Saturday, April 24, 2021

Get ready for Dynochem 6 and Scale-up Suite 2: Modeling for Everyone

Last week, Peter Clark gave a preview of new features coming with Scale-up Suite 2.  If you missed the event live, as usual you can catch the recoding in the resources site here.

Peter showed there is something for everyone in the new release.  Whatever modality of drug/ active ingredient you develop or make, whether a small or large molecule or somewhere in between, whether made with cell culture or synthetic organic chemistry, your teams and your enterprise can obtain value daily from our tools.  That takes us several steps closer to our vision to positively impact development of every potential medicine.

Scale-up Suite 2 includes:

  • Powerful equipment calculators for scale-up, scale-down and tech transfer, leveraging our industry standard vessel database format
  • Rigorous material properties calculators for pure components, mixtures and your proprietary molecules
  • Empirical / machine learning tools, to build and use regression models from your data with just a few clicks; including support for DRSM
  • Mechanistic modeling of any unit operation, in user-friendly authoring and model development environments
  • Hybrid modeling, combining the best of both worlds
  • Interactive data visualization, including parallel coordinates and animated contour plots for multidimensional datasets
  • New features to make modeling faster, more productive and more enjoyable, incorporating ideas suggested by customers and from our own team
  • New capabilities for autonomous creation of models, parameter fitting and process optimization 'headless' on the fly, as well as incorporation of real time data and access from any device.
We believe that:
  • Interdisciplinary collaboration accelerates process development and innovation
  • Models facilitate collaboration and knowledge exchange
  • Interactive, real-time simulations save days and weeks of speculation
  • Models are documents with a lifecycle extending from discovery to patient
  • Model authoring tools must be convenient and easy to use
  • Teams needs models that are easily shared
  • Enterprises need tools that embed a modeling culture and support wide participation.
In other words, modeling should be an option for Everyone.  To make that a reality for you, we support our software tools with:

  • an Online Library, containing hundreds of templates, documentation and self-paced training
  • Free 1-hour on-line training events monthly
  • Half-day and full day options for face to face training, available globally
  • A free certification program to formally recognize your progress and skills
  • Outstanding user support from PhD qualified experts with experience supporting hundreds of projects like yours
  • A thriving user community, with round tables and regular customer presentations sharing knowledge and best practices.

We're celebrating 21 years serving the industry this year, supporting more than 20,000 user projects annually, for more than 100 customers all over the world, including 15 of the top 15 pharma companies.

If you're an industry, academic or regulatory practitioner, we invite you to join our user community and start to reap the benefits for your projects.

Tuesday, March 23, 2021

Bioreactor mass transfer: kLa (O2) versus kLa (CO2)

kLa is an emotive term for many in process development.  It evokes a certain mystery for those whose background is not chemical engineering, a 'TLA' they hear over and over again.  Obtaining values for this scale-dependent 'mass transfer' parameter can be a significant undertaking, whether by experiments, empirical correlations or even CFD.  We provide purpose-designed tools to support fitting kLa to experimental data and for estimation using established correlations.  The experimental approach is the subject of this post.

The dominant experimental technique is the dynamic gassing out method, where dissolved gas concentration is followed versus time using a probe in the liquid phase.  A shortcut method allows kLa to be backed out from a semi-log plot; an implicit assumption here is that there is an abundance of gas.  A more rigorous approach that we advocate fits kLa to a model tracking multi-component mass and composition in both the liquid and gas phases.

The shortcut method contributes to confusion about kLa(O2) versus kLa(CO2), two important gases in cell culture.  Dissolved CO2 can be followed using pH probes.  Practitioners sometimes report separate values for kLa(O2) and kLa(CO2), with kLa(CO2) typically lower and insensitive to agitation.

CO2 is much more soluble than O2 and the two mass transfers are usually in opposite directions in a bioreactor: O2 from gas to liquid and CO2 from liquid to gas.  Incoming air bubbles become saturated with CO2 after a relatively short period of contact, whereas they continue to liberate O2 for most or all of their contact time.  That leads to different sensitivities of dissolved O2 and CO2 to agitation and gas flow rate; and therefore different abilities to measure something close to kLa.  A very nice study of the gas phase in bioreactors by Christian Sieblist and colleagues from Roche bears out this trend.

Practitioners report that successful bioreactor operation and adequate control over both O2 and CO2 (and hence pH) depends strongly on agitation in the case of O2 and gas flow rate in the case of CO2.  In fact, it's a spectrum and kLa and gas flow rate may both be somewhat important for both responses and the particular combination of kLa and gas flow (Qgas) determines the sensitivities for both gases.

