International Journal of Pharmaceutical Creativity and Innovation (IJPCI)
Title: International Journal of Pharmaceutical Creativity and Innovation (IJPCI) Publisher: Wissira Research Lab Publisher Address: 1743 Kenmare Drive, Indiana, Carmel, United States- 46074 E-mail to submit manuscripts: editor@ijpci.org Publisher Email: editor@wissira.us ISSN (online): Request Pending Starting Year: 2024 The International Journal of Pharmaceutical Creativity and Innovation (IJPCI) is a peer-reviewed, open-access international journal dedicated to advancing knowledge, innovation, and creative research in pharmaceutical sciences and related disciplines. The journal provides a global platform for researchers, academicians, healthcare professionals, industry experts, and scholars to disseminate high-quality scientific findings that contribute to the development of pharmaceutical education, research, and practice. IJPCI aims to promote interdisciplinary research and foster innovation in drug discovery, formulation development, pharmaceutical technology, clinical pharmacy, pharmacology, pharmaceutical analysis, biotechnology, regulatory affairs, and healthcare advancements. The journal encourages the publication of original research articles, review papers, case studies, short communications, technical notes, and innovative perspectives that address current challenges and emerging trends in pharmaceutical sciences. The journal is committed to maintaining high standards of academic integrity, transparency, and ethical publishing practices through a rigorous double-blind peer-review process. By facilitating the exchange of scientific knowledge and innovative ideas, IJPCI seeks to bridge the gap between research and practical applications, ultimately contributing to improved healthcare outcomes and pharmaceutical advancements worldwide. Aim To promote excellence in pharmaceutical research and innovation by providing an accessible and credible platform for the dissemination of high-quality scientific knowledge and creative solutions addressing contemporary healthcare challenges. Objectives To publish original and impactful research in pharmaceutical sciences. To encourage innovation in drug discovery, development, and delivery systems. To promote interdisciplinary collaboration among researchers and healthcare professionals. To support evidence-based pharmaceutical practices and healthcare improvements. To disseminate emerging technologies and novel methodologies in pharmaceutical research. To provide a global forum for academic and industrial researchers. To uphold ethical standards and quality in scientific publishing. Scope of the Journal The journal welcomes submissions in, but not limited to, the following areas: Pharmaceutics and Novel Drug Delivery Systems Pharmaceutical Chemistry Medicinal Chemistry Pharmacology and Toxicology Clinical Pharmacy and Pharmacy Practice Pharmacognosy and Phytochemistry Pharmaceutical Biotechnology Pharmaceutical Analysis and Quality Assurance Pharmaceutical Microbiology Drug Discovery and Development Regulatory Affairs Pharmacovigilance Nanotechnology in Drug Delivery Biopharmaceutics and Pharmacokinetics Industrial Pharmacy Hospital and Community Pharmacy Herbal and Traditional Medicines Healthcare Management Artificial Intelligence and Digital Technologies in Pharmacy Pharmaceutical Education and Innovation Publication Frequency IJPCI publishes high-quality scholarly articles on a Quarterly basis and welcomes submissions from researchers worldwide. Article Types Original Research Articles Review Articles Short Communications Case Reports Technical Notes Mini Reviews Perspectives Editorials Letters to the Editor Book Reviews Open Access Policy IJPCI follows an open-access publishing model, ensuring that all published articles are freely accessible to readers worldwide without subscription barriers, thereby promoting the wider dissemination and impact of scientific knowledge.
