Gotowa bibliografia na temat „DRUG DISCOVERY TOOLS”

Drug discovery and development field has entered into a revolutionary phase with the introduction of Computer Aided Drug Designing (CADD) tools in the designing and development of new drugs. Traditional drug discovery and designing is a tedious, expensive and time-consuming process. Pharmaceutical industries spend billions of dollars to launch a potential drug candidate into the drug market. It takes 15-20 years of research to discover a new drug candidate. The advancements in the Computer Aided Drug Designing techniques have significantly contributed towards lowering the cost and time involved in new drug discovery. Different types of approaches are used to find out the potential drug candidates. Numerous compounds have been successfully discovered and launched into the market using computational tools. Various novel software-based methods like Structure- Based Drug Designing (SBDD), Ligand-Based Drug Designing (LBDD), Pharmacophore Mapping and Fragment-Based Drug Designing (FBDD) are considered as powerful tools for determining the pharmacokinetics, pharmacodynamics and structure activity relationship between target protein and its ligand. These tools provide valuable information about experimental findings and the mechanism of action of drug molecules. This has greatly expedited the discovery of promising drug candidates by sidestepping the lengthy steps involved in the synthesis of unnecessary compounds.

The medicinal chemist plays the most important role in drug design, discovery and development. The primary goal is to discover leads and optimize them to develop clinically useful drug candidates. This process requires the medicinal chemist to deal with large sets of data containing chemical descriptors, pharmacological data, pharmacokinetics parameters, and in silico predictions. The modern medicinal chemist has a large number of tools and technologies to aid him in creating strategies and supporting decision-making. Alongside with these tools, human cognition, experience and creativity are fundamental to drug research and are important for the chemical intuition of medicinal chemists. Therefore, fine-tuning of data processing and in-house experience are essential to reach clinical trials. In this article, we will provide an expert opinion on how chemical intuition contributes to the discovery of drugs, discuss where it is involved in the modern drug discovery process, and demonstrate how multidisciplinary teams can create the optimal environment for drug design, discovery, and development.

Spectroscopic, scattering, and imaging methods play an important role in advancing the study of pharmaceutical and biopharmaceutical therapies. The tools more familiar to scientists within industry and beyond, such as nuclear magnetic resonance and fluorescence spectroscopy, serve two functions: as simple high-throughput techniques for identification and purity analysis, and as potential tools for measuring dynamics and structures of complex biological systems, from proteins and nucleic acids to membranes and nanoparticle delivery systems. With the expansion of commercial small-angle x-ray scattering instruments into the laboratory setting and the accessibility of industrial researchers to small-angle neutron scattering facilities, scattering methods are now used more frequently in the industrial research setting, and probe-less time-resolved small-angle scattering experiments are now able to be conducted to truly probe the mechanism of reactions and the location of individual components in complex model or biological systems. The availability of atomic force microscopes in the past several decades enables measurements that are, in some ways, complementary to the spectroscopic techniques, and wholly orthogonal in others, such as those related to nanomechanics. As therapies have advanced from small molecules to protein biologics and now messenger RNA vaccines, the depth of biophysical knowledge must continue to serve in drug discovery and development to ensure quality of the drug, and the characterization toolbox must be opened up to adapt traditional spectroscopic methods and adopt new techniques for unraveling the complexities of the new modalities. The overview of the biophysical methods in this review is meant to showcase the uses of multiple techniques for different modalities and present recent applications for tackling particularly challenging situations in drug development that can be solved with the aid of fluorescence spectroscopy, nuclear magnetic resonance spectroscopy, atomic force microscopy, and small-angle scattering.

Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery.

Background: Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug. Methods: In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Results: A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given. Conclusion: The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

