In this QCast episode, co-hosts Jullia and Tom examine how machine learning is being applied across the pharmaceutical industry, cutting through the hype to focus on where these methods deliver real value. They explain what machine learning means in a regulated drug development setting, how it differs from traditional statistical approaches, and why it is increasingly relevant across discovery, clinical development, and post-approval activities. The discussion explores practical use cases, regulatory considerations, and the challenges teams must manage to use machine learning responsibly and effectively.
What Machine Learning Is and Why It Matters in Pharma
Machine learning refers to a group of analytical methods that allow models to learn patterns from data and improve predictions as they are exposed to more information. In the pharmaceutical industry, these methods are used to support tasks such as compound screening, patient stratification, enrolment forecasting, and safety signal detection. Unlike traditional statistical models, which are often designed for inference and hypothesis testing, machine learning is particularly well suited to prediction and pattern recognition in complex, high-dimensional datasets. When applied appropriately, it can help teams make better use of growing volumes of clinical, biological, and real-world data, supporting faster and more informed decision making across the drug development lifecycle.
How Machine Learning Is Used in Practice
In early research, machine learning supports target identification and compound optimisation by analysing large chemical and biological datasets. As programmes move into clinical development, applications shift towards operational and analytical support. Predictive models can be used to inform site selection, forecast recruitment, and identify patients at higher risk of dropout. During trial conduct, algorithms may support risk-based monitoring by highlighting unusual data patterns or operational signals that warrant closer review. Beyond clinical trials, machine learning is increasingly used to analyse real-world data and support pharmacovigilance activities. Across these settings, the most effective implementations integrate machine learning outputs into existing workflows, with clear oversight from clinical, statistical, and operational experts.
Regulatory Expectations and Governance Considerations
The use of machine learning in pharma is shaped by the same regulatory principles that apply to other analytical methods. Regulators expect models to be fit for purpose, well documented, and supported by appropriate validation. Particular emphasis is placed on data integrity, traceability, and change control, especially for models that evolve over time. Transparency and explainability are also important, particularly when model outputs influence decisions related to patient safety or primary trial analyses. Strong governance frameworks, including clear ownership, role-based access, and audit trails, are essential to ensure machine learning tools can be used with confidence in regulated environments.
Common Challenges and How to Address Them
One of the most significant challenges is data quality. Incomplete, inconsistent, or biased data can undermine model performance and lead to misleading results. Generalisability is another concern, as models trained on historical data may not perform as expected in new studies or populations. Integration into existing systems and workflows can also be difficult, particularly if stakeholders do not understand or trust model outputs. These challenges can be addressed through careful upfront planning, robust validation strategies, ongoing performance monitoring, and close collaboration between data scientists, biostatisticians, clinicians, and operational teams.
Practical Takeaways for Drug Development Teams
Successful use of machine learning starts with clearly defined questions and an understanding of how outputs will inform decisions. Investing in data quality, governance, and documentation is critical from the outset. Teams should select methods that balance predictive performance with transparency, particularly in regulatory-facing contexts. Validation and monitoring should be treated as ongoing activities rather than one-off exercises. When machine learning is positioned as a complement to, rather than a replacement for, established statistical and clinical expertise, it can deliver meaningful improvements in efficiency, insight, and decision making across pharmaceutical development.
Jullia
Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.
Tom
I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.
Jullia
Whether you’re in biotech, pharma or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.
Tom
Today we’re focusing on machine learning in the pharmaceutical industry. Jullia, why don’t you start us off? How would you define machine learning in this context, and how is it different from the analytics teams have always used?
Jullia
So, in simple terms, machine learning is a set of methods where algorithms learn patterns from data and improve their performance as they see more examples. In the pharmaceutical industry, that usually means using historical and real time data to support decisions across discovery, development, and post approval activities. The key difference from traditional statistical approaches is not that statistics disappear, but that machine learning focuses on prediction and pattern recognition at scale. Instead of specifying a model structure upfront and testing a small number of hypotheses, we allow algorithms to explore complex relationships across many variables. That can be particularly useful when data are high dimensional, messy, or evolving quickly, which is common in areas like genomics, imaging, and real world data. That said, these methods still rely on sound data foundations, clear objectives, and careful validation, so they complement rather than replace established biostatistical practice.
Tom
That distinction is helpful. Now, where are we actually seeing machine learning being applied today across the pharma value chain, rather than just talked about?
Jullia
There are several well-established use cases. In early discovery, machine learning supports target identification, compound screening, and structure activity relationships by analysing large chemical and biological datasets. As programmes move into development, the focus shifts. We see applications in trial design, such as predicting enrolment rates, optimising site selection, and identifying patient subgroups more likely to respond. During trial conduct, algorithms can be used for risk-based monitoring, flagging unusual data patterns or operational risks earlier than manual review alone. Beyond trials, machine learning plays a role in pharmacovigilance, for example by supporting signal detection from safety databases and unstructured sources. Real world evidence is another area, where models help analyse healthcare records to understand treatment pathways and outcomes. Across all these stages, the common theme is using data more efficiently to support faster, more informed decisions, while recognising the need for human oversight.
