京都大学 / 0048 / 新制・論文博士 / 博士(医科学) / 乙第13373号 / 論医科博第6号 / 新制||医科||8(附属図書館) / (主査)教授 長船 健二, 教授 稲垣 暢也, 教授 渡邊 直樹 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

<p>    </p> <p>DNA-encoded chemical libraries (DELs) are collections of DNA-linked small molecules, where each synthetic small molecule is covalently attached to a unique DNA barcode that encodes its identity. This technology harnesses the power of organic chemistry and genetics, which extends the application of molecular evolution and natural selection to the discovery of specific small molecules binders to protein targets of interest. Rather than discretely screening individual molecules, up to billions of DNA-encoded small molecules can be assessed collectively by a selection assay in a single tube. As a result, the high sensitivity, low cost, and unprecedented level of molecular complexity of DELs allow rapid generation of novel bioactive compounds. While powerful, this approach has its own limitations, including limited target scope and selection strategies. Currently, DEL targets have been largely limited to biochemically purified proteins and used in affinity-based selections assays. In the first area of this work, we address both these limitations by capitalizing on the power of affinity labeling. This allows DELs to be applied to protein targets within and on living cells and expands the power of DNA-encoding to the identification of small molecules with specific biological functions beyond binding. </p> <p>In the second area, we harnessed affinity labeling and DNA sequence analysis to develop multiplexed small molecule ligand binding assays. This method is the initial demonstration of split-and-pool ligand binding assays using DNA-linked small molecule probes. We used this approach in a high-throughput screening campaign to identify selective inhibitors by screening 1000 compounds against 5 bromodomain proteins concurrently. In addition, this approach was utilized to rank order the affinity of a 96-member library of DNA-linked ligands to a protein simultaneously, which significantly increases the throughput of ligand binding assays while keeps the cost low. </p> <p>Lastly, we developed proximity-induced selection assays to enrich ligands from DELs. This approach involves uncaging or installation of a biotin purification tag on the DNA construct either through photo-deprotection of a protected biotin group using a light emitting protein tag or by amine acylation using an engineered biotin ligase. Compared to affinity labeling-based selection approaches, this approach results in improved recovery of ligands and, at the same time, removes the onerous requirement of protein purification. The enzyme-mediated proximity labeling approach should serve as a convenient tool for molecular discovery with DELs. </p>

This document is the Accepted Manuscript version of a Published Work that appeared in final form in the Journal of the American Chemical Society, copyright ©American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://dx.doi.org/10.1021/jacs.5b10162. / 京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19611号 / 医博第4118号 / 新制||医||1015(附属図書館) / 32647 / 京都大学大学院医学研究科医学専攻 / (主査)教授 齊藤 博英, 教授 渡邊 直樹, 教授 松原 和夫 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

De nouveaux domaines ont vu le jour, à l’interface entre biologie, chimie et informatique, afin de répondre aux multiples problématiques liées à la recherche de médicaments. Cette thèse se situe à l’interface de plusieurs de ces domaines, regroupés sous la bannière de la chémo-informatique. Récent à l’échelle humaine, ce domaine fait néanmoins déjà partie intégrante de la recherche pharmaceutique. De manière analogue à la bioinformatique, son pilier fondateur reste le stockage, la représentation, la gestion et l’exploitation par ordinateur de données provenant de la chimie. La chémoinformatique est aujourd’hui utilisée principalement dans les phases amont de la recherche de médicaments. En combinant des méthodes issues de différents domaines (chimie, informatique, mathématique, apprentissage, statistiques, etc.), elle permet la mise en oeuvre d’outils informatiques adaptés aux problématiques et données spécifiques de la chimie, tels que le stockage de l’information chimique en base de données, la recherche par sous-structure, la visualisation de données, ou encore la prédiction de propriétés physico-chimiques et biologiques.Dans ce cadre pluri-disciplinaire, le travail présenté dans cette thèse porte sur deux aspects importants liés à la chémoinformatique : (1) le développement de nouvelles méthodes permettant de faciliter la visualisation, l’analyse et l’interprétation des données liées aux ensembles de molécules, plus communément appelés chimiothèques, et (2) le développement d’outils informatiques permettant de mettre en oeuvre ces méthodes. / Some news areas in biology ,chemistry and computing interface, have emerged in order to respond the numerous problematics linked to the drug research. This is what this thesis is all about, as an interface gathered under the banner of chimocomputing. Though, new on a human scale, these domains are nevertheless, already an integral part of the drugs and medicines research. As the Biocomputing, his fundamental pillar remains storage, representation, management and the exploitation through computing of chemistry data. Chimocomputing is now mostly used in the upstream phases of drug research. Combining methods from various fields ( chime, computing, maths, apprenticeship, statistics, etc…) allows the implantation of computing tools adapted to the specific problematics and data of chime such as chemical database storage, understructure research, data visualisation or physoco-chimecals and biologics properties prediction.In that multidisciplinary frame, the work done in this thesis pointed out two important aspects, both related to chimocomputing : (1) The new methods development allowing to ease the visualization, analysis and interpretation of data related to set of the molecules, currently known as chimocomputing and (2) the computing tools development enabling the implantation of these methods.