El cribratge virtual és un mètode quimioinformàtic que consisteix en cribrar molècules bioactives de grans bases de dades de molècules petites. Això permet als investigadors d’estalviar-se el cost de provar experimentalment cents o milers de compostos candidats, reduïnt-ne el nombre fins a quantitats manejables. Per a la validació dels mètodes de cribratge virtual calen biblioteques de molècules cimbell. El programari DecoyFinder fou desenvolupat com a aplicació gràfica de fàcil ús per a la construcció de biblioteques de molècules cimbell, i fou posteriorment ampliat amb les troballes de recerca posterior sobre la construcció i rendiment de biblioteques de molècules cimbell. El Protein Data Bank (PDB) és molt útil perquè proporciona estructures tridimensionals per a complexos proteïna-lligand, i per tant, informació sobre com interactuen. Pels mètodes de cribratge virtual que en depenen, n’és extremadament important la seva fiabilitat. El VHELIBS fou desenvolupat com a eina per a inspeccionar i identificar, fàcilment i intuitiva, les estructures fiables del PDB, basant-se en com de bo n’és l’encaix amb els seus corresponents mapes de densitat electrònica. Mentre que el cribratge virtual prova de trobar noves molècules bioactives per determinades dianes, l’enfoc invers també s’empra: arran d’una molècula, cercar-ne dianes amb activitat biològica no documentada. Aquest cribratge invers és conegut en anglès com a “in silico target fishing”, o pesca de dianes “in silico”, i és especialment útil a l’àmbit de la reutilització de fàrmacs En començar aquesta tesi, no hi havia cap plataforma de “target fishing” de lliure accés, i tot i que durant els anys se n’han desenvolupat algunes, en tots els casos la seva predicció de bioactivitat és qualitativa. Per això es desenvolupà una plataforma pròpia de “target fishing” de lliure accés, amb la implementació d’un nou mètode que proporciona la primera predicció quantitativa de bioactivitat per aquest tipus de plataforma.
El cribado virtual es un método quimioinformático que consiste en la criba de moléculas bioactivas de grandes bases de datos de moléculas pequeñas. Esto permite a los investigadores ahorrarse el coste de probar experimentalmente cientos o miles de compuestos candidatos, reduciéndolos hasta cantidades manejables. Para la validación de los métodos de cribado virtual hacen falta bibliotecas de moléculas señuelo. El software DecoyFinder fue desarrollado como aplicación gráfica de fácil uso para la construcción de bibliotecas de moléculas señuelo, y fue posteriormente ampliado con los hallazgos de investigación posterior sobre la construcción i rendimiento de bibliotecas de moléculas señuelo. El Protein Data Bank (PDB) es muy útil porque proporciona estructuras tridimensionales para complejos proteina-ligando, y por tanto, información sobre como interactúan. Para los métodos de cribado virtual que dependen de ellas, es extremadamente importante su fiabilidad. VHELIBS fue desarrollado como herramienta para inspeccionar e identificar, fácil e intuitivamente, las estructuras fiables del PDB, basándose en como de bueno es su encaje con sus correspondientes mapas de densidad electrónica. Mientras que el cribado virtual intenta encontrar nuevas moléculas bioactivas para determinadas dianas, el enfoque inverso también se utiliza: a partir de una molécula, buscar dianas donde presente actividad biológica no documentada. Este cribado inverso es conocido en inglés como “in silico target fishing”, o pesca de dianas “in silico”, y es especialmente útil en el ámbito de la reutilización de fármacos. Al comenzar esta tesis, no había ninguna plataforma de “target fishing” de libre acceso, y aunque durante los años se han desarrollado algunas, en todos los casos su predicción de bioactividad es cualitativa. Por eso se desarrolló una plataforma propia de “target fishing” de libre acceso, con la implementación de un nuevo método que proporciona la primera predicción cuantitativa de bioactividad para este tipo de plataforma.
Virtual screening is a cheminformatics method that consists of screening large small-molecule databases for bioactive molecules. This enables the researcher to avoid the cost of experimentally testing hundreds or thousands of compounds by reducing the number of candidate molecules to be tested to manageable numbers. For their validation, virtual screening approaches need decoy molecule libraries. DecoyFinder was developed as an easy to use graphical application for decoy library building, and later updated after some research into decoy library building and their performance when used for 2D similarity approaches. The Protein Data Bank (PDB) is very useful because it provides 3D structures for protein-ligand complexes and, therefore, information on how certain ligands bind and interact with their targets. For virtual screening apporaches relying on these structures, it is of the utmost importance that the data available on the PDB for the ligand and its binding site are reliable. VHELIBS was developed as a tool to easily and intuitively inspect and identify reliable PDB structures based on the goodness of fitting between ligands and binding sites and their corresponding electron density map. While virtual screening aims to find new bioactive molecules for certain targets, the opposite approach is also used: starting from a given molecule, to search for a biological target for which it presents previously undocumented bioactivity. This reverse screening is known as in silico or computational target fishing or reverse pharmacognosy, and it is specially useful for drug repurposing or repositioning. When this thesis was started, there were no freely available target fishing platforms, but some have been developed during the years. However, they are qualitative in the nature of their activity prediction, and thus we set out to develop a freely accessible target fishing web service implementing a novel method which provides the first quantitative activity prediction: Anglerfish.

A major bottleneck in drug discovery is the production of soluble human recombinant protein for functional, biochemical and structural analyses. The level of recombinant protein expression is controlled by a complex relationship between both biological and engineering variables. Due to the inter-play between these variables and standard experimental methods, the identification of the key variables which lead to improved protein expression can sometimes be missed. This thesis presents a framework which underpins the generation of large quantities of soluble recombinant protein in E. coli in a rapid and cost-effective manner. To achieve this goal, Design of Experiments (DoE) was first employed in combination with microwell plate (MWP) fermentations to investigate the wide array of protein expression variables. These tools are well suited to high-throughput expression requirements as they afford large savings in time, cost and resource requirements. The information generated from these MWP experiments was then exploited to devise a strategy for reproducing the process within stirred- tank reactors (STRs). The DoE methodology was first used to identify relevant protein expression variables including fermentation variables (media type and fermentation time), protein induction variables (inducer concentration and induction time) and environmental variables such as oxygen transfer rate, temperature and pH. Ten factors were screened overall at the microwell scale and three were investigated further through optimisation designs. The application of DoE led to a robust understanding of the process and resulted in protein yields five-fold greater than those obtained under standard shake-flask conditions. The most significant factors were post-induction period and shaking speed, the latter of which is strongly related to the mass transfer coefficient, faa. In order to translate this stable and optimised small-scale expression system to a production-scale stirred-tank reactor (STR), an understanding of the engineering parameters at both scales of operation was crucial. This need was complicated by significant differences between the MWPs and STRs such as geometry, mode of aeration and agitation, and the effects of surface tension. In this work, the MWP fermentation results led to the hypothesis that operation at a constant kia value would facilitate predictable scale translation. However, there currently exists very little published work on the characterisation of kia within MWPs. Miniature oxygen probes were, therefore, used to characterise MWP kia values directly via the static gassing-out method over a range of square-well MWP formats and shaking speeds. This information was then used to translate the performance of a 3ml MWP E. coli fermentation, on the basis of matched faa, to STR working volumes of 51 and 451. The efficacy of scale-up was confirmed by performing F tests on pairs of profiles for cell growth and expression levels of recombinant firefly luciferase. This rapid, accurate and direct method of kia characterisation within MWPs enabled a 15,000-fold direct scale-up of fermentation performance in terms of cell growth and protein expression from MWP to STR.

