The scale of this challenge becomes clear when examining the evolution of vulnerabilities. In 2025, more than 48,000 new cybersecurity vulnerabilities were identified, approximately 22% more than the previous year. This increase does not necessarily mean that all systems are becoming less secure, but it does reflect the complexity of an constantly expanding technological ecosystem in which software, hardware, distributed services, and connected devices coexist.

The challenge is not limited to detecting and correcting vulnerabilities. Today’s infrastructures depend on multiple components and providers, meaning that a localized vulnerability can spread through shared services or compromise functions that initially appeared independent. Security must therefore consider both the individual behavior of each system and the interactions between them.

In this context, cybersecurity has become a multidisciplinary research field in which cryptography, systems engineering, applied mathematics, and artificial intelligence converge.

To mark European Cybersecurity Month, held every October, we examine the technological foundations of this discipline, the advances transforming its protection mechanisms, and the scientific challenges that remain unresolved.

What Is cybersecurity from a technological perspective?

Cybersecurity encompasses the principles, architectures, and mechanisms designed to ensure that digital systems maintain their security properties even when exposed to failures, unauthorized access, or malicious behavior.

Its purpose is not exclusively to prevent intrusions, but also to preserve process integrity, information confidentiality, and service availability. These principles, known as the CIA triad, provide a fundamental reference framework, although their application depends on the technological context.

In industrial infrastructure, for example, the integrity of control signals may be just as critical as data confidentiality. In space communication systems, the authenticity of received commands and the ability to maintain operations during disruptions become particularly important. Security must therefore be adapted to the physical, operational, and functional characteristics of each environment.

From an engineering perspective, one of the fundamental concepts is threat modeling, which identifies the assets that need protection, the vulnerabilities that could be exploited, and the potential consequences of a system compromise. This analysis informs decisions concerning network architecture, authentication, encryption, segmentation, and recovery mechanisms.

Current research also places particular emphasis on security throughout the entire technology lifecycle. Protection cannot rely solely on controls introduced after development; it must be integrated from the design stage and maintained throughout operation, updates, and system decommissioning.

This approach is reflected in the Cybersecurity Framework 2.0, which organizes risk management around six complementary functions: Govern, Identify, Protect, Detect, Respond, and Recover. Its significance lies in understanding cybersecurity as a continuous operational capability rather than a static collection of tools.

From individual vulnerabilities to systemic risk

The increasing interconnection of digital infrastructures has transformed how cybersecurity is understood. Many applications that previously operated as relatively independent systems now rely on distributed architectures, where cloud services, connected devices, and external platforms work together to perform a single function. This structure provides flexibility and scalability, but it also introduces new dependencies and expands the attack surface: the points through which a system could potentially be compromised.

However, risk does not depend solely on the number of existing vulnerabilities, but also on how components interact, which permissions they share, and how far unauthorized access can spread. The emergence of artificial intelligence agents capable of performing tasks and interacting with external systems adds a new dimension to this challenge.

A particularly illustrative case occurred in July 2026, during internal cybersecurity evaluations conducted by OpenAI. Several AI agents managed to bypass restrictions in their execution environments, exploit vulnerabilities, and interact with infrastructure they were not authorized to access, including the Hugging Face platform. The incident, which occurred in an evaluation environment, highlighted the challenges of controlling autonomous systems capable of performing operations beyond their intended scope.

Situations of this kind raise a fundamental question: how can we ensure that a system respects its operational boundaries when it has access to tools capable of interacting with other services? Modern cybersecurity therefore requires analyzing infrastructures as interconnected systems, where protecting individual components does not necessarily guarantee the security of the system as a whole.

Artificial Intelligence: new detection capabilities and new challenges

Artificial intelligence is transforming cybersecurity from two perspectives. On the one hand, it enables the analysis of large volumes of information and the identification of anomalous behavior that might go unnoticed using traditional methods. On the other, it introduces vulnerabilities associated with the operation of AI models themselves and their interactions with external systems.

Machine learning techniques can be used to analyze network traffic, activity logs, or authentication patterns to detect deviations from normal behavior. However, their effectiveness depends on data quality and the ability of models to adapt to operational changes. This phenomenon, known as concept drift, can reduce the accuracy of detection systems and generate false alerts as conditions evolve.

At the same time, AI models can themselves become targets of attacks. Among the most extensively studied threats are adversarial attacks, which manipulate input data to trigger incorrect responses, and data poisoning, which seeks to alter model behavior through manipulated training information.

In generative AI systems, prompt injection is another significant threat. This technique introduces malicious instructions into content that a model is expected to process. The risk increases when the system has access to external tools, as it may attempt to execute unauthorized actions.

These threats form part of the research field known as Adversarial Machine Learning, which studies how models can be manipulated and develops mechanisms to improve their robustness.

Post-Quantum Cryptography: preparing for the next security transition

Cryptography is one of the mathematical foundations of digital security. However, advances in quantum computing pose a challenge to certain public-key algorithms currently in use.

In 1994, mathematician Peter Shor theoretically demonstrated that a sufficiently advanced quantum computer could solve certain mathematical problems, such as integer factorization, far more efficiently than the best known classical methods. This discovery has direct implications for cryptographic systems such as RSA and those based on elliptic curves.

One of the main responses to this challenge is post-quantum cryptography, which develops security algorithms based on mathematical problems for which no efficient solutions are currently known, using either conventional or quantum computers. Unlike other alternatives, such as quantum cryptography, these algorithms can be integrated into existing digital infrastructures without requiring specialized quantum communication equipment. In August 2024, the first three post-quantum cryptography standards were published, designed to protect key establishment and digital signatures.

The need to prepare in advance is also driven by the risk known as harvest now, decrypt later, which involves collecting encrypted communications today with the intention of decrypting them in the future, once sufficient quantum computing capabilities become available.

This transition presents technical challenges related to energy consumption, memory requirements, and execution times, particularly in industrial systems, embedded devices, and space communications. Research therefore focuses not only on ensuring the mathematical security of algorithms, but also on developing efficient implementations for practical applications.

Cybersecurity at ARQUIMEA Research Center

Research at ARQUIMEA Research Center also addresses the security challenges associated with emerging technologies. One of its key research areas is post-quantum cryptography, which combines mathematical research, electronic engineering, and computational optimization to develop protection mechanisms adapted to future technological requirements.

One of the developments within this research area is an implementation of the FrodoKEM post-quantum cryptographic algorithm on programmable hardware devices (FPGAs), which has achieved a tenfold performance improvement compared with certain conventional software-based implementations. This work forms part of a broader research effort combining hardware and software development to accelerate cryptographic operations, strengthen resistance to attacks that exploit the physical characteristics of devices or deliberately induce faults, and facilitate adaptation to new security standards. The team has also developed implementations of digital signature algorithms, such as Falcon and MAYO, and key encapsulation mechanisms for secure cryptographic key establishment, including ML-KEM and HQC.

