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.

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.

[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.

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules.

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.

CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achieved good results in prediction accuracy, they often suffer from insufficient accuracy when dealing with data with irregular topological structures. METHODS: In view of this, this study proposes a pharmacokinetic parameter prediction framework based on graph convolutional networks (GCN), which predicts the PPBR and OBA of small molecule drugs. In the framework, GCN is first used to extract spatial feature information on the topological structure of drug molecules, in order to better learn node features and association information between nodes. Then, based on the principle of drug similarity, this study calculates the similarity between small molecule drugs, selects different thresholds to construct datasets, and establishes a prediction model centered on the GCN algorithm. The experimental results show that compared with traditional machine learning prediction models, the prediction model constructed based on the GCN method performs best on PPBR and OBA datasets with an inter-molecular similarity threshold of 0.25, with MAE of 0.155 and 0.167, respectively. In addition, in order to further improve the accuracy of the prediction model, GCN is combined with other algorithms. Compared to using a single GCN method, the distribution of the predicted values obtained by the combined model is highly consistent with the true values. In summary, this work provides a new method for improving the rate of early drug screening in the future.

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.

After 50 Years of Hepatic Clearance Models, Where Should We Go from Here? Improvements and Implications for Physiologically Based Pharmacokinetic Modeling.

There is overwhelming preference for application of the unphysiologic, well-stirred model (WSM) over the parallel tube model (PTM) and dispersion model (DM) to predict hepatic drug clearance, CLH , despite that liver blood flow is dispersive and closer to the DM in nature. The reasoning is the ease in computation relating the hepatic intrinsic clearance ( CLint ), hepatic blood flow ( QH ), unbound fraction in blood ( fub ) and the transmembrane clearances ( CLin and CLef ) to CLH for the WSM. However, the WSM, being the least efficient liver model, predicts a lower EH that is associated with the in vitro CLint ( Vmax / Km ), therefore requiring scale-up to predict CLH in vivo. By contrast, the miniPTM, a three-subcompartment tank-in-series model of uniform enzymes, closely mimics the DM and yielded similar patterns for CLint versus EH , substrate concentration [S] , and KL / B , the tissue to outflow blood concentration ratio. We placed these liver models nested within physiologically based pharmacokinetic models to describe the kinetics of the flow-limited, phenolic substrate, harmol, using the WSM (single compartment) and the miniPTM and zonal liver models (ZLMs) of evenly and unevenly distributed glucuronidation and sulfation activities, respectively, to predict CLH For the same, given CLint ( Vmax and Km ), the WSM again furnished the lowest extraction ratio ( EH,WSM = 0.5) compared with the miniPTM and ZLM (>0.68). Values of EH,WSM were elevated to those for EH, PTM and EH, ZLM when the Vmax s for sulfation and glucuronidation were raised 5.7- to 1.15-fold. The miniPTM is easily manageable mathematically and should be the new normal for liver/physiologic modeling. SIGNIFICANCE STATEMENT: Selection of the proper liver clearance model impacts strongly on CLH predictions. The authors recommend use of the tank-in-series miniPTM (3 compartments mini-parallel tube model), which displays similar properties as the dispersion model (DM) in relating CLint and [ S ] to CLH as a stand-in for the DM, which best describes the liver microcirculation. The miniPTM is readily modified to accommodate enzyme and transporter zonation.

GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph.

BACKGROUND: Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery. RESULTS: We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs. CONCLUSIONS: GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo's dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

The impact of centralized band purchasing of pharmaceuticals on innovation of Chinese pharmaceutical firms: an empirical study based on double difference models.

In 2018, China began to gradually promote the pilot policy of centralized band purchasing of medicines. Implementing this policy has resulted in a significant decrease in drug prices. However, there needs to be a clear consensus on the impact and mechanism of action on the innovation of pharmaceutical companies. Therefore. Taking the data of Chinese Shanghai and Shenzhen A-share pharmaceutical listed companies from 2016 to 2022 as a sample, this paper empirically investigates the impact of the centralized banded purchasing policy of drugs on the innovation of pharmaceutical enterprises by using a double difference model and further analyzes the mechanism of its action. The results show that implementing the centralized banded purchasing policy can promote pharmaceutical enterprises' innovation input and output, which is robust under the parallel trend and placebo tests. Further exploring the impact mechanism of the centralized band purchasing policy on pharmaceutical enterprises' innovation, it can be found that it promotes innovation inputs through three channels: government subsidies, enterprise profits, and operating income. In addition, the impact of centralized band purchasing on enterprise innovation is heterogeneous in terms of region, enterprise nature, and scale. Therefore, the positive effects of the centralized band purchasing policy on promoting innovation in pharmaceutical enterprises should be fully recognized, and enterprise heterogeneity should be taken into account when implementing the policy. This study provides empirical evidence on the implementation effect of the centralized banded purchasing policy and provides lessons for continuously optimizing the policy to promote the high-quality development of pharmaceutical enterprises.

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.

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/.

Cheaper is not always better: Drug shortages in the United States and a value-based solution to alleviate them.

