Daniela Gabriel

Bio: Daniela Gabriel is an academic researcher from Novartis. The author has contributed to research in topics: High-content screening & Deep learning. The author has an hindex of 12, co-authored 18 publications receiving 1524 citations.

Identification and characterization of NVP-BEZ235, a new orally available dual phosphatidylinositol 3-kinase/mammalian target of rapamycin inhibitor with potent in vivo antitumor activity

Abstract: The phosphatidylinositol 3-kinase (PI3K)/Akt/mammalian target of rapamycin inhibitor (mTOR) pathway is often constitutively activated in human tumor cells, providing unique opportunities for anticancer therapeutic intervention. NVP-BEZ235 is an imidazo[4,5-c]quinoline derivative that inhibits PI3K and mTOR kinase activity by binding to the ATP-binding cleft of these enzymes. In cellular settings using human tumor cell lines, this molecule is able to effectively and specifically block the dysfunctional activation of the PI3K pathway, inducing G(1) arrest. The cellular activity of NVP-BEZ235 translates well in in vivo models of human cancer. Thus, the compound was well tolerated, displayed disease stasis when administered orally, and enhanced the efficacy of other anticancer agents when used in in vivo combination studies. Ex vivo pharmacokinetic/pharmacodynamic analyses of tumor tissues showed a time-dependent correlation between compound concentration and PI3K/Akt pathway inhibition. Collectively, the preclinical data show that NVP-BEZ235 is a potent dual PI3K/mTOR modulator with favorable pharmaceutical properties. NVP-BEZ235 is currently in phase I clinical trials.

High throughput screening technologies for direct cyclic AMP measurement.

Abstract: A study comparing five different cAMP detection technologies in terms of sensitivity, robustness, and feasibility for HTS is presented. In this report, the following methods are described: a nonhomogeneous DELFIA, and the homogeneous methods based on time-resolved fluorescence (HTRF), luminescent singlet oxygen channeling or ALPHAScreen, FP, and high-affinity enzyme complementation. DELFIA had the highest sensitivity, whereas ALPHAScreen and HTRF shared several advantages, including high sensitivity, broad dynamic range, and minimal reagent addition steps. For G(s)-coupled antagonist screens, we found HTRF and ALPHAScreen the more sensitive and HTS-compatible techniques.

Integration of multiple readouts into the z' factor for assay quality assessment.

Abstract: Methods that monitor the quality of a biological assay (i.e., its ability to discriminate between positive and negative controls) are essential for the development of robust assays. In screening, the most commonly used parameter for monitoring assay quality is the Z' factor, which is based on 1 selected readout. However, biological assays are able to monitor multiple readouts. For example, novel multiparametric screening technologies such as high-content screening provide information-rich data sets with multiple readouts on a compound's effect. Still, assay quality is commonly assessed by the Z' factor based on a single selected readout. This report suggests an extension of the Z' factor, which integrates multiple readouts for assay quality assessment. Using linear projections, multiple readouts are condensed to a single parameter, based on which the assay quality is monitored. The authors illustrate and evaluate this approach using simulated data and real-world data from a high-content screen. The suggested approach is applicable during assay development, to optimize the image analysis, as well as during screening to monitor assay robustness. Furthermore, data sets from high-content imaging assays and other state-of-the-art multiparametric screening technologies, such as flow cytometry or transcript analysis, could be analyzed.

Linking phenotypes and modes of action through high-content screen fingerprints.