Malaria is a leading cause of morbidity and mortality, causing more than 400,000 deaths per year. Malaria is caused by parasites of the Plasmodium genus with most deaths due to P. falciparum infection. The control of malaria is complicated by the lack of a widely effective vaccine, the spread of mosquito resistance to insecticides and Plasmodium parasite resistance to available drugs, including the gold standard artemisinin-combination therapies. Thus, there is an urgent requirement for the development of new antimalarials, in particular those with different modes of action to existing drugs to limit potential problems of cross-resistance. Plasmodium species have a complex lifecycle that includes transmission from the female Anopheles mosquito vector to a human host requiring significant morphological changes. These morphological changes are associated with stage-specific changes in transcription regulated by epigenetic mechanisms. The proteins involved in these processes are potential new therapeutic targets for malaria. This includes histone deacetylases (HDACs), which together with histone acetyltransferases (HATs), are involved in reversible posttranslational acetylation of histone and non-histone proteins, regulating transcription and other cellular processes. To date, over 650 HDAC inhibitors have been investigated for in vitro activity against malaria parasites. Some inhibitors, particularly those with a hydroxamic acid zinc-binding group that targets inhibitors to the HDAC active site, have demonstrated low nM in vitro potency against P. falciparum and selectivity for the parasite over human cells. However, antiplasmodial HDAC inhibitor drug development has been hindered by factors including the lack of recombinant P. falciparum HDACs (only one available and purity is low), the lack of HDAC crystal structures (none available) and low throughput activity assays that are largely indirect measures of HDAC inhibition. Without these tools, mode of action studies, the rational design of new and improved inhibitors and the prioritisation of compounds for preclinical testing remains difficult. To address some of these challenges and further progress the development of antimalarial HDAC inhibitors, the current study employed a multi-pronged approach, including: (i) investigating the in vitro and in vivo activity of new HDAC inhibitors; (ii) establishing a higher throughput ELISA method to analyse P. falciparum lysine acetylation alterations and; (iii) developing a quantitative structure-activity relationship (QSAR) model based on classification algorithms. HDAC inhibitors typically have a pharmacophore comprising a zinc-binding group that interacts with the zinc ion in the active site of the enzyme, a linker unit and a cap group promoting hydrophobic interaction with amino acid residues at the entry of the active site. Here, a set of 26 new HDAC inhibitors with a peptoid-based scaffold was tested in vitro against drug sensitive asexual intraerythrocytic-stage P. falciparum 3D7 parasites. The set are analogues of compounds that have previously shown in vitro dual-stage antiplasmodial activity against asexual intraerythrocytic and exoerythrocytic stages and includes 16 compounds with a hydroxamic acid zinc-binding group and 10 prodrugs of this compound class. The unprotected hydroxamate-based inhibitors demonstrated growth inhibition of P. falciparum 3D7 asexual intraerythrocytic-stage parasites in the nanomolar to micromolar range (50% growth inhibition values (PfIC50) 0.008-1.04 μM) and up to 1,250-fold selectivity (selectivity indices (SI; PfIC50/human cell IC50): 10-1,250) for the parasite compared to human cells. Structure-activity relationship (SAR) analysis of cap region residues (carbonyl region, carboxylic region and isocyanide region) indicated that benzyl groups in the isocyanide region and alkyl groups in the para position of the carboxylic region are associated with increased antiplasmodial activity. In addition, methyl groups in the carbonyl region of the cap group demonstrated reduced cytotoxicity against neonatal foreskin fibroblasts (NFF), however, also somewhat reduced activity against asexual blood-stage parasites. Work by collaborators demonstrated micromolar in vitro activity of several compounds of this set against exoerythrocytic P. berghei parasite forms indicating dual-stage activity. The compound with the greatest dual-stage activity displayed an IC50 of 8 nM against asexual blood-stage P. falciparum and an IC50 of 60 nM against exoerythrocytic P. berghei in vitro. Compounds with PfIC50 of 100 nM or lower were tested against the multi-drug resistant P. falciparum Dd2 line (resistant to chloroquine, pyrimethamine, mefloquine, and other antimalarial drugs), and demonstrated a resistance index (RI) <1 indicating a lack of cross-resistance by this parasite line. The same subset of compounds was investigated for their ability to hyperacetylate P. falciparum histone H4; differential effects were observed with some compounds causing up to ~2.5-fold hyperacetylation compared to untreated controls. 10 prodrug peptoid-based HDAC inhibitors were also investigated. The prodrug strategy seeks to make the hydroxamic acid-based inhibitors more stable and bioavailable for in vivo applications as they are prone to degradation processes such as hydrolysis or reduction. These compounds were synthesised with masked hydroxamate functionalities that may undergo activation in vitro. Preliminary data demonstrated in vitro PfIC50 of 0.014-1.75 μM and 6-642-fold selectivity for the parasite over human fibroblasts. Three of these compounds displayed PfIC50 <0.1 μM and SI >100 and may therefore be of interest in further studies. Based on the in vitro antiplasmodial activity, selectivity and chemical diversity in the cap region, five peptoid-based compounds (3a, 3c, 3f, 3m, 3n, Pf3D7 IC50 0.008-0.034 μM, SI 97-625) were further investigated for in vivo efficacy against Plasmodium parasites. In addition, four analogues of the tethered phenylbutyrate-based HDAC inhibitor AR42 (Pf3D7 IC50 0.02 μM, SI 39) were also investigated in vivo (JT21b, JT83, JT92a, JT94; Pf3D7 IC50 0.005-0.21 μM, SI 55-118, (data generated by Dr MJ Chua, personal communication)). AR42 is currently in phase 1 clinical trials against various types of cancer and demonstrates an improved pharmacokinetic profile compared to a number of clinically approved HDAC inhibitors (e.g. AR42 Cmax 14.7 μM compared to vorinostat Cmax 1.9 μM, AR42 t1/2 11.1 h compared to vorinostat t1/2 0.75 h; tested in mice). AR42 analogues were of interest as AR42 has previously been shown to cure Plasmodium infections in mice (Dr MJ Chua, Griffith Institute for Drug Discovery; unpublished). While the two analogue sets differ significantly in linker and cap group, both bear a hydroxamic acid zinc-binding group. Compounds were tested in groups of two female BALB/c mice infected with P. berghei ANKA infected erythrocytes. Dosing was via oral gavage at 25 mg/kg twice daily with four hours between dosing (beginning 2 h post infection) for four consecutive days. Peripheral blood parasitemia was monitored by microscopic evaluation of stained thin blood films from day four post infection. None of the peptoid-based HDAC inhibitors attenuated P. berghei growth in BALB/c mice by more than 33% (3f (31%) and 3n (33%) on day 6 post infection). Data from collaborators demonstrated 3n to have the best metabolic stability (t1/2 271 min, Clint 6 μL/min/mg in mice; Prof Finn Hansen, University of Bonn, Germany) which may have contributed to this compound’s improved activity compared to some other analogues. In comparison, AR42 and two if its analogues cured mice of infection (AR42, 1 of 2 mice; JT21b, 2 of 2 mice; JT83 2 of 2 mice), up until day 24 post infection, at which point the mice were euthanised. AR42 and analogues are the first demonstration of oral cures in mice with a HDAC inhibitor (manuscript in preparation) and these data will be pursued in future work to further develop this HDAC inhibitor chemotype for malaria. One of the current limitations in the field is the lack of recombinant P. falciparum HDACs and the need to rely on low throughput assays to demonstrate HDAC inhibitor action via reduced total deacetylase activity or in situ lysine acetylation alterations. While deacetylase assays do not allow the differentiation of compound effects, Western blot using different acetyl-lysine antibodies can reveal compound specific acetylation profiles. Here, two higher throughput methods, dot blot and ELISA, were investigated to assess the effects of HDAC inhibitors on lysine acetylation. Using the control hydroxamate HDAC inhibitor vorinostat (first HDAC inhibitor clinically approved for cancer), the ELISA method was demonstrated to be more reliable than dot blot in detecting acetylation changes in protein lysates from P. falciparum trophozoites exposed to compound for 3 h. ELISA was therefore used to investigate histone H3 and H4 lysine acetylation alterations following exposure of P. falciparum to six commercially available anti-cancer HDAC inhibitors (vorinostat, panobinostat, trichostatin A, romidepsin, entinostat and tubastatin A). All compounds have in vitro activity against asexual intraerythrocytic P. falciparum parasites (Pf3D7), with tubastatin A activity reported for the first time here (PfIC50 0.15 ± 0.03 μM). All compounds were also shown to inhibit >84% deacetylase activity using P. falciparum protein lysates in an in vitro assay at 1 μM, with the exception of entinostat (~50% inhibition at 1 μM); this compound was also the least active against the parasite (PfIC50 11.5 μM). Using ELISA, vorinostat, panobinostat, trichostatin A, romidepsin and entinostat were all found to cause a ~3-fold increase in the signal detected using an anti-tetra-acetyl-lysine antibody. In comparison, the only human HDAC6-specific inhibitor tested, tubastatin A, caused 1.8-fold histone H4 hyperacetylation compared to the control. Further investigations of the individual N-terminal H4 lysine residues using antibodies specific to acetylated lysine 5, 8, 12 or 16 revealed that all compounds, except tubastatin A, caused hyperacetylation using each antibody. No differential effect was observed for histone H3 acetylation, with all compounds causing an ~1.8-fold increased signal using an acetyl-H3 antibody. The new ELISA method developed here provides a higher throughput way to assess differential compound induced lysine acetylation alterations in P. falciparum and therefore represents a valuable new tool to aid the investigation of HDAC inhibitors for malaria. As discussed above, the lack of tools, such as recombinant P. falciparum HDAC proteins, crystal structures and homology models, has meant that the identification of antiplasmodial HDAC inhibitors has been limited to whole-cell screening approaches which can be time-consuming and costly. To begin to address this problem, quantitative structure-activity relationship (QSAR) models were developed based on logistic algorithms with the aim of providing a new tool to triage compounds for in vitro testing. A database of 457 antiplasmodial HDAC inhibitors was assembled with published data on PfIC50 and, for 292 of those compounds with data on plasmodial selectivity. Two independent prediction algorithms based on logistic regression were developed to classify (1) antiplasmodial activity or (2) selectivity of hydroxamate-based HDAC inhibitors. Seven different activity and five different selectivity models were built, each with individual decision cut-offs defining active/selective and non-active/unselective compounds (e.g. PfIC50: active compound <0.1 μM> non-active compound; SI: selective compound >100< unselective compound). Activity model A7 revealed the highest prediction performance by predicting 93% of the training compound set and 87% of the external test compound set correctly. Cross validation revealed a prediction accuracy of 91%. The most accurate selectivity model S4 demonstrated a slightly poorer prediction performance due to a much smaller initial data set as not all the HDAC inhibitors had reported selectivity information (64%). Despite this, the selectivity model demonstrated an internal prediction accuracy of 91%, a cross-validated (internal) prediction accuracy of 82% and an external prediction accuracy of moderate 72%. To validate the prediction performance of the activity model further, they were applied to a set of 22 experimentally untested compounds (validation set) and the prediction performance compared to their experimental antiplasmodial activity. Applying prediction model A7 to this compound set predicted three hit compounds (two of which were confirmed by experimental assay data) and 12 non-actives (confirmed for 11 based on experimental assay data). The experimental PfIC50 assessment revealed asexual blood-stage PfIC50s for the whole set in the nanomolar to micromolar range (PfIC50 0.006-8.45 μM; data from Dr MJ Chua), with the correctly predicted hits (S2_E10 and LD016) having PfIC50 <0.008 μM. Overall, virtual screen using QSAR model A7 identified 87% of the validation compounds correctly and revealed high prediction specificity, identifying 92% of the non-active compounds correctly. Due to a lack of available data sets with selectivity index information (and time constraints for this project), the selectivity models were not able to be tested with an external set. These activity and selectivity QSAR models are the first generated for antiplasmodial HDAC inhibitors. These models will aid the in silico assessment of antiplasmodial activity and selectivity of hydroxamate-based HDAC inhibitors and therefore represent useful new tools in the investigation of HDAC inhibitors for malaria. In summary, data presented in this thesis include the identification of novel antiplasmodial HDAC inhibitors with activity against asexual intraerythrocytic-stage P. falciparum parasites, in vivo data demonstrating oral cures in mice for two analogues of the anti-cancer HDAC inhibitor AR42, a new ELISA method to allow higher throughput assessment of HDAC inhibitor induced changes to histone lysine residues and the first antiplasmodial HDAC inhibitor QSAR models. HDAC inhibitors identified in this study with promising in vitro and in vivo antiplasmodial activity profiles are new starting points for further development of HDAC inhibitors for malaria. In addition, the in vitro and in silico approaches developed in this study are useful new tools to facilitate the discovery of HDAC inhibitors and the understanding of their biological effects on the parasite.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Environment and Sc
Science, Environment, Engineering and Technology
Full Text

