Wednesday, April 24, 2013

Viral Inactivation of Cell Culture Media HTST

For all this talk of bioreactor sterility, the vast majority of this contamination talk refers to bacterial contamination.

What about viral contamination?

Viral contamination is mitigated with HTST treatment of cell culture media. This is where you put the cell culture media in continuous flow while subject to a high temperature for a short period time.
  • High-temperature inactivates the virus.
  • Short-time ensures that cell culture media components do not denature.
The best time to put the media in continuous flow is when you pump the media from the media prep tank to the bioreactor, so viral inactivation often happens during this transfer. The HTST unit is essentially:
  • Heater
  • Hold-tube (insulated pipe)
  • Cooler
At large-scale, the first plug of media that goes through the HTST may not meet the specification, so this plug of media cannot be permitted to be delivered to the bioreactor. This plug may be sent to drain or recycled through the HTST unit until the HTST unit reaches steady-state.

Once at steady-state, the remainder of the media is pumped (through a sterile-filter and subsequent sterile pipes) into the bioreactor; when batch volume is reached, the remaining media is sent to drain.

Simple enough, right?

The hard part is when your HTST performance begins to degrade:
  • Perhaps your sterile filter starts clogging
  • Perhaps your heater controller output maxes out
  • Perhaps your media-prep post-use is showing problems
As recently as January of this year, Amgen's Drug Product Development published a paper titled, "Identification and root cause analysis of cell culture media precipitates in the viral deactivation treatment with high-temperature/short-time method."

I haven't read the paper, but my manufacturing sciences consulting experience predicts it to say the following:

The calcium phosphate in the cell culture media becomes insoluble at the high temperatures during the HTST. This calcium phosphate precipitate may collect on the surface of the holding-tubes thereby decreasing the heat-transfer coefficient, sporadically causing the HTST to fail.

This calcium phosphate (sandy white stuff) may also clog up the 0.1 micron sterile filter causing a high delta-pressure across the sterile filter, maybe even diminishing the media flow rate.

The problem is that calcium phosphate stays in solution except when both the temperature is high and the pH is high.

Unfortunately, high-temperature is a requirement of HTST; which means the only solution to preventing calcium phosphate precipitation and the ensuing HTST performance degradation and filter clogging is to run the media through at low pH.

See Our Fix

This is a classic multivariate problem where operating in a different range will solve the problem.

See also:

Tuesday, April 23, 2013

Are You Seeing An Uptick in Bioreactor Contaminations?

There's been an uptick in the number of requests we're seeing from biotech manufacturers regarding our bioreactor sterility services.

A lot of new biotech manufacturers have questions like:
  • What is everyone else's contamination rate?
  • What's a good benchmark to target?
  • What are "low-hanging-fruit" items I can work on now?
The reality of [bioreactor] contamination response is that it can be un-scientific. And the reason it's hard to be scientific about responding to contamination is because losing run slots can be emotional.

If you're reading this wonkish blog, you know what I'm talking about. The first thing is that there's a lot of work that went into a contaminated batch and all that work is wasted. It's demoralizing. Not meeting campaign goals may disrupt supply of your API. Patients may not get medicine. And worst of all, you might get sucked into convening a contamination investigation.

As an employee, I've sat in on innumerable contamination responses. And when we've brought in outside consultants, we were in crisis mode. These guys are generally pretty good at helping everyone keep calm and help people walk through whatever system of thinking:
  • Kepner-Tregoe
  • Six-Sigma (DMAIC)
  • 5 Why's
These guys are usually generalists, and that's fine. A lot of times, applying a problem-solving system will help get the emotions out and the science back into contamination response. And if you're a big biotech, there's the perception that you have all the experience in-house and it's a matter of applying the resources properly.

The reason we offer biotech veterans is because large-scale sterility operating principles get lost through the years. A lot of companies operate off tribal knowledge. There's that one guy that's been there for 25-years and he's about to retire. Or that your memorandums are stuck on some share drive (or worse, LiveLink or SharePoint) where you can't find them.

Sometimes, good sterility practices are coded into the automation or written into SOPs and the reason for these changes are stuck in the change-control system where most people can't get to it. Institutional knowledge gets fragmented and lost through the years.

This is why we offer biotech veterans for contamination responses.  We will deliver:
  • Statement of Most Probable Cause (MPC) of acute contaminations
  • Rank-ordered list of sterility risks
  • Summary/Assessment of operations
With this report, you get both action to actually solve the problem. And you get perceived action to help you "manage up."

