Solid Experience applying Artificial Intelligence tools in R&D and Clinical
With over 16 years of experience in developing Artificial Intelligence (AI) tools and analysis, our team has successfully completed more than 180 projects across Europe, Canada, the United States, and Japan.
Our expertise extends across a wide range of medical fields, including Immunology, Rare Diseases, Neurology, Oncology/Haematology, Gastroenterology, and Cardiovascular/Metabolism areas. Our company takes pride in its proven track record of long-term collaborations with major Pharma R&D departments, which have yielded significant advancements in medical fields.
We participate in numerous competitive grants, including 22 European Union projects (FP7, H2020, and RIS3CAT). These grants further corroborate the robustness and effectiveness of our technology.
Our work has led to the publication of over 85 influential publications and patents, underpinning the impact and innovation derived from our projects.
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The following Track Record includes all the Anaxomics publications as well as congresses participations, grants obtained and patent applications:
Most relevant peer-reviewed Anaxomics publications
Unveiling chronic spontaneous urticaria pathophysiology through systems biology
Journal and year: The Journal of Allergy and Clinical Immunology, 2023 [+]
Background: Chronic spontaneous urticaria (CSU) is a rare, heterogeneous, severely debilitating, and often poorly controlled skin disease resulting in an itchy eruption that can be persistent. Antihistamines and omalizumab, an anti-IgE mAb, are the only licensed therapies. Although CSU pathogenesis is not yet fully understood, mast cell activation through the IgE:high-affinity IgE receptor (FcεRI) axis appears central to the disease process.
Objective: We sought to model CSU pathophysiology and identify in silico the mechanism of action of different CSU therapeutic strategies currently in use or under development.
Methods: Therapeutic performance mapping system technology, based on systems biology and machine learning, was used to create a CSU interactome validated with gene expression data from patients with CSU and a CSU model that was used to evaluate CSU pathophysiology and the mechanism of action of different therapeutic strategies.
Results: : Our models reflect the known role of mast cell activation as a central process of CSU pathophysiology, as well as recognized roles for different therapeutic strategies in this and other innate and adaptive immune processes. They also allow determining similarities and differences between them; anti-IgE and Bruton tyrosine kinase inhibitors play a more direct role in mast cell biology through abrogation of FcεRI signaling activity, whereas anti-interleukins and anti-Siglec-8 have a role in adaptive immunity modulation.
Conclusion: In silico CSU models reproduced known CSU and therapeutic strategies features. Our results could help advance understanding of therapeutic mechanisms of action and further advance treatment research by patient profile.
Authors: Segu-Verges, C., J. Gomez., P. Terradas-Montana, L. Artigas, S. Smeets, M. Ferrer and S. Savic.Download original article
Artificial Intelligence Assessment of the Potential of Tocilizumab Along with Corticosteroids Therapy for the Management of Covid-19 Evoked Acute Respiratory Distress Syndrome
Journal and year: PLoS One, 2023 [+]
Acute respiratory distress syndrome (ARDS), associated with high mortality rate, affects up to 67% of hospitalized COVID-19 patients. Early evidence indicated that the pathogenesis of COVID-19 evoked ARDS is, at least partially, mediated by hyperinflammatory cytokine storm in which interleukin 6 (IL-6) plays an essential role. The corticosteroid dexamethasone is an effective treatment for severe COVID-19 related ARDS. However, trials of other immunomodulatory therapies, including anti-IL6 agents such as tocilizumab and sarilumab, have shown limited evidence of benefit as monotherapy. But recently published large trials have reported added benefit of tocilizumab in combination with dexamethasone in severe COVID-19 related ARDS. In silico tools can be useful to shed light on the mechanisms evoked by SARS-CoV-2 infection and of the potential therapeutic approaches. Therapeutic performance mapping system (TPMS), based on systems biology and artificial intelligence, integrate available biological, pharmacological and medical knowledge to create mathematical models of the disease. This technology was used to identify the pharmacological mechanism of dexamethasone, with or without tocilizumab, in the management of COVID-19 evoked ARDS. The results showed that while dexamethasone would be addressing a wider range of pathological processes with low intensity, tocilizumab might provide a more direct and intense effect upon the cytokine storm. Based on this in silico study, we conclude that the use of tocilizumab alongside dexamethasone is predicted to induce a synergistic effect in dampening inflammation and subsequent pathological processes, supporting the beneficial effect of the combined therapy in critically ill patients. Future research will allow identifying the ideal subpopulation of patients that would benefit better from this combined treatment.
