Issue:June 2025
CLINICAL TRIALS - Solving Clinical Trial Challenges Through Sub-Population Optimization & Modeling Solution
INTRODUCTION
Clinical trials are the foundation of medical research, paving the way for groundbreaking therapies and treatments. However, these trials often face significant challenges, including high failure rates, substantial costs, and complex patient recruitment. In recent years, a revolutionary approach known as Sub-population Optimization & Modeling Solution (SOMS) has emerged to transform the landscape of clinical trials and offer new hope for more efficient and successful studies.
WHAT IS SOMS?
SOMS represents a shift in how clinical trials are conducted and analyzed. A sophisticated tool, SOMS leverages artificial intelligence (AI) and advanced analytics to identify biomarkers within specific patient subgroups that can predict treatment response. This capability helps researchers identify patient subgroups with higher efficacy and/or risk for adverse events, which significantly boosts the likelihood of trial success.
A MULTITUDE OF BENEFITS
One significant advantage of SOMS is its ability to progressively enhance trials throughout the process. From the outset, researchers can track subgroups, validate initial hypotheses, and run ongoing biomarker analyses to uncover new subgroups. This dynamic approach stands in stark contrast to traditional methods, which often rely on predetermined variables and static analysis. Trials that do not start with SOMS can still benefit from its introduction later, especially in cases of slow patient recruitment or minimal differences in treatment responses between treatment and placebo groups.
The power of SOMS includes real-time analysis, trial simulation, and benchmarking. Using simulated or real-world data, researchers can predict trial outcomes based on specific patient pool characteristics. This feature allows sponsors to simulate various scenarios and incorporate historical data to create more accurate patient pools. Moreover, SOMS can compare standard treatments or other therapies in a specific therapeutic area/indication to provide important insights into the potential performance of a new therapy in real-world situations.
The importance of SOMS becomes particularly apparent when considering the substantial costs and high failure rates associated with clinical trials. Taking a therapy through all trial stages can exceed $1 billion, with failure rates of 65% in Phase 2 and 35% in Phase 3. Even more concerning, only 12% of therapies that complete Phase 3 receive FDA approval.1 In this challenging landscape, SOMS offers a lifeline, helping to optimize trials and control for elements that might otherwise go unnoticed or take too long to identify.
One of the key strengths of SOMS lies in its AI integration. In contrast to traditional statistical methods in which statisticians decide variables beforehand, SOMS uses a data-driven approach to analyze multiple variables at once. This allows the AI to evaluate exponential permutations and comprehensively analyze all possible patient subgroups that might respond better to therapy or have improved safety profiles.
SOMS demonstrates impressive processing efficiency. Once data has been prepared, SOMS can generate analyses within 30 seconds for Phase 1 and Phase 2 data, or within a couple of hours for larger Phase 3 datasets. A comprehensive analysis, including data preparation, can be achieved within a week. This translates to a significant, near 20x reduction in analysis time compared to traditional methods, enabling continuous optimization throughout the trial lifecycle.
SOMS’s credibility stems from its use of validated, open-source algorithms with a proven track record, which lends weight to results presented to health authorities. The system also offers flexibility, allowing users to modify various criteria as needed. Its repeatability across trials within a portfolio, with consistently prepared data and adapted algorithms, has proven effective across multiple therapeutic areas.
KEY REAL-WORLD APPLICATIONS
One of the most critical applications of SOMS is to implement rescue strategies for struggling clinical trials. Common indicators of a trial in distress include unexpected adverse events or lack of efficacy in the treatment group. When such issues arise, SOMS can intervene with targeted rescue strategies. For instance, in a Phase 3 trial with serious adverse events in a subset of patients, SOMS can quickly analyze the data to predict which subgroups are more susceptible to these events.
A real-world example of SOMS’s effectiveness is from a Phase 3 trial in multiple myeloma that needed to identify patient subgroups at increased risk for cardiac failure. Using SOMS, researchers analyzed 25 various biomarkers, including demographic and disease characteristics. The analysis revealed two specific biomarkers within the patient population that indicated an increased risk of cardiac issues. This data empowered sponsors to focus on high-risk groups, implement protective measures and introduce interventions to mitigate the incidence of cardiac failure.
SOMS’s ability to identify and optimize effective subgroups within a trial is another crucial feature. To address various analytical needs, the platform offers three versions of the core algorithm, called Subgroup Identification Based on Differential Effect Search (SIDES). These variations include the basic SIDES algorithm, fixed SIDES and an adaptive SIDES. These algorithmic methods enable SOMS to analyze trial data with high accuracy and configurability to effectively identify patient subgroups with precision.
