Trends in Cell Biology: ddn Interview with Dr. V. Jo Davisson

Expert in the cell biology field identifies some of the themes dominating this growing research area

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As many of our readers gear upfor the American Society for Cell Biology's (ASCB) annual meeting in Decemberin Denver, the attention of laboratory researchers is turning to the discovery of the molecular basisfor specificity in biological systems and the use of this information in drugdiscovery and development. Many current research projects are focused on developingand implementing new methods and technologies to measure and quantify thedynamics of biological systems. Tools are deployed in approaches to define markersof disease, understand drug mechanisms of action and discover new drugs. Theseexperimental approaches rely on a variety of analytical, chemical, genetic andbiophysical methodologies.
 
For this special feature ontrends in cell biology, ddn turned toan expert in the field to identify some of the themes dominating this growingresearch area: Dr. V. Jo Davisson, professor of medicinal chemistry andmolecular pharmacology in Purdue University's College of Pharmacy. As aprofessor, Davisson specializes in natural product drugs, chemical biology andbionanotechnology. He has had numerous studies published, most recently in the Journal of Proteome Research, AdvancedSynthesis and Catalysis and the Journalof the American Chemical Society. Davisson received a B.A. degree fromWittenberg University in 1978, an M.S. degree from the Indiana UniversitySchool 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/cellbiology advancements in the last five years?
 
Davisson: Significantimprovements have been made with single-cell analysis technologies, includingcytometry and imaging. The capacity to conduct higher-throughput datacollection and analysis, coupled with improved molecular technologies, ischanging the way the cell models can be more accurately quantified. Thehardware capabilities have been advanced for some time now, and certainly majoradvances in this area have been available. Now, the integration with themolecular/'omics content has made the analysis of gene-gene interactions,specific protein content alterations and even genome-wide analyses connectbetter with functional consequences to cell phenotypes. There is some notion ofthe cell being the primary unit to describe molecular content, and thereforethe term "cytomics" might well be applied. The capacity to conduct these typesof studies in a higher-throughput format opens many additional avenues forapplications in drug discovery.
 
ddn: Of these, which advancements have changed the way youpersonally perform research?
 
Davisson: Thecytometry-based measurement tools combined with higher-content data analysis.
 
ddn: In what ways are molecular/cell biology research effortsimpacting the way certain drug discovery activities (e.g., problem identification, early discovery, lead optimizationand preclinical development) are being organized and executed?
 
Davisson: Thereare now multiple complementary approaches to address questions of function andconsequences of protein or gene alterations. This activity has played a majorrole in the process of target validation, and will likely continue to grow withthe advent of newer cell technology platforms that allow cross-validation indiffering biological or disease contexts.
There is evidence that the increased capacity andcapabilities of cellular technologies will provide improved approaches for hitde-replication screens. There is continued importance in the capability to havecost-effective cell models that can provide insights of risk. Screens usinghigher-content information can inform benefits as well as provide earlyindications of the drug-like properties of new molecular entities. Therefore,there is growth in the utility of using multiple cellular models for theprocess of hit-to-lead definition and further lead optimization.
 
The throughput, analytical tools and knowledge bases forinterpretation of phenotypic changes in cell response to chemical action havepromoted a return to cell-based screens for hit definition as well. A traditionaloperational paradigm for hit identification has been the use of biomolecularscreens and assays. An alternative is now defined by cell-based or even modelorganism based phenotypic screens; this is perhaps a re-invention of hitidentification.
 
The capacity to define meaningful outcomes using cell-basedphenotypic approaches for drug discovery is nicely illustrated by recentsuccesses of the Eli Lilly Phenotypic Drug Discovery Program. In this context,the in-vitro models for discovery andearly-stage development have grown in favor of using cell-based systems.
 
ddn: How has the systems biology approach impacted the way drugdiscovery is performed?
 
Davisson: Theimpact so far on early discovery has been slow coming, but it is beginning tocast a mold for new targets to be discovered and completed. There areincreasing genomic data resources and bioinformatics tools to mine and generatehypotheses regarding the roles of targeted and improved strategies forintervention. The area of more immediate growth has been the impact onlater-stage preclinical development, where the capacity to make predictionsabout response will lead to personalized Rx/Dx.
 
ddn: How closely is academia working with industry in cellularresearch? Is one party currently more influential than the other in early-phasedrug discovery? How can we achieve the best of both worlds?
 
Davisson: I thinkthere are some natural hesitations to over-invest in the areas.First-generation versions of the cell analysis tools used in early discoveryand optimization were poorly defined and often closed systems. Most of thesetools were not well-suited for the pre-defined pipeline models in pharma. Also,the intensity of biocomputation and the degree of underdeveloped methodologiesled to several levels of disappointment. The complexity of data and the lack ofsimple interoperability, and in several cases, the lack of standards, have madethe more uniform adoption of cellular analysis tools slow in discovery phase.The trends have changed significantly in the last five years as more academicand industrial groups conduct research in the process of discovery andclassification of biological phenotypes in the high-content screening world. Agreater appreciation of the ability of single-cell analysis tools to quantifypopulation effects and complexity is enabling these approaches to gaintraction.
 
ddn: How common is the outsourcing of cellular research-basedactivities to contract research organizations (CROs)?
 
Davisson: I thinkthere is opportunity here, but these are not yet standard operations and notlikely to fit the traditional CRO models of business. It is more than likely anarea where industrial-academic collaboration has the highest impact.
 
ddn: Personalized medicine and companion diagnostics are very hottopics these days. How are they related to cell biology, and how might theychange the current paradigm of drug discovery in the next decade?
 
Davisson: I thinkthis is where the systems biology perspective will likely have the largestimpact on applications on discovery and development. As I have stated, beingable to interrogate high-content molecular data, and in combination with high-contentcellular data, enables functional correlation of specific signatures. Thesesignatures have the potential for translation to the clinic, which isespecially key for the personalized medicine concepts when consideringpharmacotherapies. This means those signatures are in effect related to markersof effect and can aid in defining predictable outcomes of individual patientresponse.
 
ddn: Are there any technology/tool shortcomings or challenges thatare holding back cellular research? What can we do to overcome them?
 
Davisson: Theseapproaches are still nascent, but growing at a higher rate. The capacity todeal with multi-dimensional and multi-parametric data structures is afundamental limitation in the field. Object-oriented approaches and thecomputational tools to create meaningful statistical models that reveal drugeffects on cellular systems is an area that will certainly offer a way toovercome some of these core challenges.



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