Authors: Segu-Verges, C., L. Artigas, M. Coma and R. W. Peck.Download original article
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
Journal and year: Arthritis Care Res (Hoboken), 2023 [+]
Objective:: Real-world studies are needed to identify factors associated with response to biologic therapies in patients with axial spondyloarthritis (SpA). The objective was to assess sex differences in response to tumor necrosis factor inhibitors (TNFi) and to explore possible risk factors associated with TNFi efficacy.
Methods: A total of 969 patients with axial SpA (315 females, 654 males) enrolled in the BIOBADASER registry (2000-2019) who initiated a TNFi (first, second, or further lines) were studied. Statistical and artificial intelligence (AI)-based data analyses were used to explore the association of sex differences and other factors to TNFi response, using the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), to calculate the BASDAI50, with an improvement of at least 50% of the BASDAI score, and using the Ankylosing Spondylitis Disease Activity Score, calculated using the C-reactive protein level (ASDAS-CRP).
Results: : Females had a lower probability of reaching a BASDAI50 response with a first line TNFi treatment at the second year of follow-up (P = 0.018) and a lesser reduction of the ASDAS-CRP at this time point. The logistic regression model showed lower BASDAI50 responses to TNFi in females (P = 0.05). Other factors, such as older age (P = 0.004), were associated with unfavorable responses. The AI data analyses reinforced the idea that age at the beginning of the treatment was the main factor associated with an unfavorable response. The combination of age with other clinical characteristics (female sex or cardiovascular risk factors and events) potentially contributed to an unfavorable response to TNFi.
Conclusion: : In this national multicenter registry, female sex was associated with less response to a first-line TNFi by the second year of follow-up. A higher age at the start of the TNFi was the main factor associated with an unfavorable response to TNFi.
Authors: Fernandez-Carballido, C., C. Sanchez-Piedra, R. Valls, K. Garg, F. Sanchez-Alonso, L. Artigas, J. M. Mas, V. Jovani, S. Manrique, C. Campos, M. Freire, O. Martinez-Gonzalez, I. Castrejon, C. Perella, M. Coma and I. E. van der Horst-BruinsmaDownload original article
Exploring the synergistic effects of cabozantinib and a programmed cell death protein 1 inhibitor in metastatic renal cell carcinoma with machine learning
Journal and year: Oncotarget, 2022 [+]
Clinical evidence supports the combination of cabozantinib with an immune checkpoint inhibitor for the treatment of metastatic clear cell renal cell carcinoma (mccRCC) and suggests a synergistic antitumour activity of this combination. Nevertheless, the biological basis of this synergy is not fully characterized. We studied the mechanisms underpinning the potential synergism of cabozantinib combined with a PD1 inhibitor in mccRCC and delved into cabozantinib monotherapy properties supporting its role to partner these combinations. To model physiological drug action, we used a machine learning-based technology known as Therapeutic Performance Mapping Systems, applying two approaches: Artificial Neural Networks and Sampling Methods. We found that the combined therapy was predicted to exert a wide therapeutic action in the tumour and the microenvironment. Cabozantinib may enhance the effects of PD1 inhibitors on immunosurveillance by modulating the innate and adaptive immune system, through the inhibition of VEGF-VEGFR and Gas6- AXL/TYRO3/MER (TAM) axes, while the PD1 inhibitors may boost the antiangiogenic and pro–apoptotic effects of cabozantinib by modulating angiogenesis and T-cell cytotoxicity. Cabozantinib alone was predicted to restore cellular adhesion and hamper tumour proliferation and invasion. These data provide a biological rationale and further support for cabozantinib plus PD1 inhibitor combination and may guide future nonclinical and clinical research.
Authors: Duran, I., D. Castellano, J. Puente, L. Martin-Couce, E. Bello, U. Anido, J. M. Mas and L. Costa.Download original article
Methods to Develop an in silico Clinical Trial: Computational Head-to-Head Comparison of Lisdexamfetamine and Methylphenidate
Journal and year: Frontiers in Psychiatry, 2021 [+]
Regulatory agencies encourage computer modeling and simulation to reduce the time and cost of clinical trials. Although still not classified in formal guidelines, system biology-based models represent a powerful tool for generating hypotheses with great molecular detail. Herein, we have applied a mechanistic head-to-head in silico clinical trial (ISCT) between two treatments for attention-deficit/hyperactivity disorder, to wit lisdexamfetamine (LDX) and methylphenidate (MPH). The ISCT was generated through three phases comprising (i) the molecular characterization of drugs and pathologies, (ii) the generation of adult and children virtual populations (vPOPs) totaling 2,600 individuals and the creation of physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models, and (iii) data analysis with artificial intelligence methods. The characteristics of our vPOPs were in close agreement with real reference populations extracted from clinical trials, as did our PBPK models with in vivo parameters. The mechanisms of action of LDX and MPH were obtained from QSP models combining PBPK modeling of dosing schemes and systems biology-based modeling technology, i.e., therapeutic performance mapping system. The step-by-step process described here to undertake a head-to-head ISCT would allow obtaining mechanistic conclusions that could be extrapolated or used for predictions to a certain extent at the clinical level. Altogether, these computational techniques are proven an excellent tool for hypothesis-generation and would help reach a personalized medicine.
