Explore the vast range of titles available.

Introduction Ciltacabtagene autoleucel (cilta‐cel) is a novel chimeric antigen receptor T‐cell therapy that is being evaluated in the CARTITUDE‐1 trial (NCT03548207) in patients with relapsed or refractory multiple myeloma (RRMM) who received as part of their previous therapy an immunomodulatory drug, proteasome inhibitor, and an anti‐CD38 monoclonal antibody (i.e., triple‐class exposed). Given the absence of a control arm in CARTITUDE‐1, this study assessed the comparative effectiveness of cilta‐cel and physician's choice of treatment (PCT) using an external real‐world control arm from the Flatiron Health multiple myeloma cohort registry. Methods Given the availability of individual patient data for cilta‐cel from CARTITUDE‐1 and PCT in Flatiron, inverse probability of treatment weighting was used to adjust for unbalanced baseline covariates of prognostic significance: refractory status, cytogenetic profile, International Staging System stage, time to progression on last regimen, number of prior lines of therapy, years since diagnosis, and age. Comparative effectiveness was estimated for progression‐free survival (PFS), time to next treatment (TTNT), and overall survival (OS). A range of sensitivity analyses were conducted. Results Baseline characteristics were similar between the two cohorts after propensity score weighting. Patients with cilta‐cel had improved PFS (HR: 0.18 [95% CI: 0.12, 0.27; p < 0.0001]), TTNT (HR: 0.15 [95% CI: 0.09, 0.22; p < 0.0001]), and OS (HR: 0.25 [95% CI: 0.13, 0.46; p < 0.0001]) versus PCT. Cilta‐cel treatment benefit was robust and consistent across all sensitivity analyses. Conclusion Cilta‐cel demonstrated significantly superior effectiveness over PCT for all outcomes, highlighting its potential as an effective therapy in patients with triple‐class exposed RRMM.

Flatiron ( ffe ) mice display features of “ferroportin disease” or Type IV hereditary hemochromatosis. While it is known that both Fe and Mn metabolism are impaired in flatiron mice, the effects of ferroportin (Fpn) deficiency on physiological distribution of these and other biometals is unknown. We hypothesized that Fe, Mn, Zn and/or Cu distribution would be altered in ffe/ + compared to wild-type (+ / + ) mice. ICP-MS analysis showed that Mn, Zn and Cu levels were significantly reduced in femurs from ffe/ + mice. Bone deposits reflect metal accumulation, therefore these data indicate that Mn, Zn and Cu metabolism are affected by Fpn deficiency. The observations that muscle Cu, lung Mn, and kidney Cu and Zn levels were reduced in ffe/ + mice support the idea that metal metabolism is impaired. While all four biometals appeared to accumulate in brains of flatiron mice, significant gender effects were observed for Mn and Zn levels in male ffe/ + mice. Metals were higher in olfactory bulbs of ffe/ + mice regardless of gender. To further study brain metal distribution, 54 MnCl 2 was administered by intravenous injection and total brain 54 Mn was measured over time. At 72 h, 54 Mn was significantly greater in brains of ffe/ + mice compared to + / + mice while blood 54 Mn was cleared to the same levels by 24 h. Taken together, these results indicate that Fpn deficiency decreases Mn trafficking out of the brain, alters body Fe, Mn, Zn and Cu levels, and promotes metal accumulation in olfactory bulbs.

There is a need to understand how patients are managed in the real world to better understand disease burden and unmet need. Traditional approaches to gather these data include the use of electronic medical record (EMR) or claims databases; however, in many cases data access policies prevent rapid insight gathering. Social media may provide a potential source of real-world data to assess treatment patterns, but the limitations and biases of doing so have not yet been evaluated. Here, we assessed whether patient treatment patterns extracted from publicly available patient forums compare to results from more traditional EMR and claims databases. We observed that the 95% confidence intervals of proportions of treatments received at first, second, and third line for advanced/metastatic melanoma generated from unstructured social media data overlapped with 95% confidence intervals from proportions obtained from 1 or more traditional EMR/Claims databases. Social media may offer a valid data option to understand treatment patterns in the real world.