24/7 BIOPHARMA - issue 1 / October 2024

CPHI REPORT

nature of health authorities. The framework for developing drugs has been in place for decades and straying means potentially overlooking a risk or deficiency with consequences on patient safety or drug efficacy. This creates a dichotomy where drug innovators seek to minimise risk exposure as imposed by regulatory compliance, while the same regulatory machine expects drug companies to innovate. Innovations can create new and greater understanding that challenges what we know and what we perceive of as risk. With the emergence of new complex modalities health authorities have become more comfortable with innovative solutions; however, industry has been slow to pounce on the shift in thinking. One need only observe the sluggish adoption of PAT, or of rapid microbial testing and pharmaceutical continuous manufacturing as proof. Health authorities continue to look to industry and academia to characterise and address the difficult questions in hopes of providing enough guidance and direction for industry to continue to innovate. As part of the FDA Modernisation Act signed into law in December, 2023 3 , the FDA stated it would no longer require animal testing for new drug applications – an opportunity to replace a poor predictive model with a better approach. Animal models are poor proxies to humans as they themselves are complex systems. By logic, the output of an operating complex system should provide insight, but without full knowledge of system mechanics. When paired with an innovation tool such as the organ-on-a-chip for example, these new tools approach a more representative human mimetic partially because they lack the dynamic of a complex system. This affords drug developers a human light model. It is both the lack of complexity and more approximate human mimetic that scares risk averse developers and regulators alike. It is left to industry to argue the merits of a surrogate approach to animal testing, which has been tough sledding for a drug development framework built upon risk aversion. Shifting to more predictive digital models, whether at the molecule selection stage or at the manufacturing stage, is a step toward greater insight and greater uncertainty at the same time. Most organisations embarking on digital transformation only realise a small percentage of the opportunity. A McKinsey survey 4 concluded that only 16% of respondents say their organisations’ digital transformations have improved performance and equipped them to sustain changes in the Digital models

long term. The reasons for not realising the full benefits range from no clear vision as to why these innovations are being pursued, to poor change management, lack of a digital expertise or a lack of a digital culture and infrastructure. While there is no problem generating data sets, data translation is the issue. Impediments to adoption arise as new approaches spread across an organisation, with innovations typically evaluated in isolation. Combining new technology or a new analytical approach with a traditional methodology would provide a comprehensive view and reduce uncertainty for a more informed choice about which drug candidates to advance, prioritise or discontinue – ultimately leading to more efficient and successful drug development processes. From CRISPR to Chat GPT, advanced technologies affect our industry in many ways. The following innovations have the potential to impact our industry’s ability to identify molecule candidates with a high probability of success: - Artificial Intelligence (AI). - Organ on a Chip (OOC). - Quantitative Systems Pharmacology (QSP). - Digital and Predictive Modelling Techniques. - Data as a Product: Intelligent Data Management (see figure 1). Organisations that approach intelligent data management should view data as a precious asset to be carefully curated, managed and leveraged. Intelligent data management practices ensure that data is high-quality, well-governed, accessible and continuously improved. These factors can drive innovation in drug development, where data is the foundation for making informed decisions, optimising processes and ultimately bringing safe and effective drugs to market more efficiently. Intelligent data management in drug development will help organisations better manage their data assets, leading to more successful outcomes for patients. Even with focused initiatives like Big Data and Pharma 4.0, many small to mid-size organisations confront data management in piecemeal fashion. The notion of curating data is relatively new in our industry. As we realise the benefits of accumulating and analysing data to understand where targeted improvements can be made, data confidence and What is the missing link? Innovations shaping drug development

78 TWENTYFOURSEVENBIOPHARMA Issue 1 / October 2024