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This paper presents the findings of multiple investigations into the newly developed Bentorb sorbent, derived from winemaking byproducts, specifically the adhesive residues of yellow blood salt. Elemental analysis revealed that the predominant components of Bentorb include oxygen, carbon, silicon, aluminum, iron, nitrogen, and magnesium, which together make up the majority of the sorbent’s composition. Toxicological assessments of Bentorb were conducted using laboratory animals. To evaluate acute toxicity, 60 white mongrel rats, each weighing approximately 237 ± 7 g, were subjected to the substance. The results showed no significant changes in the general clinical condition of rats in either the experimental or control groups, and all animals survived the tests. Chronic toxicity was assessed in 60 white mice and 40 Wistar rats, each weighing 185 ± 12 g. Over the study period, no notable differences in health or survival rates were observed between the experimental and control groups. The effects of Bentorb on gastrointestinal function were examined in piglets aged 40-80 days. Additionally, the potential embryotoxicity of Bentorb was investigated in pregnant Wistar rats weighing 200-240 g. The study also included analyses of body weight and various internal organs in both control and experimental groups that received Bentorb.

Current pharmaceutical development often treats formulation design, process control, and patient-focused performance as sequential domains rather than mutually dependent components of one technology system. This separation can produce technically elegant formulations that are difficult to manufacture, tightly controlled processes that do not fully serve patient needs, or patient-friendly dosage forms that lack robust process translation. A systems perspective is therefore needed to connect product intent, manufacturing feasibility, and real-world usability from the earliest stages of development. The central problem is the absence of an integrated theory that explains how formulation decisions, process control strategies, and patient-centric targets should be co-optimised. Existing development pathways often allow these domains to interact only after critical decisions have already been made. This creates avoidable friction during scale-up, regulatory justification, and clinical implementation. The objective of this article is to propose a theory-driven systems framework for applied pharmaceutical technologies. The framework integrates formulation design logic, process control logic, and patient-centric performance into a unified conceptual model. It is intended to guide early decision-making, cross-functional communication, and translational planning. The resulting framework identifies three interacting pillars: formulation design as the material and biopharmaceutical architecture of the product, process control as the mechanism for assuring reproducible quality, and patient-centric performance as the translation of product attributes into acceptability, adherence, and therapeutic usability. Four tables capture the formulation parameters, process control strategies, patient-centric targets, and integrated framework components. Together, these elements define a systems logic for pharmaceutical technology development. The proposed framework provides a conceptual blueprint for developing pharmaceutical products that are simultaneously manufacturable, quality-assured, and optimised for patients. It supports earlier recognition of trade-offs, clearer integration of predictive models, and stronger alignment between development choices and clinical use. Its broader value lies in reframing pharmaceutical technology as a patient-anchored system rather than a sequence of isolated technical operations.

Artificial intelligence is rapidly being adopted to optimise pharmaceutical formulation and manufacturing processes, yet its inherent opacity poses a fundamental challenge to regulatory frameworks built on transparency, scientific justification, and mechanistic understanding. In pharmaceutical development, AI systems may influence formulation selection, process parameter optimisation, release modelling, stability prediction, and scale-up strategy. These decisions can directly or indirectly affect product quality and patient safety. The regulatory issue is therefore not whether AI can improve development efficiency, but whether its outputs can be explained, justified, verified, and governed. There is currently no clear regulatory consensus on what constitutes sufficient explainability for AI models used in pharmaceutical formulation and process development. Existing expectations for pharmaceutical development presume that sponsors can describe the relationship between material attributes, process parameters, critical quality attributes, and clinical performance. Many AI models, particularly neural networks, ensemble methods, and adaptive systems, challenge this assumption because their internal decision logic may not be readily interpretable. This uncertainty creates difficulty for industry, regulators, and quality units seeking to evaluate whether AI-supported decisions are scientifically sound. This article identifies the regulatory gaps and risk domains associated with non-explainable AI in pharmaceutical development. It examines how AI is being used across formulation design, process development, manufacturing optimisation, and data-rich pharmaceutical quality systems. It then defines explainability and evidence requirements appropriate for different levels of regulatory risk. The central argument is that explainability should be treated as a regulatory quality attribute of AI systems, not merely as a technical preference. The proposed framework categorises AI applications according to their intended use, model complexity, degree of influence on quality decisions, and potential impact on patient safety. It links these categories to evidence requirements, including training data documentation, performance validation, uncertainty assessment, interpretability justification, human oversight, and lifecycle change controls. Four tables present the AI application landscape, regulatory gap analysis, explainability requirements, and the proposed framework. Together, these elements provide a structured basis for regulatory dialogue and future guidance development. A structured, risk-based approach to regulatory explainability can enable responsible adoption of AI while protecting the integrity of the pharmaceutical regulatory system. Low-risk AI tools may require documented performance and traceability, whereas high-risk tools that influence critical quality decisions require stronger interpretability, validation, and governance. The proposed approach does not require all models to be fully transparent, but it does require that the explanation provided be proportionate to the decision being supported. Regulatory explainability is therefore presented as a necessary bridge between innovation, quality assurance, and patient protection.