A specific challenge to the translation of cell therapies and stem-cell derived products is the ability to develop and manufacture such products in a cost-effective, scalable and robust manner. To this end, this thesis investigates the creation and application of a set of computational tools designed to aid bioprocess design decisions for cell therapy and stem-cell derived research products. The decision-support tools comprise advanced bioprocess economics models with databases tailored to cellular products. These are linked to Monte Carlo simulation for uncertainty analysis and techniques to identify optimal bioprocess designs that include brute-force search algorithms, an evolutionary algorithm, and multi-attribute decision making analysis. A trio of industrially-relevant case studies is presented within this thesis, along with an additional study included in the appendices of this work, in order to demonstrate the applicability of the decisional tools to bioprocess design for different cell therapies (allogeneic, human embryonic stem cell-derived retinal pigment epithelial (RPE) cells for macular degeneration, allogeneic CAR-T cells for oncology) and induced pluripotent stem cells (iPSCs) for drug discovery applications. Questions tackled included manual versus automated production, costeffective inflection points of planar vs microcarrier-based bioprocess strategies, and the identification optimal process technologies for an allogeneic CAR-T cell therapy based on both qualitative and quantitative attributes. The analyses highlighted key bioprocess economic drivers and process bottlenecks. Furthermore, the Monte Carlo simulation technique was used in order to capture the effects of the inherent uncertainty associated with cell therapy bioprocessing on manufacturing costs and process throughputs. Future process improvements required to create financially feasible bioprocesses were also identified. This thesis presents the application of a series of decisional tools to bioprocess design problems and demonstrates how they can facilitate informed decisions regarding cost-effective process design in the cell therapy sector.

