Emerging use-cases for AI in clinical trials

Overview

Clinical trials are becoming more expensive where, according to a Deloitte’s report1, the average cost to get a drug to market in the USA was $1.188 billion in 2010, and $1.981 billion in 2019. This increase in cost reflects the difficulties that are associated with current linear clinical trial designs. Clinical trials can take a long time due to difficulty in finding suitable/eligible patients for each study and the growing amount of data available used to plan or inform a trial. Current clinical trials are still evolving to make use of the rapidly developing technologies, scientific methods, and data availability of recent years.

One possible way to improve and transform the clinical trial process as we know it would be through the implementation of Artificial Intelligence (AI). AI incorporates all intelligence demonstrated by machines and includes important aspects such as Machine Learning and Natural Language Processing. It is already widely used in modern technology, such as in smartphones and online website searches, but has also been used more recently to innovate the drug discovery process. AI implementation could be of benefit across diverse tasks is the planning, execution and analysis stages of clinical trials to improve cost effectiveness, study time, treatment efficacy, and quality of data2.

Many emerging developments in artificial intelligence (AI) have the potential to benefit the clinical trials landscape.

AI in Adaptive clinical trial design

Many clinical trials are designed linearly, however adaptive designs are being used to allow predetermined changes during a study in response to ongoing trial data. Adaptive designs provide the flexibility to optimise resource allocation, end an unproductive trial early, and better characterise a treatment’s efficacy and safety through multiple endpoints. AI can help to inform and optimise adaptive designs through the analysis of healthcare data to select optimal endpoints, determine and monitor parameters for early stopping, and identify appropriate protocols for the trial3. These study design changes can increase the efficiency of a study, resulting in a trial which is more cost and time effective while maintaining high quality data collection and analysis.

Meta-analysis

AI can also enhance the exploratory analysis of data from previous trials. AI-enabled technology can collect, organise, and analyse increasing amounts of data, which could be applied to data from previous trials. This is normally performed manually by a biostatistician as part of a meta-analysis, but AI could help to gather and perform initial analyses before a more in-depth statistical approach is taken. This could highlight potentially important patterns in collected evidence which would then be used in informing trial design.

Synthetic control arms

Current clinical trials typically compare an experimental treatment to placebo and established treatments, assigning enrolled patients to either a treatment or control group. Synthetic control arms are an AI-driven solution for having a control group in a single-arm trial, which usually have only a treatment group. Synthetic control arms use data from previous studies to simulate the control treatment in patients. This would allow for all patients in a trial to receive active treatment to provide more evidence for the treatment efficacy and safety at each clinical trial stage. This may also have an impact on patient enrolment as patients generally show less interest in enrolling on placebo-controlled trials4,5.

As synthetic control arms are relatively novel, comparisons should be made with traditional control groups. In a typical blinded trial, patients are unaware if they are receiving the experimental active treatment, or a placebo. This is to test that the new treatment is causative of any clinically meaningful response. Synthetic control arms could create more single-arm clinical trials, and the fact that patients are aware of receiving active treatment would be a factor given clinically meaningful results are found.

Site selection

Identification of suitable sites and investigators to perform a trial is an important factor in study efficiency and feasibility. A study site should be amply equipped to carry out a study, must be of a suitable size to process the needs of study participants, and be located in an accessible area to potential participants and investigators. Identification of target locations and investigators can be optimised through AI implementation, which also enables real-time monitoring of site performance once the trial has started. Study sites can be evaluated and compared through the development of a points-based algorithm which could factor in location, site size, and equipment.

Patient enrolment/recruitment

Most patients enrol on a clinical trial if they have not responded to existing treatments in a clinically meaningful way. However, there are often strict eligibility criteria required for patients to enrol for a trial, including diagnostic tests, biomarker profiles, and demographics. Currently, patients find out about clinical trials either through manually searching online databases or, on occasion, through a clinician’s recommendation. This puts a lot of responsibility on patients to search for potential trials, just to be faced with trying to understand eligibility criteria full of medical jargon. This can lead to low patient recruitment.

AI and natural language processing offer a potential solution for this issue. Natural language processing could be used to match patients with trials based on eligibility criteria and patient electronic health records. Potentially suitable trials could then be suggested to patients or their clinicians, making it easier for patients to find suitable trials and for trials to recruit patients. While this is an improvement to the current recruitment method, natural language processing may initially have some difficulty with clinical notes due to heavy use of acronyms, medical jargon, and deciphering of hand-written clinical notes. These problems aren’t specific to AI though, as currently patients looking for suitable trials have the same difficulties.

AI-driven patient recruitment could also be used to reduce population heterogeneity and use prognostic and predictive enrichment to increase study power. While reduced heterogeneity can be beneficial (e.g. in testing the safety of drugs for patients unable to enrol on early clinical trials), caution should be taken with limiting patient diversity. Selectively enrolling patients who are more likely to respond well to treatment initially could lead to advancing a treatment that may not be as wel tolerated in the post-market patient population.

