A review on reactive oxygen species-induced mechanism pathways of pharmaceutical waste degradation: Acetaminophen as a drug waste model.

Innately designed to induce physiological changes, pharmaceuticals are foreknowingly hazardous to the ecosystem. Advanced oxidation processes (AOPs) are recognized as a set of contemporary and highly efficient methods being used as a contrivance for the removal of pharmaceutical residues. Since reactive oxygen species (ROS) are formed in these processes to interact and contribute directly toward the oxidation of target contaminant(s), a profound insight regarding the mechanisms of ROS leading to the degradation of pharmaceuticals is fundamentally significant. The conceptualization of some specific reaction mechanisms allows the design of an effective and safe degradation process that can empirically reduce the environmental impact of the micropollutants. This review mainly deliberates the mechanistic reaction pathways for ROS-mediated degradation of pharmaceuticals often leading to complete mineralization, with a focus on acetaminophen as a drug waste model.

Development of an eco-friendly fluorescent probe for mefenamic acid sensing in pharmaceuticals and biofluids.

Mefenamic acid, renowned for its analgesic properties, stands as a reliable choice for alleviating mild to moderate pain. However, its versatility extends beyond pain relief, with ongoing research unveiling its promising therapeutic potential across diverse domains. A straightforward, environmentally friendly, and sensitive spectrofluorometric technique has been developed for the precise quantification of the analgesic medication, mefenamic acid. This method relies on the immediate reduction of fluorescence emitted by a probe upon interaction with varying concentrations of the drug. The fluorescent probe utilized, N-phenyl-1-naphthylamine (NPNA), was synthesized in a single step, and the fluorescence intensities were measured at 480 nm using synchronous fluorescence spectroscopy with a wavelength difference of 200 nm. Temperature variations and lifetime studies indicated that the quenching process was static. The calibration curve exhibited linearity within the concentration range of 0.50-9.00 µg/mL, with a detection limit of 60.00 ng/mL. Various experimental parameters affecting the quenching process were meticulously examined and optimized. The proposed technique was successfully applied to determine mefenamic acid in pharmaceutical formulations, plasma, and urine, yielding excellent recoveries ranging from 98% to 100.5%. The greenness of the developed method was evaluated using three metrics: the Analytical Eco-scale, AGREE, and the Green Analytical Procedure Index.

[Establishment of collection methods and laboratory testing methods for active pharmaceutical ingredient in the workplace air].

Objective: To establish collection methods and laboratory testing methods for qualitative and quantitative analysis of 9 typical active pharmaceutical ingredient in the workplace air. Methods: In December 2021, a mixed solution of nine analytes was prepared and then dispersed in aerosol state to simulate sampling. Glass fiber filter membrane was selected as air collector and collected active pharmaceutical ingredient in the air at a rate of 2.0 L/min for 15 minutes. Then, the obtained filter membrane samples were eluted with 25%ACN/75%MeOH. Finally, the eluent was qualitatively and quantitatively analyzed with liquid chromatography-triple quadrupole mass spectrometer. Results: This method could effectively collect active pharmaceutical ingredient in the air, with an average sampling efficiency of more than 98.5%. The linear correlation coefficient r was greater than 0.9990. The lower limit of quantification for each analyte ranged from 0.6~500.0 ng/ml, and the average recovery rate ranged from 97.6%~102.5%. Conclusion: This method could simultaneously collect 9 active pharmaceutical ingredient in the workplace air, and could provide accurate qualitative and quantitative analysis in subsequent laboratory tests.

A hybrid evaluation of the intestinal absorption performance of compounds from molecular structure.

Intestinal absorption of compounds is significant in drug research and development. To evaluate this efficiently, a method combining mathematical modeling and molecular simulation was proposed, from the perspective of molecular structure. Based on the quantitative structure-property relationship study, the model between molecular structure and their apparent permeability coefficients was successfully constructed and verified, predicting intestinal absorption of drugs and interpreting decisive structural factors, such as AlogP98, Hydrogen bond donor and Ellipsoidal volume. The molecules with strong lipophilicity, less hydrogen bond donors and receptors, and small molecular volume are more easily absorbed. Then, the molecular dynamics simulation and molecular docking were utilized to study the mechanism of differences in intestinal absorption of drugs and investigate the role of molecular structure. Results indicated that molecules with strong lipophilicity and small volume interacted with the membrane at a lower energy and were easier to penetrate the membrane. Likewise, they had weaker interaction with P-glycoprotein and were easier to escape from it and harder to export from the body. More in, less out, is the main reason these molecules absorb well.

