InSyBio

InSyBio has been selected to join the ScaleUps Studio track of the Start4Health programme, an initiative led by Pfizer’s Center for Digital Innovation (CDI). This selection represents an important milestone for the company and further reinforces its position within the digital health innovation ecosystem.

Start4Health is designed to connect innovative companies with Pfizer’s innovation ecosystem and to facilitate structured engagement around the co-development and real-world application of digital health solutions. Within this framework, the ScaleUps Studio track is specifically tailored to more mature startups and scaleups, with the aim of enabling them to discuss and co-design potential collaboration projects with Pfizer. According to Pfizer CDI, the track is intended to support established companies in scaling operations, expanding market reach, and further optimising their solutions through the exploration of a possible collaboration with Pfizer’s Center for Digital Innovation.

As part of the ScaleUps Studio activities scheduled for February–March 2026, InSyBio will participate in a tailored sequence of actions designed to deepen mutual understanding and identify concrete collaboration opportunities. These include preparation workshops delivered with the support of Found.ation to shape collaboration concepts, introductory sessions with Pfizer’s Embedded Innovation team, and a dedicated Solution Exploration Workshop with Pfizer business units.

The broader Start4Health framework has been developed to support meaningful interaction between participating companies and Pfizer CDI stakeholders, allowing both sides to identify common ground, potential challenges, and next steps. This structured approach is intended to create a basis for high-quality engagement and to ensure that collaboration discussions are grounded in both strategic relevance and practical feasibility.

For InSyBio, participation in Start4Health is closely aligned with its commitment to advancing innovation in bioinformatics, artificial intelligence, and digital health. Being selected for the ScaleUps Studio track validates the company’s expertise and opens new pathways for translating its technologies into impactful healthcare solutions.

InSyBio looks forward to collaborating with Pfizer and the wider innovation ecosystem through this programme and to exploring how its capabilities can contribute to the future of digital health. This selection constitutes another important step in the company’s effort to bridge research excellence with real-world healthcare impact.

Link references
Official Start4Health page by Pfizer CDI: https://centerfordigitalinnovation.pfizer.com/start4health

InSyBio is happy to announce a new scientific publication led by Filip Stojceski, titled “Molecular-level understanding of the aging bone and regeneration mechanisms using computational methods”, published in Osteoporosis International. The article is available as an open-access publication (https://doi.org/10.1007/s00198-026-07903-z )and was developed in the context of the REGENERATION project.

The publication provides a comprehensive review of current computational research on bone ageing, regeneration, and osteoporosis, with particular emphasis on how molecular dynamics, molecular docking, and bioinformatics approaches can support a deeper understanding of bone biology and contribute to the development of more targeted therapeutic strategies.

More specifically, the paper examines three major scientific areas: first, the structural interactions within the bone matrix; second, the role of key non-collagenous proteins in the mineralisation process; and third, computational strategies for drug discovery targeting pathways such as sclerostin, RANKL, and estrogen receptors. The publication also underscores the growing importance of integrating computational predictions with experimental and clinical data in order to move toward more personalised and effective approaches for the treatment of osteoporosis.

InSyBio is especially pleased to have contributed to this scientific output through the participation of Harry Zaverdas, Konstantinos Theofilatos, and Seferina Mavroudi among the co-authors. Through its involvement in REGENERATION, InSyBio works alongside academic and industrial partners to advance innovative approaches for aged bone tissue research and regenerative medicine.

Congratulations are due to Filip Stojceski of the Dalle Molle Institute for Artificial Intelligence, USI-SUPSI, for leading this publication, and to all co-authors and consortium partners involved, including Andrea Danani and Gianvito Grasso from the Dalle Molle Institute for Artificial Intelligence, USI-SUPSI; Alessia Mengoni and Mario Ledda from the Institute of Translational Pharmacology, National Research Council; Giuseppe Falvo D’Urso Labate from Cellex S.R.L.; and Athanasios Kalogeras from the Industrial Systems Institute, Athena Research Center.

The REGENERATION project is described in the source text as a Horizon Europe Marie Skłodowska-Curie Actions Staff Exchange project focused on the development of reliable and sustainable in vitro models of healthy and aged bone tissue, including models treated with Quantum Molecular Resonance (QMR), with the broader aim of enabling more personalised therapies, improved preventive care for older people, and better treatment and pain management for bone damage. The Springer article additionally states that the work was developed as part of the REGENERATION project and cites EU funding support.

