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Item does not contain fulltext 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. 01 januari 2023

Acute myeloid leukemia (AML), a hematologic malignancy that results in bone marrow failure, is the most common acute leukemia in adults. The presence of FMS-related tyrosine kinase 3 (FLT3) mutations is associated with a poor prognosis, making the evaluation of FLT3-inhibitors an imperative goal in clinical trials. Midostaurin was the first FLT3-inhibitor approved by the FDA and EMA for the treatment of FLT3-mutated AML, and it showed a significant improvement in overall survival for newly diagnosed patients treated with midostaurin, in combination with standard chemotherapy (RATIFY study). The main interest of midostaurin has been the FLT3-specific inhibition, but little is known about its role as a multikinase inhibitor and whether it may be used in relapse and maintenance therapy. Here, we used systems biology- and machine learning-based approaches to deepen the potential benefits of the multitarget activity of midostaurin and to better understand its anti-leukemic effect on FLT3-mutated AML. The resulting in silico study revealed that the multikinase activity of midostaurin may play a role in the treatment’s efficacy. Additionally, we propose a series of molecular mechanisms that support a potential benefit of midostaurin as a maintenance therapy in FLT3-mutated AML, by regulating the microenvironment. The obtained results are backed up using independent gene expression data.

Ischemic stroke is a primary cause of morbidity and mortality worldwide. Beyond the approved thrombolytic therapies, there is no effective treatment to mitigate its progression. Drug repositioning combinational therapies are becoming promising approaches to identify new uses of existing drugs to synergically target multiple disease-response mechanisms underlying complex pathologies. Here, we used a systems biology-based approach based on artificial intelligence and pattern recognition tools to generate in silico mathematical models mimicking the ischemic stroke pathology. Combinational treatments were acquired by screening these models with more than 5 million two-by-two combinations of drugs. A drug combination (CA) formed by ceruletide and alpha-1 antitrypsin showing a predicted value of neuroprotection of 92% was evaluated for their synergic neuroprotective effects in a mouse pre-clinical stroke model. The administration of both drugs in combination was safe and effective in reducing by 39.42% the infarct volume 24 h after cerebral ischemia. This neuroprotection was not observed when drugs were given individually. Importantly, potential incompatibilities of the drug combination with tPA thrombolysis were discarded in vitro and in vivo by using a mouse thromboembolic stroke model with t-PA-induced reperfusion, revealing an improvement in the forepaw strength 72 h after stroke in CA-treated mice. Finally, we identified the predicted mechanisms of action of ceruletide and alpha-1 antitrypsin and we demonstrated that CA modulates EGFR and ANGPT-1 levels in circulation within the acute phase after stroke. In conclusion, we have identified a promising combinational treatment with neuroprotective effects for the treatment of ischemic stroke., Neurovascular Research Laboratory acknowledges funding for this project by PI15/00354 and PI18/00804 grants from Instituto Carlos III; the Spanish neurovascular research network INVICTUS PLUS (RD16/0019/0021) also from the Instituto de Salud Carlos III (co-financed by the European Regional Development Fund, FEDER); the program 2017-SGR-1427 from the Generalitat de Catalunya; and the “Advanced Neuroprotection Repurposing Drugs and Nutraceuticals for Stroke in Andalusia: NARDNIA Project,” and COMBINA2 project both funded by the Andalusian Ministry of Health (PE-0527–2019 and PY20-01351). L.R. is supported by a predoctoral fellowship grant (IFI17/00012), from Instituto de Salud Carlos III.

Embryo implantation is one of the most inefficient steps in assisted reproduction, so the identifying drugs with a potential clinical application to improve it has a strong interest. This work applies artificial intelligence and systems biology-based mathematical modeling strategies to unveil potential treatments by computationally analyzing and integrating available molecular and clinical data from patients. The mathematical models of embryo implantation computationally generated here simulate the molecular networks underneath this biological process. Once generated, these models were analyzed in order to identify potential repositioned drugs (drugs already used for other indications) able to improve embryo implantation by modulating the molecular pathways involved. Interestingly, the repositioning analysis has identified drugs considering two endpoints: (1) drugs able to modulate the activity of proteins whose role in embryo implantation is already bibliographically acknowledged, and (2) drugs that modulate key proteins in embryo implantation previously predicted through a mechanistic analysis of the mathematical models. This second approach increases the scope open for examination and potential novelty of the repositioning strategy. As a result, a list of 23 drug candidates to improve embryo implantation after IVF was identified by the mathematical models. This list includes many of the compounds already tested for this purpose, which reinforces the predictive capacity of our approach, together with novel repositioned candidates (e.g., Infliximab, Polaprezinc, and Amrinone). In conclusion, the present study exploits existing molecular and clinical information to offer new hypotheses regarding molecular mechanisms in embryo implantation and therapeutic candidates to improve it. This information will be very useful to guide future research. Abbreviations: IVF: in vitro fertilization; EI: Embryo implantation; TPMS: Therapeutic Performance Mapping System; MM: mathematical models; ANN: Artificial Neuronal Networks; TNFα: tumour necrosis factor factor-alpha; HSPs: heat shock proteins; VEGF: vascular endothelial growth factor; PPARA: peroxisome proliferator activated receptor-α PXR: pregnane X receptor; TTR: transthyretin; BED: Biological Effectors Database; MLP: multilayer perceptron

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. LA, MC, PMF, JF, RV and JMM have commercial affiliation to Anaxomics Biotech SL. The funders provided support in the form of salaries for authors JAP, NFF and PMF, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. JAP, NFF and BO acknowledge support from the Spanish Ministry of Economy (MINECO) [BIO2017-85329-R] [RYC-2015-17519]; Unidad de Excelencia María de Maeztu”, funded by the Spanish Ministry of Economy [ref: MDM-2014-0370].PMF has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 765912.

