• Universitat de València
• Universidad de Málaga
• The University of Texas health science center at Houston

• Jorge Gálvez Álvarez
• Jesús Hierrezuelo León
• María Luz Blasco Santamaría
• David Vela Corcia
• Jesús Cámara Almirón
• María Luisa Antequera Gómez
• Antonio de Vicente Moreno 
• Diego Romero
• Rubén Gómez Gutiérrez
• Rodrigo Morales Loyola

The misfolding of certain proteins, caused by genetic mutations or changes in the cellular environment, alters the normal function of proteins and favors the formation of plaques in specific tissues. This accumulation causes the progressive degradation of cells in the central and peripheral nervous system, generating irreversible damage due to the limited capacity for neuronal regeneration.

Diseases such as Alzheimer's, Parkinson's, Creutzfeldt-Jakob disease and various cerebral and peripheral amyloidoses are closely related to this process. Current treatments focus on alleviating the symptoms or slowing the progression of the disease, but do not address its cause. It is therefore crucial to develop new therapeutic strategies capable of blocking the aggregation of misfolded proteins.

Researchers from the Universitat de València, the University of Málaga and the University of Texas System have developed a new computational method to identify chemical compounds with protein antiamyloid activity.

This approach combines advanced molecular modeling techniques, such as the Quantitative Structure-Activity Relationship (QSAR) methodology, with machine learning tools to predict and select molecules with the ability to inhibit protein aggregation.

The results have shown that several compounds identified by this strategy are highly effective in preventing protein aggregation, a key process in the development of neurodegenerative diseases. This finding represents a new avenue for the design of more effective and targeted therapies.

This invention has great potential in the pharmaceutical and biotechnological sector, especially in the treatment of diseases such as Alzheimer's and Parkinson's, both related to the abnormal accumulation of amyloid proteins.

The identification of protein aggregation inhibitors could be useful in the research of other similar pathologies, such as prion diseases or amyotrophic lateral sclerosis (ALS).

The use of machine learning in QSAR models represents a significant advance in the search for new experimental treatments within biomedical research and the pharmaceutical industry, providing a powerful tool for the discovery of drugs with anti-amyloid activity.

This breakthrough could represent a significant step towards the development of innovative therapies that could transform the treatment of neurodegenerative diseases, offering a therapeutic alternative to millions of patients worldwide.

The main advantages of the invention are:

High efficiency in the identification of anti-amyloid molecules:

The method allows the selection of chemical compounds capable of inhibiting the aggregation of proteins involved in neurodegenerative processes. This is especially relevant for diseases such as Alzheimer's and Parkinson's, where aggregation of misfolded amyloid proteins plays a central role in their development and progression.

Potent anti-aggregation activity:

Molecules identified using this methodology have demonstrated an exceptional ability to prevent the formation of protein aggregates, making them promising candidates for the development of therapies aimed at containing the progression of neurodegenerative diseases.

Reduced research costs and time:

By using commercially accessible molecules and combining advanced computational models, this approach accelerates the drug discovery process, significantly reducing the costs and time needed to reach clinical trials and potential treatments.

Potential application in multiple diseases:

In addition to Alzheimer's and Parkinson's, this method could be applied in the study of other diseases related to protein aggregation, such as prion diseases, amyotrophic lateral sclerosis (ALS) and various forms of amyloidosis.

Preventive and therapeutic approach:

The molecules identified could not only treat symptoms, but also prevent the formation of protein aggregates in early stages of the disease. This opens the door to a new paradigm in the treatment of neurodegenerative diseases. After the identification of the chemo-mathematical pattern associated with antiaggregating activity, this methodology could be applied in the search for natural products (derived from plants and foods) with preventive effect in patients with genetic predisposition.

Impact on biomedical research and the pharmaceutical industry:

This method represents a powerful tool for the scientific community and the pharmaceutical industry, facilitating the discovery of new anti-amyloid compounds and opening new possibilities for the development of innovative therapies.

• Patent applied