This publication constitutes an important scientific contribution to the field of bone regeneration and osteoporosis research and reflects the value of interdisciplinary collaboration across computational science, bioinformatics, and translational biomedical research.

Link references
You can find the article here: https://doi.org/10.1007/s00198-026-07903-z

More information about the REGENERATION project can be found here: https://regenerationhorizon.eu/

InSyBio has been accepted into the A4SUSTINNO accelerator programme, a significant step in the company’s innovation and growth trajectory and an important opportunity to further strengthen its activities in the fields of bioinformatics, artificial intelligence, and digital health.

The A4SUSTINNO accelerator is a free acceleration programme addressed to startups and teams active in the digital technologies sector that wish to develop their business ideas and advance them in a structured and dynamic way. The initiative is organised by Patras Science Park in collaboration with the entrepreneurship and innovation hub Point of Synergy, offering:

• At least 40 hours of educational content
• 4 hours of personalised support for each participating company or team
• 2 events dedicated to the presentation of innovative ideas and projects.

Through this structure, the programme aims to enhance the skills of participants and support the development and maturation of promising ventures in digital technologies.  An equally important component of the programme is the opportunity for networking with entrepreneurs, mentors, and experts, enabling the creation of valuable collaborations and laying the groundwork for future growth opportunities. In this way, A4SUSTINNO seeks not only to accelerate individual teams but also to contribute more broadly to the Greek innovation ecosystem.

For InSyBio, participation in A4SUSTINNO is fully aligned with its strategic objective of transforming advanced research into practical innovation with real-world applications. The programme provides a structured environment in which the company can further refine its solutions, strengthen its business development capabilities, and expand its connections within the national innovation landscape.

InSyBio would like to thank Patras Science Park and Point of Synergy for this opportunity and for their contribution to supporting entrepreneurship and innovation in Greece. The company looks forward to the next stages of the programme and to further advancing its ideas toward impactful implementation!

Find more information on the project, the partners and organizers in the following links!

A4SUSTINNO announcement and information: https://psp.org.gr/en/main-activities/projects/a4sustinno/

Patras Science Park official website: https://psp.org.gr/

Point of Synergy official website: https://pointofsynergy.com/

InSyBio participated in the midterm meeting of the GALATEA Horizon Europe Project, which took place in Athens on 11–12 March 2026 at the Athena Research Center. The meeting marked an important milestone in the implementation of the project and brought together consortium partners from across Europe to review the progress achieved during the first phase of the action.

During the two-day meeting, participants reviewed the scientific and technical work carried out to date, with particular emphasis on research progress, training activities, and secondments. The meeting also provided an opportunity to assess the results obtained so far, exchange expertise across disciplines, and discuss the planning of the next phase of the project.

InSyBio contributed to the meeting through the participation of its members, Harry Zaverdas, Konstantinos Theofilatos, Konstantina Liontou, and Foteini Kiskira, who took part in the consortium discussions and knowledge-exchange activities.

A central element of the meeting was the dynamic presentation of experiences and results from researchers and secondees, highlighting the interdisciplinary collaboration and knowledge transfer that constitute a core component of GALATEA. The consortium also welcomed the Project Officer, whose feedback and strategic guidance were especially valuable for shaping the next implementation period.

GALATEA is focused on advancing innovation in digital health through the development of AI-driven digital twin technologies for personalised infant nutrition. The project continues to bring together expertise from multiple disciplines in order to contribute to better-informed, data-driven approaches for the healthiest possible start in life.

InSyBio would like to thank all consortium partners for the constructive collaboration, as well as the Athena Research Center for hosting a productive and inspiring meeting in Athens. Further information on the project is available on the official GALATEA project website.

For more information on the programme, you can also visit theofficial GALATEA project website: https://galateahe.eu/

The new version also introduces an enhanced protein analysis module that allows users to retrieve rich structural, sequential, and functional information for any protein, including detailed GO terms, miRNA regulators, and protein interaction partners enriched with a wide range of structural, co-expression, and physicochemical features. These data can be instantly visualized through an upgraded, interactive network interface that supports multiple layouts and downloadable graph formats.