This research area is connected to the capabilities of QCIRCLE, the International Centre of Excellence led by ARQUIMEA Research Center, which includes communications and cryptography among its technological areas, alongside quantum computing and photonics.

The connection between these disciplines is essential for anticipating future digital security challenges. As computing capabilities evolve, so must the mechanisms that protect our infrastructures. In this context, scientific research plays a decisive role in developing security technologies capable of addressing both current threats and those that will emerge over the coming decades.

Before manufacturing a machine, operating an energy system, or even making certain decisions about our planet, what if we could first test what might happen using a digital replica capable of behaving much like the real system?

This possibility has been used in engineering for years, but it is now reaching an entirely different scale. Today, there are digital twins of factories, engines, robots, and infrastructure, while projects such as Destination Earth are taking the concept much further: creating digital replicas of Earth systems to support research and decision-making.

The evolution is significant. A technology originally conceived to better understand the behavior of industrial systems is now finding applications in medicine, robotics, climate research, and space exploration. Behind all of them lies the same idea: experimenting first on a sufficiently accurate digital representation can help anticipate behavior, evaluate scenarios, and reduce the cost and risk of experimenting directly on the physical system.

But achieving this requires much more than building a good 3D model. A true digital twin is considerably more complex.

What is a Digital Twin?

A digital twin is a virtual representation of a physical object, process, or system that uses models and data to reproduce its behavior and evolve alongside it. The fundamental difference between a digital twin and a conventional simulation lies precisely in this connection to reality.

A simulation can represent how a system should behave under certain conditions. A digital twin goes further by incorporating information from its physical counterpart, such as sensor data, operating conditions, or experimental results, to update its state and progressively improve the correspondence between the two worlds. This relationship can be bidirectional, allowing the model to be used to evaluate decisions before applying them to the real system. This dynamic, connected approach is also reflected in the scientific literature on digital twins in fields such as medicine.

Its architecture typically combines three elements. First, there is the physical system, which can range from an actuator to an entire infrastructure. Second, there is its digital representation, built using physical, mathematical, or data-driven models. Between them lies an information layer that keeps the two states connected.

The greater the fidelity of this relationship, the greater the potential of the twin. It is no longer limited to visualizing what is happening; it can also be used to ask what will happen next or what might happen if certain conditions are changed.

That is where much of its value lies.

From observing the present to experimenting with the future

One of the most established applications of digital twins is in industry. Sensors installed in machinery can feed models that track variables such as temperature, vibration, energy consumption, or wear. By comparing expected behavior with observed behavior, anomalies can be detected and certain failures anticipated before they cause downtime.

However, the concept has evolved far beyond predictive maintenance.

During the design of a system, a digital twin can be used to explore different configurations before physical components are manufactured. Once the system has been built, the twin can help optimize its operation. And throughout its service life, data collected from the real system can be fed back into the model to progressively improve its predictive capabilities.

The result is a continuous cycle of design, simulation, experimentation, and operation. Rather than waiting for a prototype to be built before discovering its limitations, part of that learning process can take place in the virtual environment.

This idea has particularly interesting precedents in the history of space exploration. Following the Apollo 13 accident, NASA used simulators and models of the spacecraft to recreate the conditions of the damaged vehicle and test possible solutions on Earth before communicating them to the astronauts. NASA itself identifies these approaches as conceptual predecessors of today’s digital twins.

More than half a century later, the underlying principle remains recognizable: use a sufficiently accurate representation of a physical system to experiment where doing so on the real object would be too expensive, too slow, too dangerous, or simply impossible.

When the Digital Twin enters the laboratory

What is particularly interesting is that this logic is moving beyond industrial environments. Digital twins are beginning to be used as scientific tools for studying systems whose complexity makes direct experimentation difficult.

One of the most ambitious examples is Destination Earth (DestinE). This European initiative is developing high-precision digital twins of the Earth system by combining real-time observations, high-resolution predictive models, artificial intelligence, and high-performance computing. Its initial applications include climate change adaptation and the simulation of extreme weather events.

The scale of the project illustrates just how far the concept can go. Rather than reproducing a machine, the aim is to represent interactions between the atmosphere, oceans, land surface, and human activity in order to explore scenarios that can support decision-making before certain events occur.

Biomedicine presents an entirely different challenge. Researchers are working on models capable of representing organs, physiological processes, or even the specific characteristics of an individual patient. The long-term goal would be to use these replicas to simulate disease progression or anticipate the response to an intervention before it is applied.

However, biological variability, data availability, and the need for rigorous model validation make this field considerably more complex than many industrial applications. A recent review highlights that the credibility and validation of these models will be essential if they are to move from basic and preclinical research toward broader biomedical applications.

Digital twins are therefore evolving from representations of relatively well-defined objects toward attempts to reproduce complex, dynamic, multiscale systems.

The invisible ingredients: data, physics, and Artificial Intelligence

A digital twin is only as useful as the model it contains and the data that feeds it.

Sensors provide information about the state of the physical system. Mathematical models describe the relationships between its variables. Simulation reproduces its evolution and, increasingly, artificial intelligence helps identify patterns that are difficult to capture using purely deterministic models.

Combining these approaches is giving rise to hybrid digital twins. A physics-based model can provide consistency and interpretability, while machine learning methods can capture behaviors that are difficult to model or correct discrepancies between simulation and reality.

But increasing complexity does not automatically result in a better digital twin. For its predictions to be useful, it is essential to continuously assess how accurately they represent the real behavior of the system. Verification, validation, and uncertainty quantification are therefore central considerations.

This becomes particularly important when a digital twin moves beyond optimizing a machine and begins informing decisions related to healthcare, critical infrastructure, or environmental phenomena. In these scenarios, knowing how much confidence can be placed in a prediction is almost as important as the prediction itself.

Robotics: bridging the gap between simulation and reality

Robotics is one of the areas where this technology is showing particularly significant potential.

Training a robot directly on physical hardware has obvious limitations. Testing takes time, causes wear, and can result in damage when experimenting with movements, controllers, or strategies that are still under development. Simulation makes it possible to multiply the number of experiments, but introduces another well-known challenge: the sim-to-real gap, the difference between how a robot behaves in a virtual environment and how it responds when the same control strategy is transferred to physical hardware.

Digital twins seek precisely to reduce that gap.

To achieve this, the model must reproduce aspects that a simplified simulation may overlook, including friction, saturation effects, current and torque limits, transmission dynamics, sensor behavior, and the operation of internal controllers.

The more accurate this representation becomes, the greater the ability to design, train, and validate strategies in simulation before transferring them to the physical robot. A recent scientific review on robotic arms and reinforcement learning identifies digital twins as one of the technologies capable of improving the connection between virtual training and real-world manipulation.

