161 research outputs found
In silico screening on the herg potassium channel
WĂ€hrend des Arzneistoffentwicklungsprozesses scheitern fast 35% der Arzneistoffe wegen
schlechter Absorption, Verteilung, Metabolismus, Ausscheidung und ToxizitÀt (ADMET). Ein
wichtiger Bestandteil dieses Scheiterns ist die Interaktion mit Anti-Target Proteinen wie
Cytochrom P450, P-glycoprotein und dem hERG Kaliumkanal.
Der hERG Kaliumkanal ist in vielen verschiedenen Zellen und Geweben wie dem Herz, Nerven
und glatten Muskelzellen vorhanden. Im Herzen spielt der hERG Kanal wÀhrend des
Aktionspotentials in der dritten Phase der kardialen Repolarisierung wegen der Weiterleitung des
schnellen Kalium Ausstroms (Ikr) eine wichtige Rolle. Ein Verzögern dieser Phase fĂŒhrt zum
Long QT Syndrom (LQTs), das eine potenziell tödliche Arrhythmie verursachen kann. Viele
Klassen von Medikamenten wurden wegen ihren Wechselwirkungen mit dem hERG Kanal in
den letzten zehn Jahren vom Markt zurĂŒckgezogen. Wie auch andere Anti-Target Proteine, ist
der hERG Kanal in der Ligandenerkennung unspezifisch, weshalb er mit vielen Klassen von
Arzneistoffen wie Psychopharmaka, Antihistaminika, Antiarrhythmika und Antibiotika
interagieren kann. Viele Studien zeigen, dass eine erhebliche Anzahl von MolekĂŒlen wĂ€hrend
der SchlieĂung des Kanals nicht dissoziieren und im geschlossenen Zustand des hERG Kanals
gefangen bleiben.
In dieser Studie wurden Propafenon und dessen Derivate in ein Homologie-Modell des hERG
Kanals im geschlossenen und geöffneten Zustand gedockt, um die hERG Hemmung und das
âdrug trappingâ besser verstehen zu können.
Ziel war es, die Wechselwirkungen zwischen dem hERG Kanal im geschlossenen Zustand und
den Liganden zu untersuchen. Aufgrund dessen wurde eine Serie von âtrappedâ Propafenon-
Derivaten im hERG Kanal, welcher sich im geschlossenen Zustand befand, mit Dock, einem
Docking Modul des Programms MOE, und GLIDE, dem Docking-Programm von Schrödinger,
gedockt. Es wurde ein svl-Skript, genannt ROTALI, verwendet, um RMSD Matrizen zu
erstellen, mit welchen die Duplikate unter den Posen, die in Bezug auf die Central Cavity
unterschiedlich positioniert waren, zu erkennen und zu löschen. In weiterer Forlge wurden die
möglichen binding modes durch agglomeratives hierarchisches Clustering identifiziert. Die
Analyse der Posen fĂŒhrte zur Identifizierung von zwei möglichen Binding Modes. Derselbe Prozess wurde angewandt, um eine Serie von Propafenon-Derivaten in ein Homologie-
Modell des hERG Kanals im geöffneten Zustand zu docken. Drei mögliche Binding Modes
wurden durch die agglomerative Cluster Analyse der RMSD Matrix identifiziert, welche durch
das gemeinsame GerĂŒst der Propafenon Derivate und jenen AminosĂ€uren generiert wurde, die
mit den MolekĂŒlen interagierten. Um die FlexibilitĂ€t des Proteins zu berĂŒcksichtigen wurden die
Propafenon Derivate zusĂ€tzlich in acht verschiedene SchnappschĂŒsse einer MolekĂŒldynamik des
Homologie-Modelles des hERG Kanals im geöffneten Zustand gedockt. In diesem Fall wurden
zwei Binding Modes selektiert.
Interessanterweise war es durch das Einordnen der Posen der fĂŒnf oben genannten Cluster nach
der potenziellen Energie des R1 Substituenten, geteilt durch die Anzahl an Schweratomen,
möglich, zwischen den âTrappedâ und ânon-Trappedâ Propafenon-Derivaten zu unterscheiden.
