Jaydeep Unni, Ph.D.
Sr. Project Manager, Robert Bosch Healthcare Systems, Palo Alto, CA
Remote Patient Monitoring – Challenges and Opportunities
Remote Patient Monitoring (RPM) is gaining importance and acceptance with rising number of chronic disease conditions and with increase in the aging population. As instances of Heart diseases, Diabetes etc are increasing the demand for these technologies are increasing. RPM devices typically collect patient vital sign data and in some case also patient responses to health related questions. Thus collected data is then transmitted through various modalities (wireless/Bluetooth/cellular) to Hospitals/Doctor’s office for clinical evaluation. With these solutions Doctors are able to access patient’s vital data ‘any time any where’ thus enabling them to intervene on a timely and effective manner. For older adult population chronic disease management, post-acute care management and safety monitoring are areas were RPM finds application. That said, there are significant challenges in adoption of Remote Patient Monitoring including patient willingness and compliance for adoption, affordability, availability of simpler/smarter technology to mention a few. But experts contend that if implemented correctly Remote Patient Monitoring can contain healthcare expenditure by reducing avoidable hospitalization while greatly improving quality of care.
Kal Ramnarayan, Ph.D.
Co-founder President & Chief Scientific Officer, Sapient Discovery, San Diego, CA, USA
A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry
With the mapping of the human genome completed almost a decade ago, efforts are still underway to understand the gene products (i.e., proteins) in the human biological and disease pathways. Deciphering such information is very important for the discovery and development of small molecule drugs as well as protein therapeutics for various human diseases for which no cure exists. As an example, with more than 500 members, the kinase family of protein targets continues to be an important and attractive class for drug discovery. While how many of the members in this family are actually druggable is still to be established, there are several ongoing efforts on this class of proteins across a broad spectrum of disease categories. Even though in general the protein structural topology might looks similar, there are issues with respect selectivity of identified small molecule inhibitors when, the lead molecule discovery is carried out at the ATP binding site. As an added complexity, allosteric modulators are needed for some of the members, but the actual site for such modulation on the protein target can not resolved with uncertainty. In this presentation we will describe a bioinformatics and computational based platform for small molecule discovery for protein targets that are involved in protein-protein interactions as well as targets like kinases and phosphatases. We will describe a computational approach in which we have used an informatics based platform with several hundred kinases to sort through in silico and identify inhibitors that are likely to be highly selective in the lead generation phase. We will discuss the implication of this approach on the drug discovery of the kinase and phosphatase classes in general and independent of the disease category.
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.