AUGUR: Digital Twins at PULSAR HRI

This pursuit of simulation capable of predicting hardware behavior with greater accuracy lies at the heart of the work carried out by PULSAR HRI, ARQUIMEA’s technology unit specializing in advanced robotics technologies.

Its AUGUR ecosystem is built around an actuator-level digital twin designed to reproduce the electromechanical characteristics of PULSE actuators. The model incorporates aspects such as motor physics, transmission friction and dynamics, current and torque limits, integrated control loops, and sensor noise.

The difference can be measured. In tests conducted with the PULSE115 actuator, AUGUR reproduced position and torque dynamics with errors approximately one order of magnitude lower than those obtained using a simplified model. When the system was scaled to a virtual humanoid arm with four actuators, a 41-second motion sequence could be simulated in just over six seconds on a conventional laptop, while maintaining detailed modeling of actuator dynamics.

This capability makes it possible to test controllers, trajectories, and configurations before deploying them on physical hardware. In the development of the PULSE robotic arm, the virtual model is also used to evaluate mass, workspace, payloads, power consumption, structural optimization, and standardized accuracy and tracking metrics. In certain configurations, 36 different static positions and payloads of up to 3 kg were analyzed before completing the physical optimization of the system.

The result clearly illustrates how the concept of the digital twin is evolving. It is no longer simply about creating a digital replica that shows what a system looks like, but about providing an environment in which to design, experiment, validate, and anticipate how it will behave before building or physically modifying it.

From a factory to a robot, an organism, or even the planet itself, the scale may change dramatically, but the underlying logic remains the same. When a digital model maintains a sufficiently close relationship with reality, it stops being merely a representation and becomes a new tool for research.

And perhaps that is where the true potential of digital twins lies: not in copying the physical world, but in allowing us to experiment with it before intervening in reality.

This transformation has led to a quiet yet profound paradigm shift: data is no longer simply an output of research but has become an essential infrastructure underpinning modern science. Just as a laboratory needs electricity, instrumentation, or Internet access to operate, research today depends on ecosystems capable of managing volumes of information that would have been unimaginable only a few years ago.

The scale of this shift is clear. The Large Hadron Collider (LHC) at CERN generates around one trillion (10¹²) particle collisions per second. Although only a small fraction of these events is retained for analysis, the experiments produce tens of petabytes of scientific data every year, which must be processed through a distributed computing infrastructure spanning more than 170 computing centers across over 40 countries. Known as the Worldwide LHC Computing Grid, this network is one of the largest scientific computing systems in the world.

This example illustrates a reality shared by virtually every scientific discipline. Astronomy continuously observes the universe through ground- and space-based telescopes; genomics sequences millions of DNA fragments every day; Earth observation satellites constantly collect information on oceans, forests, and the atmosphere; and laboratories generate ever-growing volumes of images, simulations, and experimental results. In every case, data is what makes the research itself possible.

When Data Stops Being a Resource and Becomes Infrastructure

When we think of scientific infrastructure, particle accelerators, telescopes, or supercomputers usually come to mind. However, an increasing number of institutions now consider data itself to be an infrastructure of equal strategic importance.

But what does it really mean to think of data as infrastructure?

It is not simply a matter of having large storage capacities. Data infrastructure encompasses all the elements required to make information usable by the scientific community: acquisition systems, secure storage, high-speed communication networks, computing platforms, common standards, analytical tools, and mechanisms that enable results to be shared efficiently and reproducibly.

In other words, data only becomes valuable when it can be found, understood, combined, and reused.

This is the principle behind the FAIR principles, promoted by the international scientific community, which establish that data should be Findable, Accessible, Interoperable, and Reusable. Far from being merely a best practice, these principles have become a reference framework for universities, research centers, and European programs seeking to maximize the impact of publicly funded research.

This shift in approach also responds to the need for greater scientific efficiency. When data is properly documented and reusable, other research groups can validate results, develop new lines of research, or combine information from entirely different disciplines. Research no longer advances in isolation but builds upon shared knowledge.

It is no coincidence that the European Union is promoting initiatives such as the European Open Science Cloud (EOSC), a digital infrastructure designed to enable researchers across different countries to securely access, share, and reuse scientific data in a standardized way.

Science That Moves Petabytes

The volume of information generated by modern science is difficult to imagine. A few decades ago, a researcher could store years of work on a handful of hard drives. Today, some scientific projects generate more data in a single day than many institutions once produced over an entire decade.

One of the most representative examples is the Square Kilometre Array Observatory (SKAO), the largest radio telescope ever built. Once fully operational, it will produce several hundred petabytes of scientific data every year, making it one of the largest generators of information on the planet.

Earth observation provides another particularly relevant example. The European Copernicus program, coordinated by the European Commission and the European Space Agency (ESA), provides continuous, open data on the state of our planet through its constellation of Sentinel satellites. This information is essential for monitoring wildfires, droughts, air quality, sea ice evolution, agriculture, and responses to natural disasters.

What is particularly significant is that none of these projects would be possible through better telescopes or satellites alone. Their success also depends on infrastructures capable of transporting, storing, processing, and distributing massive amounts of information almost in real time.

In this context, the ability to manage data has become a scientific advantage as important as having better observational instruments. Those capable of extracting knowledge faster and more accurately are also better positioned to address the major scientific and technological challenges of our time.

Artificial Intelligence Needs Data, but Above All, Quality Data

The rise of artificial intelligence has placed data at the heart of scientific and technological debate. However, there is a widespread misconception that the success of an AI model depends solely on the sophistication of its algorithm. In reality, its performance is largely determined by the quality, diversity, and reliability of the data on which it has been trained.

In scientific research, this is particularly critical. A model may analyze millions of medical images, physical simulations, or experimental records within minutes, but its results will only be as robust as the information it receives. Incomplete, poorly labeled, or biased data can lead to incorrect conclusions, even when the most advanced tools are used.

This is why the concept of data stewardship is becoming increasingly important. It refers to the responsible management of data throughout its entire life cycle. From acquisition and validation to storage, documentation, and reuse, every stage directly influences the quality of research and the ability to reproduce its results.

This need is also driving the development of new methodologies for integrating data from very different sources. A single project may combine satellite imagery, IoT sensors, physical models, computational simulations, and experimental laboratory data. In many cases, the ability to connect all this information coherently is what enables new discoveries.

The Next Scientific Infrastructure Will Not Be Only Physical

For decades, investment in science was primarily associated with the construction of major infrastructures: particle accelerators, astronomical observatories, laboratories, and supercomputing centers. All of these remain essential, but it is becoming increasingly clear that their true potential depends on another, less visible but equally critical infrastructure: the systems that manage and process the data they generate.

This evolution explains the growth of High Performance Computing (HPC) centers, cloud computing platforms for research, and international scientific data-sharing networks. European supercomputers such as MareNostrum 5, in Barcelona, make it possible to run simulations and analyses that only a few years ago would have required months of computation. Today, these infrastructures are strategic assets for disciplines as diverse as new materials design, weather forecasting, biomedical research, and space exploration.

