477 research outputs found

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    Rational development of stabilized cyclic disulfide redox probes and bioreductive prodrugs to target dithiol oxidoreductases

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    Countless biological processes allow cells to develop, survive, and proliferate. Among these, tightly balanced regulatory enzymatic pathways that can respond rapidly to external impacts maintain dynamic physiological homeostasis. More specifically, redox homeostasis broadly affects cellular metabolism and proliferation, with major contributions by thiol/disulfide oxidoreductase systems, in particular, the Thioredoxin Reductase Thioredoxin (TrxR/Trx) and the Glutathione Reductase-Glutathione-Glutaredoxin (GR/GSH/Grx) systems. These cascades drive vital cellular functions in many ways through signaling, regulating other proteins' activity by redox switches, and by stoichiometric reductant transfers in metabolism and antioxidant systems. Increasing evidence argues that there is a persistent alteration of the redox environment in certain pathological states, such as cancer, that heavily involve the Trx system: upregulation and/or overactivity of the Trx system may support or drive cancer progression, making both TrxR and Trx promising targets for anti-cancer drug development. Understanding the biochemical mechanisms and connections between certain redox cascades requires research tools that interact with them. The state-of-the-art genetic tools are mostly ratiometric reporters that measure reduced:oxidized ratios of selected redox pairs or the general thiol pool. However, the precise cellular roles of the central oxidoreductase systems, including TrxR and Trx, remain inaccessible due to the lack of probes to selectively measure turnover by either of these proteins. However, such probes would allow measuring their effective reductive activity apart from expression levels in native systems, including in cells, animals, or patient samples. They are also of high interest to identify chemical inhibitors for TrxR/Trx in cells and to validate their potential use as anti-cancer agents (to date, there is no selective cellular Trx inhibitor, and most known TrxR inhibitors were not comprehensively evaluated considering selectivity and potential off-targets). However, small molecule redox imaging tools are underdeveloped: their protein specificity, spectral properties, and applicability remain poorly precedented. This work aimed to address this opportunity gap and develop novel, small molecule diagnostic and therapeutic tools to selectively target the Trx system based on a modular trigger cargo design: artificial cyclic disulfide substrates (trigger) for oxidoreductases are tethered to molecular agents (cargo) such that the cargo’s activity is masked and is re-established only through reduction by a target protein. The rational design of these novel reduction sensors to target the cell's strongest disulfide-reducing enzymes was driven by the following principles: (i) cyclic disulfide triggers with stabilized ring systems were used to gain low reduction potentials that should resist reduction except by the strongest cellular reductases, such as Trx; and (ii) the cyclic topology also offers the potential for kinetic reversibility that should select for dithiol-type redox proteins over the cellular monothiol background. Creating imaging agents based on such two-component designs to selectively measure redox protein activity in native cells required to combine the correct trigger reducibility, probe activation kinetics, and imaging modalities and to consider the overall molecular architecture. The