Authors: Gutiérrez-Casares, JR., J. Quintero, G. Jorba, V. Junet, V. Martínez, T. Pozo-Rubio, B. Oliva, X. Daura, JM. Mas and C. MontotoDownload original article
Application of systems biology-based in silico tools to optimize treatment strategy identification in Still's disease
Journal and year: Arthritis Research & Therapy, 2021 [+]
Background: Systemic juvenile idiopathic arthritis (sJIA) and adult-onset
Still’s disease (AOSD) are manifestations ofan autoinflammatory disorder with
complex pathophysiology and significant morbidity, together also termed
Still’sdisease. The objective of the current study is to set in silico models based
on systems biology and investigate theoptimal treat-to-target strategy for Still’s
disease as a proof-of-concept of the modeling approach
Methods: Molecular characteristics of Still’s disease and data on biological inhibitors of interleukin (IL)-1 (anakinra,canakinumab), IL-6 (tocilizumab, sarilumab), and glucocorticoids as well as conventional disease-modifying antirheumatic drugs (DMARDs, methotrexate) were used to construct in silico mechanisms of action (MoA) models bymeans of Therapeutic Performance Mapping System (TPMS) technology. TPMS combines artificial neuronal networks, sampling-based methods, and artificial intelligence. Model outcomes were validated with published expression data from sJIA patients.
Results: Biologicals demonstrated more pathophysiology-directed efficiency than non-biological drugs. IL-1 blockade mainly acts on proteins implicated in the innate immune system, while IL-6 signaling blockade has a weaker effect on innate immunity and rather affects adaptive immune mechanisms. The MoA models showed that in the autoinflammatory/systemic phases of Still’s disease, in which the innate immunity plays a pivotal role, the IL1β-neutralizing antibody canakinumab is more efficient than the IL-6 receptor-inhibiting antibody tocilizumab. MoA models reproduced 67% of the information obtained from expression data.
Conclusions: Systems biology-based modeling supported the preferred use of biologics as an immunomodulatory treatment strategy for Still’s disease. Our results reinforce the role for IL-1 blockade on innate immunity regulation, which is critical in systemic autoinflammatory diseases. This further encourages early use on Still’s disease IL-1 blockade to prevent the development of disease or drug-related complications. Further analysis at the clinical level will validate the findings and help determining the timeframe of the window of opportunity for canakinumab treatment.
Keywords: Systems biology, Systemic juvenile idiopathic arthritis, Adult-onset Still’s disease, Treat-to-target, Artificial intelligence, Machine learning, Personalized medicine
Authors: Segú-Vergés C, Coma M, Kessel C, Smeets S, Foell D, Aldea A.