AI balancing of diversity in clinical trials could be used to balance this. It is important to test safety and efficacy in with differing demographics for a treatment to be properly characterised. This may include different ethnic groups, body types (height, weight, BMI), ages, and sexes. For example, a drug intended for female patients should specify dosage programs and any specific adverse effects in female patients before approval.

Patient diagnostics

Trial data often uses diagnostic tests to measure a patient’s response to treatment. AI can be used to improve diagnostic accuracy and objectivity during trials, reduce the potential for bias, and help with blinding a trial. AI programs have already been shown to provide more accurate diagnoses compared to clinicians6, which could be extended to use in clinical trials. AI can also be used to integrate multiple biomarkers or large data sets (e.g. bioinformatics data) to better monitor and understand a patient’s response, and to make any needed changes in dosing.

Another application of AI in clinical trials would be in patient management. Wearable devices or apps could be used to provide real time safety and effectiveness data to both clinicians and patients, leading to better data quality and higher patient retention.

Patient monitoring, retainment, & medication adherence

AI could be used to monitor patients through automatic data capture and digital clinical assessments. Automated monitoring with AI can allow for personalised adherence alerts, and wearable devices could provide real time safety and efficacy data shared with both patient and clinician to increase retainment and adherence rates. Video consultations with clinicians could improve retainment by reducing travelling required from patients, but there would still be a risk of drop due to travel as some diagnostic tests would need to be done at an appropriate site, which may warrant additional costs if tests for research purposes are not covered by a patient’s health insurance or national health service.

Currently, medication adherence is mostly dependent on each patient’s diary/record keeping or memory, which is then discussed with clinicians during routine appointments. This can make it difficult to accurately track adherence. Digitising this through use of a website/app would allow for more accurate adherence data to be obtained, in addition to providing patients with notifications, educational content, and adherence records. Other medical devices such as timed medication bottles could also be used to ensure medication is used in appropriate intervals, and smart bottles could be used to synchronise this with a smartphone app if applicable.

Data Cleaning

Data cleaning for clinical trials is typically performed by trained biostatisticians and is essential to ensure that collected data is consistently formatted and free from inputting errors. However, the data cleaning process can be time-consuming, especially with large datasets collected during clinical trials. AI could be implemented through machine learning methods to identify and correct errors found in clinical trial datasets7, leading to better quality data which is optimised for analysis. An AI approach may also reduce the amount of time spent on data cleaning.

AI implementation and clinical trial digitisation

AI implementation is relevant in several aspects of clinical trials, be it in study design, patient diagnostics, or trial management. Several tech giants (including Apple & Google) have invested in developing solutions to process electronic health records, monitor patients remotely, and integrate healthcare data into devices. By improving the cost and time effectiveness of clinical trials, both patients and pharma-tech companies benefit with more affordably priced treatment costs for patients and greater return of investment for companies.

However, for clinical trials to implement AI successfully, many aspects of clinical trials would first require digitisation. Many trials still use paper documents instead of digital alternatives, which results in lost documents and slowed trial progression. Integrating electronic health records, digital copies of clinicians’ notes, and digital patient monitoring alone would help in designing and managing a trial. There is a concern that a switch to digital may be difficult for patients unfamiliar with technology, or for those who might prefer to keep paper diaries. However, digital solutions would allow for the development and implementation of AI-based solutions which would modernise and streamline the clinical trial process.

References

  1. Taylor K, Properzi F, Cruz M, Ronte H, Haughey J. Intelligent clinical trials [Internet]. www2.deloitte.com. 2020 [cited 16 March 2022]. Available from: https://www2.deloitte.com/content/dam/insights/us/articles/22934_intelligent-clinical-trials/DI_Intelligent-clinical-trials.pdf

  2. Glass L, Shorter G, Patil R. AI IN CLINICAL DEVELOPMENT [Internet]. IQVIA. 2019 [cited 16 March 2022]. Available from: https://www.iqvia.com/-/media/iqvia/pdfs/library/white-papers/ai-in-clinical-development.pdf
  • Bhatt A. Artificial intelligence in managing clinical trial design and conduct: Man and machine still on the learning curve?. Perspectives in Clinical Research. 2021;12(1):1-3. Available from: https://doi.org/10.4103/picr.PICR_312_20

Clinical Trial Phases in Drug Development


The development of new drugs starts far before they are even seen in clinical trials. The discovery of multiple candidate drugs occur early on in the development process, often as a result of new information about how a disease functions, large-scale screening of small molecules, or the release of a new technology.

After a promising drug has been found, pre-clinical studies can be performed. A pre-clinical study for a new drug is used to determine important information about toxicity and suitable dosage amounts. These studies can be in vitro (in cell culture) and/or in vivo (in animal models) and determine whether a treatment will continue to the clinical trials stage.

