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The Ebola virus is a highly infectious pathogen with no effective antiviral treatments currently available, prompting ongoing research into potential therapeutic options. This study evaluated the inhibitory effects of licensed non-viral drugs on Ebola virus entry and replication using bioinformatic tools. A descriptive-analytical approach was used, in which the chemical structures of selected drugs were first generated in ChemDraw Ultra 10.0 and then energy-optimized in Hyperchem 8.0. Molecular docking was performed using AutoDock4.2 to simulate interactions between the drugs and viral proteins. The analysis revealed that the interactions involved primarily hydrophobic, π-π stacking, hydrogen bonding, and cation-π interactions. Chloroquine, diphenoxylate, and amodiaquine showed the strongest binding affinity, with the most negative docking energies, indicating their potential as effective inhibitors of the GP and VP40 proteins. Conversely, erythromycin and dirithromycin, due to their high hydrophilicity, exhibited weaker binding results. Overall, the study highlighted that drugs with hydrophobic components, effective hydrogen bonding, and tertiary amines tend to show enhanced anti-Ebola properties. The bioinformatic analysis suggests that these drugs could serve as promising candidates for inhibiting Ebola virus entry and replication.

Testicular toxicity is recognized as an underlying factor contributing to male infertility. This study evaluated the protective effects of Artemisia herba-alba against calcium tetrachloride (CCl₄)-induced toxicity in rats, focusing on its impact on ERCC1 gene expression. 20 male Wistar rats were randomly divided into four groups (n = 5 per group). Group I served as the untreated control. Group II received oral CCl₄ (0.4 ml/200g) every other day for three weeks. Group III was administered Artemisia herba-alba (ART) extract orally at 500 mg/kg body weight every other day for three weeks. Group IV was treated with both ART extract (500 mg/kg b.w.) and CCl₄ (0.4 ml/200g) on alternating days over three weeks. Parameters assessed included body weight, relative kidney weight, serum testosterone, tissue oxidative stress markers, ERCC1 gene expression, and testicular histology. The results revealed that CCl₄ exposure led to reduced body weight, lower tissue glutathione (GSH), decreased serum testosterone, elevated lipid peroxidation, upregulated ERCC1 expression, and disrupted testicular histoarchitecture. Conversely, ART co-treatment mitigated these effects, improving testicular histology, downregulating ERCC1 expression, and partially preserving body weight and testosterone levels. Further research with extended treatment periods is recommended to confirm the therapeutic potential of ART in managing testicular toxicity.

Artificial intelligence is rapidly entering pharmaceutical manufacturing through predictive analytics, process analytical technology, continuous manufacturing platforms, and data-driven quality control. These developments promise earlier detection of process deviation, adaptive optimization of critical process parameters, and more responsive assurance of critical quality attributes. Yet the dominant regulatory and quality vocabulary remains anchored in Quality-by-Design, a paradigm built around pre-defined knowledge, structured risk assessment, and validated control strategies. The central problem is that AI-assisted manufacturing systems do not behave like conventional pharmaceutical processes governed solely through fixed design spaces. Machine learning models may change performance as data distributions shift, as sensors age, as materials vary, or as feedback loops alter the operating environment. This creates a governance gap between the static logic of pre-validation and the dynamic reality of algorithmic decision-making. This critical conceptual review examines whether Quality-by-Design remains sufficient for AI-assisted pharmaceutical manufacturing. It argues that QbD is still necessary but no longer sufficient because it was designed for processes whose boundaries, models, and control logic can be defined before routine operation. AI-assisted systems require an additional governance logic capable of supervising data, models, explanations, adaptation, and accountability throughout the product lifecycle. The review develops the concept of Quality-by-Intelligence as a forward-looking governance framework. QbI does not discard QbD; rather, it extends it by embedding data stewardship, model lifecycle management, explainable decision-making, adaptive risk control, and regulatory translation into the pharmaceutical quality system. Its purpose is to govern not only the manufacturing process, but also the intelligence layer that increasingly mediates quality decisions. Quality-by-Intelligence reframes pharmaceutical quality as an adaptive, evidence-generating, and accountable system rather than a one-time design achievement. The transition will require new regulatory expectations, new validation evidence, new operator competencies, and stronger collaboration between industry, regulators, and academic experts. Without this shift, AI may be deployed into manufacturing faster than the quality systems needed to govern it.