Expanding the Role of Chiral Drugs and Chiral Nanomaterials as a Potential Therapeutic Tool.

Chirality, the property of molecules having mirror-image forms, plays a crucial role in pharmaceutical and biomedical research. This review highlights its growing importance, emphasizing how chiral drugs and nanomaterials impact drug effectiveness, safety, and diagnostics. Chiral molecules serve as precise diagnostic tools, aiding in accurate disease detection through unique biomolecule interactions. The article extensively covers chiral drug applications in treating cardiovascular diseases, CNS disorders, local anesthesia, anti-inflammatories, antimicrobials, and anticancer drugs. Additionally, it explores the emerging field of chiral nanomaterials, highlighting their suitability for biomedical applications in diagnostics and therapeutics, enhancing medical treatments.

Coping with drug shortages: A study of government-enterprise option cooperation stockpiling strategies for drugs in shortage considering API surrogate stockpiling subsidies.

Drug shortage is a global problem, and the development of government-enterprise cooperative stockpiles of drugs in shortage, combining physical and production capacity, has become one of the most important means of coping with drug shortages. However, existing studies have tended to overlook the fact that shortages of Active Pharmaceutical Ingredients (APIs) have become an important constraint on production capacity stockpiling and that the lack of incentives and provisions for coordination of benefits have led to a double marginal effect of joint stockpiling by government and enterprises of drugs in shortage. Accordingly, this study introduced the option contract to the drug supply system composed of government and pharmaceutical enterprises and used the subsidy of API storage in lieu as an important initiative to incentivize the reserve of APIs, to construct a model of shortage drug reserve under the government's leadership. This study aims to improve the effect of government-enterprise joint stockpiling of drugs in shortage, which is of great theoretical and practical significance. According to the classification of production license types of pharmaceutical enterprises, this study established a three-level supply chain decentralized decision-making model consisting of the government, formulation enterprises, and API enterprises, and a two-level supply chain centralized decision-making model consisting of the government and API Formulation (API-F) integrated enterprises, respectively. By solving the inverse order derivation, the government-enterprise option cooperation conditions and optimal decision-making strategy were derived. The study results showed that: (i) The addition of enterprise API stockpiling mode can help the government conventional reserves, and enterprise production capacity reserves, broaden the way of drug reserves, and improve the effect of government-enterprise option cooperation; (ii) when the probability of drug shortages is high, the government should prefer the cooperation of API-F integrated enterprises, which is conducive to reducing intermediate links and government costs and improving the supply responsiveness to shortages of medicines; (iii) Setting appropriate government subsidies for API storage can incentivize enterprises to stockpile APIs and improve drug production capacity and physical supply response capability. This study took the problem of socialized stockpiling of drugs in shortage as an entry point and explored the problems and solution strategies in the government-enterprise cooperative stockpiling of drugs in shortage, which not only made some theoretical contributions to the application of options contract in the government-enterprise cooperative stockpiling of drugs in shortage but also provided new ideas and theoretical basis for the improvement of the stockpiling work of drugs in shortage.

DrugDomain: The evolutionary context of drugs and small molecules bound to domains.

Interactions between proteins and small organic compounds play a crucial role in regulating protein functions. These interactions can modulate various aspects of protein behavior, including enzymatic activity, signaling cascades, and structural stability. By binding to specific sites on proteins, small organic compounds can induce conformational changes, alter protein-protein interactions, or directly affect catalytic activity. Therefore, many drugs available on the market today are small molecules (72% of all approved drugs in the last 5 years). Proteins are composed of one or more domains: evolutionary units that convey function or fitness either singly or in concert with others. Understanding which domain(s) of the target protein binds to a drug can lead to additional opportunities for discovering novel targets. The evolutionary classification of protein domains (ECOD) classifies domains into an evolutionary hierarchy that focuses on distant homology. Previously, no structure-based protein domain classification existed that included information about both the interaction between small molecules or drugs and the structural domains of a target protein. This data is especially important for multidomain proteins and large complexes. Here, we present the DrugDomain database that reports the interaction between ECOD of human target proteins and DrugBank molecules and drugs. The pilot version of DrugDomain describes the interaction of 5160 DrugBank molecules associated with 2573 human proteins. It describes domains for all experimentally determined structures of these proteins and incorporates AlphaFold models when such structures are unavailable. The DrugDomain database is available online: http://prodata.swmed.edu/DrugDomain/.