We made some response surface plots from a series of gassing out simulations to illustrate.  These show the final amount of dissolved gas in solution at the end of each experiment, when kLa and Qgas are varied systematically in a 'virtual DOE'.  The initial liquid contained no O2 and some dissolved CO2 that was stripped during the experiment; the gas feed was air, so that dissolved O2 increased during the experiment.

Dissolved O2 at the end of a set of kLa measurement experiments in which kLa and Qgas were varied. The final O2 concentration is always sensitive to kLa and only sensitive to Qgas at very low gas flow rates. 

Dissolved CO2 at the end of a set of kLa measurement experiments in which kLa and Qgas were varied. The final CO2 concentration depends only on Qgas at low gas flows; and is sensitive to kLa only at relatively high gas flows. 

Transient concentrations of O2 and CO2 at low gas flow respond differently to changes in kLa.  In this illustration kLa has been increased between runs from 7 1/hr (dashed line) to 21 1/hr (solid line). The dissolved oxygen profile responds but the CO2 profile remains unchanged (click to enlarge).  Clearly, kLa(CO2) cannot be inferred from these data.

Wednesday, January 27, 2021

Dynochem biologics model library released

Many thanks to customers who engaged with Scale-up Systems as we "built, broke and bettered" our biologics model library in the run-up to release late last year.

More than one hundred biopharmaceutical companies in the Scale-up Suite global user community can now access the tools for immediate use here (https://dcresources.scale-up.com/?q=bio).  An overview of the biologics library is available here.

We expect each tool to grow and be refined by the repeated use that is typical of customer activity and we look forward to supporting more users in taking up the tools in their daily work.

Much like the small molecule opportunity, mechanistic modeling has great potential to accelerate the development of large molecules by shortening development time, making best use of experiments and anticipating manufacturing challenges.  Ours is the first fit-for-purpose and comprehensive mechanistic model library to be built and released in this space, another first of which we are very proud.

Using the Dynochem biologics library delivers daily benefits in development and scale-up while creating digital twins to support your digitalization strategy

Training opportunities using the new tools will be available at regular intervals this year.  Let us know if you'd like a dedicated session for your company or site.

Feel free to share this post with anyone you think may benefit.

Tuesday, December 1, 2020

Digital Tech Transfer using the Dynochem Vessel Database

The pharma industry practice of 'process fit', which allows the manufacture of most products by re-using existing physical assets, raises the perennial question of whether a given process running well at Lab A or Site B can also be run well at Site C.  Anyone who cooks or bakes even occasionally in their own kitchen will know that equipment dimensions and operating conditions affect product quality (and cycle time) and the same is true at manufacturing scale.

This problem used to be handled with a 'boots on the ground' approach, where extensive air travel and time on site allowed detailed oversight, some costly experimentation and tweaks locally before manufacturing.  With a large portion of manufacturing now contracted out to CDMOs, tech transfer remains challenging unless you have the right tools.

Working with over 100 companies engaged in the development and manufacture of pharmaceuticals, we get an up-close look at the issues, challenges and opportunities around tech transfer.  Probably the single biggest factor that makes our tools indispensable to accelerate this work is the Dynochem Vessel Database.

Users like to achieve 'equivalence' between equipment performance at the transferring and receiving sites.  Equivalence may sound simple but the different scaling laws that apply to mixing, heat transfer, solids suspension and mass transfer make this complex; and that's before even considering meso-mixing and micromixing.  Apparently inconsequential differences that are easy to miss, such as materials of construction, heat transfer fluids, impeller types, sizes and positions and even feed locations can have a large impact on performance at the receiving site.  

The likelihood of Right First Time tech transfer increases dramatically with a sufficiently detailed Vessel Database that accurately stores the configuration of site equipment.  Link that with the recipe of the target process, our equipment calculators and peer-reviewed physical properties from our Materials System and you can perform Digital Tech Transfer quickly and accurately without leaving your desk.

If you haven't already created the Vessel Database for your site or wider organization, you can start here from our template.  It's an ideal project for a young engineer and once done correctly, saves time for everyone on the team.

Selection of 'impeller' types in the Dynochem Vessel Database; users may also add custom impellers and internals

Friday, September 25, 2020

Inspired by the industry response

Many of our posts on the blog this year have been about the pandemic, predicting its course and interpreting reported data for cases and deaths.  

We have seen that population level Dynochem models have been sufficiently accurate to describe the data for each country and quantify the potential future impact of the outbreak as well as the effectiveness of non-pharmaceutical measures, such as lockdowns and the wearing of masks.  

Our models for the outbreak will remain available to the user community on our COVID site.  We do not plan to further develop or update the models for the foreseeable future.  

