Trastuzumab, a humanized monoclonal antibody against human epidermal growth factor receptor 2 (HER2), offers a promising strategy of anticancer drug targeting to HER2-expressing cancer cells. Conjugation of trastuzumab to dendrimers, repeatedly branched polymers with a highly functionalized surface, can enhance the drug loading capacity. However, typical dendrimers such as cationic polyamidoamine dendrimers have exhibited a nonspecific cytotoxicity. In the present study, we developed a novel biocompatible amino acid dendrimer with potentially less toxicity by surface modification of the sixth generation lysine dendrimer with glutamate (KG6E). The synthesized KG6E showed a well-controlled particle size around 5-6 nm with low polydispersibility and negative surface potentials for negligible cytotoxicity. Next, the targeting efficiency of the fluorescent-labeled KG6E-trastuzumab conjugate was evaluated in HER2-positive (SKBR3) and -negative (MCF7) human breast cancer cell lines compared to free trastuzumab and KG6E dendrimers. The KG6E-trastuzumab conjugate was specifically bound to SKBR3 cells in a dose-dependent manner with low binding affinity to MCF7 cells. Furthermore, the conjugate was significantly internalized in SKBR3 cells and then trafficked to lysosomes. These results indicate the potential of KG6E-trastuzumab conjugates as HER2-targeting carriers for therapeutic and diagnostic approaches to cancer therapy.

Background: Several biologics for atopic dermatitis (AD) have demonstrated good efficacy in clinical trials, but with a substantial proportion of patients being identified as poor responders. This study aims to understand the pathophysiological backgrounds of patient variability in drug response, especially for dupilumab, and to identify promising drug targets in dupilumab poor responders.Methods: We conducted model-based meta-analysis of recent clinical trials of AD biologics and developed a mathematical model that reproduces reported clinical efficacies for nine biological drugs (dupilumab, lebrikizumab, tralokinumab, secukinumab, fezakinumab, nemolizumab, tezepelumab, GBR 830, and recombinant interferongamma) by describing system-level AD pathogenesis. Using this model, we simulated the clinical efficacy of hypothetical therapies on virtual patients. Results:Our model reproduced reported time courses of %improved EASI and EASI-75 of the nine drugs. The global sensitivity analysis and model simulation indicated the baseline level of IL-13 could stratify dupilumab good responders. Model simulation on the efficacies of hypothetical therapies revealed that simultaneous inhibition of IL-13 and IL-22 was effective, whereas application of the nine biologic drugs was ineffective, for dupilumab poor responders (EASI-75 at 24 weeks: 21.6% vs. max. 1.9%). Conclusion:Our model identified IL-13 as a potential predictive biomarker to stratify dupilumab good responders, and simultaneous inhibition of IL-13 and IL-22 as a promising drug therapy for dupilumab poor responders. This model will serve as a computational platform for model-informed drug development for precision medicine, as it allows evaluation of the effects of new potential drug targets and the mechanisms behind patient variability in drug response.

This work presents a decision support method for the choice between batch and continuous technologies in solid drug product manufacturing based on the economic evaluation. The method consists of four steps: (I) modeling of operating costs, (II) evaluation, (III) sensitivity analysis, and (IV) interpretation, with iterations. For a given design situation, manufacturing processes are modeled and evaluated with consideration for the characteristics of the two technologies. The sensitivity of the input parameters is analyzed; after interpreting all results, the economically preferable technology is suggested. As a case study, the method was applied to a situation where a new product was in the late development stage, and one of the two technologies needs to be chosen. After executing the four steps, the comparison result of the net present cost was obtained as the decision support information.

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