Mucoadhesive Nanoparticles for Localized Therapy
Localized drug delivery using mucoadhesive nanoparticles has emerged as a promising strategy for improving therapeutic efficacy while minimizing systemic toxicity. Despite rapid progress in nanotechnology, selecting optimal nanoparticle formulations for different mucosal tissues remains largely dependent on trial-anderror experimentation. This study identifies the lack of intelligent prediction of nanoparticle–mucus interactions as a major research gap. We propose an Artificial Intelligence-assisted framework that integrates physicochemical descriptors with machine learning to estimate mucoadhesion strength and localized drug retention before laboratory validation. The approach combines material characteristics, formulation variables, and biological parameters to support rational formulation design. Recent advances in explainable AI are incorporated to improve transparency of prediction models. The proposed framework aims to reduce formulation development time while improving reproducibility across oral, nasal, ocular, pulmonary, and vaginal drug delivery systems. This research establishes a foundation for AI-guided personalized localized therapy using next-generation mucoadhesive nanomedicine
Copyright (c) 2025 International Journal of Pharmaceutical Creativity and Innovation (IJPCI)
Hydrogel-Based Controlled Release Drug Platforms
Hydrogel-based drug delivery systems have transformed controlled drug release by providing biocompatible matrices capable of sustained therapeutic delivery. Despite substantial advances in hydrogel engineering, current platforms rarely incorporate adaptive artificial intelligence (AI) models capable of predicting release behavior under dynamic physiological conditions before experimental validation. This study addresses this gap by proposing an AI-assisted predictive framework that integrates physicochemical descriptors of hydrogel formulations with machine learning to estimate controlled drug release performance. The research introduces a data-driven strategy for optimizing hydrogel composition while minimizing laboratory iterations. Recent developments in polymer science, explainable AI, and digital pharmaceutical manufacturing provide the foundation for the proposed framework. The study emphasizes prediction accuracy, formulation optimization, and computational efficiency for nextgeneration hydrogel design. The proposed approach aims to support precision drug delivery while reducing experimental costs and accelerating formulation development. The findings contribute to the growing intersection of biomaterials, artificial intelligence, and digital health
3D Printed Personalized Pharmaceutical Dosage Forms
Three-dimensional (3D) printing has transformed pharmaceutical manufacturing by enabling the production of individualized dosage forms tailored to patient-specific therapeutic needs. Despite remarkable progress, current formulation workflows still depend heavily on empirical optimization, making personalized drug production time-consuming and resource-intensive. This study identifies a critical research gap: the absence of an integrated artificial intelligence (AI) framework capable of simultaneously predicting formulation composition, printability, and patient-specific therapeutic performance before manufacturing. To address this limitation, the paper proposes an explainable machine learning approach that combines patient characteristics, physicochemical drug properties, and printable material attributes for intelligent formulation recommendation. Recent developments in AI, additive manufacturing, and digital health are synthesized to establish the conceptual foundation of the framework. The manuscript also reviews contemporary advances in pharmaceutical 3D printing between 2021 and 2026 while highlighting unresolved technical and regulatory challenges. The proposed research aims to improve formulation efficiency, reduce experimental iterations, and support precision medicine through intelligent pharmaceutical manufacturing
pH-Responsive Smart Oral Drug Delivery Systems
Oral drug delivery remains the most preferred route of administration because of its convenience and high patient acceptance. However, conventional oral formulations often exhibit poor site specificity, premature drug degradation, and inconsistent bioavailability due to variations in gastrointestinal pH. Although pH-responsive nanocarriers have demonstrated promising controlled-release behavior, limited research has explored the integration of artificial intelligence (AI) for predicting polymer behavior and optimizing formulation performance before laboratory development. This study addresses this gap by proposing an AI-assisted framework for designing pH-responsive smart oral drug delivery systems capable of adapting drug release according to gastrointestinal pH conditions. The proposed framework combines machine learning-based polymer selection with predictive release modeling to minimize experimental iterations while improving formulation efficiency. Recent advances in intelligent pharmaceutical formulation design are incorporated to develop a datadriven optimization strategy. The proposed concept has the potential to enhance therapeutic precision, reduce formulation costs, and accelerate pharmaceutical development. The findings provide a foundation for future AI-enabled intelligent oral drug delivery platforms
Bioinspired Nanocarriers for Targeted Drug Delivery
Targeted drug delivery remains a major challenge because conventional nanocarriers often exhibit limited biological compatibility, rapid immune clearance, and inconsistent accumulation at diseased tissues. Bioinspired nanocarriers, designed by mimicking natural cellular membranes and biological transport mechanisms, offer a promising alternative for improving therapeutic precision. Despite rapid progress, limited research has integrated artificial intelligence (AI) with bioinspired nanocarrier engineering to optimize carrier selection, targeting efficiency, and therapeutic performance simultaneously. This study addresses that gap by proposing an AI-assisted framework for predicting the performance of bioinspired nanocarriers using multimodal physicochemical and biological characteristics. The framework combines deep feature learning with explainable machine learning to identify critical design parameters influencing delivery efficiency. A digital simulation environment is developed to evaluate targeting accuracy, cellular uptake, and controlled drug release. The proposed approach demonstrates how computational intelligence can accelerate the rational design of bioinspired drug delivery systems while reducing experimental complexity. The findings contribute to the growing intersection of nanomedicine, artificial intelligence, and precision therapeutics
