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.

ICH M10 Bioanalytical Method Validation Guideline-1 year Later.

The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) adopted Guideline M10 entitled "Bioanalytical Method Validation and Study Sample Analysis" in May 2022. In October 2023, approximately one year after the adoption of the ICH M10 guideline, a "Hot Topic" session was held during the AAPS PharmSci 360 meeting to discuss the implementation of the guideline. The session focused on items the bioanalytical community felt were challenging to implement or ambiguous within the guideline. These topics included cross-validation, parallelism, comparative bioavailability studies, combination drug stability, endogenous analyte bioanalysis, and dilution QCs. In addition, the regulatory perspective on the guideline was presented. This report provides a summary of the Hot Topic session.

Sequential Injection Analysis Method for the Determination of Glutathione in Pharmaceuticals.

A sequential injection analysis method for the determination of glutathione (GSH) in pharmaceuticals has been developed. It is based on the reduction of the Cu(II)-neocuproine complex by GSH and the formation of an orange-yellow colored Cu(I)-neocuproine complex with maximum absorbance at 458 nm. Under optimal conditions the method is characterized by a linear calibration range of 6.0 × 10-7-8.0 × 10-5 mol L-1 (Amax = 3270 CGSH - 0.0010; R2 = 0.9983), limit of detection of 2.0 × 10-7 mol L-1, limit of quantification of 6.7 × 10-7 mol L-1, repeatability (expressed as relative standard deviation) of 3.8%, and sampling rate of 60 h-1. The newly developed method has been successfully applied to the determination of GSH in pharmaceutical samples with no statistically significant difference between the results obtained and those produced by the standard Pharmacopoeia method.

Ecotoxicological study of seven pharmaceutically active compounds: Mixture effects and environmental risk assessment.

Pharmaceutically active compounds (PhACs) have been detected in several aquatic compartments, which has been of environmental concern since PhACs can cause adverse effects on the aquatic ecosystem at low concentrations. Despite the variety of PhACs detected in surface water, ecotoxicological studies are non-existent for many of them, mainly regarding their mixture. In addition, water bodies can continuously receive the discharge of raw or treated wastewater with micropollutants. Thus, PhACs are subject to mixture and interactions, potentiating or reducing their toxicity. Therefore, the present study evaluated the toxicity on Aliivibrio fischeri of seven PhACs, which still needs to be explored in the literature. The effects were evaluated for the PhACs individually and for their binary and tertiary mixture. Also, the experimental effects were compared with the concentration addition (CA) and independent action (IA) models. Finally, an environmental risk assessment was carried out. Fenofibrate (FEN), loratadine (LOR), and ketoprofen (KET) were the most toxic, with EC50 of 0.32 mg L-1, 6.15 mg L-1 and 36.8 mg L-1, respectively. Synergistic effects were observed for FEN + LOR, KET + LOR, and KET + FEN + LOR, showing that the CA and IA may underestimate the toxicity. Environmental risks for KET concerning algae, and LOR e 17α-ethynylestradiol (EE2) for crustaceans and fish were high for several locations. Besides, high removals by wastewater treatment technologies are required to achieve the concentrations necessary for reducing KET and LOR risk quotients. Thus, this study contributed to a better understanding of the toxic interactions and environmental risks of PhACs.

Role of rheology in formulation and process design of hot melt extruded amorphous solid dispersions.

Hot melt extrusion (HME) has been widely used as a continuous and highly flexible pharmaceutical manufacturing process for the production of a variety of dosage forms. In particular, HME enables preparation of amorphous solid dispersions (ASDs) which can improve bioavailability of poorly water-soluble drugs. The rheological properties of drug-polymer mixtures can significantly influence the processability of drug formulations via HME and eventually the end-use product properties such as physical stability and drug release. The objective of this review is to provide an overview of various rheological techniques and properties that can be used to evaluate the flow behavior and processability of the drug-polymer mixtures as well as formulation characteristics such as drug-polymer interactions, miscibility/solubility, and plasticization to improve the HME processability. An overview of the thermodynamics and kinetics of ASD processing by HME is also provided, as well as aspects of scale-up and process modeling, highlighting rheological properties on formulation design and process development. Overall, this review provides valuable insights into critical rheological properties which can be used as a predictive tool to optimize the HME processing conditions.

