this allowed utilisation of starting materials which contained a cyclic ketone. Of these azetidinone was a good candidate due to the fact it was a commercially available building block and allowed access to spirocycles containing a 4-membered ring; an underrepresented ring size. Finally, computation analysis was carried out on the library to assess it diversity and any potential biological targets which these fragments may inhibit.

Drugs for treating rare diseases had been neglected by the pharmaceutical industry for a long time, due to the complex and costly drug R&D process as well as a small unprofitable market. Since its introduction in 1983, the Orphan Drug Act (ODA) has sought to prompt the innovation of drugs for minority diseases by reducing the regulatory and economic barriers. The incentives of the ODA have been effected through market protection, tax credit, fee waiver and grants to increase the accessibility of orphan products for the public. The number of orphan drugs available in the market has risen sharply from just ten in the decade before 1983 to over 400 since 1983. This increase implies a substantial improvement of the healthcare of patients suffering rare diseases and a success of the orphan drug legislation with the aim to motivate the development and manufacture of products that have low commercial potentials. Although it is evident that the ODA has successfully stimulated drug companies to develop numerous orphan products, treatments are very expensive. The sales of blockbuster orphan drugs have provided drug companies with unusually highprofit margins and limited patient access to treatments. The dilemma presented by the ODA reflects many of the issues currently faced by policymakers. In this thesis, we have analyzed the long-term evolution of the biopharmaceutical industry. In particular, we have examined drug discovery in the period of random screening, rational design and network collaboration, and explored the influence of the ODA. We have taken the theory of the sectoral system of innovation, and combined it with the complex adaptive model of innovation, and found that the complex version of that theory is capable of explaining the comprehensive drug innovation system. A Multi-agent Based Model has been introduced to identify and analyze the dynamics of bio-pharmaceutical innovation. The model has explored the roles of the main players in the sector and the influence of their relationships embedded in the process of orphan drug innovation. Through this model, we have investigated the mechanisms of how the incentives stimulate orphan drug innovation during the period from 1983- 2012. Moreover, the model has been applied to solve the dilemma of the ODA through analyzing how to achieve the best trade-off between orphan drug developments. Drawing upon the results of the simulation, we provide a sound basis for adjusting the ODA incentives to strikes an appropriate balance between stimulating orphan drug innovation and providing benefits to society, propose some resolutions to the ODA, while also to motivate orphan drug development in a financial way. The Advice for other countries planning to enact the orphan drug legislation and directions for further research suggested by this model have been put forward.

The proteasome is a large protein complex which is responsible for the majority of protein degradation in eukaryotes. Following FDA approval of the first proteasome inhibitor bortezomib for the treatment of multiple myeloma (MM) in 2003, there has been an increasing awareness of the significant therapeutic potential of proteasome inhibitors in the treatment of cancer. As of 2017, three proteasome inhibitors are approved for the treatment of MM but in clinical trials with patients bearing solid tumors these existing proteasome inhibitors have demonstrated poor results. Notably, all three FDA-approved proteasome inhibitors rely on the combination a peptide backbone and reactive electrophilic warhead to target the proteasome, and all three primarily target the catalytic subunits conferring the proteasome’s chymotrypsin-like (CT-L) activity. It is our hypothesis that compounds with non-peptidic structures, non-covalent and reversible modes of action, and unique selectivity profiles against the proteasome’s distinct catalytic subunits could have superior pharmacodynamic and pharmacokinetic properties and may bear improved activity against solid tumors relative to existing proteasome inhibitors. In an effort to discover such compounds we have employed an approach which combines computational drug screening methods with conventional screening and classic medicinal chemistry. Our efforts began with a computational screen performed in the lab of Dr. Chang-Guo Zhan. This virtual screen narrowed a library of over 300,000 drug-like compounds down to under 300 virtual hits which were then screened for proteasome inhibitory activity in an in vitro assay. Despite screening a relatively small pool of compounds, we were able to identify 18 active compounds. The majority of these hits were non-peptide in structure and lacked any hallmarks of covalent inhibition. The further development of one compound, a tri-substituted pyrazole, provided us with a proteasome inhibitor which demonstrated cytotoxic activity in a variety of human solid cancer cell lines as well as significant anti-tumor activity in a prostate cancer mouse xenograft model. We have also evaluated the in vitro pharmacokinetic properties of our lead compound and investigated its ability to evade cross-resistance phenomena in proteasome inhibitor-resistant cell lines. We believe that our lead compound as well as our drug discovery approach itself will be of interest and use to other researchers. We hope that this research effort may aid in the further development of reversible non-peptide proteasome inhibitors and may eventually deliver new therapeutic options for patients with difficult-to-treat solid tumors.

