Search Results (120)

Peptide therapeutics offer high target selectivity and low toxicity, but their clinical utility remains constrained by rapid proteolysis in vivo and negligible oral bioavailability. Incorporating non-canonical amino acids (ncAAs) provides a robust molecular engineering framework to overcome these pharmacokinetic bottlenecks. This review analyzes the structural and biophysical design rules of ncAA-mediated peptide stabilization, categorizing them into side-chain steric shielding, backbone conformational constraint, and stereochemical evasion of L-specific proteases. We systematically evaluate the biosynthetic milestones enabling this field, focusing on engineered orthogonal translation systems (tRNA/synthetase pairs, orthogonal ribosomes, quadruplet codons) and metabolic engineering strategies that supply fluorinated and other ncAA precursors de novo. Furthermore, we examine the translation of these technologies into clinical candidates (e.g., modified antimicrobial peptides, antibody–drug conjugates, and PROTACs) and identify scaling, immunogenicity, and computational modeling as key bottlenecks. This review serves as a technical reference for designing next-generation, hyper-stable peptide therapeutics. Full article

Background/Objectives: Listeria monocytogenes is a critical foodborne pathogen, with poultry products serving as a potential reservoir. Its ability to form biofilms may aid in its persistence on processing equipment and food-contact surfaces, while antibiotic resistance complicates efforts to control and treat infections. This study aimed to characterize, in parallel, the biofilm-forming capacity and antibiotic susceptibility of a large collection of poultry-derived L. monocytogenes isolates (n = 93) to better understand their potential for persistence and to clarify how the biofilm phenotype may relate to the bacterial antibiotic response and to inform risk assessment and targeted control strategies along poultry processing and supply chains. Methods: Biofilms were evaluated on polystyrene microtiter plates at 12 and 30 °C in a nutrient-rich laboratory medium. Susceptibility to eight clinically and food-relevant antibiotics was tested using disk diffusion and interpreted according to European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints when available. Results: At 30 °C for 48 h, 69.9% of isolates were classified as weak biofilm formers and 30.1% as non-biofilm formers, whereas at 12 °C for 120 h, 55.9% were weak, 16.1% moderate, and 28.0% non-biofilm formers, with no strong biofilm producers identified under either condition. Overall, the isolates remained largely susceptible to ampicillin, penicillin G, vancomycin, tetracycline, and chloramphenicol, with 87.3% of inhibition zones across all drugs falling within the 20–29 mm and 30–39 mm categories, while small subpopulations showed reduced susceptibility or resistance to trimethoprim–sulphamethoxazole (TMP-SMX) and, particularly, erythromycin and streptomycin. No consistent correlation was found between biofilm-forming ability and antibiotic susceptibility, indicating that these phenotypic traits are largely independent in this collection. Conclusions: These findings reveal that poultry-derived L. monocytogenes isolates can form weak to moderate biofilms under the tested monoculture conditions while generally maintaining susceptibility to first-line antibiotics. However, the development of macrolide- and aminoglycoside-resistant subpopulations, along with the potential for increased colonization within complex multispecies biofilms in real processing environments, emphasizes the importance of ongoing integrated surveillance across animal food systems. Full article

Background/Objectives: Drug supply disruptions represent an increasingly serious problem for health systems worldwide, with systemic antibiotics among the most frequently affected therapeutic categories. Although regulatory authorities have repeatedly signaled this risk, comparative studies analyzing patterns of antibiotic shortages across multiple countries simultaneously remain scarce. Methods: We performed a cross-sectional comparative analysis based on data from public national shortage registries in seven jurisdictions: Belgium, France, Germany, Romania, Spain, the United States (FDA), and the Kingdom of Saudi Arabia. All records corresponding to systemic antibiotics in ATC group J01 were extracted, harmonized, and analyzed, with the active substance (INN) as the unit of analysis. The association between critical drug status according to the EMA list and the multinational recurrence of shortages was assessed using chi-square tests, the Mann–Whitney U test, and multivariate logistic regression. To verify the robustness of the results, a sensitivity analysis was also performed using alternative thresholds for jurisdictions. Results: A total of 350 shortage records were mapped, corresponding to 64 unique active pharmaceutical ingredients. On average, each active substance was reported as out of stock in 3.48 jurisdictions (SD = 1.46). Macrolides (J01F) and quinolones (J01M) exhibited the widest geographic spread of shortages. Antibiotics included on the EMA’s list of critical medicines were reported as missing in multiple countries simultaneously significantly more frequently than those not included on this list (82.86% vs. 37.14%; χ² = 71.99, p < 0.001; Cramer’s V = 0.454). In the multivariate logistic regression model, EMA critical medicine status remained an independent predictor of multinational recurrence of shortages (OR = 8.29; 95% CI: 4.93–13.94; p < 0.001), while the injectable route of administration did not reach the threshold for statistical significance (OR = 0.78; p = 0.341). Sensitivity analysis confirmed that this association remains statistically significant regardless of the threshold chosen. Conclusions: Shortages of systemic antibiotics tend to occur simultaneously in multiple countries, and drugs designated as critical by the EMA are disproportionately affected. The results suggest that the identified weaknesses are not specific to a single health system but reflect structural fragilities in international antibiotic supply chains. This underscores the need for internationally coordinated strategies, both for monitoring the availability of essential antibiotics and for preventing and managing shortages. Full article