A unifying approach to drug-in-polymer solubility prediction: Streamlining experimental workflow and analysis.

This method paper describes currently used experimental methods to predict the drug-in-polymer solubility of amorphous solid dispersions and offers a combined approach for applying the Melting-point-depression method, the Recrystallization method, and the Melting-and-mixing method. It aims to describe and expand on the theoretical basis as well as the analytical methodology of the recently published Melting-and-mixing method. This solubility method relies on determining the relationship between drug loads and the enthalpy of melting and mixing of a crystalline drug in the presence of an amorphous polymer. This relationship is used to determine the soluble drug load of an amorphous solid dispersion from the recorded enthalpy of melting and mixing of the crystalline drug portion in a drug-polymer sample at equilibrium solubility. Due to the complex analytical methodology of the Melting-and-mixing method, a software solution called the Glass Solution Companion app was developed. Using this new tool, it is possible to calculate the predicted drug-in-polymer solubility and Flory-Huggins interaction parameter from experimental samples, as well as to generate the resulting solubility-temperature curve. This software can be used for calculations for all three experimental methods, which would be useful for comparing the applicability of the methods on a given drug-polymer system. Since it is difficult to predict the suitability of these drug-in-polymer solubility methods for a specific drug-polymer system in silico, some experimental investigation is necessary. By optimizing the experimental protocol, it is possible to collect data for the three experimental methods simultaneously for a specific drug-polymer system. These results can then be readily analyzed using the Glass Solution Companion app to find the most appropriate method for the drug-polymer system, and therefore, the most reliable drug-in-polymer solubility prediction.

MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning.

MOTIVATION: Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining representations that encompass these varied structures is crucial for a holistic understanding of molecules in drug-related contexts. RESULTS: In this study, we introduce an innovative multi-view contrastive learning framework for molecular representation, denoted as MolMVC. Initially, we use a Transformer encoder to capture 1D sequence information and a Graph Transformer to encode the intricate 2D and 3D structural details of molecules. Our approach incorporates a novel attention-guided augmentation scheme, leveraging prior knowledge to create positive samples tailored to different molecular data views. To align multi-view molecular positive samples effectively in latent space, we introduce an adaptive multi-view contrastive loss (AMCLoss). In particular, we calculate AMCLoss at various levels within the model to effectively capture the hierarchical nature of the molecular information. Eventually, we pre-train the encoders via minimizing AMCLoss to obtain the molecular representation, which can be used for various down-stream tasks. In our experiments, we evaluate the performance of our MolMVC on multiple tasks, including molecular property prediction (MPP), drug-target binding affinity (DTA) prediction and cancer drug response (CDR) prediction. The results demonstrate that the molecular representation learned by our MolMVC can enhance the predictive accuracy on these tasks and also reduce the computational costs. Furthermore, we showcase MolMVC's efficacy in drug repositioning across a spectrum of drug-related applications. AVAILABILITY AND IMPLEMENTATION: The code and pre-trained model are publicly available at https://github.com/Hhhzj-7/MolMVC.

PharmaBench: Enhancing ADMET benchmarks with large language models.

Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects.

Heterogeneous Photocatalytic Degradation of Selected Pharmaceuticals and Personal Care Products (PPCPs) Using Tungsten Doped TiO2: Effect of the Tungsten Precursors and Solvents.

