Advancing Drug Stability: The Stability Modeller Project with Novartis

In a strategic collaboration with Novartis, Axiologo has embarked on an ambitious project known as the Stability Modeller. This initiative focuses on enhancing the predictability of drug stability, a critical factor in the pharmaceutical industry that encompasses both physical (crystallization, state change, etc.) and chemical (impurities, degradation, etc.) aspects. The project’s goal is to develop predictive models that can forecast the outcomes of long-term stability tests based on short-term stress testing data, thereby streamlining the R&D process and facilitating the quicker introduction of stable, market-ready pharmaceutical products.

Project Overview

Key Goals:

  • Predictive Modeling: To develop and implement models capable of accurately predicting the outcomes of stability tests, thus informing more effective R&D strategies.
  • R&D Process Optimization: Utilize these predictions to guide the formulation, bill of materials (BOM), and machine settings, ensuring the final product’s stability.

Technologies Deployed:

  • Python Modelling: Leveraged for its robust data processing and modeling capabilities, enabling the development of accurate stability prediction models.
  • Dash Frontend: Employed to provide an intuitive interface for interacting with the models, facilitating easy access and analysis for R&D teams.


  • Enhanced R&D Decision-Making: By accurately predicting stability outcomes, the project has significantly improved the direction of R&D efforts, ensuring formulations and processes are optimized for stability from the outset.
  • Improved Drug Development Efficiency: The ability to anticipate stability issues early in the development process has streamlined R&D workflows, reducing time and resources spent on iterative testing.

The Path Forward

The Stability Modeller project has underscored the paramount importance of stability in the development and registration of new generic drugs. With the predictive capabilities now in place, Novartis is better equipped to make informed decisions throughout the drug development process, significantly increasing the likelihood of market success. The profound impact of this project lies in its contribution to ensuring that new pharmaceuticals are not only effective but also stable, safe, and quickly brought to market. Although quantifying the exact impact on market success rates can be challenging, the implications for operational efficiency and product quality are clear and substantial.

As Axiologo continues to refine and expand upon the capabilities introduced by the Stability Modeller, the future of pharmaceutical R&D looks increasingly promising, with AI-driven innovations paving the way for faster, more reliable drug development cycles.