Among its activities, iBMB Srls conducts research and development and develops, produces and markets innovative services and products in the biotechnology sector.

While developing POEM—Predictive Oncology via Engineering Modeling—we received a Proof of Excellence through INNOLABS, a Horizon 2020-funded innovation programme. This recognition reshaped our corporate vision.

The acceleration experience taught us that a promising idea is only the starting point. We learned how to define the business need and the proposed solution, manage intellectual property, analyse the market, establish a pricing strategy, prepare a business plan, and address essential legal and financial matters, including cash-flow, income and balance-sheet planning.

The experience also highlighted the capabilities required for the company’s next stage: supply-chain management, human-resource development, recruitment, fundraising, investor relations and the disciplined execution of an operational plan. At the same time, it reinforced the central importance of communication, networking and a coherent sales strategy.

Above all, we learned how demanding it is to build a company from the ground up—and how many different disciplines must work together to turn research into a viable enterprise.

The Spin-off was approved by the University of Basilicata and incorporated in 2023; VAT ID 02156870764. Its activity beginnning presently pending, iBMB Srls is going to be appointed (as per the Italian Law) as an Innovative Startup.

Email: ibmb4info@gmail.com, ibmbsrls@pec.it

Currently, the following 2 projects are being exploited by the Spin-off:

• INGambAS: Sustainable Engineering Process for Valorizing Extracted Natural biomasses.
  Chitin is a polysaccharide that is the primary component of crustaceans and insects. The process of obtaining this valuable component typically involves several steps, including pre-treatment, demineralization, deproteinization, and decolorization. Pre-treatment is an important step in this process, as it involves the removal of proteins and other impurities from the natural substrate. This is typically accomplished through a combination of mechanical, chemical, and enzymatic methods, such as grinding, boiling, and the use of proteolytic enzymes. The goal is to break down the protein matrix of the biomass and expose the chitin, which can then be processed into chitosan. Pre-treatments can be optimized to ensure the highest yield and purity of these components, while realizing an environmentally sustainable process. This involves the use of different variables such as process temperature, pH, and enzyme concentration. In particular, we have realized a microwave-assisted extraction by using a fluidized bed to optimize exposure of subject material. Resulting chitosan is suitable for a range of applications in fields such as biomedicine, agriculture, and materials science.
  This project is brought forth in collaboration with the CNR-IPCB research institute of Pozzuoli, Naples (Italy), and the Consorzio Nazionale Italiano delle Biotecnologie, delle Metodologie dell’Acquacoltura e della Ricerca Ecocompatibile e Sostenibile "Biotecnomares" Cagliari (Italy).

• CancerMate: computational prognosis post-therapy outcome in breast cancer.
  Breast cancer is one of the most common types of cancer in women worldwide. While advancements in medical research have led to improved treatments and survival rates, accurately predicting the progression and prognosis of breast cancer remains a significant challenge. Computational prognosis of breast cancer involves the use of advanced algorithms and machine learning techniques to analyze large amounts of clinical data in retrospective, in order to predict the progression of breast cancer and patient outcomes. In particular, deterministic models can be cast that are based on mathematical equations that describe the physical and biological processes underlying cancer growth and treatment. These models typically require a large amount of data, including patient-specific clinical and biological data, to calibrate and validate the model. Once the model is validated, it can be used to predict cancer growth and treatment outcomes for individual patients.
  There are several reasons why the discipline of computational prognosis of breast cancer is important and should be studied and empowered in medical oncology:
  -> Improved accuracy and personalized treatment: computational prognosis can provide more accurate predictions of disease progression and personalized treatment plans for individual patients.
  ->Patient stratification: computational prognosis can help identify patients who should be treated with a given neoadjuvant therapy in a given time window, depending on the personal burden and the risk of developing breast cancer with may be surgically removed with more impactful ways.
  -> Accelerating research: computational prognosis can help accelerate the pace of medical research by providing a tool for analyzing large amounts of clinical and computational data.
  -> Better resource allocation: accurate predictions of disease progression and patient outcomes can help allocate medical resources more effectively and efficiently.
  Please browse about the specific website for this project, that we have cast: virtualcancercure.com.
  Here you find our Technology Offer within the Enterprise Europe Network.