[On the issue of logistic support of export of medicinal preparations of national production].

The current geopolitical situation raised pointed question of developing new supply chains and looking for rolling stock to develop newly formed cargo flows, including medicinal preparations transportation. Considering necessity in timely and safe supply of medicines, it is necessary to develop set of measures permitting to implement export of this production of national industry to ensure ultimate independence from unfriendly states. The article considers main indicators of import and export operations of medicinal preparations and measures taken by the state to support industry in current conditions, requirements for international transportation of this category of goods. The measures increasing exports within the framework of the Pharmaceutical Industry Development Strategy until 2030, such as expansion of fleet of autonomous refrigerated containers, use of consolidation warehouses in Turkey and Kazakhstan to ensure decreasing of cost of multi-modal transportation of medicinal preparations, as well as validation of rolling stock in accordance with GDP requirements.

[The long 1992 in medicinal (non-)security of Russia. Report II. Market networks and institutes].

The year of 1992 is very important milestone in history of Russian pharmaceutical market. It began not in January 1922, but in December 1991, when the President and Government enactments to open free market for medications were promulgated. The advisers of B. N. Yeltsin considered market economy as reliable mean to overcome medicinal deficiencies, corruption and bureaucratic monopoly. However, panacea did not work. Moreover, it caused completely new problems. The human and state security implies protection from threats of dependencies. The unprepared dive into market plunged Russians and the country into severe dependence on import of medications and foreign pharmaceutical companies. The proposed study expands our publications in this journal and in journal "Pharmacy" in 2022. At that time, analysis of medication scarcity was implemented on the basis of published sources and relied on journalistic evidences. The current study is carried out on the basis of archival documents of the Ministry of Health Care of the Russian Federation and authors interviews of representatives of regulator. The second report reconstructs the sequence of the emergence and correlation of the structural elements of the Russian pharmaceutical market.

Occurrence and risks of pharmaceuticals, personal care products, and endocrine-disrupting compounds in Chinese surface waters.

The continuous and rapid increase of chemical pollution in surface waters has become a pressing and widely recognized global concern. As emerging contaminants (ECs) in surface waters, pharmaceutical and personal care products (PPCPs), and endocrine-disrupting compounds (EDCs) have attracted considerable attention due to their wide occurrence and potential threat to human health. Therefore, a comprehensive understanding of the occurrence and risks of ECs in Chinese surface waters is urgently required. This study summarizes and assesses the environmental occurrence concentrations and ecological risks of 42 pharmaceuticals, 15 personal care products (PCPs), and 20 EDCs frequently detected in Chinese surface waters. The ECs were primarily detected in China's densely populated and highly industrialized regions. Most detected PPCPs and EDCs had concentrations between ng/L to µg/L, whereas norfloxacin, caffeine, and erythromycin had relatively high contamination levels, even exceeding 2000 ng/L. Risk evaluation based on the risk quotient method revealed that 34 PPCPs and EDCs in Chinese surface waters did not pose a significant risk, whereas 4-nonylphenol, 4-tert-octylphenol, 17α-ethinyl estradiol, 17ß-estradiol, and triclocarban did. This review provides a comprehensive summary of the occurrence and associated hazards of typical PPCPs and EDCs in Chinese surface waters over the past decade, and will aid in the regulation and control of these ECs in Chinese surface waters.

Unused, leftover and expired medicine and disposal practices among health sciences faculty students in Burdur, Turkey.

