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Background: Identification of drug-target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug-target interaction prediction., Methods: Here, we propose DrugMAN, a deep learning model for predicting drug-target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug-target prediction., Results: DrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN's learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug-target interactions and can be a powerful tool for drug discovery and drug repurposing., Competing Interests: Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

In vitro systems such as cultured hepatocytes are used early in drug development as a proxy for in vivo data to predict metabolites in human and the potential preclinical species. These data support preclinical species selection for toxicity studies as well as provide early evidence for potential active and reactive metabolites that can be generated in human. Although in vivo data would be best to select preclinical species for a given compound, only in vitro systems are available when selecting toxicity study species. However, as with any in vitro system, the correlation to actual in vivo results can be variable. Understanding the reliability of predicting in vivo metabolites from the various available in vitro assays and determining which system may be most predictive would help de-risk drug development teams' selection process. In this manuscript, we address these questions: can in vitro systems predict circulating metabolites? If so, is predictivity quantitative or indicative of what levels may be seen circulating? Of the currently available in vitro systems, is one better than the others at generating predictive metabolites? To address the first two issues (general in vitro/in vivo predictivity, and whether any in vitro/in vivo correlations are quantitative), we used historical data from Abbott/AbbVie to compare in vitro metabolite profiles with metabolite profiles from in vivo absorption, distribution, metabolism, excretion, and clinical studies. In this retrospective analysis of historic metabolite profiling data, in vitro systems predicted ∼50% of circulating metabolites present in vivo, across preclinical species and human, with no correlation between apparent concentrations in vitro versus in vivo. To address the final question, we selected 10 commercially available compounds with published metabolism data and incubated them in five common in vitro systems (microsomes, liver S9, suspension hepatocytes, HμREL cocultured hepatocytes, and hepatocyte spheroids); the new in vitro metabolite profiling data were compared against published in vivo data to determine whether any individual system was more accurate in generating known major human circulating metabolites. Suspension hepatocytes and cocultured hepatocytes marginally outperformed the other systems. Current in vitro systems have value early in development when in vivo studies are not feasible and are required for regulatory filings to support preclinical toxicology species selection but should not be treated as wholly representative of a given drug's in vivo metabolism. SIGNIFICANCE STATEMENT: This is a comprehensive assessment of historic metabolism data quantitating the success rate of in vitro to in vivo predictivity. Reliability of in vitro systems for metabolite profiling is important for early drug development, and understanding predictivity will help give appropriate context to the data. New data were also generated to compare common in vitro liver models to determine whether any could be definitively identified as more predictive of human circulating metabolites than others., (Copyright © 2024 by The Author(s).)

The Co-administration of multiple drugs can enhance the efficacy of disease treatment by reducing drug resistance and side effects. However, it also raises the risk of adverse drug interactions, presenting a challenging problem in healthcare. Various approaches have been developed to predict drug-drug interactions (DDIs) by leveraging both knowledge graphs and drug attribute information. While these methods have shown promise, they often fail to effectively capture correlations between biomedical information in the knowledge graph and drug properties. This work introduces a novel end-to-end DDI predictor framework based on generative adversarial networks. This framework utilizes a message-passing neural network to capture molecular structure information while employing the knowledge-aware graph attention network to capture the representation of drugs in the knowledge graph through considering the importance of different multihop neighbor nodes and relationships. The dual generative adversarial networks employ two generators and two discriminators in knowledge graph embedding and molecular topology embedding for adversarial training to capture the interrelations and complementary knowledge between molecular structure information and semantic information from the knowledge graph. comprehensive experiments have demonstrated that the proposed method outperforms state-of-the-art algorithms in binary classification, with improvements of 1.0% in accuracy, 0.45% in area under the receiver operating characteristic curve (AUC), 0.24% in area under the precision-recall curve (AUPR), and 0.98% in F1 score. Furthermore, for multiclass classification tasks, improvements were observed across various evaluation metrics, including 0.9% in accuracy, 0.25% in macro precision, 0.2% in macro recall, and 0.28% in macro F1. Additionally, ablation studies were conducted to showcase the effectiveness and robustness of our method in DDI prediction tasks.

The physicochemical properties of the physiological makeup and the chemical componentof the system make this challenging throughout strenuous procedure. The current review concentrated on in silico modelling of drug disposition, involving absorption process, distribution process, and excretion process and includes thorough knowledge of various database expeditions, the development of a pharmacophore model, molecular docking studies, homology modelling supported sequence similarity and quantitative structure-activity relationships (QSAR)/ quantitative structure-property relationships (QSPR) evaluation along with all information about drug movement and related computational tools for understanding potential chemical and pathophysiological changes. The primary development in ADMET modeling in current times has been the clarification of the function and effective modeling of various transporters. In ADMET modelling, there is still work to be done on including the impact of these transporters into existing models. The present state of modelling different elements of drug disposal at the systemic level will then be discussed, along with recent developments in modelling a wide range of active transporters and their effects on drug pharmacokinetic profiles. A more thorough knowledge of the underlying processes governing different aspects of drug disposition should also lead to an increase in mechanism-based modelling methods that are simple to grasp and put into practice. These developments will hasten the transition of model construction from computational to experimental scientists.

Existing deep learning methods have shown outstanding performance in predicting drug-target interactions. However, they still have limitations: (1) the over-reliance on locally extracted features by some single encoders, with insufficient consideration of global features, and (2) the inadequate modeling and learning of local crucial interaction sites in drug-target interaction pairs. In this study, we propose a novel drug-target interaction prediction model called the Neural Fingerprint and Self-Attention Mechanism (NFSA-DTI), which effectively integrates the local information of drug molecules and target sequences with their respective global features. The neural fingerprint method is used in this model to extract global features of drug molecules, while the self-attention mechanism is utilized to enhance CNN's capability in capturing the long-distance dependencies between the subsequences in the target amino acid sequence. In the feature fusion module, we improve the bilinear attention network by incorporating attention pooling, which enhances the model's ability to learn local crucial interaction sites in the drug-target pair. The experimental results on three benchmark datasets demonstrated that NFSA-DTI outperformed all baseline models in predictive performance. Furthermore, case studies illustrated that our model could provide valuable insights for drug discovery. Moreover, our model offers molecular-level interpretations.

