Optimisation and development for clinical diagnostics tool | Sagentia Innovation
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Module optimisation and development for clinical diagnostics instrument

A global diagnostics company wanted to develop their next instrument. They asked Sagentia to perform proof of concept studies and product development for some of the key modules within their instrument. The project allowed the client to meet tight deadlines and to improve the performance and repeatability of the instrument.

Clinical-diagnistics-instrument

Challenge

The client wanted to work with an external partner to enable them to bring their launch timeline forward and in particular, they needed someone that could combine theoretical modeling, product design, prototyping, and testing. They chose Sagentia due to our combined fundamental science, technology, and product development capabilities.

Approach

The first stage of the project involved mathematical modeling and applied physics, optimizing and redesigning the fluidics and magnetics in their instrument to enable them to improve the efficiency and reduce variability.

We validated our mathematical model through proof-of-principle and analytical testing and demonstrated that we had improved both the signal strength and co-efficient of variation. Through detailed understanding of the scientific principles influencing the system, we provided a sound basis for further design.

Working seamlessly with the client’s project team, we then undertook detailed design and prototyping of two of the instrument’s modules to enable the client to carry out systems integration and testing in their own labs. The project was conducted to ISO13485 and rigorous documentation was developed, compatible with the client’s quality management system. Following analytical testing, by the client, the system will move forward into prototypes for production.

Benefits

By working with Sagentia, the client has met tight timelines and is now able to meet their target market launch for the system. The project has also resulted in improved system performance in terms of reliability, repeatability and reduced variability.