The Spanish Association of Clinical Research Companies (AECIC)formed by more than 80% of the CROs in our countrywhich has celebrated its XI Conference focused on the Future, already present, in Clinical Trials: Applicability of Artificial Intelligence (AI) in the health of patients, evolution of computer systems and updates on current regulations. Thus, the vice president of Clinical Research & PSP of Evidenze Group, Pedro Hernández, has pointed out that “the Artificial intelligence is revolutionizing clinical trials by optimizing study design, improving patient selection, and accelerating data analysis, resulting in faster, more effective treatments.”
In this context, he explained, there are two AI: lto conventionalwhich is capable of making intelligent decisions based on a specific set of rules and with a set of data, and Generative AIa variant that can create something new and original from the information provided to it.
However, “AI is not perfect, but it is not essential for it to be perfect either, it is enough for it to be better than a human, and therefore supervision is necessary for the time being,” he said. And the problem lies in the knowledge base. There are solutions that use mechanisms of GPT natural chat languagebut with a validated knowledge base.”
AI is revolutionizing the sector clinical research, and more specifically machine learning, since need meets opportunity: handling large volumes of data, slow current processes, very high current costs, need to reduce bias, etc.
The expert has emphasized patient recruitment: identification, screening and selection. As an opportunity, AI algorithms can analyze large data sets to identify suitable patients to participate in clinical trialss, which improves efficiency and precision in selection. As a disadvantage: the application of current data protection legislation or the fear/security of data hosts to use and share data.
Another positive aspect, he stated, is to facilitate remote access for participants in clinical trials through connected devices and sensors, continuous data collection in real time and one early detection of possible problems either side effectsin addition to increasing patient engagement (virtual coach), reducing abandonment.
As a barrier, the validation of devices in the field of clinical research, safety or data validity or neophobia. Another use of AI is the prediction of results, in silico or trials. By using machine learning models, AI can predict the results of clinical trials, such as the efficacy of a treatment, for example.
In this regard, the expert has cited Three AI tools for research: consensus, connected papers or perplexity. In conclusion, “AI is a new technology that is here to stay, it will optimize processes” and “neophobia (fear of the new) as well as legislative barriers must be overcome and adapted to new environments.”
For its part, Sas Maheswaranwe have attempted to demonstrate how AI is being adopted into functional workflows to accelerate clinical development.
Decentralization of monitoring. Decentralized studies: components, advantages and challenges in practice. Gina Williamson Ramirez, Digital and DCT Ops Mgr Decentralized Solutions Syneos, has spoken about the decentralized strategies and tools to use in a clinical trial, to bring the patient closer to the trial and alleviate some of the burdens identified for their participation and permanence in it.
The expert has described that “a decentralized study aims to reach patients more and make their lives easier.” In this sense, she has commented that the three types of trials from a decentralized point of view are traditional trials, hybrid decentralized trials and fully decentralized trials. Carmen RonceroResearch Nurse Manager in Spain of Illingworth Research Group, from Syneos Health Company, has highlighted the role of home nursing and patient concierge as strategies to improve the patient experience and research center involved in clinical trials. As he has emphasized, “one of the challenges in clinical trials is the recruitment and retention of participants.”
And the fact is that, “70% of participants in a clinical trial live more than 2 hours from the research center and 38% drop out due to stress from attending visits to the center.” Therefore, “the provision of home nursing services can contribute to mitigate stress “to reduce disruption to daily routine.”
Sonia Jimenez Baranda, CTM of Syneos, for its part, has highlighted 5 points to take into account in the impact of monitoring: regulatory aspects, responsibility and training logs, data review or adverse aspects such as identifying if there is under-registration of adverse effects to the review the documentation obtained or corroborate that the planned process is followed. Thus, “there is a lot to do, but it is not impossible.”
Digital biomarkers
As for the digital biomarkers They are objective, quantifiable, physiological and behavioral measures that are collected through portable digital devicesusable or digestible. And today there is a wide range of biosensors on the market, as they have specified.
Regarding the mega site model, they want to achieve a single centralized research center that carries out remote surveillance and monitoring of the visits of trial participants, in a region or country.
However, they have warned that there is still much to be done: “We have had many trials in which we have not been able to implement 100% remote monitoring.”In Spain the centers are overloaded. Having help eases that burden».