But the horizon extends even further. Quantum computing promises to address problems whose complexity exceeds the capabilities of classical systems, particularly in areas such as optimization, molecular simulation, and drug development. Although this technology is still under development, its potential will likewise depend on our ability to integrate, process, and manage enormous volumes of scientific information.

In other words, the infrastructure of the future will consist not only of buildings or instruments, but also of digital ecosystems capable of connecting data, algorithms, and knowledge on a global scale.

An Invisible Infrastructure for Tackling the Challenges of the Future

The work carried out by centers such as ARQUIMEA Research Center, within the framework of its QCIRCLE project, reflects precisely this transformation. Research in areas such as artificial intelligence, photonics, quantum computing, biotechnology, and space systems generates and uses large volumes of information that must be integrated, processed, and analyzed to transform it into useful knowledge.

In this context, simply having access to data is no longer enough. The real challenge lies in developing the technological capabilities required to manage it securely, interoperably, and efficiently, fostering collaboration across disciplines and accelerating the transfer of knowledge into new scientific and technological applications.

Just a few decades ago, having access to large amounts of information was a competitive advantage. Today, it has become an essential requirement for doing science. Yet the real value lies in building the infrastructures that make it possible to transform data into knowledge, share it efficiently, and reuse it to answer increasingly complex questions.

In the coming years, some of the greatest scientific advances will likely depend on our ability to connect millions of data points from different disciplines and transform that information into new ideas, new technologies, and new ways of understanding the world.

Because just as electricity enabled the Industrial Revolution and the Internet transformed global communication, data has become the silent infrastructure on which 21st-century science is built.

Yet beyond its visual appeal, a solar eclipse also represents an exceptional scientific opportunity. For researchers, each eclipse serves as a natural laboratory where astronomers, physicists, engineers, and Earth observation specialists can study physical processes that remain hidden under normal conditions.

In an era when satellites continuously monitor the Sun and space probes venture closer to its atmosphere than ever before, it might seem that eclipses have lost some of their scientific value. The opposite is true. They remain unique events that allow scientists to validate models, calibrate instruments, and conduct experiments that complement observations gathered from space.

Why an Eclipse Is Still Unique for Science

A solar eclipse occurs when the Moon passes between the Earth and the Sun, casting its shadow across a small region of our planet. Although the explanation seems simple, the phenomenon is only possible thanks to an extraordinary astronomical coincidence.

The Sun is approximately 400 times larger than the Moon, but it is also about 400 times farther away from Earth. As a result, both celestial bodies appear almost exactly the same size in our sky. This remarkable alignment allows the Moon to cover the solar disk almost perfectly, briefly revealing features that are normally hidden by the Sun’s intense brightness.

This balance will not last forever. The Moon is moving away from Earth at a rate of about 3.8 centimeters per year, meaning that hundreds of millions of years from now, total solar eclipses will no longer occur. From an astronomical perspective, we are living in a privileged era in which these events can still be observed.

More Than a Century of Scientific Discoveries

The scientific importance of solar eclipses has been demonstrated through some of history’s greatest discoveries. One of the best-known examples took place in 1919, when British astronomer Arthur Eddington used a total solar eclipse to measure the bending of starlight as it passed near the Sun. His observations provided the first experimental confirmation of Albert Einstein’s General Theory of Relativity, fundamentally changing our understanding of gravity and the universe.

Einstein’s theory predicted that the Sun’s enormous mass should curve space-time, slightly deflecting the light coming from stars behind it. Under normal circumstances, however, the Sun’s brightness makes those stars impossible to observe. During a total eclipse, the Moon blocks the Sun’s light for a few minutes, allowing astronomers to photograph the surrounding stars. By comparing their apparent positions with observations made when the Sun was elsewhere in the sky, Eddington confirmed that the light had bent exactly as Einstein had predicted. Those observations became the first strong experimental evidence supporting General Relativity.

More than a century later, eclipses continue to provide valuable scientific opportunities. The sudden reduction in solar radiation allows researchers to study the Earth’s atmosphere almost in real time, analyze changes in the ionosphere, observe variations in atmospheric circulation, and measure parameters such as temperature, pressure, and wind speed. These observations help refine weather models and improve our understanding of the interaction between solar radiation and our planet.

Solar eclipses also offer an exceptional opportunity to study the solar corona, the outermost layer of the Sun’s atmosphere. Despite reaching temperatures of several million degrees, the corona remains one of the greatest mysteries in solar physics. Understanding its structure and behavior helps scientists explain how the solar wind and coronal mass ejections are generated—phenomena that influence space weather and can affect satellites, navigation systems, power grids, and communications infrastructure.

The Technology Behind Eclipse Science

Far from being merely an astronomical event, solar eclipses also represent an extraordinary technological challenge.

Modern scientific campaigns combine observations from the ground, satellites, and airborne platforms to build a comprehensive picture of the event. Telescopes equipped with high-precision optics, high-speed cameras, spectrometers, thermal sensors, and advanced data acquisition systems operate in perfect synchronization to collect millions of measurements within just a few minutes.

Meanwhile, space missions have revolutionized our ability to study the Sun. Satellites such as SOHO, the European Space Agency’s Solar Orbiter, and NASA’s Parker Solar Probe provide continuous observations that allow scientists to compare eclipse data with measurements collected directly from space.

Artificial intelligence is also playing an increasingly important role. Modern algorithms can process enormous volumes of solar imagery, identify complex structures within the corona, detect subtle changes in solar activity, and improve predictions of phenomena capable of affecting Earth’s technological infrastructure.

These advances are complemented by increasingly sophisticated physical models and digital twins that integrate data from multiple instruments to simulate the Sun’s behavior with unprecedented accuracy.

The August 2026 Eclipse

The August 12, 2026 solar eclipse will be one of Europe’s most significant astronomical events of the decade. The path of totality will cross the North Atlantic, Greenland, Iceland, and northern Spain, while much of the continent will experience a partial eclipse.

Beyond its appeal to the general public, the event will bring together numerous international scientific teams conducting coordinated campaigns in solar physics, meteorology, geophysics, and atmospheric sciences.

The eclipse will also provide an opportunity to validate next-generation scientific instruments and compare ground-based observations with data collected by solar observation satellites, strengthening collaboration among space agencies, observatories, and research centers worldwide.

This eclipse will be an extraordinary event, but it also requires a few essential safety precautions. Never look directly at the Sun without proper eye protection, even when it is partially covered by the Moon, as solar radiation can cause permanent retinal damage.

To observe the eclipse safely, always use ISO 12312-2 certified eclipse glasses or indirect viewing methods, such as a pinhole projector or a telescope fitted with a dedicated solar filter.

