Rational drug design of antineoplastic agents using 3D-QSAR, cheminformatic, and virtual screening approaches

Support was kindly provided by the EU COST Action CM1406 and CA15135. KN and JV kindly acknowledge national project number 172033 supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.PostprintPeer reviewe

In Silico Strategies for Prospective Drug Repositionings

The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions

Small molecule-protein interactions exemplified on short-chain dehydrogenases/reductases

The short-chain dehydrogenase/reductase (SDRs) family represents one of the largest enzyme superfamilies, with over 80 members in the human genome. Even though the human genome project has sequenced and mapped the entire human genome, the physiological functions of more than 70% of all SDRs are currently unexplored or insufficiently characterized. To start to fill this gap, the present thesis aimed to employ a combination of molecular modeling approaches and biological assessments for the identification and characterization of novel inhibitors and/or potential substrates of different SDRs. Due to their involvement in steroid biosynthesis and metabolism, SDRs are potential targets of endocrine disrupting chemicals (EDCs). To test the use of pharmacophore-based virtual screening (VS) applications and subsequent in vitro evaluation of virtual hits for the identification and characterization of potential inhibitors, 11β-hydroxysteroid dehydrogenase 2 (11β-HSD2) was selected as an example. 11β-HSD2 has an important role in the placenta by inactivating cortisol and protecting the fetus from high maternal glucocorticoid levels. An impaired placental 11β-HSD2 function has been associated with altered fetal growth and angiogenesis as well as a higher risk for cardio-metabolic diseases in later life. Despite this vital function, 11β-HSD2 is not covered in common off-target screening approaches. Several azole fungicides were identified as 11β-HSD inhibitors amongst approved drugs by testing selected virtually retrieved hits for inhibition of cortisol to cortisone conversion in cell lysates expressing recombinant human 11β-HSD2. Moreover, a significant structure-activity relationship between azole scaffold size, 11β-HSD enzyme selectivity and potency was observed. The most potent 11β-HSD2 inhibition was obtained for itraconazole (IC50 139 ± 14 nM), for its active metabolite hydroxyitraconazole (IC50 223 ± 31 nM), and for posaconazole (IC50 460 ± 98 nM). Interestingly, substantially lower inhibitory 11β-HSD2 activity of these compounds was detected using mouse and rat kidney homogenate preparations, indicating species-specific differences. Impaired placental 11β-HSD2 function exerted by these compounds might, in addition to the known inhibition of P-glycoprotein efflux transport and cytochrome P450 enzymes, lead to locally elevated cortisol levels and thereby could affect fetal programming. Successful employment of pharmacophore-based VS applications requires suitable and reliable in vitro validation strategies. Therefore, the following study addressed the re-evaluation of a potential EDC, the widely used flame retardant tetrabromobisphenol A (TBBPA), on glucocorticoid receptor (GR) and androgen receptor (AR) function. TBBPA was reported earlier in yeast-based reporter assays to potently interfere with GR and moderately with AR function. Human HEK-293 cell-based reporter assays and cell-free receptor binding assays did not show any activity of TBBPA on GR function, which was supported by molecular docking calculations. The antiandrogenic effect, however, could be confirmed, although less pronounced than in the HEK-293 cell system. Nevertheless, the evaluation of the relevant concentrations of an EDC found in the human body is crucial for an appropriate safety assessment. Considering the rapid metabolism of TBBPA and the low concentrations observed in the human body, it is questionable whether relevant concentrations can be reached to cause harmful effects. Thus, it is vital to take the limitations of each testing system including the distinct sensitivities and specificities into account to avoid false positive or false negative results. To extend the applications of in silico tools with demonstrated proof-of-concept, they were further employed to investigate novel substrate specificities for three different SDR members: the two multi-functional enzymes, 11β-HSD1 and carbonyl reductase (CBR) 1 as well as the orphan enzyme DHRS7. A role for 11β-HSD1 in oxysterol metabolism by metabolizing 7-ketocholesterol (7kC) has already been described. However, in contrast to the known receptors for 7α,25-dihydroxycholesterol (7α25OHC), i.e. Epstein-Barr virus-induced gene 2 (EBI2), or 7β,27-dihydroxycholesterol (7β27OHC), i.e. retinoic acid related orphan receptor (ROR)γ, no endogenous receptor has been identified so far for 7kC or its metabolite 7β-hydroxycholesterol. To explore the underlying biosynthetic pathways of such dihydroxylated oxysterols, the role of 11β-HSD1 in the generation of dihydroxylated oxysterols was investigated. For the first time, the stereospecific and seemingly irreversible oxoreduction of 7-keto,25-hydroxycholesterol (7k25OHC) and 7-keto,27-hydroxycholesterol (7k27OHC) to their corresponding 7β-hydroxylated metabolites 7β25OHC and 7β27OHC by recombinant human 11β-HSD1 could be demonstrated in vitro in intact HEK-293 cells. Furthermore, 7k25OHC and 7k27OHC were found to be potently inhibited the 11β-HSD1-dependent oxoreduction of cortisone to cortisol. Molecular modeling experiments confirmed these results and suggested competition of 7k25OHC and 7k27OHC with cortisone in the enzyme binding pocket. For a more detailed enzyme characterization, 11β-HSD1 pharmacophore models were generated and employed for VS of the human metabolome database and the lipidmaps structure database. The VS yielded several hundred virtual hits, including the successful filtering of known substrates such as endogenous 11-ketoglucocorticoids, synthetic glucocorticoids, 7kC, and several bile acids known to inhibit the enzyme. Further hits comprised several eicosanoids including prostaglandins, leukotrienes, cyclopentenone isoprostanes, levuglandins or hydroxyeicosatetraenoic acids (HETEs) and compounds of the kynurenine pathway. The important role of these compounds as well as 11β-HSD1 in inflammation emphasizes a potential association. However, further biological validation is of utmost necessity to explore a potential link. The closest relative of 11β-HSD1 is the orphan enzyme DHRS7, which has been suggested to act as tumor suppressor. Among others, cortisone and 5α-dihydrotestosterone have been identified as substrates of DHRS7, although effects in functional assays could only be observed at high concentrations that may not be of physiological relevance. Hence, the existence of other yet unexplored substrates of DHRS7 can be assumed, and the generation of homology models to study the structural features of the substrate binding site of DHRS7 was employed. The predictivity of the constructed models is currently limited, due to a highly variable region comprising a part of the ligand binding site but particularly the entry of the binding pocket, and requires further optimizations. Nevertheless, the models generally displayed a cone-shaped binding site with a rather hydrophobic core. This may suggest larger metabolites to be converted by DHRS7. Moreover, the flexible loops surrounding the binding pocket may lead to the induction of an induced fit upon ligand binding. However, further studies are crucial to confirm these findings. CBR1 is well-known for its role in phase I metabolism of a variety of carbonyl containing xenobiotic compounds. Several endogenous substrates of CBR1 have been reported such as prostaglandins, S-nitrosoglutathione or lipid aldehydes. The physiological relevance of these endogenous substrates, however, is not fully understood. Thus, the physiological roles of CBR1 was further explored by identifying a novel function for CBR1 in the metabolism glucocorticoids. CBR1 was found to catalyze the conversion of cortisol into 20β-dihydrocortisol (20β-DHF), which was in turn detected as the major route of cortisol metabolism in horses and elevated in adipose tissue derived from obese horses, humans and mice. Additionally, 20β-DHF was demonstrated as weak endogenous agonist of the GR, suggesting a novel pathway to modulate GR activation by CBR1-depenent protection against excessive GR activation in obesity. In conclusion, this thesis emphasized the employment of molecular modeling approaches as an initial filter to identify toxicological relevant compound classes for the identification of potential EDCs and, moreover, as valuable tools to identify novel substrates of multifunctional SDRs and to unravel novel functions for the large majority of yet unexplored orphan SDR members, while carefully considering the limitations of this strategy

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Characterization of protein-ligand interactions : the role of thermodynamic and structural data in the drug discovery process

