Planets align

Breaking the BioPharma paradox of cutting-edge science and lagging digital transformation

We are living in a century that has witnessed huge advancements in technology, with more than half of the world’s population now having internet access through a device they keep in their pocket.

Equally, the revolution in molecular biology brings a new frontier in the fight against some of the most devastating illnesses affecting humankind.

Over the last decade, leading innovators across academia and industry have been realising the potential of this new generation of biopharmaceutical medicines (biologics) to positively impact the lives of thousands of patients across the globe.

Monoclonal antibody (mAb) therapies are already helping to treat a range of chronic diseases, like asthma and rheumatoid arthritis, as well as some of the most complex and deadly cancers.

The great news is that the pace of scientific innovation is accelerating rapidly, with the number of monoclonal antibody therapies approved per year more than ten times higher in 2021 than in 2010.

The FDA approved the 50th antibody drug in 2015, 29 years from the first one. Only six years later, in April 2021, the FDA approved the 100th – mAb Dostarlimab, GlaxoSmithKline’s anti-PD-1 drug for the treatment of endometrial cancer.

This pace of innovation continues to accelerate, with a new era of genomic medicines. These highly targeted medicines unlock the power of the human body to help in the fight against disease.

Within the past few years, we have already seen how some of these tools, like the mRNA vaccines for SARS-CoV-2, can help us manage a global pandemic and how CAR T-cell therapies can harness the power of our own cells to treat cancers like leukaemia, lymphoma and most recently myeloma.

It is in the context of this fast-paced world of biological innovation that we find a peculiar paradox. The process of discovering, developing and manufacturing these cutting-edge drugs is complex and expensive, requiring huge investment and up to 15 years.

Given the cutting-edge science involved with every step of this life cycle, you would imagine the biopharma industry would be setting the benchmark for the application of digital technology. Indeed, it is quite the opposite.

In 2017, McKinsey looked into the digital maturity of the pharma industry and revealed that pharma was a digital laggard, even behind other highly regulated industries like banking and insurance. Across four key dimensions of strategy, culture, organisation and capabilities, the gap was the biggest in strategy and organisation.

Digital initiatives were not closely or systematically linked to the broader strategy, and it was clear that a lack of senior knowledge and ownership was hindering companies in advancing their digital initiatives. This demonstrates that digital maturity can’t be gained by a bottom-up approach; a successful digital strategy needs board-level strategy and commitment.

Fast forward to the present day and further McKinsey research reveals that many pharma companies have struggled to move their digital technology implementations from single, targeted use cases to a fully scaled suite of solutions.

While these investments are considerable, in an environment of dynamic external challenges, embracing end-to-end digital empowerment can create a clear path to cost savings, improved quality and increased resilience, as well as greater employee effectiveness.

The drug development processes and associated regulatory submissions represent a significant portion of the time and cost of bringing a drug to market, yet this part of the life cycle has one of the lowest levels of digital maturity.

Traditional product life cycle management offerings (PLM) are not well-suited to the complexities of biopharmaceutical processes, as seen with monoclonal antibody drugs. Legacy systems like ELN, LIMS and MES are siloed by nature, creating walls between functions – manufacturing vs development vs research.

Constrained by their lack of awareness of the complex relationships between data and knowledge across the full life cycle, legacy systems lead to a disconnect or lack of context between the critical quality attributes and process parameters, which are crucial in advancing understanding and unlocking the power of AI.

With the vast potential of advanced analytics and AI to transform the way therapies are developed, companies are now investing in data science programmes. Many of these programmes, however, are held up by complex, expensive and long data aggregation and contextualisation projects that could be avoided by capturing context and genealogy at the point the work is done.

The full potential of AI to accelerate innovation comes into view when we zoom out and gain perspective across all the data and scientific knowledge that accompany a drug throughout its life cycle.

With this broader perspective, it’s possible to imagine how a new generation of modular software platforms can support insight, process improvement and innovation at every stage of the BioPharmaceutical life cycle.

Realising the potential of AI requires some groundwork. Ensuring that models are fed with the high-quality data is paramount. Capturing process and scientific context, as close to the point of experiment execution as possible, enables the curation of ‘model quality’ data, while removing the time-consuming and error-prone step of adding context later.

This approach in itself helps improve efficiency and quality, but more importantly, creates a data backbone that becomes the rocket fuel for data science.

By placing the power of AI and advanced analytics into the hands of every scientist, these platforms seamlessly blend the work done by people with insight delivered by software, like having a well-informed assistant helping to spot trends or make recommendations.

For example:

1. Predictive modelling can help enable ‘Experiment by exception’ to reduce the time and cost of running physical experiments, or propose options for scaling from process development to manufacturing scale
2. Combining real-world data with AI analytics to make earlier assessments of drug developability, thereby maximising the probability of clinical success
3. Acceleration of critical milestones like IND/ BLA and technology transfer, through faster access to process and preclinical data
4. Predictive monitoring of product and quality attributes through the clinical and commercial manufacturing stages of the life cycle, improving quality and reducing failure. In the case of biologics, apart from the potentially tragic impact on patients, the financial impact of a batch failure is enormous
5. Providing a common platform for transferring processes both internally and to external partners like CROs, CDMOs and CMOs improves visibility and quality across the supply chain.

Pietro Forgione, General Manager, IDBS. Go to idbs.com