Traumatic brain injury (TBI) leads to high mortality rate. We aimed to identify the key cytokines favoring TBI repair and found that patients with TBI with a better outcome robustly increased concentrations of macrophage colony-stimulating factor, interleukin-6, and transforming growth factor–β (termed M6T) in cerebrospinal fluid or plasma. Using TBI mice, we identified that M2-like macrophage, microglia, and endothelial cell were major sources to produce M6T. Together with the in vivo tracking of mCherry+ macrophages in zebrafish models, we confirmed that M6T treatment accelerated blood-borne macrophage infiltration and polarization toward a subset of tissue repair macrophages that expressed similar genes as microglia for neuroprotection, angiogenesis and cell migration. M6T therapy in TBI mice and zebrafish improved neurological function while blocking M6T-exacerbated brain injury. Considering low concentrations of M6T in some patients with poor prognostic, M6T treatment might repair TBI via generating a previously unidentified subset of tissue repair macrophages.

Summary. We propose a flexible design for the identification of optimal dose combinations in dual-agent dose finding clinical trials. The design is called AAA, standing for three adaptations: adaptive model selection, adaptive dose insertion and adaptive cohort division. The adaptations highlight the need and opportunity for innovation for dual-agent dose finding and are supported by the numerical results presented in the proposed simulation studies. To our knowledge, this is the first design that allows for all three adaptations at the same time. We find that AAA enhances the chance of finding the optimal dose combinations and shortens the trial duration. A clinical trial is being planned to apply the AAA design and a Web tool is being developed for both statisticians and non-statisticians.

Purpose Statistical designs for traditional phase I dose-finding trials consider dose-limiting toxicity in the first cycle of treatment. In reality, patients often go through multiple cycles of treatment and may experience toxicity events in more than one cycle. Therefore, it is desirable to identify the maximum tolerated sequence of three doses across three cycles of treatment. Methods Motivated by a three-cycle dose-finding clinical trial for a rare cancer with a JAK inhibitor, we proposed and implemented a simple Bayesian adaptive dose-cycle finding (BaSyc) design that allows intercycle and intrapatient dose modification. Because of the patient-specific dosing strategy over cycles, the BaSyc design is suited as a method in precision oncology. Results BaSyc is simple and transparent because its algorithm can be summarized as two tabulated decision rules before the trial starts, allowing physicians to visually examine these rules. In addition, BaSyc employs a time-saving enrollment scheme that speeds up the trial. Extensive simulation studies show that BaSyc has desirable operating characteristics in identifying the maximum tolerated sequence. Conclusion The BaSyc design provides a first-of-kind multicycle approach for dose finding and will likely lead to better and safer patient care and drug development.