AI in Clinical Trials: The Future of Drug Discovery

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Despite the significance offered by AI to pharmaceutical companies, there are several challenges that must be overcome for successful integration of tech-based tools.

Image credit: NicoElNino | stock.adobe.com

Image credit: NicoElNino | stock.adobe.com

Artificial intelligence (AI) has become a cornerstone in the healthcare and pharmaceutical industry. The introduction of AI-powered devices has facilitated early diagnosing and provide valuable insight about new drug effects on patients. AI and AI-based tools have already been used in early drug discovery—assisting in finding suitable drug targets and advancing therapeutic molecules.

To gain approval from regulatory authorities for new drugs, pharmaceutical companies must provide accurate reports from clinical trials that include success rate, optimal dosage, human/animal trial results, adverse effects, and the safety profile. The entire process of a clinical trial is time-consuming and often results in failure due to inappropriate study models, false predictions, and insufficient data.

To address these concerns, researchers are increasingly using AI for managing clinical trials, patient recruitment, data analysis, and documentation. However, clinical trial research with AI is a whole new approach to streamlining data compilation and clinical studies with a higher accuracy rate.

Automating clinical trials using AI-algorithm-based tools speeds up the drug development process, making it a more efficient and cost-saving approach. According to the Roots Analysis, the global AI in clinical trials market size is estimated to grow from $1.42 billion in 2023 to $8.5 billion by 2035, representing a compound annual growth rate of 16% during the forecast period 2023-2035.

AI in Clinical Trials: Now and Beyond

When looking into the future of clinical trials, AI is unquestionably a multifaceted technology shaping the drug discovery process. The ideology behind using AI-driven tools in clinical trials is establishing a systemic channel to evaluate vast amounts of information generated during drug research with higher accuracy.

To date, researchers are using AI tools to identify drug molecules and understand disease patterns in patients. However, when intelligence is combined with machine learning, the technology will help researchers analyze large amounts of data to modify therapeutic drugs for positive outcomes. The application of an AI algorithm automates various parts of the drug discovery process, resulting in fast, accurate, and efficient clinical trials for drug approval.

By using artificial algorithms in clinical trials, researchers are able to collect invaluable information about drug efficacy and reduce human errors by automation information analysis tasks. The incorporation of AI-powered information analysis tools will serve as a groundbreaking technology to initiate virtual clinical trials in the future to shorten the time required for drug development and reduce dependency on physical volunteers.

Regulatory Support for Breakthrough Research with AI

FDA actions are highly based on accurate data to ensure new therapeutic interventions are safe and effective for human use. Pharmaceutical companies that are developing drugs have to provide complete data in the form of documents to get approval from the regulatory authorities. Undoubtedly, AI plays a significant role in fetching precise data during clinical trials. Considering the necessity of accurate data, the FDA provided flexibility in the risk-based regulatory framework so researchers can use technologies for data prediction.

For instance, in May 2023, the FDA's Center for Drug Evaluation and Research released an initial discussion paper jointly with the Center for Biologics Evaluation and Research and the Center for Devices and Radiological Health. This published paper addresses important considerations for using AI in clinical trials, such as human-led government, data quality, and model development standards.

Information from Real World to Trial Design

Clinical trials are an ideal way to demonstrate drug safety, efficacy, and positive outcomes of treatment. The information collected from clinical trials enables researchers to decide drug dosage, frequency of dosage, and what type of patient can benefit from these drugs.

The conventional clinical trial process is complex, expensive, labor intensive, and prone to errors. There are two main reasons for high clinical trial failures: poor patient recruiting mechanism and inability to collect information from patients due to inexpression or slow expression of drug effects.

For the successful approval of drugs, pharmaceutical companies are adopting AI in clinical trials to recruit trial participants and collect information in real-time. The Hierarchical Interaction Network (HINT), an AI-powered algorithm developed by Jimeng Sun, a computer scientist at the University of Illinois Urbana Champaign, has shown the potential to predict whether a clinical trial will succeed or fail on the basis of the drug molecule, specific disease, and eligibility criteria of the patient.

Based on the information collected from the algorithm, pharmaceutical companies can make quick decisions to alter clinical trial design or develop a different drug. The approach helps save plenty of time and investment waste due to drug failure outcomes during clinical trials. It also enhances the chances of receiving drug approval from the regulatory landscape.

Simplified Patient Recruitment with AI

Patient recruitment is a tedious and time-consuming process in clinical trials, potentially taking up to one-third of the clinical study time. Currently, pharmaceutical companies are leveraging AI to collect data about suitable patients for clinical trials.