Background and Objective: Different empirical lactation models have been published to predict the milk-to-plasma (M/P) ratio of drugs to gain knowledge on the extent of drug distribution to the breastmilk. M/P ratios will likely vary across the lactation period due to differences in physiological milk pH and fat content, which are not routinely reported in clinical lactation pharmacokinetic studies. This work aims to evaluate the sensitivity of two (a theory-based phase distribution and a log-transformed regression) lactation models for M/P prediction at different physiological milk pH and fat content., Methods: A literature search was conducted to collate reported M/P ratios for different drugs and their physicochemical parameters required for the prediction of the M/P ratio. Two distribution models were used for M/P ratio predictions. The M/P ratio of drugs was predicted under the physiological milk pHs of 6.8, 7.0, 7.2, and 7.4 and at of 1%, 3%, and 6% fat content. Calculated M/P ratios were compared with the observed M/P ratios., Results: A total of 200 M/P ratios for 130 compounds (40 acids and 90 bases) were collected from clinical studies and included in the analysis. For both model, precision decreases and bias increases outside the milk pH range 7.0-7.2 and fat contents more than 3%. Significant variability exists in the observed M/P ratios. Both milk pH and fat content are important parameters for model prediction., Conclusion: Calculated M/P ratios are influenced by multiple covariates, including milk pH and fat content. The phase distribution model is less sensitive to these covariates than the log-transformed model, especially for acidic compounds. For complex matrices such as breastmilk, the actual physiological parameters of the sampled milk, at least milk fat and pH, and their distributions are required covariates to improve the prediction outcomes, design lactation pharmacokinetic studies, and inform the potential breastfed infant dose., Competing Interests: Declarations Funding No funding was provided for this work. Conflict of interest K.A. is a full-time employee of Certara Predictive Technologies, UK, and may hold shares in Certara. M.F. is a full-time employee of UCB and was a former employee of Certara, UK. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Authors’ contributions K.A. and M.F. collected the data; K.A. and M.F. analyzed the data; and K.A. and M.F. wrote the manuscript. Data availability statement All data generated or analyzed during this study are included in this published article (and its supplementary information files). Ethics approval For this study, ethical approval was not required. Code availability Not applicable. Consent to participate Not applicable. Consent for publication Not applicable., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Drug molecules exist as complexed with serum proteins such as human serum albumin (HSA) and/or unbound free form in the blood circulation. Drugs can be effective only when they are free. Thus, it is important to understand aspects that are important for interaction between drugs and interacting proteins. In this study, interactions among 2990 FDA approved drugs and HSA were computational analyzed to unravel principles that are critical for drug-HSA interactions. Docking results showed that drugs have higher affinity toward cavity-1 (C1) than cavity-2 (C2). A total of 1131 drug molecules have docking score greater than 60 while 768 molecules have docking score greater than 60 when they are docked in C2. In addition, three solvent channels have potential to direct solvent to C1 cavity while C2 does not have any effective channel. The post MD analyses demonstrated that drugs are making polar interactions with basic amino acids in the binding cavities. Verbscoside and ceftazidime both have stable low RMSD values throughout MD simulation with 2 Å on average in C1 cavity. The ligand RMSD shows less stability for verbscoside, which is around 4 Å when it is in complex with HSA in C1. The individual contribution of the residues K192, K196, R215, and R254 to ceftazidime are -1.92 ± 0.18, -3.09 ± 0.09, -2.17 ± 0.17, and - 2.32 ± 0.098, respectively. These residues contribute the binding energy of the verbscoside by -6.06 ± 0.08, -2.10 ± 0.06, and - 1.57 ± 0.03 kcal/mol individually in C1 cavity. C2 is making polar interactions with drug via R469, K472, and K488 residues and their contribution to the two drugs are -3.13 ± 0.21 kcal/mol for R469, -1.94 ± 0.18 kcal/mol for K472, and -1.96 ± 0.11 kcal/mol for K488 to total binding energy of ceftazidime. The binding energy of verbscoside is 57.17 ± 7.00 kcal/mol and Arg-407 has the highest contribution this bind energy individually with -4.29 ± 0.12 kcal/mol. Drugs with hydrogen bond donor/acceptor chemical adducts such as verbscoside involve higher hydrogen bond formation in C1 pocket. Ceftazidime makes interaction with HSA toward hydrophobic residues, L384, L404, L487, and L488 in the C2 cavity., (© 2024 The Author(s). Journal of Molecular Recognition published by John Wiley & Sons Ltd.)

Breastfeeding is the most complete nutritional method of feeding infants, but several impediments affect the decision to breastfeed, including questions of drug safety for medications needed during lactation. Despite recent FDA guidance, few labels provide clear dosing advice during lactation. Physiologically based pharmacokinetic modeling (PBPK) is well suited to mechanistically explore pharmacokinetics and dosing paradigms to fill gaps in the absence of extensive clinical studies and complement existing real-world data. For lactation-focused PBPK (Lact-PBPK) models, information on system parameters (e.g., expression of drug transporters in mammary epithelial cells) is sparse. The breast cancer resistance protein (BCRP) is expressed on the apical side of mammary epithelial cells where it actively transports drugs/substrates into milk (reported milk: plasma ratios range from 2 to 20). A critical review of BCRP and its role in lactation was conducted. Longitudinal changes in BCRP mRNA expression have been identified in women with a maximum reached around 5 months postpartum. Limited data are available on the ontogeny of BCRP in infant intestine; however, data indicate lower BCRP abundance in infants compared to adults. Current status of incorporation of drug transporter information in Lact-PBPK models to predict active secretion of drugs into breast milk and consequential exposure of breast-fed infants is discussed. In addition, this review highlights novel clinical tools for evaluation of BCRP activity, namely a potential non-invasive BCRP biomarker (riboflavin) and liquid biopsy that could be used to quantitatively elucidate the role of this transporter without the need for administration of drugs and to inform Lact-PBPK models., (© 2024 The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

Hypothetical and experimental models of unbound fraction have been proposed to facilitate predicting the hepatic clearance (CL H ) of drugs from values of intrinsic clearance for the unbound drug (CL int-in vitro-unbound ) and the well-stirred model (WSM). The hypothetical model (fu -adjusted ) is adjusting the unbound fractions determined in plasma in vitro to estimate the maximum unbound fractions at the hepatocytes if each drug-protein complex in plasma becomes fully dissociated at the membrane by any albumin (ALB)-facilitated hepatic uptake mechanism. The model of fu -adjusted is also adjusting the unbound fraction for a pH gradient effect across the membrane. Alternatively, the new experimental model (fu -dynamic ) measures the unbound fractions resulting to the dynamic dissociation kinetics from proteins in the presence of plasma and a liver enzyme in an in vitro assay. The objective of this study was to conduct an in-depth analysis of previous CL H predictions made with these unbound fractions in a companion manuscript. Furthermore, a new dataset on transporter substrates was also included in this study. Finally, the physiological basis of fu -adjusted has been redefined to extend its applicability with more drugs. In this case, there are lower concentrations of binding proteins in liver versus plasma that could also explain the higher unbound fractions for that organ. The outcomes associated to additional analyses pointed out that fu -adjusted , again, generally provided the most accurate predictions of CL H because fu -dynamic has generated superior biases of underpredictions or overpredictions. For slowly metabolized drugs bound to ALB, fu -dynamic was definitively less accurate than fu -adjusted . For other drug properties, fu -dynamic fared better but it was still not generally more accurate than fu -adjusted . Furthermore, experimental values of fu -dynamic were sometimes incoherent. For example, drugs bound to alpha-acid glycoprotein (AGP) did not follow the principle of fu -dynamic (i.e., values of fu -dynamic did not correlate with values of CL int-in vitro-unbound ) by contrast to those drugs bound to ALB. Therefore, the current experimental setting for fu -dynamic might be unsuitable in some circumstances. Overall, this study confirmed that calculated values of fu -adjusted were as accurate as experimental values of fu -dynamic and can even be more accurate. A guidance on which unbound fraction to use in the WSM is also provided., (Copyright © 2024 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.)

Pharmaceuticals released into the aquatic and soil environments can be absorbed by plants and soil organisms, potentially leading to the formation of unknown metabolites that may negatively affect these organisms or contaminate the food chain. The aim of this study was to identify pharmaceutical metabolites through a triplet approach for metabolite structure prediction (software-based predictions, literature review, and known common metabolic pathways), followed by generating in silico mass spectral libraries and applying various mass spectrometry modes for untargeted LC-qTOF analysis. Therefore, Eisenia fetida and Lactuca sativa were exposed to a pharmaceutical mixture (atenolol, enrofloxacin, erythromycin, ketoprofen, sulfametoxazole, tetracycline) under hydroponic and soil conditions at environmentally relevant concentrations. Samples collected at different time points were extracted using QuEChERS and analyzed with LC-qTOF in data-dependent (DDA) and data-independent (DIA) acquisition modes, applying both positive and negative electrospray ionization. The triplet approach for metabolite structure prediction yielded a total of 3762 pharmaceutical metabolites, and an in silico mass spectral library was created based on these predicted metabolites. This approach resulted in the identification of 26 statistically significant metabolites (p < 0.05), with DDA + and DDA - outperforming DIA modes by successfully detecting 56/67 sample type:metabolite combinations. Lettuce roots had the highest metabolite count (26), followed by leaves (6) and earthworms (2). Despite the lower metabolite count, earthworms showed the highest peak intensities, closely followed by roots, with leaves displaying the lowest intensities. Common metabolic reactions observed included hydroxylation, decarboxylation, acetylation, and glucosidation, with ketoprofen-related metabolites being the most prevalent, totaling 12 distinct metabolites. In conclusion, we developed a high-throughput workflow combining open-source software with LC-HRMS for identifying unknown metabolites across various sample types., (© 2024. The Author(s).)