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., This work was mainly supported by a grant from Fundació La Marató-TV3 (#110432, CC, AB & VP) that funded the work and contracts of TLR and MHG and partially by the Ministerio de Economía y Competitividad of Spain (#SAF 2014-59701, CC, DRG, & TLR). We are also grateful for support from CIBERNED and funding from the European Union Seventh Framework Programme for research, technological development, and demonstration (#306240, XN, FRP & CC). Part of the research leading to these results has also received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 306240.

e15108 Background: VAN is a novel bispecific mAb targeting VEGF & ANG-2. A randomized phase 2 study (McCAVE) investigating VAN & BEV, each combined with chemotherapy (mFOLFOX-6), in 1L metastatic colorectal cancer patients was completed. Reported gastrointestinal perforation (GIP) events (including GI fistula & abdominal abscess) in both arms (approx. 10%) exceeded those expected with BEV+CTx. The present work focused on identifying the explicit molecular pathways mediating VAN/BEV associated GIP. Methods: Clinical data from McCave (safety outcomes & gene expression profiling) was integrated with data from other clinical/preclinical VAN/BEV studies. Machine learning was applied to the dataset to computationally infer GIP mechanisms. The methodology was constrained by relevant biological & clinical knowledge. Literature reviews & database queries mapped 194 unique proteins to 8 biological processes previously hypothesized to play a role in GIP. Identified proteins guided construction of a GIP protein interaction network which was transformed into an artificial neural network and was fitted to the dataset to predict molecular pathways/biological processes most likely linking VEGF-A and/or ANG-2 inhibition to GIP. Results: VAN/BEV were predicted to enhance GIP risk via 4 out of 8 previously suggested biological effects: (i) reduced GI mucosal integrity; (ii) impaired wound healing; (iii) inhibition of angiogenesis (iv) increased thromboembolic risk. The specific proteins & associated interactions predicted to mediate these 4 effects were identified as solely VEGF-A inhibition driven. These effects may drive increased susceptibility to GIP triggered by secondary insults, e.g. preclinical ulcer & intramural lesion. Candidate GIP biomarkers for monitoring these effects were identified, e.g., aPTT, citrulline & I-FABP. Conclusions: The molecular pathways mediating GIP in VAN/BEV treated patients are likely driven by VEGF inhibition with no contribution from ANG-2 inhibition. It is proposed, that in addition to VAN/BEV, one or more secondary insults to the GI tract wall are required to drive perforation.

Mireia Coma,1 Raquel Valls,1 José Manuel Mas,1 Albert Pujol,1 Miquel Angel Herranz,2 Vicente Alonso,2,3 Jordi Naval21Anaxomics Biotech, Barcelona, Spain; 2Genocosmetics Laboratory, Barcelona, Spain; 3Unidad de Dermatología del Hospital Nisa 9 Octubre de Valencia, Valencia, SpainBackground: Perceived age has been defined as the age that a person is visually estimated to be on the basis of physical appearance. In a society where a youthful appearance are an object of desire for consumers, and a source of commercial profit for cosmetic companies, this concept has a prominent role. In addition, perceived age is also an indicator of overall health status in elderly people, since old-looking people tend to show higher rates of morbidity and mortality. However, there is a lack of objective methods for quantifying perceived age.Methods: In order to satisfy the need of objective approaches for estimating perceived age, a novel algorithm was created. The novel algorithm uses supervised mathematical learning techniques and error retropropagation for the creation of an artificial neural network able to learn biophysical and clinically assessed parameters of subjects. The algorithm provides a consistent estimation of an individual's perceived age, taking into account a defined set of facial skin phenotypic traits, such as wrinkles and roughness, number of wrinkles, depth of wrinkles, and pigmentation. A nonintervention, epidemiological cross-sectional study of cases and controls was conducted in 120 female volunteers for the diagnosis of perceived age using this novel algorithm. Data collection was performed by clinical assessment of an expert panel and biophysical assessment using the ANTERA 3D® device.Results and discussion: Employing phenotype data as variables and expert assignments as objective data, the algorithm was found to correctly classify the samples with an accuracy of 92.04%. Therefore, we have developed a method for determining the perceived age of a subject in a standardized, consistent manner. Further application of this algorithm is thus a promising approach for the testing and validation of cosmetic treatments and aesthetic surgery, and it also could be used as a screening method for general health status in the population.Keywords: perceived age, algorithm, dermatology, diagnostic method, skin aging