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations. Full article

Background: Drug shortages represent a growing challenge to healthcare systems worldwide, affecting treatment continuity and patient outcomes. This study assessed the burden and perceived impact of drug shortages from both healthcare professionals’ and patients’ perspectives in a Saudi tertiary hospital. Methods: A cross-sectional survey was conducted in April 2025 at King Abdulaziz Medical City, Riyadh, Saudi Arabia. Convenience sampling was used to recruit healthcare professionals with at least two years of experience and adult outpatients. Structured questionnaires assessed shortage frequency, affected drug classes, perceived impacts, and management practices. The findings were descriptively analyzed and compared with the Saudi Food and Drug Authority (SFDA) national shortage data for the corresponding 12-month period. Results: A total of 230 healthcare professionals and 243 patients participated. Among healthcare professionals, 89.1% reported experiencing at least one drug shortage, with 38.3% encountering shortages more than ten times annually. Anti-infectives (36.5%) and analgesics (35.7%) were the most frequently reported classes. The most common response was prescribing alternative medications (77.4%), with 55.3% perceived as adequately effective and 30.8% as less effective. Delayed care was the most frequently reported consequence (44.0%). Among patients, 30.9% reported experiencing shortages, 46.7% reported some degree of health impact, and 28.1% incurred additional costs. Awareness and utilization of the SFDA reporting system were low in both groups. Comparison with SFDA data revealed discrepancies between hospital-reported and nationally reported shortages. Conclusions: Drug shortages were frequently reported and associated with perceived clinical and economic consequences. Gaps between hospital experiences and national reporting highlight limitations in current surveillance systems. Strengthening reporting mechanisms, communication, and supply chain coordination may improve the management of drug shortages. Full article

Background: Blockchain is increasingly proposed to strengthen pharmaceutical traceability, anti-counterfeiting, and chain of custody in multi-actor supply chains, but the evidence base remains heterogeneous in technical rigor and operational clarity. Methods: We conducted a mapping review of Scopus and Web of Science to map publication patterns, identify dominant thematic configurations, and compare citation-salient studies across recurring solution profiles and operational design dimensions. The final corpus comprised 103 records. Results: The literature expanded rapidly from 2019 to 2025, with notable geographic concentration and dissemination mainly through technically focused outlets. Keyword analysis identified a core traceability theme, an implementation stream centered on smart contracts, Ethereum, and security, and additional streams involving vaccines and regulatory or credentialing concerns. Citation-salient studies clustered into implemented systems and prototypes, architecture or framework proposals, and contextual maturity or decision-layer evidence. Across these profiles, transferability depended less on platform choice than on governance and access-control assumptions, modular smart contract roles, and verifiable on-chain/off-chain data placement. Conclusions: Chain-of-custody semantics and evaluation methods remain inconsistently formalized, limiting cross-study comparability and the interpretability of operational claims. Benchmark-oriented assessments and minimal reporting standards specifying governance parameters, logistics scope and checkpoints, workload, measurement conditions, and concrete evidence artifacts are needed. Full article