Deconvolution and convolution are powerful tools that allow decomposition and reconstruction, respectively, of plasma versus time profiles from input and impulse functions. While deconvolution have commonly used compartmental approaches (e.g., Wagner-Nelson or Loo-Riegelman), convolution most typically used the convolution integral which can be solved with numerical methods. In 2005, an analytical solution for one-compartment pharmacokinetic was proposed and has been widely used ever since. However, to the best of our knowledge, analytical solutions for drugs distributed in more than one compartment have not been reported yet. In this paper, analytical solutions for compartmental convolution from both original and exact Loo-Riegelman approaches were developed and evaluated for different scenarios. While convolution from original approach was slightly more precise than that from the exact Loo-Riegelman, both methods were extremely accurate for reconstruction of plasma profiles after respective deconvolutions. Nonetheless, convolution from exact Loo-Riegelman was easier to interpret and to be manipulated mathematically. In fact, convolution solutions for three and more compartments can be easily written with this approach. Finally, our convolution analytical solution was applied to predict the failure in bioequivalence for levonorgestrel, demonstrating that equations in this paper may be useful tools for pharmaceutical scientists., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Industrial expansion and inadequate environmental safety measures are major contributors to environmental contamination, with heavy metals (HMs) and pharmaceutical waste playing crucial roles. Their negative effects are most noticeable in aquatic species and vegetation, where they accumulate in tissues and cause harmful results. Interactions between HMs and pharmaceutical molecules result in the production of metal-drug complexes (MDCs), which have the potential to disturb diverse ecosystems and their interdependence. However, present studies frequently focus on individual pollutants and their effects on specific environmental parameters, leaving out the cumulative effects of pollutants and their processes across several environmental domains. To address this gap, this review emphasizes the environmental sources of HMs, elucidates their emission pathways during anthropogenic activities, investigates the interactions between HMs and pharmaceutical substances, and defines the mechanisms underlying the formation of MDCs across various ecosystems. Furthermore, this review underscores the simultaneous occurrence of HMs and pharmaceutical waste across diverse ecosystems, including the atmosphere, soil, and water resources, and their incorporation into biotic organisms across trophic levels. It is important to note that these complex compounds represent a higher risk than individual contaminants.

Pharmaceuticals in aquatic environments pose threats to aquatic organisms because of their continuous release and potential accumulation. Monitoring methods for these contaminants are inadequate, with targeted analyses falling short in assessing water quality's impact on biota. The present study advocates for integrated strategies combining suspect and targeted chemical analyses with molecular biomarker approaches to better understand the risks posed by complex chemical mixtures to nontarget organisms. The research aimed to integrate chemical analysis and transcriptome changes in fathead minnows to prioritize contaminants, assess their effects, and apply this strategy in Wascana Creek, Canada. Analysis revealed higher pharmaceutical concentrations downstream of a wastewater-treatment plant, with clozapine being the most abundant in fathead minnows, showing notable bioavailability from water and sediment sources. Considering the importance of bioaccumulation factor and biota-sediment accumulation factor in risk assessment, these coefficients were calculated based on field data collected during spring, summer, and fall seasons in 2021. Bioaccumulation was classified as very bioaccumulative with values >5000 L kg -1 , suggesting the ability of pharmaceuticals to accumulate in aquatic organisms. The study highlighted the intricate relationship between nutrient availability, water quality, and key pathways affected by pharmaceuticals, personal care products, and rubber components. Prioritization of these chemicals was done through suspect analysis, supported by identifying perturbed pathways (specifically signaling and cellular processes) using transcriptomic analysis in exposed fish. This strategy not only aids in environmental risk assessment but also serves as a practical model for other watersheds, streamlining risk-assessment processes to identify environmental hazards and work toward reducing risks from contaminants of emerging concern. Environ Toxicol Chem 2024;43:2252-2273. © 2024 SETAC., (© 2024 SETAC.)

The advanced age population may be susceptible to an increased risk of adverse effects due to increased drug exposure after oral dosing. Factors such as high-interindividual variability and lack of data has led to poor characterization of absorption's role in pharmacokinetic changes in this population. Physiologically based pharmacokinetic (PBPK) models are increasingly being used during the drug development process, as their unique qualities are advantageous in atypical scenarios such as drug-drug interactions or special populations such as older people. Along with relying on various sources of data, auxiliary tools including parameter estimation and sensitivity analysis techniques are employed to support model development and other applications. However, sensitivity analyses have mostly been limited to localized techniques in the majority of reported PBPK models using them. This is disadvantageous, since local sensitivity analyses are unsuitable for risk analysis, which require assessment of parametric interactions and proper coverage of the input space to better estimate and subsequently mitigate the effects of the phenomenon of interest. For this reason, this study seeks to integrate a global sensitivity analysis screening method with PBPK models based in PK-Sim® to characterize the consequences of potential changes in absorption that are often associated with advanced age. The Elementary Effects (Morris) method and visualization of the results are implemented in R and three model drugs representing Biopharmaceutical Classification System classes I-III that are expected to exhibit some sensitivity to three age-associated hypotheses were successfully tested., Competing Interests: Declaration of competing interest The authors have no conflict of interest that are directly related to the content of this article., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Despite the benefits of breastfeeding, lactating mothers who take prescribed medications may discontinue breastfeeding due to concerns associated with infant drug exposure in breastmilk. Consolidating the current knowledge of drug transport to breastmilk may inform understanding of the safety of medication use during lactation. This literature review summarizes the mechanisms of drug transport to breastmilk, details the physicochemical drug properties that may alter the extent of passive transport, and describes the expressional changes in mammary drug transporters that may affect active transport. During the period of 20 July 2023 to 11 August 2023, PubMed® was searched to identify journal articles pertinent to the mechanisms of drug transport from maternal plasma to breastmilk and the expression of mammary drug transporters during lactation. From the 28 studies included in this review, four mechanisms were identified for transporting drugs from maternal plasma to breastmilk: passive transport, active transport, lipid co-transport, and transcytosis. The lactational expression of 20 drug transporters was further summarized, with 9 transporters demonstrating downregulated expression during lactation and 11 transporters demonstrating upregulated expression during lactation. Understanding the mechanisms of drug transport to breastmilk may aid in estimating infant drug exposure, developing physiologically based pharmacokinetic (PBPK) models that describe drug transfer, and initiating clinical drug development programs in the lactating population., (© 2024 The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

A dominant assumption in pharmacology throughout the 20th century has been that in vivo target occupancy-and attendant pharmacodynamics-depends on the systemic concentration of drug relative to the equilibrium dissociation constant for the drug-target complex. In turn, the duration of pharmacodynamics is temporally linked to the systemic pharmacokinetics of the drug. Yet, there are many examples of drugs for which pharmacodynamic effect endures long after the systemic concentration of a drug has waned to (equilibrium) insignificant levels. To reconcile such data, the drug-target residence time model was formulated, positing that it is the lifetime (or residence time) of the binary drug-target complex, and not its equilibrium affinity per se, that determines the extent and duration of drug pharmacodynamics. Here, we review this model, its evolution over time, and its applications to natural ligand-macromolecule biology and synthetic drug-target pharmacology., (© 2023 British Pharmacological Society.)

