Pfizer scientist wins Select Science award for oncology research video

A Pfizer scientist working in oncology research received the Select Science Award for Video of the Year, recognizing work on cancer research innovation and the role of analytical science in drug development. Accepting the award, the scientist emphasized the urgency of oncology research, noting that cancer rates are rising and affecting younger populations—a fact that has personal resonance, as the scientist has family members battling or lost to cancer.

The core message of the award-winning work centers on the complementary relationship between human expertise and machine intelligence. While machine learning and AI tools are transforming the speed and ease of data analysis, the scientist stressed that these technologies cannot substitute for high-quality data acquisition and rigorous human analysis. As the field moves into a more AI-oriented environment, the quality of data and the quality of analysis remain non-negotiable foundations for meaningful research outcomes.

The recognition also highlights a long-standing industrial partnership. Shimadzu, a supplier of analytical instrumentation, has collaborated with Pfizer's San Diego laboratory for 12–13 years. Over the past five to six years, that partnership has intensified around advanced analytical techniques—specifically newer approaches to chromatography and SFC (supercritical fluid chromatography). Direct collaboration between Pfizer researchers and Shimadzu engineers, including feedback loops that extend back to Japan, has shaped the development of analytical software and methodologies that support Pfizer's research pipeline. The Select Science Award recognized the value of this partnership and shared the achievement across all three parties: Shimadzu, Pfizer, and Select Science itself.

The award reflects a broader shift in how pharmaceutical research balances technological innovation with fundamental scientific rigor. The Pfizer scientist's remarks underscore a critical tension in modern drug development: as AI and machine learning tools proliferate, the bottleneck is not computation but data quality. This insight has practical implications for companies like Shimadzu, whose analytical instrumentation (chromatography and SFC systems) remains essential to generating the high-quality datasets that train and validate AI models in drug discovery and development.

The 12–13 year partnership between Pfizer and Shimadzu, intensifying over the past five to six years, suggests that as pharma companies invest in AI-driven research pipelines, they simultaneously deepen investments in upstream analytical infrastructure. For oncology specifically—where cancer incidence and age of onset are both rising—this combination of rigorous data acquisition and computational acceleration may be critical to accelerating the discovery and validation of new treatments.

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