Regulatory Considerations on the use of Machine Learning based tools in Clinical Trials.

Background: The widespread increasing use of machine learning (ML) based tools in clinical trials (CTs) impacts the activities of Regulatory Agencies (RAs) that evaluate the development of investigational medicinal products (IMPs) in clinical studies to be carried out through the use of data-driven technologies. The fast progress in this field poses the need to define new approaches and methods to support an agile and structured assessment process.

Method: The assessment of key information, characteristics and challenges deriving from the application of ML tools in CTs and their link with the principles for a trustworthy artificial intelligence (AI) that directly affect the decision-making process is investigated.

Critical Analysis and Quality Assessment of Nanomedicines and Nanocarriers in Clinical Trials: Three Years of Activity at the Clinical Trials Office.

Investigational medicinal products submitted over the course of 3 years and authorized at the Clinical Trials Office of the Italian Medicines Agency as part of a request for authorization of clinical trials were scrutinized to identify those encompassing nanomedicines. The quality assessment reports performed on the documentation submitted were analyzed, classifying and discussing the most frequently detected issues. The identification of nanomedicines retrieved and the information on their quality profiles are shared to increase the transparency and availability of information, providing feedback that can support sponsors in optimizing the quality part of the documentation and of the information submitted.

Quality Assessment of Investigational Medicinal Products in COVID-19 Clinical Trials: One Year of Activity at the Clinical Trials Office.

One year after the spread of the pandemic, we analyzed the assessment results of the quality documentation submitted to the Clinical Trials Office of the Italian Medicines Agency as part of the request for authorization of clinical trials with a COVID-19 indication. In this article, we report the classification of the documentation type, an overview of the assessment results, and the related issues focusing on the most frequently detected ones. Relevant data regarding the Investigational Medicinal Products (IMPs) tested in COVID-19 clinical trials and their quality profiles are provided in the perspective of increasing transparency and availability of information.

Clinical Trials and Machine Learning: Regulatory Approach Review.

Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has provided important contributes to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has been constantly growing and this is now affecting the National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed or that are generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning.

Surfactants, Nanomedicines and Nanocarriers: A Critical Evaluation on Clinical Trials.

Advances, perspectives and innovation in drug delivery have increased in recent years; however, there is limited information available regarding the actual presence of surfactants, nanomedicines and nanocarriers in investigational medicinal products submitted as part of a request for authorization of clinical trials, particularly for those authorized in the European Economic Area. We retrieve, analyze and report data available at the Clinical Trial Office of the Italian Medicines Agency (AIFA), increasing the transparency and availability of relevant information. An analysis of quality documentation submitted along with clinical trials authorized by the AIFA in 2018 was carried out, focusing on the key terms "surfactant", "nanomedicine" and "nanocarrier".