Can Better Modeling Reduce Pharmaceutical Development and Manufacturing Costs?

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In the U.K., a new four-year initiative has been launched to streamline drug development and manufacturing by leveraging better computer-based process modeling and simulation.

In the U.K., a new four-year initiative has been launched to streamline drug development and manufacturing by leveraging better computer-based process modeling and simulation. Dubbed Advanced Digital Design of Pharmaceutical Therapeutics (ADDoPT), the $29-million program, part of the UK's Advanced Manufacturing Supply Chain Initiative, aims to develop the tools required for an “efficient, knowledge-based, quality by design-oriented [pharmaceutical] supply chain.” Promoting the effort is the Medicines Manufacturing Industry Partnership an alliance between The Association of British Pharmaceutical Industry and the UK BioIndustry Association.

Astra-Zeneca, BMS, GSK and Pfizer are participating in the program, as are the universities of Cambridge, Leeds and Strathclyde, particularly its Center for Innovative Manufacturing in Continuous Manufacturing and Crystallization (CCMAC).

Also collaborating are the Cambridge Crystallographic Data Center, said to be the world’s largest repository of crystallization data, and the Hartree Center, a center for high-performance computing operated by the UK’s Science and Technology Facilities Council (STFC).  

Participating solution providers include Process Systems Enterprise (PSE) Ltd., a specialist in process modeling and simulation, the process-control company Perceptive Engineering, and Britest, a nonprofit organization that focuses on improving process understanding and value.

The project’s loftier goals are to develop new approaches that would allow pharmaceutical manufacturers to deliver medicines more effectively to patients, to use data analysis and first principle models to better define, design and control pharmaceutical manufacturing processes.

On a more pragmatic level, ADDoPT also aims to develop a strong knowledge-based workforce and centers of excellence within the UK, where the impact of recent downsizings have had ripple effects on the economy, most notably the partial shuttering of Pfizer’s 60-year-old R&D facility in Sandwich, Kent, which had employed more than 2,000 scientists and technicians. 

As of 2013, there were 380 pharmaceutical companies in the UK generating more than $43 billion in revenues.  According to the program’s sponsors, ADDoPT “has the potential to propel the UK to the forefront of medicinal product design and manufacturing.”

One of ADDoPT’s goals will be to diversify the modeling techniques available to pharmaceutical manufacturers, says Sean Bermingham, principal consultant and head of PSE’s life sciences division, who is the technical lead for ADDoPT.  PSE was established as a spinoff of Imperial College UK, in 1997 to develop process  modeling and optimization software for the chemicals and oil and gas sectors. In 2010, it set up a pharmaceutical and life sciences division to bring this capability to drug companies. A growing number of Big Pharma companies are using the software  One focus is on applying modeling to support continuous processing initiatives. Use of mechanistic models can be particularly important in this area.


Currently, many pharma companies rely on statistical modeling and design of experiments (DoE) approaches in their drug development programs. However, DoE can often require a large number of experiments, and considerable resources.  Mechanistic modeling uses first principles to find correlations between different variables, so that fewer, more targeted experiments could be run. 

However, using this approach will require a very different mindset and new workflows than many manufacturers currently use.  Tackling those challenges, and the training involved, is an important aspect of ADDoPT, he says.  The project will also use Big Data techniques and  high-performance computing available via the Hartree Center, to develop equations in areas where science hasn’t advanced enough to allow the use of mechanistic modeling, Bermingham says. An example would be material properties such as compressability, which can vary significantly when the blend of API particles and excipients is changed. 

“You may run tests on a compactor simulator, but when you change the blend composition or the particle size distribution of the API and/or excipients, the behavior will change. One of the research aims of ADDoPT is to generate correlations for compressability and other material properties,"  says Bermingham.  

ADDoPT's pharma partners will  provide real-world measurements, for example, of different compressability values for different blends, in order to develop a compressability correlation.  “We hope that, by applying Big Data techniques, we will be able to find overarching correlations, and predictive models,” he says. 

The efforts could have considerable impact, not only on R&D but on scaleup and tech transfer, allowing companies to weed out drug candidates with less scaleup potential, much sooner, and to focus on those that can be scaled up successfully.

The new group will soon set up a web site whose url will be  Stay tuned for more information and updates on the project in Pharmaceutical Technology.