Scientifically robust software providing an industry-standardised approach for calculating purge factors of potentially mutagenic impurities
- Mirabilis can be used to satisfy ICH M7 Control Option 4 by producing an estimated purge factor for removal of an impurity by a synthetic process.
- Mirabilis is built on expert knowledge and is semi-automated to improve the efficiency of purge analysis.
- Lhasa Limited is part of the Mirabilis Consortium, which sponsors and guides the development of Mirabilis.
ICH M7 Control Option 41 outlines that “a control strategy that relies on process controls in lieu of analytical testing can be appropriate if the process chemistry and process parameters that impact levels of mutagenic impurities are understood and the risk of an impurity residing in the final drug substance above the acceptable limit is determined to be negligible”. Importantly it goes on to clarify that “in many cases justification of this control approach based on scientific principles alone is sufficient”.
Analytical testing can be replaced by a risk assessment if:
- The impact of the synthetic process on an impurity is confidently understood and
- The risk of an impurity residing in the final drug product above an acceptable limit is determined to be negligible
The guideline states that the “risk assessment can be based on physicochemical properties and process factors that influence the fate and purge of an impurity including chemical reactivity, solubility, volatility, ionizability and any physical process steps designed to remove impurities.”
Mirabilis can be used to help satisfy Option 4 by producing an estimated reactivity purge factor for removal of the impurity by a synthetic process. As such, Mirabilis can reduce the need to set up costly and time-consuming analytical methods for the measurement of impurities that are unlikely to be present in the final drug product.
- The user enters a full synthetic scheme leading to the drug substance (API), including reagents and conditions. Operations performed during the synthesis are organised in stages and steps.
- A step is any operation: reaction, work-up, purification.
- A stage consists of one reaction step, optionally followed by one or more workup and/or purification steps.
- The user identifies the potential mutagenic impurities (PMI) of concern.
- Mirabilis can automatically recognise the structural class of an impurity and the transformations in synthetic steps if they are held within the knowledge matrix.
- Based on empirical data, Mirabilis generates a reactivity purge factor for each PMI.
- Mirabilis generates a report which is suitable for inclusion in a regulatory submission and includes robust scientific evidence to support the predicted reactivity purge factors.
Research is also taking place regarding the development of predicted solubility purge factors and other process factor purge predictions.
Lhasa’s dedicated team of scientists have developed a knowledge matrix that resides within Mirabilis. This matrix contains reactivity purge factors of structural classes for a variety of transformations. Knowledge gathering by dedicated Lhasa Scientists has ensured any crucial gaps in the matrix have been filled. The knowledge in Mirabilis will be continually expanded and refined in the future.
Lhasa Limited are collaboratively working within the Mirabilis Consortium. The consortium sponsors and guides the development of Mirabilis and the supporting scientific knowledge and methodologies.
The activities carried out by the consortium include:
- Standardisation of the calculation of purge factors and the scientific support required
- Identifying gaps in knowledge
- Testing, using and helping to develop the software prototypes
- Engaging with regulators
For more information on Mirabilis, or to request a demonstration, please contact us.
2. Teasdale et al. (2010) ‘A Tool for the Semiquantitative Assessment of Potentially Genotoxic Impurity (PGI) Carryover into API Using Physicochemical Parameters and Process Conditions’, Organic Process Research & Development, vol. 14, pp 943-945.
3. Teasdale et al. (2013) ‘Risk Assessment of Genotoxic Impurities in New Chemical Entities: Strategies To Demonstrate Control’, Organic Process Research & Development, vol. 17, pp 221-230.