Lentiviral vectors (LVs) are frequently used to engineer cell lines for preclinical research purposes including assay development and target validation. Development of production cell lines for manufacturing recombinant protein therapeutics may also benefit from the use of LVs because they may reduce timelines and generate more uniform or higher expressing stable pools and clones. In addition, LVs could be advantageous for engineering new, alternative host cell substrates due to their ability to efficiently transduce most cell types. We demonstrate here that NS0 mouse myeloma cells, a host cell frequently used for protein production, can be transduced with LVs to greater than 80% efficiency and with no cytotoxic effects. The use of LVs for engineering of production cell lines will require additional testing procedures. Since LVs have previously been used in human gene therapy clinical trials, safety testing assays and procedures have been developed that could easily be applied to the development process for manufacturing cell lines to ensure the absence of unwanted viral material in cell banks and biologic products.

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Cancer cells are capable of sphere formation (transformation) through reactive oxygen species (ROS) and glycolysis shift. Transformation is linked to tumorigenesis and therapy resistance, hence targeting regulators of ROS and glycolysis is important for cancer therapeutic candidates. Here, we demonstrate that Smac mimetic AZ58 in combination with tumour necrosis factor-α (TNF-α) was able to inhibit the production of ROS, inhibit glycolysis through Pim-1 kinase-mediated Ser-112 phosphorylation of BAD, and increase depolarization of mitochondria. We also identified mitochondrial isoforms of Pim-1 kinase that were targeted for degradation by AZ58 in combination with TNF-α or AZ58 in combination with Fas ligand (FasL) plus cycloheximide (CHX) through caspase-3 to block transformation. Our study demonstrates that Smac mimetic in combination with TNF-α is an ideal candidate to target Pim-1 expression, inhibit ROS production and to block transformation from blebbishields.
© 2016 Authors; published by Portland Press Limited.

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Starting from compound 1, a potent PI3Kα inhibitor having poor general kinase selectivity, we used structural data and modelling to identify key exploitable differences between PI3Kα and the other kinases. This approach led us to design chemical modifications of the central pyrazole, which solved the poor kinase selectivity seen as a strong liability for the initial compound 1. Amongst the modifications explored, a 1,3,4-triazole ring (as in compound 4) as a replacement of the initial pyrazole provided good potency against PI3Kα, with excellent kinase selectivity.
Copyright © 2015 Elsevier Ltd. All rights reserved.

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Complex mechanisms involving genomic aberrations in numerous proteins and pathways are believed to be a key cause of many diseases such as cancer. With recent advances in genomics, elucidating the molecular basis of cancer at a patient level is now feasible, and has led to personalized treatment strategies whereby a patient is treated according to his or her genomic profile. However, there is growing recognition that existing treatment modalities are overly simplistic, and do not fully account for the deep genomic complexity associated with sensitivity or resistance to cancer therapies. To overcome these limitations, large-scale pharmacogenomic screens of cancer cell lines–in conjunction with modern statistical learning approaches–have been used to explore the genetic underpinnings of drug response. While these analyses have demonstrated the ability to infer genetic predictors of compound sensitivity, to date most modeling approaches have been data-driven, i.e. they do not explicitly incorporate domain-specific knowledge (priors) in the process of learning a model. While a purely data-driven approach offers an unbiased perspective of the data–and may yield unexpected or novel insights–this strategy introduces challenges for both model interpretability and accuracy. In this study, we propose a novel prior-incorporated sparse regression model in which the choice of informative predictor sets is carried out by knowledge-driven priors (gene sets) in a stepwise fashion. Under regularization in a linear regression model, our algorithm is able to incorporate prior biological knowledge across the predictive variables thereby improving the interpretability of the final model with no loss–and often an improvement–in predictive performance. We evaluate the performance of our algorithm compared to well-known regularization methods such as LASSO, Ridge and Elastic net regression in the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (Sanger) pharmacogenomics datasets, demonstrating that incorporation of the biological priors selected by our model confers improved predictability and interpretability, despite much fewer predictors, over existing state-of-the-art methods.

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Menopausal symptoms (eg, hot flushes and vaginal symptoms) are common, often bothersome, and can adversely impact women’s sexual functioning, relationships, and quality of life. Estrogen-progestin therapy was previously considered the standard care for hormone therapy (HT) for managing these symptoms in nonhysterectomized women, but has a number of safety and tolerability concerns (eg, breast cancer, stroke, pulmonary embolism, breast pain/tenderness, and vaginal bleeding) and its use has declined dramatically in the past decade since the release of the Women’s Health Initiative trial results. Conjugated estrogens paired with bazedoxifene (CE/BZA) represent a newer progestin-free alternative to traditional HT for nonhysterectomized women. CE/BZA has demonstrated efficacy in reducing the frequency and severity of vasomotor symptoms and preventing loss of bone mineral density in postmenopausal women. CE/BZA provides an acceptable level of protection against endometrial hyperplasia and does not increase mammographic breast density. Compared with traditional estrogen-progestin therapy, it is associated with lower rates of breast pain/tenderness and vaginal bleeding. Patient-reported outcomes indicate that CE/BZA improves menopause-specific quality of life, sleep, some measures of sexual function (especially ease of lubrication), and treatment satisfaction. This review looks at the rationale for selection and combination of CE with BZA at the dose ratio in the approved product and provides a detailed look at the efficacy, safety, tolerability, and patient-reported outcomes from the five Phase III trials. Patient considerations in the choice between CE/BZA and traditional HT (eg, tolerability, individual symptoms, and preferences for route of administration) are also considered.

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