In-silico simulated prototype-patients using TPMS technology to study a potential adverse effect of sacubitril and valsartan
Journal and year: PLOS ONE, 2020 [+]
Unveiling the mechanism of action of a drug is key to understand the benefits and adverse reactions of a medication in an organism. However, in complex diseases such as heart diseases there is not a unique mechanism of action but a wide range of different responses depending on the patient. Exploring this collection of mechanisms is one of the clues for a future personalized medicine. The Therapeutic Performance Mapping System (TPMS) is a Systems Biology approach that generates multiple models of the mechanism of action of a drug. Each molecular mechanism generated could be associated to particular individuals, here defined as prototype-patients, hence the generation of models using TPMS technology may be used for detecting adverse effects to specific patients. TPMS operates by (1) modelling the responses in humans with an accurate description of a protein network and (2) applying a Multilayer Perceptron-like and sampling strategy to find all plausible solutions. In the present study, TPMS is applied to explore the diversity of mechanisms of action of the drug combination sacubitril/valsartan. We use TPMS to generate a wide range of models explaining the relationship between sacubitril/valsartan and heart failure (the indication), as well as evaluating their association with macular degeneration (a potential adverse effect). Among the models generated, we identify a set of mechanisms of action associated to a better response in terms of heart failure treatment, which could also be associated to macular degeneration development. Finally, a set of 30 potential biomarkers are proposed to identify mechanisms (or prototype-patients) more prone of suffering macular degeneration when presenting good heart failure response. All prototype-patients models generated are completely theoretical and therefore they do not necessarily involve clinical effects in real patients. Data and accession to software are available at http://sbi.upf.edu/data/tpms/
Authors: Jorba G, Aguirre-Plans J, Junet V, Segú-Vergés C, Ruiz JL, Pujol A, Fernández-Fuentes N, Mas JM, Oliva B.Download original article
Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization
Journal and year: Science Translational Medicine, 2020 [+]
Identifying immune correlates of protection and mechanisms of immunity accelerates and streamlines the development of vaccines. RTS,S/AS01E, the most clinically advanced malaria vaccine, has moderate efficacy in African children. In contrast, immunization with sporozoites under antimalarial chemoprophylaxis (CPS immunization) can provide 100% sterile protection in naïve adults. We used systems biology approaches to identifying correlates of vaccine-induced immunity based on transcriptomes of peripheral blood mononuclear cells from individuals immunized with RTS,S/AS01E or chemoattenuated sporozoites stimulated with parasite antigens in vitro. Specifically, we used samples of individuals from two age cohorts and three African countries participating in an RTS,S/AS01E pediatric phase 3 trial and malaria-naïve individuals participating in a CPS trial. We identified both preimmunization and postimmunization transcriptomic signatures correlating with protection. Signatures were validated in independent children and infants from the RTS,S/AS01E phase 3 trial and individuals from an independent CPS trial with high accuracies (>70%). Transcription modules revealed interferon, NF-κB, Toll-like receptor (TLR), and monocyte-related signatures associated with protection. Preimmunization signatures suggest that priming the immune system before vaccination could potentially improve vaccine immunogenicity and efficacy. Last, signatures of protection could be useful to determine efficacy in clinical trials, accelerating vaccine candidate testing. Nevertheless, signatures should be tested more extensively across multiple cohorts and trials to demonstrate their universal predictive capacity. http://sbi.upf.edu/data/tpms/
Authors: Moncunill G, Scholzen A, Mpina M, Nhabomba A, Hounkpatin AB, Osaba L, Valls R, Campo JJ, Sanz H, Jairoce C, Williams NA, Pasini EM, Arteta D, Maynou J, Palacios L, Duran-Frigola M, Aponte JJ, Kocken CHM, Agnandji ST, Mas JM, Mordmuller B, Daubenberger C, Sauerwein R, Dobaño C.Link to the original article
In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm
Journal and year: PLOS ONE, 2020 [+]
From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.
Authors: Artigas L, Coma M, Matos-Filipe P, Aguirre-Plans J, Farrés J, Valls R, Fernandez-Fuentes N, de la Haba-Rodriguez J, Olvera A, Barbera J, Morales R, Oliva B, Mas JM.Download original article
Systems biology drug screening identifies statins as enhancers of current therapies in chronic lymphocytic leukemia
Journal and year: Scientific Reports, 2020 [+]
Chronic lymphocytic leukemia (CLL) is a B lymphoid malignancy highly dependent on the microenvironment. Despite new targeted therapies such as ibrutinib and venetoclax, disease progression and relapse remain an issue. CLL cell interactions with the supportive tissue microenvironment play a critical role in disease pathogenesis. We used a platform for drug discovery based on systems biology and artifcial intelligence, to identify drugs targeting key proteins described to have a role in the microenvironment. The selected compounds were screened in CLL cell lines in the presence of stromal cells to mimic the microenvironment and validated the best candidates in primary CLL cells. Our results showed that the commercial drug simvastatin was the most efective and selective out of the tested compounds. Simvastatin decreased CLL cell survival and proliferation as well as cell adhesion. Importantly, this drug enhanced the antitumor efect of venetoclax and ibrutinib. We proposed that systems biology approaches combined with pharmacological screening could help to fnd new drugs for CLL treatment and to predict new combinations with current therapies. Our results highlight the possibility of repurposing widely used drugs such as statins to target the microenvironment and to improve the efcacy of ibrutinib or venetoclax in CLL cells.
Authors: Gimenez N, Tripathi R, Giró A, Rosich L, López-Guerra M, López-Oreja I, Playa-Albinyana H, Arenas F, Mas JM, Pérez-Galán P, Delgado J, Campo E, Farrés J, Colomer D.
