In response to the FDA and EMA's emphasis on understanding the molecular and physiological basis of therapies, Anaxomics proposes TPMS technology, enabling computational simulation of human pathophysiology.
This groundbreaking approach unveils the likely mechanisms underlying drugs' clinical responses, aligning with new regulatory requirements.
What does Anaxomics offer?
Enhancing Understanding of Disease Pathogenesis
Failures in preclinical development can often be attributed to deficiencies in our understanding of disease pathogenesis and the selection of appropriate therapeutic targets. Anaxomics is committed to improving human health by assisting clients in comprehending clinical observations and placing them within the broader context of human physiology. By doing so, we help identify potential pitfalls and refine strategies for more effective disease management.
Achieving a Comprehensive Understanding of Drug Safety and Efficacy
In the early stages of drug discovery, accurate prediction of efficacy and safety is crucial. Anaxomics' TPMS technology offers the ability to compare and select the most promising candidates while considering other therapeutic options. This ensures a comprehensive evaluation of the safety and efficacy profile during the drug discovery and development process.
ADR: adverse drug reaction
Gaining Insights into Drugs' Clinical Responses
Whether in clinical trial phases or once a drug has entered the market, understanding the mechanisms of action is of utmost importance. Anaxomics' TPMS technology facilitates the exploration of unexpected drug-induced adverse drug reactions (ADRs) observed during trials ( Wagg, 2017 ; Jorba, 2020; Córdoba, 2022). Additionally, it helps unravel the efficacy of drugs or drug combinations observed in phase II, phase III, real-world evidence (RWE), or clinical practice settings (Durán, 2022; Segú-Vergés, 2023; Bayes-Genis, 2021; Iborra-Egea, 2017; Lozano, 2021; Segú-Vergés, 2021; Díaz-Beyá, 2022; Carcereny, 2021). Furthermore, it contributes to advancing personalized medicine by enhancing our understanding of drug efficacy and safety (Segú-Vergés, 2023).
Durán, I., D. Castellano, J. Puente, L. Martín-Couce, E. Bello, U. Anido, J. M. Mas and L. Costa (2022). Exploring the synergistic effects of cabozantinib and a programmed cell death protein 1 inhibitor in metastatic renal cell carcinoma with machine learning. Oncotarget 13: p. 237-256.
The image on the left shows the molecular pathways associated with a specific mechanism of action (MoA) identified through TPMS technology. On the right, the illustration is a representation of a total of five identified MoAs, integrated into a single figure providing context and visualizing the interconnectedness of the identified mechanisms. Both images correspond to the same project carried out for IPSEN (Durán, 2022).
Publications
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Wagg, J., O. Krieter, C. Ooi, S. Croset, M. Leddin, R. Valls, J.
M. Mas and C. Boetsch (2017). Effect of molecular mechanisms mediating bevacizumab
(BEV) and vanucizumab (VAN) on gastrointestinal perforation: Use of artificial neural networks
for integrated data analysis. ASCO Annual Meeting: American Society of Clinical Oncology.
DOI: 10.1200/JCO.2017.35.15_suppl.e15108 -
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 -
Córdoba, R., D. Colomer, A. Bayés-Genís, C. Leiva Farre, E. Álvarez, M.D. López and E.
Zatarain
(2022). In silico Evaluation of BTK Inhibitors Mechanisms That Could Induce Atrial Fibrillation
and Hypertension in the Treatment of Chronic Lymphocytic Leukemia. Presented at 64th ASH Annual
Meeting & Exposition.
DOI: 10.1182/blood-2022-158963 -
Durán, I., D. Castellano, J. Puente, L. Martín-Couce, E. Bello, U. Anido, J. M.
Mas and L.
Costa
(2022). Exploring the synergistic effects of cabozantinib and a programmed cell death protein 1
inhibitor in metastatic renal cell carcinoma with machine learning. Oncotarget 13: p. 237-256.
DOI: 10.18632/oncotarget.28183 -
Segú-Vergés, C., L. Artigas, M. Coma and R. W. Peck (2023).
Artificial Intelligence
Assessment
of the Potential of Tocilizumab Along with Corticosteroids Therapy for the Management of
Covid-19 Evoked Acute Respiratory Distress Syndrome. PLoS One. 18, no. 2: e0280677.
DOI: 10.1371/journal.pone.0280677 -
Bayes-Genis, A., O. Iborra-Egea, G. Spitaleri, M. Domingo, E. Revuelta-López, P. Codina, G.
Cediel, E. Santiago-Vacas, A. Cserkóová, D. Pascual-Figal, J. Núñez and J. Lupón (2021).
Decoding empagliflozin's molecular mechanism of action in heart failure with preserved ejection
fraction using artificial intelligence. Sci Rep,. 11(1): p. 12025.
DOI: 10.1038/s41598-021-91546-z -
Iborra-Egea, O., C. Gálvez-Montón, S. Roura, I. Perea-Gil, C. Prat-Vidal, C. Soler-Botija and
A.
Bayes-Genis (2017). Mechanisms of action of sacubitril/valsartan on cardiac remodeling: a
systems biology approach. npj Systems Biology and Applications 3(12).
DOI: 10.1038/s41540-017-0013-4 -
Lozano, M. L., C. Segú-Vergés, M. Coma, M. T.
Álvarez-Roman, J. R. González-Porras, L.
Gutiérrez, D. Valcárcel and N. Butta (2021). Elucidating the Mechanism of Action of the
Attributed Immunomodulatory Role of Eltrombopag in Primary Immune Thrombocytopenia: An in Silico
Approach. Int J Mol Sci 22, no. 13.
DOI: 10.3390/ijms22136907 -
Segú-Vergés, C., M. Coma, C. Kessel, S. Smeets, D. Foell
and A. Aldea (2021). Application of
systems biology-based in silico tools to optimize treatment strategy identification in Still's
disease. Arthritis Res Ther, 23(1): p. 126.
DOI: 10.1186/s13075-021-02507-w -
Díaz-Beyá, M., M. García-Fortes, R. Valls, L. Artigas, M.
T. Gómez-Casares, P. Montesinos, F.
Sánchez-Guijo, M. Coma, M. Vendranes and J. Martínez-López (2022). A Systems
Biology- and
Machine Learning-Based Study to Unravel Potential Therapeutic Mechanisms of Midostaurin as a
Multitarget Therapy on FLT3- Mutated AML. BioMedInformatics. 2(3): p. 375-397.
DOI: 10.3390/biomedinformatics2030024 -
Carcereny, E., A. Fernández-Nistal, A. López, C. Montoto, A. Naves, C.
Segú-Vergés, M. Coma,
G.
Jorba, B. Oliva and J. M. Mas (2021). Head to head evaluation of
second generation ALK
inhibitors brigatinib and alectinib as first-line treatment for ALK+ NSCLC using an in silico
systems biology-based approach. Oncotarget, 12(4): p. 316-332.
DOI: 10.18632/oncotarget.27875 -
Segú-Vergés, C., J. Gómez., P. Terradas-Montana, L.
Artigas, S. Smeets, M. Ferrer, and S.
Savic
(2023). Unveiling chronic spontaneous urticaria pathophysiology through systems biology. The
Journal of Allergy and Clinical Immunology. 151(4): p. 1005-1014.
DOI: 10.1016/j.jaci.2022.12.809