major prior art in this field has applied cyclic 5-membered disulfides (1,2 dithiolanes) as substrates for TrxR in a similar way to create such tools. However, this motif was described elsewhere as thermodynamically instable and was due to widely used for dynamic covalent cascade reactions. By comparing a novel 1,2 dithiolane-based probe to the state-of-the-art probes, including commercial TrxR sensors, by screening a conclusive assay panel of cellular TrxR modulations, I clarified that 1,2 dithiolanes are not selective substrates for TrxR in biological settings (Nat Commun 2022). Instead, aiming for more stable ring systems and thus more robust redox probes, during this work, I developed bicyclic 6 membered disulfides (piperidine fused 1,2 dithianes) with remarkably low reduction potentials. I showed that molecular probes using them as reduction sensors can be mostly processed by thioredoxins while being stable against reduction by GSH. The thermodynamically stabilized decalin like topology of the cis-annelated 1,2 dithianes requires particularly strong reductants to be cleaved. They also select for dithiol type redox proteins, like Trx, based on kinetic reversibility and offer fast cyclization due to the preorganization by annelation (JACS 2021). This work further expanded the system’s modularity with structural cores based on piperazine-fused 1,2 dithianes with the two amines allowing independent derivatization. Diagnostic tools using them as reduction sensors proved equally robust but with highly improved activation kinetics and were thus cellularly activated. Cellular studies evolved that they are substrates for both Trxs and their protein cousins Grxs, so measuring the cellular dithiol protein pool rather than solely Trx activity (preprint 2023). Finally, a trigger based on a slightly adapted reduction sensor, a desymmetrized 1,2 thiaselenane, was designed for selective reduction by TrxR’s selenol/thiol active site, then combined with a precipitating large Stokes’ shift fluorophore and a solubilizing group, to evolve the first selective probe RX1 to measure cellular TrxR activity, which even allowed high throughput inhibitor screening (Chem 2022). The central principle of this work was further advanced to therapeutic prodrugs based on the duocarmycin cargo (CBI) with tunable potency (JACS Au 2022) that can be used to create off-to-on therapeutic prodrugs. Such CBI prodrugs employing stabilized 1,2 dichalcogenide triggers proved to be cytotoxins that depend on Trx system activity in cells. They could further be exploited for cell-line dependent reductase activity profiling by screening their redox activation indices, the reduction-dependent part of total prodrug activation, in 177 cell lines. Beyond that, these prodrugs were well-tolerated in animals and showed anti-cancer efficacy in vivo in two distinct mouse tumor models (preprint 2022). Taken together, I introduced unique monothiol-resistant reducible motifs to target the cellular Trx system with chemocompatible units for each for TrxR and Trx/Grx, where the cyclic nature of the dichalcogenides avoids activation by GSH. By using them with distinct molecular cargos, I developed novel selective fluorescent reporter probes; and introduced a new class of bioreductive therapeutic constructs based on a common modular design. These were either applied to selectively measure cellular reductase activity or to deliver cytotoxic anti cancer agents in vivo. Ongoing work aims to differentiate between the two major redox effector proteins Trx and Grx, requiring additional layers of selectivity that may be addressed by tuned molecular recognition. The flexible use of various molecular cargos allows