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Jan 15, 2010 (Vol. 30, No. 2)

Laboratory Automation Circumvents Bottlenecks

Improvements in Speed, Data Integration, and Efficiency Are Expanding the Possibilities

  • The drive to produce more data, more quickly, and at less cost is fueling new strategies for lab automation, especially as applied to the drug discovery process. Overcoming bottlenecks of speed, efficiency, and data integration are among the topics to be discussed at “LabAutomation2010” later this month in Palm Springs.

    Flow cytometry has existed for more than 20 years and taken on a number of new capabilities. Scientists at Vivia Biotech have gone back to the simple roots of flow cytometry, in screening primary cells with antibodies, but they’ve magnified the scale and brought it into the realm of personalized medicine. “We wanted to develop assays that are closer to the target than traditional screening methods and by testing patient samples directly, this brings the technology closer to personalized medicine,” says Teresa Bennett, Ph.D., vp of research.

    Vivia Biotech has engineered a fully automated process for analyzing the impact of drugs on blood or bone marrow cells from patients with hematological malignancies. “We do re-profiling screening, looking for new indications of known drugs, and also screen the drugs for a particular indication to determine ex vivo which drugs a patient may be resistant or sensitive to,” Dr. Bennett reports.

    “By using specific markers for cells along with assessing apoptosis, we can evaluate both healthy and cancerous cells simultaneously. In a short span of about 48 hours, this approach allows for the ex vivo analysis of thousands of drugs or combinations of drugs on patient samples. Traditional analysis via flow cytometry can screen only 10–100 drugs per sample.”

    To accomplish this goal, the company automated the process from the beginning. It incorporated liquid handlers to prepare samples in tissue culture hoods and developed an automated flow-cytometry system to run the assay. To handle this much data also required development of proprietary software. “One important accomplishment is that we don’t have to analyze each individual well separately, we can use one file for the whole plate and this is analyzed within a few minutes. Ultimately, this approach could tell much more rapidly how a patient would respond to a drug regimen.”

    The company has already identified a new drug candidate and will proceed into Phase I/II trials in the fall of 2010. Additionally, a clinical study for personalized medicine testing will begin in early 2010.

  • Genotype-Correlated Drug Sensitivity

    Click Image To Enlarge +
    Researchers at Harvard Medical School are making headway with a strategy that evaluates anticancer agents to uncover genotypes that confer sensitivity.

    Traditional methods of anticancer drug discovery, development, and approval have generally followed a tissue-centric approach wherein the organ from which the tumor originated has preeminence. An alternative strategy gaining headway places the genotype of the cancer cell center stage and evaluates anticancer agents to uncover genotypes that confer sensitivity.

    “This would allow for the stratification of patients for treatment with a particular drug based on their genotype without regard to the tissue from which the tumor originated,” says Sreenath Sharma, Ph.D., assistant professor of medicine at Harvard Medical School and assistant director of Molecular Therapeutics at Massachusetts General Hospital (MGH) Cancer Center.

    “We use a collection of more than 1,000 genetically characterized human tumor-derived cell lines from different organs and assess their sensitivity to anticancer drugs that are in or about to enter the clinic,” Dr. Sharma explains. “This study aims to identify specific genotypes that confer sensitivity to particular anticancer drugs and use this information to profile and identify cancer patients most likely to benefit from treatment with the drug.”

    To work with so many cell lines and drugs requires automation. “We use lots of liquid-handling workstations for what we call pushing plastic, such as for adding drugs to multiwell plates and fixing and staining cells,” Dr. Sharma reports. “Analysis also requires software able to handle the volumes of data generated in order to create heat maps of drug sensitivity. What we can’t automate is the actual handling of cell lines. Each cell line has its own personality and properties, so here we need the human touch.”

    For the future, Dr. Sharma says, “One could fantasize that a cancer patient coming into the clinic, gets a tumor biopsy that is genotyped before anything else is done. Based on the genotype of the tumor, the patient is then treated with drugs that specifically target the mutated gene driving his/her tumor. This personalized approach would maximize the benefit from the treatment while at the same time minimize the side effects of the drug. In some current trials, only 10% of patients respond to therapy, indicating that a lot of individuals are getting treated unnecessarily.

    “At MGH with genotype-based patient preselection we can increase response rates from 10 to 80%, in some cases. The field is definitely headed in this direction as the paradigm changes from the less effective tissue-centric therapy to a more specific molecularly targeted treatment approach that is guided by the genotype of the patient’s tumor.”

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