Comparing biological activities and thermodynamic profiles obtained for one compound toward proteins, differing only in few residues is a promising strategy to increase the knowledge of protein-ligand interactions and active site topologies thereby enabling rational drug design. For this purpose, wild type proteins and their mutants carrying a set of point mutations can be used. Such an approach was applied in the present study to guide the development of small-molecule antagonists targeting PqsR, a novel target to limit Pseudomonas aeruginosa pathogenicity. It resulted in two chemically divers fragments with antagonistic activity and provided insights into their binding modes. The latter can be used to assist fragment optimization and therefore the identified PqsR antagonists are promising scaffolds for further drug design efforts against this important pathogen. Alternatively, proteins from various species, which are highly conserved in sequence and differ only in few residues, might be considered. This line of attack was also utilized employing human and marmoset monkey 17β-HSD1, a promising target for the treatment of estrogen-dependent diseases. By means of in silico methods a better understanding of 17β-HSD1 active site topologies and inhibitor binding modes was achieved that guided the elaboration of a lead compound. The described strategy was successfully applied for hit identification and lead optimization reflecting its benefit in drug design.Die möglichst genaue Kenntnis der räumlichen Gestalt der Ligand Bindungsstelle und der damit verbundenen Protein-Ligand-Wechselwirkungen ist eine Voraussetzung für rationales Wirkstoffdesign. Eine aussichtsreiche Strategie um diese Informationen zu erhalten, ist es biologische/biophysikalische Daten einer Verbindung gegenüber Proteinen zu vergleichen, die sich nur in wenigen Aminosäuren unterscheiden. Einerseits können dazu Wildtyp und Mutanten genutzt werden, was in dieser Studie zur Entwicklung von PqsR Antagonisten geführt hat. PqsR ist ein neues Target zur Limitierung der Pseudomonas aeruginosa Pathogenität. Zwei chemisch verschiedene, antagonistische Fragmente wurden entdeckt und Aufschlüsse über deren Bindungsmodi erhalten, die zur weiteren Optimierung genutzt werden können. Somit stellen die entdeckten Liganden vielversprechende Grundgerüste zur weiteren Wirkstoffentwicklung gegen dieses relevante Pathogen dar. Alternativ können hochkonservierte Proteine verschiedener Spezies in Betracht gezogen werden. Unter Verwendung von humaner und marmoset 17β-HSD1, einem erfolgversprechenden Target zur Behandlung estrogenabhängiger Erkrankungen, wurden durch in silico Methoden das aktive Zentrum der 17β-HSD1 sowie Bindungsmodi ausgewählter Hemmstoffe untersucht. Anhand der erstellten Modelle wurde eine Leitverbindung weiterentwickelt. Diese Strategie wurde erfolgreich für das Wirkstoffdesign bei der Hit Identifizierung sowie der Lead Optimierung angewendet

Computer-Aided Design of Ligands at Multiple Protein Targets for Multifactorial Diseases

Today, drug discovery predominately focuses on the design of ligands with high selectivity towards a specific biological target. A significant limitation in the case of multi-factorial diseases (e.g. neurodegenerative disorders) is that effective therapy may require multi-target drugs addressing the complexity of multi-factorial pathologies. Here, single- and multi-target ligand design was investigated to discover novel compounds active at multiple proteins/multiple binding sites including allosteric ligands. Calpain-1, a challenging target, was selected to develop and evaluate computational approaches to the discovery of novel ligands. Current selective calpain-1 inhibitors are reported to bind to an allosteric site and their mode of action has remained elusive. To elucidate this, a structure-based virtual screening protocol was implemented to find chemically novel compounds with improved selectivity and a reduced side-effects profile. To develop methods for the discovery of multi-target ligands, a multi-target design approach, which could be beneficial in the treatment of Lung Carcinoma and Neurodegenerative diseases, was investigated. A novel ensemble of proteins was targeted to elevate intracellular cAMP, deemed to be beneficial in these diseases resulting in the discovery of ligands with high binding affinity at three targets, PDE10A, A1 and A2A receptors. In tandem, functional activity at the A2A receptor and PDE10A was investigated, resulting in the discovery of novel compounds, which exhibited anti-proliferative effects in lung carcinoma cell-lines correlating with the co-expression of the two targets and increased cAMP levels. Critically, the dynamics of one amino acid residue, Val84, was identified as a novel conformational descriptor of A2A receptor activation. Overall, novel single- and multi-target ligand design approaches are presented in this work, which could be applicable to a wide range of ligand design problems, across (multi-factorial) disease areas and target families. The findings may facilitate improved design of allosteric calpain-1 inhibitors using the PEF(S) domain, and encourage investigating the therapeutic benefits of dual ligands at the A2A receptor and PDE10A against lung cancer in vivo.IDB Cambridge International Scholarship and ERC Starting Grant (No. 336159

