Artem Cherkasov

“Undruggable” or “difficult to drug” targets make up most of the human proteome; these proteins are described as such when it is considered impossible to pharmacologically target them. Therefore, “undruggable and difficult to drug” proteins represent significant challenges to the established drug discovery pipelines, but their successful inhibition would enable access to a wider range of therapeutic opportunities.In this thesis, we utilized various in silico tools to identify and design small molecule drug prototypes that could inhibit such undruggable and difficult targets. First, we introduced the recent development of new computer-aided drug design (CADD) methodologies and tools such as consensus docking, and Deep Docking. We then described the deployment of these innovative CADD methodologies to discover novel small molecule therapeutics against considered-as-difficult targets such as N-Myc, SARS-CoV-2 PLpro and Mpro. N-Myc is a highly desirable oncoprotein involved in many cancers and there is significant interest for targeting N-Myc in prostate cancer (PCa) and particularly in neuroendocrine prostate cancer (NEPC, an advanced, low-survival stage of PCa). However, N-Myc is considered an undruggable and unsuitable for small molecule inhibition due to its overall disordered structure. Thus, we developed new N-Myc specific small molecules with established and newly developed CADD protocols. Other examples of high value, but challenging targets are the viral main protease (Mpro) and the papain-like protease (PLpro) from severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2), as they are central pieces in its replication-transcription complex. However, intrinsic flexibility, multiple protonation state, solvent-exposed nature and other active site features unique to each protease decrease the success rate of conventional drug discovery protocols. Therefore, we discussed the identification of Mpro hits through naïve large virtual screening and ultra-large virtual screening that incorporated advanced consensus approaches. We also recapitulated the identification of new PLpro inhibitors through fine-tuned pharmacophore modelling and large-scale virtual screening with Deep Docking. In this thesis, we identified and designed small molecule inhibitors for each mentioned target of interest using state-of-the-art CADD methodologies. Notably, the compounds presented in this thesis provide the initial blueprints for the potential development of new anticancer and antiviral drugs.

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Prostate cancer is one of the leading causes of cancer-related death in men worldwide. If diagnosed early, prostate cancer can be treated by surgery and/or radiotherapy. In cases where the cancer has returned or is more aggressive and has metastasized, hormone therapy is the standard treatment. While initially effective, resistance to hormone therapy often occurs. Therefore, there is a pressing demand for new therapeutics to be developed to treat this disease. Previous studies have established that in up to 50% of all prostate cancer cases, a genomic irregularity involving the ETS-related gene (ERG) is present. This alteration results in the aberrant production of predominantly amino-terminal truncated ERG proteins in the prostate where it is linked to disease development and progression. This thesis tested the hypothesis that direct, small molecule targeting of ERG DNA binding could result in inhibition of the metastatic potential of PCa through the following specific aims: a) develop and apply in vitro assays to validate inhibitory activities/mechanisms of lead anti-ERG compounds, and b) determine the therapeutic effects of the lead compounds based on their effects and activity in in vivo xenograft models. The results demonstrate the direct binding of a novel small molecule, VPC-18005, with the ERG-ETS domain using biophysical approaches. This was further supported by reduced migration and invasion rates of ERG expressing prostate cancer cells, and reduced metastasis in a zebrafish xenograft model following exposure to VPC-18005. These results support the concept that small molecules targeting the ERG-ETS domain that suppress transcriptional activity and reverse transformed characteristics of prostate cancers aberrantly expressing ERG can be developed. It is hoped that these approaches might lead to identification of small molecules that can be further developed as drug candidates as alternatives to, or in combination with, current therapies for prostate cancer patients harboring ERG fusions.

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To date, castration-resistant prostate cancer (CRPC) remains an incurable disease, as conventional therapeutic inhibition of androgen receptor (AR) signaling with anti-androgens inevitably leads to treatment-resistance, further progression to final stage neuroendocrine prostate cancer (NEPC), and rapid demise. Therefore, development of novel targeted therapies for CRPC and NEPC is of paramount importance.Targeting the oncogenic activity of Myc family of transcription factors has long been and currently is a major topic of cancer research. While Myc is an essential regulator of normal growth, its exacerbated expression is a hallmark of human cancer. Amplifications of Myc family members play critical roles in prostate cancer progression and therapy-resistance. c-Myc is amplified across its full-spectrum and has special relevance in CRPC as it positively regulates the expression and activity of AR itself as well as of ligand-independent AR-V truncated variants, such as AR-V7 that confers resistance to anti-androgens. Moreover, N-Myc amplifications induce NEPC phenotype.Although Myc proteins are high-value targets for therapeutic intervention, clinically viable Myc-directed inhibitors await discovery. Intrinsically disordered, Myc lacks effective binding pockets. Therefore, the use of conventional methods of structure-based drug discovery is an inherent challenge. Moreover, the oncogenic function of Myc is dependent on its dimerization with the obligate partner Max, which together form a functional transcriptional complex capable of activating critical genomic targets and eliciting oncogenic effects.This dissertation describes the discovery and development of novel small-molecule inhibitors targeting the oncogenic activity of Myc-Max complexes. Specifically, we utilized methods of computer-aided drug discovery (CADD) to target directly complexes of c-Myc and N-Myc with Max, as well as Myc-upregulated hnRNP A1 splicing factor. The use of CADD enabled us to identify small-molecule drug candidates, which selectively disrupt critical protein-nucleic acid interactions – a therapeutic approach that has not been previously exercised for these targets. The CADD techniques encompassed large-scale structure-based docking and molecular dynamics simulations along with ligand-based approaches including pharmacophore modeling, chemical similarity searches and ADMET profiling, complemented by experimental validation. On the outlook, the identified lead inhibitors lay the foundation for development of safe and effective clinical candidates that may serve as prospective therapeutics for CRPC and NEPC.

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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.

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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.

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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.

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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.

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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.

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