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Mechanistic advances in nanomedicine, nucleic acid therapies, and AI-driven research on cervical cancer.

Jul 2026 · Journal of Pharmacy and Science · pp. 104442 · 0 citations · 137 references
Medicine

TL;DR

A translational framework for overcoming key barriers in pharmaceutical intervention design for oncology applications is outlined, integrating drug delivery engineering, molecular biopharmaceutics, and computational optimization.

Abstract

Cervical cancer pharmacotherapy is significantly limited by physiological and cellular barriers that restrict drug access to therapeutic targets, resulting in suboptimal biodistribution, systemic toxicity, and the emergence of drug resistance. This review provides a mechanistic and biopharmaceutics-centered analysis of how advanced drug delivery systems are being engineered to overcome these limitations. We critically examine the role of nanocarriers, including lipid-based vesicles, polymeric nanoparticles, and inorganic hybrid systems, in modulating absorption, distribution, and tumor-targeting efficiency, with emphasis on their physicochemical properties and interaction with biological barriers such as the tumor microenvironment and cellular uptake pathways. In parallel, we analyze nucleic acid-based therapeutics (CRISPR/Cas systems, miRNA, and antisense oligonucleotides) from a pharmaceutical sciences perspective, focusing on delivery constraints, stability, intracellular trafficking, and their ability to modulate pharmacological response and drug resistance mechanisms. The review also discusses the integration of immunomodulatory strategies within nanodelivery platforms as a means to alter disease-related biological barriers and improve therapeutic index. Finally, we explore the emerging role of AI-assisted models in optimizing formulation design, predicting pharmacokinetic behavior, and supporting precision dosing strategies in drug development workflows. By integrating drug delivery engineering, molecular biopharmaceutics, and computational optimization, this work outlines a translational framework for overcoming key barriers in pharmaceutical intervention design for oncology applications.

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