New content on the blog will return to our core focus, positively impacting the development of medicines by our customers, the global pharmaceutical industry.  

We are proud to serve the pharmaceutical industry, supporting daily core business activities at more than 100 organizations that develop or make medicines. The industry response to COVID-19 has been inspiring and no less than we expected, having worked with some of these companies for two decades.

All of our normal activities including software development, user support and training have continued in a fully operational state and we have seen increased activity from customers both using and learning the tools. Of course we are delivering all events on-line for now. Public training events are half price during the outbreak and we have been offering training licenses to customers delivering their own internal curriculum.

We have ramped up our own support for unit operations likely to be involved in manufacturing vaccines and treatments, including bioreactors and lyophilization.

We would be delighted to hear from members of the community anytime if you have ideas or suggestions as to how we could do more, by email to support@scale-up.com.

Reaction Lab in action
Our Reaction Lab software helps chemists develop kinetic models, maximize yield and minimize impurities.

Thursday, July 23, 2020

COVID-19 models including reopening and second wave

Reopening of many countries has begun and testing rates have also ramped up.  Some countries are now better able to detect infections and the age profile of infected people has in many cases shifted towards younger people.  Some countries have the outbreak under good apparent control while others have not yet seen even the first peak.  

We have updated our models for Ireland, the US and Italy to include the latest ECDC data.  On the basis of our modeling assumptions, and in the absence of further closures/ measures, Italy may not see a second wave for a considerable period.  Ireland may see this second wave sooner (and authorities will act to control it).  

A population (whole country) based model of any country is an approximation, as there are always localized effects, especially when applied to large countries like the US.  For example, cities in the North East had large first waves and are having relatively small second waves on reopening.  Cities in other regions had small first waves that never really fell away and are now seeing the effects of those waves continuing to build after somewhat earlier reopening.  Viewed as a whole, the US clearly has a second wave, but for the above reasons that terminology is not necessarily accurate locally.

Model fit to death rates in the US as a whole, up to 23 July 2020.  Model predictions indicate that a second peak in death rate has started and tightening of measures is required to limit its severity.

We have included in the latest models parameters to reflect increased detection by testing (as positive test rates are reducing in many nations) and also reduced mortality as the age profile shifts.  Those extra parameters add uncertainty but are necessary in order to continue to describe the evolving situation well.  On the basis of the model and its prior versions referenced in previous posts, parts of the US will need to continue to take all possible measures to limit the spread of COVID-19 in order to prevent a significantly larger death toll.

Sunday, June 14, 2020

COVID-19 second wave risk: models for Ireland, Italy, US and Singapore

We've taken another look at ECDC data to date and updated the models for the US, Italy and Ireland with data to 13 June.  We've also added a model fitted to Singapore data.

To date, the same model structure has fitted every region and inferred rates of physical contact between those infected with the disease and the broader community have tallied qualitatively with mobility data from Apple and Google.  That continues to be true for Ireland, Italy and the United States.  

Now that many countries are easing restrictions, the risk of a second wave of infections is growing.  In the case of the US, model parameters are tending to suggest that a second wave could be nearer than in the other regions.  Every feasible way to reduce transmission should be considered and it is possible that relatively small measures such as widespread wearing of masks could make the difference between a manageable and unmanageable second wave.

We've added some factors to the scenarios tabs of the models that define how restrictions are lifted.  That makes it easier for you to change those (in Simulator>Set Parameters) and you can use them to make response surfaces.  We took Ireland as an example and estimated the number of reported deaths at end 2020 for a variety of 'new normal' levels of contact (m_normal) and periods over which we adjust from lockdown to those levels (t_normal).  
Response surface (contour plot) for projected Year End 2020 reported deaths in Ireland from COVID-19, as a function of 'new normal' levels of movement / transmission (m_normal) relative to baseline and the length of time over which we move from lockdown to new normal (t_normal).  While there is some sensitivity to t_normal, the main sensitivity is to m_normal (vertical axis).  Every reasonable step should be taken to keep this value as low as possible.
Here's hoping that our greater knowledge about COVID-19 puts us in a position to avoid the less favourable regions on this diagram.

We added the Singapore model at the request of a customer there.  Singapore was highly praised in the early stages of the outbreak, with a small number of cases, extensive testing and vigilance and a very low mortality rate from COVID-19.  However an outbreak in dormitories used by migrant workers led to a spike in cases and a period of lockdown referred to as the 'circuit breaker'.  Mortality rates remain low and this is attributed to both compliance and the young age profile of the majority of cases / migrant workers.  To fit the data, we had to allow for a burst of increased contact (the breakout in dormitories) and the model now fits the data well.

All models are available here as usual.  All you need to run them is the Excel file and Dynochem.

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