Design, Synthesis, Characterisation and Biological Evaluation of Some Novel 1, 3, 4-Thiadiazole Derivatives as Anti-Tubercular Agents Targeting Decaprenyl Phosphoryl Beta-D-Ribose 2’ Epimerase-1

INTRODUCTION:Tuberculosis [TB] is caused by mycobacterium tuberculosis that most often affect the lungs. Tuberculosis is curable and preventable. In 1882, the German physician Robert Koch isolated the bacterium. Tuberculosis is contagious and airborne disease. In 1944, streptomycin was to treat tuberculosis [TB]. This amino glycoside interferes with protein biosynthesis through an interaction with the small 30s subunit of the ribosome. The discovery of Para amino salicylic acid in 1946 was quickly followed by the important discovery of Isoniazid [INH], as one of the most active anti-TB drugs used today. Inhibition of mycolic acids biosynthesis, one of the essential components of the mycobacterium cell wall was determined as the mechanism of action. Pyrazinamide [PZA] appeared as a potential Anti-TB drug in 1952. The TB treatment in the 1980s was a great success as it allowed to shorten the duration of the therapy from 9 to 6 months. Ethambutol [EMB] and Rifampin [RIF], the two last derivatives used in the TB first-line treatment, were discovered during the 60s. Ethambutol is an ethylenediamine discovered in 1961, which affects the cell wall by specifically targeting the polymerization of arabinogalactan and lipoarabinomannan. Finally, Rifampin appeared as a drug of choice for TB treatment around 1970, by acting on replicating and non-replicating mycobacteria. This derivative belongs to the rifampicin family and inhibits bacterial RNA synthesis by binding to the b-subunit of the DNA-dependent polymerase. AIM: The aim of this project is to design molecules with potential anti-tubercular activity that is capable of inhibiting cell wall synthesis by inhibiting Decaprenylphosphoryl-beta-D-ribose2-epimerase-1. The designed compounds will be synthesized, characterized and evaluated for biological activity and toxicity. OBJECTIVE:The compounds are designed and docked against a specific crucial target, Decaprenylphosphoryl-beta-D-ribose2epimerase-1.This is involved in the cell wall biosynthesis and Lipid metabolism. The synthesized compounds are expected to act on the same.SUMMARY:Decaprenylphosphoryl-beta-D-ribose 2-epimerase a critical enzyme for the growth of Mycobacterium tuberculosis was chosen for our study after review of literature. It belongs to the Oxidoreductase family. A database of 200 molecules with high prospects of inhibiting the target Dpre1 were carefully chosen by making changes to the known hit molecules, here the thiadiazole nucleus was chosen. Selected molecules were designed and docked against Dpre1 using Argus lab® software. Six molecules with good docking score [lower binding energy] and interactions were shortlisted for synthesis. Reaction conditions were optimized. The selected molecules were subjected to toxicity prediction assessment by OSIRIS® property explorer developed by Acetilon Pharmaceuticals limited which is available online. The results are color coded as green color which predicts the drug likeness and possibly better activity. The molecules were labelled as SDK1, SDK2, SDK3, SDK5, PAA, HA, and were synthesized with satisfactory yield. The purity of the synthesized compounds was ensured by repeated recrystallization. Further the compounds were evaluated by TLC and Melting point determination. The characterization of the synthesized compounds was done using Infra-red, Nuclear Magnetic Resonance [H1 NMR] and Mass spectrometric methods [LC-MS, GC-MS]. All the Synthesized compounds exhibited molecular ion peak (M+) of varying intensities. The final pure compounds were screened for Anti-mycobacterial activity by in vitro method called Micro plate Alamar Blue Assay [MABA]. The synthesized compounds showed sensitivity [Minimum inhibitory concentration] at 3.12mcg/ml. The standard drugs Pyrazinamide, Streptomycin, Ciprofloxacin exhibited anti mycobacterial activity at 3.125mcg/ml, 6.25mcg/ml, and 3.125mcg/ml concentrations respectively. This indicates that the synthesized compounds are as Potent as the standard drugs.CONCLUSION:All the compounds gave Docking score between -8.73 to 11.37 kcal/mol Pyrazinamide gave docking score 11.55kcal/mol for 4P8Y, Streptomycin gave docking score of 10.87kcal/mol for 4P8Y and Ciprofloxacin gave docking score of 11.25kcal/mol for 4P8Y. There is a correlation between the score and activities of all the compounds which were tested and compared with the standard drugs. This goes to prove that Decaprenyl phosphoryl beta-D-ribose 2’ epimerase-1’ (PDBID: 4P8Y) is a critical enzyme for anti-mycobacterial activity. So the fine tuning the structures of these compounds will yield molecules with better anti mycobacterial activity. Further structural modifications of the synthesized compounds will aid in the development of potential molecules against the tuberculosis pathogen

