- A collaboration of pharmaceutical organisations sharing Acceptable Intake (AI) and Permitted Daily Exposure (PDE) data.
- The AI/PDE project aids in regulatory submission under various guidelines including ICH M7and ICH Q3A, ICH Q3B and ICH Q3C.
Initiated in 2017 and using Lhasa’s Vitic platform, the AI/PDE project brings together top pharmaceutical organisations to share and harmonise Acceptable Intake (AI) and Permitted Daily Exposure (PDE) data.
The calculation of compound specific AIs/PDEs are required in regulatory submission under various guidelines including ICH M7 and ICH Q3A, ICH Q3B and ICH Q3C. This process is often time consuming, with the potential for duplicate or non-equivalent assessments across pharmaceutical organisations worldwide.
AI/PDE data sharing can enhance the safety of drug substances and streamline the risk assessment workflow by allowing organisations to rapidly access a harmonised and agreed-upon series of AIs/PDEs for commonly used reagents and solvents.
You can find out more about the 2022.1.0 version of the database here.
The approach of the consortium is to share and harmonise AI/PDE safety assessment data. The project will include:
- Identification of a series of high-value AIs/PDEs for common impurities in APIs.
- Generation of a harmonised, industry-accepted approach to safety assessment and AI/PDE derivation.
- Storage and retrieval of agreed AI/PDE data from a structure searchable database.
- Time & Cost Savings – AI and PDE assessments take time to put together and rarely make use of proprietary data. The sharing of data in this context will save an organisation significant time, effort and money without disclosing sensitive information.
- Avoid duplication of effort – As a requirement of regulatory guidelines, all pharmaceutical organisations are required to submit AI and PDE assessments for certain impurities, with the high possibility that organisations are submitting reports for the same compounds. Sharing and harmonising the data in an easily searchable database avoids the need to duplicate work that has already been done.
- Standardisation – As different data or safety factors can be applied when generating an AI/PDE, it is plausible that regulators could be presented with inconsistent AI/PDE values for the same chemical. Working collaboratively to harmonise the AI/PDE data shared will allow for a degree of consistency in regulatory submission.
Lhasa Limited - an honest broker and trusted holder of data
Lhasa is a not-for-profit organisation and believes that shared knowledge can lead to shared progress. Recognised as the original "Honest Broker", Lhasa Limited has repeatedly been trusted with proprietary data and this can be seen with our involvement in other collaborative data sharing projects.
To find out more about data sharing opportunities at Lhasa Limited, please get in touch.
- Bercu et al. (2018) 'Potential impurities in drug substances: Compound-specific toxicology limits for 20 synthetic reagents and by-products, and a class-specific toxicology limit for alkyl bromides', Regulatory Toxicology and Pharmacology, April 2018, Volume 94, Pages 172 - 182. https://doi.org/10.1016/j.yrtph.2018.02.001