Case Study
Case Study: In-silico stability testing
- Technology Innovation
The Background:
When creating a new product (skincare, vitamins, cosmetics or other) it is customary to run stability testing, to assess how the formulation will hold up during its lifetime without any change in appearance, smell, pH, etc.
The testing is normally carried out over a period of 1 to 3 months, putting the formulation under extreme environments that simulate real life conditions along the product’s shelf life. This is time consuming and expensive, and therefore it is reserved for formulations that have a high likelihood of hitting the shelves.
This does not leave much room for experimental formulation changes, as testing these would be too costly.
Client's Need
The client (with a range of established nutraceuticals) wishes to explore existing and emerging software-based predictive testing processes that could be applied to their formulations, together with service providers/experts in this area.
Adopting such technology would allow them to:
- Screen a broader range of potential ingredients and formulations
- Speed up the time to market
This approach would ultimately enhance their innovation pipeline while reducing reliance on lengthy, traditional testing methods.
IDYL's Approach
Idyl carried out a structured program of research, covering:
- The identification and understanding of existing in-silico solutions for predictive testing
- The identification of solutions under development
- The identification of experts (both commercial and academic) with relevant knowledge
- Interviews with said experts and assessment of their capabilities
IDYL's Solution
In-silico predictive testing solutions are very much tailored to specific applications, specific formulations and also specific chemicals. In other words, a “one size fits all” solution does not exist, and commercial solutions generally speaking are aimed at the pharmaceutical industry rather than nutraceuticals formulations.
- Therefore, Idyl proposed to set up an in-house team to implement a Big Data approach to predictive testing; the team would consist of key experts with knowledge across in-silico predictive testing, chemical storage data, computational chemistry and experiment design.
- Idyl provided the client with a list of suitable key experts with relevant knowledge, no conflicts of interest and willingness to engage.
Idyl provided the client with a list of suitable key experts with relevant knowledge, no conflicts of interest and a willingness to engage.
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