Ethical Challenges of AI Adoption in Pharmacy Practice
Artificial intelligence is increasingly being incorporated into medication management, clinical decision support, dispensing operations, pharmacovigilance, patient counselling, and pharmaceutical supply systems. Despite these developments, existing ethical guidance largely addresses healthcare AI broadly and does not adequately reflect the interconnected responsibilities of pharmacists across the medication-use pathway. This study identifies a research gap in the absence of pharmacy-specific methods for assessing how ethical risks accumulate when AI recommendations move between prescribing, verification, dispensing, counselling, monitoring, and follow-up stages. It proposes a pathway-oriented ethical assessment framework centred on algorithmic fairness, patient autonomy, data governance, explainability, professional accountability, and continuous human oversight. The proposed investigation will evaluate AIsupported pharmacy decisions through simulated medication-use cases representing diverse demographic, clinical, linguistic, and socioeconomic conditions. Unlike studies that assess predictive accuracy alone, the framework treats ethical reliability as a multidimensional and context-dependent outcome. Particular attention is given to automation bias, responsibility diffusion, unequal system performance, confidentiality risks, and the erosion of pharmacistpatient trust. The study establishes a foundation for developing measurable governance standards that support responsible, equitable, and professionally accountable AI adoption in pharmacy practice
Generative AI for Pharmaceutical Literature Mining
The rapid expansion of pharmaceutical publications has made conventional keyword-based literature review increasingly difficult to scale, reproduce, and update. Generative artificial intelligence offers new capabilities for interpreting scientific language, connecting dispersed evidence, extracting drug-related relationships, and producing structured knowledge summaries. However, existing generative systems often optimize linguistic fluency without adequately verifying whether generated claims are supported by authentic, temporally relevant, and contextually consistent sources. This study identifies a research gap in the absence of an integrated literaturemining framework that jointly evaluates evidence grounding, citation fidelity, contradiction risk, and knowledge freshness. To address this gap, the study proposes a temporally aware, evidence-traceable generative literature-mining architecture for pharmaceutical research. The proposed approach combines hybrid document retrieval, biomedical entity normalization, claim-level evidence alignment, contradiction detection, and uncertainty-sensitive text generation. The framework is designed to support drug discovery, safety surveillance, therapeutic comparison, formulation research, and regulatory intelligence without treating generated narratives as substitutes for expert judgement. The anticipated contribution is a reproducible and auditable method for transforming fragmented pharmaceutical literature into structured evidence while reducing unsupported synthesis and citation-related
Data-Driven Drug Utilization Review Systems
Drug utilization review systems are essential for identifying inappropriate prescribing, preventable medication-related harm, and inefficient pharmaceutical use across healthcare settings. However, most existing systems rely on static rules that assess prescriptions individually and often overlook longitudinal treatment patterns, evolving patient conditions, and uncertainty in clinical data. This study addresses that gap by proposing a data-driven drug utilization review framework that integrates temporal prescription modelling, patientcontext representation, population-level utilization signals, and uncertainty-aware risk stratification. The framework is designed to evaluate drug–drug interactions, therapeutic duplication, dosage suitability, treatment duration, monitoring requirements, and deviations from expected prescribing trajectories. Unlike conventional alert engines, the proposed approach distinguishes clinically meaningful medication risks from low-priority rule violations by learning from electronic health records, pharmacy claims, laboratory observations, and documented pharmacist interventions. A hybrid architecture combining temporal graph learning, gradient-boosted risk estimation, and calibrated explanation generation is conceptualized to support pharmacist-led review rather than autonomous prescribing decisions. The principal research objective is to improve the clinical relevance, prioritization, interpretability, and operational efficiency of drug utilization review while reducing unnecessary alert burden. The proposed investigation provides a foundation for adaptive medication-surveillance systems capable of supporting safer, more equitable, and evidence-informed pharmaceutical care
Vaccine distribution systems frequently rely on periodic demand estimates and fixed replenishment rules that respond poorly to local epidemiological changes, population mobility, cold-chain disruptions, and unequal healthcare access. This study identifies a research gap in the absence of an integrated artificial intelligence framework that simultaneously predicts demand, estimates cold-chain failure risk, and protects geographic and socioeconomic equity during vaccine allocation. A precision distribution framework is proposed in which spatiotemporal forecasting, graphbased logistics modelling, and equity-constrained optimization are combined within a continuously updated decision architecture. The system is designed to generate facility-level vaccine requirements while accounting for disease incidence, historical uptake, demographic vulnerability, travel accessibility, stock availability, storage capacity, and temperaturemonitoring signals. Unlike conventional cost-centred distribution models, the proposed approach treats preventable stock-outs, vaccine wastage, excessive travel burden, and inequitable coverage as interconnected operational outcomes. Uncertainty-aware predictions are incorporated so that allocation decisions remain cautious in districts with incomplete, delayed, or unreliable health data. The framework also supports interpretable recommendations by reporting the demand, risk, accessibility, and fairness factors influencing each shipment decision. The proposed research establishes a foundation for adaptive, transparent, and equitable vaccine logistics in resource-constrained and geographically diverse public-health systems
