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Biomarker discovery is of great significance in biomedical applications and across different stages of drug development, spanning from early stage to clinical trials. Anaxomics’ employs the power of its TPMS technology to accurately identify measurable and reliable molecules that hold immense potential as biomarkers.

What does Anaxomics offer?

Anaxomics’ TPMS proprietary technology allows to identify potential biomarkers through the mathematical modelling of pathological conditions and other physiological states. Using TPMS, we can analyse clinical and -omics data from patients and animals to identify reliable biomarkers that exhibit differential activity or expression associated with specific physiological states.

Our methodology, which has been previously described (Jorba, 2020; Gil, 2022), has proven successful in identifying biomarkers across different fields and approaches

Identifying biomarkers of treatment response

TPMS technology has been used to identify biomarkers of treatment response in various conditions. For example, it has been applied to analyse corticoids response in ulcerative colitis (EP17382246.1. (2017)), malaria vaccine response (Moncunill, 2020), TNFi response in axSpA (Fernández‐Carballido, 2023) or somatostatin receptor ligands response in acromegaly (Gil, 2022).

The figure below shows the data mining process followed in the specific project around the acromegaly therapeutic response (Gil, 2022).


Gil, J., et al (2022). Data mining analyses for precision medicine in acromegaly: a proof of concept. Sci Rep. 12(1): p. 8979.

Identifying biomarkers of prognosis and diagnosis

We have successfully identified biomarkers not only for treatment response but also for prognosis and diagnosis in a wide range of medical conditions. For instance, we have identified biomarkers for prognosis in macular degeneration (Jorba, 2020) and in diabetes nephropathy (Guillén-Gómez, 2018).

Moreover, our expertise in identifying biomarkers for diagnosis and follow-up is extensive. We have delved into various areas, including Adult Onset Pompe disease (Carrasco-Rozas, 2019), colorectal cancer (Herreros-Villanueva, 2018), multiple sclerosis (Navarro-Barriuso, 2019), Alzheimer's disease and dementia with Lewy bodies (Gámez-Valero, 2019), fertility (Azkargorta, 2018), as well as diabetes and obesity (Gómez-Serrano, 2016), among others.


Examples of biomarker identification without data provided by the client

  • Gomez, J., L. Artigas, R. Valls and J. Gervas-Arruga (2023). An in silico approach to identify early damage biomarker candidates in metachromatic leukodystrophy.
    Mol Genet Metab Rep. 35: p. 100974
    DOI: 10.1016/j.ymgmr.2023.100974

  • Gil, J., M. Marques-Pamies, M. Sampedro, S. M. Webb, G. Serra, I. Salinas, A. Blanco, E. Valassi, C. Carrato, A. Picó, A. García-Martínez, L. Martel-Duguech, T. Sardon, A. Simó-Servat, B. Biagetti, C. Villabona, R. Cámara, C. Fajardo-Montañana, C. Álvarez-Escolá, C. Lamas, C. V. Alvarez, I. Bernabéu, M. Marazuela, M. Jordà and M. Puig-Domingo (2022). Data mining analyses for precision medicine in acromegaly: a proof of concept. Sci Rep. 12(1): p. 8979.
    DOI: 10.1038/s41598-022-12955-2

  • Jorba G, J. Aguirre-Plans, V. Junet, C. Segú-Vergés, J. L. Ruiz, A. Pujol, N. Fernández-Fuentes, J. M. Mas, B. Oliva (2020). In-silico simulated prototype-patients using TPMS technology to study a potential adverse effect of sacubitril and valsartan. PLoS One. Feb 13;15(2):e0228926.
    DOI: 10.1371/journal.pone.0228926

Examples of biomarker identification by analyzing the data provided by the client

