Does this follow the ICH Q8 (R2) and ICH Q9 Guidelines?
Yes. This product is in compliance with ICH Q8 (R2) and ICH Q9, Q10. In fact, it was designed to follow the ICH guidelines.
Do QbD professionals use this software?
Absolutely! Many QbD practitioners, including ourselves, are currently using the software. We are constantly improving the product based on their feedback and field testing. Currently companies like Medtronic, Merck and many others are using this software.
Do you provide customer support?
Absolutely! You can communicate directly with the developers of the product. How often does that happen?
How easy is it to use this software?
LeanQbD was designed to be intuitive, so easy to use that you can use it right away without training. We are constantly improving the usability and user experience. Documentation is also provided.
Is my data secure?
Absolutely! The standard High-Security measures have been taken for our cloud service, and we also offer the on-premise solution for companies preferring to keep their data in-house.
How is the correlation number calculated?
I love answering this question because this core feature is how we can correlate a process parameter to the patient mathematically.
There are 3 layers of data that scientists and regulators want to correlate:
QTPP (patient) – CQA (product specs) – CPP/CMA (controlled by operations)
In LeanQbD software, we do this step-by-step.
First, establish relationship between QTPP to CQA. (Ask: How much does this QA affect this TPP?)
Next, establish relationship between CQA to CPP. (Ask: How much does this PP affect this QA?)
Finally, we tie these together with simple linear algebra (multiplication and addition).
As you can see, this is very logical and mathematical.
Now contrast this with traditional FMEA where we try to achieve the correlation by asking, how likely is this process parameter to harm the patient? The issue with this question is that our brains can’t make such a far cognitive jump — pH in media vs duration time of lyophilization: which process parameter will affect the QTPP more? Now do this for 1000+ parameters…
In the traditional FMEA, we skipped a layers of data, hence trying to make a cognitive jump without the intermediate step. As a result, scientists feel fmea sessions are very subjective, tiring and only get mediocre results.
Notice how easier it is for scientists to answer:
How much does this QA affect this TPP? & How much does this Parameter affect this Quality Attribute?
These are scientific questions. We can express them as: Y=f(x) or QTPP=f(CQA) and CQA = f(CPP).
So as you can see, we just broke down the question into 2 logical steps.
The alternative is excel based FMEA which can NOT correlate the impact that a Process parameter has on the patient (QTPP).
Other industries have been using a similar approach. We just adapted it to our industry.
In the Modify Drug section, you assign a ranking/score to QTPP’s (default is medium)
In the QTPP-CQA tab, when you are assigning a ranking to CQA’s,
software multiplies individual QTPP ranking and individual CQA ranking. i.e. 3 x 3 = 9.
You do that for all QTPP and CQA combinations.
Now you have a total score of each CQA.
In CQA-CPP, you do the same for all combinations of CQA-CPP’s.
Now you have a total score of each CPP.
To compare apples to apples, the software normalizes (changes raw scores into relative percentages)
Normalisation is: individual CPP score / total score of CPP x 100% = Relative Importance of CPP
This goes the same for CQA and Occurrence.
If you have any further questions, please feel free to ask!
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