So whether your a new Korean CMO or NJ Biopharma, the big question is: can you afford to wait until crisis mode before bringing in veteran contamination consultant?

Friday, April 12, 2013

Compile Error in Hidden Module: modAddin (x64) (Office 2003, 2007)

You're here because of this:

modAddin excel compile error in hidden module
...and because you're using a 64-bit system (x64).

The fix, like the 32-bit solution is to un-register and re-register the mscomctl.ocx.

But it's slightly different on a 64-bit system.

The mscomctl.ocx file is located here:


Un-register and re-register that file and you'll be gold.

p.s. - Thanks for all the commenters with this.

Friday, April 5, 2013

How To Interpret Distributions (Histograms)

Here's a set of Y-Distributions (histograms) I saw on the data visualization sub-Reddit.

On the left side, we have Polish language scores. On the right, we have mathematics.

Each row is a year... 2010 through 2012.

According to the notes on the page, these are the high-school exit exam scores for which passing is to receive 30% of the total available points.

Most people know what a "bell-shaped" curve looks like and those Polish language scores don't look like bells. In fact, it looks like right around the 30% mark, someone took the non-passing scores that were "close enough" and just handed out the passing score.

We sometimes see this in biotech manufacturing... where in order to proceed to the next step, you need to take a sample and measure the result. If there is a specification, you'll see a lot of just-passing results. What is euphemistically called, "Wishful sampling."

The process is the process and if the sampling is random, you expect a bell-shaped curve. In the case of Polish high school students, their Polish skills are what they are. What you're seeing is an artifact of the people grading the tests. I would bet a fair amount of money that teachers or schools are rewarded according to the number of students who pass this test.

Let's look at the mathematics scores. This "wishful grading" is going on in mathematics, but is far less pronounced. What is crazy is how different the distributions look from year to year (compared to the language histograms).

It's hard for me to think that mathematics skills of students across Poland vary that much from year to year. Like the U.S. News and World Reports rankings of schools, it's more likely that the difficulty of the test changes significantly from year to year... in this case with 2011 tests with particularly difficult questions.

Histograms say quite a bit about your process. What they never tell you is that the histograms also tell you quite a bit about your process specifications and how truthful your measurement systems are.

If I were the FDA... and I wanted to be mean about it, I'd request a distribution of measurements for every single process specification, and if I saw something like this "Polish language" test, someone has some explaining to do.

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Monday, April 1, 2013

Dr. Tom Little - Stats Ninja

There is an epidemic of statistical dunderheads working in the biotech industry. This epidemic probably plagues other sectors of the economy as well, but I'm not qualified to speak to that.

The reason for this complete lack of statistical knowledge (I think) is that statistics is not a part of the standard engineering curriculum. You get differential and integral calculus like crazy, but just one semester of basic engineering statistics and here's your diploma.

And as with most of undergraduate academia, it's not practical.

At my first job, we used the statistical software program - JMP - a lot. We were making a minimally invasive glucose monitor called the, GlucoWatch® Biographer and my entire job as a research engineer was to run in-house clinical studies and correlate the biographer performance against over-the-counter glucose meters. So we did a lot of linear correlations, I got to understand what p-values meant. And I figured out the primary purpose of engineering a system was to figure out what was signal and what was noise.

I think I might have even landed my second job because I knew how to use JMP. In fact, my second week on the job, the boss had his entire group go get JMP training in San Francisco where I had the luck of sharing a computer terminal with him.

Whatever the case, understanding enough statistics to know what tests are applicable when is really important. And when your group gets big enough that sending your team to off-site training becomes impractical, there is Dr. Thomas Little who will send practical stats gurus to train you.

Dr. Tom Little Statistics Consulting

Dr. Tom trained us (in a computer room) setting and a lot of this stuff was new at the time I learned it. ANOVA... Multivariate Analysis. Why use backwards stepwise regression... how to read the normal quantile plot... Capability... Control Charting.... All the things that are relevant to monitoring a production campaign.

When you get out of the class, you've leveled up in the world of biologics manufacturing and you look around and wonder why maintaining spreadsheets of cell culture data qualifies as plant support. You also start wondering why process development spends more time swinging male genitalia over higher titers rather than defining critical process parameters (CPPs) and identifying proven acceptable ranges (PARs).

Dr. Tom is pretty well-known in the world of biologics. I run into his team of consultants every third place I go. If your team isn't making statistically-sound, data-drive decisions, you seriously need to give him a call.

Call Dr. Tom