Abstract: High-content screening (HCS) is a powerful technique for monitoring phenotypic responses to treatments on a cellular and subcellular level. Cellular phenotypes can be characterized by multivariate image readouts such as shape, intensity, or texture. The corresponding feature vectors can thus be defined as HCS fingerprints that serve as a powerful biological compound descriptor. Therefore, clustering or classification of HCS fingerprints across compound treatments allows for the identification of similarities in protein targets or pathways. We developed an HCS-based profiling panel that serves as basis for characterizing the mode of action of compounds. This panel measures phenotypic effects in six different compartments of U-2OS cells, namely the nucleus, the cytoplasm, the endoplasmic reticulum, the Golgi apparatus, and the cytoskeleton. We profiled a set of 2,725 well-annotated compounds and clustered their corresponding HCS fingerprints to establish links between predominant cellular phenotypes and cellular processes and protein targets. We found various different clusters enriched for individual targets (e.g., HDAC, HSP90, TOP1, HMGCR, TUB), signaling pathways (e.g., PIK3/AKT/mTOR), or gene sets associated with diseases (e.g., psoriasis, leukemia). Based on this clustering we were able to identify novel compound-target associations for selected compounds such as a submicromolar inhibitory activity of Silmitasertib (a casein kinase inhibitor) on PI3K and mTOR.

Recommendations for the reduction of compound artifacts in time-resolved fluorescence resonance energy transfer assays.

Abstract: Time-resolved (TR) fluorescence resonance energy transfer (FRET) is a widely accepted technology for high throughput screening (HTS), being able to detect and quantify the interactions of specific biomolecules in a homogeneous format. TR-FRET has several advantages for HTS applications that reduce assay artifacts such as compound interference. However, in some cases artifacts due to compound autofluorescence, color quenching, or signal stability are still observed. This report presents strategies addressing these issues by several means. One recommendation is the recording and visualization of differences in the donor/acceptor fluorescence, which allows the identification of compound artifacts. Another suggestion is to adjust the time delay, between excitation and recording of the fluorescence, in order to reduce compound interference. Furthermore, configuring the assay to allow the TR-FRET measurement to be taken at different time points, creating a reaction time course, allows background correction for each sample. Finally, the optimization of the FRET pair, to ensure assay signal stability under screening conditions, can improve the assay quality. This report presents examples of how these simple steps can be applied to enhance the quality of TR-FRET screening campaigns.

Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

Abstract: Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, we analyzed gene expression (GE) profiles from 21 breast cancer data sets and identified 587 TNBC cases. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem–like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed us to identify TNBC cell line models representative of these subtypes. Predicted “driver” signaling pathways were pharmacologically targeted in these cell line models as proof of concept that analysis of distinct GE signatures can inform therapy selection. BL1 and BL2 subtypes had higher expression of cell cycle and DNA damage response genes, and representative cell lines preferentially responded to cisplatin. M and MSL subtypes were enriched in GE for epithelial-mesenchymal transition, and growth factor pathways and cell models responded to NVP-BEZ235 (a PI3K/mTOR inhibitor) and dasatinib (an abl/src inhibitor). The LAR subtype includes patients with decreased relapse-free survival and was characterized by androgen receptor (AR) signaling. LAR cell lines were uniquely sensitive to bicalutamide (an AR antagonist). These data may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.

Targeting the phosphoinositide 3-kinase pathway in cancer.

Abstract: The phosphoinositide 3-kinase (PI3K) pathway is a key signal transduction system that links oncogenes and multiple receptor classes to many essential cellular functions, and is perhaps the most commonly activated signalling pathway in human cancer. This pathway therefore presents both an opportunity and a challenge for cancer therapy. Even as inhibitors that target PI3K isoforms and other major nodes in the pathway, including AKT and mammalian target of rapamycin (mTOR), reach clinical trials, major issues remain. Here, we highlight recent progress that has been made in our understanding of the PI3K pathway and discuss the potential of and challenges for the development of therapeutic agents that target this pathway in cancer.

Targeting PI3K signalling in cancer: opportunities, challenges and limitations.

Abstract: There are ample genetic and laboratory studies that suggest the PI3K-Akt pathway is vital to the growth and survival of cancer cells. Inhibitors targeting this pathway are entering the clinic at a rapid pace. In this Review, the therapeutic potential of drugs targeting PI3K-Akt signalling for the treatment of cancer is discussed. I focus on the advantages and drawbacks of different treatment strategies for targeting this pathway, the cancers that might respond best to these therapies and the challenges and limitations that confront their clinical development.