Generative AI: Its Role in Clinical Trials

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As they say, laughter is not the best medicine - penicillin is! It took modern science over ten years to successfully refine penicillin from Penicillium mold. And it was much longer before it was commercially available. We’ve come a long way since then, but drug development still isn’t easy!

It’s expensive, complex, and takes a lot of time. But that’s when it’s done traditionally. AI-powered drug development speeds up the process, makes it easier, and makes it more effective, too. For example, consider Insilico Medicine . It supplies AI solutions for every stage of the drug development process. Insilico has recently developed the world’s first fully AI-generated drug . It’s currently in Phase II trials. This drug has been designed to slow the spread of idiopathic pulmonary fibrosis (IPF). The disease currently has no cure.

Real-world data for rare diseases is limited, and hard to access. As a result, the development of drugs to address IPF is slow and costly. It is a challenge to identify strong drug candidates.

As part of their AI-powered drug development, Insilico Medicine used advanced AI algorithms to simulate realistic patient profiles. This ensured that they could model diverse populations, without increasing their patient selection costs. The team then ran simulations within the cohort for each proposed treatment, to study the response of these virtual patients. Promising drug candidates were identified by Insilico’s AI system, and clinical trial design was further optimized.

AI-based development has been revolutionary. It saved Insilico Medicine months of planning and significantly cut costs. It also improved the accuracy of patient cohort selection, resulting in overall better trial outcomes. It’s an industry path-breaker.

AI in clinical trialsWhat’s the difference between traditional and generative AI? Traditional AI focuses on recognizing patterns in data and making predictions. Generative AI goes one step further, and can generate synthetic data based on existing information. This means it’s particularly useful when real-world data is scarce. Both kinds of AI help improve the modern drug development process, but generative AI is proving to be a game-changer.In the period 1999-2022, the US registered over 160,000 clinical trials. As much as 90% of these trials fail! This could be for several reasons, including poor trial design, side effects and market failure. Generative AI can solve each of these issues in different ways.

AI-designed trials can create virtual patient data, improving sample size and patient selection. Even better, AI can predict the potential drawbacks of each drug. This could be possible side effects, adverse interactions and even toxicities at different concentrations. AI predictive analytics can help forecast and address complex market factors.

Clinical trials are essential for the development of new treatments, but they’re a challenge to design and run. It’s hard to find a strong, diverse cohort of patients that represent reality. The trials can become costly and time-consuming. Generative AI has the potential to address these issues when leveraged correctly. Here’s how it can help your business achieve greater efficiency and accuracy in trial design, patient cohort selection and trial simulation.

Optimizing patient cohort selection

Clinical trials test the new chemical on a specific patient cohort. For that reason, it’s crucial to work with the right patient cohort. Poor patient selection leads to inconclusive results. It also causes increased dropout rates as the patients are not properly aligned with the test. This can also result in delay or failure to bring new treatments to market. Generative AI tools work with criteria (like demographics, medical history, genetic factors, etc) to identify the ideal patient group. It can also generate a “virtual” patient cohort to speed up testing. Using AI, researchers gain a number of benefits:

Identifying rare disease populations: Patients with rare diseases are hard to find! Since generative AI can analyze extensive datasets quickly, it can run multifactor filters to identify potential participants.

Selecting patients based on criteria: In addition to suffering the disease, each trial has criteria that participants have to meet. Generative AI works with extensive databases to select patients based on trial requirements. This improves the chance of trial success.

Reducing dropouts: Another factor in patient selection is the participant’s likelihood to complete the trial. This can be predicted based on existing data. Better patient selection also leads to higher engagement and thus lower dropout rates.

Simulating tests for better trial design

AI-powered trial simulations are very realistic, and offer a great alternative to in-person trials. Generative AI creates virtual patient models that closely mimic the complexities of real human biology. AI simulations are then run on these patient models, to test various trial scenarios. These simulations predict patient outcomes and identify potential risks, even before real-world trials are conducted. They offer a comprehensive understanding of how different groups might respond to new treatments. Such simulations improve trial efficiency, safety, and chances of success. They also help by:

Reducing costs and timelines: Virtual trials do away with the need for patient recruitment, travel, and site management. This significantly reduces the costs associated. They also shorten timelines by allowing rapid iteration and simultaneous testing of several trial designs.