harnessing the same cellular redox machinery by either probes or prodrugs. This allows predictive conclusions from diagnostics to be directly translated into therapy and offers great potential for future adaptation to other enzyme classes and therapeutic venues.Die zellulĂ€re Redox-Homöostase hĂ€ngt von Thiol/Disulfid-Oxidoreduktasen ab, die den Stoffwechsel, die Proliferation und die antioxidative Antwort von Zellen beeinflussen. Die wichtigsten Netzwerke sind die Thioredoxin Reduktase-Thioredoxin (TrxR/Trx) und Glutathion Reduktase-Glutathion-Glutaredoxin (GR/GSH/Grx) Systeme, die ĂŒber Redox-Schalter in Substratproteinen lebenswichtige zellulĂ€re Funktionen steuern und so an der Redox-Regulation und -SignalĂŒbertragung beteiligt sind. Persistente VerĂ€nderungen des Redoxmilieus in pathologischen ZustĂ€nden, wie z. B. bei Krebs, sind in hohem Maße mit dem Trx-System verbunden. Eine Hochregulierung und/oder ÜberaktivitĂ€t des Trx-Systems, die bei vielen Krebsarten auftreten, unterstĂŒtzt zudem das Fortschreiten des Krebswachstums, was TrxR/Trx zu vielversprechenden Zielproteinen fĂŒr die Entwicklung neuer Krebsmedikamente macht. Um die biochemischen Prozesse dahinter zu erforschen, sind spezielle Techniken zur Visualisierung und Messung enzymatischer AktivitĂ€t nötig. Die hierzu geeigneten, meist genetischen Sensoren messen ratiometrisch das VerhĂ€ltnis reduzierter/oxidierter Spezies in zellulĂ€rem Umfeld oder spezifisch ausgewĂ€hlte Redoxpaare. Die weitere Erforschung der exakten Funktion von TrxR/Trx und deren Substrate ist jedoch durch mangelnde Nachweismethoden limitiert. Diese sind außerdem zur Validierung chemischer Hemmstoffe fĂŒr TrxR/Trx in Zellen und deren potenziellen Verwendung als Krebsmittel von großem Interesse. Bislang gibt es keinen selektiven zellulĂ€ren Trx-Inhibitor und potenzielle Off-Target-Effekte der bekannten TrxR-Inhibitoren wurden nicht abschließend bewertet. Ziel dieser Arbeit ist die Entwicklung niedermolekularer, diagnostischer und therapeutischer Werkzeuge, die selektiv auf das Trx-System abzielen und auf einem modularen Trigger-Cargo Design basieren. Hierzu werden zyklische Disulfid-Substrate (Trigger) fĂŒr Oxidoreduktasen so mit molekularen Wirkstoffen (Cargo) verknĂŒpft, dass dabei die WirkstoffaktivitĂ€t maskiert, und erst nach Reduktion durch ein Zielprotein wiederhergestellt wird. Diese neuartigen, synthetischen Reduktionssensoren basieren auf den folgenden Grundprinzipien: (i) Zyklische Disulfide sind thermodynamisch stabilisiert und können nur durch die stĂ€rksten Reduktasen gespalten werden; und (ii) die zyklische Topologie ermöglicht die kinetische ReversibilitĂ€t der zwei Thiol-Disulfid-Austauschreaktionen, die eine erste Reaktion mit Monothiolen, wie z. B. GSH, sofort umkehrt und so eine vollstĂ€ndige Reduktion verhindert. Die meisten frĂŒheren Arbeiten auf diesem Gebiet verwendeten ein zyklisches, fĂŒnfgliedriges Disulfid (1,2 Dithiolan) als Substrat fĂŒr TrxR. Das gleiche Strukturmotiv wurde jedoch an anderer Stelle als thermodynamisch instabil beschrieben und aufgrund dieser Eigenschaft explizit fĂŒr dynamische Kaskadenreaktionen verwendet. Deshalb vergleicht diese Arbeit zu Beginn einen neuen 1,2 Dithiolan basierten fluorogenen Indikator mit bestehenden, z. T. kommerziellen, Redox Sonden fĂŒr TrxR in einer Reihe von Zellkultur-Experimenten unter Modulation der zellulĂ€ren TrxR AktivitĂ€t und stellt so einen Widerspruch in der Literatur klar: 1,2 Dithiolane eignen sich nicht als selektive Substrate fĂŒr TrxR, da sie labil sowohl gegen die Reduktion durch andere Redoxproteine, als auch gegen den Monothiol Hintergrund in Zellen sind (Nat. Commun. 