The split-and-pool method has been widely used to synthesize chemical libraries of a large size for early drug discovery, albeit without the possibility of meaningful quality control. In contrast, a self-assembled DNA-encoded chemical library (DEL) allows us to construct an m x n-member library by mixing an m-member and an n-member pre-purified sub-library. Herein, we report a trio-pharmacophore DEL (T-DEL) of m x l x n members through assembling three pre-purified and validated sub-libraries. The middle sub-library is synthesized using DNA-templated synthesis with different reaction mechanisms and designed as a linkage connecting the fragments displayed on the flanking two sub-libraries. Despite assembling three fragments, the resulting compounds do not exceed the up-to-date standard of molecular weight regarding drug-likeness. We demonstrate the utility of T-DEL in linker optimization for known binding fragments against trypsin and carbonic anhydrase II and by de novo selections against matrix metalloprotease-2 and −9.

L’identification d’antiviraux à large spectre est un des défis majeurs de la rechercheactuelle en virologie. Une des stratégies les plus prometteuses consiste à cibler une interactionvirus/hôte conservée. Ainsi, avec la technique d’AlphaScreen® et le modèle d’interactionprotéine VI de l’Adénovirus (AdV)/Nedd4-2, nous avons réalisé un criblage biochimique àhaut débit contre l’interaction virus/hôte [LP]PxY/Nedd4, commune à différentes familles devirus. Nous avons trouvé des candidats inhibiteurs issus d’une banque de composés approuvéspar les agences de santé. Nous les avons testés, caractérisés et validé leur effet antiviral surdeux familles de virus totalement différentes. Ainsi, les composés C9 (Sulconazole) et C4(Flunarizine) que nous avons identifiés diminuent la réplication de l’AdV, un virus à ADNenveloppé et du virus de Marburg, un virus à ARN, non enveloppé de la famille desFiloviridae. Ces résultats ont permis de valider l’interaction [LP]PxY/Nedd4 comme unecible idéale d’un antiviral à large spectre et de proposer un repositionnement de ces moléculesC9 et C4 comme antiviraux potentiels. Nous avons également synthétisé de nouvellesmolécules analogues du composé C9 et démontré qu’elles étaient tout aussi efficaces que lecomposé lui-même sur la réplication de l’AdV. Ces résultats nous ont permis de présenter laclasse des dérivés imidazolés comme structure de base pour l’élaboration de nouveauxantiviraux, potentiellement à large spectre. / Broad-spectrum antiviral identification is considered as one of the major aims of theactual virology research and one strategy consists in targeting virus/host interaction. Using theAlphaScreen® technology and the adenoviral model protein VI/Nedd4-2, we performed highthroughputbiochemical screening targeting the [LP]PxY/Nedd4 interaction, a commoninteraction of different virus families. We identified candidate inhibitors from a librarycompound approved by health agencies. We tested, characterized and validated the antiviraleffect of those compounds on two very different virus families. Indeed, compounds C9(Sulconazole) and C4 (Flunarizine) decrease replication of the adenovirus, a DNA nonenvelopedvirus and the replication of the Marburg virus, an RNA enveloped virus from theFilovirus family. Taken together, those results permit us to validate the [LP]PxY/Nedd4interaction as good target for a broad spectrum antiviral and to propose the “repositioning” ofcompounds C4 and C9 as antivirals. Moreover, we have synthesized new analogues from C9showing similar effect on AdV replication compared to the original molecule (C9). Inconclusion, our work on developing new broad-spectrum antivirals highlights the possibilityto use imidazole derivatives as a new class of antiviral compounds.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22733号 / 医博第4651号 / 新制||医||1046(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 萩原 正敏, 教授 岩田 想, 教授 渡邊 直樹 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Cancer immunotherapy is an encompassing term referring to therapeutic strategies that aim to boost the immune system to fight cancer. These strategies include administering immune cells that have been altered to have greater anti-tumor activity or using biologics and small molecules that target immune components to also promote tumor clearance. Natural Killer (NK) cells are cells of the innate immune system that recognize and kill abnormal cells such as cancer cells and play an important role in the anti-tumor response. Because of their crucial role in tumor immunity, NK cells are prime targets for immunotherapies. Repurposing small molecule drugs is an attractive strategy to identify new immunotherapies from already approved drugs. Here, we screened 1,200 approved drugs from the Prestwick Chemical Library to identify drugs that increase NK cell cytotoxicity. We used a high-throughput luciferase-release cytotoxicity assay to measure the killing of the myeloid leukemia cell line, K562 cells expressing nano luciferase (NL) by NK92 cells, a human NK cell line. From the drug candidates identified from the screening assay, the antibiotic colistin sulfate increased cytotoxicity of the NK92 cell line and unstimulated human NK cells towards K562-NL cells. This increase in NK cytotoxicity was short-lived as pre-treating NK92 cells with colistin for 1 hour or 24 hours did not increase cytotoxicity. Also, we show pre-treating K562-NL target cells with colistin does not sensitize them to NK-mediated killing. Further studies are needed to uncover the mechanism of action of colistin, thus contributing to knowledge of fundamental NK cell biology regarding NK cell cytotoxicity which will aid in identifying additional small molecule drugs that enhance NK cell activity.

