How do nanoparticle properties shape pharmacokinetics and pharmacodynamics? A mechanistic review

Many therapeutics fail due to suboptimal pharmacokinetics/pharmacodynamics (PK/PD), and the development of nanomedicine remains partly empirical. We present a protocol-guided, mechanistic review that links nanoparticle (NP) physicochemical traits (such as size, surface charge/hydrophobicity, elasticity, and composition) to protein-corona remodeling, immune recognition, biodistribution, and clearance across the ADME, and examines how these shifts translate into pharmacodynamics and therapeutic index. We systematically searched PubMed, Web of Science, Scopus, and ClinicalTrials.gov (primarily 2000-2025) using predefined Boolean strings. We included in vivo animal or human studies directly comparing the same drug in NP versus non-NP formulations with matched route and comparable dose, reporting at least one PK and/or PD endpoint, while excluding in vitro-only reports, reviews/opinion articles, and studies with undisclosed or non-comparable vehicles. We curated in vivo, route-matched comparisons of the same drug in NP versus non-NP forms and compiled an eight-drug cross-compound dataset. Where variance permitted, we quantified effects using standardized ln-ratios for AUC, half-life, and clearance, and flagged vehicle-matched comparisons to avoid overattributing gains to nanoscale architecture. Across oral and intravenous delivery, NPs consistently increased systemic exposure and prolonged half-life, with the most significant benefits in poorly soluble, lipophilic molecules; effect sizes were context-dependent and, in some formulations, reached multi-fold improvements. Mechanistically, we delineate when lymphatic uptake (long-chain lipid carriers), receptor-mediated transcytosis (e.g., BBB-oriented designs), or context-limited EPR are most contributory, and we highlight stiffness-dependent corona effects (e.g., ApoA-I enrichment) as a lever to extend circulation. To enable translation, we contribute: (i) a mechanism-to-metric map that connects trait tuning to PK readouts and PD/TI implications; (ii) a standardized, route-stratified table of ln-ratio effects with explicit vehicle/excipient flags; and (iii) a pragmatic decision framework to triage candidates (by logP/solubility and barrier goals) and design reproducible studies with safety checks (CARPA, anti-PEG/ABC, inorganic retention). Collectively, these elements advance anticipatory, model-informed nanoformulation design and provide quantitative anchors for future vehicle-matched, mechanism-driven comparisons.