Background: Background: Teclistamab (JNJ-64007957) is a T cell redirecting bispecific antibody that targets both B-cell maturation antigen (BCMA) and CD3 receptors to induce T cell mediated cytotoxicity of BCMA-expressing myeloma cells. MajesTEC-1 is an open-label, multicohort, phase 1/2 study evaluating teclistamab in patients (pts) with relapsed/ refractory multiple myeloma (RRMM) previously treated with ≥3 prior lines of therapy (LOT). An overall response rate (ORR) of 62.0% in pts with no prior anti-BCMA treatment (tx) was previously reported in a pooled analysis from phase 1 and phase 2 cohort A at a median follow-up of 7.8 mo. Aims:Aims: We report efficacy and safety results of teclistamab from cohort C, which enrolled patients who had prior exposure to anti-BCMA treatment. Methods:Methods: Eligible pts (age ≥18 y) had multiple myeloma (MM) per IMWG criteria and were previously treated with ≥3 prior LOT, including a PI, IMiD, anti-CD38 antibody, and anti-BCMA treatment (chimeric antigen receptor T cell therapy [CAR-T] or Ab drug conjugate [ADC]). Pts were enrolled using a Simon's stage design to receive weekly subcutaneous teclistamab 1.5 mg/kg (step-up doses of 0.06 and 0.3 mg/kg). ORR per IMWG 2016 criteria was the primary endpoint. All AEs were graded per CTCAE v4.03; immune effector cell-associated neurotoxicity syndrome (ICANS) and cytokine release syndrome (CRS) were graded per ASTCT guidelines.

Abstract. This contribution explores a new approach to forecast multivariate covariances for atmospheric chemistry through the use of the parametric Kalman filter (PKF). In the PKF formalism, the error covariance matrix is modelized by a covariance model relying on parameters, for which the dynamics is then computed. The PKF has been formulated in univariate cases, and a multivariate extension for chemical transport models is explored here. To do so, a simplified two-species chemical transport model over a 1D domain is introduced, based on the nonlinear Lotka-Volterra equations, which allows to propose a multivariate pseudo covariance model. Then, the multivariate PKF dynamics is formulated and its results are compared with a large ensemble Kalman filter (EnKF) in several numerical experiments. In these experiments, the PKF accurately reproduces the EnKF. Eventually, the PKF is formulated for a more complex chemical model composed of six chemical species (Generic Reaction Set). Again, the PKF succeeds at reproducing the multivariate covariances diagnosed on the large ensemble.

Abstract. This contribution explores a new approach to forecasting multivariate covariances for atmospheric chemistry through the use of the parametric Kalman filter (PKF). In the PKF formalism, the error covariance matrix is modellized by a covariance model relying on parameters, for which the dynamics are then computed. The PKF has been previously formulated in univariate cases, and a multivariate extension for chemical transport models is explored here. This contribution focuses on the situation where the uncertainty is due to the chemistry but not due to the uncertainty of the weather. To do so, a simplified two-species chemical transport model over a 1D domain is introduced, based on the non-linear Lotka–Volterra equations, which allows us to propose a multivariate pseudo covariance model. Then, the multivariate PKF dynamics are formulated and their results are compared with a large ensemble Kalman filter (EnKF) in several numerical experiments. In these experiments, the PKF accurately reproduces the EnKF. Eventually, the PKF is formulated for a more complex chemical model composed of six chemical species (generic reaction set). Again, the PKF succeeds at reproducing the multivariate covariances diagnosed on the large ensemble.

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