Dieser Wert war bei den ânon-Trappedâ Substanzen immer höher als bei den âTrappedâ
MolekĂŒlen. Der Umstand, dass dies auch bei den Vertretern des fĂŒnften Clusters möglich ist, bei
denen der R1 Substituent unterhalb der vier Phe656 zum Liegen kommt, deutet darauf hin, dass
das PhÀnomen des Drug-Trappings mehr auf die inhÀrenten Eigenschaften des R1 Substituenten
als auf seine Konformation zurĂŒckzufĂŒhren ist, wenn er mit dem hERG Kanal interagiert. Dies
könnte bedeuten, dass die Starrheit und die Sperrigkeit der Substituenten bestimmt ob
Propafenon und dessen Derivate âTrappedâ sind oder
nicht, unabhÀngig vom Bindemodus im hERG Kanal.During the drug development process, almost 35% of the compounds fail due to poor absorption,
distribution, metabolism, excretion and toxicity (ADMET). An important role on these failures is
played by improper interactions with antitarget proteins, such as cytocrome P450, P-glycoprotein
and the hERG potassium channel.
The hERG potassium channel is expressed in various cells and tissues, such as heart, neurons
and smooth muscle. In the heart, the hERG channel plays an important role in the third phase of
heart repolarization, due to the conduction of the rapid delayed rectifier K+ current (Ikr). A delay
of this phase of repolarization leads to a syndrome called Long QT syndrome (LQTs) which
might cause a potentially fatal arrhythmia called Torsade de Pointes (TdP). Many different
classes of compounds were withdrawn from the market in the past decade due to their interaction
with the hERG channel. Similar to other antitarget proteins, the hERG channel is polyspecific in
the ligand recognition, hence it can interact with many classes of compounds, such as
psychiatric, antihistaminic, antiarrhytmic and antimicrobial drugs. Several studies show that
some molecules do not dissociate during the channel gating and are trapped in the closed state of
the hERG channel.
In this study, propafenone and derivatives were docked into homology models of the hERG
channel in the closed and open states to shed more light on hERG inhibition and on drug
trapping.
With the aim to investigate the interactions between the hERG channel in the closed state and the
compounds investigated, a series of trapped propafenone derivatives were docked into the
homology model of the hERG channel in the closed conformation using Dock, the docking tool
of MOE, and Glide, the docking tool of Schrödinger. A svl script called ROTALI was used to
generate RMSD matrices with which the duplicate poses lying in different directions of the
central cavity were detected and deleted, thus allowing to identify possible binding modes
through agglomerative hierarchical clustering. This analysis led to the identification of two
possible binding modes.
The same process was applied to the poses obtained by docking the propafenones into a
homology model of the hERG channel in the open state. Three possible binding modes were selected through agglomerative cluster analysis of the RMSD matrix generated taking into
account the propafenone derivativesâ common scaffold and the amino acids that might interact.
Finally, in order to take into account protein flexibility, nine propafenone derivatives were
docked into eight models of the hERG channel in the open state obtained from snapshots of
molecular dynamics simulations. Clustering both according to the common scaffold RMSD and
the RMSD matrix of the amino acids interacting with the poses, two binding modes were
selected. Biological studies suggest that non-trapped propafenones hinder the hERG channel
gating with a mechanism called âfoot in the doorâ. In four out of the five selected clusters, it is
possible to explain the âfoot in the doorâ mechanism.
Interestingly, ranking the poses of the five clusters above-mentioned according to the potential
energy values of the R1 substituent, and according to this value divided by the number of heavy
atoms, it is possible to distinguish between trapped and non-trapped propafenones. In the nontrapped
compounds, this value is always higher than in the trapped ones. The fact that it works
also in cluster five, where the R1 substituents are placed under the ring formed by the four
Phe656, might indicate that drug trapping phenomena depend more on intrinsic properties of the
R1 susbstituent rather than on its conformation when it interacts with the hERG channel. Hence,
this might indicate that the rigidity and the bulkyness of the substituent determines whether a
propafenone derivatives is trapped or not independently of the binding mode in the hERG
channel
Development of in silico models for the prediction of toxicity incorporating ADME information
Drug discovery is a process that requires a significant investment in both time and resources. Although recent developments have reduced the number of drugs failing at the later stages of development due to poor pharmacokinetic and/or toxicokinetic profiles, late stage attrition of drug candidates remains a problem. Additionally, there is a need to reduce animal testing for toxicological risk assessment for ethical and financial reasons. In silico methods offer an alternative that can address these challenges.