Digital twins promise to reshape pharmaceutical development and manufacturing by creating virtual replicas that mirror physical systems over time. Their value lies in linking process knowledge, real-time data, predictive modelling, and decision support into a single operational framework. In pharmaceutical contexts, this promise is especially relevant because product quality is tightly coupled to process history, material attributes, and patient-facing performance. The core problem is that digital twin development in pharma remains fragmented across manufacturing, drug delivery, regulatory modelling, and digital transformation literatures. Manufacturing studies often focus on process control, PAT, and continuous production, whereas drug delivery studies emphasise physiological prediction, formulation performance, and patient-specific behaviour. These areas share mechanistic foundations, but they are rarely treated as parts of a unified pharmaceutical digital twin ecosystem. This conceptual review analyses digital twin logic across pharmaceutical manufacturing and drug delivery systems. It focuses on how mechanistic models, hybrid modelling, and real-time data infrastructures can be combined to support quality, performance prediction, and regulatory decision-making. The central argument is that digital twins must become not only predictive but also explainable and governable. The synthesis defines the architecture of pharmaceutical digital twins, catalogues their manufacturing and drug delivery applications, and identifies unresolved challenges in model coupling, parameter identifiability, uncertainty handling, and regulatory credibility. It also maps technological, organisational, economic, and regulatory barriers that prevent promising models from becoming routine industrial tools. Five tables summarise the conceptual architecture, application domains, model architectures, integration problems, and implementation barriers. Realising the full potential of pharmaceutical digital twins will require mechanistic rigour, explainable analytics, high-quality data connectivity, and early alignment with regulatory expectations. The future of the field will depend less on isolated demonstrations and more on reusable validation strategies, transparent model governance, and cross-sector collaboration. Digital twins should therefore be understood as evolving regulatory-scientific infrastructures rather than as standalone computational artefacts.

Continuous manufacturing has been promoted as a transformative alternative to batch pharmaceutical production because it can reduce equipment footprint, shorten development-to-commercialisation timelines, and enable more responsive quality assurance. Its appeal rests on the idea that material flows through an integrated process rather than waiting in isolated unit operations. Yet the technical maturity required to make that flow reliable is often understated. The central problem is that continuous operation is sometimes treated as intrinsically superior to batch production, as though continuity alone guarantees better quality. In practice, quality depends on the ability of sensors, models, actuators, supervisory logic, and operators to detect and correct deviations fast enough to prevent poor material from propagating through the line. Without that control capability, continuous systems may amplify rather than resolve process vulnerability. This critical review examines continuous pharmaceutical manufacturing through three linked lenses: control architecture design, process robustness, and regulatory translation. It asks whether current systems are sufficiently controlled to justify claims of superior quality, whether robustness is assessed with appropriate metrics, and whether regulatory frameworks have matured enough to support dynamic manufacturing strategies. The review deliberately treats continuous manufacturing as a socio-technical system rather than as a purely technological upgrade. The evidence indicates that continuous manufacturing has advanced substantially, particularly in direct compression, wet granulation, process analytical technology, residence-time modelling, soft sensing, and model predictive control. However, many demonstrations remain limited by narrow disturbance scenarios, incomplete treatment of start-up and shutdown, uncertain model maintenance requirements, and uneven translation into routine good manufacturing practice. Regulatory acceptance is progressing, but unresolved questions remain around batch definition, traceability, adaptive control validation, and lifecycle change management. The review concludes that continuous manufacturing is not an automatic guarantor of pharmaceutical quality. Its value depends on control-centric process design, standardised robustness assessment, credible digital and analytical infrastructure, and regulatory alignment that recognises dynamic process operation. The future pathway requires stronger pre-competitive collaboration, more realistic stress testing, and regulatory science that keeps pace with advanced control strategies.

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.

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.

Excipients are conventionally described through intrinsic material properties, pharmacopeial specifications, and functional labels such as binder, disintegrant, solubiliser, stabiliser, or release modifier. This vocabulary has supported pharmaceutical development for decades because it simplifies excipient selection and links material identity to expected product performance. Yet the same vocabulary becomes unstable when dosage forms are compositionally dense, structurally heterogeneous, and highly dependent on manufacturing history. The central problem is that excipient performance in complex dosage forms often deviates from what would be predicted by isolated material tests. A polymer that stabilises supersaturation in one amorphous solid dispersion may fail in another, while a lipid excipient that improves solubilisation under one digestion condition may promote precipitation under another. Such behaviour suggests that excipient functionality is not merely carried by the excipient molecule, but is produced within the dosage form system. This article proposes a theoretical reframing of excipient functionality as a system property. In this view, functionality emerges from the combined effects of formulation composition, spatial architecture, and processing history. The purpose is not to replace molecular or compendial characterisation, but to relocate those measurements within a broader systems framework. The proposed theory defines excipient functionality as an emergent outcome of interactions among drugs, excipients, process energy, phase behaviour, and microstructural organisation. It explains why apparently similar formulations can display different dissolution, supersaturation, release, or stability behaviours when their processing route or internal architecture differs. Three tables are used to contrast the reductionist and system-property paradigms, map overlooked interactions, and identify design implications. Adopting a system-property view would shift pharmaceutical development from selecting excipients as isolated ingredients toward designing excipient functions as relational outcomes. It would encourage formulation scientists to evaluate not only what an excipient is, but what it becomes within a particular dosage form. This perspective offers a conceptual basis for more predictive, adaptive, and robust pharmaceutical product design.