Rationale: This study focuses on the advantage of using the novel electron-activated dissociation (EAD) technology on the QTOF system for structural elucidation of conjugation metabolites. In drug metabolite identification, conceptual "boxes" are generally used to represent potential sites of modifications, which are proposed based on MS/MS data. Electron-activated dissociation (EAD) provides unique fragmentation patterns, potentially allowing for more precise localization of the metabolic modification sites compared to CID, particularly for conjugations., Method: Known compounds were incubated with rat liver microsomes in the presence of nicotinamide adenine dinucleotide phosphate (NADPH), uridine dihosphate-glucuronic acid (UDPGA), and glutathione. Conjugation metabolites were analyzed using the QTOF system. High-resolution MS/MS spectra were collected using EAD and CID fragmentations along with TOF MS full scan for tested drugs and metabolites. Fragmentation patterns were compared to evaluate their efficiency in structural elucidation., Results: Metabolite profiling identified conjugation metabolites (glucuronides and GSH adducts), using characteristic mass shifts. A comparison of EAD and CID fragmentation revealed EAD-specific fragments for most conjugates. EAD was able to break the relatively stable bonds on parent drug motifs while keeping relatively weak conjugation bonds intact, despite the generally low intensity of EAD. EAD effectively narrowed the conceptual "box" representing modification sites, providing more definitive information on conjugation sites and facilitating the structural elucidation of conjugated metabolites., Conclusion: EAD is a powerful tool for metabolite profiling in drug development, particularly for identifying conjugation sites. EAD-enabled MS/MS spectra offer a greater variety of signature fragments compared to CID, resulting in more comprehensive and unique structural information for metabolic modification analysis. Overall, EAD, complementary to CID, has the potential to narrow down potential modification sites, significantly enhancing the precision of conjugation metabolite structure elucidation., (© 2024 John Wiley & Sons Ltd.)

Biomass energy recovery from sewage sludge through anaerobic treatment is vital for environmental sustainability and a circular economy. However, large amounts of pharmaceutical and personal care products (PPCPs) remain in sludge, and their interactions with microbes and enzymes would affect resource recovery. This article reviews the effects and mechanisms of PPCPs on anaerobic sludge treatment. Most PPCPs posed adverse impacts on methane production, while certain low-toxicity PPCPs could stimulate volatile fatty acids and biohydrogen accumulation. Changes in the microbial community structure and functional enzyme bioactivities were also summarized with PPCPs exposure. Notably, PPCPs such as carbamazepine could bind with the active sites of the enzyme and induce microbial stress responses. The fate of various PPCPs during anaerobic sludge treatment indicated that PPCPs featuring electron-donating groups (e.g., ·-NH 2 and ·-OH), hydrophilicity, and low molecular weight were more susceptible to microbial utilization. Key biodegrading enzymes (e.g., cytochrome P450 and amidase) were crucial for PPCP degradation, although several PPCPs remain refractory to biotransformation. Therefore, remediation technologies including physical pretreatment, chemicals, bioaugmentation, and their combinations for enhancing PPCPs degradation were outlined. Among these strategies, advanced oxidation processes and combined strategies effectively removed complex and refractory PPCPs mainly by generating free radicals, providing recommendations for improving sludge detoxification.

The regulation of drug-metabolizing enzymes and transporters by cytokines has been extensively studied in vitro and in clinic. Cytokine-mediated suppression of cytochrome P450 (CYP) or drug transporters may increase or decrease the systemic clearance of drug substrates that are primarily cleared via these pathways; neutralization of cytokines by therapeutic proteins may thereby alter systemic exposures of such drug substrates. The Food and Drug Administration recommends evaluating such clinical drug interactions during clinical development and has provided labeling recommendations for therapeutic proteins. To determine the clinical relevance of these drug interactions to dose adjustments, trends in steady-state exposures of CYP-sensitive substrates coadministered with cytokine modulators as reported in the University of Washington Drug Interaction Database were extracted and examined for each of the CYPs. Coadministration of cytochrome P450 family 3 subfamily A (CYP3A) (midazolam/simvastatin), cytochrome P450 subfamily 2C19 (omeprazole), or cytochrome P450 subfamily 1A2 (caffeine/tizanidine) substrates with anti-interleukin-6 and with anti-interleukin-23 therapeutics led to changes in systemic exposures of CYP substrates ranging from ∼ -58% to ∼35%; no significant trends were observed for cytochrome P450 subfamily 2D6 (dextromethorphan) and cytochrome P450 subfamily 2C9 (warfarin) substrates. Although none of these changes in systemic exposures have been reported as clinically meaningful, dose adjustment of midazolam for optimal sedation in acute care settings has been reported. Simulated concentration-time profiles of midazolam under conditions of elevated cytokine levels when coadministered with tocilizumab, suggest a ∼six- to sevenfold increase in midazolam clearance, suggesting potential implications of cytokine-CYP drug interactions on dose adjustments of sensitive CYP3A substrates in acute care settings. Additionally, this article also provides a brief overview of nonclinical and clinical assessments of cytokine-CYP drug interactions in drug discovery and development. SIGNIFICANCE STATEMENT: There has been significant progress in understanding cytokine-mediated drug interactions for CYP-sensitive substrates. This article provides an overview of the progress in this field, including a trend analysis of systemic exposures of CYP-sensitive substrates coadministered with anti-interleukin therapeutics. In addition, the review also provides a perspective of current methods used to assess these drug interactions during drug development and a focus on individualized medicine, particularly in acute care settings., (Copyright © 2024 by The Author(s).)

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental, and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport, and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help describe factors mediating drug disposition, efficacy, and toxicity. While important advancements in this area have been made, there remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy. SIGNIFICANCE STATEMENT: Pharmacometabolomics has emerged as an approach to identify metabolites that allow for implementation of precision medicine approaches to pharmacotherapy. This review article provides an overview of pharmacometabolomics including highlights of important examples., (Copyright © 2024 by The American Society for Pharmacology and Experimental Therapeutics.)

Hydrolases represent an essential class of enzymes indispensable for the metabolism of various clinically essential medications. Individuals exhibit marked differences in the expression and activation of hydrolases, resulting in significant variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of drugs metabolized by these enzymes. The regulation of hydrolase expression and activity involves both genetic polymorphisms and nongenetic factors. This review examines the current understanding of genetic and nongenetic regulators of six clinically significant hydrolases, including carboxylesterase (CES)-1 CES2, arylacetamide deacetylase (AADAC), paraoxonase (PON)-1 PON3, and cathepsin A (CTSA). We explore genetic variants linked to the expression and activity of the hydrolases and their effects on the PK and PD of their substrate drugs. Regarding nongenetic regulators, we focus on the inhibitors and inducers of these enzymes. Additionally, we examine the developmental expression patterns and gender differences in the hydrolases when pertinent information was available. Many genetic and nongenetic regulators were found to be associated with the expression and activity of the hydrolases and PK and PD. However, hydrolases remain generally understudied compared with other drug-metabolizing enzymes, such as cytochrome P450s. The clinical significance of genetic and nongenetic regulators has not yet been firmly established for the majority of hydrolases. Comprehending the mechanisms that underpin the regulation of these enzymes holds the potential to refine therapeutic regimens, thereby enhancing the efficacy and safety of drugs metabolized by the hydrolases. SIGNIFICANCE STATEMENT: Hydrolases play a crucial role in the metabolism of numerous clinically important medications. Genetic polymorphisms and nongenetic regulators can affect hydrolases' expression and activity, consequently influencing the exposure and clinical outcomes of hydrolase substrate drugs. A comprehensive understanding of hydrolase regulation can refine therapeutic regimens, ultimately enhancing the efficacy and safety of drugs metabolized by the enzymes., (Copyright © 2024 by The American Society for Pharmacology and Experimental Therapeutics.)

