Depression treatment continues to pose major challenges for psychiatry today. Although various effective treatment options are available, the choice of medication is often based on a trial-and-error principle. For patients, this can mean having to undergo several treatment attempts before an effective therapy is found—a process that takes time and is associated with uncertainty. In the latest episode of the Talking Life Science podcast, Ivan Orban from the Aristo Group talks to Mateo de Bardeci, co-founder of DeepPsy, about this very issue and how data-driven approaches can help make therapy decisions more structured and informed.
Why trial and error is still a reality in depression treatment
Depression is one of the most common mental illnesses worldwide. At the same time, not all patients respond to the same therapy in the same way. In clinical practice, doctors are often faced with the task of choosing between several approved treatment options without knowing in advance which one is best suited to the individual patient.
As Mateo de Bardeci explains in the podcast, in many cases there is a lack of objective additional information that could support this decision. As a result, treatments are started, adjusted, or changed until a sufficient effect is achieved. This approach is stressful for everyone involved.
This is precisely where DeepPsy comes in. The company analyzes EEG and ECG data from patients with depression to identify patterns in brain signals. This information is compiled in a structured report that helps psychiatrists make informed decisions between already approved treatment options.
Importantly, DeepPsy does not make diagnoses or recommend new or experimental treatments. The approach aims to translate existing scientific findings from neuroscience into everyday clinical practice and expand the existing basis for decision-making.
The conversation also covers the path from scientific research to a regulated medical device. Mateo reports on the founding of DeepPsy, the regulatory requirements in the medical device environment, and certifications in Switzerland and the UK. The planned EU certification and cooperation with clinics and hospitals are also discussed.
This highlights how complex it is to implement innovative data-based solutions in a highly regulated environment and why quality assurance, regulatory understanding, and a clear long-term vision are crucial.
The particular challenges facing life science startups
Mateo describes how young companies not only have to develop innovative technologies, but also have to meet extensive regulatory, qualitative, and organizational requirements at the same time.
Software-based medical devices in particular often operate in areas that are complex or not clearly defined in regulatory terms. For startups, this means a lot of work dealing with regulations, creating documentation, setting up a quality management system, and coordinating with authorities. These tasks are time-consuming and resource-intensive and run parallel to product development, research, and market preparation.
The podcast makes it clear that while regulatory requirements are a key challenge, they also contribute significantly to the quality and safety of medical products. For founders, this means that the path to market is almost impossible to navigate without experienced experts. Especially in a highly regulated environment, it is crucial to draw on expertise in regulation, quality, and certification at an early stage in order to make informed decisions and advance the development process in a targeted manner. This is an area in which we, as a personalized personnel consulting firm, have supported DeepPsy by providing relevant experts.
The conversation also touches on DeepPsy’s regulatory journey. The company is active in Switzerland, has certification in the United Kingdom, and is working toward certification in the European Union.
These international activities illustrate how different regulatory frameworks can be and how important it is to incorporate regulatory expertise into the development process at an early stage. At the same time, it shows that market access in healthcare does not depend solely on technology, but also on trust, clinical acceptance, and clear processes.
A key conclusion of the episode: Advances in the treatment of depression do not necessarily have to come from new medications. Various effective treatment options already exist today. The decisive factor is which treatment has the best chance of success for which patients.
Data-driven approaches such as DeepPsy can help reduce uncertainty and better support therapy decisions. This opens up new perspectives for more precise, individualized psychiatric care.
