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Lenalidomide (LEN) maintenance (MT) post autologous stem cell transplantation (ASCT) is standard of care in newly diagnosed multiple myeloma (MM) but has not been compared to other agents in clinical trials. We retrospectively compared bortezomib (BTZ; n = 138) or LEN (n = 183) MT from two subsequent GMMG phase III trials. All patients received three cycles of BTZ-based triplet induction and post-ASCT MT. BTZ MT (1.3 mg/m2 i.v.) was administered every 2 weeks for 2 years. LEN MT included two consolidation cycles (25 mg p.o., days 1–21 of 28 day cycles) followed by 10–15 mg/day for 2 years. The BTZ cohort more frequently received tandem ASCT (91% vs. 33%) due to different tandem ASCT strategies. In the LEN and BTZ cohort, 43% and 46% of patients completed 2 years of MT as intended (p = 0.57). Progression-free survival (PFS; HR = 0.83, p = 0.18) and overall survival (OS; HR = 0.70, p = 0.15) did not differ significantly with LEN vs. BTZ MT. Patients with <nCR after first ASCT were assigned tandem ASCT in both trials. In patients with <nCR and tandem ASCT (LEN: n = 54 vs. BTZ: n = 84), LEN MT significantly improved PFS (HR = 0.61, p = 0.04) but not OS (HR = 0.46, p = 0.09). In conclusion, the significant PFS benefit after eliminating the impact of different tandem ASCT rates supports the current standard of LEN MT after ASCT.
Background: Despite novel therapeutic agents, most multiple myeloma (MM) patients eventually relapse. Two large phase III trials have shown significantly improved response rates (RR) of lenalidomide/dexamethasone compared with placebo/dexamethasone in relapsed MM (RMM) patients. These results have led to the approval of lenalidomide for RMM patients and lenalidomide/dexamethasone has since become a widely accepted second-line treatment. Furthermore, in RMM patients consolidation with high-dose chemotherapy plus autologous stem cell transplantation has been shown to significantly increase progression free survival (PFS) as compared to cyclophosphamide in a phase III trial. The randomized prospective ReLApsE trial is designed to evaluate PFS after lenalidomide/dexamethasone induction, high-dose chemotherapy consolidation plus autologous stem cell transplantation and lenalidomide maintenance compared with the well-established lenalidomide/dexamethasone regimen in RMM patients.
Methods/Design: ReLApsE is a randomized, open, multicenter phase III trial in a planned study population of 282 RMM patients. All patients receive three lenalidomide/dexamethasone cycles and - in absence of available stem cells from earlier harvesting - undergo peripheral blood stem cell mobilization and harvesting. Subsequently, patients in arm A continue on consecutive lenalidomide/dexamethasone cycles, patients in arm B undergo high dose chemotherapy plus autologous stem cell transplantation followed by lenalidomide maintenance until discontinuation criteria are met. Therapeutic response is evaluated after the 3rd (arm A + B) and the 5th lenalidomide/dexamethasone cycle (arm A) or 2 months after autologous stem cell transplantation (arm B) and every 3 months thereafter (arm A + B). After finishing the study treatment, patients are followed up for survival and subsequent myeloma therapies. The expected trial duration is 6.25 years from first patient in to last patient out. The primary endpoint is PFS, secondary endpoints include overall survival (OS), RR, time to best response and the influence of early versus late salvage high dose chemotherapy plus autologous stem cell transplantation on OS.
Discussion: This phase III trial is designed to evaluate whether high dose chemotherapy plus autologous stem cell transplantation and lenalidomide maintenance after lenalidomide/dexamethasone induction improves PFS compared with the well-established continued lenalidomide/dexamethasone regimen in RMM patients. Trial registration: ISRCTN16345835 (date of registration 2010-08-24).
A new method to bridge the gap between ligand and receptor-based methods in virtual screening (VS) is presented. We introduce a structure-derived virtual ligand (VL) model as an extension to a previously published pseudo-ligand technique [1]: LIQUID [2] fuzzy pharmacophore virtual screening is combined with grid-based protein binding site predictions of PocketPicker [3]. This approach might help reduce bias introduced by manual selection of binding site residues and introduces pocket shape information to the VL. It allows for a combination of several protein structure models into a single "fuzzy" VL representation, which can be used to scan screening compound collections for ligand structures with a similar potential pharmacophore. PocketPicker employs an elaborate grid-based scanning procedure to determine buried cavities and depressions on the protein's surface. Potential binding sites are represented by clusters of grid probes characterizing the shape and accessibility of a cavity. A rule-based system is then applied to project reverse pharmacophore types onto the grid probes of a selected pocket. The pocket pharmacophore types are assigned depending on the properties and geometry of the protein residues surrounding the pocket with regard to their relative position towards the grid probes. LIQUID is used to cluster representative pocket probes by their pharmacophore types describing a fuzzy VL model. The VL is encoded in a correlation vector, which can then be compared to a database of pre-calculated ligand models. A retrospective screening using the fuzzy VL and several protein structures was evaluated by ten fold cross-validation with ROC-AUC and BEDROC metrics, obtaining a significant enrichment of actives. Future work will be devoted to prospective screening using a novel protein target of Helicobacter pylori and compounds from commercial providers.