Polymers are fundamental components of modern pharmaceutical manufacturing, serving critical roles as excipients, binders, coatings, and matrices for controlled drug delivery systems. However, the conventional production of pharmaceutical polymers relies heavily on petrochemical feedstocks, energy-intensive processes, and hazardous solvents, leading to significant environmental and economic burdens. In recent years, increasing regulatory pressure, environmental awareness, and sustainability goals have driven the pharmaceutical industry toward greener manufacturing strategies. This review critically examines sustainable green polymer production for pharmaceutical applications, with a focus on both environmental and economic impacts. The review discusses the role of polymers in pharmaceutical manufacturing, outlines the limitations of conventional polymer synthesis, and highlights the relevance of green chemistry principles in addressing these challenges. Key green polymer synthesis techniques, including biopolymer production, enzymatic polymerization, microwave-assisted synthesis, supercritical CO₂ processing, and the use of ionic liquids and deep eutectic solvents, are systematically evaluated. Additionally, life-cycle assessment (LCA) approaches are explored to assess the environmental performance of green polymer processes in comparison with traditional methods. Beyond environmental sustainability, this review emphasizes the importance of pharmacoeconomic evaluation in determining the feasibility of adopting green polymers at an industrial scale. Cost–benefit analyses, manufacturing cost comparisons, long-term economic advantages, and health–economic outcomes are discussed in the context of pharmaceutical supply chains. Regulatory perspectives, industrial implementation challenges, and future directions are also addressed. Overall, this review highlights sustainable polymer innovation as a critical pathway toward environmentally responsible, economically viable, and future-ready pharmaceutical manufacturing. Full article

Background/Objectives: We conducted this study to develop a generic amiodarone tablet pharmaceutically equivalent to the reference drug. This development is crucial for securing a stable supply chain for this orphan drug, which currently faces domestic market instability. Amiodarone, a national essential medicine, often experiences unstable supply due to its limited profitability. Methods: To secure this stable supply chain, we employed a factorial design, utilizing a Quality by Design (QbD) approach, to create the most suitable formulation. Initially, we observed a limitation where the formulation exhibited a flowability of 25% based on the Carr’s Index, which exceeded the target of 20%. To address this challenge, we incorporated lactose monohydrate during the pre-mixing stage rather than the post-mixing stage. Subsequently, we identified the binder content and the amount of granulation solvent as Critical Material Attributes (CMAs), and we performed a Design of Experiments (DoE). Result: Based on these investigations, we determined that the optimal prescription utilizes 5.71% povidone K25 and 40 mg/T of purified water. The final formulation successfully achieved an excellent flowability of 15.8%. Furthermore, this formulation showed a dissolution and bioequivalence PK profile equivalent to the reference drug in pH 1.2, 4.0, and 6.8 buffer solutions, each containing 1% Tween 80. Conclusions: Ultimately, the developed formulation is anticipated to establish a stable domestic supply chain and concurrently reduce national healthcare costs. These research findings also establish the groundwork for future continuous manufacturing implementation. Full article

Pharmaceutical supply chains face high inventory and stockout risks because of short product shelf lives and volatile demand. To enhance coordination efficiency and reduce drug waste, this study examines a two-echelon supply chain comprising a manufacturer and multiple medical institutions. We built a joint ordering and transshipment optimization model that simultaneously incorporates shelf-life constraints, the first-in–first-out (FIFO) policy, inventory capacity limits, and peer-level transshipment. Under deterministic and stochastic demand, we solved the model using Bayesian optimization and Monte Carlo simulation. The results show that moderate inventory transshipment effectively mitigates risk from demand uncertainty and increases total supply-chain profit; under stochastic demand, the optimal strategy relies more heavily on coordinated transshipment to reduce excess inventory and near-expiry waste. Full article

Pharmaceutical manufacturing and logistics rely on accurate prediction and decision making to safeguard product quality, delivery reliability, and patient outcomes. Despite rapid advances in artificial intelligence (AI) and machine learning (ML), few studies benchmark model performance across the diverse operational demands of global pharmaceutical supply chains. Predictive setbacks contribute to financial losses, reduced supply chain efficacy, and potential adverse health consequences, yet understanding these failures offers firms opportunities to refine strategy and strengthen resilience. Drawing on 1.2 million shipments spanning 39 countries, we compare traditional statistical models (ARIMA), ensemble methods (random forests, gradient boosting), and deep neural networks (LSTM, GRU, CNN, ANN) across pricing, demand forecasting, vendor management, and shipment planning. Gradient boosting produced the strongest pricing performance, while ARIMA delivered the lowest demand-forecasting errors but with limited explanatory power; neural networks captured nonlinear demand shocks and achieved superior maintenance-risk classification. We also identified three vendor performance clusters—high-performing, cost-efficient, and mixed-reliability vendors—enabling firms to better align shipment criticality with vendor capabilities by prioritizing high performers for urgent deliveries, leveraging cost-efficient vendors for non-urgent volumes, and managing mixed performers through targeted oversight. These insights highlight the value of our evidence-based roadmap for selecting algorithms in high-stakes healthcare logistics, in rapidly evolving, technologically complex global contexts where increasing algorithmic sophistication elevates the standards for safer, smarter pharmaceutical supply chains. Full article

