Fiandaca Giada, Campanile Elio, Leonardelli Lorena, Pettinà Elisa, Giampiccolo Stefano, Carstens Elizabeth J, Dasti Lorenzo, Zangani Natascia, Marchetti Luca
Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
Fondazione the Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Mol Ther Nucleic Acids. 2025 Jun 16;36(3):102606. doi: 10.1016/j.omtn.2025.102606. eCollection 2025 Sep 9.
In the field of cancer therapy, bispecific T cell engagers (BiTEs) have demonstrated significant potential. However, their clinical application is constrained by challenges in production and limited plasma half-life. -transcribed (IVT) mRNA formulations emerge as a promising alternative, offering adaptability and cost-efficiency. Yet, the intricate relationship between mRNA dosage, antibody production, and the distribution of mRNA and proteins requires a deeper understanding. To address these issues, we present a novel physiologically based pharmacokinetic (PBPK) model to characterize the pharmacokinetics of BiTEs. This model predicts the distribution patterns of both recombinant and mRNA-encoded BiTEs by extending an established PBPK model with a hierarchical multiscale framework calibrated and validated using preclinical data from existing literature. The extended PBPK model can be adapted to various mRNA-based therapeutic formulations, facilitating exploration of different drug administration scenarios. It can provide valuable support for optimizing dose and schedule and allows the efficient investigation of drug distribution at a whole-body scale. This approach promises to enhance the personalization and effectiveness of cancer therapies, reduce research time and costs, and significantly advance the development of mRNA-based BiTEs for cancer treatment.
在癌症治疗领域，双特异性T细胞衔接器（BiTEs）已展现出巨大潜力。然而，其临床应用受到生产挑战和血浆半衰期有限的限制。体外转录（IVT）mRNA制剂作为一种有前景的替代方案出现，具有适应性和成本效益。然而，mRNA剂量、抗体产生以及mRNA和蛋白质分布之间的复杂关系需要更深入的理解。为解决这些问题，我们提出了一种新型的基于生理的药代动力学（PBPK）模型来表征BiTEs的药代动力学。该模型通过使用现有文献中的临床前数据校准和验证的分层多尺度框架扩展已建立的PBPK模型，预测重组和mRNA编码的BiTEs的分布模式。扩展的PBPK模型可适应各种基于mRNA的治疗制剂，便于探索不同的给药方案。它可为优化剂量和给药时间表提供有价值的支持，并允许在全身尺度上高效研究药物分布。这种方法有望提高癌症治疗的个性化和有效性，减少研究时间和成本，并显著推进基于mRNA的BiTEs用于癌症治疗的开发。