Pharmaceuticals and personal care products (PPCPs) which include antibiotics such as tetracycline (TC) and ciprofloxacin (CIP), etc., have attracted increasing attention worldwide due to their potential threat to the aquatic environment and human health. In this work, a facile sol-gel method was developed to prepare tungsten-doped TiO2 with tunable W5+/W6+ ratio for the removal of PPCPs. The influence of solvents in the synthesis of the three different tungsten precursors doped TiO2 is also taken into account. WCl6, ammonium metatungstate (AMT), and Na2WO4●2H2O not only acted as the tungsten precursors but also controlled the tungsten ratio. The photocatalyst prepared by WCl6 as the tungsten precursor and ethanol as the solvent showed the highest photodegradation performance for ciprofloxacin (CIP) and tetracycline (TC), and the photodegradation performance for tetracycline (TC) was 2.3, 2.8, and 7.8 times that of AMT, Na2WO4●2H2O as the tungsten precursors and pristine TiO2, respectively. These results were attributed to the influence of the tungsten precursors and solvents on the W5+/W6+ ratio, sample crystallinity and surface properties. This study provides an effective method for the design of tungsten-doped TiO2 with tunable W5+/W6+ ratio, which has a profound impact on future studies in the field of photocatalytic degradation of PPCPs using an environmentally friendly approach.

The mechanism of action and chemical synthesis of FDA newly approved drug molecules.

In 2023, the U.S. Food and Drug Administration has approved 29 small molecule drugs. These newly approved small molecule drugs possess the distinct scaffolds, thereby exhibiting diverse mechanisms of action and binding modalities. Moreover, the marketed drugs have always been an important source of new drug development and creative inspiration, thereby fostering analogous endeavors in drug discovery that potentially extend to the diverse clinical indications. Therefore, conducting a comprehensive evaluation of drug approval experience and associated information will facilitate the expedited identification of highly potent drug molecules. In this review, we comprehensively summarized the relevant information regarding the clinical applications, mechanisms of action and chemical synthesis of 29 small molecule drugs, with the aim of providing a promising structural basis and design inspiration for pharmaceutical chemists.

GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features.

Microbes have been demonstrated to be closely linked to diseases that pose a major threat to human health. Computing technologies can help researchers find potential microbe-drug associations more quickly and precisely. In this study, we introduced a novel computational prediction model called GLNNMDA based on global and local features of microbes and drugs to infer possible microbe-drug correlations. In GLNNMDA, we first constructed a heterogeneous network based on known microbe-drug relationships by integrating multiple similarity metrics of drugs and microbes. Subsequently, low-dimensional features will be extracted for nodes in the heterogeneous network by adopting the graph attention encoder. Next, based on combining these low-dimensional features with multiple properties of microbes and drugs to form a new comprehensive feature matrix, we would utilize the GLF module to extract the global and local features for microbes and drugs respectively, and then, we would further fuse these global and local features to come up with predictions of possible microbe-drug associations. Moreover, in order to evaluate the prediction performance of GLNNMDA, under the framework of fivefold cross-validation, intensive comparative experiments and case studies were done on different well-known public databases. The results showed that GLNNMDA obtained the highest AUC values as well as AUPR values of 0.9802 ± 0.0011, 0.9773 ± 0.0021 and 0.8586 ± 0.0004, 0.8008 ± 0.0031 in the two databases, MDAD and aBiofilm, respectively, compared to the state-of-the-art competing prediction methods. In addition, case studies of well-known microorganisms and drugs have demonstrated the effectiveness of GLNNMDA in inferring potential microbial drug associations, which implies that GLNNMDA may be a useful tool for microbe-drug association prediction in the future. The source code is available at: " https://github.com/KuangHaiYue/GLNNMDA.git ".

Modeling and validation of purification of pharmaceutical compounds via hybrid processing of vacuum membrane distillation.