Virtual screening of potential bioactive substances using the support vector machine approach

Die vorliegende Dissertation stellt eine kumulative Arbeit dar, die in insgesamt acht wissenschaftlichen Publikationen (fünf publiziert, zwei eingerichtet und eine in Vorbereitung) dargelegt ist. In diesem Forschungsprojekt wurden Anwendungen von maschinellem Lernen für das virtuelle Screening von Moleküldatenbanken durchgeführt. Das Ziel war primär die Einführung und Überprüfung des Support-Vector-Machine (SVM) Ansatzes für das virtuelle Screening nach potentiellen Wirkstoffkandidaten. In der Einleitung der Arbeit ist die Rolle des virtuellen Screenings im Wirkstoffdesign beschrieben. Methoden des virtuellen Screenings können fast in jedem Bereich der gesamten pharmazeutischen Forschung angewendet werden. Maschinelles Lernen kann einen Einsatz finden von der Auswahl der ersten Moleküle, der Optimierung der Leitstrukturen bis hin zur Vorhersage von ADMET (Absorption, Distribution, Metabolism, Toxicity) Eigenschaften. In Abschnitt 4.2 werden möglichen Verfahren dargestellt, die zur Beschreibung von chemischen Strukturen eingesetzt werden können, um diese Strukturen in ein Format zu bringen (Deskriptoren), das man als Eingabe für maschinelle Lernverfahren wie Neuronale Netze oder SVM nutzen kann. Der Fokus ist dabei auf diejenigen Verfahren gerichtet, die in der vorliegenden Arbeit verwendet wurden. Die meisten Methoden berechnen Deskriptoren, die nur auf der zweidimensionalen (2D) Struktur basieren. Standard-Beispiele hierfür sind physikochemische Eigenschaften, Atom- und Bindungsanzahl etc. (Abschnitt 4.2.1). CATS Deskriptoren, ein topologisches Pharmakophorkonzept, sind ebenfalls 2D-basiert (Abschnitt 4.2.2). Ein anderer Typ von Deskriptoren beschreibt Eigenschaften, die aus einem dreidimensionalen (3D) Molekülmodell abgeleitet werden. Der Erfolg dieser Beschreibung hangt sehr stark davon ab, wie repräsentativ die 3D-Konformation ist, die für die Berechnung des Deskriptors angewendet wurde. Eine weitere Beschreibung, die wir in unserer Arbeit eingesetzt haben, waren Fingerprints. In unserem Fall waren die verwendeten Fingerprints ungeeignet zum Trainieren von Neuronale Netzen, da der Fingerprintvektor zu viele Dimensionen (~ 10 hoch 5) hatte. Im Gegensatz dazu hat das Training von SVM mit Fingerprints funktioniert. SVM hat den Vorteil im Vergleich zu anderen Methoden, dass sie in sehr hochdimensionalen Räumen gut klassifizieren kann. Dieser Zusammenhang zwischen SVM und Fingerprints war eine Neuheit, und wurde von uns erstmalig in die Chemieinformatik eingeführt. In Abschnitt 4.3 fokussiere ich mich auf die SVM-Methode. Für fast alle Klassifikationsaufgaben in dieser Arbeit wurde der SVM-Ansatz verwendet. Ein Schwerpunkt der Dissertation lag auf der SVM-Methode. Wegen Platzbeschränkungen wurde in den beigefügten Veröffentlichungen auf eine detaillierte Beschreibung der SVM verzichtet. Aus diesem Grund wird in Abschnitt 4.3 eine vollständige Einführung in SVM gegeben. Darin enthalten ist eine vollständige Diskussion der SVM Theorie: optimale Hyperfläche, Soft-Margin-Hyperfläche, quadratische Programmierung als Technik, um diese optimale Hyperfläche zu finden. Abschnitt 4.3 enthält auch eine Diskussion von Kernel-Funktionen, welche die genaue Form der optimalen Hyperfläche bestimmen. In Abschnitt 4.4 ist eine Einleitung in verschiede Methoden gegeben, die wir für die Auswahl