The same precaution applies to cameras, smartphones, binoculars, telescopes, and other optical devices. Pointing any of these directly at the Sun without a certified solar filter can damage both the device’s sensor and your eyesight.

An eclipse is a unique opportunity for scientific observation and public engagement, but only when viewed using the appropriate safety measures.

Three Fascinating Facts You May Not Know

Solar eclipses continue to surprise even those who have studied them for decades.

• During a total solar eclipse, air temperature can drop by several degrees within just a few minutes because of the sudden reduction in solar radiation.
• The Moon’s shadow can travel across Earth’s surface at speeds exceeding 2,000 km/h, depending on the eclipse geometry. This explains why totality lasts only a few minutes at any given location and why scientific campaigns require extremely precise planning.
• Some species of birds, insects, and other animals temporarily change their behavior during an eclipse. Many birds stop singing and return to their nests, crickets and cicadas begin their nighttime activity, and even some bats emerge to fly, interpreting the sudden darkness as nightfall. Totality during a solar eclipse can never exceed approximately 7 minutes and 30 seconds, a limit imposed by the geometry of the Earth–Moon–Sun system.

Solar eclipses demonstrate that some of science’s greatest advances arise from the ability to observe natural phenomena with extraordinary precision. For just a few minutes, they make it possible to validate theories, improve observational technologies, and gather data that continues to expand our understanding of the Sun and its influence on Earth.

That ongoing dialogue between science and technology is also one of the principles driving research at ARQUIMEA Research Center: transforming every new observation into knowledge, and that knowledge into new capabilities for tackling the scientific challenges of the future.

The volume of knowledge generated by the global scientific community is difficult to imagine. In 2022 alone, nearly 3.3 million scientific articles were published, yet only a fraction of those results will ever become an applied technology, a new company, or a solution capable of generating impact beyond the academic sphere.

Between discovery and application lies a complex journey that requires validation, development, protection and, above all, the ability to connect research with real-world needs. It is precisely in this space that technology transfer comes into play, a fundamental process that prevents ideas from remaining confined to the laboratory and allows them to be transformed into innovation.

What is technology transfer?

Technology transfer emerges precisely in the space that separates scientific discovery from its practical application. Its function is to facilitate the evolution of knowledge generated in research environments into solutions with industrial, economic, or social impact, using tools that go far beyond traditional intellectual protection.

Its relevance has grown considerably in recent decades. In a context where innovation has become a strategic factor for competitiveness, the ability to transform scientific knowledge into applicable technologies is considered one of the main drivers of economic and technological development. The World Intellectual Property Organization (WIPO) highlights technology transfer as a fundamental element for connecting research, innovation and growth.

Although it is often associated with patents or licences, in reality it encompasses a much broader range of activities. It can take shape through collaboration agreements with companies, co-development projects, the creation of spin-offs, the transfer of specialised knowledge, or the incorporation of emerging technologies into existing industrial processes.

In essence, it is about reducing the distance between those who generate knowledge and those with the capacity to transform it into applicable solutions. As the volume of scientific research and the complexity of emerging technologies increase, this connection becomes ever more important.

It is no coincidence that the most advanced innovation ecosystems devote growing resources to strengthening these dynamics. Knowledge generation remains the foundation of scientific progress, but it is transfer that allows that knowledge to translate into new industries, competitive advantages, and opportunities for technological development.

From discovery to application

There is a fairly widespread perception that innovations follow a relatively linear trajectory: an idea emerges in a laboratory, is developed, and eventually becomes a product or service. The reality is usually considerably more complex.

Many promising technologies never manage to get past the initial development phases. This phenomenon is so common that it even has its own name: the Valley of Death, or the technological “valley of death”. The term describes the gap that exists between a successful scientific demonstration and the ability to turn that technology into a solution adopted by industry or the market.

At this stage, challenges related to funding, technical validation, scalability, regulation, and commercial viability converge. It is not uncommon for technologies with a solid scientific foundation to fail at this point due to the difficulty of demonstrating their usefulness outside the experimental environment.

Technology transfer emerges precisely to reduce this gap, providing the necessary mechanisms for innovations to advance from research towards practical adoption.

This challenge is particularly visible in Deep Tech technologies, such as quantum computing, photonics, biotechnology, or advanced artificial intelligence, where development cycles tend to be longer and validation requirements considerably more demanding.

How a technology reaches the market

There is no single formula for transferring technology, but there are a number of elements that tend to be decisive in enabling an innovation to advance beyond the laboratory.

The first is technological validation. A promising idea must demonstrate that it works outside the experimental environment and that it can respond to a concrete need. The more mature a technology is, the lower the uncertainty associated with its adoption and the greater its chances of finding application.

A second factor is the identification of market opportunities. Not all technologies find their value in the domain for which they were originally conceived. In many cases, success depends on identifying sectors, applications, or problems where the innovation can provide a differential advantage.

Intellectual property also plays a relevant role throughout this journey. Patents, trade secrets, or copyrights make it possible to protect the knowledge generated and create a framework that facilitates collaboration with companies, investors, or technology partners. However, they represent only one piece within a much broader process.

Another fundamental element is collaboration between research and industry. Early involvement of potential users or industrial partners allows technological development to be oriented towards real needs, reducing risks and accelerating adoption.

Finally, aspects such as funding, the availability of adequate infrastructure, or access to specialised talent often make the difference between a technology that remains in the experimental phase and one that manages to reach the market.

Taken together, these factors reflect a reality common to any technology transfer process: developing a good solution is not enough. It is also necessary to build the right environment for that solution to grow, be validated, and generate impact.

Different pathways to transfer

Technologies do not follow a single path to generate impact. In some cases, the most appropriate route involves licensing a technology so that a specialised company can continue its development and commercialisation. In others, direct collaboration with industry allows the validation and adaptation of the innovation to specific needs to be accelerated.

In recent years, the creation of spin-offs has established itself as one of the most dynamic mechanisms of technology transfer. These companies are born directly from scientific results and offer a particularly suitable environment for developing emerging technologies that have not yet found a place within consolidated business structures.

At the same time, open innovation programmes, co-development agreements, and technology consortia have significantly expanded the possibilities for collaboration between research and industry.

For this reason, technology transfer is increasingly understood not as a one-off transaction but as a continuous process of interaction between science, market, and society.

Technology transfer at ARQUIMEA Research Center

At our centre of excellence QCIRCLE, technology transfer is part of the innovation model itself. The objective is not merely to generate scientific knowledge, but to identify those technologies with genuine potential to become applicable solutions.

This process begins with Technology Scouting activities, aimed at identifying and validating emerging technologies with disruptive potential in strategic areas. The combination of technology monitoring, scientific analysis, and trend assessment makes it possible to detect opportunities before they reach commercial maturity.

