10 Ways to Tell If You Suck at Cell Culture Support

Here are 10 ways to tell if your support of large-scale cell culture, well, sucks:

1. Key performance indicators.
   You don't know what the right KPIs are for cell culture, but you're 100% certain that it's titer.
2. Proven Acceptable Limits.
   You don't have any defined for your critical process parameters and you failed to demand them of Process Development.
3. Control charts. You're not using them or you don't know how to make them, and your bar graphs are just fine, thankyouverymuch. They're not just fine and it's because you can't identify:
4. Special cause vs. common cause variability.
   You investigate common cause variability because that titer seemed too low or too high.
5. C_pK. You don't know what process capability is and you're not calculating them.
6. Histograms. You aren't looking at the distribution of your KPIs.
7. Bi-variate Analysis.
   Linear-regressions, ANOVA, Tukey-Kramer.  You have no idea what this stuff is, 我還不如寫中文.
8. Multivariate Analysis.
   You're not doing these and when you do, Y-responses are treated as X-factors.
9. Local Lab. You don't have a local MSAT lab to run satellite experiments to confirm the hypothesis generated from the plant.
   
   A lot of people assume that you can use the resources of a distant process development lab; but then again, a lot of people like blood sausage.
10. Excel. You're still using Excel to store data. You're still using Excel to analyze data. If you're looking to play varsity cell culture support, you really need to be using a cell culture data management system.

by Oliver Yu

See also:

• MSAT plant support: local data
• Campaign monitoring v. Process Improvement
• MSAT consulting