Tom
Those opportunities sound compelling, but pharma is also a highly regulated environment. How do regulators view machine learning, especially when models influence clinical decisions?
Jullia
Regulators are generally supportive, but cautious. Current regulatory guidance emphasises that any method used in drug development must be fit for purpose, transparent, and well controlled. For machine learning, that means clear documentation of how models are developed, trained, and validated, and how they’re used in decision making. There’s particular focus on data integrity, audit trails, and version control, especially when models are updated over time. Explainability is another important point. While not every model needs to be fully interpretable, sponsors are expected to understand and justify how outputs are generated, particularly if they affect patient safety or primary analyses. Regulators also expect appropriate governance, including role-based access and change management. So, while there’s no blanket prohibition on machine learning, its use must align with existing principles around quality, traceability, and scientific validity.
Tom
You mentioned explainability, which often comes up as a challenge. From a practical perspective, what makes machine learning difficult to implement well in pharma?
Jullia
So, there’s a couple of recurring challenges. Data quality is the first. Machine learning models are only as good as the data they are trained on, and clinical data can be incomplete, inconsistent, or biased. If those issues aren’t addressed, models may produce misleading outputs. The second challenge is generalisability. A model that performs well on historical trial data may not behave the same way in a new study, a different population, or a changed operational context. That’s why external validation and ongoing performance monitoring are so important. The third challenge is integration. Models need to fit into existing workflows, systems, and governance structures. If outputs are not trusted or understood by clinicians, statisticians, or regulators, they won’t be used. Finally, there’s the skills gap. Effective use of machine learning requires collaboration between data scientists, statisticians, clinicians, and operational teams, which can be difficult to coordinate.
Tom
Alright so given those hurdles, how should teams decide when machine learning is appropriate and when simpler approaches might be better?
Jullia
That decision should start with the question rather than the method. Teams need to be clear about the problem they’re trying to solve and what success looks like. If the objective is inference, estimation, or hypothesis testing with clear regulatory expectations, traditional statistical models may be more appropriate. If the goal is prediction, prioritisation, or early signal detection in complex data, machine learning can add value. It’s also important to consider the cost of complexity. More complex models require more data, more validation, and more governance. In some cases, a simpler approach that stakeholders understand and trust will deliver more impact than a sophisticated model that’s hard to explain. A pragmatic mindset, where machine learning is one tool among many, tends to work best in practice.
Tom
Great, now let’s talk about clinical trials specifically. How is machine learning changing the way trials are ran and designed?
Jullia
Yeah so in trials, machine learning is most often used to support operational efficiency and risk management. For example, predictive models can help forecast enrolment and identify sites likely to struggle, allowing proactive intervention. Patient level models can support eligibility screening or stratification by identifying patterns associated with response or dropout. In monitoring, algorithms can scan incoming data to flag unusual trends, such as protocol deviations or data anomalies, supporting a risk-based approach. Importantly, these tools are typically used alongside, not instead of, established processes. They help teams focus attention where it’s most needed, rather than replacing clinical judgement or statistical oversight. When used appropriately, this can reduce burden, improve data quality, and support more timely decisions during trial conduct.
Tom
I feel like that raises an important point about roles. How do biostatisticians fit into this picture as machine learning becomes more common?
Jullia
Biostatisticians remain central. Their expertise in study design, bias, variability, and interpretation is critical to ensuring machine learning is applied appropriately. In many organisations, statisticians work closely with data scientists to define objectives, select suitable methods, and design validation strategies. They also play a key role in communicating results to clinical and regulatory stakeholders, translating model outputs into meaningful insights. Rather than being displaced, statisticians often act as a bridge between advanced analytics and regulatory grade evidence. This collaboration helps ensure that innovation doesn’t come at the expense of scientific rigour or credibility.
Tom
We’ve talked about benefits and challenges. Are there any common misconceptions about machine learning in pharma that are worth addressing?
Jullia
Definitely. So, one common misconception is that machine learning automatically delivers better results than traditional methods. In reality, performance depends on context, data, and implementation. Another is that these models remove the need for human judgement. In practice, human oversight is essential at every stage, from data preparation to interpretation and action. There’s also sometimes an assumption that regulators are resistant to innovation, when in fact they’re open to new approaches that are well justified and controlled. Addressing these misconceptions helps set realistic expectations and supports more effective adoption.
Tom
Now before we wrap up, could you summarise a few practical takeaways for teams considering machine learning in their programmes?
Jullia
Of course. So first, start with a clear question and define how the output will be used. Second, invest in data quality and governance from the outset, as these underpin everything else. Third, choose methods that balance performance with transparency and trust, especially in regulated settings. Fourth, validate models thoroughly and monitor them over time, rather than treating them as one-off solutions. Finally, encourage collaboration across disciplines, bringing together statistical, clinical, operational, and data science expertise. These steps will help ensure machine learning delivers real, sustainable value rather than short term novelty.
Jullia
With that, we’ve come to the end of today’s episode on machine learning in the pharmaceutical industry. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode and share it with a colleague. And if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.
Tom
Thanks for tuning in, and we’ll see you in the next episode.
QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.
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