AI in clinical trial systems harnesses natural language processing tools (an algorithm designed to allow computers to determine and interpret human language) that can learn patient data and clinical trial protocol. These systems extract vital information to make a well-informed decision about the eligibility of patients. Moreover, several clinicians are accelerating the patient recruitment process by relaxing eligibility criteria while adhering to standard safety protocols.

For example, Trial Pathfinder is an AI-powered system developed by biomedical data scientist James Zou, that is useful for analysis of completed clinical trials. This system also helps to interpret how adjusting the patient eligibility criteria such as blood pressure, thresholds, and lymphocytes can benefit the trial process. These patient eligibility criteria may affect the hazard ratio and rate of serious illness among patients. By using Pathfinder, researchers can adjust the patient eligibility criteria, thereby doubling the patient recruitment process without affecting the hazard ratio.

The technology helps prioritize the small set of clinical trials for which patients are qualified. The integration of AI in clinical trials can reduce guesswork and eliminate human errors by optimizing patient eligibility criteria for successful outcomes.

Digital Twins and AI Models: Future Vision of Clinical Trials

AI combined with digital technologies, such as digital twins and organ-on-a-chip, act as virtual models of patients' physiological characteristics. This virtual model of clinical trial helps to achieve invaluable data insight about patient health, drug effects, and can help determine the impact of adverse therapies. Presently, several pharmaceutical companies are leveraging digital technologies to predict disease progression and evaluate post-market drug surveillance to take a proactive approach to drug development.

In the future, AI-powered devices such as wearable sensors and smart watches will be combined with clinical trials to collect patient health information in real time. The integration of AI technologies enables efficient and constant monitoring of data during clinical trial periods. Moreover, it significantly reduced the burden of compiling patients at trial sites with personalized patient surveillance systems.

The system automatically collects information from different sources and transfers it to clinical trial systems for easy prediction. Additionally, wearable technologies enable automated collection and management of data with dropout for particular patients, making clinical trials efficient and less risky.

Challenges to be Addressed

AI in clinical trials helps to overcome several limitations posed by conventional drug discovery processes. Despite enormous benefits, several challenges must be overcome for the effective implementation of AI in clinical trials and other drug development applications. One of the biggest challenges is a need for more standardization and issues with data quality and data security.

AI-based tools rely on high quality data to predict clinical trial patterns and patient response accurately. If the data collected from different sources are biased, AI algorithm-based tools may not be able to provide accurate information, resulting in clinical trial failure.

Furthermore, data security challenges are a major concern while using AI in clinical trials. Data collected from patients are extremely crucial for drug development; therefore, it has to be protected from unauthorized access by third parties. Researchers must take appropriate cybersecurity measures or multi-factor authentication to ensure patient data are completely secured.

A Promising Future of Drug Discovery Harnessing AI

With the above outlined innovations and the role of AI in clinical trials, it is clear AI will be the future of clinical trials. The development of new drugs will be accelerated with a real time analysis approach offered by AI-driven tools.

Moreover, implementation of AI shortens the time of drug development, encouraging pharmaceutical companies to invest valuable time in research and development processes with higher precision. The flexibility provided by the regulatory authorities for using innovative technologies in drug development played a significant role in widespread adoption of AI in clinical trials. Despite the significance offered by AI to pharmaceutical companies, there are several challenges that must be overcome for successful integration of tech-based tools.

For instance, robust cybersecurity guidelines and software must be incorporated with AI tools to prevent data breaching. Furthermore, advanced computer modeling combined with AI and machine learning helps in the regulatory evaluation of newly discovered drugs.

The widespread adoption of digital technologies, virtual learning tools, and artificial intelligence will be the future of clinical studies. With integration of technologies, there will be less financial burden on pharmaceutical companies for drug development. In the future, the focus of pharmaceutical companies will be on using AI applications for developing patient-centric drugs with seamless accuracy—thereby bridging the gap between drug discovery, development, clinical trials, approval, and market supply.

About the Author

Nancy Kapila is a seasoned pharmaceutical consultant with over five years of varied experience and a Master’s in Pharmaceutics from Panjab University. She excels in drug mechanisms and interactions. Her career highlights include collaborating with numerous pharmaceutical companies and offering strategic insights and guidance. Nancy stands out for her dedication to keeping abreast of pharmaceutical advancements, regulatory changes, and emerging trends. She believes in continuous learning to navigate the industry’s complexities and provide innovative client solutions. Fascinated by the role of data analytics in decision-making, Nancy delves into data to uncover patterns and opportunities, offering evidence-based recommendations for process optimization, product development, and operational efficiency. Her career is driven by a relentless pursuit of knowledge, passion for data insights, and commitment to leading pharmaceutical companies towards success in a dynamic industry.

Works Cited

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