Neuroprotective Drug for Nerve Trauma Revealed Using Artificial Intelligence
Journal and year: Scientific Reports, 2018 [+]
Here we used a systems biology approach and artificial intelligence to identify a neuroprotective agent for the treatment of peripheral nerve root avulsion. Based on accumulated knowledge of the neurodegenerative and neuroprotective processes that occur in motoneurons after root avulsion, we built up protein networks and converted them into mathematical models. Unbiased proteomic data from our preclinical models were used for machine learning algorithms and for restrictions to be imposed on mathematical solutions. Solutions allowed us to identify combinations of repurposed drugs as potential neuroprotective agents and we validated them in our preclinical models. The best one, NeuroHeal, neuroprotected motoneurons, exerted anti-inflammatory properties and promoted functional locomotor recovery. NeuroHeal endorsed the activation of Sirtuin 1, which was essential for its neuroprotective effect. These results support the value of network-centric approaches for drug discovery and demonstrate the efficacy of NeuroHeal as adjuvant treatment with surgical repair for nervous system trauma.
Authors: Romeo-Guitart D, Fores J, Herrando-Grabulosa M, Valls R, Leiva-Rodriguez T, Galea E, Gonzalez-Perez F, Navarro X, Petegnie, V, Bosch A, Coma M, Mas J M, Casas C.Download original article
Most relevant congress participationsᐯ
Unveiling the likely pharmacological mechanisms of brigatinib on brain metastasis in ALK+ patients with non-small cell lung cancer: a systems biology and artificial intelligence-based approach
Congress and year: European Lung Cancer Congress, 2023 [+]
Introduction: Brain metastasis occurs in 10% of patients with non-small cell lung cancer (NSCLC) and it is more frequent in ALK+ NSCLC, with a frequency of around 20–30% at the time of diagnosis [doi 10.18632/ oncotarget.26073]. In a multinational, phase III study (ALTA-1L), brigatinib significantly improved progression-free survival, the confirmed objective response rate (ORR) and the confirmed intracranial ORR compared with crizotinib in ALK+ NSCLC patients. Interestingly, in silico studies unveiled the potential of brigatinib in modulating proteins associated with metastasis [doi 10.18632/oncotarget.27875], however the molecular mechanisms still need to be elucidated. The aim of the present study was the creation of in silico systems biology and artificial intelligence-based models to unveil brigatinib’s effects on metastatic processes both in the primary tumor (PT) and established brain metastases (BM) of ALK+ NSCLC.
Methods: We used Therapeutic Performance Mapping System technology, based on bibliographical molecular characterization of PT with metastatic capability and BM in NSCLC cohorts, and machine learning for mathematically modelling of the pharmacological mechanisms of brigatinib in the PT and already established BM. Models were constrained with publicly available gene expression data (GSE31210 and GSE128309).
Results:: The results suggest that brigatinib has the potential to successfully modulate a wide array of metastasis-involved proteins both in the PM and in BM, acting mainly through IGFR1, EGFR, FLT3, ALK and ROS1. Brigatinib modulation of the effectors of PT with metastatic capability seems to be derived from a downregulation of STAT5 and 3, CXCR4, ETS1, AKT3, CTNB1, ERBB2 and MAPK and an activation of CADH1, whereas its action on the effectors of BM seems to be derived from a downregulation of YAP1, FGFR1, ABL1, CTNB1, NFKB1 and PI3 K/AKT/mTOR pathway.
Conclusion:In silico models have revealed the potential of brigatinib to successfully modulate a wide array of metastasis-involved proteins both in the PT and in the BM. Further clinical studies are needed to validate these potential results before translation into clinical practice.
Authors: Carcereny, E., L. Artigas, A. Martinez-Cardús, A. Lopez and M. Coma.Link to the original abstract
Understanding the impact of previous tnf-α inhibitors on vedolizumab response in patients with ulcerative colitis and crohn’s disease using systematic review and gene expression data
Congress and year: UEG week, 2022 [+]
Introduction: Vedolizumab (VDZ) is a gut-selective anti-lymphocyte trafficking (GSALT) humanised monoclonal antibody able to bind α4β7 integrin expressed on circulating lymphocytes, selectively preventing their migration into the gut mucosa. It is approved for moderate‐to‐severe Crohn’s disease (CD) and ulcerative colitis (UC) patients who do not respond or are intolerant to conventional therapy or TNF-α inhibitors. To shed light on the impact of previous TNF-α inhibitors in the outcomes of VDZ in CD and UC, we explored data from clinical trials and observational studies, as well as patient gene-expression data publicly available. In particular, we intended to address the question of whether prior exposure to TNF-α inhibitors alters the mucosal immune responses, and/or immune cell trafficking patterns characterizing the response to VDZ as a second- or further line of treatment.
