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.
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).
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
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.
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.
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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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,
- 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