2022). Als alternatives Strukturmotiv wird in dieser Arbeit ein bizyklisches sechsgliedriges Disulfid (anneliertes 1,2 Dithian) etabliert. Durch sein niedriges Reduktionspotenzial, also seine hohe Resistenz gegen Reduktion, werden molekulare Sonden basierend auf diesem 1,2 Dithian als Reduktionssensor fast ausschließlich von Trx aktiviert, nicht aber von TrxR oder GSH (JACS 2021). Dieses Kernmotiv bestimmt dabei die Reduzierbarkeit, und damit die EnzymspezifitĂ€t, durch seine zyklische Natur und die Annelierung, auch unter Verwendung unterschiedlicher Farb-/Wirkstoffe. Auf dieser Grundlage konnte die molekulare Struktur durch einen weiteren Modifikationspunkt fĂŒr die flexible Verwendung weiterer funktioneller Einheiten ergĂ€nzt werden. Obwohl zellulĂ€re Studien ergaben, dass diese neuartigen 1,2 Dithian Einheiten in Zellen sowohl Trx als auch das strukturell verwandte Grx adressieren, sind die daraus resultierenden diagnostischen MolekĂŒle wertvoll, um den katalytischen Umsatz zellulĂ€rer Dithiol-Reduktasen, der sogenannten Trx Superfamilie, selektiv anzuzeigen (Preprint 2023). BegĂŒnstigt durch das modulare MolekĂŒldesign stellt diese Arbeit zudem das erste Reportersystem RX1 zum selektiven Nachweis der TrxR-AktivitĂ€t in Zellen vor. Es basiert auf der Verwendung eines zyklischen, unsymmetrischen Selenenylsulfid-Sensors (1,2 Thiaselenan), der selektiv von dem einzigartigen Selenolat der TrxR angegriffen wird, und dadurch letztlich nur von TrxR reduziert werden kann. RX1 eignete sich zudem fĂŒr eine Hochdurchsatz-Validierung bestehender TrxR Inhibitoren und unterstreicht dadurch den kommerziellen Nutzen derartiger Diagnostika (Chem 2022). Das zentrale Trigger-Cargo Konzept dieser Arbeit wurde fĂŒr therapeutische Zwecke weiterentwickelt und nutzt dabei den einzigartigen Wirkmechanismus der Duocarmycin-Naturstoffklasse (CBI) (JACS Au 2022) zur Entwicklung reduktiv aktivierbarer Therapeutika. CBI Prodrugs basierend auf stabilisierten Redox-Schaltern (1,2 Dithiane fĂŒr Trx; 1,2 Thiaselenan fĂŒr TrxR) reagierten signifikant auf TrxR-Modulation in Zellen. Sie wurden darĂŒber hinaus durch das Referenzieren ihrer AktivitĂ€t gegenĂŒber nicht-reduzierbaren KontrollmolekĂŒle fĂŒr die Erstellung zelllinienabhĂ€ngiger Profile der ReduktaseaktivitĂ€t in 177 Zelllinien genutzt. Schließlich waren diese neuen Krebsmittel im Tiermodell gut vertrĂ€glich und zeigten in zwei verschiedenen Mausmodellen eine krebshemmende Wirkung (Preprint 2022b). Zusammenfassend prĂ€sentiert diese Dissertation monothiol-resistente reduzierbare Trigger-Einheiten fĂŒr das zellulĂ€re Trx-System zur Entwicklung neuartiger, selektiver Reporter-Sonden, sowie eine neue Klasse reduktiv aktivierbarer Krebsmittel auf Basis eines adaptierbaren Trigger-Cargo Designs. Diese fanden entweder zur selektiven Messung zellulĂ€rer ProteinaktivitĂ€t oder zum Einsatz als Antikrebsmittel Verwendung. Es wurden chemokompatible Motive sowohl fĂŒr TrxR als auch fĂŒr Trx/Grx identifiziert, wobei deren zyklische Natur eine Aktivierung durch GSH verhindert. Eine weitere Differenzierung zwischen den beiden Redox-Proteinen Trx und Grx und anderen Proteinen der Trx-Superfamilie erfordert eine zusĂ€tzliche Ebene der Selektierung, z. B. durch molekulare Erkennung, und ist Gegenstand laufender Arbeiten. Die flexible Verwendung verschiedener molekularer Wirkstoffe ermöglicht dabei die „Pipeline-Entwicklung“ von Diagnostika und Therapeutika, die von der zellulĂ€ren Redox-Maschinerie analog umgesetzt werden, und dadurch Schlussfolgerungen aus der Diagnostik direkt auf eine Therapie ĂŒbertragbar machen. Dies birgt großes Potenzial fĂŒr kĂŒnftige Entwicklungen bei einer potenziellen Übertragung des modularen Konzepts auf andere Enzymklassen und therapeutische Einsatzgebiete