El paradigma clàssic on un medicament interacciona amb un únic target biològic vinculat a una malaltia es posa en dubte. Actualment es reconeix que un medicament interacciona amb múltiples targets biològics i que aquests targets estan involucrats en multitud de pathways i que s’expressen en una varietat d’òrgans. Amb el creixent reconeixement d’aquesta complexitat, la estratègia reduccionista del procés de descoberta de nous medicaments ha evolucionat cap a estratègies sistèmiques multinivell. Gràcies als avenços tecnològics, hi ha hagut un gran increment de les dades generades en les diverses àrees rellevants en la descoberta de nous medicaments: química, farmacologia, toxicologia, genòmica, metabolòmica, etc fet que ha expandit considerablement la nostra habilitat per general models computacionals amb un rendiment i cobertura creixents. Però darrerament, extreure coneixement d’aquest complex, vast i heterogeni volum de dades no és simple. El principal objectiu d’aquesta tesi es desenvolupar noves eines analítiques i de visualització i investigar la seva capacitat per extreure nou coneixement de dades altament interconnectades; eines integrades a una plataforma flexible que per obtenir respostes simples a preguntes complexes. En particular, farem èmfasi en la navegació per les relacions entre les entitats del sistema (molècules petites i els seus metabòlits, proteïnes com a targets biològics, termes de safety).
The classical silver bullet paradigm of one drug interacting with a single target linked to a disease is currently challenged. It is now widely recognized that one drug interacts with multiple targets and these targets are involved in many biological pathways and expressed in a variety of organs. As the notion of complexity has been gradually accepted, the reductionist drug discovery approach has naturally evolved towards systems multilevel strategies. Thanks to technological advances, there has been a huge increase of data generated in the various fields relevant to drug discovery, namely, chemistry, pharmacology, toxicology, genomics, metabolomics, etc., which has expanded dramatically our ability to generate computational models with increasing performance and coverage. But ultimately, extracting knowledge from this complex, vast and heterogeneous amount of data is not straightforward. The main objective of this Thesis is to develop new interactive analytics and visualization tools and investigate their ability to extract knowledge from highly interconnected data when implemented into an integrated flexible platform to facilitate drawing simple answers from complex questions. In particular, special emphasis will be put in the navigation aspects of the relationships between systemic entities (small molecules and their metabolite, protein targets, safety terms).

