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BioPharma Europe Initiative: giving Pharma manufacturing a distinctive voice in Brussels

Through our involvement with the pre-competitive collaboration centre SSPC, we have attended a number of events organized by BioPharma Europe, a growing SSPC-led initiative that is raising awareness in the European Parliament and Commission of the unique role, position and needs of the European Pharma industry and seeking policy initiatives that support a strong future for Pharmaceutical Manufacturing in Europe.

Source: EFPIA

After a good start, this group is now seeking to build support from a wider network of European pharma industry stakeholders in the next phase of discussion with Europe's research and regulatory policymakers, such that future policy decisions support this strategically important industry in the globally competitive landscape.

At Scale-up Systems, we are proud of our excellent relationships with pharma companies, CMOs and CROs and are delighted to bring BioPharma Europe to the attention of our customers.  Organizations wishing to find out more about BioPharma Europe should contact Aisling Arthur at SSPC.

A PSD trend that is not widely reported - thanks, Orel

While supporting customers who apply DynoChem for crystallization modeling, we have seen several cases where some of the familiar quantiles of the PSD (D10, D50, D90) reduce with time during at least the initial part of the crystallization process.

On reflection one should not be that surprised: these are statistics rather than the sizes of any individual particles.  In fact, all particles may be getting larger but the weighting of the PSD shifts towards smaller sizes (where particles are more numerous, even without nucleation) and in certain cases, this causes D90, D50 and maybe even D10 to reduce during growth.

Last week we had an excellent Guest Webinar from Orel Mizrahi of Teva and Ariel University, who characterized a system with this behaviour, with modeling work summarised in the screenshot below.

D10, D50 and D90 trends in a seeded cooling crystallization: measured data (symbols) and model predictions (curves).

There was a good discussion of these results during Orel's webinar and we decided to make a short animation of a similar system using results from the DynoChem Crystallization Toolbox to help illustrate the effect.

Cumulative PSD from the DynoChem Crystallization toolbox, showing the evolution of PSD shape during growth from a wide seed PSD.  The movement of quantiles D10, D50 and D90 is shown in the lines dropped to the size axis of the curve.

In this illustration, the reduction in  D50 can be seen briefly and the reduction in D90 continues through most of the process.  From the changing shape of the curve,  with most of the movement on the left hand side, most of the mass is deposited on the (much more numerous) smaller particles.

We see this trend even in growth-dominated systems, when the seed PSD is wide.

Run typical crystallization experimental design in silico using DynoChem

Faced with challenging timelines for crystallization process development, practitioners typically find themselves running a DOE (statistical design of experiments) and measuring end-point results to see what factors most affect the outcome (often PSD, D10, D50, D90, span).  Thermodynamic, scale-independent effects (like solubility) may be muddled with scale-dependent kinetic effects (like seed temperature and cooling rate or time) in these studies, making results harder to generalize and scale.

First-principles models of crystallization may never be quantitatively perfect - the phenomena are complex and measurement data are limited - but even a semi-quantitative first-principles kinetic model can inform and guide experimentation in a way that DOE or trial and error experimentation can not, leading to a reduction in overall effort and a gain in process understanding, as long as the model is easy to build.

Scale-up predictions for crystallization are often based on maintaining similar agitation and power per unit mass (or volume) is a typical check, even if the geometry on scale is very different to the lab.  A first principles approach considers additional factors such as whether the solids are fully suspended or over-agitated, how well the heat transfer surface can remove heat and the mixing time associated with the incoming antisolvent feed.

The DynoChem crystallization library and the associated online training exercises and utilities show how to integrate all of these factors by designing focused experiments and making quick calculations  to obtain separately thermodynamic, kinetic and vessel performance data before integrating these to both optimize and scale process performance.

Users can easily perform an automated in-silico version of the typical lab DOE in minutes, with 'virtual experiments' reflecting performance of the scaled-up process.  Even if the results are not fully quantitative, users learn about the sensitivities and robustness of their process as well as its scale-dependence.  This heightened awareness alone may be sufficient to resolve problems that arise later in development and scale-up, in a calm and rational manner.  Some sample results of a virtual DOE are given below by way of example.

Heat-map of in-silico DOE at plant scale agitation conditions, showing the effects of four typical factors on D50. 
The largest D50 is obtained in this case with the highest seeding temperature,  lowest seed loading and longest addition (phase 1) time. Cooling time (phase 2) has a weak effect over the range considered.

Click here to learn how to apply these tools.