Clinical trials test whether these experimental treatments are safe for use in humans, and whether they are more effective in treating or preventing a disease when compared to existing treatments. Clinical trials consist of several stages, called phases, where each phase is focused on answering a different clinical question: Progression of a treatment to the next phase requires the study to meet several parameters to ensure a treatment’s safety or efficacy.

  • Phase 0: Is the new treatment safe to use in humans in small doses?
  • Phase I: Is the new treatment safe to use in humans in therapeutic doses?
  • Phase II: Is the new treatment effective in humans?
  • Phase III: Is the new treatment more effective than existing treatments?
  • Phase IV: Does the new treatment remain safe and effective post-market?
Key phases of a pharmaceutical clinical trial

Phase 0: Small dose safety

Phase 0 studies can help to streamline the other clinical trial phases. Phase 0 consists of giving a few patients small, sub-therapeutic doses of the new treatment. This is to make sure that the new treatment behaves as expected by researchers and isn’t harmful to humans prior to using higher doses in phase I trials.

Phase I: Therapeutic dose safety

Phase I studies evaluate the safety of various doses of the new treatment in humans. This takes several months with typically around 20-80 healthy volunteers. In some cases, such as in anti-cancer drug trials, the study participants are patients with the targeted cancer type. A treatment may not pass phase I if the treatment leads to any serious adverse events.

Initial dosages in phase I studies can be informed based on data obtained during pre-clinical animal studies, and adjustments can be made to investigate the treatment’s side effect profile and develop an optimal dosing program. This could also include comparing different methods of giving a drug to patients (e.g., oral, intravenous etc.).

Phase II: Treatment efficacy

After passing phase I trials and having proven safety in humans, a new treatment advances to phase II studies designed to assess whether it may prevent or treat a disease. This phase can take between several months to 2 years, testing the new treatment in up to several hundred patients with the disease. Using a larger number of patients over a longer time period provides researchers with additional safety and effectiveness data, which is essential for the design of phase III trials.

To further test safety and efficacy, it is common to have a control group that receives either a placebo (a harmless pill or injection without the new treatment) or other current treatment (in trials where the disease is fatal unless treated e.g., cancer).

Phase III: Comparing to current treatments

Phase III studies are the last stage of a clinical trial before a new treatment can be approved for market use. The primary focus of a phase III study is to compare the safety and efficacy of a new treatment with current, existing treatments in patients with the target disease. Anywhere from several hundred to 3,000 patients may be included in a phase III study for between 1 to 4 years. Due to the scale of this phase, long-term or rare side effects are more likely to be uncovered.

Phase III studies are often randomised control trials, where patients will be randomly designated to different treatment groups. These groups may receive placebo, a current treatment (control group), the new treatment, or variations of the new treatment (e.g., different drug combinations). Randomised control trials are often double-blinded, where both the patient and the clinician administering their treatment do not know which treatment group they are assigned to.

A new treatment may continue to market and phase IV trials if the results prove it is as safe and effective as an existing treatment.

Phase IV: Post-market surveillance

If a new treatment passes phase III and is approved by the MHRA, FDA, or other national regulatory agency, it can be put to market. Phase IV is carried out in the post-market surveillance of the new treatment to keep updated on any emerging or long-term safety and efficacy concerns. This may include rare or long-term adverse side effects that were not yet discovered, or long-term analyses to see if the new treatment improves the life expectancy of a patient after recovery from disease.

Summary

Clinical trials are ultimately designed to mitigate risk. This includes the risk to the safety of trial participants by limiting the use of potentially unsafe treatments to small doses in a small number of patients before scaling up to testing therapeutic dose safety. Risk mitigation is not only for patient safety but also for preventing financial misspending as a treatment that is deemed unsafe in phase 0 would not proceed to the later, more costly clinical trial phases.

Not all clinical trials are the same, however, as each trial will have a different disease and treatment context. Trials for medical devices are somewhat different from pharmaceutical trials (for more information about the differences between medical device and pharma trials, click here). In addition, while sample sizes expand with phase progression, the required sample size for each trial and each phase is dependent on several factors including disease context (a rare disease may require lower sample sizes), patient availability (location of trial), trial budget and effect size. The sample size values mentioned earlier in this blog are purely indications of what each phase may use (for more information on how a biostatistician determines a suitable sample size, click here).

References

https://www.fda.gov/patients/drug-development-process/step-3-clinical-research

https://www.cancer.org/treatment/treatments-and-side-effects/clinical-trials/what-you-need-to-know/phases-of-clinicaltrials.html

https://www.healthline.com/health/clinical-trial-phaseshttps://www.brightfocus.org/clinical-trials/how-clinical-trials-work/phases-clinical-trials