首页|Researchers from Dana-Farber Cancer Institute Discuss Findings in Robotics (Automated Radiolabeling and Handling of 177lu- and 225ac-psma-617 Using a Robotic Pipettor)

Researchers from Dana-Farber Cancer Institute Discuss Findings in Robotics (Automated Radiolabeling and Handling of 177lu- and 225ac-psma-617 Using a Robotic Pipettor)

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Investigators publish new report on Robotics. According to news originating from Boston, Massachusetts, by NewsRx correspondents, research stated, “While automated modules for F- 18 and C-11 radiosyntheses are standardized with features such as multiple reactors, vacuum connection and semi-preparative HPLC, labeling and processing of compounds with radiometals such as Zr-89, Lu- 177 and Ac-225 often do not require complex manipulations and are frequently performed manually by a radiochemist. These procedures typically involve transferring solutions to and from vials using pipettes followed by heating of the reaction mixture, and do not require all the features found in most commercial automated synthesis units marketed as F-18 or C-11 modules.” Our news journalists obtained a quote from the research from Dana-Farber Cancer Institute, “Here we present an efficient automated method for performing radiosyntheses involving radiometals by adapting a commercially available robotic pipettor originally developed for high-throughput processing of biological samples. While a robotic pipettor is less costly than a radiosynthesis module, it holds many similar advantages over manual radiosynthesis such as minimization of operator error, lower operator exposure rates, and abbreviated synthesis times, among others. To demonstrate the feasibility of using the OpenTrons OT-2 robotic pipettor to perform automated radiosyntheses, we radiolabeled and formulated (177) Lu- PSMA-617 and (225) Ac-PSMA-617 on the system.”

BostonMassachusettsUnited StatesNorth and Central AmericaEmerging TechnologiesMachine LearningRoboticsRobotsDana-Farber Cancer Institute

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.4)
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