Our knowledge of the roles of individual cytochrome P450 (P450) enzymes in drug metabolism has developed considerably in the past 30 years, and this base has been of considerable use in avoiding serious issues with drug interactions and issues due to variations. Some newer approaches are being considered for "phenotyping" metabolism reactions with new drug candidates. Endogenous biomarkers are being used for noninvasive estimation of levels of individual P450 enzymes. There is also the matter of some remaining "orphan" P450s, which have yet to be assigned reactions. Practical problems that continue in drug development include predicting drug-drug interactions, predicting the effects of polymorphic and other P450 variations, and evaluating interspecies differences in drug metabolism, particularly in the context of "metabolism in safety testing" regulatory issues ["disproportionate (human) metabolites"]. SIGNIFICANCE STATEMENT: Cytochrome P450 enzymes are the major catalysts involved in drug metabolism. The characterization of their individual roles has major implications in drug development and clinical practice., (Copyright © 2024 by The American Society for Pharmacology and Experimental Therapeutics.)

Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are useful for scaling in vitro and animal data to humans for accurate prediction and interpretation of drug clearance and toxicity. Targeted DMET proteomics that relies on synthetic stable isotope-labeled surrogate peptides as calibrators is routinely used for the quantification of selected proteins; however, the technique is limited to the quantification of a small number of proteins. Although the global proteomics-based total protein approach (TPA) is emerging as a better alternative for large-scale protein quantification, the conventional TPA does not consider differential sequence coverage by identifying unique peptides across proteins. Here, we optimized the TPA approach by correcting protein abundance data by the sequence coverage, which was applied to quantify 54 DMETs for characterization of 1) differential tissue DMET abundance in the human liver, kidney, and intestine, and 2) interindividual variability of DMET proteins in individual intestinal samples ( n = 13). Uridine diphosphate-glucuronosyltransferase 2B7 (UGT2B7), microsomal glutathione S-transferases (MGST1, MGST2, and MGST3) carboxylesterase 2 (CES2), and multidrug resistance-associated protein 2 (MRP2) were expressed in all three tissues, whereas, as expected, four cytochrome P450s (CYP3A4, CYP3A5, CYP2C9, and CYP4F2), UGT1A1, UGT2B17, CES1, flavin-containing monooxygenase 5, MRP3, and P-glycoprotein were present in the liver and intestine. The top three DMET proteins in individual tissues were: CES1>CYP2E1>UGT2B7 (liver), CES2>UGT2B17>CYP3A4 (intestine), and MGST1>UGT1A6>MGST2 (kidney). CYP3A4, CYP3A5, UGT2B17, CES2, and MGST2 showed high interindividual variability in the intestine. These data are relevant for enhancing in vitro to in vivo extrapolation of drug absorption and disposition and can be used to enhance the accuracy of physiologically based pharmacokinetic prediction of systemic and tissue concentration of drugs. SIGNIFICANCE STATEMENT: This study quantified the abundance and compositions of drug-metabolizing enzymes and transporters in pooled human liver, intestine, and kidney microsomes as well as individual intestinal microsomes using an optimized global proteomics approach. The data revealed large intertissue differences in the abundance of these proteins and high intestinal interindividual variability in the levels of cytochrome P450s (e.g., CYP3A4 and CYP3A5), uridine diphosphate-glucuronosyltransferase 2B17, carboxylesterase 2, and microsomal glutathione S-transferase 2. These data are applicable for the prediction of first-pass metabolism and tissue-specific drug clearance., (Copyright © 2024 by The American Society for Pharmacology and Experimental Therapeutics.)

Historically, the intestinal lymphatics were considered passive conduits for fluids, immune cells, dietary lipids, lipid soluble vitamins, and lipophilic drugs. Studies of intestinal lymphatic drug delivery in the late 20th century focused primarily on the drugs' physicochemical properties, especially high lipophilicity, that resulted in intestinal lymphatic transport. More recent discoveries have changed our traditional view by demonstrating that the lymphatics are active, plastic, and tissue-specific players in a range of biological and pathological processes, including within the intestine. These findings have, in turn, inspired exploration of lymph-specific therapies for a range of diseases, as well as the development of more sophisticated strategies to actively deliver drugs or vaccines to the intestinal lymph, including a range of nanotechnologies, lipid prodrugs, and lipid-conjugated materials that "hitchhike" onto lymphatic transport pathways. With the increasing development of novel therapeutics such as biologics, there has been interest in whether these therapeutics are absorbed and transported through intestinal lymph after oral administration. Here we review the current state of understanding of the anatomy and physiology of the gastrointestinal lymphatic system in health and disease, with a focus on aspects relevant to drug delivery. We summarize the current state-of-the-art approaches to deliver drugs and quantify their uptake into the intestinal lymphatic system. Finally, and excitingly, we discuss recent examples of significant pharmacokinetic and therapeutic benefits achieved via intestinal lymphatic drug delivery. We also propose approaches to advance the development and clinical application of intestinal lymphatic delivery strategies in the future. SIGNIFICANCE STATEMENT: This comprehensive review details the understanding of the anatomy and physiology of the intestinal lymphatic system in health and disease, with a focus on aspects relevant to drug delivery. It highlights current state-of-the-art approaches to deliver drugs to the intestinal lymphatics and the shift toward the use of these strategies to achieve pharmacokinetic and therapeutic benefits for patients., (Copyright © 2024 by The American Society for Pharmacology and Experimental Therapeutics.)

A selective and sensitive liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) method was developed and validated for simultaneous quantitation of a cassette of 8 drugs, including docetaxel, erlotinib, loperamide, riluzole, vemurafenib, verapamil, elacridar and tariquidar. Stable isotopically labeled compounds were available for use as internal standards for all compounds, except for tariquidar for which we used elacridar-d4. Sample pre-treatment involved liquid-liquid extraction using tert-butyl-methyl ether as this resulted in good recovery and low ion suppression. Chromatographic separation was achieved using a Zorbax Extend C18 analytical column and a linear gradient from 20 % to 95 % methanol in 0.1 % (v/v) formic acid in water. MS/MS detection using multiple reaction monitoring was done in positive ionization mode. We validated this assay for human and mouse plasma and mouse brain homogenates. The calibration curves were linear over a range 1-200 nM for each drug in the mix, except for tariquidar probably due to the lack of a stable isotope labeled analog. The intra-day and inter-day accuracies were within the 85-115 % range for all compounds at low, medium and high concentrations in the three different matrices. Similarly, the precision for all compounds at three different concentration levels ranged below 15 %, with the exception of tariquidar in mouse plasma and brain homogenate and riluzole in brain homogenate. Pilot studies have confirmed that the method is suitable for the analysis of mouse plasma samples and brain homogenates following cassette dosing of this mixture in mice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The study of protein-drug interaction plays a crucial role in understanding drug mechanisms, identifying new drug targets and biomarkers, and facilitating drug development and disease treatment. In recent years, significant progress has been made in various protein-drug interaction research methods due to the rapid development and in-depth application of mass spectrometry, nuclear magnetic resonance, Raman spectroscopy, and other technologies. The progress has enhanced the sensitivity, precision, accuracy, and applicability of analytical methods, enabling the establishment of drug-protein interaction networks. This review discusses various emerging research methods, such as native mass spectrometry, infrared spectroscopy, nuclear magnetic resonance and spectrum, biosensor technologies employing surface enhanced Raman, electrochemistry, and magneto resistive signals, as well as affinity magnetic levitation and affinity chromatography. The article also delves into the principles, applications, advantages, and limitations of these technologies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Pharmaceuticals (PhACs) are increasingly detected in aquatic ecosystems, yet their effects on biota remain largely unknown. The environmentally relevant concentrations of many PhACs may not result in individual-level responses, like mortality or growth inhibition, traditional toxicity endpoints. However, this doesn't imply the absence of negative effects on biota. Metabolomics offers a more sensitive approach, detecting responses at molecular and cellular levels and providing mechanistic understanding of adverse effects. We evaluated bioaccumulation and metabolic alterations in a benthic ostracod, Heterocypris incongruens, exposed to a mixture of five PhACs (carbamazepine, tiapride, tolperisone, propranolol and amlodipine) at environmentally relevant concentrations for 7 days using liquid chromatography coupled with mass spectrometry. The selection of PhACs was based, among other factors, on risk quotient values determined using toxicological data available in the literature and concentrations of PhACs quantified in our previous research in the sediments of the Odra River estuary. This represents a novel approach to PhACs selection for metabolomic studies that considers strictly quantitative data. Amlodipine and tolperisone exhibited the highest bioaccumulation. Significant impacts were observed in Alanine, aspartate and glutamate metabolism, Starch and sucrose metabolism, Arginine biosynthesis, Histidine metabolism, Tryptophan metabolism, Glycerophospholipid metabolism, and Glutathione metabolism pathways. Most of the below-individual-level responses were likely nonspecific and related to dysregulation in energy metabolism and oxidative stress response. Additionally, some pharmaceutical-specific responses were also observed. Therefore, untargeted metabolomics can be used to detect metabolic changes resulting from environmentally relevant concentrations of PhACs in aquatic ecosystems and to understand their underlying mechanism., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Joanna Giebultowicz reports financial support was provided by National Science Centre Poland. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Benjamin Long reports financial support was provided by Barwon Region Water Corporation. Benjamin Long reports financial support was provided by Central Highlands Region Water Corporation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2024. Published by Elsevier B.V. All rights reserved.)

