The Integration of Artificial Intelligence into Clinical Practice

Self Cite

Sample size is a key factor in bioequivalence and clinical trials. An appropriately large sample is necessary to gain valuable insights into a designated population. However, large sample sizes lead to increased human exposure, costs, and a longer time for completion. In a previous study, we introduced the idea of using variational autoencoders (VAEs), a type of artificial neural network, to synthetically create in clinical studies. In this work, we further elaborate on this idea and expand it in the field of bioequivalence (BE) studies. A computational methodology was developed, combining Monte Carlo simulations of 2 × 2 crossover BE trials with deep learning algorithms, specifically VAEs. Various scenarios, including variability levels, the actual sample size, the VAE-generated sample size, and the difference in performance between the two pharmaceutical products under comparison, were explored. All simulations showed that incorporating AI generative algorithms for creating virtual populations in BE trials has many advantages, as less actual human data can be used to achieve similar, and even better, results. Overall, this work shows how the application of generative AI algorithms, like VAEs, in clinical/bioequivalence studies can be a modern tool to significantly reduce human exposure, costs, and trial completion time.

Section: Discussionmentioning

confidence: 99%

Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies

Papadopoulos,

Karalis

2024

Self Cite

“…Additionally, AI has the potential to predict treatments most likely to yield positive outcomes for individual patients, enabling personalized medicine tailored to unique patient characteristics. By optimizing administrative processes and reducing redundant tests, AI also holds promise for reducing healthcare expenses, both in terms of finances and time [11].…”

Section: Discussionmentioning

confidence: 99%

“…The number of AI applications has been increasing exponentially in recent years. In healthcare, there are numerous use cases of AI and machine learning, including drug discovery, medicine, dentistry, anesthesiology, and ophthalmology [11][12][13][14][15][16]. One recent application of AI proposed by our research group is data augmentation, which involves virtually increasing a sample by generating new data from existing data [17,18].…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Generative AI Algorithm for Virtually Increasing the Sample Size of Clinical Studies

Nikolopoulos,

Karalis

2024

Self Cite

Determining the appropriate sample size is crucial in clinical studies due to the potential limitations of small sample sizes in detecting true effects. This work introduces the use of Wasserstein Generative Adversarial Networks (WGANs) to create virtual subjects and reduce the need for recruiting actual human volunteers. The proposed idea suggests that only a small subset (“sample”) of the true population can be used along with WGANs to create a virtual population (“generated” dataset). To demonstrate the suitability of the WGAN-based approach, a new methodological procedure was also required to be established and applied. Monte Carlo simulations of clinical studies were performed to compare the performance of the WGAN-synthesized virtual subjects (i.e., the “generated” dataset) against both the entire population (the so-called “original” dataset) and a subset of it, the “sample”. After training and tuning the WGAN, various scenarios were explored, and the comparative performance of the three datasets was evaluated, as well as the similarity in the results against the population data. Across all scenarios tested, integrating WGANs and their corresponding generated populations consistently exhibited superior performance compared with those from samples alone. The generated datasets also exhibited quite similar performance compared with the “original” (i.e., population) data. By introducing virtual patients, WGANs effectively augment sample size, reducing the risk of type II errors. The proposed WGAN approach has the potential to decrease costs, time, and ethical concerns associated with human participation in clinical trials.

“…Ethical considerations also include obtaining consent for using clinical data in generating synthetic datasets and addressing potential biases to ensure fairness and representativeness. Adhering to these steps will ensure that the synthetic data generated by generative AI algorithms are scientifically valid and ethically sound for clinical research [21].…”

Section: Discussionmentioning

confidence: 99%

“…The number of AI applications has been increasing exponentially in recent years. In healthcare, there are numerous use cases of AI and machine learning, including drug discovery, medicine, dentistry, anesthesiology, and ophthalmology [16][17][18][19][20][21]. Data augmentation has not been widely used in the healthcare industry; however a recent study from our lab has shown the efficiency and advantages of this approach in the field of clinical trials [22].…”

Section: Introductionmentioning

confidence: 99%

Bioequivalence Studies of Highly Variable Drugs: An Old Problem Addressed by Artificial Neural Networks

Papadopoulos,

Karali,

Karalis

2024

Self Cite

The bioequivalence (BE) of highly variable drugs is a complex issue in the pharmaceutical industry. The impact of this variability can significantly affect the required sample size and statistical power. In order to address this issue, the EMA and FDA propose the utilization of scaled limits. This study suggests the use of generative artificial intelligence (AI) algorithms, particularly variational autoencoders (VAEs), to virtually increase sample size and therefore reduce the need for actual human subjects in the BE studies of highly variable drugs. The primary aim of this study was to show the capability of using VAEs with constant acceptance limits (80–125%) and small sample sizes to achieve high statistical power. Monte Carlo simulations, incorporating two levels of stochasticity (between-subject and within-subject), were used to synthesize the virtual population. Various scenarios focusing on high variabilities were simulated. The performance of the VAE-generated datasets was compared to the official approaches imposed by the FDA and EMA, using either the constant 80–125% limits or scaled BE limits. To demonstrate the ability of AI generative algorithms to create virtual populations, no scaling was applied to the VAE-generated datasets, only to the actual data of the comparators. Across all scenarios, the VAE-generated datasets demonstrated superior performance compared to scaled or unscaled BE approaches, even with less than half of the typically required sample size. Overall, this study proposes the use of VAEs as a method to reduce the necessity of recruiting large numbers of subjects in BE studies.