Artificial intelligence is increasingly being positioned as a transformative tool in pharmaceutical formulation and process development because it can model complex relationships among molecular properties, excipient behaviour, formulation variables, processing conditions, and product performance. Machine learning, deep learning, hybrid modelling, and optimisation algorithms are now used to support decisions that were previously dominated by empirical screening and expert judgement. Despite this promise, the practical translation of artificial intelligence into pharmaceutical development remains uneven. Many models demonstrate high retrospective accuracy but provide limited mechanistic insight, weak interpretability, and uncertain relevance when moved beyond the specific datasets, formulations, equipment, or scales on which they were trained. This critical review examines artificial intelligence in pharmaceutical formulation and process development through three linked lenses: explainability, transferability, and regulatory trust. It argues that these issues are not secondary implementation details but core determinants of whether artificial intelligence can become credible within quality-driven pharmaceutical development. The analysis shows that artificial intelligence can support formulation and process understanding only when predictive performance is accompanied by transparent reasoning, domain-aware validation, lifecycle governance, and evidence of transferability across development contexts. A coordinated pathway involving explainable-by-design models, standardised transferability testing, and regulatory learning environments is required to move pharmaceutical artificial intelligence from technical promise toward justified regulatory trust.

Drug–device combination products occupy a technically demanding position between pharmaceutical formulation, engineered device performance, and real-world user interaction. Their development requires the simultaneous control of drug product quality, delivery-system reliability, and safe administration by intended users. Yet these domains are still frequently treated as separable workstreams rather than as mutually shaping elements of one system. This conceptual framework article addresses the limitations of sequential development models in which formulation is stabilised first, device selection follows, and user validation is deferred until late-stage development. Such sequencing may appear efficient during early development, but it can conceal incompatibilities that only emerge during device verification, usability testing, clinical bridging, or regulatory review. The result is often redesign, delayed translation, or unresolved uncertainty about whether the final product can perform reliably under intended conditions of use. The objective of this article is to propose and defend the Formulation–Device–User Triangle as a unified design logic for drug–device combination products. The triangle positions formulation, device, and user as co-equal vertices that continuously constrain and enable one another. It is intended not as a replacement for existing quality, design-control, or human-factors processes, but as an integrating framework that makes their interdependence explicit. The proposed triangle reframes combination product design as a system-level co-development problem. It argues that a product is not ready for translation simply because its formulation is stable, its device is functional, or its users can pass a summative test. Readiness depends on whether the formulation tolerates device action, the device accommodates formulation variability, and the user interface supports reliable administration across real-world conditions.

Pharmaceutical translation depends on coordinated movement across discovery, formulation, engineering, clinical development, regulatory assessment, and real-world adoption. Yet these domains are not organised around a single language, evidence standard, or professional logic. Scientists, engineers, clinicians, and regulators often evaluate the same innovation through different assumptions about value, risk, feasibility, and acceptable uncertainty. A critical but under-recognised cause of translational delay is the absence of shared artefacts that can carry meaning across these disciplinary worlds. When a formulation concept, manufacturing constraint, clinical use condition, or regulatory concern is represented only in the vocabulary of one group, it becomes difficult for others to interpret its implications. This produces misaligned specifications, late-stage redesign, evidence fragmentation, and avoidable regulatory friction. This article introduces boundary object theory as a translational lens for pharmaceutical innovation. Boundary objects are artefacts that remain stable enough to support shared work while remaining flexible enough to be interpreted by different professional communities. Applied to pharmaceutical development, they include target product profiles, critical quality attributes, design space diagrams, pharmacokinetic–pharmacodynamic models, control strategies, clinical administration guides, and regulatory summaries. The article constructs an original translational framework that identifies how boundary objects can be designed, evaluated, and positioned across formulation science, process engineering, clinical practice, and regulatory assessment. It argues that pharmaceutical translation should not be understood only as the transfer of data or documentation, but as the progressive alignment of meanings, expectations, and decisions through structured artefacts. The proposed framework positions boundary object design as a practical intervention for improving translational continuity. By making assumptions, sensitivities, use conditions, uncertainties, and decision consequences visible across disciplines, boundary objects can reduce communication failure and support more coherent innovation pathways. The article calls for deliberate integration of boundary object thinking into pharmaceutical development programmes, regulatory communication, and translational training.

Generative artificial intelligence is becoming increasingly relevant to pharmaceutical formulation because it can propose compositions, excipient combinations, processing conditions, and optimisation trajectories that may not be obvious through conventional experimental design. These capabilities create the possibility of faster development, broader exploration of formulation space, and more systematic use of prior knowledge. Yet the same models that expand formulation creativity often operate through complex latent representations that are difficult to interpret. This creates a trust problem for both scientific and regulatory decision-making. The central problem is that an AI-generated formulation is not only a predicted technical solution but also a claim about product performance, manufacturability, and quality. If the rationale behind that claim cannot be explained, formulation scientists may struggle to convert model outputs into mechanistic understanding. Regulators may likewise find it difficult to assess whether the proposed formulation is supported by transparent evidence. Opaque formulation design therefore risks becoming a translational bottleneck rather than an innovation accelerator. This perspective develops a conceptual framework for dual-purpose explainability in AI-generated pharmaceutical formulation design. The framework is designed to serve two decision contexts simultaneously. Scientific decision-makers require explanations that clarify formulation logic, reveal influential variables, and support hypothesis generation. Regulatory decision-makers require explanations that are auditable, reproducible, uncertainty-aware, and connected to product quality and safety evidence. The article first defines the conceptual gap between existing AI formulation capabilities and explainability expectations. It then describes the logic of AI-generated formulation, identifies distinct scientific and regulatory explanation requirements, and analyses transparency barriers. The proposed framework integrates global model explanations, local formulation-specific explanations, mechanistic interpretation, uncertainty communication, and regulatory evidence packaging. Three tables summarise the gap analysis, explainability requirements, and framework architecture. The article concludes that explainability must be treated as a design requirement rather than a post hoc add-on to pharmaceutical AI. AI-generated formulation designs will become useful only when their rationale can be interrogated, documented, challenged, and connected to established principles of product and process understanding. A dual-purpose explainability framework can help move the field from black-box prediction toward transparent, accountable, and scientifically meaningful formulation intelligence.