The identification of drug-target interaction (DTI) is crucial for drug discovery. However, how to reduce the graph neural network's false positives due to its bias and negative transfer in the original bipartite graph remains to be clarified. Considering that the impact of heterogeneous auxiliary information on DTI varies depending on the drug and target, we established an adaptive enhanced personalized meta-knowledge transfer network named M eta G raph A ssociation-Aware C ontrastive L earning (MGACL), which can transfer personalized heterogeneous auxiliary information from different nodes and reduce data bias. Meanwhile, we propose a novel DTI association-aware contrastive learning strategy that aligns high-frequency drug representations with learned auxiliary graph representations to prevent negative transfer. Our study improves the DTI prediction performance by about 3%, evaluated by analyzing the area under the curve (AUC) and area under the precision-recall curve (AUPRC) compared with existing methods, which is more conducive to accurately identifying drug targets for the development of new drugs.

Pharmaceuticals and personal care products (PPCPs) exhibit varying biodegradability during the acidogenic and methanogenic phases of anaerobic digestion. However, there is limited information regarding the end products generated during these processes. This work investigates the biotransformation products (BTPs) generated in a two-phase (TP) acidogenic-methanogenic (Ac-Mt) bioreactor using advanced suspect and nontarget strategies. Fourteen BTPs were confidently identified from ten parent PPCPs including carbamazepine (CBZ), naproxen (NPX), diclofenac (DCF), ibuprofen (IBU), acetaminophen (ACT), metoprolol (MTP), sulfamethoxazole (SMX), ciprofloxacin (CIP), methylparaben (MPB) and propylparaben (PPB). These BTPs were linked with oxidation reactions such as hydroxylation, demethylation and epoxidation. Their generation was related to organic acid production, since all metabolites were detected during acidogenesis, with some being subsequently consumed during methanogenesis, e.g., aminothiophenol and kynurenic acid. Another group of BTPs showed increased concentrations under methanogenic conditions, e.g., hydroxy-diclofenac and epoxy-carbamazepine. The most PPCPs showed high removal efficiencies (> 90 %) - SMX, CIP, NPX, MTP, ACT, MPB, PPB, while DCF, CBZ and IBU demonstrated higher persistence - DCF (42 %); CBZ (40 %), IBU (47 %). The phase separation of anaerobic digestion provided a deeper understanding of the biotransformation pathways of PPCPs, in addition to enhancing the biodegradability of the most persistent compounds, i.e., DCF, CBZ and IBU., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:, (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: Poly(amidoamine) dendrimers have been widely investigated as potential nanomaterials that can enhance the skin permeation of topically applied drugs. This article reviews the studies that have used dendrimers as penetration enhancers and examines the mechanisms by which enhancement is claimed., Key Findings: A wide range of studies have demonstrated that, in certain circumstances and for certain drugs, the incorporation of dendrimers into a topically applied formulation can significantly increase the amount of drug passing into and through the skin. In some cases, dendrimers offered little or no enhancement of skin permeation, suggesting that the drug-dendrimer interaction and the selection of a specific dendrimer were central to ensuring optimal enhancement of skin permeation. Significant interactions between dendrimers and other formulation components were also reported in some cases., Summary: Dendrimers offer substantial potential for enhancing drug delivery into and across the skin, putatively by mechanisms that include occlusion and changes to surface tension. However, most of these studies are conducted in vitro and limited progress has been made beyond such laboratory studies, some of which are conducted using membranes of limited relevance to humans, such as rodent skin. Thus, the outcomes and claims of such studies should be treated with caution., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Royal Pharmaceutical Society.)

The oral route stands out as the most commonly used method for drug administration, prized for its non-invasive nature, patient compliance, and easy administration. Several elements influence the absorption of oral medications, including their solubility, permeability across mucosal membranes, and stability within the gastrointestinal (GI) environment. Research has delved into comprehending physicochemical, biochemical, metabolic, and biological obstacles that impact the bioavailability of a drug. To improve oral drug absorption, several pharmaceutical technologies and delivery methods have been studied, including cyclodextrins, micelles, nanocarriers, and lipid-based carriers. This review examines both traditional and innovative drug delivery methods, as well as the physiological and pharmacological barriers influencing medication bioavailability when taken orally. Additionally, it describes the challenges and advancements in developing formulations suitable for oral use., (© 2024. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.)

Motivation: The prediction of drug-target interaction is a vital task in the biomedical field, aiding in the discovery of potential molecular targets of drugs and the development of targeted therapy methods with higher efficacy and fewer side effects. Although there are various methods for drug-target interaction (DTI) prediction based on heterogeneous information networks, these methods face challenges in capturing the fundamental interaction between drugs and targets and ensuring the interpretability of the model. Moreover, they need to construct meta-paths artificially or a lot of feature engineering (prior knowledge), and graph generation can fuse information more flexibly without meta-path selection., Results: We propose a causal enhanced method for drug-target interaction (CE-DTI) prediction that integrates graph generation and multi-source information fusion. First, we represent drugs and targets by modeling the fusion of their multi-source information through automatic graph generation. Once drugs and targets are combined, a network of drug-target pairs is constructed, transforming the prediction of drug-target interactions into a node classification problem. Specifically, the influence of surrounding nodes on the central node is separated into two groups: causal and non-causal variable nodes. Causal variable nodes significantly impact the central node's classification, while non-causal variable nodes do not. Causal invariance is then used to enhance the contrastive learning of the drug-target pairs network. Our method demonstrates excellent performance compared with other competitive benchmark methods across multiple datasets. At the same time, the experimental results also show that the causal enhancement strategy can explore the potential causal effects between DTPs, and discover new potential targets. Additionally, case studies demonstrate that this method can identify potential drug targets., Availability and Implementation: The source code of AdaDR is available at: https://github.com/catly/CE-DTI., (© The Author(s) 2024. Published by Oxford University Press.)