A variety of computational approaches have been developed in the last two decades, these must be evaluated to ensure confidence in their use. The research presented in this thesis has assessed a range of existing tools for the prediction of toxicity and absorption, distribution, metabolism and elimination (ADME) parameters with an emphasis on absorption and xenobiotic metabolism. These two ADME properties largely determine bioavailability of a drug and, in turn, also influence toxicity. In vitro (Caco-2 cells and the parallel artificial membrane permeation assay) and in silico approaches, such as various druglikeness filters, can be used to estimate human intestinal absorption; a comparison between different methods was performed to identify relative strengths and weaknesses of the approaches. In terms of xenobiotic metabolism it is not only important to predict metabolites correctly, but it is also crucial to identify those compounds that can be biotransformed into species that can covalently bind to biomolecules. Structural alerts are routinely used to screen for such potential reactive metabolites. The balance between sensitivity and specificity of such reactive metabolite alerts has been discussed in the context of correctly predicting reactive metabolites of pharmaceuticals (using data available from DrugBank). Off-target toxicity, exemplified by human Ether-Ă -go-go-Related Gene (hERG) channel inhibition, was also explored. A number of novel structural alerts for hERG toxicity were developed based on groups of structurally similar compounds. Finally, the importance of predicting potential ecotoxicological effects of drugs was also considered. The utility of zebrafish embryos to distinguish between baseline and excess toxicity was investigated. In evaluating this selection of existing tools, improvements to the methods have been proposed where possible
Computational Methods in Biophysics and Medicinal Chemistry: Applications and Challenges
In this thesis I described the theory and application of several computational methods in solving medicinal chemistry and biophysical tasks. I pointed out to the valuable information which could be achieved by means of computer simulations and to the possibility to predict the outcome of traditional experiments. Nowadays, computer represents an invaluable tool for chemists.
In particular, the main topics of my research consisted in the development of an automated docking protocol for the voltage-gated hERG potassium channel blockers, and the investigation of the catalytic mechanism of the human peptidyl-prolyl cis-trans isomerase Pin1
Classifier Design to Improve Pattern Classification and Knowledge Discovery for Imbalanced Datasets
Imbalanced dataset mining is a nontrivial issue. It has extensive applications in a variety of fields, such as scientific research, medical diagnosis, business, multiple industries, etc. Standard machine learning algorithms fail to produce satisfactory classifiers: they tend to over-fit the larger class but ignore the smaller class. Numerous algorithms have been developed to handle class imbalance, and limited progress has been achieved in improving prediction accuracy for smaller class. However, real world datasets may have hidden detrimental characteristics other than class imbalance. Those characteristics usually are dataset specific, and can fail otherwise robust algorithms for other imbalanced datasets. Mining such datasets can only be improved by algorithms tailored to domain characteristics (Weiss, 2004); therefore, it is important and necessary to do exploratory data analysis before classifier design. On the other hand, unmet needs in knowledge discovery, such as lead optimization during drug discovery, demand novel algorithms. In this study, we have developed a framework for imbalanced dataset mining tailored to data characteristics and adapted to knowledge discovery in chemical datasets. First, we explored the dataset and visualized domain characteristics, and then we designed different classifiers accordingly: for class imbalance, active learning (AL), cost sensitive learning (CSL) and re-sampling methods were designed; for class overlap, Class Boundary Cleaning (CBC) and Class Boundary Mining (CBM) were developed. CBM was also designed for lead optimization: ideally it would detect fine structural differences between different classes of compounds; and these differences could be options for lead optimization. Methods developed were applied to two datasets, hERG and CPDB. The results from imbalanced hERG liability dataset showed that CBC, CBM and AL were effective in correcting class imbalance/overlap and improving the classifier's performance. Highly predictive models were built; discriminating patterns were discovered; and lead optimization options were proposed. The methodology developed and knowledge discovered will benefit drug discovery, improve hazard test prioritization, risk assessment, and governmental regulatory work on human health and the environmental protection.Doctor of Philosoph
From Knowledgebases to Toxicity Prediction and Promiscuity Assessment
Polypharmacology marked a paradigm shift in drug discovery from the traditional âone drug, one targetâ approach to a multi-target perspective, indicating that highly effective drugs favorably modulate multiple biological targets. This ability of drugs to show activity towards many targets is referred to as promiscuity, an essential phenomenon that may as well lead to undesired side-effects. While activity at therapeutic targets provides desired biological response, toxicity often results from non-specific modulation of off-targets. Safety, efficacy and pharmacokinetics have been the primary concerns behind the failure of a majority of candidate drugs. Computer-based (in silico) models that can predict the pharmacological and toxicological profiles complement the ongoing efforts to lower the high attrition rates. High-confidence bioactivity data is a prerequisite for the development of robust in silico models. Additionally, data quality has been a key concern when integrating data from publicly-accessible bioactivity databases. A majority of the bioactivity data originates from high- throughput screening campaigns and medicinal chemistry literature. However, large numbers of screening hits are considered false-positives due to a number of reasons. In stark contrast, many compounds do not demonstrate biological activity despite being tested in hundreds of assays.