Intelligent Pharmaceutical Manufacturing Quality Prediction
Pharmaceutical manufacturing generates extensive process data, yet final product quality is still frequently confirmed through delayed laboratory testing and batch-end inspection. Existing machine-learning applications commonly predict isolated quality attributes from single-unit operations, limiting their ability to represent interactions across an integrated manufacturing line. This manuscript addresses the research gap concerning uncertainty-aware, cross-stage quality prediction under batch variability, sensor drift, missing measurements, and changing operating conditions. A novel intelligent framework is proposed that combines multistage process representation, temporal feature learning, domain-informed constraints, uncertainty estimation, and concept-drift monitoring. The intended system predicts critical quality attributes before batch completion while identifying the process variables and manufacturing stages responsible for elevated quality risk. Unlike conventional black-box models, the framework is designed to distinguish reliable predictions from uncertain cases that require laboratory confirmation or human review. The research further positions artificial intelligence as a decision-support component within Process Analytical Technology, Quality by Design, and real-time release testing rather than as an autonomous replacement for validated quality systems. The proposed direction may support earlier deviation detection, reduced material waste, improved batch consistency, and more scientifically defensible deployment of predictive models in regulated pharmaceutical manufacturing
AI-Assisted Clinical Trial Patient Recruitment
Clinical trial recruitment remains slow because eligibility criteria are complex, patient information is fragmented, and screening relies heavily on manual review. Recent artificial intelligence systems can retrieve trials and classify eligibility, but many treat patient suitability as a fixed binary decision. This study addresses the lack of recruitment models that account for temporal clinical changes, missing evidence, fairness, and uncertainty simultaneously. It proposes a human-supervised, uncertainty-aware framework that continuously evaluates structured records, clinical narratives, laboratory trends, and trial criteria. The framework distinguishes confirmed eligibility, confirmed ineligibility, possible eligibility, insufficient evidence, and time-dependent eligibility. A fairness-sensitive ranking mechanism is introduced to prevent high-volume demographic groups from dominating candidate recommendations. The proposed approach aims to reduce unnecessary chart review while preserving clinician control, evidential traceability, and patient privacy. The study establishes a foundation for safer and more inclusive AI-assisted recruitment across multiple clinical specialties
Copyright (c) 2024 International Journal of Pharmaceutical Creativity and Innovation (IJPCI)
AI-Enabled Medication Error Prevention Systems
Medication errors remain a persistent patient-safety concern because prescribing, dispensing, administration, and monitoring decisions are distributed across complex clinical workflows. Conventional clinical decision-support systems primarily use fixed rules and frequently generate non-specific warnings, contributing to excessive alert burden and clinician override behaviour. This study identifies a research gap in the absence of context-aware systems capable of tracing medication risk across multiple stages of the medication-use pathway. A multimodal artificial intelligence framework is proposed to combine structured electronic health records, medication orders, laboratory trends, clinical notes, barcode events, and administration histories. The proposed system employs temporal representation learning, clinical language processing, medication knowledge graphs, and uncertainty-aware risk estimation to identify potentially harmful discrepancies before they reach the patient. Unlike single-stage prediction tools, the framework models the evolving relationship between patient condition, medication intent, operational workflow, and subsequent clinical observations. It also introduces severity-sensitive alert prioritisation and evidence-based explanations to reduce low-value notifications while retaining clinically important warnings. The study establishes a foundation for evaluating AIenabled medication-error prevention through predictive performance, alert usefulness, workflow compatibility, fairness, and prospective patient-safety outcomes
Secure AI-Enhanced Gateway Architecture for Large-Scale RESTful Applications
Drug dosage adjustment is a sequential clinical decision problem requiring repeated evaluation of therapeutic response, toxicity, and changing patient physiology. Conventional dosing protocols and supervised prediction models often recommend isolated doses without adequately representing the delayed and cumulative effects of previous administrations. This study proposes a pharmacokinetically informed, uncertainty-aware offline reinforcement-learning framework for individualized dosage adjustment using longitudinal electronic health records. The identified research gap concerns the limited integration of prolonged drug-action memory, conservative policy learning, uncertainty estimation, and explicit safety constraints within a unified dosing architecture. The proposed framework represents residual drug exposure through an action-history state encoder and restricts recommendations to clinically plausible dose ranges supported by observational data. A distributional critic estimates the range of possible treatment outcomes, while a safety layer penalizes toxicity, abrupt dose changes, and departures from established therapeutic boundaries. The intended experimental design evaluates therapeutic target attainment, toxicity exposure, policy reliability, dose stability, and performance across clinically relevant patient subgroups. The framework is designed as an interpretable decision support system rather than an autonomous prescribing mechanism, enabling safer translation of reinforcement learning into precision pharmacotherapy
Predictive Analytics for Antibiotic Stewardship Programs
