Click
here to printTrends in Cell Biology: ddn Interview with Dr. V. Jo Davisson
November 2011
SHARING OPTIONS:
As many of our readers gear up
for the American Society for Cell Biology’s (ASCB) annual meeting in December
in Denver, the attention of laboratory researchers is turning to the discovery of the molecular basis
for specificity in biological systems and the use of this information in drug
discovery and development. Many current research projects are focused on developing
and implementing new methods and technologies to measure and quantify the
dynamics of biological systems. Tools are deployed in approaches to define markers
of disease, understand drug mechanisms of action and discover new drugs. These
experimental approaches rely on a variety of analytical, chemical, genetic and
biophysical methodologies.
For this special feature on
trends in cell biology, ddn turned to
an expert in the field to identify some of the themes dominating this growing
research area: Dr. V. Jo Davisson, professor of medicinal chemistry and
molecular pharmacology in Purdue University’s College of Pharmacy. As a
professor, Davisson specializes in natural product drugs, chemical biology and
bionanotechnology. He has had numerous studies published, most recently in the Journal of Proteome Research, Advanced
Synthesis and Catalysis and the Journal
of the American Chemical Society. Davisson received a B.A. degree from
Wittenberg University in 1978, an M.S. degree from the Indiana University
School of Medicine in 1983 and a Ph.D. from the University of Utah in 1988.
ddn: What have been some of the most important molecular/cell
biology advancements in the last five years?
Davisson: Significant
improvements have been made with single-cell analysis technologies, including
cytometry and imaging. The capacity to conduct higher-throughput data
collection and analysis, coupled with improved molecular technologies, is
changing the way the cell models can be more accurately quantified. The
hardware capabilities have been advanced for some time now, and certainly major
advances in this area have been available. Now, the integration with the
molecular/’omics content has made the analysis of gene-gene interactions,
specific protein content alterations and even genome-wide analyses connect
better with functional consequences to cell phenotypes. There is some notion of
the cell being the primary unit to describe molecular content, and therefore
the term “cytomics” might well be applied. The capacity to conduct these types
of studies in a higher-throughput format opens many additional avenues for
applications in drug discovery.
ddn: Of these, which advancements have changed the way you
personally perform research?
Davisson: The
cytometry-based measurement tools combined with higher-content data analysis.
ddn: In what ways are molecular/cell biology research efforts
impacting the way certain drug discovery activities (e.g., problem identification, early discovery, lead optimization
and preclinical development) are being organized and executed?
Davisson: There
are now multiple complementary approaches to address questions of function and
consequences of protein or gene alterations. This activity has played a major
role in the process of target validation, and will likely continue to grow with
the advent of newer cell technology platforms that allow cross-validation in
differing biological or disease contexts.
There is evidence that the increased capacity and
capabilities of cellular technologies will provide improved approaches for hit
de-replication screens. There is continued importance in the capability to have
cost-effective cell models that can provide insights of risk. Screens using
higher-content information can inform benefits as well as provide early
indications of the drug-like properties of new molecular entities. Therefore,
there is growth in the utility of using multiple cellular models for the
process of hit-to-lead definition and further lead optimization.
ddn: How has the systems biology approach impacted the way drug
discovery is performed?
The throughput, analytical tools and knowledge bases for
interpretation of phenotypic changes in cell response to chemical action have
promoted a return to cell-based screens for hit definition as well. A traditional
operational paradigm for hit identification has been the use of biomolecular
screens and assays. An alternative is now defined by cell-based or even model
organism based phenotypic screens; this is perhaps a re-invention of hit
identification.
The capacity to define meaningful outcomes using cell-based
phenotypic approaches for drug discovery is nicely illustrated by recent
successes of the Eli Lilly Phenotypic Drug Discovery Program. In this context,
the in-vitro models for discovery and
early-stage development have grown in favor of using cell-based systems.
Davisson: The
impact so far on early discovery has been slow coming, but it is beginning to
cast a mold for new targets to be discovered and completed. There are
increasing genomic data resources and bioinformatics tools to mine and generate
hypotheses regarding the roles of targeted and improved strategies for
intervention. The area of more immediate growth has been the impact on
later-stage preclinical development, where the capacity to make predictions
about response will lead to personalized Rx/Dx.
Davisson: I think
there is opportunity here, but these are not yet standard operations and not
likely to fit the traditional CRO models of business. It is more than likely an
area where industrial-academic collaboration has the highest impact.
ddn: How closely is academia working with industry in cellular
research? Is one party currently more influential than the other in early-phase
drug discovery? How can we achieve the best of both worlds?
Davisson: I think
there are some natural hesitations to over-invest in the areas.
First-generation versions of the cell analysis tools used in early discovery
and optimization were poorly defined and often closed systems. Most of these
tools were not well-suited for the pre-defined pipeline models in pharma. Also,
the intensity of biocomputation and the degree of underdeveloped methodologies
led to several levels of disappointment. The complexity of data and the lack of
simple interoperability, and in several cases, the lack of standards, have made
the more uniform adoption of cellular analysis tools slow in discovery phase.
The trends have changed significantly in the last five years as more academic
and industrial groups conduct research in the process of discovery and
classification of biological phenotypes in the high-content screening world. A
greater appreciation of the ability of single-cell analysis tools to quantify
population effects and complexity is enabling these approaches to gain
traction.
ddn: How common is the outsourcing of cellular research-based
activities to contract research organizations (CROs)?
ddn: Personalized medicine and companion diagnostics are very hot
topics these days. How are they related to cell biology, and how might they
change the current paradigm of drug discovery in the next decade?
Davisson: I think
this is where the systems biology perspective will likely have the largest
impact on applications on discovery and development. As I have stated, being
able to interrogate high-content molecular data, and in combination with high-content
cellular data, enables functional correlation of specific signatures. These
signatures have the potential for translation to the clinic, which is
especially key for the personalized medicine concepts when considering
pharmacotherapies. This means those signatures are in effect related to markers
of effect and can aid in defining predictable outcomes of individual patient
response.
ddn: Are there any technology/tool shortcomings or challenges that
are holding back cellular research? What can we do to overcome them?
Davisson: These
approaches are still nascent, but growing at a higher rate. The capacity to
deal with multi-dimensional and multi-parametric data structures is a
fundamental limitation in the field. Object-oriented approaches and the
computational tools to create meaningful statistical models that reveal drug
effects on cellular systems is an area that will certainly offer a way to
overcome some of these core challenges.
Code: E111133
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