Smart drug delivery systems promise to transform therapy by linking drug release to physiological need, local microenvironmental cues, or algorithmic feedback. Their ambition is not merely to administer medicines more conveniently, but to create therapeutic platforms that sense, decide, and act. Yet this promise remains vulnerable to sensor errors, biological noise, material instability, actuator failure, and unpredictable patient behaviour. The dominant design philosophy in smart delivery has been shaped by precision, specificity, and near-perfect triggering. Systems are often evaluated as though the correct signal will be detected, the intended release pathway will activate, and the therapeutic response will follow the modelled trajectory. This assumption makes many platforms appear elegant in controlled studies but fragile in messy clinical environments. This perspective argues that smart drug delivery needs a failure-tolerant design paradigm. Rather than treating malfunction as an exceptional event to be eliminated, failure-tolerant design treats drift, delay, degradation, and misclassification as expected operating conditions. The aim is not to abandon precision, but to make precision recoverable when the system deviates from its intended state. The framework proposed here integrates control logic, risk engineering, and pharmaceutical performance principles. Control logic supplies feedback, fault detection, and adaptive recovery; risk engineering supplies structured failure anticipation and mitigation; pharmaceutical performance anchors every decision in pharmacokinetics, pharmacodynamics, material stability, and patient use. Together, these domains can move smart drug delivery beyond the brittle ideal of error-free function. The central claim is that smart drug delivery systems should be designed to fail intelligently. A clinically useful system must detect its own unreliability, degrade toward a safer state, activate independent recovery pathways, and preserve therapeutic performance within acceptable bounds. Such a shift would require new engineering practice, new regulatory expectations, and a more honest understanding of biological variability.

Pharmaceutical platform technologies are reshaping drug delivery by enabling systems that can be adapted, configured, or personalised across multiple therapeutic contexts. Rather than treating each medicine as a discrete formulation problem, platform thinking emphasises reusable design principles, shared technological architectures, and translational scalability. This shift is visible across lipid nanoparticles, polymeric micelles, implantable devices, three-dimensional printed dosage forms, and digitally connected delivery systems. Despite this progress, the terminology surrounding platform technologies remains inconsistent. Terms such as smart, adaptive, programmable, modular, intelligent, responsive, and personalised are often used interchangeably, even when the systems being described operate through different mechanisms. This ambiguity limits meaningful comparison between technologies and can obscure the design assumptions that determine manufacturability, clinical suitability, and regulatory evaluation. This classification review and perspective develops a critical taxonomy of pharmaceutical platform technologies. It classifies platforms into four categories: modular, adaptive, programmable, and patient-responsive systems. The taxonomy is based on operational logic and design intent rather than material class, route of administration, or therapeutic area. The proposed taxonomy defines explicit criteria for distinguishing the four platform types. Modular systems are organised around interchangeable components, adaptive systems around dynamic response to environmental or physiological cues, programmable systems around rule-based therapeutic logic, and patient-responsive systems around real-time patient-specific data and feedback control. Five tables support the manuscript by defining classification criteria, summarising representative platform types, and comparing the translational implications of each category. This taxonomy provides a shared conceptual language for platform-based pharmaceutical development. It clarifies design philosophies, translational risks, manufacturing implications, and regulatory questions that differ across platform types. Its purpose is to support clearer communication among pharmaceutical scientists, engineers, clinicians, regulators, and translational teams as drug delivery platforms become increasingly configurable, biologically interactive, and data-linked.

Pharmaceutical technology evaluation has traditionally been organized around pharmacokinetic performance, with bioavailability occupying a privileged position as a marker of formulation success. This emphasis has been scientifically productive because it links dosage form design to systemic exposure and supports comparability across products. Yet bioavailability captures only one part of the pathway between a pharmaceutical technology and sustained therapeutic benefit. A product may deliver favorable exposure under controlled conditions while still failing when introduced into everyday patient use. The central problem is that bioavailability-centered evaluation often assumes idealized conditions of administration, storage, handling, and persistence. In practice, patients must swallow, inject, inhale, store, prepare, remember, tolerate, and continue medicines within complex personal and healthcare environments. Technologies that improve exposure may therefore generate limited value if they are difficult to use, fragile under real-world variability, or unable to support continuity of treatment over time. This creates a gap between technical success and therapeutic success. The objective of this article is to propose a systems-based evaluation model for pharmaceutical technologies. The model treats usability, robustness, and therapeutic continuity as co-equal dimensions that complement traditional pharmacokinetic endpoints. Usability captures the human–technology interface, robustness captures performance consistency under realistic variability, and therapeutic continuity captures sustained benefit across time and care settings. Together, these dimensions broaden the meaning of pharmaceutical performance. The proposed model defines each dimension, explains their interactions, and translates them into a practical evaluation framework. It argues that usability, robustness, and therapeutic continuity should not be treated as late-stage refinements after bioavailability has been optimized. Instead, they should be incorporated early in product design and carried through development, assessment, and post-translation evaluation. Two tables are used to contrast the dominant bioavailability-centered paradigm with a systems-based view and to present the operational structure of the proposed model. Adopting a systems-based evaluation paradigm can help pharmaceutical technologies become not only pharmacokinetically effective but also usable, resilient, and capable of sustaining therapeutic benefit in practice. Such a shift does not diminish the importance of bioavailability. It places bioavailability within a broader causal architecture of real-world performance. The result is a more complete foundation for pharmaceutical technology assessment and patient-centered product development.

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.