von Deskriptoren genutzt haben. In diesem Abschnitt wird der Unterschied zwischen einer „Filter“- und der „Wrapper“-basierten Auswahl von Deskriptoren herausgearbeitet. In Veröffentlichung 3 (Abschnitt 7.3) haben wir die Vorteile und Nachteile von Filter- und Wrapper-basierten Methoden im virtuellen Screening vergleichend dargestellt. Abschnitt 7 besteht aus den Publikationen, die unsere Forschungsergebnisse enthalten. Unsere erste Publikation (Veröffentlichung 1) war ein Übersichtsartikel (Abschnitt 7.1). In diesem Artikel haben wir einen Gesamtüberblick der Anwendungen von SVM in der Bio- und Chemieinformatik gegeben. Wir diskutieren Anwendungen von SVM für die Gen-Chip-Analyse, die DNASequenzanalyse und die Vorhersage von Proteinstrukturen und Proteininteraktionen. Wir haben auch Beispiele beschrieben, wo SVM für die Vorhersage der Lokalisation von Proteinen in der Zelle genutzt wurden. Es wird dabei deutlich, dass SVM im Bereich des virtuellen Screenings noch nicht verbreitet war. Um den Einsatz von SVM als Hauptmethode unserer Forschung zu begründen, haben wir in unserer nächsten Publikation (Veröffentlichung 2) (Abschnitt 7.2) einen detaillierten Vergleich zwischen SVM und verschiedenen neuronalen Netzen, die sich als eine Standardmethode im virtuellen Screening etabliert haben, durchgeführt. Verglichen wurde die Trennung von wirstoffartigen und nicht-wirkstoffartigen Molekülen („Druglikeness“-Vorhersage). Die SVM konnte 82% aller Moleküle richtig klassifizieren. Die Klassifizierung war zudem robuster als mit dreilagigen feedforward-ANN bei der Verwendung verschiedener Anzahlen an Hidden-Neuronen. In diesem Projekt haben wir verschiedene Deskriptoren zur Beschreibung der Moleküle berechnet: Ghose-Crippen Fragmentdeskriptoren [86], physikochemische Eigenschaften [9] und topologische Pharmacophore (CATS) [10]. Die Entwicklung von weiteren Verfahren, die auf dem SVM-Konzept aufbauen, haben wir in den Publikationen in den Abschnitten 7.3 und 7.8 beschrieben. Veröffentlichung 3 stellt die Entwicklung einer neuen SVM-basierten Methode zur Auswahl von relevanten Deskriptoren für eine bestimmte Aktivität dar. Eingesetzt wurden die gleichen Deskriptoren wie in dem oben beschriebenen Projekt. Als charakteristische Molekülgruppen haben wir verschiedene Untermengen der COBRA Datenbank ausgewählt: 195 Thrombin Inhibitoren, 226 Kinase Inhibitoren und 227 Faktor Xa Inhibitoren. Es ist uns gelungen, die Anzahl der Deskriptoren von ursprünglich 407 auf ungefähr 50 zu verringern ohne signifikant an Klassifizierungsgenauigkeit zu verlieren. Unsere Methode haben wir mit einer Standardmethode für diese Anwendung verglichen, der Kolmogorov-Smirnov Statistik. Die SVM-basierte Methode erwies sich hierbei in jedem betrachteten Fall als besser als die Vergleichsmethoden hinsichtlich der Vorhersagegenauigkeit bei der gleichen Anzahl an Deskriptoren. Eine ausführliche Beschreibung ist in Abschnitt 4.4 gegeben. Dort sind auch verschiedene „Wrapper“ für die Deskriptoren-Auswahl beschrieben. Veröffentlichung 8 beschreibt die Anwendung von aktivem Lernen mit SVM. Die Idee des aktiven Lernens liegt in der Auswahl von Molekülen für das Lernverfahren aus dem Bereich an der Grenze der verschiedenen zu unterscheidenden Molekülklassen. Auf diese Weise kann die lokale Klassifikation verbessert werden. Die folgenden Gruppen von Moleküle wurden genutzt: ACE (Angiotensin converting enzyme), COX2 (Cyclooxygenase 