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The translation of drug delivery innovations from laboratory design to clinical application remains uncertain, expensive, and highly selective. Many systems demonstrate promising biological activity in early studies but fail to progress because evidence of manufacturability, safety, stability, or regulatory maturity is incomplete. This creates a recurring gap between technical novelty and clinical readiness. Existing technology readiness models provide useful language for describing maturity, but they were not originally designed for pharmaceutical technologies. Drug delivery systems require simultaneous assessment of material attributes, formulation behaviour, biological performance, dose reproducibility, scale-up potential, and patient-facing utility. A single linear maturity scale is therefore insufficient for classifying readiness before first-in-human development. This article develops a novel conceptual model called the Pharmaceutical Technology Readiness Matrix. The model integrates technology readiness logic, drug delivery innovation categories, and preclinical translation criteria into a structured assessment framework. Its purpose is to support transparent classification, risk assessment, and decision-making before clinical translation. The proposed matrix adapts readiness levels for drug delivery technologies, classifies major innovation categories, defines preclinical translation criteria, and links evidence maturity to risk and Go/No-Go decisions. Five tables specify the readiness levels, innovation categories, translation criteria, matrix design, and decision pathway. Together, these elements provide a practical conceptual tool for comparing heterogeneous delivery technologies. The Pharmaceutical Technology Readiness Matrix may help researchers, investors, developers, and regulators evaluate drug delivery systems more consistently. By making evidence gaps explicit before clinical translation, the model aims to reduce avoidable attrition and guide rational allocation of development resources. Future empirical validation will be required to test its predictive value across delivery platforms and therapeutic areas.

Personalized medicine has transformed how disease risk, diagnosis, and therapeutic selection are conceptualised, yet the pharmaceutical products used to deliver many therapies remain comparatively static. In many clinical settings, personalization still means selecting a drug more precisely while administering it through conventional dosage forms. This creates a widening gap between diagnostic sophistication and pharmaceutical responsiveness. The prevailing interpretation of personalized pharmaceutics is often reduced to customized dosing. Dose adjustment is important, but it cannot by itself address fluctuating physiology, variable adherence, lifestyle change, disease progression, or treatment-emergent toxicity. A patient’s therapeutic need is not a fixed parameter but a moving target. This conceptual review reframes personalized pharmaceutics as the design of adaptive therapeutic systems. Such systems are not merely individualized at the point of prescription or manufacture; they sense, respond, and evolve with the patient over time. The central argument is that pharmaceutical technology must move from static customization toward dynamic therapeutic adaptation. The analysis shows that customized dosing addresses only one layer of patient variability, whereas adaptive therapeutic systems combine patient-specific design, feedback control, responsive materials, digital monitoring, and translational governance. Four tables clarify definitions, identify unresolved limits of dose-only personalization, describe representative adaptive systems, and map translation challenges. Together, these elements support a broader conceptual vocabulary for personalized pharmaceutics. Realising the full potential of personalized medicine requires pharmaceutical systems that co-evolve with the patient. The future of personalized pharmaceutics therefore depends not only on tailoring what dose is given, but on designing systems that can adapt when, how, where, and why therapy is delivered. This shift has implications for formulation science, device engineering, clinical trials, regulation, reimbursement, and patient participation.