Trabectedin use in soft-tissue sarcoma patients in a real-world setting: Data from an Italian national drug-access registry.

Trabectedin is a marine-derived anticancer drug approved for the treatment of patients with advanced soft-tissue sarcomas (STS). Here, we aimed to analyze its use in a large cohort of STS patients treated in Italy in a real-world setting. Data on STS patients treated with trabectedin in Italy were prospectively collected from January 2013 to December 2019 by the national drug regulator, the Italian Medicines Agency (AIFA). Time-to-off-treatment (TToT) was defined as the time between the initial prescription of trabectedin and the date of treatment discontinuation for any cause. The impact of the different baseline covariates, including the initial prescribed dose of trabectedin, on TToT was evaluated using an accelerated failure time (AFT) models with log-logistic distribution. In total, we analyzed data from 2633 sarcoma patients and 14 950 individual cycles of trabectedin. The median number of cycles of trabectedin received per patient was 3 (interquartile range 2-7). The labeled 1.5 mg/sqm dose was used in 27.3% of all first prescriptions. Overall, the median TToT was 93 days. In the final AFT model, the variables significantly associated to longer TToT were female gender (+13% increase in TToT); ECOG performance status 0 (+50%); histological diagnosis of leiomyosarcoma (+22%), well-differentiated/dedifferentiated liposarcoma (+72%) or myxoid liposarcoma (+61%); receiving treatment in a high-volume center (+23%). In this large real-world cohort of STS patients treated with trabectedin, our findings support the use of trabectedin in STS patients, in particular in leiomyosarcoma and liposarcoma patients, and highlight the role of treatment center volume in their management.

PIGSPro: prediction of immunoGlobulin structures v2.

PIGSpro is a significant upgrade of the popular PIGS server for the prediction of the structure of immunoglobulins. The software has been completely rewritten in python following a similar pipeline as in the original method, but including, at various steps, relevant modifications found to improve its prediction accuracy, as demonstrated here. The steps of the pipeline include the selection of the appropriate framework for predicting the conserved regions of the molecule by homology; the target template alignment for this portion of the molecule; the selection of the main chain conformation of the hypervariable loops according to the canonical structure model, the prediction of the third loop of the heavy chain (H3) for which complete canonical structures are not available and the packing of the light and heavy chain if derived from different templates. Each of these steps has been improved including updated methods developed along the years. Last but not least, the user interface has been completely redesigned and an automatic monthly update of the underlying database has been implemented. The method is available as a web server at http://biocomputing.it/pigspro.