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Last Updated on 14 January 2022 by Vincent Billot

Case study – Expertise for the development of predictive models for pharmaceutical tabletting

Secteur : Ingredients

Type de client : Big Account

Service interlocuteur : Data science – R&D

The client aims at developping predictive models for pharmaceutical tablets final properties. Preliminary data-science provided low level of satisfaction, so the Client asks RHEONIS for a critical scientific analysis, a neutral opinion on the feasability of the project and recommendations for giving maximum chances to the project.

Hybrid scientific, industrial and data analysis expertises

RHEONIS provided its hybrid expertises regarding process industrial science, know-how for investigating scientific litterature, techniques for data analyse and interpretation and its experience in R&D strategy.

Following actions have been taken in the context of this expertise:

  1. Client’s need analysis and building of the Sow of the expertise
  2. Phenomenological analysis of tabletting process and scientific litterature study of existing models, influencing factors and hierarchy of correlations
  3. Client’s data analysis with hybrid physical/statistical techniques following models and phenomena
  4. Brainstorming for R&D strategy
  5. Synthesis report, incluing scientific opinion about project feasability and recommendations for R&D
  6. Debriefing meeting and discussion about next steps

Industrial science and phenomenology for getting to master tabletting

Our expertise provided a critical, scientific and neutral opinion on the feasability of its ambitious project. We identified uncertainties and risks but also stable grounds and promising options.

Our hybrid approach, combining science, data analysis, industrial phenomenology and powders expertise, allowed to build and suggest R&D paths for progressively remove risks and orient the project towards its success.

Industrial Science for Data-Science

Scientific expertise of industrial processes’ physical phenomena

Identify models and parameters in scientific litterature

Guide data analysis with phenomena understanding

Build on efficient and pragmatic R&D strategy

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