Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Design and rationale of the Management of High Bleeding Risk Patients Post Bioresorbable Polymer Coated Stent Implantation With an Abbreviated Versus Standard DAPT Regimen (MASTER DAPT) Study

Background: The optimal duration of antiplatelet therapy in high–bleeding risk (HBR) patients with coronary artery disease treated with newer-generation drug-eluting bioresorbable polymer-coated stents remains unclear. Design: MASTER DAPT (clinicaltrial.gov NCT03023020) is an investigator-initiated, open-label, multicenter, randomized controlled trial comparing an abbreviated versus a standard duration of antiplatelet therapy after bioresorbable polymer-coated Ultimaster (TANSEI) sirolimus-eluting stent implantation in approximately 4,300 HBR patients recruited from ≥100 interventional cardiology centers globally. After a mandatory 30-day dual-antiplatelet therapy (DAPT) run-in phase, patients are randomized to (a) a single antiplatelet regimen until study completion or up to 5 months in patients with clinically indicated oral anticoagulation (experimental 1-month DAPT group) or (b) continue DAPT for at least 5 months in patients without or 2 in patients with concomitant indication to oral anticoagulation, followed by a single antiplatelet regimen (standard antiplatelet regimen). With a final sample size of 4,300 patients, this study is powered to assess the noninferiority of the abbreviated antiplatelet regimen with respect to the net adverse clinical and major adverse cardiac and cerebral events composite end points and if satisfied for the superiority of abbreviated as compared to standard antiplatelet therapy duration in terms of major or clinically relevant nonmajor bleeding. Study end points will be adjudicated by a blinded Clinical Events Committee. Conclusions: The MASTER DAPT study is the first randomized controlled trial aiming at ascertaining the optimal duration of antiplatelet therapy in HBR patients treated with sirolimus-eluting bioresorbable polymer-coated stent implantation