To bring a new drug to the market takes approximately 10–15 years and costs between 1.5–2 billion US dollars. As such, a failed trial can result in significant losses in both time and money. Unfortunately, clinical trial failure is not a rare occurrence; only 1 in 10 compounds that enter trials reach the market. Fundamental causes of trial failure include poor patient cohort selection, recruitment and retention issues, inadequate patient monitoring, and safety concerns. The development of medicine relies on successful clinical trials, which are indispensable in bringing innovative therapeutics from the laboratory to patients. Nevertheless, as our understanding of human complexity grows, clinical trial design is becoming increasingly challenging.
Fortunately, AI has proven incredibly useful in transforming critical steps of clinical trial design, helping overcome issues that often lead to trial failure. Innovative AI platforms have shown to be safe, cost-effective, and impactful in clinical trial optimization. NextTrial AI offers a highly collaborative, AI-driven, cloud-based data platform that simplifies all stages of the clinical trial lifecycle—from inception to commercialization. It provides a unified data repository with AI-powered decision support and complete trial oversight, streamlining the trial process, minimizing risk of failure, and accelerating research translation.
NextTrial AI in Clinical Trial Design
NextTrial AI supports clinical trial design at all stages, including selecting optimal endpoints, refining inclusion and exclusion (I/E) criteria, optimizing assessment schedules, and building a risk profile using safety data. It also provides support tools for research and analysis.
Endpoint Selection
Selection and refinement of meaningful, clinically relevant endpoints are essential for evaluating intervention effects in clinical trials. Endpoint validity should be verified against gold standards, and investigators must estimate the required sample size to meet study objectives. NextTrial AI’s unified data repository and AI-powered decision support streamline endpoint selection and sample size determination, optimizing endpoint choices to enhance intervention efficacy.
Real-World Data (RWD) and Real-World Evidence (RWE) offer additional insights into the effectiveness of interventions in real-world clinical settings, complementing clinical trial data. These resources support novel endpoint discovery, broaden insights into rare diseases, and encourage a more patient-centric approach.
I/E Criteria Refinement
Developing and refining I/E criteria is often long and complex. Criteria may be overly strict, excluding relevant patients and biasing results, or too broad, leading to suboptimal outcomes. Manual review of hundreds of clinical terms across past trials can be tedious, made more difficult by non-standardized terminology.
NextTrial AI uses proprietary Natural Language Processing (NLP) models to extract meaningful features from past trials and literature. These models identify patient characteristics most likely to yield favorable outcomes and uncover hidden clinical relationships within unstructured data. I/E variables from numerous sources are aggregated and summarized, providing insights across multiple dimensions. Extracted characteristics can then be enriched with data from disease registries and electronic health records (EHR) to further improve recruitment.
Schedule of Assessments
During trial design, identifying necessary assessment procedures is essential for collecting required data. Non-essential procedures should be eliminated to reduce participant burden, improve recruitment, and minimize dropout rates. Once disease profiles and study characteristics are optimized, NextTrial AI recommends appropriate procedures based on data from similar trials. As the trial progresses, intelligent insights into participant behavior and adherence further enhance engagement and retention.
Safety Monitoring
NextTrial AI enables users to analyze signals from RWD, historical adverse event (AE) data, regulatory databases, literature, and approved medication labels. This helps create a strong benefit–risk profile, crucial for regulatory approval. The availability of this information saves time, ensures comprehensive risk evaluation, and promotes patient safety by minimizing potential harm.
Other Relevant Tools for Research and Analysis
Beyond design processes, NextTrial AI simplifies access to essential research data such as biomedical literature, historical trial results, toxicology reports, and information on genes and biomarkers. All relevant information can be sourced, searched, summarized, and integrated seamlessly into study design. This improves productivity, reduces human error, enhances protocol integrity, and minimizes unconscious bias.
Summary and Future Perspectives
NextTrial AI leverages data from past trials, RWD, scientific literature, genomics, adverse events, disease registries, and internal datasets to simplify clinical trial design and reduce protocol amendments. This accelerates timelines and helps deliver innovative treatments to patients more efficiently.
Since its launch last year, NextTrial AI has been continuously enriched with diverse data sources and novel predictive models. In the coming years, platforms like NextTrial will further optimize and streamline clinical research processes, reduce trial failure rates, and enhance outcomes across the global healthcare landscape.