Thursday, August 14, 2014
Friday, July 25, 2014
Phenotypic screening in oncology drug discovery — Hosein Kouros-Mehr
A recent paper* summarized the history of oncology drug discovery and analyzes the contibutions of phenotypic drug discovery vs traditional target-based drug discovery. The authors analyze the development and mechanisms of all small molecule-based cancer drugs approved by the FDA over the past 15 years and those currently in clinical development.
The authors show that the majority of small molecule inhibitors for oncology originated in target-based discovery. However, a significant number of inhibitors were identified in phenotypic drug screening approaches. The authors suggest that the rate-limiting step in bringing compounds identified in phenotypic screenings into clinical development is the lack of mechanistically defined cellular models for the cancer phenotypes in question and also the reliance on traditional drug effects such as cytotoxicity and mitotic arrest, which are only a component of the hallmarks of cancer.
The authors suggest that mechanistically informed phenotypic models may better enable compounds identified in phenotypic screenings to successfully complete clinical development. These models would enable confirmation that the targeted agents have a molecular mechanism of action, which would enable PD biomarker development and development of diagnostic hypotheses and patient tailoring hypotheses.
Wednesday, July 23, 2014
Cholesterol, Wnt signaling, and Cancer - Hosein Kouros-Mehr
A recent paper* has reported a direct biological link between cholesterol and cancer. The Wnt pathway plays an important role in cancer formation and metastasis, particularly in colorectal cancer in which the Wnt-related gene APC is frequently mutated. In this report, the authors find that cholesterol is enriched around the Wnt-related Frizzled and LRP5/6 receptors in cancer cells. This suggested to the authors that perhaps cholesterol itself could activate Wnt signaling. The authors found that cholesterol directly recruits the Wnt-related Dishevelled (Dvl) scaffold protein to the cell membrane through interaction with its PDZ domain. Dvl then activates the canonical Wnt signaling pathway. In this way, cholesterol may directly regulate activation of canonical Wnt signaling and may regulate balance between canonical and non-canonical signaling. Future studies will be needed to determine effects of anti-cholesterol medicines in silencing Wnt signaling and perhaps inhibiting Wnt-driven cancers.
* Sheng, R., et. al. (2014). Cholesterol selectively activates canonical Wnt signalling over non-canonical Wnt signalling. Nature 5 (4393)
Hosein Kouros-Mehr
Tuesday, July 22, 2014
Friday, June 27, 2014
RUNX1 as a biomarker for triple-negative breast cancer - Hosein Kouros-Mehr
With the advent of microarray expression profiling over a decade ago, many new biomarkers have been identified for ER+ breast cancers, such as the transcription factors GATA-3 and XBP-1. A recent paper* identified a novel biomarker for triple-negative (ER- PR- HER2-) breast cancer. RUNX1 is a transcription factor that is the most frequently mutated gene in human leukemia, and the RUNX family plays essential roles in haematopoiesis, osteogenesis and neurogenesis.
In this report, the authors utilized a tissue microarray continaing biopsies from 483 patients with invasive ductal breast adenocarcinoma. The microarray was stained with antibodies for immunihistochemistry. RUNX1 immunostaining was signficantly asociated with pooere cancer-specific survival in patients with ER-negative and triple-negative breast cancer. However, RUNX1 was associated with progesterone receptor positive tumors as well, which is a bit confounding. Interestinlgly, RUNX1 was associated with more CD4+ and CD8+ T-lymphocyte infiltration and CD68+ macrophage infiltration, which have been observed as markers for poor prognosis in breast cancer patients.
Triple negative breast cancer is an unmet medical need and lacks suitable biomarkers for patient stratification. RUNX1 may be used as a biomarker and prognostic indicator correlating with poor prognosis specifically in the triple negative subtype of human breast cancer.
Hosein Kouros-Mehr
*Citation: Ferrari N, Mohammed ZMA, Nixon C, Mason SM, Mallon E, et al. (2014) Expression of RUNX1 Correlates with Poor Patient Prognosis in Triple Negative Breast Cancer. PLoS ONE 9(6): e100759. doi:10.1371/journal.pone.0100759
Monday, June 9, 2014
Retrospective analysis of a pharmaceutical company's R&D pipeline - Hosein Kouros-Mehr
A major pharmaceutical company has published* a thorough analysis of its R&D pipeline from 2005-2010 and summarized the primary reasons for successes and failures of its small-molecule drug projects. While R&D investment in the industry has reached record high levels and there has been a wealth of new technologies and new drug targets to explore, the rate of new drug launches has been steady and drug development costs have increased substantially. The conclusions of this article can be used to boost productivity of drug development efforts in hopes of increasing the number of successful drug launches.
An interesting point made in the paper is that volume-based metrics to boost portfolio projects and drug candidates have not necessarily yielded better performance. The authors state, "This volume-based approach damaged not only the quality and sustainability of R&D pipelines but, more importantly, also the health of the R&D organizations and their underlying scientific curiosity. This is because the focus of scientists and clinicians moved away from the more demanding goal of thoroughly understanding disease pathophysiology and the therapeutic opportunities, and instead moved towards meeting volume-based goals and identifying an unprecedented level of back-up and 'me too' drug candidates. In such an environment, 'truth-seeking' behaviours to understand disease biology may have been over-ridden by 'progression-driven' behaviours that rewarded scientists for meeting numerical volume-based goals." The authors suggest that volume-based metrics should be substituted with a more in depth understanding of drug targets, biologies, and patient selection metrics.
The company describes a comprehensive review undertaken for 142 drug discovery and development projects from candidate phase to Phase II. Data were gathered from more than 80% of these 142 projects and for 95% of projects in clinical phases. Of the projects analysed, 94 closed during the period assessed; 33 closed before clinical testing and a further 61 closed during clinical testing. The review was performed by submitting structured questionnaires to a cross-functional group of scientists and clinicians drawn from the project teams. The primary cause of failure for projects up to Phase II was unacceptale safety, which accounted for more than half of all project closures. The majority of these failures occurred before clinical testing (primarily during regulatory GLP toxicology testing), with safety issues being the reason for 82% of preclinical project closures. The analysis suggested a crucial need for teams to pay attention to preclinical safety signals.
Based on the analysis, the paper concludes that there 5 variable that predict success for an R&D portfolio:
(1) Right target - Strong link between target and disease; differentiated efficacy; available and predictive biomarkers
(2) Right tissue - Adequate bioavailability and tissue exposure; definition of PD biomarkers; clear understanding of preclinical and clinical PK/PD; understanding of drug-drug interactions
(3) Right safety - DIfferentiated and clear safety margins, understanding of secondary pharmcology risk, understanding of reactive metabolites, genotoxicity, drug-drug interactions, understanding of target liability
(4) Right patients - Identification of patient population for tailoring of molecules, definition of risk-benefit for a given population
(5) Right commercial potential - Differentiated value proposition versus future standard of care, focus on market access (payer, provider), personalized health care strategy (diagnostic, biomarkers)
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