Objective: To evaluate the opinions of university-level Health Sciences students about unused, leftover and expired medicine, as well as their disposal practices, and to classify the medicines. METHODS: The cross-sectional study was conducted from April 1 to May 31, 2023, at the Faculty of Health Sciences, Burdur Mehmet Akif Ersoy University, Turkey, and comprised those studying at the Nursing, Nutrition Dietetics and Physical Therapy and Rehabilitation departments. Data was collected using Google Forms. The Anatomical Therapeutic Chemical classification was used for classifying pharmaceutical active ingredients. Data was analysed using SPSS 24. RESULTS: Of the 373 participants, 272(73%) were females and 101(27%) were males. The overall mean age was 20.8±2.8 years. There were 348(93.3%) subejcts who reported having a total of 845 boxes of leftover and unused medicines in their homes (2.3±1.9 per capita), while 25(6.7%) participants had none. The medicines were stored in the kitchen 261(61.5%) as the storage area, and in the refrigerator 181(40.2%) as the storage unit. The expired medicine was disposed of in the garbage in 328(86.1%) cases. Self-medication was prevalent in 325(87.1%) cases. Anatomical Therapeutic Chemical classification analysis showed that paracetamol, acetylsalicylic acid, paracetamol+caffeine and metamizole sodium was the most common group of leftover and unused medicines 283(81.3%). Conclusion: High prevalence of unused and leftover medicine, disposal of medicine in household garbage, and selfmedication behaviour indicated a serious public health and environmental problem.

Leveraging AI and Machine Learning in Six-Sigma Documentation for Pharmaceutical Quality Assurance.

The pharmaceutical industry must maintain stringent quality assurance standards to ensure product safety and regulatory compliance. A key component of the well-known Six Sigma methodology for process improvement and quality control is precise and comprehensive documentation. However, there are a number of significant issues with traditional documentation procedures, including as slowness, human error, and difficulties with regulatory standards. This review research looks at innovative ways to employ machine learning (ML) and artificial intelligence (AI) to enhance Six Sigma documentation processes in the pharmaceutical sector. AI and ML provide cutting-edge technologies that have the potential to drastically alter documentation processes by automating data entry, collection, and analysis. Natural language processing (NLP) and computer vision technologies have the potential to significantly reduce human error rates and increase the efficacy of documentation processes. By applying machine learning algorithms to support real-time data analysis, predictive analytics, and proactive quality management, pharmaceutical organizations may be able to identify potential quality issues early on and take proactive efforts to address them. Combining AI and ML improves documentation accuracy and reliability while also strengthening compliance with stringent regulatory criteria. The primary barriers and limitations to the current state of Six Sigma documentation in the pharmaceutical industry are identified in this study. It examines the fundamentals of AI and ML with an emphasis on their specific applications in quality assurance and potential benefits for Six Sigma processes. The report includes extensive case studies that highlight notable developments and explain how AI/ML enhanced documentation is used in the real world.

Use of Track One Prioritized Examination for Pharmaceutical Patents.

This study examines the use of the Track One prioritized patent examination program by pharmaceutical manufacturers from 2011 to 2022.

Bioconcentration of pharmaceuticals by aquatic flora in an Australian river system.

Pharmaceuticals are emerging contaminants in the environment and are a ubiquitous presence in rivers downstream of wastewater treatment plant outfalls. Questions remain about the persistence of pharmaceuticals in rivers, and the uptake and bioconcentration of pharmaceuticals by aquatic plants. Our study took place in the Yarrowee/Leigh/Barwon River system in southeastern Australia. We quantified the concentrations of five pharmaceuticals (carbamazepine, primidone, propranolol, tramadol, and venlafaxine) in surface water at five sites along a 144-km stretch of river, downstream of the presumed primary point source (a wastewater treatment plant outfall). We quantified pharmaceuticals in the leaves of two aquatic plant species (Phragmites australis and Vallisneria australis) sampled at each site, and calculated bioconcentration factors. All five pharmaceuticals were detected in surface waters, and the highest detected concentration exceeded 500 ng.L-1 (tramadol). Four of the pharmaceuticals (all except tramadol) were detected and quantified at all sites, including the furthest site from the outfall (144 km). Carbamazepine showed less attenuation with distance from the outfall than the other pharmaceuticals. Carbamazepine and venlafaxine were quantified in the leaves of both aquatic plant species (range: 10-31 ng.g-1), and there was evidence that bioconcentration factors increased with decreasing surface water concentrations. The study demonstrates the potential long-distance persistence of pharmaceuticals in river systems, and the bioconcentration of pharmaceuticals by aquatic plants in natural ecosystems. These phenomena deserve greater attention as aquatic plants are a potential point of transfer of pharmaceuticals from aquatic ecosystems to terrestrial food webs.

The behavior of pharmaceutically active compounds and contrast agents during wastewater treatment - Combining sampling strategies and analytical techniques: A critical review.