The release features a new generation of computationally predicted protein complexes, developed using an updated EEMC algorithm. These complexes are presented with functional annotations and direct links to visualization, enabling quick identification of biologically meaningful modules.

Functional exploration is further strengthened with a redesigned GO term interface, allowing researchers to examine proteins and complexes associated with any biological process, molecular function, or cellular component. A rebuilt dataset creation module enables users to generate balanced or custom PPI datasets, including optional normalization and iRefIndex-verified interactions, with an option to store them directly within the InSyBio Data Store.

For systems-level analysis, users can now create full biological networks from lists of biomarkers or protein identifiers. These networks can be interactively visualized, explored, and exported in multiple formats, with full compatibility with InSyBio BioNets for further analysis.

With Version 3.4, InSyBio strengthens the field of PPI analysis, protein exploration, and network biology. The updated InSyBio Interact environment brings together high-accuracy PPI prediction, an expanded protein search module, and complex identification. By utilizing InSyBio’s advanced ML capabilities, researchers can move smoothly from examining a single protein’s characteristics to exploring its interaction partners, regulatory molecules, functional roles, and placement within broader biological systems.

InSyBio CEO Labros Digonis stated: “Protein interaction analysis remains fragmented and technically demanding. With Version 3.4, we integrate PPI prediction, protein analysis, complex detection, and interactive network biology into a single, user-friendly platform. Our goal is to empower researchers to extract actionable insights from complex biological data without requiring programming or specialized bioinformatics expertise.”

A detailed description of all new features is available in the InSyBio Interact User Manual v3.4: ( InSyBio Interact – InSyBio )

Researchers may request a free one-month trial of InSyBio Suite, including all new functionalities, by clicking on the following link: (https://suite.insybio.com/)

About InSyBio. InSyBio is an international pioneer biotechnology company revolutionizing the medical field through targeted biomarker discovery, highly accurate predictive analytics, and the development of personalized tests for drugs. Its solutions provide the R&D departments of Pharma and Research Institutes with the means to meet their most challenging research and innovation goals. This is done through a sophisticated, software-as-a-service, machine learning platform that comprehensively integrates multi-omics and clinical data, thus reducing the number of samples and additional validation experiments needed. Since 2013, InSyBio has worked with hundreds of academics, large and small companies, hospitals, and research centers, allowing them to succeed in their biomarker discovery tasks with an increase of at least 10% in predictive accuracy and a decrease of at least 80% in overall time and cost.

A unique Machine-Learning and Molecular Modelling pipeline focusing on the interactome of the Taste Receptors has been released in a new publication from the InSyBio team in collaboration with a team of exceptional researchers from Dalle Molle Institute for Artificial Intelligence (Switzerland), and Politecnico di Torino (Italy) in the framework of VIRTUOUS Project, an EU Horizon 2020 program.

In this study, a Machine-Learning (ML) methodology was developed for predicting binary Protein-Protein Interactions (PPIs) and ranking them based on their computationally predicted affinity. The ML models developed were then applied for resolving the Taste Receptor interactome and detecting the most important interactions of Taste Receptors from the multitude of potential ones. The detected interactions were further characterized through Molecular Dynamics analysis, verifying novel interactions of TAS2R41, such as the TAS2R41-CHMP4A. The classification and regression models applied were based on InSyBio’s multi-objective evolutionary optimization algorithm that is implemented in InSyBio Suite, showcasing the wide range of applications of InSyBio’s biomarker discovery and predictive analytics algorithms and pipelines.

This pipeline aims to facilitate taste perception research for nutrition and pharmaceutical companies and laboratories by filtering and characterizing the most significant Taste Receptor interactions for further studies. The paper was published in the NPJ Science of Food journal of the Springer Nature publishing company (https://rdcu.be/euiWe).

Image: Flowchart of the proposed ML and Molecular Dynamics analysis pipeline applied for the Taste Receptor Interactome.

Labros Digonis, CEO of InSyBio, commented on the importance of the tool:

“We are proud for this unique application of our machine learning and network analytics technology. Resolving the molecular mechanisms of taste is essential for engineering new food products and studying the health implications and downstream effects of different food products. This was an excellent collaborative result of our R&D working together with universities and research institutes in Italy, Greece and Switzerland. Congratulations to the pioneer team!”

