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Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - Nov 2019)
No abstract available.
Estrogen receptor alpha positive (ERα+) disease constitutes approximately 75% of all breast cancer (BCa) cases. However resistance to hormone therapy is observed in early-stage as well as in metastatic disease. Importantly, 70% of ERα+ primary tumors retain active ERα when they metastasize and, therefore, ERα continues to play a role in the resistant form of the disease. Moreover, the effectiveness of conventional hormone therapies is hampered due to gain-of-function mutations that may render the receptor constitutively active. Thus drugs that target the ERα estrogen binding site can become ineffective with time. Moreover, cross-talk between ERα and activated growth factor receptors, or their downstream kinases have shown to play a major role in activating ERα even in the absence of estradiol. Taken together, these observations highlight the importance of developing therapeutics that target alternative sites on the receptor, for instance, those that directly act on the co-activator binding pocket called activation function-2 (AF2) site. Using methods of in-silico screening followed by a systematic computer-guided lead optimization process, we were able to develop several promising small-molecule inhibitors that target the AF2 functional site of ERs. This thesis describes the establishment of an experimental pipeline and development of such inhibitors. The identified lead compound VPC-16606 effectively blocked ERα-co-activator interactions, demonstrated a strong anti-proliferative effect against a panel of ERα+ cells including Tamoxifen-resistant cells and down-regulated ERα-dependent genes. Most importantly, VPC-16606 successfully inhibited known constitutively active mutant forms of ERα observed in clinical settings where BCa patients have relapsed on aromatase inhibitors. Furthermore, the compound also reduced tumor burden in vivo. Overall, these studies helped to identify a novel class of ERα AF2 inhibitors which have the potential to effectively inhibit ERα activity by a unique mechanism and to circumvent the issue of hormone resistance in BCa patients.
Interest in developing androgen receptor (AR) inhibitors with novel mechanism of action for the treatment of prostate cancer (PCa) is on the rise since the commercial anti-androgens (including recently approved drug, Enzalutamide) face clinical limitations. Current therapies fail over a period of time because they all target mutation-prone androgen binding pocket on AR to which the receptor has already developed effective resistance mechanisms. Hence, there is a pressing need for novel therapeutics that inhibit the AR through alternative modes of action. To address this problem, we have used in silico drug design methodology to create new drugs that act on an entirely different site on the AR, a recently identified co-activator site called binding function-3 (BF3). This dissertation describes the discovery and development of novel anti-androgens directed towards the BF3 surface of the AR. These inhibitors were developed through a series of computational experiments followed by extensive biological validations. Based on the activity profile of the identified inhibitors, it can be anticipated that these drug prototypes will lay a foundation for the development of alternative or supplementary small-molecule therapies capable of combating PCa even in its drug resistant forms. Because the emergence of castration resistance is the lethal end stage of the disease, we anticipate that the thesis work will eventually have a substantial impact on the survival of prostate cancer patients.
Prostate cancer (PCa) is the most commonly diagnosed cancer in men, and the second leading cause of male cancer death in North America. The androgen signalling pathway plays a central role in the development and advancement of PCa as well as in its progression to a lethal castration-resistant stage (CRPC). The human androgen receptor (AR) is a master regulator of PCa progression and survival, and a well-validated drug target for PCa. All clinically used AR inhibitors (antiandrogens) are initially effective to PCa; however, they invariably cause resistance. Thus, there is a continuing need for developing novel anti-AR drugs for the treatment of PCa and CRPC. Although the mechanism of resistance to antiandrogens is not completely clear, it involves mutation-driven antagonist-to-agonist transformation of the AR response, and the emergence of AR splice variants (ARVs) lacking the entire ligand-binding domain (LBD) of the protein. This dissertation describes the discovery and development of novel AR inhibitors directed towards the conventional androgen binding site (ABS) of the receptor, as well as the discovery of an entirely novel class of inhibitors targeting the DNA-binding domain (DBD) of the AR. Both types of AR inhibitors were identified through virtual screening and molecular modeling, followed by in vitro and/or in vivo validation of developed drug prototypes. The objective of developing novel chemotypes for ABS binders and AR DBD inhibitors is to help circumvent drug resistance problem in the field of PCa.
Infectious diseases caused by bacterial pathogens continue to be major public health concerns affecting millions of human lives annually, as conventional treatment via antibiotics has lost its effectiveness due to growing problems of drug resistance. Recent advancements in systems biology, high-throughout sequencing, protein interaction study and computer-aided drug development can offer possible solutions to antibiotic resistance through discovery of novel antimicrobials. The thesis describes several bioinformatics approaches that focus on protein interaction network (PIN) studies, analyses of targetable protein indels (insertions and deletions) and virtual compound screening for new antibacterial candidates – approaches integrated into an antibiotic discovery pipeline for methicillin-resistant Staphylococcus aureus (MRSA252). In the course of the described work we identified new drug targets corresponding to highly interacting proteins (hubs) through comprehensive PIN analysis in MRSA252. The advantage of using hub proteins as targets is established by their essentiality, non-replaceable PIN position and lower rate of mutation, all of which can help to counter bacterial resistance. To accelerate these studies hub predicting tools have been developed to assist proteomics experiments for PIN discovery and to facilitate drug target identification in pathogens. Because some bacterial proteins are conserved in humans, we applied the indel (insertion or deletion) concept to locate unique compound-binding sites that enabled us to specifically target conserved and essential bacterial hubs. We demonstrated associations between the presence of sizable indels in proteins with their essentiality and network rewiring capability, which established indels as potential markers for drug targets. To provide the research community a fast and user-friendly web portal for identification and characterization of indel-bearing drug targets, the Indel PDB database has been developed to characterize the functional and structural features of 117,266 indel sites across numerous species. Finally, combining the above bioinformatics methodologies with a rapid and efficient procedure of virtual screening allowed discovery of compounds that effectively inhibited MRSA252 cell growth with no signs of human toxicity. We anticipate that the drug discovery pipeline along with established MRSA PIN resource, hub prediction tools and indel database will provide a framework for the development of next-generation antibiotics in other existing or emerging pathogens.
