Ravi, I assume the laboratories you work with have been doing laboratory-developed-type tests for many years and have no objection to validating a test in their laboratory and using their own studies.
Ravi Gupta: It is a research-use only product. Many labs have been doing this for a while and will do their due diligence, validations, and the work needed to create the evidence to support their use of the product.
Poulomi, what role does the new system and PharmacoPro plate play in overcoming the long-existing barriers to the use of pharmacogenomics in precision medicine?
Dr. Acharya: The uptake of pharmacogenomics has been slow, especially for preemptive pharmacogenomics. There have been a couple of deterrents to adoption—a clunky workflow that takes multiple days and the possibility of introducing manual error and sample contamination owing to many instrument handoff points. This workflow eliminates that, and a medical laboratory scientist can be trained to do this; it doesn’t require a PhD-level operator.
The clinical relevance of the guideline-driven content and performance of the PharmacoPro array will enable anyone who wants to start a pharmacogenomics program to say, I can adopt this workflow and have results in 30 hours to my patient or pharmacist. Modernizing the workflow, reducing touchpoints, and having the state-of-the-art content with high performance and dependable results will help push pharmacogenomics forward.
Our mission is to make the world healthier and safer, and we aim to do that with this pharmacogenomics solution. A hospital or academic medical center will see health economic benefits from adopting this type of solution and avoiding unnecessary incorrect dosing and possible adverse reactions.
Dr. Balog: On the technical side, this array offers varied content—single nucleotide variant content, copy number content—in one workflow, rather than labs having to divert samples to two different technologies to capture both variant types.
Many times customers were making a trade-off between only looking at dozens of variants or, if they wanted to go to NGS, needing to look at and aggregate many samples to make the run cost-effective. We’ve found the sweet spot in offering all the clinical required content and much of the up-and-coming research content without needing to aggregate a large amount of samples to do a run.
Ravi, can you comment on how this offering will help lead to adoption?
Ravi Gupta: Pharmacogenomics hasn’t stalled because the science is weak. The workflow is hard, and that includes turnaround time, reimbursement, and getting results into clinical decision-making. With SwiftArrayStudio, the automation and standardized reporting will start to close the gap from the start time to decision time, which will help accelerate pharmacogenomics adoption.
Final thoughts?
Dr. Balog: Our previous workflows and instruments focused on discovery and research labs. We’ve created an instrument that focuses on the clinical implementation of discoveries from the past few decades. It’s using the same robust technology, but now it can be implemented in an industrial-type setting.
Dr. Acharya: Microarrays are going to the next level with this scalable solution, from low-throughput to high-end labs with a highly standardized protocol that doesn’t require people to reengineer every time. Microarrays are here to stay. They enable scientists and clinicians to get a large amount of data cost-effectively and quickly, whether for research, translational work, or clinical applications.
We have future-proofed this instrument to run not just genomics but eventually multiomics. We hope to take this same platform, with the familiarity of the workflow, and extend it beyond genomics into proteomics to fuel better discoveries and a greater understanding of biology. We are here to power the future of genomics and future understanding of biology and its clinical application.
Ravi Gupta: The future of omics isn’t about adding instruments; it’s generating integrated insights faster and more efficiently. The pressure labs feel today is real. They need to accelerate genetic analysis, reduce bottlenecks, and enable rapid, large-scale discovery, but they can’t afford to add instruments and increase handoffs and operational complexity. Thermo Fisher Scientific’s SwiftArrayStudio allows a lab to scale genomic data production without increasing complexity. As demands grow for faster, more connected insights, scalable and standardized genotyping will remain foundational. The next era of omics will not tolerate slower workflows. It demands speed, scale, and simplicity.