• An efficient method for the detection and elimination of systematic error in high-throughput screening

High-throughput screening (HTS) is an early-stage process in drug discovery which allows thousands of chemical compounds to be tested in a single study. We report a method for correcting HTS data prior to the hit selection process (i.e. selection of active compounds). The proposed correction minimizes the impact of systematic errors which may affect the hit selection in HTS. The introduced method, called a well correction, proceeds by correcting the distribution of measurements within wells of a given HTS assay. We use simulated and experimental data to illustrate the advantages of the new method compared to other widely-used methods of data correction and hit selection in HTS.

Source: Bioinformatics 2007 23(13):1648-1657; doi:10.1093/ bioinformatics/btm145

  • Data integration: challenges for drug discovery

The integration of data from many sources, especially new 'omic' platforms, is increasingly challenging not just because of increasing volume of data but because these data are highly diverse, expecially in a drug discovery enterprise that inherently draws on many disparate types of data.  The rise of the 'omics' disciplines means that data-driven research will need to be integrated with hypothesis-driven methods, as well as experimental with observational studies, and in general data integration will have cultural consequences, because it combines results from different scientific disciplines.

Even seemingly homogeneous data create integration challenges, beginning with cross-platform normalization, meta-analysis methods, multiple testing issues and new logical and statistical complexities that only increase with greater data heterogeneity. The drug discovery enterprise, extending from molecules to human populations, depends on the combination of much more heterogenous data as well, and this carries its own challenges in terms of bridging widely varying scales and contexts. Integration increases the dimensionality of data, both in terms of its arity or number of attributes, and in terms of its degree of connectivity; both are known to place stresses on data management, visualization and algorithmic analysis.

Scientific data integration entails making choices about the representation of data (for example, tables, matrices or graphs), each of which affords different integration and analysis techniques, and also requires careful attention to the syntax (format) and semantics (meaning) of the data; the latter can require sophisticated knowledge representation tools called ontologies. Business data integration places more emphasis on decision support and on processes surrounding portfolio progression, which can be captured in a form of semantics called business rules.

Data mining is a form of integrative query that is highly exploratory and stresses pattern recognition and clue generation from heterogenous data sources, often presented on the Web.

Source: Nature Reviews Drug Discovery 4, 45-58 (2005); doi:10.1038/nrd1608

  • A New Predictive Toxicity Test

A new test for toxicity may save researchers time and money, and allow a compound’s genetic toxicity to be tested much earlier in the drug discovery process. Like the current FDA-approved toxicity test, the new test measures DNA damage in mouse red blood cells. The current test relies on injecting a compound into a live mouse and observing its effects on red blood cells produced by bone marrow. The new test looks for similar red blood cell damage, but in in vitro marrow cells. The results were published in May in Proceedings of the National Academy of Sciences.  

  • A new method of controlling bacterial infections including MRSA

Scientists have developed a method of embedding bacteriophages - naturally occurring viruses that prey on bacteria - into physical materials such as cotton, silk and polythene to prevent 'superbug' infection. The immobilised and stabilised bacteriophages can be used for a wide range of applications, from clinical use in hospitals to packaging for the food industry. In addition, the University has developed a technique of creating bacteriophage-carrying nanospheres, which can be introduced through injection into the body to treat systemic infection. More information on University of Strathclyde

  • Identifying Drug Resistance Patterns and Expanding the Druggable Target Space

Researchers are combining a range of techniques for analyzing key cell signaling pathways in drug discovery and therapeutic development, according to presentations at Cambridge Healthtech’s “Molecular Medicine Tri Conference” in San Francisco and a Keystone Symposium on “PI 3-Kinase Signaling Pathways in Disease”, held in Santa Fe. Investigators described using cell-signaling pathway analysis to characterize drug-resistance patterns in cancer cells, identify key signaling pathways impacted by specific drug treatments, and expand the druggable target space.

A common theme in the presentations was the application of molecular analysis approaches combining several technologies, such as gene array studies coupled with protein expression and the use of computer programs and computational databases to analyze the results.

Several investigators stressed the need to go beyond single-target-based drug discovery, such as cell-surface-receptor drug targets, and identify key downstream proteins involved in the pathway responses to drug-target interactions.

  • Imaging and Markers: A Pfizer Approach

One of the most obvious trends in modern drug discovery is the explosion of new imaging approaches.  In particular, these are being put to great use in oncology research.  Just a couple of years ago, Pfizer researchers demonstrated a startling example of such use in studies looking at the effects of a new drug called Sutent (sunitinib malate) in a small human trial. What was so surprising about these studies wasn't just that they quickly and efficiently proved the drug was working. Rather, it was that the studies demonstrated an unconventional marker — tumor metabolism rate instead of the typical marker of tumor size. At the time, one of the questions dogging Sutent was that the drug did not seem to shrink tumors in patients.  However, animal studies had suggested it had great promise. 

The scans showed that just days after starting Sutent therapy, glucose metabolism plummeted in the tumors, despite the fact that they remained about the same size.  The tumors were indeed being attacked by the drug, even if they weren't shrinking. The drug went on to become the first oncology product to be simultaneously approved by FDA for two indications — GIST and advanced kidney cancer.