CD19/CD20 Bispecific Chimeric Antigen Receptor (CAR) in Naive/Memory T Cells for the Treatment of Relapsed or Refractory Non-Hodgkin Lymphoma.

To address antigen escape and loss of T-cell functionality, we report a phase I clinical trial (NCT04007029) evaluating autologous naive and memory T (TN/MEM) cells engineered to express a bispecific anti-CD19/CD20 chimeric antigen receptor (CAR; CART19/20) for patients with relapsed/refractory non-Hodgkin lymphoma (NHL), with safety as the primary endpoint. Ten patients were treated with 36 × 106 to 165 × 106 CART19/20 cells. No patient experienced neurotoxicity of any grade or over grade 1 cytokine release syndrome. One case of dose-limiting toxicity (persistent cytopenia) was observed. Nine of 10 patients achieved objective response [90% overall response rate (ORR)], with seven achieving complete remission [70% complete responses (CR) rate]. One patient relapsed after 18 months in CR but returned to CR after receiving a second dose of CART19/20 cells. Median progression-free survival was 18 months and median overall survival was not reached with a 17-month median follow-up. In conclusion, CART19/20 TN/MEM cells are safe and effective in patients with relapsed/refractory NHL, with durable responses achieved at low dosage levels. SIGNIFICANCE: Autologous CD19/CD20 bispecific CAR-T cell therapy generated from TN/MEM cells for patients with NHL is safe (no neurotoxicity, maximum grade 1 cytokine release syndrome) and demonstrates strong efficacy (90% ORR, 70% CR rate) in a first-in-human, phase I dose-escalation trial. This article is highlighted in the In This Issue feature, p. 517.

Quantitative self-assembly prediction yields targeted nanomedicines.

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.