Clinical Trials and Machine Learning: Regulatory Approach Review

2021 ◽  
Vol 16 ◽  
Author(s):  
Diego Alejandro Dri ◽  
Maurizio Massella ◽  
Donatella Gramaglia ◽  
Carlotta Marianecci ◽  
Sandra Petraglia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Discovery ◽  
Clinical Applications ◽  
Clinical Development ◽  
Regulatory Approach ◽  
National Competent Authorities ◽  
Review Current

: Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or 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. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.

scholarly journals Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Javed Iqbal ◽  
Zeeshan Javed ◽  
Haleema Sadia ◽  
Ijaz A. Qureshi ◽  
Asma Irshad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Disease Risk ◽  
Clinical Applications ◽  
Optimal Decision ◽  
Great Promise ◽  
Time Range ◽  
Base System ◽  
The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

scholarly journals The exosome journey: from biogenesis to uptake and intracellular signalling

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sonam Gurung ◽  
Dany Perocheau ◽  
Loukia Touramanidou ◽  
Julien Baruteau
Keyword(s):  
Clinical Trials ◽  
Plasma Membrane ◽  
Scientific Community ◽  
Cell Types ◽  
Clinical Applications ◽  
Clinical Development ◽  
Intracellular Signalling ◽  
Clinical Settings ◽  
Health And Disease ◽  
Adequate Definition

AbstractThe use of exosomes in clinical settings is progressively becoming a reality, as clinical trials testing exosomes for diagnostic and therapeutic applications are generating remarkable interest from the scientific community and investors. Exosomes are small extracellular vesicles secreted by all cell types playing intercellular communication roles in health and disease by transferring cellular cargoes such as functional proteins, metabolites and nucleic acids to recipient cells. An in-depth understanding of exosome biology is therefore essential to ensure clinical development of exosome based investigational therapeutic products. Here we summarise the most up-to-date knowkedge about the complex biological journey of exosomes from biogenesis and secretion, transport and uptake to their intracellular signalling. We delineate the major pathways and molecular players that influence each step of exosome physiology, highlighting the routes of interest, which will be of benefit to exosome manipulation and engineering. We highlight the main controversies in the field of exosome research: their adequate definition, characterisation and biogenesis at plasma membrane. We also delineate the most common identified pitfalls affecting exosome research and development. Unravelling exosome physiology is key to their ultimate progression towards clinical applications.

scholarly journals Artificial intelligence and machine learning in clinical development: a translational perspective

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Pratik Shah ◽  
Francis Kendall ◽  
Sean Khozin ◽  
Ryan Goosen ◽  
Jianying Hu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Development

scholarly journals Applications of Artificial Intelligence (AI) in healthcare: A review

2021 ◽  
Author(s):  
Mohammed Yousef Shaheen
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Drug Discovery ◽  
Target Identification ◽  
Health Sector ◽  
Monitoring Methods ◽  
Pharmaceutical Firms ◽  
Genetic Codes ◽  
Applications Of Ai

Artificial intelligence is revolutionizing — and strengthening — modern healthcare through technologies that can predict, grasp, learn, and act, whether it's employed to identify new relationships between genetic codes or to control surgery-assisting robots. It can detect minor patterns that humans would completely overlook. This study explores and discusses the various modern applications of AI in the health sector. Particularly, the study focuses on three most emerging areas of AI-powered healthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggest that pharmaceutical firms have benefited from AI in healthcare by speeding up their drug discovery process and automating target identification. Artificial Intelligence (AI) can help also to eliminate time-consuming data monitoring methods. The findings also indicate that AI-assisted clinical trials are capable of handling massive volumes of data and producing highly accurate results. Medical AI companies develop systems that assist patients at every level. Patients' medical data is also analyzed by clinical intelligence, which provides insights to assist them improve their quality of life.

scholarly journals Prime Time for Artificial Intelligence in Interventional Radiology

2022 ◽  
Author(s):  
Jarrel Seah ◽  
Tom Boeken ◽  
Marc Sapoval ◽  
Gerard S. Goh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Interventional Radiology ◽  
Clinical Applications ◽  
Diagnostic Radiology ◽  
Machine Learning Techniques ◽  
Prime Time ◽  
Limited Data ◽  
Learning Techniques ◽  
Diagnostic Capabilities

AbstractMachine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.

Artificial Intelligence Recognizes β-Lapachone as an Allosteric 5- Lipoxygenase Inhibitor

2018 ◽  
Author(s):  
Gonçalo Bernardes ◽  
Tiago Rodrigues ◽  
Markus Werner ◽  
Jakob Roth ◽  
Eduardo H. G. da Cruz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Natural Product ◽  
Systems Pharmacology ◽  
Lipoxygenase Inhibitor ◽  
Machine Learning Methods ◽  
Biochemical Assays ◽  
Viable Approach ◽  
Chemical Matter

<div> <div> <div> <p>Chemical matter with often-discarded moieties entails opportunities for drug discovery. Relying on orthogonal ligand-centric machine learning methods, targets were consensually identified as potential counterparts for the fragment-like natural product β-lapachone. Resorting to a comprehensive range of biophysical and biochemical assays, the natural product was validated as a potent, ligand efficient, allosteric and reversible modulator of 5-lipoxygenase (5-LO). Moreover, we provide a rationale for 5-LO-inhibiting chemotypes inspired in the β-lapachone scaffold through a focused analogue library. This work demonstrates the power of artificial intelligence technologies to deconvolute complex phenotypic readouts of clinically relevant chemical matter, leverage natural product-based drug discovery, as an alternative and/or complement to chemoproteomics and as a viable approach for systems pharmacology studies. </p> </div> </div> </div>

Sign in / Sign up

Export Citation Format

Share Document