The next stage involves the development of Technology Demonstrators, where research is translated into technology demonstrators, prototypes, and transferable assets capable of validating the potential of a technology in real-world application scenarios.

Finally, the Technology Transfer phase enables these technologies to find the most appropriate mechanism for generating impact. This can materialise through their incorporation into other ARQUIMEA business lines, transfer agreements with third parties, or the creation of new specialised companies.

This entire process is supported by the Market Intelligence team, whose work consists of analysing scientific trends, industrial needs, and market opportunities to maximise the potential of the technologies developed and facilitate their transition towards real-world applications.

Ultimately, this model responds to a straightforward idea: research achieves its maximum value when it manages to transcend the laboratory. The ability to identify technologies with potential, validate them, and accompany them through to adoption allows scientific knowledge to generate real impact and contribute to addressing technological, industrial, and social challenges.

Geopolitical tensions, technological dependence on certain countries and growing concern over data sovereignty are driving a new trend: geopatriation. The term, introduced recently, refers to the strategic movement of data, workloads and digital infrastructure from large global platforms to local or sovereign environments, motivated by geopolitical and regulatory risks.

Far from being a purely technical debate, geopatriation is beginning to influence critical areas of science and technology. From artificial intelligence and cloud computing to scientific supply chains or the management of critical infrastructure, more and more organizations are asking not only what technology they use, but also where it resides, under which jurisdiction it operates and who really controls their data.

What exactly is geopatriation?

Geopatriation can be understood as an evolution of the concept of cloud repatriation, but driven less by economic reasons and more by issues of sovereignty, security and technological resilience.

In practical terms, it involves moving digital infrastructure, cloud services or sensitive data from global providers to national, regional or sovereign alternatives. This may include owned data centers, sovereign clouds or technology providers subject to jurisdictions considered safer or more aligned with the strategic interests of a country or region.

More than 75% of European and Middle Eastern companies could geopatriate part of their workloads before 2030, compared to less than 5% in 2025.

The main reason is clear: digital infrastructure is no longer perceived only as a technological resource, but as an element of sovereignty.

Geopatriation does not necessarily imply abandoning technological globalization, but redefining it. The goal is not to isolate, but to build more resilient, less dependent systems capable of operating even in scenarios of international tension.

This shift is also changing how scientific innovation is understood. The ability to develop proprietary technology, protect critical infrastructure and secure digital autonomy is beginning to be considered a strategic advantage for universities, research centers and technology companies.

In this sense, geopatriation represents something deeper than a technological tredn: it reflects a transformation in the relationship between science, technology and sovereignty.

From digital globalization to technological sovereignty

Geopatriation emerges in a context where technological globalization is beginning to show structural vulnerabilities.

The pandemic, trade tensions between the United States and China and recent geopolitical conflicts have revealed the extent to which supply chains and digital infrastructure depend on a small number of global actors. Research from the National Bureau of Economic Research (NBER) describes this phenomenon as a “great reallocation” of global value chains.

This movement is not limited to the manufacturing industry. It also affects data storage, cloud services, artificial intelligence models and the ability to process critical information within a specific jurisdiction.

In parallel, concepts such as reshoring, friendshoring or technological sovereignty have started to become part of the scientific and industrial strategy of numerous countries. Reshoring refers to the return of productive and technological capabilities to the country of origin, while friendshoring seeks to concentrate supply chains and technological collaboration in countries considered allies or politically stable. Technological sovereignty, for its part, aims to reduce dependence on critical infrastructure, data and technologies controlled by third parties. All these approaches share the same logic: strengthening control and resilience over strategic resources in a global context increasingly conditioned by geopolitical factors.

Infrastructure, AI and data centers: the new geopolitical board

One of the most visible aspects of geopatriation is the transformation of digital infrastructure.

For years, major cloud platforms concentrated a large part of the global technology ecosystem. Today, many organizations are looking for hybrid or sovereign architectures that reduce geopolitical exposure and improve control over their data.

This is driving new investments in:

• Regional data centers
• Sovereign clouds
• Local storage systems
• Hybrid infrastructure
• AI platforms with jurisdictional control

In this context, the location of a data center is becoming as strategic as the location of an industrial plant or an energy network.

Real cases: when geopatriation stops being theory

Although the concept of geopatriation is relatively recent, its effects are already beginning to appear in specific decisions made by governments, technology centers and large companies. Beyond the strategic debate, there are real movements showing how the location of data and digital infrastructure is becoming a critical issue.

Europe and sovereign clouds

One of the clearest examples is the European push towards so-called sovereign clouds. Initiatives such as GAIA-X, backed by Germany and France, were created with the aim of building an interoperable cloud infrastructure aligned with European standards for data protection and digital sovereignty.

The project emerges, in part, as a response to the strong dependence on US technology providers and to concern over extraterritorial access to sensitive data. The idea is clear: certain scientific, industrial or strategic data must remain under European jurisdiction.

Scientific infrastructure and strategic data

Geopatriation is also beginning to influence large international scientific infrastructures. In areas such as high-performance computing, artificial intelligence or biomedical research, more and more countries are seeking to make certain critical data remain within their own jurisdictions.

A clear example is the development of the European EuroHPC initiative, created to strengthen Europe’s own supercomputing and artificial intelligence capabilities. The project combines data centers, supercomputers and advanced AI platforms under European infrastructure, with the aim of reducing external technological dependencies in strategic research.

Chips, AI and technological sovereignty

Control over computing capacity has become another of the major strategic axes of this technological reorganization. The training of advanced artificial intelligence models, scientific simulation and massive data processing depend on next-generation semiconductors and high-performance systems whose global production is concentrated in very few actors.

The European Chips Act, for example, seeks to reduce Europe’s dependence on Asia for critical components for AI, computing and telecommunications.

This strategy responds to a growing concern: the possibility that geopolitical tensions or supply chain disruptions could compromise key scientific and technological infrastructure.

The role of research centers

Research centers have a particularly relevant role in this scenario. They are spaces where sensitive technologies, advanced models and strategic intellectual property are generated, often linked to critical sectors such as artificial intelligence, biotechnology, quantum computing or cybersecurity.

The need to secure technological sovereignty is driving new forms of collaboration between research, industry and public institutions, especially in Europe. The ability to develop proprietary knowledge and maintain control over infrastructure and data is beginning to be as important as scientific innovation itself.

This is the context in which initiatives such as ARQUIMEA Research Center and its QCIRCLE project are situated, focused on developing proprietary capabilities in areas such as quantum computing, photonics and artificial intelligence. Beyond fundamental research, projects of this kind reflect a growing trend: strengthening technological ecosystems capable of generating strategic innovation from Europe, reducing external dependencies in areas considered critical for the scientific and industrial future.

In a global context where technological competitiveness is crucial, protecting innovation is not only a legal matter. It is a way to ensure that research outcomes can be developed, scaled, and brought to society. According to the World Intellectual Property Organization, more than 3.5 million patent applications were filed worldwide in 2023, a record figure that reflects the growing importance of turning knowledge into tangible value.