Congratulations to Dr Jake Albrecht of BMS: Winner of AIChE QbD for Drug Substance Award, 2017

At AIChE Annual Meetings, Monday night is Awards night for the Pharma community, represented by PD2M.  This year in Minneapolis the award for Excellence in QbD for Drug Substance process development and scale-up went to Dr Jake Albrecht of Bristol-Myers Squibb.  Congratulations, Jake!

Winners are selected using a blinded judging panel selected by the Awards Chair, currently Bob Yule of GSK.  Awards criteria are:

• Requires contributions to the state of the art in the public domain (e.g. presentations, articles, publications, best practices)
• Winner may be in Industry, Academia, Regulatory or other relevant working environment
• Winner may be from any nation, working at any location
• There are no age or experience limits
• Preference is given to work that features chemical engineering

Jake was nominated by colleagues for:

• his innovative application of modeling methodologies and statistics to enable quality by design process development
• including one of the most downloaded papers in Computers and Chemical Engineering (2012-2013), “Estimating reaction model parameter uncertainty with Markov Chain Monte Carlo”
• his leadership and exemplary efforts to promote increasing adoption of modeling and statistical approaches by scientists within BMS and without
• his leadership in AIChE/PD2M through presentations, chairing meeting sessions, leading annual meeting programming and serving on the PD2M Steering Team

Scale-up Systems was delighted to be involved at the AIChE Annual Meeting this year in our continued sponsorship of this prize.  Some photos and video from the night made it onto our facebook page and more should appear soon on the PD2M website.

Jake is also a DynoChem power user and delivered a guest webinar in 2013 on connecting DynoChem to other programs, such as MatLab.

November 2017 DynoChem Crystallization Toolbox Upgrade

We're delighted that the number of DynoChem users getting value from our crystallization tools continues to grow strongly and we're grateful for the feedback and feature requests they provide to help us improve the tools.

New features released this November include:

• One-click conversion of kinetic model into predictor of the shape of the PSD
• High-resolution tracking of the distribution shape, to minimize error*
• Extended reporting and plotting of PSD shape.

Sometimes practitioners that are unaware of crystallization fundamentals, crystallize too fast and with little attention to the rate of desupersaturation.  For such a rushed process, even when seeded (2%) the operating lines might look like the picture on the left below (Figure 1). A more experienced practitioner might operate the crystallization as shown on the right (Figure 3):

The particles produced by these alternatives differ greatly in size.  The rushed crystallization leads to a multimodal distribution (red in Figure 2) with low average size, due to seeded growth and separate nucleation events during both antisolvent addition and natural cooling.  These crystals will be difficult to filter and forward-process.

More gradual addition, with attention to crystallization kinetics and both the addition and cooling rates, leads to larger crystals (blue in Figure 2) and a tighter distribution that can be further enhanced by optimizing seed loading, seeding temperature and the operating profiles.

From November 2017, these types of scenarios can be set up, illustrated and reported in minutes using the DynoChem Crystallization Toolbox.

To learn how, take the training exercise or download the latest version of the toolbox.

* We have implemented high resolution finite volume discretisation of the CSD, using the Koren flux limiter.

Simulating PFRs for flow chemistry under transient upset conditions

Readers of this blog will be aware of our RTD utility that helps characterize continuous manufacturing (CM) equipment trains and also simulate the impact of process disturbances, in the absence of chemical reactions.  Pharma CM processes typically have several layers of controls to help ensure that off-spec material is diverted when necessary and as far as possible that disturbances are minimized and detected early. 

For regulatory filings or other purposes, from time to time it may be necessary to simulate transient/ upset conditions in chemically reacting systems (e.g. making drug substance intermediates or final API) to understand the additional chemical effects and to define boundaries for acceptable levels of input variation.  We have been exploring such cases and the most effective way to model them in DynoChem.  Some interesting DC Simulator plots are shown below to illustrate when and for how long such upsets might affect the exit CQA (blue) and impurity level (green) from an example PFR (average residence time 30 minutes) with a ‘typical’ side-reaction. 

Simulation of plug flow reactor with significant and frequent fluctuations in four input variables. These unusually large variations if left unchecked would lead  in this example to a breach of the CQA limit (high impurity) twice during a 3 hour operating period. 

Simulation of plug flow reactor with a feed pump failure at 90 minutes, lasting for 30 minutes.  In addition to reducing  product output, depending on which feed pump fails, this may lead to a temporary increase in impurity level until the feed is restored.