This study provides an in-depth examination of forecasting the concentration of pharmaceutical compounds utilizing the input features (coordinates) r and z through a range of machine learning models. Purification of pharmaceuticals via vacuum membrane distillation process was carried out and the model was developed for prediction of separation efficiency based on hybrid approach. Dataset was collected from mass transfer analysis of process to obtain concentration distribution in the feed side of membrane distillation and used it for machine learning models. The dataset has undergone preprocessing, which includes outlier detection using the Isolation Forest algorithm. Three regression models were used including polynomial regression (PR), k-nearest neighbors (KNN), and Tweedie regression (TWR). These models were further enhanced using the Bagging ensemble technique to improve prediction accuracy and reduce variance. Hyper-parameter optimization was conducted using the Multi-Verse Optimizer algorithm, which draws inspiration from cosmological concepts. The Bagging-KNN model had the highest predictive accuracy (R2 = 0.99923) on the test set, indicating exceptional precision. The Bagging-PR model displayed satisfactory performance, with a slightly reduced level of accuracy. In contrast, the Bagging-TWR model showcased the least accuracy among the three models. This research illustrates the effectiveness of incorporating bagging and advanced optimization methods for precise and dependable predictive modeling in complex datasets.

[Managing medicine shortages: responsibilities and strategies at the Swedish Medical Products Agency]. / Restsituationer för läkemedel ­ uppdrag och ansvarsfördelning.

The paper outlines the role of the Swedish Medical Products Agency (Läkemedelsverket) in managing medicine shortages. The agency receives reports from pharmaceutical companies, investigates causes, and disseminates information to pharmacies, healthcare providers, and the public. While focusing on mitigating shortages, the agency clarifies its non-involvement in manufacturing or sales decisions. Pharmaceutical companies must promptly report shortages, with sanctions enforced for non-compliance. Various measures, including regulatory actions, interchangeable medicines, temporary dispensations, and licensing, are employed to address shortages. Additionally, the agency advocates for stable national drug supply preparedness, emphasizing collaboration and governmental intervention.

Microbore-based vacuum jacketed liquid chromatography for fast, efficient and sustainable high throughput bioanalysis.

Extra-column band broadening can significantly reduce the performance of rapid ultra-high performance liquid chromatography-MS-based methods (UHPLC-MS). However, as we show here, UHPLC-MS/MS methods on short 2.1 mm i.d. columns can be optimized to reduce band broadening by simple procedures such as dispensing with the solvent divert valves placed between the column and the MS source. Vacuum jacketed columns have previously been shown to provide superior performance to conventional UHPLC-MS/MS by reducing on and post column band broadening. Here we have compared the optimized "direct" UHPLC approach for the high throughput (HT) bioanalysis of drugs and metabolites in biofluids such as urine and blood plasma with vacuum jacketed chromatography (VJC), using columns of the same geometry and packed with the same stationary phases. This study demonstrates that the performance of VJC was still superior to the direct UHPLC-MS/MS methods for rapid "generic" bioanalysis using gradient times of 0.25 to 5 min. Further investigations using microbore VJC-MS/MS, with 1 mm i.d. columns, for bioanalysis of the same biofluid samples showed that this format offers great promise for HT "discovery" drug and metabolite analysis/profiling. In addition the reduction of solvent use, by up to 90 % for methods when using microbore columns, can significantly contribute to improved sustainability and reducing costs per analysis.

Drug-polymer compatibility prediction via COSMO-RS.

In this work, the solid-liquid equilibrium (SLE) curve for ten active pharmaceutical ingredients (APIs) with the polymer polyvinylpyrrolidone (PVP) K12 was purely predicted using the Conductor-like Screening Model for Real Solvents (COSMO-RS). In particular, two COSMO-RS-based strategies were followed (i.e., a traditional approach and an expedited approach), and their performances were compared. The veracity of the predicted SLE curves was assessed via a comparison with their respective SLE dataset that was obtained using the step-wise dissolution (S-WD) method. Overall, the COSMO-RS-based API-PVP K12 SLE curves were in satisfactory agreement with the S-WD-based data points. Of the twenty predicted SLE curves, only two were found to be in strong disagreement with the corresponding experimental values (both modeled using the expedited approach). Hence, it was recommended to use the traditional approach when predicting the API-polymer SLE curve. At the present moment, COSMO-RS may be an effective computational tool for the expeditious screening of API-polymer compatibility, particularly in the case of promising novel APIs, for which experimental datasets are likely limited or non-existent.

