Call for Papers and Posters

Applications of genetics, genomics and bioinformatics in drug discovery

 

Pacific Symposium on Biocomputing
January 3-7, 2018
Fairmont Orchid Resort
The Big Island of Hawaii, USA

 

Motivation

 

New drug target discovery is essential for making innovative medicines that address unmet medical needs.  Advances in basic research are increasingly being translated into breakthrough treatments in many disease areas; however, the field of drug discovery and development continues to face the challenges of rising cost and declining rates of success.  While the estimated average cost to bring a new molecular entity to market currently exceeds US$ 1.5 billion, R&D return on investment fell considerably from 10.1% in 2010 to 3.7% in 2016.  Recent advances in genetic and genomic research have accelerated our studies of disease mechanisms and underlying molecular pathophysiology, and also enabled drug target discovery in many areas.  For example, the power of human genetics in therapeutic target validation has been underscored by a retrospective analysis that selecting targets with supportive human genetics evidence doubled the success rate in clinical development.  Genomics and genetics also play an increasingly important role in other areas in drug discovery such as biomarker identification for drug efficacy and safety, understanding drug mechanisms of action, identifying alternative drug indications and anticipating on-target safety concerns, and selecting disease relevant experimental models. To facilitate the application of genomics in drug discovery, it is critical to systematically assess data quality and reproducibility, and to develop new methods and tools for integrative genomic data analysis.

 

Session topics

 

We would like to invite submissions to address the challenges and opportunities described above.  Examples of topics and problems within the scope of this session include but are not limited to:

 

·         Target identification and validation: integrative analysis of molecular data at scale: coupling genetic, epigenetic, gene expression profiling, proteomic, metabolomic, phenotypic trait measurements to disease diagnosis and clinical outcome data to generate hypotheses on molecular etiology of diseases in service of identification or validation of novel therapeutic targets.

·         Biomarker discovery: utilizing genetic and genomic data derived from cell lines, animal models, human disease tissues and PBMC to develop preclinical or clinical biomarkers for target engagement, pharmacodynamics, drug response, prognosis, and patient stratification; applying genomic profiling in clinical trials to identify early response markers to predict clinical end points.

·         Pharmacogenomics: identify associations between germline SNPs, somatic mutations, gene expression and other molecular alterations and drug responses.

·         Toxicogenomics: integrative analysis of genomic, histopathology, and clinical chemistry data to develop predictive toxicology biomarkers in preclinical 4-day, 14-day and 30-day studies and clinical studies.

·         Understanding drug mechanisms of action (MoA): applying genomic profiling to de-convolute targets and delineate MoA of non-selective drugs or drugs from phenotypic screening.

·         Characterization of mechanisms of acquired resistance: analysis of genetic and genomic data derived from preclinical isogenic models or clinical patient samples to study the mechanisms of acquired resistance.

·         Selection of disease-relevant experimental models: comparative analysis of genetic and genomic data to assess and select cell line and animal models in drug discovery that best represent the disease indications.

·         Developing drug combination strategies: analysis of genetic and genomic data to identify synthetic lethality genes as drug combination targets; computational analysis to understand gene regulatory networks to develop combination strategies that target parallel pathways or reverse drug resistance.

·         Drug repurposing: applying in silico approaches to identify new disease indications for existing drugs.

·         Novel methods and tools for multi-omics data integration, analyses, and visualization

 

Session co-chairs

 

Richard Bourgon, PhD, Genentech Inc. (bourgon.richard@gene.com)

Rick Dewey, MD, Regeneron Genetics Center (frederick.dewey@regeneron.com)

Zhengyan Kan, PhD, Pfizer Inc. (Zhengyan.Kan@pfizer.com)

Dan Li, PhD, Icahn School of Medicine at Mount Sinai (shuyudan.li@mssm.edu)

 

Primary contact: shuyudan.li@mssm.edu

 

Submission Information

 

Please note that the submitted papers are reviewed and accepted on a competitive basis.  At least three reviewers will be assigned to each submitted manuscript.

 

Important Dates

 

·         Paper submissions due: August 1, 2017

·         Notification of paper acceptance: September 11, 2017

·         Camera-ready final paper due: October 2, 2017

·         Deadline for poster abstracts: November 13, 2017

 

Paper Format

 

Please see the PSB paper format template and instructions at http://psb.stanford.edu/psb-online/psb-submit.

 

The file formats we accept are: postscript (*.ps) and Adobe Acrobat (*.pdf).  Attached files should be named with the last name of the first author (e.g. altman.ps or altman.pdf).  Hardcopy submissions or unprocessed TeX or LaTeX files will be rejected without review.

 

Each paper must be accompanied by a cover letter. The cover letter must state the following:

 

·         The email address of the corresponding author.

·         The specific PSB session that should review the paper or abstract.

·         The submitted paper contains original, unpublished results, and is not currently under consideration elsewhere.

·         All co-authors concur with the contents of the paper.

 

Submitted papers are limited to twelve (12) pages in our publication format. Please format your paper according to instructions found at http://psb.stanford.edu/psb-online/psb-submit/.  If figures cannot be easily resized and placed precisely in the text, then it should be clear that with appropriate modifications, the total manuscript length would be within the page limit.