Kenntnisse über die dreidimensionale Struktur therapeutisch relevanter Zielproteine bieten wertvolle Informationen für den rationalen Wirkstoffentwurf. Die stetig wachsende Zahl aufgeklärter Kristallstrukturen von Proteinen ermöglicht eine qualitative und quantitative rechnergestützte Untersuchung von spezifischen Protein-Liganden Wechselwirkungen. Im Rahmen dieser Arbeit wurden neue Algorithmen für die Identifikation und den Ähnlichkeitsvergleich von Proteinbindetaschen und ihren Eigenschaften entwickelt und in dem Programm PocketomePicker zusammengefasst. Die Software gliedert sich in die Routinen PocketPicker, PocketShapelets und PocketGraph. Ferner wurde in dieser Arbeit die Methode ReverseLIQUID reimplementiert und im Rahmen einer Kooperation für das strukturbasierte Virtuelle Screening angewendet. Die genannten Methoden und ihre wissenschaftliche Anwendungen sollte hier zusammengefasst werden: Die Methode PocketPicker ermöglicht die Vorhersage potentieller Bindetaschen auf Proteinoberflächen. Diese Technik implementiert einen geometrischen Ansatz auf Basis „künstlicher Gitter“ zur Identifikation zusammenhängender vergrabener Bereiche der Proteinoberfläche als Orte möglicher Ligandenbindestellen. Die Methode erreicht eine korrekte Vorhersage der tatsächlichen Bindetasche für 73 % der Einträge eines repräsentativen Datensatzes von Proteinstrukturen. Für 90 % der Proteinstrukturen wird die tatsächlich Ligandenbindestelle unter den drei wahrscheinlichsten vorhergesagten Taschen gefunden. PocketPicker übertrifft die Vorhersagequalität anderer etablierter Algorithmen und ermöglicht Taschenidentifikationen auf apo-Strukturen ohne signifikante Einbußen des Vorhersageerfolges. Andere Verfahren weisen deutlich eingeschränkte Ergebnisse bei der Anwendung auf apo-Strukturen auf. PocketPicker erlaubt den alignmentfreien Ähnlichkeitsvergleich von Bindetaschenfor-men durch die Kodierung berechneter Bindevolumen als Korrelationsdeskriptoren. Dieser Ansatz wurde erfolgreich für Funktionsvorhersage von Bindetaschen aus Homologiemodellen von APOBEC3C und Glutamat Dehydrogenase des Malariaerregers Plasmodium falciparum angewendet. Diese beiden Projekte wurden in Zusammenarbeit mit Kollaborationspartnern durchgeführt. Zudem wurden PocketPicker Korrelationsdeskriptoren erfolgreich für die automatisierte Konformationsanalyse der enzymatischen Tasche von Aldose Reduktase angewendet. Für detaillierte Analysen der Form und der physikochemischen Eigenschaften von Proteinbindetaschen wurde in dieser Arbeit die Methode PocketShapelets entwickelt. Diese Technik ermöglicht strukturelle Alignments von extrahierten Bindevolumen durch Zerlegungen der Oberfläche von Proteinbindetaschen. Die Überlagerung gelingt durch die Identifikation strukturell ähnlicher Oberflächenkurvaturen zweier Taschen. PocketShapelets wurde erfolgreich zur Analyse funktioneller Ähnlichkeit von Bindetaschen verwendet, die auf Betrachtungen physikochemischer Eigenschaften basiert. Zur Analyse der topologischen Vielfalt von Bindetaschengeometrien wurde in dieser Arbeit die Methode PocketGraph entwickelt. Dieser Ansatz nutzt das Konzept des sog. „Wachsenden Neuronalen Gases“ aus dem Bereich des maschinellen Lernens für eine automatische Extraktion des strukturellen Aufbaus von Bindetaschen. Ferner ermöglicht diese Methode die Zerlegung einer Bindestelle in ihre Subtaschen. Die von PocketPicker charakterisierten Taschenvolumen bilden die Grundlage für die Methode ReverseLIQUID. Dieses Programm wurde in dieser Arbeit weiterentwickelt und im Rahmen einer Kooperation zur Identifikation eines Inhibitors der Serinprotease HtrA des Erregers Helicobacter pylori verwendet. Mit ReverseLIQUID konnte ein strukturbasiertes Pharmakophormodell für das Virtuelle Screening erstellt werden. Dieser Ansatz ermöglichte die Identifikation einer Substanz mit niedrig mikromolarer Affinität gegenüber der Zielstruktur.
The representation of small molecules as molecular graphs is a common technique in various fields of cheminformatics. This approach employs abstract descriptions of topology and properties for rapid analyses and comparison. Receptor-based methods in contrast mostly depend on more complex representations impeding simplified analysis and limiting the possibilities of property assignment. In this study we demonstrate that ligand-based methods can be applied to receptor-derived binding site analysis. We introduce the new method PocketGraph that translates representations of binding site volumes into linear graphs and enables the application of graph-based methods to the world of protein pockets. The method uses the PocketPicker algorithm for characterization of binding site volumes and employs a Growing Neural Gas procedure to derive graph representations of pocket topologies. Self-organizing map (SOM) projections revealed a limited number of pocket topologies. We argue that there is only a small set of pocket shapes realized in the known ligand-receptor complexes.
Background Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITEcs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITEcs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusions The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections.
