Team:Manchester/HP/Gold
HP Gold
Throughout our project we addressed a variety of Human Practices issues and took into account each stakeholder’s knowledge, experience and opinion to direct our project design. All experimental decisions were guided by intense interactions with the public and important stakeholders interested in raising alcohol awareness. This has enabled the AlcoPatch to be refined, redesigned and progress in cohesion with the public needs and interests. We strived to make sure the insights of potential beneficiaries directed our project throughout the duration of the journey the AlcoPatch has taken.
Please take a look at our human practices page for more examples of the interwoven theme of human practices throughout our project.
iGEM Human Practices Gold Criteria 2016:
"Demonstrate how you have integrated the investigated issues into the design and/or execution of your project."
Three issues were identified as recurrent themes in our interactions with various stakeholders:
- Our product would have to be cheap, robust and light-weight. For example, our debates with the local police highlighted that currently only a select number of officers carry breathalysers due to their weight and cost. This suggests our AlcoPatch could be a great tool, every police officer could have.
This information lead to our cost analysis of the AlcoPatch, where the modelling team identified the optimal enzyme and dye concentrations that would yield the lowest cost for a robust performance of the detector for our cell-free mechanism. - The Second major design requirement identified by the stakeholder interactions was the need for a very rapid read-out. The police officers emphasized the need for a decision within minutes, and our industrial contacts, such as the Technology Officer from SCRAM Systems , confirmed that rapid responses would be essential for a marketable device. Also, the CEO of FredSense concurred: “Any tool which can accurately read out someone’s blood ethanol concentration would be useful. However, these tests would need to be very quick…”
To address this point, we ran a detailed modelling analysis, including real experimental data from our pilot experiment to determine the time it would take for the colour to appear under real-world conditions, such as realistic sweat-alcohol concentrations. The insights gained from this modelling exercise informed us of optimal concentrations for the final design of our AlcoPatch device. Accounting for the police requirements minimising the cost of the device. - Alternative sensors for diverse sweat compounds beyond alcohol would dramatically increase the economic viability of our product. Our project featured twice in the local newspaper Manchester Evening News, and the comments left by readers showed that there might be a market for AlcoPatch as a Personal Intoxication Awareness Tool, but it might be relatively limited (one reader said quite clearly “I never wanted to know how drunk I was when I used to gulp down fourteen pints”). This aligned with comments from Dr Smith at the iGEM UK Meet Up, who suggested that our target market should be ‘people who don’t want to get drunk’, which also an issue that came up in our interaction with Alcoholics Anonymous and the Fetal Alcohol Clinic. It was therefore quite inspiring when, at the Microbiology Society Annual Conference, it was suggested that we also try to test for other metabolites in sweat. As our discussion with the Manchester Enterprise Centre and IP Attorney from Ward Hadaway and Venner Shipley had shown the limited patent opportunities for our AlcoPatch technology we were further strengthened in our conviction that a broader market in the health sector would probably be essential to make our device economically viable.
We took note of this and decided to test whether our Cell-free AlcoPatch design could be modified to detect sweat glucose concentrations, for example in diabetes patients, who need to monitor their glucose levels.We tested this in a proof-of-concept experiment for the Cell-free Mechanism. The experiments were highly successful and generated a large amount of data, which we in turned could use to improve and validate our model of our AlcoPatch.
