Digital tools must be used in pharma manufacturing to keep pace with drug discovery and clinical development

The pace of change in drug discovery and clinical development has been unmatched in recent years, with technologies such as AI accelerating these processes. But for these advancements to continue at such a rate, the speed of this science must be better aligned with the manufacturing infrastructure around it.

Recent research from Accenture highlighted that CEOs and C-suite executives are particularly focused on how new disruptive technology, such as machine learning and AI, have created opportunities to accelerate R&D. Opportunities include the simulation scientific experiments in silico, better ways of designing trials, identifying patient groups, and avoiding potential adverse events. The research found that more than 150 pharma companies are currently using artificial intelligence to accelerate drug discovery, and more than 50 candidate molecules discovered using AI are progressing through clinical trials.

Leveraging digital tools for appropriate acceleration

The findings illustrate the industry’s desire to leverage digital tools to accelerate progress in discovering/developing new drugs. But with that pace in mind, I believe that there is a growing risk that industry investments in technology to accelerate discovery and development could be hampered by downstream bottlenecks in clinical/commercial manufacturing and this could limit or delay patient access to these new therapies.

The complexity of new drug candidates, combined with accelerated progression of assets into and through clinical trials, adds significant pressure to the scientists and engineers responsible for scaling up and gaining approval for the manufacturing processes. There is a compelling case for more investment in technology and skills in manufacturing process development to manage the costs and risks associated with accelerating R&D of more candidates.

Professor Suzanna Farid of UCL recently presented research that highlighted that process development and manufacturing can account for 20-25% of the total R&D cost of bringing a new drug to market. This is for biologic drugs known as ‘monoclonal antibodies’, with the suggestion that this percentage could increase significantly for more complex drug types, such as antibody drug conjugates, gene therapy, and cell therapy.

Increasing costs, increasing workforce burden

With cost pressures intensifying alongside the pace of scientific advancements, we can’t overlook the impact this has on the workforce and their ability to operate flawlessly under increasing time pressure. This is where technology is a crucial factor. Leaders we interviewed are acknowledging that the increased rate of change in the past few years is leading to talent exhaustion, and they are looking to appropriately reskill talent and provide the tools and environment to achieve better focus, productivity, and experiences.

As debate continues in industry on how to reduce the cost and risk of drug development, Professor Farid’s research would suggest that the area of manufacturing process development is an area where costs are significant and growing. It is therefore worth considering how digital tools (such as digital twins to support in silico experimentation on complex manufacturing processes) may have a role to play in accelerating improvements in productivity and enhance the experience of these teams of scientists and engineers.

Industry veteran and consultant Dr Brendan Hughes, chair of the scientific advisory board of Ireland’s National Institute for Bioprocess Research and Training (NIBRT), has spoken at length about the ways in which successful process development scientists and engineers can deliver long-term savings after a drug is launched by maximising productivity of the manufacturing process.

These savings in cost and carbon footprint accumulate through the lifecycle of an approved drug by optimising yield, cycle time, and minimising supply chain complexity as early as possible in the lifecycle. Evidence of significant long term cost take-out in manufacturing of monoclonal antibodies is corroborated by published academic research from Professor Farid and others, which described how the cost of these drugs has dropped from $10,000 per gram to between $1000 and $100 per gram, depending on the scale of production. The enormous progress of the biologic manufacturing space was highlighted in the way that several companies overcame pandemic constraints to achieve the rapid development and launch of a monoclonal antibody for treatment of patients infected with Covid-19.

Interoperable digital tools across the value chain

As this debate continues in the industry, cost and talent burnout risks in the area of manufacturing process development is a good place for leaders to focus. It is therefore worth considering how digital tools may have a role to play in helping improve the productivity and experience of these teams of scientists and engineers. It’s clear that the academic community is prioritising this, and I have seen multiple examples of research (advanced analytics and digital twins in biotech manufacturing) and educational programmes aimed at increasing the supply of undergraduate and postgraduate talent with combined scientific and digital areas, which is hugely encouraging.

As scientific developments progress at pace, it is also essential that the industry continues to minimise silos and reinvent the end-to-end value chain. It is my view that this reinvention will be enabled by investing in digital reskilling and upskilling of scientists and engineers and providing a strong digital core based on cloud, data, and AI through an interoperable set of systems across the value chain, from discovery through development and manufacturing.