%0 Journal Article
%T Predictive Modeling of Pharmaceutical Nanocrystals from 2017 to 2026 Across Particle Engineering, Physical Stability, Dissolution, and Oral Performance
%A Pierre Dubois
%A Marc Lefevre
%A Claire Moreau
%A Julien Martin
%A Thomas Dupont
%J International Journal of Pharmaceutical And Phytopharmacological Research
%@ 2250-1029
%D 2026
%V 16
%N 4
%R 10.51847/uxtBp7XmZI
%P 60-70
%X Pharmaceutical nanocrystal development increasingly relies on computational, mechanistic, statistical, and data-driven approaches to anticipate formulation behavior instead of depending solely on iterative experimentation. However, prediction targets differ substantially across particle engineering, stabilization, physical stability, dissolution, and oral performance, which makes apparently similar modeling studies difficult to compare directly. This evidence-mapping review examined peer-reviewed pharmaceutical nanocrystal and closely bounded nanosuspension modeling literature published from 2017 through 2026. Eligible evidence was charted by prediction target, material and process context, model family, experimental scale, validation design, uncertainty source, and intended formulation decision. The verified DOI-level screening inventory comprised 52 eligible candidate articles, of which 15 were excluded after evidence-fit assessment and 37 were included. The resulting map indicates an uneven predictive landscape. Modeling is comparatively mature where controllable process or formulation inputs can be linked to particle-size distributions, stabilizer behavior, or physical-stability endpoints, whereas evidence becomes less continuous across dissolution, supersaturation, permeation, and organism-level oral performance. Machine-learning, mechanistic, population-balance, Quality-by-Design, molecular, and biopharmaceutic models therefore represent distinct predictive logics rather than interchangeable approaches. A proposed stage-and-validation framework distinguishes what a model predicts from how far that prediction has been experimentally tested. Pharmaceutical nanocrystal modeling is becoming more predictive, but the available evidence does not yet support a unified end-to-end predictive framework. Translation requires endpoint-specific validation, explicit applicability boundaries, and separation of predictive accuracy from mechanistic or biopharmaceutic validity.
%U https://eijppr.com/article/predictive-modeling-of-pharmaceutical-nanocrystals-from-2017-to-2026-across-particle-engineering-ph-q7canezu1nk4hkp