Antibiotic stewardship programs frequently rely on retrospective audits, fixed clinical rules, and manual review of large numbers of prescriptions. Although machine-learning models have been developed for resistance prediction and antibiotic selection, comparatively little attention has been given to forecasting which active prescriptions require immediate stewardship intervention. This study addresses that gap by proposing an uncertainty-aware predictive framework for prioritizing hospitalized patients according to the expected clinical and ecological value of antibiotic review. The framework is designed to combine longitudinal electronic health records, microbiological findings, local resistance patterns, treatment duration, organ-function indicators, and World Health Organization AccessWatch–Reserve categories. Rather than producing a simple appropriate-orinappropriate classification, the proposed system estimates distinct risks relating to ineffective therapy, unnecessary broad-spectrum exposure, delayed deescalation, excessive treatment duration, and avoidable intravenous administration. Temporal modelling is combined with calibrated risk estimation and interpretable patient-level explanations to support, rather than replace, infectious-disease specialists and clinical pharmacists. The anticipated contribution is a clinically actionable prioritization mechanism that reduces low-value alert generation while directing limited stewardship resources toward prescriptions with the greatest potential benefit. The research establishes a foundation for evaluating predictive stewardship systems through discrimination, calibration, workload reduction, intervention yield, subgroup fairness, and patient-safety outcomes
Natural Language Processing for Pharmacovigilance Signal Detection
Pharmacovigilance increasingly relies on large volumes of unstructured clinical narratives, spontaneous safety reports, and digital health records, making conventional adverse drug event surveillance inefficient. This study proposes a novel Context-Aware Hierarchical Transformer with Signal Prioritization (CHT-SP) framework for early pharmacovigilance signal detection using natural language processing. Unlike existing approaches that mainly focus on adverse event extraction, the proposed framework jointly models contextual relationships among drugs, symptoms, temporal expressions, and clinical uncertainty to improve signal prioritization. A hybrid semantic attention mechanism is introduced to distinguish clinically meaningful safety signals from irrelevant textual noise. The proposed architecture is designed to support scalable surveillance across heterogeneous healthcare documents while maintaining interpretability. Recent advances in transformer-based language models, biomedical embeddings, and contextual learning motivate the proposed methodology. The study addresses an important research gap in integrating contextual semantic reasoning with pharmacovigilance signal ranking. The proposed framework aims to improve early detection accuracy, reduce false alerts, and strengthen post-marketing drug safety surveillance
AI-Driven Counterfeit Drug Identification Systems
Counterfeit medicines remain a critical public health challenge, particularly in regions with fragmented pharmaceutical supply chains and limited regulatory oversight. Recent advances in artificial intelligence have enabled automated visual inspection of medicines; however, most existing systems rely on isolated image analysis and fail to integrate packaging authentication with tablet-level verification. This study addresses this limitation by proposing a multi-stage AI framework that combines package feature extraction, pill surface characterization, and anomaly-aware decision fusion for counterfeit drug identification. The proposed research gap focuses on improving detection reliability when counterfeit products imitate authentic packaging but differ in microscopic physical characteristics. A hybrid deep learning architecture is conceptualized to jointly analyze multiple visual representations while incorporating uncertainty estimation during prediction. The framework is designed to reduce false authentication of sophisticated counterfeit medicines and improve robustness under varying imaging conditions. The proposed methodology provides a scalable solution for pharmacies, manufacturers, and regulatory agencies. The study contributes toward secure pharmaceutical distribution through intelligent, explainable, and automated counterfeit detection
Cloud-Based Pharmacovigilance Analytics Platforms
Pharmacovigilance has entered a new era as healthcare systems increasingly generate large-scale digital data through electronic health records, spontaneous reporting systems, wearable devices, and telemedicine platforms. Although cloud computing offers scalable infrastructure for processing these heterogeneous datasets, concerns interoperability, and regarding secure patient privacy, multi-institutional collaboration continue to limit its adoption. This study identifies a research gap in the absence of privacypreserving cloud-native pharmacovigilance architectures capable of collaborative machine learning without centralized patient-level data sharing. To address this limitation, a federated cloud-based pharmacovigilance analytics framework is proposed for distributed adverse drug event (ADE) signal detection. The framework integrates cloud orchestration, federated learning, explainable artificial intelligence, and automated data harmonization to improve scalability while maintaining regulatory compliance. The proposed approach is designed to support real-time safety surveillance across geographically distributed healthcare institutions
AI-Guided Selection of Pharmaceutical Excipients
The selection of pharmaceutical excipients remains a knowledge-intensive process that relies heavily on empirical expertise and iterative laboratory experimentation. Although artificial intelligence (AI) has demonstrated significant success in drug discovery and formulation optimization, its application to excipient recommendation remains fragmented and largely focused on isolated formulation variables. This study addresses the research gap by proposing an AI-guided framework that predicts excipient compatibility through multiattribute formulation learning rather than simple ingredient matching. The framework integrates physicochemical properties of active pharmaceutical ingredients (APIs), formulation objectives, and excipient functional characteristics into a unified machine learning pipeline. Unlike traditional decision-support systems, the proposed approach emphasizes explainable predictions to improve formulation transparency. The manuscript discusses recent advances in AI-enabled pharmaceutical development, identifies limitations of existing excipient selection strategies, and establishes a foundation for intelligent formulation design. The proposed framework aims to reduce experimental workload while supporting faster and more reproducible pharmaceutical product development.
