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Nanomedicines promise therapeutic advantages that are difficult to achieve with conventional dosage forms, including improved targeting, altered pharmacokinetics, protected delivery of fragile payloads, and platform adaptability across drug classes. Yet these benefits depend on manufacturing systems that can repeatedly generate nanoscale products within narrow physicochemical and biological performance windows. The central difficulty is that small changes in process conditions can produce disproportionate changes in particle size, morphology, surface properties, encapsulation efficiency, release behaviour, and biological interaction. Despite decades of academic activity, the translation of nanomedicine from laboratory prototypes to routine clinical and industrial products remains limited. Many formulations are optimised in small batches under conditions that are poorly suited to pilot or commercial production. As a result, promising nanocarriers may fail not because their therapeutic concept is weak, but because their quality attributes cannot be reproduced reliably at scale. The analysis identifies a systemic mismatch between laboratory-optimised formulation methods and industrial requirements for control, documentation, comparability, continuous monitoring, and GMP-ready reproducibility. Four tables synthesise manufacturing platforms, scale-up fragility points, reproducibility challenges, and regulatory or industrial barriers. Together, these syntheses support a translational quality framework centred on process understanding, platform logic, critical quality attributes, and regulatory co-evolution. The review concludes that nanomedicine translation cannot rely on linear scale-up assumptions. Robust translation requires a shift toward platform-based manufacturing, quality-by-design development, integrated process analytical technology, orthogonal characterisation, predictive scale-down models, and shared regulatory learning. Nanomedicines will become clinically credible only when therapeutic innovation is matched by reproducible, affordable, and auditable manufacturing systems.

Advanced drug delivery now includes lipid nanoparticles, polymeric carriers, implants, and 3D-printed dosage forms, each offering distinct advantages for controlling where, when, and how drugs are released. These platforms differ in their suitability for nucleic acids, small molecules, biologics, local therapy, systemic exposure, long-acting treatment, and personalised dosing. The breadth of available options has expanded faster than the decision tools used to choose among them. Platform selection is often driven by familiarity, institutional capability, technological enthusiasm, or precedent within a therapeutic area. Such heuristic decisions can produce poor alignment between the drug, the target product profile, the patient population, and the manufacturing pathway. A structured framework is therefore needed to make platform choice more transparent, reproducible, and development-relevant. This article constructs an original decision framework for selecting among lipid nanoparticles, polymeric carriers, implants, and 3D-printed dosage platforms. The framework treats platform choice as a multi-criteria decision problem rather than as a single-attribute optimisation exercise. It integrates drug properties, release requirements, stability, scalability, usability, regulatory precedent, and cost into a practical selection process. A criteria-driven platform selection process can help development teams avoid technology-led formulation choices and instead align delivery strategy with therapeutic purpose. The proposed framework is intended to support early-stage screening, translational planning, quality-by-design discussions, and portfolio decisions. Its central argument is that no advanced delivery platform is inherently superior; the best platform is the one that best satisfies the target product profile under real development constraints.

The clinical success of mRNA–lipid nanoparticle vaccines transformed lipid nanoparticle technology from a specialised drug delivery field into a central modality for modern biopharmaceutical development. That success demonstrated that nucleic acid therapeutics can be manufactured, distributed, and deployed at unprecedented speed when formulation science, process engineering, and regulatory urgency align. Yet the same success also exposed how dependent LNP products remain on tightly constrained composition, process history, and cold-chain stability. The central problem is that processes optimised rapidly under pandemic conditions do not automatically constitute robust manufacturing platforms. Many LNP processes remain product-specific, empirically tuned, and sensitive to changes in lipid composition, aqueous phase conditions, mixing geometry, and downstream handling. The language of “platform” is therefore often stronger than the underlying evidence for generalisable process robustness. The review maps the structural and functional logic of LNP platforms, evaluates how mRNA delivery requirements shaped formulation choices, and assesses preclinical and manufacturing evidence across laboratory, preclinical, and scalable production contexts. It identifies recurrent fragility points including mixing sensitivity, particle heterogeneity, aggregation, mRNA degradation, storage instability, and incomplete comparability evidence after process change. Five tables summarise platform design, mRNA delivery requirements, manufacturing evidence, fragility points, and a system design strategy for robust LNP production. The post-mRNA era requires a shift from emergency product development to platform-centred system design. LNP manufacturing must become modular, measurable, scalable, and quality-resilient rather than merely reproducible under narrowly defined conditions. Achieving this transition is essential if LNP technologies are to move beyond COVID-19 vaccines into broader therapeutic applications and more equitable global health deployment.