The emergence of pathogens resistant to available drug therapies is a pressing global health problem. Antimicrobial peptides (AMPs) may potentially form new therapeutics to counter these pathogens. AMPs are key components in the mammalian innate immune system and are responsible for both direct killing and immunomodulatory effects in host defense against pathogenic organisms. This thesis describes computational methods for the identification of novel natural and synthetic AMPs. A bioinformatic resource was constructed for classification and discovery of gene- coded AMPs, consisting of a database of clustered known AMPs and a set of hidden Markov models (HMMs). One set of 146 clusters was based on the mature peptide sequence, and one set of 40 clusters was based on propeptide sequence. The bovine genome was analyzed using the AMPer resources, and 27 of the 34 known bovine AMPs were identified with high confidence and up to 69 AMPs were predicted to be novel peptides. One novel cathelicidin AMP was experimentally verified as up-regulated in response to infection in bovine intestinal tissue. A chemoinformatic analysis was performed to model the antibacterial activity of short synthetic peptides. Using high-throughput screening data for the activities of over 1400 peptides of diverse sequence, quantitative structure-activity relation (QSAR) models were created using artificial neural networks and physical characteristics of the peptide that included three-dimensional atomic structure. The models were used to predict the activity of a set of approximately 100,000 peptide sequence variants. After ranking the predicted activity, the models were shown to be very accurate. When 200 peptides were synthesized and screened using four levels of expected activity, 94% of the top 50 peptides expected to have the highest level of activity were found to be highly active. Several promising candidates were synthesized with high quality and tested against several multi- antibiotic-resistant pathogens including clinical strains of Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecalis and Escherichia coli. These peptides were found to be highly active against these pathogens as determined by minimal inhibitory concentration; this serves as independent confirmation of the effectiveness of high-throughput screening and in silico analysis for identifying peptide antibiotic drug leads.
Master's Student Supervision (2010 - 2018)
The human Androgen Receptor (AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of prostate cancer (PCa). AR is also critical for the survival of many forms of castration resistant prostate cancer (CRPC). The currently used AR inhibitors (anti-androgens) face clinical limitations as drug resistance has been reported in patients, both primary and acquired. In 20% of the CRPC patients resistance to AR antagonists arise due to the mutations in the androgen binding site (ABS) of the receptor. Some mutations can convert antagonist to agonist. Such gain-of-function mutations have been reported across the length of the ligand binding domain (LBD) of AR that contains the ABS, it is imperative to develop a prognostic personalized therapy platform which would equip clinicians with actionable strategies in regard to previously unreported AR aberrations when they are encountered in clinical samples. The goal of this study is to develop a theoretical approach that can characterize such previously unreported AR mutants and predict their response to the currently used anti-androgens. Thus, a novel ‘in-silico’ pipeline has been created that amalgamates the state-of-the-art cheminformatics methods with experimental assays that enable predicting AR mutants and characterizing their drug responses with high accuracy. The corresponding pipeline utilizes QSAR approach that extracts key protein-ligand interactions quantified by the in-house developed 4D-inductive molecular descriptors. The developed QSAR models reach about 90% accuracy that forecasts agonist or antagonist behaviors of AR mutants caused by clinically used and experimental anti-androgens. Furthermore, a previously unreported mutant, T878G has been predicted to be activated by both first and second generation anti-androgens and the corresponding experimental evaluation confirmed this prediction. Finally, the applicability and adaptability of the developed cheminformatics pipeline was tested against an experimental anti-androgen drug ODM-201 which was not a part of the QSAR training dataset, and the predictions were confirmed by experimental evaluations. Overall, the developed pipeline can provide useful insights towards understanding the changing genomic landscape of advanced PCa.
Androgen receptor (AR) plays a critical role in prostate cancer development and progression. Allcurrent therapeutic AR inhibitors modulate the receptor via direct binding to its HormoneBinding Site (HBS). Despite the identification of other small molecule binding areas on the ARsurface including Activation Function 2 (AF2), binding function 3 (BF3), and N-terminal domain(NTD), HBS continues to be the major target site for AR antagonists (even though this site isprone to resistant mutations). Thus, there is a high need for the identification and development ofnovel antagonists targeting HBS of the AR.In this study, an effective QSAR modeling pipeline was set up and proved to be capable ofidentifying new AR antagonists from a large ZINC collection of purchasable chemicals. Inparticular, we have utilized DRAGON, INDUCTIVE and MOE descriptors to create variousbinary QSAR models of anti-AR activity. When we have applied the developed QSAR solutionsto screen more than 2 million chemicals from the ZINC database, we were able to identify 39potential candidate AR HBS binders. When they were tested in the DHT displacement assay, 9chemicals demonstrated the corresponding IC₅₀ values in efficient low-micromole range. Ofthose, 9 compounds later exhibited ability to inhibit AR in the eGFP transcriptional assay withthe IC₅₀ values established at 1.04-16.18 μM level. Notably, 6 discovered chemicalsdemonstrated concentration-dependent suppression of survival of LNCaP prostate cancer celllines.The results of this study set a ground for the development of an entire novel chemical class ofAR antagonists that are distinct for the currently marketed drugs such as Nitalutamide,Flutomide, Cassodex, and MDV3100 that all share significant structural similarity.