Internet of Medical Things for Smart Medication Monitoring
Medication non-adherence remains one of the leading causes of preventable hospitalizations, treatment failures, and rising healthcare expenditures despite continuous advances in digital health technologies. The Internet of Medical Things (IoMT) has created opportunities to monitor medication intake through interconnected wearable devices, smart dispensers, physiological sensors, and cloud-based healthcare platforms. However, most existing IoMT solutions focus on event logging rather than continuously interpreting patient behavior and physiological responses in real time. This study identifies this limitation and proposes a behavior-aware smart medication monitoring framework integrating multimodal IoMT sensing with adaptive machine learning for personalized adherence assessment. The framework aims to distinguish intentional, accidental, and clinically significant deviations in medication routines while minimizing false alerts. A hybrid edge-cloud intelligence mechanism is designed to support timely clinical intervention with reduced latency. The proposed architecture is intended to improve monitoring reliability, patient engagement, and decision support in digital healthcare environments.
Predictive Modeling of Drug Stability Using Artificial Intelligence
Drug stability is a fundamental determinant of pharmaceutical quality, efficacy, and patient safety throughout a product's shelf life. Conventional stability assessment relies on long-term experimental studies that require substantial time, laboratory resources, and financial investment. Recent advances in artificial intelligence present opportunities to accelerate stability prediction while reducing dependence on exhaustive experimental testing. This study proposes an explainable multimodal machine learning framework that integrates physicochemical drug properties, storage conditions, and packaging characteristics for predictive stability modeling. The proposed approach employs an ensemble learning strategy combined with feature attribution techniques to improve prediction accuracy and interpretability. A comprehensive experimental protocol is designed using publicly available pharmaceutical descriptors and simulated environmental stability profiles following ICH stability principles. The framework is evaluated using multiple regression performance metrics and compared with conventional machine learning algorithms. The findings demonstrate the potential of explainable AI to support early formulation development, shelf-life estimation, and regulatory decision-making in modern pharmaceutical manufacturing.
Machine Learning-Based Prediction of Tablet Dissolution Profiles
Tablet dissolution testing is a critical quality attribute in pharmaceutical manufacturing because it directly influences drug bioavailability and therapeutic performance. Conventional dissolution studies require repeated laboratory experiments, making formulation optimization time-consuming and resource-intensive. This study identifies a research gap in the limited use of hybrid machine learning frameworks that simultaneously integrate formulation composition and manufacturing process variables for dissolution profile prediction. To address this challenge, a multi-stage ensemble learning framework is proposed to estimate dissolution behavior at multiple time intervals without replacing regulatory dissolution testing. The proposed framework combines physicochemical descriptors, manufacturing parameters, and dissolution conditions into a unified predictive model. The study emphasizes explainable machine learning to identify formulation variables that most strongly influence dissolution kinetics. Such an approach can support formulation scientists during early-stage product development by reducing experimental iterations. The proposed framework demonstrates the potential of artificial intelligence as a decision-support tool for pharmaceutical quality-by-design (QbD) applications.
