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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