2), CRF (Corticotropin releasing factor) Antagonisten, DPP (Dipeptidylpeptidase) IV, HIV (Human immunodeficiency virus) protease, Nuclear Receptors, NK (Neurokinin receptors), PPAR (peroxisome proliferator-activated receptor), Thrombin, GPCR und Matrix Metalloproteinasen. Aktives Lernen konnte die Leistungsfähigkeit des virtuellen Screenings verbessern, wie sich in dieser retrospektiven Studie zeigte. Es bleibt abzuwarten, ob sich das Verfahren durchsetzen wird, denn trotzt des Gewinns an Vorhersagegenauigkeit ist es aufgrund des mehrfachen SVMTrainings aufwändig. Die Publikationen aus den Abschnitten 7.5, 7.6 und 7.7 (Veröffentlichungen 5-7) zeigen praktische Anwendungen unserer SVM-Methoden im Wirkstoffdesign in Kombination mit anderen Verfahren, wie der Ähnlichkeitssuche und neuronalen Netzen zur Eigenschaftsvorhersage. In zwei Fällen haben wir mit dem Verfahren neuartige Liganden für COX-2 (cyclooxygenase 2) und dopamine D3/D2 Rezeptoren gefunden. Wir konnten somit klar zeigen, dass SVM-Methoden für das virtuelle Screening von Substanzdatensammlungen sinnvoll eingesetzt werden können. Es wurde im Rahmen der Arbeit auch ein schnelles Verfahren zur Erzeugung großer kombinatorischer Molekülbibliotheken entwickelt, welches auf der SMILES Notation aufbaut. Im frühen Stadium des Wirstoffdesigns ist es wichtig, eine möglichst „diverse“ Gruppe von Molekülen zu testen. Es gibt verschiedene etablierte Methoden, die eine solche Untermenge auswählen können. Wir haben eine neue Methode entwickelt, die genauer als die bekannte MaxMin-Methode sein sollte. Als erster Schritt wurde die „Probability Density Estimation“ (PDE) für die verfügbaren Moleküle berechnet. [78] Dafür haben wir jedes Molekül mit Deskriptoren beschrieben und die PDE im N-dimensionalen Deskriptorraum berechnet. Die Moleküle wurde mit dem Metropolis Algorithmus ausgewählt. [87] Die Idee liegt darin, wenige Moleküle aus den Bereichen mit hoher Dichte auszuwählen und mehr Moleküle aus den Bereichen mit niedriger Dichte. Die erhaltenen Ergebnisse wiesen jedoch auf zwei Nachteile hin. Erstens wurden Moleküle mit unrealistischen Deskriptorwerten ausgewählt und zweitens war unser Algorithmus zu langsam. Dieser Aspekt der Arbeit wurde daher nicht weiter verfolgt. In Veröffentlichung 6 (Abschnitt 7.6) haben wir in Zusammenarbeit mit der Molecular-Modeling Gruppe von Aventis-Pharma Deutschland (Frankfurt) einen SVM-basierten ADME Filter zur Früherkennung von CYP 2C9 Liganden entwickelt. Dieser nichtlineare SVM-Filter erreichte eine signifikant höhere Vorhersagegenauigkeit (q2 = 0.48) als ein auf den gleichen Daten entwickelten PLS-Modell (q2 = 0.34). Es wurden hierbei Dreipunkt-Pharmakophordeskriptoren eingesetzt, die auf einem dreidimensionalen Molekülmodell aufbauen. Eines der wichtigen Probleme im computerbasierten Wirkstoffdesign ist die Auswahl einer geeigneten Konformation für ein Molekül. Wir haben versucht, SVM auf dieses Problem anzuwenden. Der Trainingdatensatz wurde dazu mit jeweils mehreren Konformationen pro Molekül angereichert und ein SVM Modell gerechnet. Es wurden anschließend die Konformationen mit den am schlechtesten vorhergesagten IC50 Wert aussortiert. Die verbliebenen gemäß dem SVM-Modell bevorzugten Konformationen waren jedoch unrealistisch. Dieses Ergebnis zeigt Grenzen des SVM-Ansatzes auf. Wir glauben jedoch, dass weitere Forschung auf diesem Gebiet zu besseren Ergebnissen führen kann