DNA-encoded chemical libraries (DEL) allows an exhaustive chemical space sampling with a large-scale data consisting of compounds produced through combinatorial synthesis. This novel technology was utilized in the early drug discovery stages for robust hit identification and lead optimization. In this project, the aim was to build a Machine Learning- based QSAR model with conformal prediction for hit identification on two different target proteins, the DEL was assayed on. An initial investigation was conducted on a pilot project with 1000 compounds and the analyses and the conclusions drawn from this part were later applied to a larger dataset with 1.2 million compounds. With this classification model, the prediction of the compound activity in the DEL as well as in an external dataset was aimed to be analyzed with identification of the top hits to evaluate model’s performance and applicability. Support Vector Machine (SVM) and Random Forest (RF) models were built on both the pilot and the main datasets with different descriptor sets of Signature Fingerprints, RDKIT and CDK. In addition, an Autoencoder was used to supply data-driven descriptors on the pilot data as well. The Libsvm and the Liblinear implementations were explored and compared based on the models’ performances. The comparisons were made by considering the key concepts of conformal prediction such as the trade-off between validity and efficiency, observed fuzziness and the calibration against a range of significance levels. The top hits were determined by two sorting methods, credibility and p-value differences between the binary classes. The assignment of correct single-labels to the true actives over a wide range of significance levels regardless of the similarity of the test compounds to the training set was confirmed for the models. Furthermore, an accumulation of these true actives in the models’ top hit selections was observed according to the latter sorting method and additional investigations on the similarity and the building block enrichments in the top 50 and 100 compounds were conducted. The Tanimoto similarity demonstrated the model’s predictive power in selecting structurally dissimilar compounds while the building block enrichment analysis showed the selectivity of the binding pocket where the target protein B was determined to be more selective. All of these comparison methods enabled an extensive study on the model evaluation and performance. In conclusion, the Liblinear model with the Signature Fingerprints was concluded to give the best model performance for both the pilot and the main datasets with the considerations of the model performances and the computational power requirements. However, an external set prediction was not successful due to the low structural diversity in the DEL which the model was trained on.

The first part of the thesis deals with the synthesis and biological evaluation of new platelets aggregation inhibitors, based on 12-HETE, 13-HODE and their analogues. In the second part we are interested in novel methodologies for the preparation of CF₂-containing molecules : First, a flexible strategy for the synthesis of gem-difluoro-bisarylic derivatives and heteroaromatic analogues was designed based on the easy synthesis and the reactivity of gem-difluoro propargylic intermediates, which by Diels-Alder cycloaddition and 1,3-dipolar cycloadditions afforded respectively the bisarylic and mixed arylic heteroarylic scaffolds. In addition, two small libraries were constructed around a bisarylic scaffold as representative examples. Second, we were interested in the synthesis of optically active functionalized molecules containing a gem-difluoro group, using asymmetric organocatalysis protocols. After preparation of the gem-difluoro enals, from their difluoropropargylic precursors, asymmetric organocalytic Diels-Alder cycloaddition and 1,4-conjugated additions were successfully performed.