Aims & Methods: A systematic literature search was performed across several databases including PubMed, www.clinicaltrials.org, Cochrane Library, Embase as well as ECCO, DDW and UEG Week congress abstracts from 2008 to 2021. In addition, gene-expression data in patients with active IBD treated with the TNF-α inhibitor infliximab and/or VDZ were collected from the publicly available Gene Expression Omnibus database, retrieving datasets GSE16879 and GSE73661. Dataset GSE16879, included sixty-one patients with active IBD, (24 with UC, 19 with colonic CD and 18 with ileal CD), and a healthy control group of 12 individuals (6 with colonic samples and 6 with ileal samples) who underwent endoscopy for polyp screening. Dataset GSE73661 includes 44 UC patients undergoing vedolizumab treatment at different time points, as well as 12 healthy controls and colonic biopsies of 23 patients prior to and after TNF-α inhibitors treatment.
Results: A total of 194 studies (UC:59; CD:48; Both:87) were retrieved through the systematic search. Analysis showed that previous TNF-α inhibitor treatment negatively influences the response to VDZ treatment. However, no significant differences between the influence of this factor on CD and UC have been observed. Results show a higher proportion of CD patients (median 88%) being exposed to prior TNF-α inhibitors than UC patients (median 67%). The analysis of publicly available gene-expression data has identified a potential impact of TNF-α inhibitors on a set of key proteins involved in leukocyte rolling and migration, as well as on biomarkers reported to be associated with VDZ response. Among others, genes such as CXCL5, CXCL6, CCL20 and Selectin-L show higher expression in patients refractory to TNF-α inhibitors who will not respond to vedolizumab therapy than in patients refractory to TNF-α inhibitors that will respond to it.
Conclusion:Our analyses highlight potential pharmacological mechanisms behind the observed differences on VDZ outcomes when used first-line biologic or after TNF-α inhibitors in clinical practice.
Authors: Regí, B., I. Tagarro, A. Lagarda, T. Letosa, J. Aparicio, M. Coma, C. Montoto, J. Manye and E. Domenech.Link to the original abstract
In silico Evaluation of BTK Inhibitors Mechanisms That Could Induce Atrial Fibrillation and Hypertension in the Treatment of Chronic Lymphocytic Leukemia
Congress and year: ASH, 2022 [+]
Introduction: Ibrutinib (IBRU) and acalabrutinib (ACALA) are first and second generation Bruton Tyrosine Kinase inhibitors (BTKi), respectively, approved for the treatment of chronic lymphocytic leukemia (CLL). Beside the main target, BTKis may inhibit other tyrosine kinases leading to unfavorable off-target effects. The introduction of second generation BTKis was based on their potential of greater selectivity and affinity for BTK inhibition and therefore fewer off-target side effects. Despite many survival benefits, IBRU is associated with adverse events probably due to the off-target inhibition; of concerns are the cardiovascular events. The second-generation BTKi ACALA is more selective to BTK and with less off-target inhibition. A randomized phase III trial comparing ACALA and IBRU in CLL patients demonstrated non-inferior progression free survival with ACALA, along with fewer cardiovascular events, such as atrial fibrillation (AF) and hypertension (HT). Here we present a systems biology- and artificial intelligence-based in silico study to infer the likely pharmacological mechanisms that underlie AF and HT induced by these drugs.
Methods: Therapeutic Performance Mapping System (TPMS) technology (Anaxomics Biotech, Spain) was used to create mechanistic models of ACALA and IBRU at the protein interactome level. First, published evidence was reviewed and compiled to molecularly characterize CLL, AF, HT and the target profile of these drugs. Then, supervised machine learning algorithms were applied to computationally infer AF and HT mechanisms. Model-derived measures were used to rank the targets more likely to trigger AF and HT. Finally, the potential mechanisms of action of inhibiting these targets were identified. Although the models are protein-based, the interactome in which they are build include gene and RNA regulation data; for standardization purposes, gene names are used for genes/proteins mentioned in this abstract.