    Profiling Of Volatiles In Tissues Of Salacia Reticulata Wight. With Anti-Diabetic Potential Using GC-MS And Molecular Docking

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    Type 2 diabetes mellitus is a global pandemic, it is a chronic, progressive and an incompletely understood metabolic condition. The disease is characterized by higher levels of sugar in blood caused either due to insufficient production of insulin or because of insulin resistance. Major drugs used for the treatment of the condition are fraught with side effects. Hence, it is becoming an obligation to gaze at alternative agents showing marginal adverse effects. An important source of such agents are the medicinal plants. Several plants have been positively identified to show anti-diabetic effects. The species Salacia reticulata Wight., belonging to the family Celastraceae which is found in the forests of Southern India is one such promising plant to tackle type 2 diabetics. In this study, numerous volatile compounds were identified from various tissues through GC-MS analysis. Among these the compounds possessed suitable ADMET properties, and high binding affinities were compared with two approved {\alpha}-glucosidase inhibitors, Acarbose and Miglitol. The analysis indicated that the compounds with PubChem IDs, CID-240051 and CID-533471 exhibited potential as inhibitors of Human Maltase-Glucoamylase enzyme

    Dual Piperidine-Based Histamine H3 and Sigma-1 Receptor Ligands in the Treatment of Nociceptive and Neuropathic Pain

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    In search of new dual-acting histamine H3/sigma-1 receptor ligands, we designed a series of compounds structurally based on highly active in vivo ligands previously studied and described by our team. However, we kept in mind that within the previous series, a pair of closely related compounds, KSK67 and KSK68, differing only in the piperazine/piperidine moiety in the structural core showed a significantly different affinity at sigma-1 receptors (σ1Rs). Therefore, we first focused on an in-depth analysis of the protonation states of piperazine and piperidine derivatives in the studied compounds. In a series of 16 new ligands, mainly based on the piperidine core, we selected three lead structures (3, 7, and 12) for further biological evaluation. Compound 12 showed a broad spectrum of analgesic activity in both nociceptive and neuropathic pain models based on the novel molecular mechanism

    Evaluation of dormancy states in cancer and associated therapeutic opportunities

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    Tumour mass dormancy and cancer cell quiescence represent the two facets of cancer dormancy and play key roles in cancer development and progression. Quiescence describes the reversible, proliferative arrest of individual cancer cells that has been observed as a contributing factor of resistance to chemotherapy and other treatments targeting cycling cells. In contrast, tumour mass dormancy describes the state of no net tumour growth, which can arise due to inadequate tumour vascularisation or anti- tumour immune response, during which tumours can acquire additional mutations and establish a microenvironment permissive for growth. Currently, both dormancy states remain poorly characterised. This thesis presents computational frameworks for evaluating the two states and comprehensively profiles their abundance and associated genomic and cellular features across 31 solid cancers from the Cancer Genome Atlas. Using machine learning approaches, I demonstrate that cancer cell quiescence preferentially arises in less mutated tumours with intact TP53 and DNA damage repair pathways. I also highlight novel genomic dependencies, such as CEP89 amplification, which drive an impairment of quiescence. Similarly, mutations within CASP8 and HRAS oncogenes are shown to be enriched and positively selected in samples with tumour mass dormancy. I also highlight an association between APOBEC mutagenesis and both dormancy states. Moreover, tumour mass dormancy is shown to be associated with infiltration with macrophages and cytotoxic and regulatory T cells but a decreased infiltration with Th17 cells. Lastly, using single-cell data, I demonstrate that quiescence underlies resistance to a wide range of therapies, including treatments targeting cell cycle regulation, proliferative kinase signalling and epigenetic regulation. Ultimately, this analysis sheds light on the underlying biology of cancer dormancy states, potentially highlighting vulnerabilities that can be targeted in the clinic. It also provides a transcriptional signature of therapy-tolerant quiescent cells that could be explored further in the clinic to monitor patient therapy response

    Strategies to hijack MTs dysfunction in neurodegenerative tauopathies: Design and synthesis of novel CNS-disease-modifying tools

