Why Clinical Trial Simulation Is Reshaping Drug Development: A Q&A with Dr. Aiden Flynn

Clinical trial simulation is rapidly becoming one of the most important tools in trial design, helping Sponsors make more informed decisions, reduce risk, and avoid costly delays. With the growing complexity of clinical trials and increasing pressure to deliver results faster, the need for smarter planning has never been greater.

One of the platforms leading this shift is KerusCloud®, and now part of the MMS technology portfolio. To explore how simulation is helping reshape drug development and what this means for sponsors, we spoke with Dr. Aiden Flynn, Senior Vice President at MMS to share his insights on the power of simulation, its role in engaging regulators, and what the future may hold for clinical trial design.

Aiden, it’s been an exciting time with the acquisition of Exploristics by MMS. Can you share how this partnership is amplifying the impact of KerusCloud on drug development?

Aiden: Exploristics was a small company with limited resources. We had grown steadily through recommendations and referrals, but there were large parts of the market we couldn’t reach on our own. Partnering with MMS gave us that reach and route to market. It provides access to a broader set of customers and more projects that demonstrate the value of KerusCloud, offering further compelling validation of our approach.

For sponsors, we can now deliver more efficient and smarter study designs and development programs that reduce the risks, costs, and timelines of drug development. Ultimately, this means more treatment options for the patients who need them. Like many founders, I wanted to build something that made a meaningful difference. Realizing that vision required a partner who understood it, and who could accelerate the adoption of the technology. That partner was MMS.

How is simulation technology, like that used in KerusCloud, transforming clinical trial design and helping the industry make better early-phase decisions?

Aiden: Clinical trials are complex, with many moving and interacting parts. The data we collect is increasingly varied and multi-dimensional. Innovations in clinical trial design and analysis have emerged, but traditional approaches often oversimplify this complexity, leaving valuable information unused.

With improved processing power, cloud infrastructure, and tools like KerusCloud, we can now run intensive simulations in minutes to bring real-time answers to complex design questions. This is especially valuable in early-phase trials, where risk is highest and many design choices must still be made. Phase 2 is particularly risky, yet we must define success criteria that justify investment in late-stage development. Choosing the wrong design or success metrics can have serious consequences.

Clinical trial simulation helps mitigate that risk. Our current success rates are more than double the historical average when we apply simulation to early-phase planning.

That’s incredible to hear! In what ways can tools like KerusCloud address long-standing challenges such as underpowered studies, patient recruitment difficulties, and trial delays?

Aiden: Traditional study design often relies on a small, fixed set of assumptions, many of which may not hold true. We also assume perfect data collection, ignoring real-world factors like missingness, variable recruitment rates, and uncertain effect sizes. Many trials are designed in silos, with statisticians brought in too late to simply calculate sample size while the rest of the protocol is already finalized.

This sequence is flawed. The statistical sections are the only ones that quantitatively assess study risks, yet they are often an afterthought. Later, we may discover the target population does not exist or is difficult to recruit. These issues can delay or derail a clinical trial.

As an industry, we need to invest more time in the trial design stage. That means identifying risks early, using available data to quantify them, and developing mitigation strategies. Design should be collaborative and iterative, with input from clinical, statistical, operational, regulatory, and commercial stakeholders. Simulation is a key enabler of that process. It provides a decision framework where risks can be quickly evaluated, alternatives explored, and suboptimal paths ruled out. Spending more time up front pays dividends later by avoiding costly amendments and delays.

How are sponsors leveraging simulation and modeling to improve engagement with regulatory authorities, particularly when proposing complex or innovative trial designs?

Aiden: Both Sponsors and global health authorities understand the challenges of high attrition rates and the need for new approaches in drug development. Regulators have supported innovation through pilot programs and guidance on complex clinical trial designs and model-informed drug development (MIDD).

When proposing complex studies, clinical trial simulation is critical. It quantifies risks across multiple, interconnected design elements. These risks include stopping a clinical trial too early, missing an effective treatment, or exposing patients to ineffective or unsafe drugs. Some clinical trial designs improve decision-making speed or accuracy, which benefits all stakeholders, including regulators.

Engaging regulators requires a clear plan and rationale. Simulation provides a decision framework to support that rationale, allowing for transparent conversations about design choices. It helps avoid unnecessary debate over hypotheticals by showing why decisions were made. Sponsors who can demonstrate that their designs are informed by rigorous analysis are typically viewed more favorably.

Sometimes, Sponsors try to answer too many questions in one clinical trial, leading to over-complicated trial designs with no accepted statistical methods to assess risk. Regulators are not there to peer review novel methods. In cases like this, simulation helps clarify what is feasible and appropriate, even in traditional designs, by exploring alternative scenarios and understanding the impact of unexpected issues. Other industries have already embraced simulation to manage risk and improve outcomes. In pharma and biotech, it will soon be routine and we will wonder why it took so long to get there.

That’s very interesting! As the industry pushes for more diverse and inclusive clinical trials, how can simulation platforms help sponsors plan for and achieve more representative study populations?

Aiden: This is a compelling topic. Regulators have issued guidance to encourage broader participation, which promotes health equity and provides better data on treatment risks and benefits for the real-world population. However, the pharmaceutical and biotech industry tends to be conservative and risk-averse. Clinical trial designs are often based on what has been done before, in part because regulatory guidance relies on published data or previous studies. If those studies excluded underrepresented groups, we are left without the data needed to confidently adjust our assumptions.

This creates an interesting dilemma. Sponsors may default to familiar designs to minimize perceived risk, and existing recruitment networks may not be well-positioned to engage underserved populations. Still, clinical trial simulation can help. Even when information is limited, it is rare to have nothing to work with. Simulations allow us to evaluate the impact of including more diverse populations and identify when those differences matter to study success.

In many cases, diversity can improve recruitment efficiency. Simulation gives Sponsors a way to bridge the gap between regulatory encouragement and operational reality by supporting informed, evidence-based decisions about clinical trial design.

Looking ahead, how do you see simulation-driven trial design evolving over the next 5 to 10 years, especially with trends like precision medicine, decentralized trials, and AI?

Aiden: Predicting the future is tricky, but we know change is coming. What will not change is the need for good science, solid evidence, and informed decision-making. Everyone recognizes the high attrition rate in drug development. Many new tools promise to fix this, but no single approach is a silver bullet. Instead, we need a suite of technologies that each add value under specific conditions. Simulation plays a central role in identifying when and where these tools are effective. It helps answer key “what if” questions before decisions are made.

While clinical trials generate high-quality data, the industry also collects vast amounts of real-world, unstructured, and metadata. These secondary data sources often lack the design rigor of clinical trial data, making them difficult to reuse. Often, they just were not collected with a clear purpose in mind.

Better decisions start with better data. Many failures could be avoided by linking clinical study design to expected outcomes, defining where new tools are useful, and closing the loop through a learning ecosystem that improves models over time. Clinical trial simulation can help establish that loop. It enables smarter, more data-driven design choices that evolve with the science, and it supports the broader goal of delivering better treatments, faster.

To speak with Aiden yourself, email info@mmsholdings.com and we will connect you!