Katy Moore, a registered pharmacist with over 29 years' experience in the pharmaceutical industry, has held various leadership roles in clinical pharmacology, modelling and simulation and strategic business development. She has contributed to the development of numerous medicines for HIV, other viral diseases and cancer, including worldwide regulatory submission and commercialization. Moore has over 94 publications and holds an undergraduate degree in Biological Sciences, Zoology and Pharmacy from Colorado State University and University of Colorado, respectively. She received her PharmD from University of North Carolina School of Pharmacy.
Alexander MacDonald, a pharmaceutical industry veteran of over 23 years, has held key roles in quantitative and clinical pharmacology at several companies, honing his expertise in clinical pharmacology and model-informed drug development (MIDD). He has supported numerous successful global regulatory filings, including Nirsevimab for severe respiratory syncytial virus infection, Tezepelumab for severe asthma and Anifrolumab for systemic lupus erythematosus. MacDonald has published extensively in peer-reviewed journals across various therapeutic areas, including respiratory and immunology, oncology and metabolic diseases. He holds a Ph.D. in Quantitative Pharmacology and Toxicology and an M.Sc. in Control Systems from the University of Sheffield. To give readers a glimpse into your backgrounds, could you both share some defining moments that have shaped your careers so far? Katy Moore: My journey began as a clinical pharmacologist. In the 1990s, I pursued postdoctoral research focused on cutting-edge pharmacokinetic/pharmacodynamic modelling. After nearly three impactful decades with big pharma companies, my role progressed to managing full departments in hands-on modelling and immersive clinical trials, as well as business development assessing novel assets and platforms. Throughout my career, I have helped enable numerous successful global regulatory submissions and drug approvals for adults and children with infectious disease, cancer and other diseases. Now, at Allucent, I leverage this expertise to spearhead industry-topping capabilities. These aid sponsors seeking to understand, optimise and confirm product value through translational and model-informed drug development approaches. My responsibility is steering our clinical pharmacology, modelling and drug development teams who partner with sponsors on these approaches. Alex MacDonald: I started my career focusing on pharmacokinetic/pharmacodynamic modelling. This led me to a pivotal role as a toxicology risk assessor for the UK government, before transitioning to translational medicine and clinical pharmacology with pharmaceutical companies. Most recently, I led the clinical pharmacology and modelling group for respiratory and immunology at AstraZeneca. Career highlights include contributing to the development and regulatory approvals of breakthrough treatments for diseases like lupus. I've also worked extensively in oncology drug development programs. Some of my most rewarding experiences come from partnering with small biotech companies, which often collaborate with large pharma groups to advance innovative new medicines. Throughout my varied roles, I've enjoyed working cross-functionally to apply modelling approaches that ultimately help deliver life-changing therapies to patients in need. From your perspective, what are the challenges within the pharmaceutical industry, regarding the use of interim analysis, integration of external evidence and managing analytical complexities? Katy Moore: Clinical trials are costly and time-consuming and leveraging real-time data to inform decision-making is crucial. While regulatory authorities recognise the value of such approaches, hurdles remain in rapidly implementing them. For example, real-time data analytics in oncology settings aid in monitoring drug safety and target pharmacology, allowing for informed decisions during trials. Incorporating interim analyses and data visualisation techniques is vital for efficient decision-making, particularly in early trials where data flows continuously. At Allucent, we have several examples where our teams have influenced the dose escalation strategy and the number of patients required for each cohort in the initial study design and critically modified the study dose and design during study conduct based on real-time analysis in earlier cohorts. Effectively navigating these situations with sponsors not only ensures safety of the participants but streamlines their decision-making, saves time, resources which most importantly, benefits both patients and the development of the compound. Alex MacDonald: It is also crucial to anticipate potential outcomes in a clinical trial and proactively plan for contingencies, whether determining optimal dosing or integrating interim analyses to guide adaptive trial designs. However, it's essential to note that the implementation of real-time data analytics comes with its own set of challenges, particularly in ensuring data integrity and presenting findings in a digestible format. While late-stage trials may have stringent data protection measures in place, early-phase trials often operate with more flexibility, allowing for daily data flows and real-time