  • Fernández-Carballido, C., C. Sanchez-Piedra, R. Valls, K. Garg, F. Sánchez-Alonso, L. Artigas, J. M. Mas, V. Jovaní, S. Manrique, C. Campos, M. Freire, O. Martínez-González, I. Castrejón, C. Perella, M. Coma and I. E. van der Horst-Bruinsma (2023). Female Sex, Age, and Unfavorable Response to Tumor Necrosis Factor Inhibitors in Patients With Axial Spondyloarthritis: Results of Statistical and Artificial Intelligence-Based Data Analyses of a National Multicenter Prospective Registry. Arthritis Care Res (Hoboken). 75(1): p. 115-124.
    DOI: 10.1002/acr.25048

  • Moncunill, G., A. Scholzen, M. Mpina, A. Nhabomba, A. B. Hounkpatin, L. Osaba, R. Valls, J. J. Campo, H. Sanz, C. Jairoce, N. A. Williams, E. M. Pasini, D. Arteta, J. Maynou, L. Palacios, M. Duran-Frigola, J. J. Aponte, C. H. M. Kocken, S. T. Agnandji, J. M. Mas, B. Mordmüller, C. Daubenberger, R. Sauerwein and C. Dobaño (2020). Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization. Sci Transl Med 12(543).
    DOI: 10.1126/scitranslmed.aay8924

  • Carrasco-Rozas, A., E. Fernández-Simón, M. C. Lleixà, I. Belmonte, I. Pedrosa-Hernandez, E. Montiel-Morillo, C. Nuñez-Peralta, J. Llauger Rossello, S. Segovia, N. De Luna, X. Suarez-Calvet, I. Illa, g. Pompe Spanish Study, J. Díaz-Manera and E. Gallardo (2019). Identification of serum microRNAs as potential biomarkers in Pompe disease. Ann Clin Transl Neurol 6(7): 1214-1224.
    DOI: 10.1002/acn3.50800

  • Gámez-Valero, A., J. Campdelacreu, D. Vilas, L. Ispierto, R. Reñé, R. Álvarez, M. P. Armengol, F. E. Borràs and K. Beyer (2019). Exploratory study on microRNA profiles from plasma-derived extracellular vesicles in Alzheimer's disease and dementia with Lewy bodies. Transl Neurodegener. 8: p. 31.
    DOI: 10.1186/s40035-019-0169-5

  • Guillén-Gómez, E., B. Bardají-de-Quixano, S. Ferrer, C. Brotons, M. A. Knepper, M. Carrascal, J. Abian, J. M. Mas, F. Calero, J. A. Ballarín and P. Fernández-Llama (2018). Urinary Proteome Analysis Identified Neprilysin and VCAM as Proteins Involved in Diabetic Nephropathy. Journal of Diabetes Research 2018: 12.
    DOI: 10.1155/2018/6165303

  • Herreros-Villanueva, M., R. Pérez-Palacios, S. Castillo, C. Segú, T. Sardón, J. M. Mas, A. C. Martín and R. Arroyo (2018). Biological Relationships between miRNAs used for Colorectal Cancer Screening. Journal of Molecular Biomarkers & Diagnosis 9: 398.
    DOI: 10.4172/2155-9929.1000398

  • Azkargorta, M., I. Escobes, I. Iloro, N. Osinalde, B. Corral, J. Ibañez-Perez, A. Exposito, B. Prieto, F. Elortza and R. Matorras (2018). Differential proteomic analysis of endometrial fluid suggests increased inflammation and impaired glucose metabolism in non-implantative IVF cycles and pinpoints PYGB as a putative implantation marker. Hum Reprod, 1;33(10):1898-1906.
    DOI: 10.1093/humrep/dey274

  • Gómez-Serrano, M., E. Camafeita, E. García-Santos, J. A. López, M. A. Rubio, A. Sánchez-Pernaute, A. Torres, J. Vázquez and B. Peral (2016). Proteome-wide alterations on adipose tissue from obese patients as age-, diabetes- and gender-specific hallmarks. Sci Rep 6: 25756.
    DOI: 10.1038/srep25756

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