Patient stratification is a fundamental aspect of personalized medicine, where patients are grouped based on their individual characteristics. Anaxomics’ TPMS technology allows the biomolecular characterization of treatments and patients by groups, providing valuable insights for disease classification, prognostic assessment, treatment selection, monitoring treatment response, clinical trial design, and guiding long-term patient management.
What does Anaxomics offer
Unraveling Mechanisms of Action
The analysis of a population by subgroups of individuals is extremely complex due to the enormous variability between patients and the limited ability of statistical approaches to deal with analytical variables. Anaxomics' TPMS faces the challenge of limited data sets and complements conventional statistical analysis through individually modelling the patients and then segmenting them according to qualitative or quantitative characteristics identifying mechanisms of action of a specific subgroup.
Harnessing the Power of Data Science for Biomarkers Identification
Anaxomics’ strength lies in its ability to identify biomarkers, critical indicators of disease and treatment response, despite common data limitations, such as population variability and small samples sizes. Anaxomics’ data science strategies enable unveiling complex biomarker signatures by harnessing advanced algorithms and statistical methods, and analyzing diverse data types (molecular, clinical, or both).
A Data Science and Mathematical Modeling Approach
Anaxomics' TPMS technology joins the power of systems biology and machine learning-based mathematical models by simulating the biological process of interest while incorporating statistical and data mining analysis to identify biomarkers and stratify patients effectively. By segmenting patients based on their unique molecular profiles, Anaxomics provides mechanistic rationales for clinical characteristics associated with specific subgroups: i) acquiring the disease vs healthy status, ii) different pathophysiological situations, iii) the response mechanism of the drug under study.
Publications
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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 - 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 - Gámez-Valero, A., J. Campdelacreu, D. Vilas, L. Ispierto, J. Gascón-Bayarri,
R. Reñé, R. Álvarez, M.
P. Armengol, F. E. Borràs and K. Beyer (2021). Platelet miRNA Biosignature Discriminates between
Dementia with Lewy Bodies and Alzheimer's Disease. Biomedicines. 9(9).
DOI: 10.3390/biomedicines9091272 - Jorba, G., J. Aguirre-Plans, V. Junet, C. Segú-Vergés, J. L. Ruiz, A. Pujol,
N. Fernández-Fuentes, J.
M. Mas and 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 - 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 - Lorén, V., A. Garcia-Jaraquemada, J. E. Naves, X. Carmona, M. Mañosa, A. M.
Aransay, J. L. Lavin, I.
Sánchez, E. Cabré, J. Manyé and E. Domènech (2019). ANP32E, a protein involved in
steroid-refractoriness
in ulcerative colitis, identified by a systems biology approach. J Crohns Colitis.
DOI: 10.1093/ecco-jcc/jjy171 - 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