Proteins are dynamic molecules capable of performing complex biological functions necessary for life. The impact of protein dynamics in the development of medicines is often understated. Science is only now beginning to unravel the numerous consequences of protein flexibility on structure and function. This thesis will encompass two case studies in developing small molecule inhibitors targeting flexible enzymes, and provide a thorough evaluation of their inhibitory mechanisms of action. The first case study focuses on caspases, a family of cysteine proteases responsible for executing the final steps of apoptosis. Consequently, they have been the subject of intense research due to the critical role they play in the pathogenesis of various cardiovascular and neurodegenerative diseases. A fragment-based screening campaign against human caspase-7 resulted in the identification of a novel series of allosteric inhibitors, which were characterized by numerous biophysical methods, including an X-ray co-crystal structure of an inhibitory fragment with caspase-7. The fragments described herein appear to have a significant impact on the substrate binding loop dynamics and the orientation of the catalytic Cys-His dyad, which appears to be the origin of their inhibition. This screening effort serves the dual purpose of laying the foundation for future medicinal chemistry efforts targeting caspase proteins, and for probing the allosteric regulation of this interesting class of hydrolases. The second case study focuses on glutamate racemase, another dynamic enzyme responsible for the stereoinversion of glutamate, providing the essential function of D-glutamate production for the crosslinking of peptidoglycan in all bacteria. Herein, I present a series of covalent inhibitors of an antimicrobial drug target, glutamate racemase. The application of covalent inhibitors has experienced a renaissance within drug discovery programs in the last decade. To leverage the superior potency and drug target residence time of covalent inhibitors, there have been extensive efforts to develop highly specific covalent modifications to reduce off-target liabilities. A combination of enzyme kinetics, mass spectrometry, and surface-plasmon resonance experiments details a highly specific 1,4-conjugate addition of a small molecule inhibitor with the catalytic Cys74 of glutamate racemase. Molecular dynamics simulations and quantum mechanics-molecular mechanics geometry optimizations reveal, with unprecedented detail, the chemistry of the conjugate addition. Two compounds from this series of inhibitors display antimicrobial potency comparable to β-lactam antibiotics, with significant activity against methicillin-resistant S. aureus strains. This study elucidates a detailed chemical rationale for covalent inhibition and provides a platform for the development of antimicrobials with a novel mechanism of action.

A drug discovery project typically starts with a pharmacological hypothesis: that the modulation of a specific molecular biological mechanism would be beneficial in the treatment of the targeted disease. In a small-molecule project, the next step is to identify hits, i.e. molecules that can effect this modulation. These hits are subsequently expanded into hit series, which are optimised with respect to pharmacodynamic and pharmacokinetic properties, through medicinal chemistry. Finally, a drug candidate is clinically developed into a new drug. This thesis concerns the use of structure-based virtual screening in the hit identification phase of drug discovery. Structure-based virtual screening involves using the known 3D structure of a target protein to predict binders, through the process of docking and scoring. Docking is the prediction of potential binding poses, and scoring is the prediction of the free energy of binding from those poses. Two new methodologies, based on post-processing of scoring results, were developed and evaluated using model systems. Both methods significantly increased the enrichment of true positives. Furthermore, correlation was observed between scores and simple molecular properties, and identified as a source of false positives in structure-based virtual screening. Two target proteins, Mycobacterium tuberculosis ribose-5-phosphate isomerase, a potential drug target in tuberculosis, and Plasmodium falciparum spermidine synthase, a potential drug target in malaria, were subjected to docking and virtual screening. Docking of substrates and products of ribose-5-phosphate isomerase led to hypotheses on the role of individual residues in the active site. Additionally, virtual screening was used to predict 48 potential inhibitors, but none was confirmed as an inhibitor or binder to the target enzyme. For spermidine synthase, structure-based virtual screening was used to predict 32 potential active-site binders. Seven of these were confirmed to bind in the active site.

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.

Docking is one of the methods in virtual screeing. Studies from around 1980 to now, many docking software have been developed, but these software have many short comings. The software currently used for docking have many disadvantage: poor efficiency, rigid structure of the proteins and the ligands, poor accuracy, without the polarization after binding, leading virtual screening is still stuck in a supporting role. Our experiment with new method improves those shortcomings of docking. With this new method, we obtain the following improvements in docking process: better efficiency, flexible structure of the proteins and the ligands, better accuracy. In the depression-related protein docked with traditional Chinese medicine test. We change the conformations of ligands with the shapes of active sites before posing, this makes the conformation of complex much more reasonable, even more complicated, large ligands. In the experiment of random sites docking, we found a possible path for compounds traveling into active sites. We illustrate a docking area by linking all possible docking sites. The lead compound may not successfully travel into active site when this area is occupied by other proteins or ligands. In the docking experiment with side-chain rotation, we rotate the torsion angle to make side chains relax. We obtained a similar result with molecular dynamics, and saved a lot of time.