High drug attrition rate during clinical and post market phases is one of the major factors contributing to the pharmaceutical industry productivity crisis. This problem is especially worrisome in the cancer field, where it is two to four times higher than in other health sectors. Most of the drugs are discarded due to safety (mainly cardio-, neuro-, and hepato-toxicities) and efficacy issues, which reflect the limitations of current preclinical models in anticipating such drawbacks. In this context, new models are needed in order to tackle these problems and to accomplish with the new demands (higher throughput and predictivity) of the modern research and development (R&D) processes. Zebrafish is a vertebrate with elevated homology to humans and unique biological properties, which make it suitable for high throughput studies. The final objective of my thesis is to improve the use of this animal model in an attempt to ameliorate the overall R&D process efficiency and thus, ease the productivity crisis. First, a semi-high throughput methodology has been generated for the assessment of cardio-, neuro- and hepato-toxicities in the same animal, thus, impacting the 3Rs principle. Second, xenografts of human cancer cells into zebrafish larvae for the study of anti-tumour drug efficacy have been standardised, validated and automated. Results obtained help to consolidate and validate the use of the zebrafish in the R&D process of new drugs, as a bridge between in vitro models and in vivo mammalian models.
La alta tasa de deserción de medicamentos durante fases clínicas y posteriores a la comercialización es uno de los factores principales que contribuyen a la crisis de productividad que afecta a la industria farmacéutica hoy en día. Este problema es especialmente preocupante en el sector del cáncer, donde es de dos a cuatro veces mayor que en otros sectores de la salud. La mayoría de estos medicamentos son descartados debido a problemas de seguridad (principalmente cardio, neuro, y hepatotoxicidad) y de eficacia, lo que reflejan las limitaciones de los modelos preclínicos actuales para anticipar tales inconvenientes. En este contexto, se necesitan nuevos modelos para abordar este problema y cumplir con las nuevas demandas (mayor rendimiento y predictividad) de los procesos de investigación y desarrollo (I+D). El pez cebra es un vertebrado con alta homología con los humanos y propiedades biológicas únicas, que lo hacen adecuado para estudios de alto rendimiento. El objetivo final de mi tesis es mejorar el uso de este modelo animal en un intento de mejorar la eficiencia general del proceso de I+D y, así, aliviar la crisis de productividad. Primero, se ha generado una metodología de rendimiento medio para la evaluación in vivo de las toxicidades cardíaca, neuronal, y hepática en un mismo animal, en línea con en el principio de las 3Rs. En segundo lugar, se ha estandardizado, validado y automatizado, el xenotrasplante de células tumorales humanas en larvas de pez cebra para el estudio de la eficacia de fármacos antitumorales. Los resultados obtenidos ayudan a consolidar y validar el uso del pez cebra en el proceso de I+D de nuevos fármacos, como puente entre los modelos in vitro y los modelos in vivo de mamíferos.