Improving trial efficiency: AI-powered simulations let researchers test multiple scenarios. That means that those delivering the most promising outcomes can be identified quickly. These scenarios or compounds can alone be taken forward to in-person trials. This shortened list of drug candidates increases trial design efficiency.

Identifying risk areas early: Through simulations, researchers can identify potential risk areas before actual trials begin. This proactive approach allows for adjustments that can prevent costly delays and failures.

Monitoring patients in real time

In a traditional clinical trial, patient monitoring and data collection rely heavily on manual processes. The patient may be required to visit the clinic in person for periodic check-ins. They may need to self-report side effects. This can lead to gaps in data, delays in identifying adverse impacts, and trial inefficiencies. Thanks to sophisticated real-time monitoring tools, generative AI can automate and enhance data collection and analysis. It helps with patient monitoring through:

Real-time health monitoring: The AI tracks vital signs gathered in real time from wearable devices and sensors. The system can then detect variations and predict potential health issues. Let’s say a patient’s blood pressure spikes. The tech immediately alerts healthcare providers. This speeds up necessary intervention and also improves record keeping. This proactive approach improves patient safety and also keeps trial data accurate.

Enhanced patient compliance: How do researchers ensure that patients adhere to the trial protocols? AI tools can help by providing personalized reminders and follow ups to the patient’s personal device. They can also analyze patterns in patient behavior. Certain aspects of the protocol may be missed regularly. For example, the patient may be sleeping fewer than the prescribed number of hours everyday. The AI system may make recommendations to adjust the protocol to suit that patient’s needs. This results in improved compliance, increasing the likelihood of trial success.

Predictive analytics for patient outcomes: Generative AI can analyze historical and real-time data to predict outcomes for individual patients. Based on various factors, AI can forecast how a patient is likely to respond to a treatment, identifying those at higher risk or most likely to benefit. Trials can be adjusted dynamically to improve safety, efficiency and trial success rates.

The ethics of AI in pharmaceutical trials

The benefits of generative AI in clinical trials are clear. However, certain ethical considerations need to be addressed.

1. Data privacy and security: AI models require vast amounts of patient data, including sensitive health information. In order to use such data, it needs to be anonymized, securely stored, and used ethically. In addition to being essential for patient trust, strong data privacy protocols help maintain compliance with data protection regulations like GDPR.
2. Bias in AI algorithms: Whatever the biases in the training data, the generative AI model inherits them too. This can lead to skewed results in patient selection and in other recommendations. These biases can disproportionately affect underrepresented groups and can compromise trial outcomes if not addressed.
3. Informed consent and patient autonomy: It may be difficult for patients to fully understand how AI works and give truly informed consent. Participants need to be properly informed on how the AI is being used, what data is being collected, and the implications of AI-driven decisions. AI-driven recommendations and decisions can also influence patient choices, potentially limiting their autonomy.
4. Regulatory compliance: The use of generative AI in clinical trials is still a relatively new field with evolving regulatory standards. Under these circumstances, obtaining approval from oversight bodies can be a significant challenge.
5. Over-dependency and misinterpretation: Some of the data produced by generative AI models can appear highly realistic. However, it’s still synthetic! Researchers might over-rely on AI-generated simulations without adequate validation. Misinterpretation of simulations and simulated data can lead to erroneous conclusions, affecting trial design and patient safety.

AI is no longer a subject of science fiction. It’s a fact of science today. Generative AI holds great promise when it comes to the future of clinical trials. As AI technologies continue to advance, we can expect further improvements as well.

Generative AI can streamline clinical trials, shorten drug development timelines and reduce costs. This can revolutionize the industry as new treatments are brought to market faster and more efficiently.

Ask our AI experts how we can help you make your clinical trials more efficient with the right Gen AI tools.

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