With the increasing globalization of supply chains, ensuring food safety has become more complex, necessitating advanced approaches for risk assessment. This study aims to review the transformative role of machine learning (ML) and deep learning (DL) in enabling intelligent food safety management by efficiently analyzing high-quality and nonlinear data. We systematically summarize recent advances in the application of ML and DL, focusing on key areas such as biotoxin detection, heavy metal contamination, analysis of pesticide and veterinary drug residues, and microbial risk prediction. While traditional algorithms including support vector machines and random forests demonstrate strong performance in classification and risk evaluation, unsupervised methods such as K-means and hierarchical cluster analysis facilitate pattern recognition in unlabeled datasets. Furthermore, novel DL architectures, such as convolutional neural networks, recurrent neural networks, and transformers, enable automated feature extraction and multimodal data integration, substantially improving detection accuracy and efficiency. In conclusion, we recommend future work to emphasize model interpretability, multi-modal data fusion, and integration into HACCP systems, thereby supporting intelligent, interpretable, and real-time food safety management. Full article

Background: Mobile antimicrobial resistance genes (ARGs) on plasmids or other elements enable Salmonella Typhimurium to spread resistance across hosts and environments. The emergence of multi-drug resistance (MDR) Salmonella Typhimurium has raised global concern, yet little is reported about these mobile elements from the Thailand pork supply chain, where this risk of transfer to humans remains largely uncharacterized. Methods: Between March 2023 and February 2024, 25 S. Typhimurium isolates were collected from pig carcasses in slaughterhouses and pork swabs from retail markets in northeastern Thailand. Nine representative isolates, sampled across three seasons, were subjected to Illumina whole-genome sequencing. Assemblies were analyzed for sequence types, phylogenetic relationships, antimicrobial resistance (AMR) determinants, plasmid replicons and mobilization features, functional annotation based on COG (Clusters of Orthologous Groups of proteins) classification, and comparative genomics against a reference strain. Results: Genome assemblies ranged from 4.76 to 5.00 Mb with consistent GC (guanine-cytosine) content (52.0–52.2%). Phylogenetic analysis revealed three sequence types: ST34 (77.8%), ST19, and ST1543. ST34 isolates displayed the broadest AMR gene repertoires, carrying tetracycline (tetA/tetB), sulfonamide (sul1/sul2/sul3), aminoglycoside (aadA, aph(6)-Id, aph(3″)-Ib), phenicol (floR, catA1), and β-lactam (bla_TEM-1B) genes, whereas non-ST34 isolates harbored fewer determinants. ARGs frequently co-localized with IncQ1 and Col-type plasmid replicons, MOB_H/MobA relaxases (enzymes that initiate plasmid transfer), and conjugation modules (type IV secretion and coupling proteins), often alongside virulence loci and metal resistance operons. Functional annotation showed highly conserved metabolic and housekeeping functions, while comparative genomics confirmed >90% core genome conservation, with variability concentrated in genomic islands encoding hypothetical proteins. These genomic patterns were inferred from a limited WGS dataset (nine isolates) and should therefore be considered exploratory and require confirmation in larger collections. Conclusions: Multi-drug resistant ST34 Salmonella Typhimurium predominated in the northeastern Thailand pork supply chain, with diverse resistance genes carried on IncQ1/Col-type plasmids linked to MOB_H relaxases and conjugation modules. The stability of these mobilizable elements underscores their role in sustaining MDR traits and highlights the risk of foodborne AMR transmission, reinforcing the need for continuous genomic surveillance under a One Health framework. Full article