Digital Twins for Personalized Drug Response Prediction
Personalized medicine requires predictive models capable of estimating how individual patients respond to specific medications before treatment begins. Digital twin technology offers a promising solution by constructing virtual patient representations that evolve alongside real physiological changes. However, current digital twin systems often rely on static datasets and limited biological modalities, reducing their clinical applicability. This study proposes a Dynamic MultiModal Digital Twin (DMDT) framework that integrates longitudinal electronic health records, genomic biomarkers, wearable sensor measurements, and laboratory investigations into a continuously updated virtual patient model. The framework combines temporal transformers with graph neural networks to capture both time-dependent physiological variations and complex biological relationships. The proposed methodology aims to improve individualized drug response prediction while supporting adaptive treatment planning. The study addresses existing limitations in multimodal integration and uncertainty-aware prediction, providing a scalable foundation for next-generation AI-assisted precision therapeutics.
AI-Assisted Pharmaceutical Inventory Optimization
Efficient pharmaceutical inventory management is fundamental to ensuring uninterrupted healthcare services while minimizing medicine wastage and operational costs. Traditional inventory control systems often rely on static reorder policies that cannot adequately respond to dynamic demand patterns, fluctuating supplier lead times, and medicine expiration. This research proposes an AI-assisted pharmaceutical inventory optimization framework integrating deep learning-based demand forecasting, expiry-risk prediction, and reinforcement learning-driven inventory decision support. The proposed framework combines Temporal Fusion Transformer models for forecasting medicine demand with Gradient Boosting algorithms for estimating expiry probability and a Deep Q-Network for adaptive inventory optimization. Unlike conventional forecasting-only approaches, the proposed framework simultaneously considers inventory availability, storage cost, expiration losses, and service-level objectives. The study establishes a scalable architecture suitable for hospital pharmacies and regional healthcare distribution centers. The proposed methodology aims to reduce inventory inefficiencies while improving medicine accessibility and operational sustainability in modern pharmaceutical supply chains
Smart Prescription Validation Using Artificial Intelligence
Prescription errors remain one of the leading causes of preventable adverse drug events despite the widespread adoption of electronic health records and digital prescribing systems. Conventional prescription verification techniques primarily rely on predefined rules and are unable to interpret complex contextual relationships among medications, diagnoses, and patient characteristics. This study proposes a Hybrid Semantic Prescription Validation Framework (HSPVF) that integrates transformer-based clinical language understanding with graph-based pharmaceutical knowledge modeling for intelligent prescription assessment. The proposed framework performs semantic validation by jointly evaluating dosage appropriateness, therapeutic duplication, contraindications, drug interactions, and prescription completeness. Unlike conventional rule-based approaches, the model adapts to contextual clinical information while maintaining explainable validation decisions. Recent advances in clinical natural language processing and knowledge graph learning are incorporated to improve decision reliability. The proposed framework aims to enhance medication safety, reduce pharmacist workload, and strengthen clinical decision support in digital healthcare environments.
Intelligent Chatbots for Medication Counseling
Medication counseling remains an essential component of pharmaceutical care, yet healthcare systems frequently struggle to provide timely, personalized, and continuous patient guidance. Recent advances in conversational artificial intelligence have enabled intelligent chatbots to support medication-related inquiries, but existing systems primarily emphasize factual accuracy while overlooking individualized patient context and conversational continuity. This study identifies a research gap in adaptive context-aware medication counseling capable of integrating patient characteristics, medication history, and dialogue memory without compromising response reliability. A hybrid framework combining transformer-based language models, retrieval-augmented generation, and medical knowledge validation is proposed to improve counseling quality. The framework is designed to enhance patient comprehension, reduce inconsistent recommendations, and promote safe medication usage. Experimental evaluation compares the proposed approach with conventional chatbot architectures using multiple natural language understanding and healthcare-specific evaluation metrics. Results indicate that contextual reasoning significantly improves counseling effectiveness, response consistency, and user satisfaction. The proposed architecture demonstrates the potential of trustworthy conversational AI in future digital pharmacy services.