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    Neuronal microtubules assembly and dynamics are regulated by several proteins including (MT)-associated protein tau, whose aberrant hyperphosphorylation promotes its dissociation from MTs and its abnormal deposition into neurofibrillary tangles, a common neurotoxic hallmarks of neurodegenerative tauopathies. To date, no disease-modifying drugs have been approved to combat CNS tau-related diseases. The multifactorial etiology of these conditions represents one of the major limits in the discovery of effective therapeutic options. In addition, tau protein functions are orchestrated by diverse post-translational modifications among which phosphorylation mediated by PKs plays a leading role. In this context, conventional single-target therapies are often inadequate in restoring perturbed networks and fraught with adverse side-effects. This thesis reports two distinct approaches to hijack MT defects in neurons. The first is focused on the rational design and synthesis of first-in-class triple inhibitors of GSK-3ÎČ, FYN, and DYRK1A, three close-related PKs, which act as master regulators of aberrant tau hyperphosphorylation. A merged multi-target pharmacophore strategy was applied to simultaneously modulate all three targets and achieve a disease-modifying effect. Optimization of ARN25068 by a computationally and crystallographic driven SAR exploration, allowed to rationalize the key structural modifications to maintain a balanced potency against all three targets and develop a new generation of quite well-balanced analogs exhibiting improved physicochemical properties, a good in vitro ADME profile, and promising cell-based anti-tau phosphorylation activity. In Part II, MT-stabilizing compounds have been developed to compensate MT defects in tau-related pathologies. Intensive chemical effort has been devoted to scaling up BL-0884, identified as a promising MT-normalizing TPD, which exhibited favorable ADME-PK, including brain penetration, oral bioavailability, and brain pharmacodynamic activity. A suitable functionalization of the exposed hydroxyl moiety of BL-0884 was carried out to generate corresponding esters and amides possessing a wide range of applications as prodrugs and active targeting for cancer chemotherapy

    Machine Learning for Kinase Drug Discovery

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    Cancer is one of the major public health issues, causing several million losses every year. Although anti-cancer drugs have been developed and are globally administered, mild to severe side effects are known to occur during treatment. Computer-aided drug discovery has become a cornerstone for unveiling treatments of existing as well as emerging diseases. Computational methods aim to not only speed up the drug design process, but to also reduce time-consuming, costly experiments, as well as in vivo animal testing. In this context, over the last decade especially, deep learning began to play a prominent role in the prediction of molecular activity, property and toxicity. However, there are still major challenges when applying deep learning models in drug discovery. Those challenges include data scarcity for physicochemical tasks, the difficulty of interpreting the prediction made by deep neural networks, and the necessity of open-source and robust workflows to ensure reproducibility and reusability. In this thesis, after reviewing the state-of-the-art in deep learning applied to virtual screening, we address the previously mentioned challenges as follows: Regarding data scarcity in the context of deep learning applied to small molecules, we developed data augmentation techniques based on the SMILES encoding. This linear string notation enumerates the atoms present in a compound by following a path along the molecule graph. Multiplicity of SMILES for a single compound can be reached by traversing the graph using different paths. We applied the developed augmentation techniques to three different deep learning models, including convolutional and recurrent neural networks, and to four property and activity data sets. The results show that augmentation improves the model accuracy independently of the deep learning model, as well as of the data set size. Moreover, we computed the uncertainty of a model by using augmentation at inference time. In this regard, we have shown that the more confident the model is in its prediction, the smaller is the error, implying that a given prediction can be trusted and is close to the target value. The software and associated documentation allows making predictions for novel compounds and have been made freely available. Trusting predictions blindly from algorithms may have serious consequences in areas of healthcare. In this context, better understanding how a neural network classifies a compound based on its input features is highly beneficial by helping to de-risk and optimize compounds. In this research project, we decomposed the inner layers of a deep neural network to identify the toxic substructures, the toxicophores, of a compound that led to the toxicity classification. Using molecular fingerprints —vectors that indicate the presence or absence of a particular atomic environment —we were able to map a toxicity score to each of these substructures. Moreover, we developed a method to visualize in 2D the toxicophores within a compound, the so- called cytotoxicity maps, which could be of great use to medicinal chemists in identifying ways to modify molecules to eliminate toxicity. Not only does the deep learning model reach state-of-the-art results, but the identified toxicophores confirm known toxic substructures, as well as expand new potential candidates. In order to speed up the drug discovery process, the accessibility to robust and modular workflows is extremely advantageous. In this context, the fully open-source TeachOpenCADD project was developed. Significant tasks in both cheminformatics and bioinformatics are implemented in a pedagogical fashion, allowing the material to be used for teaching as well as the starting point for novel research. In this framework, a special pipeline is dedicated to kinases, a family of proteins which are known to be involved in diseases such as cancer. The aim is to gain insights into off-targets, i.e. proteins that are unintentionally affected by a compound, and that can cause adverse effects in treatments. Four measures of kinase similarity are implemented, taking into account sequence, and structural information, as well as protein-ligand interaction, and ligand profiling data. The workflow provides clustering of a set of kinases, which can be further analyzed to understand off-target effects of inhibitors. Results show that analyzing kinases using several perspectives is crucial for the insight into off-target prediction, and gaining a global perspective of the kinome. These novel methods can be exploited in the discovery of new drugs, and more specifically diseases involved in the dysregulation of kinases, such as cancer