About Virtuous: The EU-funded VIRTUOUS project (Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) – https://virtuoush2020.com/) is dedicated to the development of a virtual tongue made by an integrated computational framework that can screen food for natural components by binding taste receptors. This international consortium includes eight academic and industry participants from four European countries. Using a variety of computational and experimental data, partners plan to create an algorithm for the prediction of the organoleptic profile of the food based on its chemical composition. The potential application of this platform as a taste predictor is in the food industry, modern precision agriculture, and as a link to neuroscience for taste determination.

About InSyBio: InSyBio is an international pioneer bioinformatics revolutionizing the medical field, clinical trials and food/nutrition industries through targeted biomarker discovery and highly-accurate predictive analytics.

InSyBio solutions provide the R&D departments of Pharma, Research Institutes and Food/Nutrition companies, with the means both analytically and computationally, to meet their challenging research and innovation goals (such as robust biomarker discovery and accurate predictive models). This is done through a sophisticated, software-as-a-service, machine learning platform which comprehensively integrates multi-omics data reducing the number of samples and additional validation experiments needed. Since 2013, InSyBio has worked with hundreds of academics, large and small companies, hospitals and research centers allowing them to succeed in their biomarker discovery tasks with an increase of at least 10% in predictive accuracy and decrease of at least 80% in overall time and cost.

Visit www.insybio.com to see how InSyBio can help you reach your R&D goals in the era of Precision Medicine and Nutrition.

A novel predictor for multiple taste sensations based on a multi-objective Machine Learning method was published by InSyBio in the NPJ Science of Food journal of the Springer Nature publishing company (https://rdcu.be/dOVbA). This new predictor, namely Virtuous Multi-Taste, was developed in collaboration with a team of exceptional researchers from Politecnico di Torino (Italy), Industrial Systems Institute (Greece) and Dalle Molle Institute for Artificial Intelligence (Switzerland) in the framework of VIRTUOUS Project, an EU Horizon 2020 program.

This work provides a valuable tool for predicting the taste of chemical compounds based on their most important physiochemical properties. The tool is designed to analyze different compounds and provide as output the percentage of contribution of four different tastes- sweet, umami, bitter and other- to the overall taste of each query compound. The multi-class predictor was created based on InSyBio’s multi-objective evolutionary optimization algorithm that is implemented in InSyBio Suite, showcasing the wide range of applications of InSyBio’s biomarker discovery and predictive analytic algorithms and pipelines.

The Virtuous Multi-Taste tool will accelerate taste perception research by providing nutrition and pharmaceutical companies and laboratories with a much-needed tool for compound design based on taste. The tool itself can be accessed through a web-based interface: https://virtuous.isi.gr/#/virtuous-multitaste.

Image: (a) The Virtuous Multi-Taste web-based interface and the result of compound search indicating (b) each compound’s structure and (c) the percentage of each taste’s representation in the final compound taste.

The vision of the Virtuous project, as previously stated by Dr. Marco Agostino Deriu, was to “build an intelligent machine able to decrypt a sensation by modelling the biophysical architecture of our body, covering all required competencies for this ambitious challenge, starting from molecular and multiscale modelling techniques, to artificial intelligence algorithms; experimental facilities to analyze natural product typical of the Mediterranean diet such as wine and oil“

Labros Digonis, CEO of InSyBio, commented on the importance of the tool:

“We are proud of acquiring, at last, the ability to predict the taste of chemical compounds based on their physiochemical properties. The potential to revolutionize multiple industries by improving product development processes, enhancing consumer satisfaction and fostering innovation in taste-related research is inherent to this revolutionary discovery and is here to stay. We will be seeing a lot of new, cost-efficient, more targeted and senses-satisfying products based on this. Congratulations to the pioneer team!”

About Virtuous: The EU-funded VIRTUOUS project (Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) – https://virtuoush2020.com/) is dedicated to the development of a virtual tongue made by an integrated computational framework that can screen food for natural components by binding taste receptors. This international consortium includes eight academic and industry participants from four European countries. Using a variety of computational and experimental data, partners plan to create an algorithm for the prediction of the organoleptic profile of the food based on its chemical composition. The potential application of this platform as a taste predictor is in the food industry, modern precision agriculture, and as a link to neuroscience for taste determination.

