Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

BACKGROUND: P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. METHODOLOGY/PRINCIPAL FINDINGS: An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. CONCLUSIONS/SIGNIFICANCE: This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile

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Combined ligand- and structure-based studies on inhibitors of P-glycoprotein

Der aktive Effluxtransporter P-Glykoprotein (P-gp) ist verantwortlich für Multidrug Resistenz (MDR) in Tumoren und beeinflusst außerdem die ADME Eigenschaften von Arzneistoffkandidaten. P-gp zeigt eine sehr breite Substratspezifität und transportiert daher eine hohe Anzahl von strukturell und funktionell diversen Substanzen aus Tumorzellen hinaus und über physiologische Barrieren hinweg. Obwohl in den letzten zwei Jahrzehnten einige Inhibitoren von P-gp identifiziert wurden, scheiterten alle von ihnen in klinischen Studien, entweder wegen schwerwiegenden Nebenwirkungen, oder wegen fehlender Wirksamkeit. Dies betont die Notwendigkeit von verlässlichen in silico Modellen für die Vorhersage von P-gp Substraten und Inhibitoren bereits in frühen Phasen der Wirkstoffentwicklung. In dieser Arbeit wurden daher unterschiedliche in silico Methoden verwendet um Einblicke in die dreidimensionalen strukturellen Voraussetzungen der Liganden, ihren Bindungsmodus und ihre Stereoselektivität gegenüber P-gp zu erhalten. Verschiedene 2D- und 3D-QSAR Modelle wurden mit einfachen physicochemischen sowie komplexen 3D-Deskriptoren (GRIND) für unterschiedliche chemische Grundkörper erstellt, um globale strukturelle Merkmale von P-gp Inhibitoren zu untersuchen. Um die vielversprechendsten P-gp Liganden mit dem besten Wirksamkeits/Lipophilie- oder Größenverhältnis zu identifizieren, verwendeten wir zum bisher ersten Mal ligandeneffizienz- und lipophilieeffizienzbasierte Ansätze. Interessanterweise überschritt keine der vielversprechendsten Substanzen den LipE Grenzwert von 5. Dies könnte mit dem einzigartigen Zugangsweg der Substanzen zusammenhängen, der anders als bei anderen Transportern oder Ionenkanälen direkt aus der Zellmembran erfolgt. Unsere Dockingstudien bieten einen ersten Nachweis über unterschiedliche Bindungsareale für zwei diastereomere Substanzserien und zeigen eine stereoselektive Ligandenerkennung von P-gp. Zusätzlich war es uns möglich zu zeigen, dass sich ein Benzophenon-Dimer so platzieren lässt, dass diese beiden Areale verbunden werden, was die Hypothese von mehreren, teilweise überlappenden, Bindunsarealen von P-gp verstärkt. Die in dieser Dissertation beschriebene Arbeit wird den Weg für die Entwicklung von zukünftlichen neuen und vielversprechenderen Inhibitoren von P-gp bereiten, die bessere ADME Eigenschaften und verringerte Toxizität besitzen.The drug efflux pump P-glycoprotein (P-gp) has been shown to cause multidrug resistance (MDR) in tumors as well as to influence ADME properties of drug candidates. P-gp is highly promiscuous in its ligand recognition profiles and thus transports numerous structurally and functionally diverse compounds out of tumor cells and accross physiological barriers. Several inhibitors of P-gp mediated drug efflux have been identified in the past two decades, but all of them failed in clinical trials due to severe side effects and lack of efficacy. This further emphasizes the necessity of reliable in-silico tools for prediction of P-gp substrates and inhibitors during the early phases of drug discovery. Therefore, in this thesis, various in silico tools have been utilized to get insights into 3D structural requirements of ligands, their binding modes, as well as their stereoselectivity towards P-gp. Different 2D- and 3D-QSAR models using simple physicochemical and GRID independent molecular descriptors have been constructed across different chemical scaffolds to investigate global structural attributes of P-gp inhibitors. In order to identify most promising P-gp ligands with best potency/lipophilicity or size ratio, we, for the first time, also used ligand efficiency and lipophilic efficiency based approaches. Interestingly, none of the P-gp inhibitors/substrates cross the LipE threshold of 5 for highly promising compounds. This might be linked to the unique entry pathway directly from the membrane bilayer, which is rather unique for transporters and ion channels. Our docking studies provide the first evidence for different binding areas of two diastereomeric compound series and provides evidence for stereoselective ligand recognition of by P-gp. In addition we could show that a benzophenone dimer is well docked in a pose bridging these two distinct binding sites, which further strengthens the hypothesis of multiple, partly overlapping binding sites at P-gp. The work described in this thesis will pave the way for the design of new and more promising inhibitors of P-gp in the future with better ADME properties and reduced toxicity

Statistical learning approaches for predicting pharmacological properties of pharmaceutical agents

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