Identifying sustainable compounds for use in consumer products
Identifying safer and more sustainable product ingredients which are both cost effective and easy to acquire can be a challenge within the consumer goods industry.
With the dramatic rise in antimicrobial resistance, companies are seeking to swap out compounds used in their products which are potential contributors to the rise of resistant bacteria.
Cataloguing all the appropriate data and identifying the right criteria for data validation are time consuming, resource heavy activities which often require specialist skills and can hamper enterprise organisations in the development of new and existing products.
A global consumer goods company was seeking to identify compounds which could provide a safer and more sustainable alternative to existing ingredients used in its household products.
However, the organisation not only needed to gather more relevant data to create an informed overview, but also needed to identify criteria which would enable selection of the most valuable data and in-turn reveal the relevant compounds.
This would then allow its researchers to carry out further analysis on compounds with a drastically improved chance of success.
The enterprise approached Eagle Genomics for help. After reviewing the scope of the organization’s current datasets, the Eagle Genomics team identified additional relevant open-source datasets which were then integrated with the institutional data to create the widest, richest and most informed data landscape possible.
Working closely with the global organization, the Eagle team established customized data valuation criteria including toxicity level, antimicrobial activity and availability of the compound.
The team was then able to design a conceptual data model, enabling analyses of the data to provide a table of potential compounds ranked according to the valuation criteria.
Sustainable compounds which could be used to replace current ingredients in household products were identified for further investigation
Evaluation of more than 50 relevant databases to provide the widest, richest and most informed view possible
Production of a data model capable of distinguishing attributes that are important contributors to antimicrobial efficacy as well as factoring-in the impact of interaction with other entities
Data easily exported for further analysis