This thesis work employs cheminformatics approaches to contribute to the aforementioned diverse, yet highly related, aspects that are crucial in rationalizing and expediting drug discovery. Knowledgebase resources of approved and withdrawn drugs were established and enriched with information integrated from multiple databases. These resources are not only useful in small molecule discovery and optimization, but also in the elucidation of mechanisms of action and off- target effects. In silico models were developed to predict the effects of small molecules on nuclear receptor and stress response pathways and human Ether-aÌ-go-go-Related Gene encoded potassium channel. Chemical similarity and machine-learning based methods were evaluated while highlighting the challenges involved in the development of robust models using public domain bioactivity data. Furthermore, the true promiscuity of the potentially frequent hitter compounds was identified and their mechanisms of action were explored at the molecular level by investigating target-ligand complexes. Finally, the chemical and biological spaces of the extensively tested, yet inactive, compounds were investigated to reconfirm their potential to be promising candidates.Die Polypharmakologie beschreibt einen Paradigmenwechsel von "einem Wirkstoff - ein ZielmolekuÌl" zu "einem Wirkstoff - viele ZielmolekuÌle" und zeigt zugleich auf, dass hochwirksame Medikamente nur durch die Interaktion mit mehreren ZielmolekuÌlen Ihre komplette Wirkung entfalten koÌnnen.
Hierbei ist die biologische AktivitaÌt eines Medikamentes direkt mit deren Nebenwirkungen assoziiert, was durch die Interaktion mit therapeutischen bzw. Off-Targets erklaÌrt werden kann (PromiskuitaÌt). Ein Ungleichgewicht dieser Wechselwirkungen resultiert oftmals in mangelnder Wirksamkeit, ToxizitaÌt oder einer unguÌnstigen Pharmakokinetik, anhand dessen man das Scheitern mehrerer potentieller Wirkstoffe in ihrer praÌklinischen und klinischen Entwicklungsphase aufzeigen kann. Die fruÌhzeitige Vorhersage des pharmakologischen und toxikologischen Profils durch computergestuÌtzte Modelle (in-silico) anhand der chemischen Struktur kann helfen den Prozess der Medikamentenentwicklung zu verbessern. Eine Voraussetzung fuÌr die erfolgreiche Vorhersage stellen zuverlaÌssige BioaktivitaÌtsdaten dar. Allerdings ist die DatenqualitaÌt oftmals ein zentrales Problem bei der Datenintegration. Die Ursache hierfuÌr ist die Verwendung von verschiedenen Bioassays und âReadoutsâ, deren Daten zum GroĂteil aus primaÌren und bestaÌtigenden Bioassays gewonnen werden. WaÌhrend ein GroĂteil der Treffer aus primaÌren Assays als falsch-positiv eingestuft werden, zeigen einige Substanzen keine biologische AktivitaÌt, obwohl sie in beiden Assay- Typen ausgiebig getestet wurden (âextensively assayed compoundsâ).
In diese Arbeit wurden verschiedene chemoinformatische Methoden entwickelt und angewandt, um die zuvor genannten Probleme zu thematisieren sowie LoÌsungsansaÌtze aufzuzeigen und im Endeffekt die Arzneimittelforschung zu beschleunigen. HierfuÌr wurden nicht redundante, Hand-validierte Wissensdatenbanken fuÌr zugelassene und zuruÌckgezogene Medikamente erstellt und mit weiterfuÌhrenden Informationen angereichert, um die Entdeckung und Optimierung kleiner organischer MolekuÌle voran zu treiben. Ein entscheidendes Tool ist hierbei die AufklaÌrung derer Wirkmechanismen sowie Off-Target-Interaktionen.
