Wednesday, October 25, 2017

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.

Tuesday, October 3, 2017

DOE has "virtually no role at all" in Lyophilization

We've been working away for a little while now with a group of customers to develop improved models for Lyophilization.  The fruits of these labours are available as the current Lyo model in DynoChem Resources.  This handles multi-component (e.g. water, acetic acid) freezing (rate-based approach to SLE) and sublimation (rate-based approach to SVE), with pressure-dependent heat transfer, radiation and a sublimation rate that depends on the thickness of the dry product layer.  You can obtain a predictive model for your system using this template and a few key experiments.

In researching the field while putting this model together, among Mike Pikal's excellent writings we found this useful presentation from a meeting in Bologna, 2012 [The Scientific Basis of QbD: Developing a Scientifically Sound Formulation and Optimizing the Lyophilization Process] and our favourite slide from the deck is reproduced below.

We are used to delivering this message in the context of characterizing, optimizing and scaling other unit operations (e.g. reactions, crystallization) and it is no surprise to see that the same principles hold for Lyo.

Download the model to simulate Lyophilization, fit parameters, predict scale-up and optimize. Download the full slide deck for a good introduction to Lyo.

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