Results:: The results show that BTK inhibition is not the only factor contributing to AF and HT induction, as they could be mediated by other off-targets impacting pathophysiological processes (Tables 1 and 2). Regarding AF, potential off-target mechanism of action is the inhibition of TEC and ERBB4, which can downregulate PIK3CA and NOS3 involved in atrial fibrosis and downregulate ZFHX3 and the sodium channels SCN1B and SCN5A having an impact in electrophysiology regulation. While no ACALA-specific AF off-targets were identified, IBRU data showed specific off-targets potentially involved in AF mechanisms such as structural remodeling and atrial fibrosis (HCK, FGR, LYN, FYN, YES1 and FLT3), electrophysiology regulation (LYN and SRC), and autonomic nervous system remodeling (CSK). Regarding HT, potential off-target mechanism of action is the inhibition of RIPK2 and ERBB4, which can trigger HT through the following common pathways: modulation of pro- and anti-inflammatory cytokines (IL6, IL10 and TNFA), induction of oxidative stress (NOS3), and endothelial dysfunction (MME, ROCK1, ROCK2, VEGFA and VEGFD). No ACALA-specific HT off-targets or mechanisms were detected, while seven IBRU-specific off-targets were involved in inflammation related to HT (LCK, JAK3 and FLT3) and oxidative stress and endothelial dysfunction (ERBB2, BLK, SRC and CSK).
Conclusion:: BTKi selectivity is key for CLL disease control and BTK inhibition is not the only factor for AF and HT. This analysis supports that BTKi-induced AF and HT are off-target effects that can partly be mediated by TEC and ERBB4, and RIPK2 and ERBB4 inhibition, respectively. However, other IBRU-specific off-targets and mechanisms could also explain its association with the higher incidence of AF and HT. Although potential literature bias and inhibition potency should be considered in prospective studies, our findings are a starting point to understand ACALA and IBRU differences regarding AF and HT incidence in CLL patients.
Authors: Cordoba R., D. Colomer, A. Bayés-Genís, C. Leiva Farre, E. Álvarez, M. D. López and E. ZatarainLink to the original abstract
Exploring the synergistic effects of cabozantinib (cabo) and a programmed cell death protein 1 (PD1) inhibitor in metastatic renal cell carcinoma (mRCC) with artificial intelligence (AI)
Congress and year: Genitourinary Cancers Symposium: American Society of Clinical Oncology, 2021 [+]
Background: Nonclinical and clinical data suggest that cabo with a PD1
inhibitor provides synergistic antitumor activity in patients with mRCC, possibly by
a cabo-induced switch to an immunopermissive tumor microenvironment. We used a
complementary, unbiased, AI approach to gain a holistic view of the complex
interplay between multiple pathways, cells and molecules and identify the mechanisms
that may underpin this synergism.
Methods: Biological targets associated with mRCC pathophysiology or drug actions were identified from proteomic, genomic and transcriptomic databases and literature. Using systems- and AI-based technology, the data were integrated using machine learning into mathematical models of the human mRCC protein network topology. The combined effects of cabo and a PD1 inhibitor on biological targets were simulated assuming target receptors were fully activated or fully inhibited. Relevant effects on known cancer processes (e.g. angiogenesis, metastasis, cell proliferation, immune evasion) were identified using artificial neural networks. Biologically plausible synergistic mechanisms were described with sampling methods.
Results: Inhibition of VEGF/VEGFR and GAS6/TAMR axes by cabo enhanced the known effects of PD1 inhibitors on immune evasion mechanisms by modulating multiple humoral and cellular components of the innate and adaptive immune responses. PD1 inhibitors further enhanced the anti-angiogenic and tumor pro-apoptotic effects of cabo by modulating pro- and anti-angiogenic factors and T cell cytotoxicity..
Conclusions: These data provide a mechanistic rationale and further support for the beneficial combination of cabo and a PD1 inhibitor and may guide future nonclinical and clinical research.
Authors: Authors: Costa L, Castellano D, Puente J, Martin L, Anido U, Gómez J, Duran I.Link to the original abstract
Application of systems biology-based in silico tools to optimize treatment strategy in Still's disease
Congress and year: EULAR E-Congress , 2020 [+]
Background: Systemic Juvenile Idiopathic Arthritis (sJIA) and Adult Onset
Still’s Disease (AOSD) are manifestations of an autoinflammatory disorder with
complex pathophysiology and significant morbidity, together also termed Still’s
Objectives: To investigate the optimal treat-to-target strategy for Still’s disease by in silico models based on systems biology.
