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<p>The clinical translation of engineered nanocarriers is currently encumbered by dismal target delivery efficiencies averaging a median delivery efficiency of less than 0.1% for non-targeted CNS nanocarriers—primarily due to the steric and physiological constraints of the blood-brain barrier (BBB) and a persistent reliance on resource-intensive, empirical “trial-and-error” methodologies. To mitigate this bottleneck, we present Nano-Predictor Pro (v1.0), a high-fidelity computational framework engineered to simulate the systemic trajectory and target bioavailability of nanoparticles in silico. The simulator employs a stochastic-algorithmic architecture that integrates fundamental thermodynamic principles, such as the Boltzmann distribution, with rigid physiological constraints including renal filtration and opsonization-mediated clearance. The engine was rigorously benchmarked against three clinical gold standards: PEGylated liposomal doxorubicin (Doxil®), albumin-bound paclitaxel (Abraxane®), and polymeric micelles (Genexol-PM®). Subsequently, the platform was deployed to optimize an Apolipoprotein E (ApoE)-targeted lipid nanoparticle (LNP) library for Alzheimer’s disease. Validation against clinical datasets demonstrated high predictive fidelity across heterogenous nanostructures. In the Alzheimer’s case study, the algorithm delineated a precise “Optimal Design Window” for central nervous system delivery, establishing that a hydrodynamic diameter (Dh) ≤ 85 nm, a near-neutral surface charge (− 5 to − 10 mV), and high-affinity ligand modification (ΔG ≤ − 10.5 kcal/mol) are the critical determinants for maximizing parenchymal accumulation. This optimized configuration yielded a predicted target bioavailability of 3.42 ± 0.24%, whereas marginal deviations in size or charge resulted in near-total therapeutic failure due to either steric exclusion (1.8% accumulation) or rapid sequestration by the mononuclear phagocyte system (80.1% loss). Nano-Predictor Pro (v1.0) represents a pivotal advancement in computational nanopharmacology, offering an ethical and economically sustainable alternative to traditional experimental screening. By facilitating “in silico-first” workflows, this tool supports the 3Rs principle in animal research and provides a robust blueprint for accelerating the development of the next generation of precision neuro-nanotherapeutics.</p>

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In Silico Pharmacokinetic Prediction of Nanoparticle Systemic Fate and Bioavailability: The Nano-Predictor Pro Framework

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Batuhan Yurtseven,
  • Esra Aydemir,
  • Furkan Ayaz

摘要

The clinical translation of engineered nanocarriers is currently encumbered by dismal target delivery efficiencies averaging a median delivery efficiency of less than 0.1% for non-targeted CNS nanocarriers—primarily due to the steric and physiological constraints of the blood-brain barrier (BBB) and a persistent reliance on resource-intensive, empirical “trial-and-error” methodologies. To mitigate this bottleneck, we present Nano-Predictor Pro (v1.0), a high-fidelity computational framework engineered to simulate the systemic trajectory and target bioavailability of nanoparticles in silico. The simulator employs a stochastic-algorithmic architecture that integrates fundamental thermodynamic principles, such as the Boltzmann distribution, with rigid physiological constraints including renal filtration and opsonization-mediated clearance. The engine was rigorously benchmarked against three clinical gold standards: PEGylated liposomal doxorubicin (Doxil®), albumin-bound paclitaxel (Abraxane®), and polymeric micelles (Genexol-PM®). Subsequently, the platform was deployed to optimize an Apolipoprotein E (ApoE)-targeted lipid nanoparticle (LNP) library for Alzheimer’s disease. Validation against clinical datasets demonstrated high predictive fidelity across heterogenous nanostructures. In the Alzheimer’s case study, the algorithm delineated a precise “Optimal Design Window” for central nervous system delivery, establishing that a hydrodynamic diameter (Dh) ≤ 85 nm, a near-neutral surface charge (− 5 to − 10 mV), and high-affinity ligand modification (ΔG ≤ − 10.5 kcal/mol) are the critical determinants for maximizing parenchymal accumulation. This optimized configuration yielded a predicted target bioavailability of 3.42 ± 0.24%, whereas marginal deviations in size or charge resulted in near-total therapeutic failure due to either steric exclusion (1.8% accumulation) or rapid sequestration by the mononuclear phagocyte system (80.1% loss). Nano-Predictor Pro (v1.0) represents a pivotal advancement in computational nanopharmacology, offering an ethical and economically sustainable alternative to traditional experimental screening. By facilitating “in silico-first” workflows, this tool supports the 3Rs principle in animal research and provides a robust blueprint for accelerating the development of the next generation of precision neuro-nanotherapeutics.