Blockchain-Based Pharmaceutical Supply Chain Authentication
The increasing prevalence of counterfeit medicines has become a critical global healthcare challenge, threatening patient safety, reducing public trust, and causing significant economic losses. Although blockchain technology has emerged as a promising solution for pharmaceutical supply chain traceability, existing implementations primarily emphasize immutable record keeping while providing limited support for intelligent authenticity verification across heterogeneous stakeholders. This study identifies the research gap of adaptive trust-aware pharmaceutical authentication integrating blockchain with artificial intelligence for anomaly detection. A decentralized framework is proposed in which blockchain ensures tamper-resistant transaction recording while machine learning identifies suspicious supply chain activities in real time. Smart contracts automate product verification, ownership transfer, and compliance validation throughout manufacturing, distribution, and retail processes. The proposed framework is designed to improve transparency, reduce counterfeit circulation, and enhance regulatory oversight without compromising data integrity. Recent developments in blockchain interoperability, AI-assisted fraud detection, and pharmaceutical digitization motivate this research. The study contributes a scalable architecture that strengthens medicine authentication and supports secure digital healthcare ecosystems
AI-Driven Drug Repurposing for Rare Diseases
Rare diseases collectively affect millions of individuals worldwide, yet therapeutic development remains limited because conventional drug discovery is expensive and time-intensive. Artificial intelligence has emerged as an effective approach for accelerating drug repurposing by identifying new therapeutic applications for approved compounds. Nevertheless, existing AI models frequently rely on homogeneous biomedical data and rarely quantify prediction uncertainty, reducing confidence in computational recommendations for clinically underrepresented diseases. This study introduces an Uncertainty-Aware Multi-Modal Graph Learning Framework that combines biomedical knowledge graphs, transcriptomic profiles, pathway representations, and protein interaction networks to predict novel drug–disease associations. Bayesian uncertainty estimation is incorporated to distinguish high-confidence predictions from uncertain recommendations, improving decision support for downstream biological validation. The proposed framework emphasizes interpretability through attention-based graph aggregation and biological pathway contribution analysis. The research demonstrates how integrating heterogeneous biomedical evidence with uncertainty-aware learning can improve reliable candidate prioritization for rare diseases. The findings contribute toward trustworthy AI-assisted drug repurposing systems capable of supporting precision medicine while reducing computational bias associated with limited rare disease data
Deep Learning Models for Early Detection of Adverse Drug Reactions
Adverse Drug Reactions (ADRs) remain one of the leading causes of preventable morbidity and hospitalization, highlighting the need for intelligent systems capable of identifying high-risk cases before clinical deterioration occurs. Existing pharmacovigilance approaches primarily depend on spontaneous reporting systems, which often detect ADRs only after significant patient harm has occurred. This study addresses the research gap by proposing a temporal deep learning framework that combines sequential medication histories, laboratory trends, and patient-specific clinical characteristics for proactive ADR prediction. Unlike conventional classification models that analyze isolated patient records, the proposed approach learns longitudinal clinical dependencies using a hybrid Bidirectional Long Short-Term Memory (BiLSTM) and attention mechanism. The framework is designed to improve prediction interpretability accuracy through while attention-based preserving feature importance. Experimental evaluation demonstrates consistent improvements over conventional machine learning baselines across multiple evaluation metrics. The findings indicate that integrating temporal clinical information substantially enhances early ADR detection and may support safer medication management in digital healthcare environments
Copyright (c) 2023 International Journal of Pharmaceutical Creativity and Innovation (IJPCI)
Predictive Analytics for Personalized Medication Adherence
Medication non-adherence remains one of the most persistent barriers to effective healthcare, contributing to disease progression, avoidable hospitalizations, and increased healthcare expenditure. Existing predictive models primarily estimate whether a patient will adhere to treatment but rarely account for the dynamic behavioral transitions that occur throughout long-term therapy. This study addresses this limitation by proposing a predictive analytics framework centered on temporal adherence trajectories rather than static adherence classification. The proposed research integrates longitudinal electronic health records, pharmacy refill behavior, wearable-derived activity indicators, and patient-reported outcomes to forecast adherence deterioration before missed medication events occur. Recent advances in explainable artificial intelligence and temporal deep learning are incorporated to improve both predictive performance and clinical interpretability. The study aims to support proactive clinical interventions by identifying individualized adherence risk patterns across different stages of treatment. The framework emphasizes transparent decision support suitable for integration into digital health ecosystems. This manuscript establishes the conceptual foundation and literature context for developing next-generation personalized medication adherence prediction systems
Explainable AI in Clinical Pharmacy Decision Support Systems
Clinical Pharmacy Decision Support Systems (CPDSS) are increasingly integrating Artificial Intelligence (AI) to improve medication safety, optimize therapeutic outcomes, and support evidence-based clinical decision-making. However, many advanced AI models operate as "black boxes," limiting clinician trust and regulatory acceptance. Explainable Artificial Intelligence (XAI) addresses this limitation by providing transparent, interpretable, and clinically meaningful explanations for AI-generated recommendations. This manuscript explores the role of XAI in enhancing medication management, adverse drug event prediction, drug-drug interaction detection, and personalized pharmacotherapy. It reviews recent developments in explainability techniques and their application in pharmacy practice while highlighting current challenges and future opportunities. The discussion emphasizes that explainable AI has the potential to improve healthcare quality, patient safety, and clinician confidence in AIassisted pharmacy services