Full Schedule

Ganesh Sambasivam, Ph.D.
CSO & Co-Founder, Anthem Biosciences India

Preclinical Outsourcing to India

The outsourcing segment is witnessing rapid changes with respect to the nature of work outsourced and the location. Cost is the major driver but other considerations such as infrastructure and government policies can also be important drivers for decision making. The last couple of years have been a trying time for all CROs. The global economic meltdown has hit research budgets especially hard. The new challenges facing Contract Research Organizations call for a radically revised approach and a new model that would push the boundaries of this business further and would blur the line between client and vendor further. I believe the term Contract Research Organization (CRO), is a misnomer to begin with (often confused with Clinical Research Organization), has now morphed into a new type of company viz Contract Innovation Services (CIS). Clients are no longer just happy to outsource odds and ends of the development piece but are looking to their vendors for a massive amount of innovation input. This input is increasingly across both the chemistry and discovery domains. This new paradigm calls for CIS companies to develop new platforms, create intellectual propertythat is of service to clients andinnovate processes to meet new found customer expectations.

Ayyappan Nair, Ph.D.
Head, Business Development (Technologies, Discovery Biology), Anthem Biosciences & DavosPharma, New Jersey, USA

Inhibition of NF-κB regulated gene expression by chrysoeriol suppresses tumorigenesis in breast cancer cells

Amrutha K¹, Pandurangan Nanjan¹, Sanu K Shaji¹, Damu Sunilkumar¹, Subhalakshmi K¹, Rashmi U Nair¹, Lakshmi Rajakrishna², Asoke Banerji¹, Ayyappan Ramesh Nair^1*,2

1. School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Clappana P.O., Kollam – 690 525, Kerala, India
2. Anthem Biosciences, No 49, Canara Bank Road, Bommasandra Industrial Area, Phase 1,  Hosur Road, Bangalore – 560 099, Karnataka, India

Abstract:  A large number of effective cancer-preventing compounds inhibit the activation of nuclear factor-κ B (NF-κB).  It has been previously demonstrated that some flavonoids that are a vital component of our diet inhibits this pathway. As a consequence, many flavonoids inhibit genes involved in various aspects of tumorigenesis and have thus emerged as potential chemopreventive candidates for cancer treatment. We studied the effect of 17 different flavonoids, including the highly evaluated quercetin on the NF-κB pathway, and on the expression of MMP-9 and COX-2 (two NF-κB regulated genes involved in metastasis) in the highly invasive human breast cancer cell line MDA-MB-231.  The findings suggest that not all the quercetin like flavone backbone compounds inhibit the NF-κB pathway, and that the highly hydoxylated flavonols quercetagetin and gossypetin did not inhibit this pathway, nor did it inhibit the expression of MMP-9 and COX-2.  This indicates a correlation between inhibition of NF-κB and subsequent suppression of these NF-κB regulated genes. Here, we also report the novel observation that the not so well characterized methoxylated flavone chrysoeriol inhibited the NF-κB pathway, and was most potent in reducing the expression of MMP-9 and COX-2.  Based on these observations, the cellular effects of chrysoeriol were evaluated in MDA-MB-231.  Chrysoeriol caused cell cycle arrest at G2/M, inhibited migration and invasion, and caused cell death of macrophages that contributed to migration of these cancer cells.  These effects of chrysoeriol make it a potential therapeutic candidate for breast cancer metastasis.

Shigeki Miyamoto, Ph.D.
Professor, McArdle Laboratory for Cancer Research – UW Carbone Cancer Center
Department of Oncology, School of Medicine and Public Health
University of Wisconsin-Madison

“Inside-out” NF-κB signaling in cancer and other pathologies

The NF-κB/Rel family of transcription factors contributes to critical cellular processes, including immune, inflammatory and cell survival responses. As such, NF-κB is implicated in immunity-related diseases, as well as multiple types of human malignancies. Indeed, genetic alterations in the NF-κB signaling pathway are frequently observed in multiple human malignancies. NF-κB is normally kept inactive in the cytoplasm by inhibitor proteins. Extracellular ligands can induce the release of NF-κB from the inhibitors to allow its migration into the nucleus to regulate a variety of target genes.  NF-κB activation is also induced in response to multiple stress conditions, including those induced by DNA-damaging anticancer agents. Although precise mechanisms are still unclear, research from our group has revealed a unique nuclear-to-cytoplasmic signaling pathway. In collaboration with bioengineers, clinicians and pharmaceutical industry, our lab has developed new methods to analyze primary cancer patient samples and identified several compounds with different mechanisms that mitigate this cell survival pathway.  Further contributions from other labs have also revealed additional mechanisms and molecular players in this “inside-out” signaling pathway and expanded its role in other physiological and pathological processes, including B cell development, premature aging and therapy resistance of certain cancers. Our own new findings, along with these recent developments in the field, will be highlighted.

Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA

Applying Machine learning for Automated Identification of Patient Cohorts

Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland

Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.