About InSyBio: InSyBio is an international pioneer bioinformatics revolutionizing the medical field, clinical trials and food/nutrition industries through targeted biomarker discovery and highly-accurate predictive analytics.

InSyBio solutions provide the R&D departments of Pharma, Research Institutes and Food/Nutrition companies, with the means both analytically and computationally, to meet their challenging research and innovation goals (such as robust biomarker discovery and accurate predictive models). This is done through a sophisticated, software-as-a-service, machine learning platform which comprehensively integrates multi-omics data reducing the number of samples and additional validation experiments needed. Since 2013, InSyBio has worked with hundreds of academics, large and small companies, hospitals and research centers allowing them to succeed in their biomarker discovery tasks with an increase of at least 10% in predictive accuracy and decrease of at least 80% in overall time and cost.

Cardiovascular diseases (CVDs) are the leading cause of death globally, with 17.9 million annual deaths. InSyBio has partnered with Syndesis, the data and technology catalyst to a truly global, multi-continent network of healthcare and life sciences organizations, to address this enormous global health issue.

We focused on two specific heart diseases, congestive heart failure (CHF) and hypertrophic cardiomyopathy (HCM). Congestive heart failure (CHF), or heart failure, is a long-term condition characterized by the inability of the myocardium to meet the body’s blood supply needs. Hypertrophic cardiomyopathy (HCM) is a genetic heart muscle disease that causes alterations in heart’s structure. It is associated with high mortality due to sudden cardiac arrest and frequently requires heart transplant. When faced with patients suffering from CHF or HCM, doctors are often challenged in ensuring the optimal therapeutic protocol that reduces the risk of dangerous complications or death.

Existing clinical scores (Qrisk2, SCORE, Framingham) perform poorly in predicting 5-year mortality and short-term outcome (<60% Area Under the Curve, AUC) in both disease areas. Artificial Intelligence can provide significant support in such clinical decisions, by predicting outcomes for patients based on certain therapeutic protocols.

PrecisionCHF and PrecisionHCM

InSyBio analyzed a highly curated cardiovascular data set using Syntium, Syndesis Health’s data platform of anonymized clinical data, in order to develop two Precision Medicine Tools based on Artificial Intelligence. These tools predict CHF and HCM risk and can help to reduce it by recommending changes to the therapeutic protocol of the patients suffering from these diseases.

The AI tools – “PrecisionCHF” and “PrecisionHCM” – were trained on data from 1044 patients hospitalized with Congestive Heart Failure (CHF) and 255 Hypertrophic cardiomyopathy (HCM) patients with full clinical annotation and outcomes mined from Syntium.

Following training on the data, the AI tools performed impressively well: PrecisionCHF was able to predict 5-year mortality for CHF with 75% AUC, while PrecisionHCM was able to predict short term outcome (i.e. mortality until discharge) with 97% AUC. Using the AI tools 37% of CHF and 28% of HCM patients at risk can substantially reduce their risk (>5% reduction) with the optimization of their medication plan. Moreover, 12% of the rest of CHF and 11% of the rest of HCM who are not at risk can further reduce their risk by at least 5%.

The collaboration between InSyBio and Syndesis has demonstrated the enormous value of training AI pipelines for medical applications using relatively “small” but highly curated and validated clinical data, and the importance of innovative platforms such as InSyBio Diagnostics Platform and Syntium.

These tools can be accessed via the newly launched InSyBio Diagnostics Platform: https://diagnostics.insybio.com

About InSyBio. InSyBio is an international pioneer biotechnology company revolutionizing the medical field through targeted biomarker discovery, highly accurate predictive analytics and the development of personalization tests for drugs. Its solutions provide the R&D departments of Pharma and Research Institutes with the means to meet their most challenging research and innovation goals. This is done through a sophisticated, software-as-a-service, machine learning platform which comprehensively integrates multi-omics and clinical data, thus reducing the number of samples and additional validation experiments needed. Since 2013, InSyBio has worked with hundreds of academics, large and small companies, hospitals, and research centres allowing them to succeed in their biomarker discovery tasks with an increase of at least 10% in predictive accuracy and a decrease of at least 80% in overall time and cost.