    Targeting clinical mechanisms of therapy resistance in ER+ breast cancer using an AR agonistic approach

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    Breast cancer is now the most common cancer and cause of cancer-related death in women. With over 2 million new cases in 2018, and approximately 30% of these patients dying from the disease, the burden for patients, families and society is high. In the estrogen receptor positive (ER+) setting, the majority of these deaths are attributed to metastatic disease (stage IV), as it is often intractable due to resistance to conventional endocrine therapies (ET) and cyclin dependent kinase 4/6 inhibitors (CDK4/6i). With multi-drug resistant ER+ tumours acquiring mechanisms to push through the cell cycle and proliferate, it is critical that alternative pathways are exploited. While it is recognised that the androgen receptor (AR) plays a role in breast cancer, controversy surrounding its exact nature has impeded progress in the development of targeted therapies. Given that nuclear receptors have similar consensus DNA binding motifs and commonly use the same co-factors for activity, it is not surprising that substantial cross-talk occurs between nuclear receptors in cancer. Activation or inhibition of parallel hormonal pathways, may therefore, impact on the steroid hormone receptors responsible for driving tumour growth. Recent studies have demonstrated that activation of the AR using selective AR modulators is an effective therapeutic strategy in preclinical treatment- naïve and -resistant ER+ models. As such, further investigation paired with clinical trials are required to fully elucidate the effectiveness of AR agonism in both the primary and metastatic setting. Here, I have investigated the efficacy of AR agonists, both as a monotherapy and in combination with a CDK4/6i, as a therapeutic strategy in treatment refractory ER+/AR+ preclinical breast cancer models. I have developed 3 novel patient derived xenograft (PDX) models of CDK4/6i resistance, and have characterised them for response to standard of care therapy, genome and transcriptome. Using these models alongside ET ± CDK4/6i resistant cell lines, I demonstrated the tumour suppressive nature of the AR in the context of CDK4/6i resistance, and showed that combination AR agonist + CDK4/6i was an optimal therapeutic strategy. Finally, I provided evidence that that AR activity is enhanced in tumours treated with CDK4/6i, providing a rationale for this therapy. In summary, a therapeutic rationale for combination AR agonist + CDK4/6i was validated

    Uncovering the effects and molecular mechanism of Astragalus membranaceus (Fisch.) Bunge and its bioactive ingredients formononetin and calycosin against colon cancer: An integrated approach based on network pharmacology analysis coupled with experimental validation and molecular docking

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    Colon cancer is a highly malignant cancer with poor prognosis. Astragalus membranaceus (Fisch.) Bunge (Huang Qi in Chinese, HQ), a well-known Chinese herbal medicine and a popular food additive, possesses various biological functions and has been frequently used for clinical treatment of colon cancer. However, the underlying mechanism is not fully understood. Isoflavonoids, including formononetin (FMNT) and calycosin (CS), are the main bioactive ingredients isolated from HQ. Thus, this study aimed to explore the inhibitory effects and mechanism of HQ, FMNT and CS against colon cancer by using network pharmacology coupled with experimental validation and molecular docking. The network pharmacology analysis revealed that FMNT and CS exerted their anticarcinogenic actions against colon cancer by regulating multiple signaling molecules and pathways, including MAPK and PI3K-Akt signaling pathways. The experimental validation data showed that HQ, FMNT and CS significantly suppressed the viability and proliferation, and promoted the apoptosis in colon cancer Caco2 and HT-29 cells. HQ, FMNT and CS also markedly inhibited the migration of Caco2 and HT-29 cells, accompanied by a marked increase in E-cadherin expression, and a notable decrease in N-cadherin and Vimentin expression. In addition, HQ, FMNT and CS strikingly decreased the expression of ERK1/2 phosphorylation (p-ERK1/2) without marked change in total ERK1/2 expression. They also slightly downregulated the p-Akt expression without significant alteration in total Akt expression. Pearson correlation analysis showed a significant positive correlation between the inactivation of ERK1/2 signaling pathway and the HQ, FMNT and CS-induced suppression of colon cancer. The molecular docking results indicated that FMNT and CS had a strong binding affinity for the key molecules of ERK1/2 signaling pathway. Conclusively, HQ, FMNT and CS exerted good therapeutic effects against colon cancer by mainly inhibiting the ERK1/2 signaling pathway, suggesting that HQ, FMNT and CS could be useful supplements that may enhance chemotherapeutic outcomes and benefit colon cancer patients