Methods: Molecular characteristics of Still’s disease and data on biological inhibitors of interleukin (IL)-1 (anakinra, canakinumab), IL-6 (tocilizumab, sarilumab), glucocorticoids as well as conventional disease-modifying anti-rheumatic drugs (DMARDs, methotrexate) were used to construct in silico mechanisms of action (MoA) models by means of Therapeutic Performance Mapping System technology (TPMS). TPMS combines artificial neuronal networks (ANN), sampling-based methods and artificial intelligence. The models were validated with publicly available expression data from sJIA patients.
Results: Biologicals demonstrated more pathophysiology-directed efficiency than non-biological drugs. IL-1 blockade mainly acts on the innate immune system, while IL-6 signaling blockade has a weaker activity on the innate immunity and rather affects the adaptive immunity. The MoA models showed that the IL-1β inhibitor canakinumab is more efficient than the IL-6 receptor inhibiting antibody tocilizumab in the autoinflammatory/systemic phases of Still’s disease. MoA models reproduced 67% of the information obtained from expression data.
Conclusion: Systems biology-based modelling supported the preferred use of biologics as immunomodulatory treatment strategy for Still’s disease. This further encourages early IL-1β blockade in initial autoinflammatory/systemic phases of Still’s Disease to prevent the development of disease or drug-related complications. Further studies are needed to determine the optimal timeframe of the window of opportunity for canakinumab treatment.
Disclosure of Interests: : None declared
Authors: Coma M, Segu-Verges C, Kessel C, Smeets S, Foell D, Aldea A.Download original abstract
Application of Systems Biology-Based In Silico Tools for Optimal Treatment Strategy Identification in Still’s Disease
Congress and year: ACR/ARP Annual Meeting, 2019 [+]
Background/Purpose: Systemic JIA (sJIA) and Adult Onset Still’s Disease may
represent a disease continuum1 of the same autoinflammatory disorder, Still’s
Disease. Current challenges in its management include the complex disease clinical
phenotypes (systemic/rheumatic symptoms) and the absence of optimal treatment
guidelines. Several efforts are being made to identify the best targeted strategy to
take advantage of the “window of opportunity” and prevent complications and damage2.
Systems biology-based methods are becoming reliable tools to understand the
molecular effects of drugs in complex clinical settings. In this study, we
constructed in silico models to explore and compare the mode of action (MoA) in
Still’s Disease of current biologicals, anakinra (ANA), canakinumab (CAN) and
tocilizumab (TCZ), in addition to non-biological drugs such as corticosteroids and
methotrexate, aiming to advance towards an optimal treatment
Methods: Therapeutic Performance Mapping System was used to create Still’s Disease pathophysiology and drug MoA models. Drugs’ efficacies were compared using artificial neuronal networks, and detailed MoA of IL-1β and IL-6 blockers was modeled using sampling methods. Available expression data in sJIA patients was used for model validation (GSE80060, GSE21521, GSE8361, GSE7753, GSE76492).
Results: The models reflected human physiology ( >90% accuracy) and Still’s Disease pathophysiology (Figure 1A). Biologicals were found more efficient than non-biologicals (Table 1). IL-1 blockers behaved similarly (CAN as IL-1β-specific blocker was used for further analyses) and presented an innate immune system-centered mechanism, while TCZ acted over the adaptive immune system. A detailed evaluation of the MoA of CAN and TCZ on the innate immune system showed some well-known proteins (Figure 1B) differentially modulated. While CAN inhibits NF-κB, CXCL8 and S100A9 more effectively, CD64 (FCGR1A) is preferentially inhibited by TCZ. The CAN and TCZ MoA models reproduced 67% of the information obtained from expression data (Figure 2).
Conclusion: The created in silico Still’s Disease models reproduce known clinical and molecular findings, which render them a good tool for future patient profiling, biomarker identification and treatment strategy designing. According to the models, the biologicals tested for proof-of-concept purposes provide a more pathophysiology-directed MoA than non-biological drugs, and are similarly effective on both systemic and rheumatic disease features. IL-1 blockers, specifically CAN, might be more efficient than TCZ in initial autoinflammatory/systemic phases of Still’s Disease that is dominated by innate immune dysregulation. Key innate immune mediators are hereby proposed to explain the differences observed between CAN and TCZ MoA. Our systems biology data may thus support the development of therapeutic strategies fostering early intervention with CAN during the window of opportunity to prevent the development of destructive chronic arthritis and treatment- or disease-associated complications long-term.
Authors: Segu-Verges C, Coma M, Kessel C, Smeets S, Foell D, Aldea A.Link to the original abstract