Increasing consumption of pharmaceuticals and the respective consequences for the aquatic environment have been the focus of many studies over the last thirty years. Various aspects in this field were investigated, considering diverse pharmaceutical groups and employing a wide range of research methodologies. Various questions from the perspectives of different research areas were devised and answered, resulting in a large mix of individual findings and conclusions. Collectively, the results of the studies offer a comprehensive overview. The large variety of methods and strategies, however, demands close attention when comparing and combining information from heterogeneous projects. This review critically examines the application of diverse sampling techniques as well as analytical methods in investigations concerning the behavior of pharmaceutically active compounds (PhACs) and contrast agents (CAs) in wastewater treatment plants (WWTPs). The combination of sampling and analysis is discussed with regard to its suitability for specific scientific problems. Different research focuses need different methods and answer different questions. An overview of studies dealing with the fate and degradation of PhACs and CAs in WWTPs is presented, discussing their strategic approaches and findings. This review includes surveys of anticancer drugs, antibiotics, analgesics and anti-inflammatory drugs, antidiabetics, beta blockers, hormonal contraceptives, lipid lowering agents, antidepressants as well as contrast agents for X-ray and magnetic resonance imaging.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features.

Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due to its powerful performance. However, the models trained on limited known DTI data struggle to generalize effectively to novel drug-target pairs. In this work, we propose a strategy to train an ensemble of models by capturing both domain-generic and domain-specific features (E-DIS) to learn diverse domain features and adapt them to out-of-distribution data. Multiple experts were trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. E-DIS provides a comprehensive representation of proteins and ligands by capturing diverse features. Experimental results on four benchmark data sets in both in-domain and cross-domain settings demonstrated that E-DIS significantly improved model performance and domain generalization compared to existing methods. Our approach presents a significant advancement in DTI prediction by combining domain-generic and domain-specific features, enhancing the generalization ability of the DTI prediction model.

Distribution Clearance: Significance and Underlying Mechanisms.

PURPOSE: Evaluation of distribution kinetics is a neglected aspect of pharmacokinetics. This study examines the utility of the model-independent parameter whole body distribution clearance (CLD) in this respect. METHODS: Since mammillary compartmental models are widely used, CLD was calculated in terms of parameters of this model for 15 drugs. The underlying distribution processes were explored by assessment of relationships to pharmacokinetic parameters and covariates. RESULTS: The model-independence of the definition of the parameter CLD allowed a comparison of distributional properties of different drugs and provided physiological insight. Significant changes in CLD were observed as a result of drug-drug interactions, transporter polymorphisms and a diseased state. CONCLUSION: Total distribution clearance CLD is a useful parameter to evaluate distribution kinetics of drugs. Its estimation as an adjunct to the model-independent parameters clearance and steady-state volume of distribution is advocated.

A Comparative Metagenomic Analysis of Specified Microorganisms in Groundwater for Non-Sterilized Pharmaceutical Products.

In pharmaceutical manufacturing, ensuring product safety involves the detection and identification of microorganisms with human pathogenic potential, including Burkholderia cepacia complex (BCC), Escherichia coli, Pseudomonas aeruginosa, Salmonella enterica, Staphylococcus aureus, Clostridium sporogenes, Candida albicans, and Mycoplasma spp., some of which may be missed or not identified by traditional culture-dependent methods. In this study, we employed a metagenomic approach to detect these taxa, avoiding the limitations of conventional cultivation methods. We assessed the groundwater microbiome's taxonomic and functional features from samples collected at two locations in the spring and summer. All datasets comprised 436-557 genera with Proteobacteria, Bacteroidota, Firmicutes, Actinobacteria, and Cyanobacteria accounting for > 95% of microbial DNA sequences. The aforementioned species constituted less than 18.3% of relative abundance. Escherichia and Salmonella were mainly detected in Hot Springs, relative to Jefferson, while Clostridium and Pseudomonas were mainly found in Jefferson relative to Hot Springs. Multidrug resistance efflux pumps and BlaR1 family regulatory sensor-transducer disambiguation dominated in Hot Springs and in Jefferson. These initial results provide insight into the detection of specified microorganisms and could constitute a framework for the establishment of comprehensive metagenomic analysis for the microbiological evaluation of pharmaceutical-grade water and other non-sterile pharmaceutical products, ensuring public safety.