Development of improved methodologies for the discovery of novel bioactive compounds and their application to problems of biomedical relevance.

Chemoinformatics is a discipline that has positioned itself both in the world of aca- demic research and in the industrial field as a fundamental tool given its high effi- ciency/cost ratio. However, it also has certain limitations that have been addressed in the development of this industrial thesis. The principal objective on which this work is based is fundamentally to apply methodological improvements in chemoinformatic techniques for the discovery of new bioactive compounds against different therapeutic targets, both in an academic and industrial context. The computational techniques used are based on both ligand structure (phar- macophore modeling, QSAR, three-dimensional similarity) and protein structure (docking and molecular dynamics). Some of the therapeutic targets studied have been Zika virus protease NS2B-NS3, acetyl and butyrylcholinesterase enzymes, the α-galactosidase enzyme and various proteins involved in diabetes. The first contribution of this thesis (which is presented as a compendium of publications) investigated the discovery of a pharmacological chaperone for the enzyme α-galactosidase with the ability to bind to the enzyme in a region other than the active site of the enzyme so that it would stabilize its three-dimensional structure and recover its functionality without inhibiting it at high concentrations as Migalastat (the only drug approved by the Food and Drug Administration for the treatment of Fabry disease) does. Using a combined strategy of virtual screening based on the three-dimensional shape of the chemical compounds and the modeling of the molecular coupling we discovered a compound, 2,6-ditiopurine, which after laboratory tests was found to be able to stabilize and promote the maturation of the enzyme. In the following three contributions, docking techniques were used to describe the potential mechanism of action as acetyl and butyrylcholinesterase inhibitors of N-acetyl-tryptophan compounds, different classes of tanshinones, rosmarinic acid, hyperforin and hyguanin C. In all these cases, our simulation results predict that more stable binding of these compounds with enzymes takes place in the active site of the same, so that the compounds would block the entry of the substrate, which would explain their activity as inhibitors. In the penultimate contribution we screened Drugbankn˜(Law et al., 2013), a public database containing FDA-approved drugs and compounds used in clinical research) for searching compounds suitable for the treatment of Zika virus infection. In this case, protease NS2B-NS3 was selected as a therapeutic target for which 8 enzyme-inhibiting compounds were found by means of structure based virtual screening. Of these, the Novobiocin compound proved to inhibit both the protein in vitro and in vivo, obtaining 100 % survival of the animal models used for the experiment. An on-line platform was finally developed for the identification of new anti- diabetic compounds. This thesis presents a work derived from the use of this platform to carry out an inverse screening and identify the potential therapeutic target of different natural compounds. We have found bibliographic evidences of experimental validation for eight of the compounds for which the computational method predicted a possible therapeutic target confirming the the have certain activity.FarmaciaMedicin