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Trends in Cell Biology: ddn Interview with Dr. V. Jo Davisson
November 2011
by Amy Swinderman  |  Email the author


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.
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.
ddn: How has the systems biology approach impacted the way drug discovery is performed?
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.
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)?
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: 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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