The subject of this thesis is multicolour bioluminescence analysis and how it can provide new tools for drug discovery and development.The mechanism of color tuning in bioluminescent reactions is not fully understood yet but it is object of intense research and several hypothesis have been generated. In the past decade key residues of the active site of the enzyme or in the surface surrounding the active site have been identified as responsible of different color emission. Anyway since bioluminescence reaction is strictly dependent from the interaction between the enzyme and its substrate D-luciferin, modification of the substrate can lead to a different emission spectrum too. In the recent years firefly luciferase and other luciferases underwent mutagenesis in order to obtain mutants with different emission characteristics. Thanks to these new discoveries in the bioluminescence field multicolour luciferases can be nowadays employed in bioanalysis for assay developments and imaging purposes. The use of multicolor bioluminescent enzymes expanded the potential of a range of application in vitro and in vivo. Multiple analysis and more information can be obtained from the same analytical session saving cost and time. This thesis focuses on several application of multicolour bioluminescence for high-throughput screening and in vivo imaging. Multicolor luciferases can be employed as new tools for drug discovery and developments and some examples are provided in the different chapters. New red codon optimized luciferase have been demonstrated to be improved tools for bioluminescence imaging in small animal and the possibility to combine red and green luciferases for BLI has been achieved even if some aspects of the methodology remain challenging and need further improvement. In vivo Bioluminescence imaging has known a rapid progress since its first application no more than 15 years ago. It is becoming an indispensable tool in pharmacological research. At the same time the development of more sensitive and implemented microscopes and low-light imager for a better visualization and quantification of multicolor signals would boost the research and the discoveries in life sciences in general and in drug discovery and development in particular.

The subject of this thesis is multicolour bioluminescence analysis and how it can provide new tools for drug discovery and development.The mechanism of color tuning in bioluminescent reactions is not fully understood yet but it is object of intense research and several hypothesis have been generated. In the past decade key residues of the active site of the enzyme or in the surface surrounding the active site have been identified as responsible of different color emission. Anyway since bioluminescence reaction is strictly dependent from the interaction between the enzyme and its substrate D-luciferin, modification of the substrate can lead to a different emission spectrum too. In the recent years firefly luciferase and other luciferases underwent mutagenesis in order to obtain mutants with different emission characteristics. Thanks to these new discoveries in the bioluminescence field multicolour luciferases can be nowadays employed in bioanalysis for assay developments and imaging purposes. The use of multicolor bioluminescent enzymes expanded the potential of a range of application in vitro and in vivo. Multiple analysis and more information can be obtained from the same analytical session saving cost and time. This thesis focuses on several application of multicolour bioluminescence for high-throughput screening and in vivo imaging. Multicolor luciferases can be employed as new tools for drug discovery and developments and some examples are provided in the different chapters. New red codon optimized luciferase have been demonstrated to be improved tools for bioluminescence imaging in small animal and the possibility to combine red and green luciferases for BLI has been achieved even if some aspects of the methodology remain challenging and need further improvement. In vivo Bioluminescence imaging has known a rapid progress since its first application no more than 15 years ago. It is becoming an indispensable tool in pharmacological research. At the same time the development of more sensitive and implemented microscopes and low-light imager for a better visualization and quantification of multicolor signals would boost the research and the discoveries in life sciences in general and in drug discovery and development in particular.

The scope of this work focuses on computationally modeling compounds with protein structures. While the impetus of drug discovery is the innovation of new therapeutic molecules, it also involves distinguishing molecules that would not be an effective drug. This can be achieved by inventing new tools or by refining old tools. Virtual screening (VS, also called docking), the computational modeling of a molecule in a receptor structure, is a staple in predicting a molecule's affinity for an intended target. In our Virtual Target Screening system (also called inverse-docking), VS is used to find high-affinity targets, which can potentially explain absorption, distribution, metabolism, and excretion (ADME) of a molecule of interest in the human body. The next project, low-mode docking (LD), attempts to improve VS by incorporating protein flexibility into traditional docking where a static receptor structure has potential to produce poor results due to incorrectly predicted ligand poses. Finally, VS, performed mostly on small molecules, is scaled up to cyclic peptides by employing Monte Carlo simulations and molecular dynamics to mimic the steps of small molecule VS. The first project discussed is Virtual Target Screening (also called inverse-docking) where a small molecule is virtually screened against a library of protein structures. Predicting receptors to which a synthesized compound may bind would give insights to drug repurposing, metabolism, toxicity, and lead optimization. Our protocol calibrates each protein entry with a diverse set of small molecule structures, the NCI Diversity Set I. Our test set, 20 kinase inhibitors, was predicted to have a high percentage of kinase "hits" among approximately 1500 protein structures. Further, approved drugs within the test set generally had better rates of kinase hits. Next, normal mode analysis (NMA), which can computationally describe the fundamental motions of a receptor structure, is utilized to approach the rigid body bias problem in traditional docking techniques. Traditional docking involves the selection of a static receptor structure for VS; however, protein structures are dynamic. Simulation of the induced fit effect in protein-ligand binding events is modeled by full articulation of the approximated large-scale low-frequency normal modes of vibration, or "low-modes," coupled with the docking of a ligand structure. Low-mode dockings of 40 cyclin dependent 2 (CDK2) inhibitors into 54 low-modes of CDK2 yielded minimum root-mean-square deviation (RMSD) values of 1.82 – 1.20 Å when compared to known coordinate data. The choice of pose is currently limited to docking score, however, with ligand pose RMSD values of 3.87 – 2.07 Å. When compared to corresponding traditional dockings with RMSD values of 5.89 – 2.33 Å, low-mode docking was more accurate. The last discussion involves the rational docking of a cyclic peptide to the murine double minute 2 (MDM2) oncoprotein. The affinity for a cyclic peptide (synthesized by Priyesh Jain, McLaughin Lab, University of South Florida), PJ-8-73, in MDM2 was found to be within an order of magnitude of a cyclic peptide from the Robinson Lab at the University of Zurich in Switzerland. Both are Β-hairpin cyclic peptides with IC50 values of 650 nm and 140 nm, respectively. Using the co-crystalized structure of the Robinson peptide (PDB 2AXI), we modeled the McLaughlin peptide based on an important interaction of the 6-chloro-tryptophan residue of the Robinson peptide occupying the same pocket in MDM2 as the tryptophan residue by the native p53 transactivation helical domain. By preserving this interaction in initial cyclic peptide poses, the resulting pose of PJ-8-73 structure in MDM2 possessed comparable active site residue contacts and surface area. These protocols will aid medical research by using computer technology to reduce cost and time. VTS utilizes a unique structural and statistical calibration to virtually assay thousands of protein structures to predict high affinity binding. Determining unintended protein targets aids in creating more effective drugs. In low-mode docking, the accuracy of virtual screening was increased by including the fundamental motions of proteins. This newfound accuracy can decrease false negative results common in virtual screening. Lastly, docking techniques, usually for small molecules, were applied to larger peptide molecules. These modifications allow for the prediction of peptide therapeutics in protein-protein interaction modulation, a growing interest in medicine. Impactful in their own ways, these procedures contribute to the discovery of drugs, whether they are small molecules or cyclic peptides.