What is intellectual property in research

Intellectual property encompasses the rights that protect creations of the human intellect, from inventions to software, processes, or designs. Within this framework, there are different forms of protection, such as patents, copyrights, trademarks, or industrial designs, each adapted to different types of innovation. In the scientific and technological field, the most relevant tool is usually the patent, which grants its holder the exclusive right to exploit an invention for a defined period, typically 20 years.

Patents play a central role in the innovation ecosystem, as they allow research results to be protected and facilitate their transfer to real applications. In practice, they act as a mechanism that connects scientific knowledge with industrial development by providing a framework that encourages investment and collaboration between research centers and companies.

In research centers, this is particularly relevant because much of the knowledge generated is in early stages, when there is still no final product, but there is a high potential for application.

The role of patents in research centers

Research centers, such as ARQUIMEA Research Center, occupy a key position within the innovation chain. It is in these environments where many of the ideas that eventually become applied technologies or solutions with real industrial impact are born. A significant part of innovation in sectors such as biotechnology, energy, or digital technologies originates in universities and scientific institutions.

Patents also play a fundamental role in ensuring that this knowledge does not remain confined to the academic sphere. They allow research results to be structured and protected, facilitating their transfer to companies, their development into new products, and their ability to attract investment. In this process, intellectual property acts as an essential link between basic research and its practical application.

Publish or protect: a strategic decision

One of the most interesting dilemmas in research is finding the balance between publishing and protecting.

Scientific publication is essential for the advancement of knowledge, but if it is done before filing a patent, it may prevent the invention from being protected. This forces research centers to integrate intellectual property management into the scientific process itself.

For example, the Organisation for Economic Co-operation and Development highlights that collaboration between research centers and industry is one of the most important factors for transforming knowledge into economic innovation.

For this reason, more and more institutions are developing specific structures to manage this balance, such as technology transfer offices or teams specialized in intellectual property.

Data that help understand its impact

Beyond the theoretical framework, there are a number of indicators that help measure the real weight of intellectual property in today’s scientific and technological system.

One of the most revealing is the shift in the nature of corporate value. Today, more than 90% of the value of companies in the S&P 500 is linked to intangible assets such as patents, technical knowledge, and innovation capabilities, a figure that reflects a profound transformation in the global economy where protected knowledge has become the main driver of value creation.

In the scientific field, the impact of intellectual property is also reflected in development timelines. In sectors such as biomedicine, the path from a patented invention to its commercial application can exceed 10 years. This highlights the importance of protecting results at very early stages, when there is still no final product, but there is already a technological foundation with development potential.

At the same time, the global distribution of patent applications helps identify where innovation is heading. Technologies such as artificial intelligence, biotechnology, or clean energy account for a large share of growth, indicating not only where research is most active, but also where the capabilities that will shape the future are being developed.

Taken together, these data show that intellectual property is not a secondary element, but a key infrastructure of the innovation system.

Current challenges in intellectual property

Despite its relevance, managing patents in research presents several challenges.

One of the main issues is technological complexity. Today’s innovations often combine multiple disciplines, making it difficult to define precisely what is patentable and how to protect it.

Another major challenge is cost and time. International protection requires significant resources and a clear strategy.

There is also an ongoing tension between open access to knowledge and intellectual property protection. Science advances by sharing results, but innovation requires protection to be developed.

Finally, global competition in technology makes intellectual property increasingly strategic, especially in critical areas such as artificial intelligence, biotechnology, or quantum computing.

Beyond protection: intellectual property as a strategic asset

Today, intellectual property is increasingly understood as a strategic asset.

A strong patent portfolio allows a research center to position itself in key technological areas, facilitate international collaborations, generate revenue through licensing, and increase its capacity for technology transfer.

In this context, intellectual property moves beyond being an administrative element to become a central part of scientific and technological strategy.

Intellectual property at ARQUIMEA Research Center

At ARQUIMEA Research Center, intellectual property is an integral part of the research process. It is not only about protecting results, but also about identifying which developments have real potential to become applicable solutions. To achieve this, different forms of protection are combined, such as patents, trademarks, industrial designs, trade secrets, or copyrights, with the aim of strengthening the protection of each innovation from multiple angles. This approach makes it possible to adapt the IP strategy to the nature of each development and maximize its long term value.

In addition, ARQUIMEA diversifies its intellectual property exploitation models through licensing agreements, facilitating technology transfer and its development in specialized environments.

The center currently holds patent activity across multiple areas, including biotechnology, robotics, cybersecurity, and artificial intelligence, reflecting the breadth of its technological approach and its commitment to turning knowledge into innovation with real impact, ensuring that the knowledge generated goes beyond the laboratory and achieves tangible application.

In this context, heat pipes, passive devices that transfer heat through the latent heat associated with phase changes of an internal working fluid, have become a key component in the thermal architecture of satellites and space missions. Their ability to transfer heat efficiently, without moving parts and with high reliability, makes them an essential solution for maintaining systems within safe operating temperature ranges.

However, the performance of heat pipes in space does not depend solely on their structural design. The working fluid circulating inside them ultimately determines their thermal behavior. In the space sector, ammonia has become one of the most widely used working fluids in heat pipes, as it provides highly efficient thermal performance within the temperature range in which these systems typically operate.

What Are Heat Pipes?

Heat pipes are hermetically sealed, two-phase heat transfer devices designed to transport high heat fluxes with minimal temperature gradients. Their operation is based on the phase change of a working fluid contained inside and on the action of a capillary structure that provides the driving force required to circulate the fluid and sustain the evaporation–condensation cycle.

Operating Principle

From a physical standpoint, the operation of a heat pipe is based on three main processes:

1. Evaporation: the working fluid absorbs heat and vaporizes in the high-temperature region.
2. Vapor transport: the generated vapor flows naturally toward the colder region, carrying heat with minimal losses.
3. Condensation and capillary return: in the condenser, the vapor releases latent heat and condenses. The resulting liquid returns to the evaporator through the capillary structure. This mechanism enables operation in microgravity conditions.

Heat pipes are fully passive systems: they do not require pumps, compressors, or external power input. Fluid circulation is driven solely by internal pressure differences and capillary forces. This architecture eliminates mechanical wear mechanisms, resulting in long operational lifetimes and extremely high reliability.

Advantages Over Other Thermal Control Systems

Compared to solid conduction-based solutions or active cooling systems, heat pipes offer clear advantages:

• Ability to transport high heat fluxes with minimal temperature gradients
• Uniform heat distribution across satellite structures
• No vibrations or mechanical interference
• Stable operation in microgravity thanks to capillary-driven return
• Lower structural complexity and reduced mass compared to active systems

In space platforms, where constraints on mass, volume, and power consumption are critical, these characteristics make heat pipes a technically superior solution for passive thermal management. In the absence of convection, heat can only be managed through conduction and radiation, further reinforcing their role within spacecraft thermal control architectures.

