Aug
12
Mon
2013
Plenary Talk: Nano-biotechnology: Omega-3 Oils and Nanofibres @ Sathyam Hall
Aug 12 @ 1:30 pm – 2:05 pm

collinColin Barrow, Ph.D.
Chair in Biotechnology, School of Life & Environmental Sciences, Deakin University, Australia


Nano-biotechnology: Omega-3 Oils and Nanofibres

The health benefits of long-chain omega-3 fatty acids are well established, especially for eicosapentaenoic acid (EPA) and docosapentaenoic acid (DHA) from fish and microbial sources. In fact, a billion dollar market exists for these compounds as nutritional supplements, functional foods and pharmaceuticals. This presentation will describe some aspects of our omega-3 biotechnology research that are at the intersection of Nano-biotechnology and oil chemistry. These include the use of lipases for the concentration of omega-3 fats, through immobilization of these lipases on nanoparticles, and the microencapsulation and stabilization of omega-3 oils for functional foods. I will also describe some of our work on the enzymatic production of resolvins using lipoxygenases, and the fermentation of omega-3 oils from marine micro-organisms. Finally, I will describe some of our work on the formation of amyloid fibrils and graphene for various applications in nano-biotechnology.

 

Aug
13
Tue
2013
Plenary Address: Making sense of pathogen sensors of Innate Immunity: Utility of their ligands as antiviral agens and adjuvants for vaccines. @ Acharya Hall
Aug 13 @ 9:17 am – 9:55 am

SuryaprakashSuryaprakash Sambhara, DVM, Ph.D
Chief, Immunology Section, Influenza Division, CDC, Atlanta, USA


Making sense of pathogen sensors of Innate Immunity: Utility of their ligands as antiviral agents and adjuvants for vaccines.

Currently used antiviral agents act by inhibiting viral entry, replication, or release of viral progeny.  However, recent emergence of drug-resistant viruses has become a major public health concern as it is limiting our ability to prevent and treat viral diseases.  Furthermore, very few antiviral agents with novel modes of action are currently in development.  It is well established that the innate immune system is the first line of defense against invading pathogens.  The recognition of diverse pathogen-associated molecular patterns (PAMPs) is accomplished by several classes of pattern recognition receptors (PRRs) and the ligand/receptor interactions trigger an effective innate antiviral response.  In the past several years, remarkable progress has been made towards understanding both the structural and functional nature of PAMPs and PRRs.  As a result of their indispensable role in virus infection, these ligands have become potential pharmacological agents against viral infections.  Since their pathways of action are evolutionarily conserved, the likelihood of viruses developing resistance to PRR activation is diminished.  I will discuss the recent developments investigating the potential utility of the ligands of innate immune receptors as antiviral agents and molecular adjuvants for vaccines.

Suryaprakash (1) Suryaprakash (4) Suryaprakash-Nagaraja

Invited Talk: Applying Machine learning for Automated Identification of Patient Cohorts @ Sathyam Hall
Aug 13 @ 2:40 pm – 3:05 pm

SriSairamSrisairam 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.

Invited Talk: Changing landscapes of Biosimilars @ Acharya Hall
Aug 13 @ 5:28 pm – 6:03 pm

RustomModyRustom Mody, Ph.D.
Head R & D Lupin Ltd., Pune


Biosimilars are follow-on biologics also known as Similar Biologics – terms used to describe officially approved subsequent versions of innovator biopharmaceutical products made by rDNA technology when made by a different sponsor following patent expiry on the innovator product. These products are drawing global attention as a large number of block buster biopharmaceuticals have expired and many will soon seize to have patent protection in the coming years, opening the doors for the entry of biosimilars. However, the regulatory landscape is getting complex across the globe. The talk focuses on opportunities and challenges in the field of biosimilars and the future of biosimilar companies in India.