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An Overview of 10 Applications in AI Clinical Drug Development Trials

An Overview of 10 Applications in AI Clinical Drug Development Trials

Insight by: Jon Warner

As a very general average, it has been said that bringing a new drug or therapy to market takes over $1 Billion and around a decade in total, assuming the therapy gets successfully through phase 1, 2, and 3 trials. Interest in therefore lowering the cost and/or accelerating the time it takes to develop a new drug has been very high on pharmaceutical companies’ agendas for many years. While general digitization has made some inroads, recent advances in the deployment of artificial intelligence (or AI-enabled technology solutions) are showing significant potential to make a much bigger impact in the future. While it is early days, here are 10 areas in which we are already seeing early investment:

 AI Can:

 Accelerate and Increase the Accuracy of Participant Screening 

Screening potential participants for clinical trials typically involves a significant amount of manual processing, which is time-consuming and prone to errors. However, by utilizing AI technology, real-time automated assessments of potential participant eligibility can be conducted, resulting in improved efficiency in the enrollment process.

Shorten study build timelines for clinical trials.

The process of building a clinical trial study can be highly manual and repetitive. Data managers are required to read the study protocol and create multiple case report forms, which can be a time-consuming task. However, the use of automated text reading can streamline this process by parsing, categorizing, and stratifying words. This can enable the software to automatically generate initial draft case reports and produce a data capture matrix for review. By leveraging this technology, data managers can optimize their workflow and improve the efficiency of clinical trial study build.

Improve Diversity in Clinical Trials 

AI has the potential to enhance diversity in clinical trials by aiding in the identification and recruitment of participants from underrepresented populations, tailoring the informed consent process, devising more inclusive trial protocols, and monitoring trial participation to ensure the achievement of diversity objectives.

 Bridge Gaps Between Trial Research and Clinical Care 

AI software has demonstrated a significant ability to expedite enrollment rates and eliminate communication barriers between clinical and research teams. Moreover, AI-powered capabilities can aid in clinical trial research, as well as clinical data integration and interpretation.

Speed up Clinical trial patient recruitment and management

AI has the ability to analyze electronic health records and other medical data sources, which helps in identifying potential participants for clinical trials. With the help of AI algorithms, a vast amount of data can be searched through to identify patients who meet specific criteria for a clinical trial. These criteria may include age, gender, medical history, and diagnosis. Subsequently, AI can suggest the most suitable candidates for recruitment and provide insights on how to manage their involvement in the trial effectively.

Enable Faster Time to Market

AI offers significant advantages in automating time-consuming and labor-intensive tasks that are integral to clinical trials, affording a potentially considerably faster path to market. This includes data collection, analysis, and selecting cohorts and patients. For instance, AI algorithms can analyze large volumes of patient data to pinpoint high-potential candidates for clinical trials. Additionally, algorithms can scrutinize patient behavioral patterns and provide recommendations for designing a trial. These benefits demonstrate the efficiency and accuracy that AI brings to clinical trials.

 Reduce trial/development costs.

AI can significantly decrease the expenses associated with workflow automation, such as patient recruitment and safety monitoring. This enables R&D teams, in particular, to save time and money. Furthermore, AI can expedite critical stages in the pharmaceutical R&D process, such as the discovery of novel drug compounds and the design of clinical drug trials.

Facilitate more Accurate Data Analysis

By utilizing algorithms, researchers can efficiently analyze vast amounts of data and uncover patterns and trends that clinical researchers might otherwise miss or take an extended time to recognize. For instance, AI-based models have the capability of predicting the potential toxicity of drug candidates, enabling research teams to discard unsuitable compounds and move forward with promising ones.

Allow greater predictive modeling.

By leveraging predictive modeling, clinical trial researchers can pinpoint patient populations that are most appropriate for specific treatments and modify trial design to suit them. This approach enhances the likelihood of success while mitigating the risk of trial failure or harm to patients. Furthermore, predictive modeling enables the identification of potential safety concerns in the drug development process at an earlier stage.

Increase the potential for Personalized medicine.

Each patient possesses a distinct set of attributes, which contributes to the challenge of assessing the efficacy of treatments. However, AI can assist in pinpointing specific patient demographics that are more probable to benefit from certain medications, such as by analyzing genetic variations and lifestyle patterns. Additionally, AI tools can personalize treatment by determining the appropriate dosage and frequency based on the patient’s unique characteristics, including their personal and family medical history.

Improve Patient Outcomes 

Each novel drug discovery, whether it’s a new therapy or a previously unrecognized application of an existing medication, broadens the range of options available to patients in need. AI-powered analysis of existing drugs and treatments is an effective strategy for identifying new applications. Machine learning algorithms can be employed to scrutinize patient data and provide early warning signals, enabling faster intervention. Additionally, AI-based reinforcement learning techniques can enhance participants’ adherence to trial protocols, resulting in improved outcomes.

In summary, any life sciences organization conducting clinical trials can gain many benefits from deploying Artificial Intelligence more widely and thoughtfully, but needs to ensure that it has knowledgeable and skilled human resources who can apply the right strategy and tactics to do so effectively. I will elaborate further on these individual items in future posts.