Drug shortages threaten patients' access to medications and are associated with adverse health outcomes and increased costs. Drug shortages disproportionately occur among generic drugs of limited profitability, most notably drugs administered by injection. In this perspective, we discuss how reimbursement and purchasing practices that were meant to create an efficient marketplace for generics have generated strong price pressure that threatens profitability in certain markets. We further explain how, faced with limited profitability, manufacturers lack incentives to invest in resilient supply chains, and in some cases, engage in cost-containment strategies or decide to exit the market, ultimately contributing to shortages. We propose the development and implementation of value-based reimbursement to provide needed incentives for drug purchasers and manufacturers to establish a more reliable supply chain as part of the policy solution to reduce the number and extent of drug shortages. This reimbursement model would necessitate the development of a rating system that measures supply chain resilience and maturity for each generic product. This rating would then be applied as a value-based modifier to reimbursement rates for generic products. The proposed model would result in higher reimbursement rates for generic products from more dependable supply chains, generating incentives for manufacturers to invest in supply chain resiliency. We propose the application of this reimbursement system originally in Medicare given Congressional interest on reforming Medicare payment to prevent drug shortages.

Are pharmaceutical residues in crops a threat to human health?

The application of biosolids, manure, and slurry onto agricultural soils and the growing use of treated wastewater in agriculture result in the introduction of human and veterinary pharmaceuticals to the environment. Once in the soil environment, pharmaceuticals may be taken up by crops, resulting in consequent human exposure to pharmaceutical residues. The potential side effects of pharmaceuticals administered in human medicine are widely documented; however, far less is known regarding the risks that arise from incidental dietary exposure. The aim of this study was to evaluate human exposure to pharmaceutical residues in crops and assess the associated risk to health for a range of pharmaceuticals frequently detected in soils. Estimated concentrations of carbamazepine, oxytetracycline, sulfamethoxazole, trimethoprim, and tetracycline in soil were used in conjunction with plant uptake and crop consumption data to estimate daily exposures to each compound. Exposure concentrations were compared to Acceptable Daily Intakes (ADIs) to determine the level of risk. Generally, exposure concentrations were lower than ADIs. The exceptions were carbamazepine, and trimethoprim and sulfamethoxazole under conservative, worst-case scenarios, where a potential risk to human health was predicted. Future research therefore needs to prioritize investigation into the health effects following exposure to these compounds from consumption of contaminated crops.

Mechanisms in the photocatalytic breakdown of persistent pharmaceutical and pesticide molecules over TiO2-based photocatalysts: A review.

Titanium dioxide (TiO2) based photocatalysts have been widely used as a photocatalyst for the degradation of various persistent organic compounds in water and air. The degradation mechanism involves the generation of highly reactive oxygen species, such as hydroxyl radicals, which react with organic compounds to break down their chemical bonds and ultimately mineralize them into harmless products. In the case of pharmaceutical and pesticide molecules, TiO2and modified TiO2photocatalysis effectively degrade a wide range of compounds, including antibiotics, pesticides, and herbicides. The main downside is the production of dangerous intermediate products, which are not frequently addressed in the literature that is currently available. The degradation rate of these compounds by TiO2photocatalysis depends on factors such as the chemical structure of the compounds, the concentration of the TiO2catalyst, the intensity, the light source, and the presence of other organic or inorganic species in the solution. The comprehension of the degradation mechanism is explored to gain insights into the intermediates. Additionally, the utilization of response surface methodology is addressed, offering a potential avenue for enhancing the scalability of the reactors. Overall, TiO2photocatalysis is a promising technology for the treatment of pharmaceutical and agrochemical wastewater, but further research is needed to optimize the process conditions and to understand the fate and toxicity of the degradation products.

Predicting pharmaceutical prices. Advances based on purchase-level data and machine learning.

BACKGROUND: Increased costs in the health sector have put considerable strain on the public budgets allocated to pharmaceutical purchases. Faced with such pressures amplified by financial crises and pandemics, national purchasing authorities are presented with a puzzle: how to procure pharmaceuticals of the highest quality for the lowest price. The literature explored a range of impactful factors using data on producer and reference prices, but largely foregone the use of data on individual purchases by diverse public buyers. METHODS: Leveraging the availability of open data in public procurement from official government portals, the article examines the relationship between unit prices and a host of predictors that account for policies that can be amended nationally or locally. The study uses traditional linear regression (OLS) and a machine learning model, random forest, to identify the best models for predicting pharmaceutical unit prices. To explore the association between a wide variety of predictors and unit prices, the study relies on more than 200,000 purchases in more than 800 standardized pharmaceutical product categories from 10 countries and territories. RESULTS: The results show significant price variation of standardized products between and within countries. Although both models present substantial potential for predicting unit prices, the random forest model, which can incorporate non-linear relationships, leads to higher explained variance (R2 = 0.85) and lower prediction error (RMSE = 0.81). CONCLUSIONS: The results demonstrate the potential of i) tapping into large quantities of purchase-level data in the health care sector and ii) using machine learning models for explaining and predicting pharmaceutical prices. The explanatory models identify data-driven policy interventions for decision-makers seeking to improve value for money.

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.