Autonomous CPCA Web-based calculator FREE

[DennisZhu]

Hi,

I am the author this application, It will be great that you could also share 15 structures with me once you are OK.

Thanks
Jiazhou

[DennisZhu]

Hi Yosukemino,

Thanks for coming up with beta-methyl issue, In the application, 'there is a requirement for the chain to be branched at the position’ this rule is not counted since it’s not included in the guidance. If this rule is something that should be considered first when to come to count a beta-methyl, I can actually fix it in the application.

Thanks
Jiazhou

[vinod.thorat]

This is the API (Quetipine fumarate) and identified impurities are mentioned below
and i considered these impurities are NDSRIs . Is it correct?>???

[vinod.thorat]

Autonomous CPCA Web-based calculator FREE

Have any of you tried this resource? @Yosukemino @JavierFernandez [image]

Request for ur expert opinion on above question ??

[SSimon]

Yes, I’ve also tried it. Looks really promising! Thanks a lot for sharing.

[JavierFernandez]

Yes, all of them could be potential nitrosamines, although the last one looks the least likely

[Naiffer_Host]

A similar tool posted by @diullio on CPCA calculator on Python Desktop

CPCA Calculation System (Python Desktop)

Dear Nitrosamine Exchange Members, I come to post for you a CPCA calculation tool based on Python (open source), to facilitate the calculation of the Carcinogenic Potency Categorization. The tool uses smiles (chemical structure) to calculate the categorization, automatically recognizing category 5 nitrosamines. [1] If it is necessary to calculate the potency score, the system will indicate to select the activating and deactivating groups in the structure, and will finalize the category based…

[lucas10mauriz]

Previously available as a preprint and not yet subjected to peer review, the work of Jiazhou Zhu, Qu, and Ye (2024) has now been published.

An automated carcinogenic potency categorization approach for nitrosamine drug substance-related impurities

The algorithm quickly and accurately categorized the risks of over 6000 chemicals, with high agreement compared to the dataset labels.

Further analysis revealed a correlation between high pKaH values of the parent amine (the basicity of an amine where the susceptibility to nitrosation is related to the nitrogen basicity of the amine) and low risk of N-nitrosamine.

To ensure the algorithm’s robustness and reliability, testing was conducted on a larger dataset containing NDSRIs. This dataset was obtained from the referenced article:

The Landscape of Potential Small and Drug Substance Related Nitrosamines in Pharmaceuticals

The algorithm accurately calculated the potency category for each chemical within seconds, while also identifying that 16 molecules failed due to an invalid SMILES string.

Since the posting date of the preprint in this community until now, with the article already published, for those who have been using the tool, what new recommendations do you have?

[SachinKamble]

It is very useful . I have applied in many application and found suitable