By: Dr. Sachin Dighe, Associate Director – LifeScience

Clinical trials evaluate the efficacy and safety of a new drug before it comes into the market. The complexity of the trial makes the outcomes very uncertain. The only way to minimize this uncertainty is to build scientifically valid and practically feasible study designs that can be easily executed during clinical operations.

Many critical components in the trial design directly impact the study outcome. Many potential trials fail to demonstrate ‘statistically significant efficacy’ just because of an insufficient number of trial subjects (sample size), inadequate study duration, wrong comparator (placebo or active comparator), inappropriate patient selection, or wrong statistical considerations. Sometimes, the patient eligibility criteria and trial methodology are complex and practically difficult to execute. All these design challenges lead to a higher probability of trial failures.

These design challenges can be minimized by a retrospective analysis of historical clinical trials published on PubMed (https://pmc.ncbi.nlm.nih.gov/), ClinicalTrials.gov (https://clinicaltrials.gov/),Cochrane Library (https://www.cochranelibrary.com/), and other databases. Evidence from Real-world Clinical Trial Data (RWCTD) and other insights helps a protocol writer make informed decisions while building the trial design.

However, in the current data-centric healthcare environment, the industry is overwhelmed with extensive historical clinical trial information. The fragmented nature of this data makes it difficult for a protocol writer to access, interpret, and utilize it effectively. The manual review and analysis of the RWCTD is time-consuming and prone to observational errors. Most of this information often goes underused because of the difficulties associated with accessing and interpreting isolated data.

This process can be optimized with Generative AI-enabled search optimization, which can perform a semantic search of relevant data points across clinical trial databases, extract them according to the user’s intent, and provide a logical summarization of key insights.

Generative AI-enabled RWCTD Search Optimizer

An RWCTD search optimizer harnesses the power of Gen AI, combined with natural language processing (NLP) and machine learning techniques, to access, extract, and interpret historical clinical trial data.

The user inputs a natural language query related to clinical trial design in a query bot. The query is processed using NLP techniques to understand the intent and extract key entities and parameters. Based on the extracted entities and parameters, an SQL query is dynamically generated to search the clinical trial database. The generated SQL query is executed against the clinical trial database to retrieve relevant data, which is then extracted and pre-processed for further analysis. Gen AI capabilities are used to perform a semantic search on the extracted data, identifying relevant data points even if they are not explicitly included in the query. Gen AI models summarize the extracted data logically, providing a coherent and concise summary tailored to the user’s query. The summarized data is finally presented to the user in an easily understandable format.

Business Benefits:

When Gen AI is coupled with an RWCTD search optimizer, the following benefits can be realized.

• Better utilization of historical clinical trial data for optimization of study design
• Enhance patient recruitment by practically feasible endpoints and enrolment criteria
• Enhance probability of statistically significant outcomes with sample size validation against industry benchmarks
• Accelerated clinical trial operations due to optimized trial methodology

Prospects

Clinical trials play a crucial role in the global drug development process. There has been a notable rise in the number of registered clinical trials in recent years. As of November 8, 2024, more than 515,000 clinical studies were recorded worldwide. This figure represents a substantial increase from just 2,119 registered trials in 2000.[i]

Overall, the complexity of clinical trials has grown in recent years, emphasizing the need for technology interventions in designing scientifically validated, practically feasible and commercially viable clinical trials.

The Gen AI-enabled RWCTD search optimization solution offers an excellent opportunity to accelerate clinical trials by generating more viable protocol designs. Upcoming innovations in AI technology will further improve protocol execution efficiency by anticipating future risks.

This can be further leveraged in Adaptive Trial Design that allows modifications to the trial and/or statistical procedures of the trial after its initiation without challenging its validity and integrity. The Gen AI-enabled search optimizer can help modify trial components based on interim analysis.

Conclusion

Generative AI-enabled RWCTD search optimization is revolutionizing clinical trials by addressing the challenges of fragmented data. As technology progresses, AI-driven solutions will lead to more efficient, effective, and personalized research. Now is the time to embrace the future of clinical trial design with Gen AI and tap into the complete potential of historical clinical trial data. The journey has just begun, and the opportunities are infinite.

[i] https://www.statista.com/statistics/732997/number-of-registered-clinical-studies-worldwide/