Track 6 Systems Pharmacology: Pathways to Patient Response @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA
Reporter: Aviva Lev-Ari, PhD, RN
April 30, 2014
Modeling: Novel Tools
10:50 Chairperson’s Remarks
Avi Ma’ayan, Ph.D., Associate Professor, Pharmacology and Systems
Therapeutics, Icahn School of Medicine at Mount Sinai
11:00 The Human Avatar: Quantitative Systems Pharmacology to Support Physician Decision Making in Neurology and Psychiatry
Hugo Geerts, Ph.D., MBA, BA, CSO, In Silico Biosciences;
Adjunct Associate Professor, Perelman School of Medicine, University of Pennsylvania
CNS Quantitative Systems Pharmacology uses computer-based mechanistic modeling integrating brain network neurophysiology, functional imaging of
genetics, pharmacology of drug-receptor interactions and parameterization with clinical data. A patient model (“human avatar”) can be developed
accounting for polypharmacy and life history of traumatic events to help identify optimal treatments.
11:30 VisANT: An Integrative Network Platform to Connect Genes, Drugs, Diseases and Therapies
Zhenjun Hu, Ph.D., Research Associate Professor, Center for Advanced Genomic Technology,
Bioinformatics Program, Boston University
With the rapid accumulation of our knowledge on diseases, disease-related genes and drug targets, network-based analysis plays an increasingly
important role in systems biology, systems pharmacology and translational science. The new release of VisANT aims to provide new functions to facilitate
the convenient network analysis of diseases, therapies, genes and drugs.
12:00 pm Selected Oral Poster Presentation: Individualized PK/PD Biosimulations for Precision Drug Dosing: Diabetes Mellitus
Clyde Phelix, Ph.D., Associate Professor, Biology,
University of Texas San Antonio
Individualized biosimulations offer many advantages to precision medicine. Using one’s transcriptome to determine parameters of kinetic models of metabolism reanimates that individual for in silico testing. The Transcriptome-To-Metabolome™ Model is multiorgan and multicompartmental, including over 30 primary and secondary metabolic pathways and transport processes. Thus pharmacokinetics/pharmacodynamics studies can be performed in silico before treating each patient.
12:40 Luncheon Presentations (Sponsorship Opportunities Available) or Lunch on Your Own
Modeling: Cancer
1:50 Chairperson’s Remarks
Hugo Geerts, Ph.D., MBA, BA, CSO, In Silico Biosciences; Adjunct Associate Professor, Perelman School of Medicine, University of Pennsylvania
In REAL TIME
»»1:55 FEATURED PRESENTATION
Identifying Drug Targets from Drug-Induced Changes in Genome-Wide mRNA Expression
Avi Ma’ayan, Ph.D., Associate Professor, Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai
We collected and organized publicly available genome-wide gene expression data where hundreds of drugs were used to treat mammalian cells and changes in expression were compared to a control. We then developed computational methods that try to find the drug targets from the expression changes. We show that different steps in the analysis can contribute to approaching the right answer.
In REAL TIME
System biology and drug related by phynotypes, drugs causes diseasespatient and side effects
Networs,
Gene-set Libraries stored in Gene Matrix Transpose(GMT) files, KEGG Example
Drug-set Libraries
Drug-Drug similarity data, SIDER 2 Side Effect Resource, FDA adverse effect Report data
Connactivity Map: Broad Institute, L1000 cell lines microarray, different drug dose, DRUG effect on GENES
- develop new compondts,
- measure toxicity
LINC-L1000 data overview, Drug-drug similarity structure, connversion
for Vector side effect
LINCS Canvas Browser
Cell-Line/Drug Browser
New method for clustering patient by outcomes, survival analysis
http://www/maayanlab.net/LINCS/LCB/
Drug interact with target drug vs transcription factors, over expression
Over expression of transcription factors vs knock out for validation
2:25 Infrastructure for Comparison of Systematically Generated Cancer Networks vs. Literature Models
Dexter Pratt, Project Director,
NDEx, Cytoscape Consortium
Cancer subtype genetic networks can be generated by systematic analysis of patient somatic mutation data. Comparison to existing models of cancer
mechanisms is an important step in investigating these data-derived models. Recent work on Network Based Stratification (NBS) at the Ideker Lab will be
described along with tools for network comparison under development in the NDEx project.
In REAL TIME
Network based classification, unsupervised methoods
Ovarian cancer- sparse mutations, no two patients share same mutation, clustering by expression profile – can be cause, gene – gene interaction, smooth knowlede,
Reference networks, Common Entity identification system used, started at UCSD. overlap of curated PATHWAYS, query, neighborhoods in the reference network,
Using mapping tables to mapp identifiers for entity correspondence
Complex Reference Networks N:1 and 1:N
Transcriptionalcontrol motif, extract motifs mapp data to motifs, concordence, and other metrics to be computed fromreferenced data,
Boundaries of Pathways – Reaction chain, Differentially expressed genes –>> enzymes –>>> reactions (differentilly regulated) –>> smaoll molecules
CONCLUTIONS
Cliniccal relevance, hypothesis motifs and interactions.
MAY 1, 2014
Modeling: Drug/Dose Response
1:55 Chairperson’s Remarks
Birgit Schoeberl, Ph.D., Vice President, Research, Merrimack Pharmaceuticals
»»2:00 FEATURED PRESENTATION
Systems Approaches to Risk Assessment
Lawrence J. Lesko, Ph.D., FCP, Clinical Professor and Director, Center for Pharmacometrics and Systems Pharmacology, University of Florida
“Idiosyncratic” adverse drug events (ADEs) are a substantial societal burden in terms of morbidity, mortality and healthcare costs. Predicting who
will suffer ADEs from what medications is extremely difficult with current observational or surveillance approaches. A new mechanistic approach to
drug safety science is sorely needed. Systems approaches may address this unmet medical need.
2:30 Pharmacodynamic Characterization of Compounds in Drug Discovery
Rui-Ru Ji, Ph.D., Principal Scientist, Genomics, Bristol-Myers Squibb
The transcriptome reacts in a dose-dependent manner to compound treatment. We will present methodology and will discuss multiple applications of dose
response profiling of the whole transcriptome.
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