Customized and precision medicine becomes a reality thanks to artificial intelligence

Contribute to the development of new drugs with more precise therapeutic targets

Drafting between sister companies05/25/2022

InNorMadrid, along with the health care center of multinational technology company GMV, brought together experts in biomedical research and cybersecurity at the Pons Foundation headquarters for the forum moderated by journalist Francisco Garcia Cabello ‘Innovation and Security in Biomedical Research: Two Sides of the Coin’. He delved into responsible and safe innovation, as well as the role of digital technologies in the development of new drugs.

Photo of the event in Madrid.

At the opening of the event, Cesar Hernández, Head of Medicines for Human Use at the Spanish Agency for Medicines and Health Products, AEMPS declared that “we must move towards transparent data management regarding the assessment of safety and efficacy from clinical trials and new medicines. Likewise, as Isabel Garcia, Director of Inormadrid noted, , “Artificial intelligence contributes to improving clinical and health research, and has a direct impact on patients’ well-being.”

In this sense, the cybersecurity of the pharmaceutical industry is absolutely fundamental, because it works with highly sensitive information related to clinical trials, new drug development, patents, intellectual property, etc., which undoubtedly attracts the interest of consumers. Internet criminals. As Javier Candau, CCN-CERT Head of Cyber ​​Security noted, “There is no digital transformation without cybersecurity. Monitoring against potential cyberattacks must be continuous and by default. We have to practice ‘zero trust’, zero trust.”

Emerging Technologies and Cyber ​​Security

GlobalData’s “Surveying Emerging Technology Trends” report highlights the concern of pharmaceutical industry executives about this problem. Thus, more than 70% of those surveyed associated with the implementation of emerging technologies will prioritize areas such as cybersecurity (73%), cloud solutions (71%) and big data (71%). The large amounts of data they handle require a high level of security associated with the processing, transmission and storage of information.

Indeed, it is difficult for health organizations to have tools that guarantee 100% data anonymity for research purposes in collaboration with other entities. As a result, clinical data for each health event resides in so-called data silos. Even conducting research internationally can be complicated because national laws prevent data from being shared or transferred across countries.

According to Javier Zubieta, Director of Marketing and Communications at GMV’s Secure e Solutions and cybersecurity expert, “Artificial intelligence needs data and the more data, the more efficient its algorithms are. However, if the data is not sufficiently protected, the entire system is either limp or It disrupts directly, without forgetting the regulatory or legislative pressure that requires higher protection measures in the health context. Therefore, cybersecurity ensures the protection and privacy of data and acts as an enabler and lever for artificial intelligence.”

Accounts securely and confidentially

Advanced analytics algorithms used to extract evidence from the vast world of data generated by hospitals and clinical trials allow us to deliver conclusions capable of increasing the accuracy of diagnosis and disease prognosis. All of this, in turn, leads to greater efficiency and effectiveness in the work of health professionals, who can see their diagnoses backed up or “adjusted” by the data provided by this high-impact technology.

In this sense, the creation of specific algorithms in drug development offers several advantages: it allows sequential ordering of operations, which reduces the range of errors; It is also accurate and allows you to stick to a specific guide; They shorten the times of the various preclinical and clinical phases with the intent of making the commercialization period as long as possible and make it easier to work with large amounts of distributed data, without revealing or transferring it from organizations, thus avoiding privacy problems.

This is the case of Use PET (Privacy Enhancement Technologies), a technology solution developed by GMV that securely and confidentially performs computations on distributed patient data, allowing research centers and the pharmaceutical industry to capture important information such as survival itself: biomarker value, prognosis, and average patient age , etc., from clinical treatments. With uTile, we don’t need to choose between data privacy and usability, as it takes advantage of advanced encryption methods that keep the data encrypted while performing all necessary calculations.

It allows leveraging confidential data to improve machine learning algorithms and analytical models, complying at all times with regulatory requirements, ensuring privacy, as well as with current regulations. Protection of personal data is a fundamental right enshrined in Article 18.4 of the Spanish Constitution and regulated by the European Data Protection Regulation (RGPD) and LOPDGDD, and supplemented by the Patient Autonomy Act.

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Clinical applications of uTile PET are very diverse, ranging from improving the accuracy of AI techniques to existing models that predict different tumors (prostate cancer, breast cancer, etc.), reducing the need for biopsies, through research into rare diseases, to collecting data that has been reported about the patient.

Clinical data on people with rare disease (RD) are scarce and scattered in hospitals. Even with the national registries for rare diseases, the amount of data is still limited. uTile allows the use of all information available in various local and national registries as well as European reference networks to discover hidden relationships in the data and answer research questions. Similarly, in some cases, symptoms associated with adverse outcomes of medical treatment are not recorded in patients’ clinical records, and thus this knowledge is lost. uTile is an opportunity to collect daily information from patients using a smartphone, and without sharing this data outside the patient/doctor’s field, use AI techniques to improve and increase the body of knowledge about the disease.

As agreed at the event, we can conclude that digital health is the driver for Personalized Precision Medicine (PPM). The ability to manage large amounts of data makes it possible to use information useful for health decision-making and enhance PPM, adjusting it to the needs of each patient.

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