    Structural Cheminformatics for Kinase-Centric Drug Design

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    Drug development is a long, expensive, and iterative process with a high failure rate, while patients wait impatiently for treatment. Kinases are one of the main drug targets studied for the last decades to combat cancer, the second leading cause of death worldwide. These efforts resulted in a plethora of structural, chemical, and pharmacological kinase data, which are collected in the KLIFS database. In this thesis, we apply ideas from structural cheminformatics to the rich KLIFS dataset, aiming to provide computational tools that speed up the complex drug discovery process. We focus on methods for target prediction and fragment-based drug design that study characteristics of kinase binding sites (also called pockets). First, we introduce the concept of computational target prediction, which is vital in the early stages of drug discovery. This approach identifies biological entities such as proteins that may (i) modulate a disease of interest (targets or on-targets) or (ii) cause unwanted side effects due to their similarity to on-targets (off-targets). We focus on the research field of binding site comparison, which lacked a freely available and efficient tool to determine similarities between the highly conserved kinase pockets. We fill this gap with the novel method KiSSim, which encodes and compares spatial and physicochemical pocket properties for all kinases (kinome) that are structurally resolved. We study kinase similarities in the form of kinome-wide phylogenetic trees and detect expected and unexpected off-targets. To allow multiple perspectives on kinase similarity, we propose an automated and production-ready pipeline; user-defined kinases can be inspected complementarily based on their pocket sequence and structure (KiSSim), pocket-ligand interactions, and ligand profiles. Second, we introduce the concept of fragment-based drug design, which is useful to identify and optimize active and promising molecules (hits and leads). This approach identifies low-molecular-weight molecules (fragments) that bind weakly to a target and are then grown into larger high-affinity drug-like molecules. With the novel method KinFragLib, we provide a fragment dataset for kinases (fragment library) by viewing kinase inhibitors as combinations of fragments. Kinases have a highly conserved pocket with well-defined regions (subpockets); based on the subpockets that they occupy, we fragment kinase inhibitors in experimentally resolved protein-ligand complexes. The resulting dataset is used to generate novel kinase-focused molecules that are recombinations of the previously fragmented kinase inhibitors while considering their subpockets. The KinFragLib and KiSSim methods are published as freely available Python tools. Third, we advocate for open and reproducible research that applies FAIR principles ---data and software shall be findable, accessible, interoperable, and reusable--- and software best practices. In this context, we present the TeachOpenCADD platform that contains pipelines for computer-aided drug design. We use open source software and data to demonstrate ligand-based applications from cheminformatics and structure-based applications from structural bioinformatics. To emphasize the importance of FAIR data, we dedicate several topics to accessing life science databases such as ChEMBL, PubChem, PDB, and KLIFS. These pipelines are not only useful to novices in the field to gain domain-specific skills but can also serve as a starting point to study research questions. Furthermore, we show an example of how to build a stand-alone tool that formalizes reoccurring project-overarching tasks: OpenCADD-KLIFS offers a clean and user-friendly Python API to interact with the KLIFS database and fetch different kinase data types. This tool has been used in this thesis and beyond to support kinase-focused projects. We believe that the FAIR-based methods, tools, and pipelines presented in this thesis (i) are valuable additions to the toolbox for kinase research, (ii) provide relevant material for scientists who seek to learn, teach, or answer questions in the realm of computer-aided drug design, and (iii) contribute to making drug discovery more efficient, reproducible, and reusable
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