The role of AI in optimizing clinical trials| HCLTech

The role of AI in optimizing clinical trials

Artificial intelligence is playing an important role in optimizing clinical trials, including site identification and selection
4 minutes 18 seconds read
Dr. Shyam Sunder Rao Chepur
Dr. Shyam Sunder Rao Chepur
Associate Vice President, ERS
4 minutes 18 seconds read
The role of AI in optimizing clinical trials

Clinical trials are crucial for medical research and drug development, enabling pharmaceutical organizations to gather vital data on the safety and efficacy of health interventions.

These trials, ranging from small-scale Phase 1 studies involving 20 to 100 participants to late-stage, large-scale Phase 3 studies involving over 1,000 volunteers, play a pivotal role in advancing medical knowledge and bringing new treatments to patients. The duration of these studies can vary from several months to several years. 

Challenges in conducting a clinical trial

Clinical research conducted by Contract Research Organizations (CRO) operates within a vast ecosystem. This ecosystem includes various stakeholders such as pharmaceutical companies, biomedical researchers, regulatory authorities, patients and healthcare providers. It is characterized by countless connections and several permutations and combinations. Despite great strides in the pharmaceutical industry and biomedical research, the journey of bringing drugs to market remains complex, with significant room for enhancement. Clinical trials, due to their time-consuming nature and high costs, pose challenges that often lie beyond a company’s direct control, including regulatory constraints, patient access, costs, infrastructure and trial complexities. 

Choosing the appropriate site is one of the most critical decisions that significantly impacts trial success. Optimal site selection can alleviate challenges related to infrastructure, patient availability and cost. 

Here, we delve into the challenges faced in optimizing site selection for a clinical research trial:

  • Right experience: Selecting the right clinical trial site requires evaluating numerous factors. This includes assessing the number of patients screened, prioritizing quality over quantity and investigating screen failures to optimize recruitment strategies. Efficient patient enrollment, understanding the average time taken to recruit one patient, monitoring dropout rates and focusing on retaining patients are crucial throughout the study. Lastly, it is important to ensure adherence to the study protocol by minimizing protocol deviations. Adopting an integrated approach considers both quantitative metrics and qualitative aspects such as site expertise and commitment to trial success, as well as the availability of the right equipment.

  • Improper facility: Adequate infrastructure facility and equipment are essential for a successful trial. Inadequate facilities can result in errors, delays or compromised data quality. Sites should be well-equipped with laboratories, storage areas and specialized tools relevant to the study. The presence of calibrated instruments and trained personnel is essential. Regular maintenance and quality checks for equipment are imperative to prevent disruptions during the trial.

  • Access to the right or suitable subjects for the trial: Identifying sites with access to the target patient population is pivotal. Consider local demographics, disease prevalence and healthcare infrastructure. Sites located near hospitals or clinics where potential participants seek medical care are more beneficial. Collaborating with such sites can enhance subject recruitment and retention.

The data overload conundrum

Selecting the right site for a trial requires extensive data collection, encompassing various aspects such as patient demographics, site capabilities and regulatory considerations. However, challenges such as ensuring data completeness, quality maintenance and managing inconsistency hinder the effectiveness of traditional methods in utilizing the data. 

By employing data analytics and AI techniques, clinical trials can foster more objective and data-driven decision-making. Effective data management optimizes resource utilization, increases scientific collaboration and improves decision-making. Furthermore, leveraging this data helps in predicting the site performance, thereby improving the overall trial efficiency.

How artificial intelligence and machine learning transform site selection

Advancements in AI empower CROs in their search for Optimal Clinical Trial Site identification by churning and turning enormous amounts of data into actionable insights, facilitating informed decision-making on site selection or ejection. Despite the data being readily available, segregating, analyzing and understanding if it aligns with the requirements can be overwhelming and can even paralyze the process.

AI automates data cleaning processes, improving data quality and completeness. ML algorithms detect patterns in historical data to forecast site performance, refining site selection efficiency. Real-time monitoring of trial data facilitated by AI also enables proactive decision-making. Furthermore, AI and ML personalize patient recruitment strategies based on data trends, enhancing patient enrollment rates. Lastly, they model complex relationships between variables, supplementing traditional statistical methods for more accurate site selection.

But how much data is too much data?

Now, how do we strike the right balance? By using AI and ML, we can make sense of this data overload. Instead of drowning in too much or too little information, we focus on the important stuff. Here is how:

  1. Previous enrolment metrics: AI models can quickly scrutinize historical data points related to patient enrollment in past clinical trials. This analysis predicts enrollment rates for future trials and helps optimize planning and resource allocation.

  2. Patient access data: By identifying potential barriers to enrollment and suggesting ways to improve patient access by utilizing information about patients’ ability to access trial sites, their health status and other relevant factors.

  3. Data available on equipment: Advanced AI solutions can help ensure that trials are well-equipped and predict and prevent potential equipment-related issues by accessing information about the medical equipment used in trials, such as its availability, functionality and usage.

Leveraging AI and ML can help prevent trial sponsors from compromising on the data volume, enabling them to make an informed decision on site selection and adhering to all the criteria required to be an optimal site for the trial.

HCLTech’s top 10 tech trends

Watch the video

The path forward

As AI solidifies its presence, the clinical trial landscape is poised for transformation. Success lies in identifying sites with principal investigators capable of attracting high-quality subjects meeting baseline clinical trial requirements. AI models, under the direction of CRO experts, can meticulously analyze data and rank results, institutions, sites, investigators, countries and geos, empowering the trial sponsor to zero down on the right sites. This helps in seamless engagement and conduct of the trial.

By embracing AI-enabled capabilities, biopharma companies can:

  1. Optimize site selection
  2. Develop core AI competencies
  3. Reinvest resources strategically

The proliferation of AI within the clinical domain is both promising and well-deserved. However, this symbiotic relationship between technology and biopharma necessitates robust collaboration, rigorous testing and a deeper collective comprehension.

Share On