While SOMS excels at finding patterns when they exist, it is important to note that not every trial will have clear subgroup distinctions. The impact of SOMS-driven safety measures on trial integrity and success rates depends on several factors, including the specific findings for each trial, the nature of the identified risks, and how researchers choose to act on the information SOMS provides.
When addressing patient recruitment challenges, SOMS takes a unique approach. Rather than directly improving recruitment numbers, SOMS enhances the quality and relevance of the recruited patients. This “fewer but right” approach can lead to better trial outcomes in the long-term, including potentially faster recruitment due to a more focused pool.
The therapeutic and financial outcomes of using SOMS in clinical trials are significant. In one instance, a Phase 3 trial for a new antibacterial treatment initially showed no overall treatment effect. By analyzing 26 biomarkers, SOMS identified a subpopulation with a strong enough response to secure FDA approval. This intervention not only resulted in a successful drug launch for a particular patient subgroup but also prevented a late-stage failure, potentially saving pharmaceutical companies hundreds of millions of dollars.
SOMS offers valuable support to sponsors throughout clinical trials, from design to closeout. Its versatility allows application across the entire trial lifecycle, with a particularly significant impact between Phase 2 and Phase 3. In Phase 1, SOMS can leverage existing data from the therapy in different indications or from similar compounds to simulate patient responses. The simulated data helps inform Phase 2 design and patient selection.
The transition from Phase 2 to Phase 3 often sees the greatest impact of SOMS. By analyzing data from both Phase 1 and Phase 2, SOMS can refine inclusion/exclusion criteria, identify responsive subgroups, and ultimately optimize overall trial design for Phase 3. These capabilities significantly increase the chances of trial success and regulatory approval. SOMS’s value extends beyond Phase 3. It can also be used in post-approval phases to benchmark the therapy against standards of care.
LOOKING AHEAD
SOMS holds significant promise for the future of clinical trials. While SOMS currently operates independently, future developments aim to integrate it with data management systems. This will enable real-time analysis as data streams in, allowing for dynamic trial management with identification of emerging risks and opportunities. In addition, incorporating SOMS into risk-based quality management tools will enhance risk prediction and mitigation to leverage automated workflows.
The evolution of algorithms is also a critical area. Currently, SOMS uses a generalized approach across various indications. The future lies in specialized algorithms for specific therapeutic areas. These will provide more nuanced and accurate insights, especially in early signal detection. Custom algorithms tailored to specific contexts are being developed to better predict efficacy and safety events, leading to more precise insights for different therapeutic areas and trial types.
As AI and advanced analytics mature, their role in clinical trial optimization will grow significantly. The trend is toward more integrated, intelligent, and responsive systems. AI is likely to impact various aspects of trial design, conduct, and analysis, including sophisticated predictive modeling, automated patient matching and recruitment, real-time data analysis and AI-assisted protocol design.
Advanced analytics hold the potential for targeted approaches leading to adaptive trial designs that evolve based on incoming data. This could significantly reduce the time and cost of bringing new treatments to market. Integrating diverse data sources, like real-world evidence and genetic information, could lead to a more comprehensive understanding of treatment effects and patient responses, with AI playing a crucial role in synthesizing insights from these complex datasets.
As these technologies evolve, ethical considerations and explainable AI will be increasingly important. AI-driven decisions in clinical trials must remain transparent, interpretable and aligned with patient safety and regulatory requirements. The future of clinical trial optimization with tools like SOMS and other AI-driven solutions points toward more efficient, precise, and adaptive trials. This has the potential to revolutionize how new therapies are developed and brought to market.
SOMS is a powerful solution for many clinical trial challenges. By leveraging advanced analytics and AI, it offers a data-driven approach to optimizing patient subgroups, predicting outcomes and enhancing overall trial efficiency. As the field continues to evolve, SOMS and similar technologies are poised to play a crucial role in shaping the future of medical research and drug development, ultimately leading to better treatments and improved patient outcomes.
REFERENCE

Adrian Kizewski is Associate Director of Product Management at IQVIA. He brings expertise spanning R&D and clinical life sciences, business analysis, process design and improvement, and product implementation. He is currently a lead for IQVIA’s Clinical Data Analytics Solution (CDAS) as well as Sub-population Optimization and Modeling Solution (SOMS). He earned his MBA from the McDonough School of Business at Georgetown University, in addition to his MSc in Pharmacology from The Johns Hopkins University School of Medicine and a BSc in Biochemistry from Temple University.
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