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Programmable drug delivery systems are emerging as a transformative class of therapeutic technologies because they can adjust drug release according to physiological signals, algorithmic rules, or external commands. Their appeal lies in the promise of more responsive, individualised, and continuous therapy than is possible with conventional dosage forms. Closed-loop insulin delivery, implantable programmable pumps, responsive antidote systems, and digitally mediated delivery platforms all illustrate this shift from passive administration to active therapeutic control. This shift also changes the ethical character of drug delivery. When a device senses, interprets, and acts on behalf of a patient, dosing becomes partly delegated to software, control architecture, and design assumptions. The ethical question is therefore not only whether the system works, but whether it preserves the patient’s agency while pursuing therapeutic optimisation. The core problem is that programmable delivery systems combine pharmacological intervention, medical device operation, data processing, and algorithmic decision-making in a single therapeutic object. This convergence creates tensions between efficiency and autonomy, adaptability and safety assurance, and automation and human oversight. Existing ethical and regulatory vocabularies do not fully capture these tensions because they often treat drugs, devices, software, and clinical decisions as separable domains. This critical perspective argues that programmable drug delivery requires an ethical design approach from the earliest stages of development. Autonomy must be translated into design features such as consent clarity, override capacity, patient-facing explanation, and withdrawal options. Safety must be treated as a lifecycle property rather than a static pre-market claim. The article proposes a critical framework for classifying programmable delivery systems according to autonomy level, identifying ethical pressure points, and linking them to design and governance requirements. It argues that trustworthy programmable delivery depends not merely on technical performance, but on the deliberate preservation of meaningful human control. Ethical foresight must therefore become part of the engineering logic of programmable drug delivery itself.

Pharmaceutical manufacturing is moving from automated equipment and digitally assisted control toward more autonomous systems capable of interpreting process data, adjusting operating conditions, and supporting quality decisions. This trajectory promises faster response, improved consistency, and more adaptive control across complex production environments. Yet autonomy also changes the nature of manufacturing responsibility because technical decisions increasingly occur inside algorithmic systems rather than through visible human judgement alone. The central problem addressed in this article is the trust deficit created by autonomous pharmaceutical manufacturing. When an algorithm modifies a critical process parameter, detects an anomaly, recommends batch continuation, or contributes to a quality disposition, regulators, operators, quality units, and patients require confidence that the decision remains safe, explainable, reversible, and accountable. Trust cannot be assumed simply because the system performs well during validation; it must be sustained over time as processes, materials, sensors, models, and organisational practices evolve. This article develops an original theory-driven framework for trustworthy autonomous pharmaceutical manufacturing. The framework is structured around three interdependent pillars: human oversight, model drift management, and quality accountability. These pillars are treated not as separate compliance add-ons but as mutually reinforcing design requirements for autonomous manufacturing systems operating in Good Manufacturing Practice environments. The article draws on a theoretical synthesis of peer-reviewed literature on pharmaceutical manufacturing automation, process analytical technology, machine learning, trust in automation, human–autonomy teaming, resilience engineering, socio-technical systems, and model drift. It reframes autonomous manufacturing as a socio-technical trust problem rather than a purely technical optimisation problem. Four tables map the theoretical foundations, oversight architectures, drift-management logic, and integrated Trustworthy System Framework. The proposed framework argues that trustworthiness in autonomous pharmaceutical manufacturing is not a property of an algorithm alone. It emerges from the designed relationship among people, models, process controls, quality systems, audit trails, and governance responsibilities. Autonomous manufacturing will become viable only when the system can remain technically reliable, humanly overseen, and institutionally accountable throughout its lifecycle.