FuÌr die weiterfuÌhrende Charakterisierung von Nebenwirkungen, wurde ein Hauptaugenmerk auf Nuklearrezeptoren, Pathways in welchen Stressrezeptoren involviert sind sowie den hERG-Kanal gelegt und mit in-silico Modellen simuliert. Die Erstellung dieser Modelle wurden Mithilfe eines integrativen Ansatzes aus âstate-of-the-artâ Algorithmen wie AÌhnlichkeitsvergleiche und âMachine- Learningâ umgesetzt. Um ein hohes MaĂ an VorhersagequalitaÌt zu gewaÌhrleisten, wurde bei der Evaluierung der DatensaÌtze explizit auf die DatenqualitaÌt und deren chemische Vielfalt geachtet. WeiterfuÌhrend wurden die in-silico-Modelle dahingehend erweitert, das Substrukturfilter genauer betrachtet wurden, um richtige Wirkmechanismen von unspezifischen Bindungsverhalten (falsch- positive Substanzen) zu unterscheiden. AbschlieĂend wurden der chemische und biologische Raum ausgiebig getesteter, jedoch inaktiver, kleiner organischer MolekuÌle (âextensively assayed compoundsâ) untersucht und mit aktuell zugelassenen Medikamenten verglichen, um ihr Potenzial als vielversprechende Kandidaten zu bestaÌtigen
Synthesis of Biologically Active Small Molecules: Different Approaches to Drug Design
In the past years, genome biology had disclosed an ever-growing kind of biological targets that emerged as ideal points for therapeutic intervention. Nevertheless, the number of new chemical entities (NCEs) translated into effective therapies employed in the clinic, still not observed. Innovative strategies in drug discovery combined with different approaches to drug design should be searched for bridge this gap. In this context organic synthetic chemistry had to provide for effective strategies to achieve biologically active small molecules to consider not only as potentially drug candidates, but also as chemical tools to dissect biological systems.
In this scenario, during my PhD, inspired by the Biology-oriented Synthesis approach, a small library of hybrid molecules endowed with privileged scaffolds, able to block cell cycle and to induce apoptosis and cell differentiation, merged with natural-like cores were synthesized. A synthetic platform which joined a Domino Knoevenagel-Diels Alder reaction with a Suzuki coupling was performed in order to reach the hybrid compounds. These molecules can represent either antitumor lead candidates, or valuable chemical tools to study molecular pathways in cancer cells. The biological profile expressed by some of these derivatives showed a well defined antiproliferative activity on leukemia Bcr-Abl expressing K562 cell lines.
A parallel project regarded the rational design and synthesis of minimally structurally hERG blockers with the purpose of enhancing the SAR studies of a previously synthesized collection. A Target-Oriented Synthesis approach was applied. Combining conventional and microwave heating, the desired final compounds were achieved in good yields and reaction rates. The preliminary biological results of the compounds, showed a potent blocking activity. The obtained small set of hERG blockers, was able to gain more insight the minimal structural requirements for hERG liability, which is mandatory to investigate in order to reduce the risk of potential side effects of new drug candidates
Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics
This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs
In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.
Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds
Allosteric modulation and ligand binding kinetics at the Kv11.1 channel
Kv11.1-induced cardiotoxicity has emerged as an unanticipated adverse effect of many pharmacological agents and has become a major obstacle in drug development over the past decades. In this thesis, allosteric modulation of the Kv11.1 channel has been extensively explored, and negative allosteric modulators were shown to relieve the proarrhythmic effects of structurally and therapeutically diverse Kv11.1 blockers. The most potent modulators may be developed as a new class of antiarrhythmic medications in the future. On the other hand, kinetic binding parameters of a wide range of Kv11.1 blockers at the channel have been thoroughly investigated in this thesis. Association and dissociation rates or residence times are strongly suggested to be integrated with equilibrium affinity values into the future paradigms for a better and more comprehensive evaluation of Kv11.1 liability of drug candidates. The __kon-koff-KD__ kinetic map provides a first and promising classification of Kv11.1 blockers, which could be beneficial and indicative for drug researchers to design compounds with less Kv11.1-mediated cardiac side effects in the early stage of drug development. Hopefully, all findings in this thesis have brought new insights into Kv11.1-induced cardiac arrhythmias, and will offer opportunities for restoring or preventing this kind of arrhythmias in the near future.Chinese Scholarship CouncilUBL - phd migration 201
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