Wednesday, June 27, 2012
Thursday, April 12, 2012
CSD modeling using population balances in DynoChem
This week we have published a new KB article on CSD modeling using population
balances, together with several new example models. You can
download the article here: Modeling
crystal nucleation, growth and size distribution using DynoChem. The
article contains links to the example models.
This is a challenging, ‘power
user’ topic, of most interest to academic researchers.
Typical output of one of the population balance models (a 'moving sectional' model) is shown below for a seeded crystallization with an ageing period, followed by cooling and some additional nucleation.
Friday, March 23, 2012
Pharmaceutical Quality by Design: Review of Progress and Challenges
This presentation was posted recently by Ajaz Hussain on slideshare and to some groups on LinkedIn. It contains lots of links to useful content on QbD.
There are some nice references to 'making science visible' and that brings to mind a problem the industry needs to stay on top of.
In a competitive workplace environment where people are rewarded for appearing to be better than / outdo their colleagues, the kind of information sharing that leads to good scientific outcomes could be hard to achieve. If the reward system is based on metrics like how many experiments are done, rather than how much has been learned, a lower standard of work, favoring quantity over quality, may be the result.
These are problems that managers, especially senior managers can limit, so that 'teams' really operate as teams and that data may be questioned in the interest of gaining further valuable knowledge. When oversights or mistakes are found, these should be celebrated (in a way) if the resulting lessons are learned and the team moves forward with greater knowledge and improved methods.
So for example as often happens in our work, when a DynoChem model indicates there may be an inconsistency in HPLC data, the team needs to be open to question these data. It is not enough to say 'I measured it, don't question it'; we also need to ask 'how did you measure it?' and keep going down this track until we know we can trust the data.
Managers can help to ensure that this open environment for genuine teamwork and good science is nurtured.
There are some nice references to 'making science visible' and that brings to mind a problem the industry needs to stay on top of.
In a competitive workplace environment where people are rewarded for appearing to be better than / outdo their colleagues, the kind of information sharing that leads to good scientific outcomes could be hard to achieve. If the reward system is based on metrics like how many experiments are done, rather than how much has been learned, a lower standard of work, favoring quantity over quality, may be the result.
These are problems that managers, especially senior managers can limit, so that 'teams' really operate as teams and that data may be questioned in the interest of gaining further valuable knowledge. When oversights or mistakes are found, these should be celebrated (in a way) if the resulting lessons are learned and the team moves forward with greater knowledge and improved methods.
So for example as often happens in our work, when a DynoChem model indicates there may be an inconsistency in HPLC data, the team needs to be open to question these data. It is not enough to say 'I measured it, don't question it'; we also need to ask 'how did you measure it?' and keep going down this track until we know we can trust the data.
Managers can help to ensure that this open environment for genuine teamwork and good science is nurtured.
Thursday, March 15, 2012
Wilfried Hoffmann talks about the use of DynoChem in processdevelopment.
See the full version at http://dcresources.scale-up.com.