For a long time, research has been forced to work within this gap: detecting damage only once it was visible and intervening when the disease was already advanced. The ability to identify biological changes before symptoms emerge, together with a more detailed understanding of the underlying cellular mechanisms, is driving a shift that is not only conceptual. It also opens the door to earlier, more precise, and potentially more impactful interventions.

Neurological disorders are currently one of the leading causes of disability and mortality worldwide. It is estimated that more than 55 million people are living with dementia globally, a figure that could double in the coming decades. At the same time, the healthcare and economic burden associated with conditions such as Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and hereditary ataxias continues to rise in increasingly aging societies.

A heterogeneous group with shared biological foundations

The term “neurodegenerative diseases” encompasses a wide range of conditions that differ significantly from one another, yet share a common biological pattern: progressive neuronal loss, accumulation of misfolded proteins, and gradual deterioration of the mechanisms that maintain cellular homeostasis. What varies from one disease to another is less the nature of the process than its location, its progression rate, and the functions it ultimately affects.

Among the most studied are several conditions that, despite being well known, still pose major unanswered questions:

• Alzheimer’s disease, the most common form of dementia, initially presents as memory loss and progressive cognitive decline. Biologically, it is associated with the accumulation of beta-amyloid and tau proteins, which disrupt neuronal communication and ultimately compromise cell survival.
• Parkinson’s disease, characterized by loss of motor control, tremors, and muscle rigidity. In this case, the problem originates from the degeneration of dopamine-producing neurons, along with the accumulation of alpha-synuclein in structures known as Lewy bodies.
• Amyotrophic lateral sclerosis (ALS), a particularly aggressive disease that affects motor neurons responsible for voluntary movement. As these neurons degenerate, patients progressively lose the ability to move, speak, and breathe, while cognitive functions often remain intact in many stages of the disease.
• Prion diseases, much rarer but extremely rapid in progression, in which a misfolded protein triggers a chain reaction that alters healthy proteins, leading to severe neurological deterioration in a short period of time.

For years, research focused on these aggregated proteins as the main cause of neuronal damage. However, current knowledge points to a more complex scenario. Today, it is understood that multiple biological processes converge in these diseases, including mitochondrial dysfunction, alterations in energy metabolism, failures in cellular degradation systems, neuroinflammation, and genetic predisposition. This more integrated view, supported by recent studies, has shifted the focus from a single trigger to interconnected pathological networks, where multiple factors interact and reinforce one another.

From symptom to molecular mechanism

En In the study of neurodegenerative diseases, the clinical analysis of symptoms has long been the foundation for understanding and diagnosing these conditions. In recent years, this approach has been complemented by greater attention to the biological processes that precede those symptoms, expanding the ability to characterize disease in earlier stages.

In Alzheimer’s disease, for example, the criteria established by the Alzheimer’s Association now incorporate measurable biological indicators, such as beta-amyloid and tau accumulation, detectable through PET imaging or cerebrospinal fluid analysis even before symptoms appear. This shift enables identification of the disease at much earlier stages.

This approach has directly influenced therapeutic development. Antibodies targeting beta-amyloid protein, although their clinical results remain under debate, reflect an effort to intervene in the underlying mechanism rather than merely treating symptoms.

At the same time, genetic sequencing has made it possible to identify molecular subtypes in diseases such as ALS, as well as to classify the disease as sporadic (90–95% of cases) or familial (5–10%). Mutations in genes such as SOD1 have opened the door to targeted therapies, such as antisense oligonucleotides, designed to reduce the production of altered proteins. These strategies represent some of the first advances toward precision medicine in neurodegeneration.

ALS: a paradigm of scientific urgency

La Amyotrophic lateral sclerosis occupies a particular place within this landscape. It is a rare but devastating disease, with an average survival of three to five years after diagnosis, which typically occurs when symptoms have already been present for a long time. It selectively affects cortical and spinal motor neurons, leading to progressive muscle weakness and respiratory failure.

For years, therapeutic options were limited and had modest impact. However, the identification of genetic subtypes and a deeper understanding of the underlying cellular processes are reshaping research in this field. International initiatives such as the Project MinE consortium have generated large-scale genomic datasets to accelerate the discovery of therapeutic targets.

ALS illustrates how our understanding of these diseases is evolving. It is increasingly viewed not as a single uniform entity, but as a set of molecular alterations converging into a common clinical phenotype. This deeper biological characterization enables more targeted therapeutic strategies, although it also introduces new challenges in clinical trial design, which must adapt to greater diversity in patient profiles.

Beyond neurons: the role of the cellular environment

Another important shift is the recognition of the role of non-neuronal cells. The activation of microglia and astrocytes, alterations in the blood–brain barrier, and the involvement of the peripheral immune system are now understood as part of the pathological landscape. Neurodegeneration is increasingly seen as a multicellular process.

This broader perspective has driven the search for therapies that modulate inflammation, metabolism, or intercellular communication. The challenge lies in intervening without disrupting essential physiological functions, which requires a nuanced understanding of the biological balances involved.

Molefy Pharma and the search for new targets

In this context, biotech spin-offs are playing an increasingly important role in translating scientific knowledge into concrete therapeutic solutions. At ARQUIMEA, this commitment is embodied in Molefy Pharma, a company founded in 2024 with a clear objective: to develop therapies capable of changing the course of neurodegenerative diseases, with a primary focus on amyotrophic lateral sclerosis (ALS).

Unlike more traditional approaches, Molefy Pharma focuses its research on identifying new molecular targets that allow intervention in the cellular processes driving the disease. Its main line of work centers on the development of AP-2, a small molecule designed to restore cellular balance by regulating a key kinase involved in the homeostasis of the TDP-43 protein, whose abnormal accumulation is present in the vast majority of ALS cases.

This approach does not aim merely to slow disease progression, but to go a step further: to intervene in the mechanisms that originate the disease with the goal of reversing cellular dysfunction. Data obtained from patient-derived cellular models and preclinical animal studies show a promising profile in terms of both efficacy and safety, positioning these strategies as highly relevant within current therapeutic development.

The work of Molefy Pharma reflects a broader trend in the field: moving toward therapies that act on specific biological processes, integrating molecular knowledge with pharmaceutical development. In diseases such as ALS, where clinical urgency is particularly high, this approach not only expands the range of available options but also introduces a different expectation, the possibility of intervening more directly at the root of the disease.

In this sense, research in neurodegeneration is no longer limited to describing deterioration, but to understanding it with sufficient precision to attempt to modify it. It is at this intersection between fundamental science and applied development where initiatives such as Molefy Pharma find their purpose, providing new tools to a field that, until recently, offered very few real therapeutic alternatives.