decision-making. Presenting data in a manner that is both meaningful and actionable for clinicians and decision-makers is key to maximising the impact of real-time analytics. As technological advancements continue to shape the pharmaceutical industry, which specific capabilities do you find most promising for enhancing decision-making processes? Alex MacDonald: An emerging technology with significant potential is the proliferation of data capture, characterised by its speed, frequency and volume, facilitated by wearables and frequent imaging. This abundance of data, nearly in real-time, has the potential to revolutionise clinical trials. Advancements in data analytics are gradually catching up, particularly notable in the oncology space with liquid biopsy and circulating tumour DNA (ctDNA) analysis, offering real-time insights into disease progression and treatment response. However, the challenge lies in effectively managing and interpreting this vast amount of data, necessitating advancements in visualisation techniques and using artificial intelligence. While the full potential of AI in this context remains to be realised, it will play a critical role in streamlining data processing and decision-making in the near future. "Presenting data in a manner that is both meaningful and actionable for clinicians and decision-makers is key to maximising the impact of real-time analytics." The evolving skill sets required in pharmaceutical research, particularly in data science and AI, are of equal importance. It's crucial to supplement AI expertise with a robust foundation in statistical science, as statistics continues to be indispensable in drug development. A thorough understanding of pharmacology and clinical pharmacology is essential for interpreting early data effectively. Finding individuals proficient in all three areas is challenging yet essential for success. Postgraduate degree programs produce such professionals, yet they remain in short supply. Looking ahead, the demand for these multifaceted skill sets will only escalate alongside technological advancements. Katy Moore: Clinical pharmacology has been at the forefront of innovation, particularly in response to increasing regulatory emphasis on quantitative analysis. The integration of data analytics has enabled drug developers to recognise the value of scrutinising data more closely. In addition to rapid data integration, proactive discussions with the team upfront are essential. Focusing on key data points rather than the entire dataset allows for targeted analysis and ensures alignment with treatment goals and patient needs. Combining analysis with comprehensive team discussions, this integrated approach facilitates informed decision-making in drug development processes. As pharmaceutical leaders, what advice would you give fellow CROs to foster a data-driven culture and successfully adopt interim analysis and use of external data and advanced analytics for decision-making in drug development? Katy Moore: My core advice is to illustrate to teams, particularly those less experienced in quantitative decision-making, the tangible impact it can have. For example, in a recent collaboration at Allucent, we worked with a small biotech focusing on rare diseases. By swiftly turning around an analysis, we demonstrated a two-fold improvement in a crucial biomarker, directly translating to improved clinical outcomes. This allowed the company to accelerate their phase two program and secure the necessary investment to address the needs of patients with rare diseases. Committing to the team, educating them on the value of such analyses in driving decisions and collaborating closely to ensure alignment with clinical outcomes is crucial. Staying open to innovative approaches and proactively integrating assessments within clinical studies upfront is key to achieving desired results, where real-time adjustments may be needed to optimise treatment strategies during the study. Alex MacDonald: A well-known analogy of the learning-confirm cycle describes the progression from early to late phase drug development. This analogy was coined by a pioneer in Clinical Pharmacology modelling and simulation to capture the iterative process of learning about a drug and then confirming that knowledge through further testing. The key advice stemming from this is that early-phase clinical trials should be treated as learning studies, not confirmatory studies trying to definitively demonstrate efficacy. Importantly, use of internal and external data provides a holistic evaluation of the results for the development teams’ decision making. Attempting to design robust late-phase trials too early often leads to failure and missed opportunities to properly understand the drug. Particularly in oncology, more flexibility is needed in early trials since there is rarely a control group to compare against. Instead, quantitative and modelling techniques help gauge early activity relative to external historical data and existing standards of care. The competitive or clinical landscape may shift over just a few months in rapidly changing oncology fields. Quantitative analytics enable responsive modifications, even if they require a few extra days of analysis time, to appropriately shape the overall development program based on emerging trial data.