Medicine and vaccine supply chains in Nigeria are socio-technical systems exposed to persistent uncertainty and disruption. Existing studies rarely integrate systems thinking with uncertainty-aware decision tools to jointly prioritize challenges and policy responses. This study asks which policy mix most effectively strengthens these supply chains while balancing multiple, conflicting criteria and stakeholder judgments. We develop a two-stage Fermatean fuzzy framework that first weights 35 challenges using Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) and then ranks four policy alternatives via Fermatean Fuzzy VIšeKriterijumska Optimizacija I Kompromisno Resenje (FF-VIKOR), based on expert elicitation and linguistic assessments. Results identify interruption of drug supplies, limited vaccine funding, cold-chain potency loss, human resource shortages, and product damage as the most critical challenges. FF-VIKOR prioritizes Effective Implementation of Existing Policies as the best alternative, followed by Improving Access to Medicines and Vaccines, indicating that governance quality and access-enabling infrastructure are complementary levers for resilience. To further enhance robustness, we embed the VIKOR outcomes into a policy-oriented game-theoretic analysis, where strategic weighting scenarios (e.g., cost-focused, infrastructure-driven, human-capital focused) interact with policy choices. The equilibrium results reveal that a mixed strategy combining Effective Implementation of Existing Policies and Strengthening Distribution and Storage Systems guarantees the best compromise performance across adversarial scenarios. The proposed framework operationalizes systems thinking for uncertainty-aware and strategically robust policy design and can be extended with real-time data integration, scenario planning, and regional replication to guide adaptive supply chain governance. Full article

Optical sensing technologies are revolutionizing global food safety surveillance through exceptional sensitivity, rapid response, and high portability. This review systematically evaluates five major platforms, revealing unprecedented detection capabilities from sub-picomolar to single-cell resolution. Surface plasmon resonance achieves $0.021 \mathrm{ng}/\mathrm{mL}$ detection limits for veterinary drugs with superior molecular recognition. Quantum dot fluorescence sensors reach $0.17 \mathrm{nM}$ sensitivity for pesticides, enabling rapid on-site screening. Surface-enhanced Raman scattering attains $0.2 \mathrm{pM}$ sensitivity for heavy metals, ideal for trace contaminants. Laser-induced breakdown spectroscopy delivers multi-elemental analysis within seconds at $0.0011 \mathrm{mg}/\mathrm{L}$ detection limits. Colorimetric assays provide cost-effective preliminary screening in resource-limited settings. We propose a stratified detection framework that strategically allocates differentiated sensing technologies across food supply chain nodes, addressing heterogeneous demands while eliminating resource inefficiencies from deploying high-precision instruments for routine screening. Integration of microfluidics, artificial intelligence, and mobile platforms accelerates evolution toward multimodal fusion and decentralized deployment. Despite advances, critical challenges persist: matrix interference, environmental robustness, and standardized protocols. Future breakthroughs require interdisciplinary innovation in materials science, intelligent data processing, and system integration, transforming laboratory prototypes into intelligent early warning networks spanning the entire food supply chain. Full article

Industrialized processing has increased the complexity of the food supply chain. Concerns about food-related risks have increased consumer interest in food traceability. Traceability systems are regarded as effective tools for mitigating information asymmetry and enhancing food quality and safety. However, the design of traditional food traceability systems overlooks the risk of information overload. Based on information overload theory, this study designs an artificial intelligence (AI) traceability assistant as an innovative tool to optimize traditional food traceability systems and examines its positive effects. This study focuses on prepared foods as the research objects, selecting three types of prepared foods (Kung Pao chicken, fish-flavored shredded pork, and pickled fish) and three food traceability tasks (preservatives, sweeteners, and drug residues) as experimental stimuli. Through three online scenario experiments, 747 valid responses were collected. This study explores the impact of AI traceability assistant design on positive consumer engagement behaviors and its underlying mechanism. The results reveal that the AI traceability assistant significantly promotes positive consumer engagement behaviors. This positive effect is mediated by perceived system ease of use. Furthermore, perceived product risk positively moderates the impact of the AI traceability assistant on perceived system ease of use. Perceived product risk strengthens the mediating effect of perceived system ease of use. This study contributes a novel theoretical perspective for research on food traceability systems and reveals the underlying mechanism through which the AI traceability assistant exerts its positive effect. In practice, it provides actionable guidance for food producers implementing digital traceability solutions. Full article