Mechanical, electrical and chemical signals from the environment direct influence the physiological activities and health of a cell. While the existing trend is to study these signals separately in their respective domains, they are actually inherently coupled. Here we explore the feasibility and merits of a new instrument that can measure, for the first time, externally applied (or internally generated) forces and electrical impedance of a single cell in real time. The pico-Newton resolution micro-instrument (1 mm × 1 mm in size) will be readily compatible with conventional optical/fluorescence techniques for opto-electro-mechanical probing. Force sensing operation is unaffected by the electrical and chemical nature of cell environment and the bio-impedance measurement scheme can automatically compensate for any non-cell specific sources of error. Studies on the effects of mechanical, electrical and chemical signals with real-time opto-electro-mechanical cell characterization will impact diagnosis and cure for various diseases of the heart, skin, intestines, nerves, oncology, bones, lung — to name a few. Long-term contributions would be novel lab-on-a-chip type diagnostic/therapeutic/drug discovery tools to monitor the bio-impedance of a single cell with respect to external stimuli (toxin, drug).

It is realistic to describe Artificial Intelligence (AI) as the most important of emerging technologies because of its increasing dominance in almost every field of modern life and the crucial role it plays in boosting high-tech multidisciplinary developments integrated in steady innovations. The implementation of AI-based solutions for real world problems helps to create new insights into old problems and to produce unique knowledge about intractable problems which are too complex to be efficiently solved with conventional methods. Biomedical data analysis, computer-assisted drug discovery, pandemic predictions and preparedness are only but a few examples of applied research areas that use machine learning as a pivotal data evaluation tool. Such tools process enormous amounts of data trying to discover causal relations and risk factors and predict outcomes that for example can change the course of diseases. The growing number of remarkable achievements delivered by modern machine learning algorithms in the last years raises enthusiasm for all those things that AI can do. The value of the global artificial intelligence market was calculated at USD 136.55 billion in 2022 and is estimated to expand at an annual growth rate of 37.3% from 2023 to 2030. Novel machine-learning applications in finance, national security, health, criminal justice, transportation, smart cities etc. justify the forecast that AI will have a disruptive impact on economies, societies and governance. The traditional rule-based or expert systems, known in computer science since decades implement factual, widely accepted knowledge and heuristic of human experts and they operate by practically imitating the decision making process and reasoning functionalities of professionals. In contrast, modern statistical machine learning systems discover their own rules based on examples on the basis of vast amounts of training data introduced to them. Unfortunately the predictions of these systems are generally not understandable by humans and quite often they are neither definite or unique. Raising the accuracy of the algorithms doesn't improve the situation. Various multi-state initiatives and business programs have been already launched and are in progress to develop technical and ethical criteria for reliable and trustworthy artificial intelligence. Considering the complexity of famous leading machine learning models (up to hundreds of billion parameters) and the influence they can exercise for example by creating text and news and also fake news, generate technical articles, identify human emotions, identify illness etc. it is necessary to expand the definition of HMI (Human Machine Interface) and invent new security concepts associated with it. The definition of HMI has to be extended to account for real-time procedural interactions of humans with algorithms and machines, for instance when faces, body movement patterns, thoughts, emotions and so on are considered to become available for classification both with or without the person's consent. The focus of this work will be set upon contemporary technical shortcomings of machine learning systems that render the security of a plethora of new kinds of human machine interactions as inadequate. Examples will be given with the purpose to raise awareness about underestimated risks.