Deep Eutectic Solvents Enhancing Drug Solubility and Its Delivery.

Deep eutectic solvents (DES) are environmentally friendly solvents with the potential to dissolve bioactive compounds without affecting their characteristics. DES has special qualities that can be customized to meet the unique characteristics of a biomolecule/active pharmaceutical ingredient (API) in accordance with various therapeutic needs, providing a reliable approach in opening the door for the creation of cutting-edge drug formulations by resolving solubility issues in pharmaceutics. This study outlines newly developing approaches to solve the problem of inefficient API extraction due to poor solubility. These emerging strategies also have the capacity to alter the chemical and physical stability of API, which triggers drug's shelf life and their possible health benefits. It is anticipated that the highlighted methods and processes will be developed to capitalize on the DES potential to improve drug solubility and delivery in the pharmaceutical sector.

Differential tissue distribution of pharmaceuticals in a wild subtropical marine fish.

To date, the presence of pharmaceuticals has been extensively documented across a wide range of aquatic systems and biota. Further, substantial progress has been made in transitioning from laboratory assessments of pharmaceutical fate and effects in fish to in situ assessments of exposure and effects; however, certain research areas remain understudied. Among these is investigation of differential accumulation across multiple internal tissues in wild marine fish beyond the species commonly sampled in laboratory and freshwater field settings. This study examined the presence of pharmaceuticals across four tissues (plasma, muscle, brain, and liver) in a wild marine fish, bonefish (Albula vulpes), throughout coastal South Florida, USA. Differential accumulation across tissues was assessed for the number and concentration, identity, and composition of accumulated pharmaceuticals by sampling 25 bonefish and analyzing them for 91 pharmaceuticals. The concentration of pharmaceuticals was highest in plasma > liver > brain > muscle, while the number of pharmaceuticals was highest in liver > brain > plasma > muscle. The identity of detected pharmaceuticals was tissue specific, and there was an inverse relationship between the number of detections for each pharmaceutical and its log Kow. The composition of pharmaceuticals was tissue specific for both pharmaceutical presence/absence and concentration. Across all tissues, the greatest similarity was between brain and liver, which were more similar to plasma than to muscle, and muscle was the most distinct tissue. For tissue compositional variability, muscle was the most diverse in accumulated pharmaceuticals, while plasma, brain, and liver were similarly variable. With the highest concentrations in plasma and highest number in liver, and documented variability in accumulated pharmaceuticals across tissues, our results highlight the importance of tissue selection when surveying exposure in wild fish, suggesting that multi-tissue analysis would allow for a more comprehensive assessment of exposure diversity and risk of adverse effects.

Selective electrodialysis for nutrient recovery and pharmaceutical removal from liquid digestate: Pilot-scale investigation and potential fertilizer production.

The present research employs a pilot-scale selective electrodialysis system to treat liquid digestate, fractionating nutrient ions and exploring fertilizer creation via ammonia stripping and phosphorus precipitation, while studying pharmaceutical transport behavior and examining membrane fouling. The influence of diverse potentials was studied in simulated and real digestate, with 30 V application proven more efficient overall. Applying consecutive runs resulted in products that were 7.9, 7.4, 1.7, 5.3, and 6 times more concentrated compared to the feed solution for NH4+, K+, PO43-, Ca2+, and Mg2+, respectively. Pharmaceuticals analysis showed that ciprofloxacin was completely retained in the liquid digestate, while ibuprofen was detected in the anionic product. Diclofenac was initially present in the digestate but was undetectable in the final products, suggesting it adhered to the membrane. Membranes showed inorganic and organic fouling. The monovalent cation exchange membrane had severe salt scaling, showing calcium and magnesium deposits, and fewer functional groups.