Motivation: Exploring the association between drugs and targets is essential for drug discovery and repurposing. Comparing with the traditional methods that regard the exploration as a binary classification task, predicting the drug-target binding affinity can provide more specific information. Many studies work based on the assumption that similar drugs may interact with the same target. These methods constructed a symmetric graph according to the undirected drug similarity or target similarity. Although these similarities can measure the difference between two molecules, it is unable to analyze the inclusion relationship of their substructure. For example, if drug A contains all the substructures of drug B, then in the message-passing mechanism of the graph neural network, drug A should acquire all the properties of drug B, while drug B should only obtain some of the properties of A., Results: To this end, we proposed a structure-inclusive similarity (SIS) which measures the similarity of two drugs by considering the inclusion relationship of their substructures. Based on SIS, we constructed a drug graph and a target graph, respectively, and predicted the binding affinities between drugs and targets by a graph convolutional network-based model. Experimental results show that considering the inclusion relationship of the substructure of two molecules can effectively improve the accuracy of the prediction model. The performance of our SIS-based prediction method outperforms several state-of-the-art methods for drug-target binding affinity prediction. The case studies demonstrate that our model is a practical tool to predict the binding affinity between drugs and targets., Availability and Implementation: Source codes and data are available at https://github.com/HuangStomach/SISDTA., (© The Author(s) 2024. Published by Oxford University Press.)

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 K ow . 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Sulphotransferases (SULTs) are a major phase II metabolic enzyme class contributing ~20 % to the Phase II metabolism of FDA-approved drugs. Ignoring the potential for SULT-mediated metabolism leaves a strong potential for drug-drug interactions, often causing late-stage drug discovery failures or black-boxed warnings on FDA labels. The existing models use only accessibility descriptors and machine learning (ML) methods for class and site of sulfonation (SOS) predictions for SULT. In this study, a variety of accessibility, reactivity, and hybrid models and algorithms have been developed to make accurate substrate and SOS predictions. Unlike the literature models, reactivity parameters for the aliphatic or aromatic hydroxyl groups (R/Ar-O-H), the Bond Dissociation Energy (BDE) gave accurate models with a True Positive Rate (TPR)=0.84 for SOS predictions. We offer mechanistic insights to explain these novel findings that are not recognized in the literature. The accessibility parameters like the ratio of Chemgauss4 Score (CGS) and Molecular Weight (MW) CGS/MW and distance from cofactor (Dis) were essential for class predictions and showed TPR=0.72. Substrates consistently had lower BDE, Dis, and CGS/MW than non-substrates. Hybrid models also performed acceptablely for SOS predictions. Using the best models, Algorithms gave an acceptable performance in class prediction: TPR=0.62, False Positive Rate (FPR)=0.24, Balanced accuracy (BA)=0.69, and SOS prediction: TPR=0.98, FPR=0.60, and BA=0.69. A rule-based method was added to improve the predictive performance, which improved the algorithm TPR, FPR, and BA. Validation using an external dataset of drug-like compounds gave class prediction: TPR=0.67, FPR=0.00, and SOS prediction: TPR=0.80 and FPR=0.44 for the best Algorithm. Comparisons with standard ML models also show that our algorithm shows higher predictive performance for classification on external datasets. Overall, these models and algorithms (SOS predictor) give accurate substrate class and site (SOS) predictions for SULT-mediated Phase II metabolism and will be valuable to the drug discovery community in academia and industry. The SOS predictor is freely available for academic/non-profit research via the GitHub link., (© 2024 Wiley-VCH GmbH.)

Bioinformatics is a rapidly evolving field that applies computational methods to analyze and interpret biological data. A key task in bioinformatics is identifying novel drug-target interactions (DTIs), which plays a crucial role in drug discovery. Most computational approaches treat DTI prediction as a binary classification problem, determining whether drug-target pairs interact. However, with the growing availability of drug-target binding affinity data, this binary task can be reframed as a regression problem focused on drug-target affinity (DTA). DTA quantifies the strength of drug-target binding, offering more detailed insights than DTI and serving as a valuable tool for virtual screening in drug discovery. Accurately predicting compound interactions with targets can accelerate the drug development process. In this study, we introduce a deep learning model named TC-DTA for DTA prediction, leveraging convolutional neural networks (CNN) and the encoder module of the transformer architecture. We begin by extracting raw drug SMILES strings and protein amino acid sequences from the dataset, which are then represented using various encoding methods. Subsequently, we employ CNN and the transformer's encoder module to extract features from the drug SMILES strings and protein sequences, respectively. Finally, the feature information is concatenated and input into a multi-layer perceptron to predict binding affinity scores. We evaluated our model on two benchmark DTA datasets, Davis and KIBA, comparing it with methods such as KronRLS, SimBoost, and DeepDTA. Our model, TC-DTA, outperformed these baseline methods based on evaluation metrics like Mean Squared Error (MSE), Concordance Index (CI), and Regression towards the Mean Index ( r m 2 ). These results highlight the effectiveness of the Transformer's encoder and CNN in extracting meaningful representations from sequences, thereby enhancing DTA prediction accuracy. This deep learning model can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, machine learning technology offers a more effective and efficient approach to drug discovery.

This review considers the role of in vitro permeation testing (IVPT) for the evaluation of drug delivery from topical formulations applied to the skin. The technique was pioneered by Franz in the 1970's and today remains an important tool in the development, testing and optimization of such topical formulations. An overview of IVPT as well as selection of skin for the experiment, integrity testing of the membrane, and required number of replicate skin samples is discussed. In the literature many researchers have focused solely on permeation and have not reported amounts of the active remaining on and in the skin at the end of the IVPT. Therefore, a particular focus of this article is determination of the complete mass balance of the drug. It is noteworthy that for the evaluation of bioequivalence of topical formulations the draft guideline issued by the European Medicines Agency (EMA) requires the IVPT method to report on both the skin deposition and distribution of the active in the skin as well as amount permeated. Other aspects of current guidance from the EMA and United States Food and Drug Agency for IVPT are also compared and contrasted. Ultimately, harmonisation of IVPT protocols across the regulatory agencies will expedite the development process for novel topical formulations as well as the availability of generic products., (Copyright © 2024 The Author. Published by Elsevier B.V. All rights reserved.)

Biocatalysis has emerged as a strong tool for the synthesis of active pharmaceutical ingredients (APIs). In the early twentieth century, whole cell biocatalysis was used to develop the first industrial biocatalytic processes, and the precise work of enzymes was unknown. Biocatalysis has evolved over the years into an essential tool for modern, cost-effective, and sustainable pharmaceutical manufacturing. Meanwhile, advances in directed evolution enable the rapid production of process-stable enzymes with broad substrate scope and high selectivity. Large-scale synthetic pathways incorporating biocatalytic critical steps towards >130 APIs of authorized pharmaceuticals and drug prospects are compared in terms of steps, reaction conditions, and scale with the corresponding chemical procedures. This review is designed on the functional group developed during the reaction forming alcohol functional groups. Some important biocatalyst sources, techniques, and challenges are described. A few APIs and their utilization in pharmaceutical drugs are explained here in this review. Biocatalysis has provided shorter, more efficient, and more sustainable alternative pathways toward existing small molecule APIs. Furthermore, non-pharmaceutical applications of biocatalysts are also mentioned and discussed. Finally, this review includes the future outlook and challenges of biocatalysis. In conclusion, Further research and development of promising enzymes are required before they can be used in industry., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The Pharmpy Automatic Model Development (AMD) tool automates the building of population pharmacokinetic (popPK) models by utilizing a systematic stepwise process. In this study, the performance of the AMD tool was assessed using simulated datasets. Ten true models mimicking classical popPK models were created. From each true model, dataset replicates were simulated assuming a typical phase I study design-single and multiple ascending doses with/without dichotomous food effect, with rich PK sampling. For every dataset replicate, the AMD tool automatically built an AMD model utilizing NONMEM for parameter estimation. The AMD models were compared to the true and reference models (true model fitted to simulated datasets) based on their model components, predicted population and individual secondary PK parameters (SP) (AUC 0-24 , c max , c trough ), and model quality metrics (e.g., model convergence, parameter relative standard errors (RSEs), Bayesian Information Criterion (BIC)). The models selected by the AMD tool closely resembled the true models, particularly in terms of distribution and elimination, although differences were observed in absorption and inter-individual variability components. Bias associated with the derived SP was low. In general, discrepancies between AMD and true SP were also observed for reference models and therefore were attributed to the inherent stochasticity in simulations. In summary, the AMD tool was found to be a valuable asset in automating repetitive modeling tasks, yielding reliable PK models in the scenarios assessed. This tool has the potential to save time during early clinical drug development that can be invested in more complex modeling activities within model-informed drug development., (© 2024 Roche. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Uridine 5'-diphospho-glucuronosyltransferases (UGTs) demonstrate variable expression in the pediatric population. Thus, understanding of age-dependent maturation of UGTs is critical for accurate pediatric pharmacokinetics (PK) prediction of drugs that are susceptible for glucuronidation. Ontogeny functions of major UGTs have been previously developed and reported. However, those ontogeny functions are based on in vitro data (i.e., enzyme abundance, in vitro substrate activity, and so on) and therefore, may not translate to in vivo maturation of UGTs in the clinical setting. This report describes meta-analysis of the literature to develop and compare ontogeny functions for 8 primary UGTs (UGT1A1, UGT1A4, UGT1A6, UGT1A9, UGT2B7, UGT2B10, UGT2B15, and UGT2B17) based on published in vitro and in vivo studies. Once integrated with physiologically based pharmacokinetics modeling models, in vivo activity-based ontogeny functions demonstrated somewhat greater prediction accuracy (mean squared error, MSE: 0.05) compared to in vitro activity (MSE: 0.104) and in vitro abundance-based ontogeny functions (MSE: 0.129)., (© 2024, The American College of Clinical Pharmacology.)

ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME and animal PK endpoints trained on in-house data generated at Boehringer Ingelheim. Models were evaluated both at the design stage of a compound (i. e., no experimental data of test compounds available) and at testing stage when a particular assay would be conducted (i. e., experimental data of earlier conducted assays may be available). Using realistic time-splits, we found a clear benefit in performance of multi-task graph-based neural network models over single-task model, which was even stronger when experimental data of earlier assays is available. In an attempt to explain the success of multi-task models, we found that especially endpoints with the largest numbers of data points (physicochemical endpoints, clearance in microsomes) are responsible for increased predictivity in more complex ADME and PK endpoints. In summary, our study provides insight into how data for multiple ADME/PK endpoints in a pharmaceutical company can be best leveraged to optimize predictivity of ML models., (© 2024 Wiley-VCH GmbH.)

Introduction: Determining whether a new drug is a substrate, inhibitor or inducer of efflux or uptake membrane transporters has become a routine process during drug discovery and development. In vitro assays are utilized to establish whether a new drug has the potential to be an object (substrate) or precipitant (inhibitor, inducer) in transporter-mediated clinical drug-drug interactions. The findings from these in vitro experiments are then used to determine whether further in vivo drug interaction studies are necessary for a new drug., Areas Covered: This article provides an update on in vitro transporter assays, focusing on new uses of transfected cells, time-dependent inhibition, transporter induction, and complex model systems., Expert Opinion: The newer in vitro assays add to the toolbox in defining new drugs as transporter substrates, inhibitors, or inducers. Complex models such as spheroids, organoids, and microphysiological systems require standardization and further research with model transporter substrates and inhibitors. In drug discovery, the more traditional transporter assays may be employed as substrate and inhibitor screening assays. In drug development, more complex cell models can be employed in later drug development to better understand how transporter(s) are involved in the absorption, distribution, and excretion of new drugs.

The liver plays a pivotal role in drug disposition owing to the expression of transporters accounting for the uptake at the sinusoidal membrane and the efflux across the basolateral and canalicular membranes of hepatocytes of many different compounds. Moreover, intracellular mechanisms of phases I and II biotransformation generate, in general, inactive compounds that are more polar and easier to eliminate into bile or refluxed back toward the blood for their elimination by the kidneys, which becomes crucial when the biliary route is hampered. The set of transporters expressed at a given time, i.e., the so-called transportome, is encoded by genes belonging to two gene superfamilies named Solute Carriers (SLC) and ATP-Binding Cassette (ABC), which account mainly, but not exclusively, for the uptake and efflux of endogenous substances and xenobiotics, which include many different drugs. Besides the existence of genetic variants, which determines a marked interindividual heterogeneity regarding liver drug disposition among patients, prevalent diseases, such as cirrhosis, non-alcoholic steatohepatitis, primary sclerosing cholangitis, primary biliary cirrhosis, viral hepatitis, hepatocellular carcinoma, cholangiocarcinoma, and several cholestatic liver diseases, can alter the transportome and hence affect the pharmacokinetics of drugs used to treat these patients. Moreover, hepatic drug transporters are involved in many drug-drug interactions (DDI) that challenge the safety of using a combination of agents handled by these proteins. Updated information on these questions has been organized in this article by superfamilies and families of members of the transportome involved in hepatic drug disposition., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Microplastics (MPs) and pharmaceuticals and personal care products (PPCPs) are ubiquitous in aquatic environments. Algae play an important role in aquatic environments. Thus, it is important to study the response of algae to combined exposure of MPs and PPCPs. Here, we review the effects of MPs and PPCPs on algae. First, the individual effects of MPs and PPCPs on algae were summarized. Second, the combined effects of MPs and PPCPs on algae were systematically analyzed. (1) Antagonism: ① when the MPs are too large to enter the algal cells, the adsorption of PPCPs onto MPs results in decreased the contact of MPs and PPCPs with algae; ② PPCPs and MPs have opposing actions on the same biological target; ③ MPs increase the activity of metabolic enzymes in algae, thus promoting the PPCP degradation. (2) Synergy: ① when the MPs are small enough to enter algal cells, the adsorption of PPCPs on MPs promotes the entry of PPCPs; ② when MPs are negatively charged, the adsorption of positively charged PPCPs by MPs decreases the electrostatic repulsion, increasing the interaction between algae and MPs; ③ complementary modes of action between MPs and PPCPs show combined effects on the same biological target. Third, the relative importance of the factors that impact the combined effects are evaluated using the random forest model decreased in the following order: PPCP types > algal species > MP size > MP concentration > MP types > exposure time. Finally, future directions for the combined effects of MPs and PPCPs are proposed, which will facilitate a better understanding of the environmental fate and risks of both MPs and PPCPs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Drug-target interactions (DTIs) prediction algorithms are used at various stages of the drug discovery process. In this context, specific problems such as deorphanization of a new therapeutic target or target identification of a drug candidate arising from phenotypic screens require large-scale predictions across the protein and molecule spaces. DTI prediction heavily relies on supervised learning algorithms that use known DTIs to learn associations between molecule and protein features, allowing for the prediction of new interactions based on learned patterns. The algorithms must be broadly applicable to enable reliable predictions, even in regions of the protein or molecule spaces where data may be scarce. In this paper, we address two key challenges to fulfill these goals: building large, high-quality training datasets and designing prediction methods that can scale, in order to be trained on such large datasets. First, we introduce LCIdb, a curated, large-sized dataset of DTIs, offering extensive coverage of both the molecule and druggable protein spaces. Notably, LCIdb contains a much higher number of molecules than publicly available benchmarks, expanding coverage of the molecule space. Second, we propose Komet (Kronecker Optimized METhod), a DTI prediction pipeline designed for scalability without compromising performance. Komet leverages a three-step framework, incorporating efficient computation choices tailored for large datasets and involving the Nyström approximation. Specifically, Komet employs a Kronecker interaction module for (molecule, protein) pairs, which efficiently captures determinants in DTIs, and whose structure allows for reduced computational complexity and quasi-Newton optimization, ensuring that the model can handle large training sets, without compromising on performance. Our method is implemented in open-source software, leveraging GPU parallel computation for efficiency. We demonstrate the interest of our pipeline on various datasets, showing that Komet displays superior scalability and prediction performance compared to state-of-the-art deep learning approaches. Additionally, we illustrate the generalization properties of Komet by showing its performance on an external dataset, and on the publicly available L H benchmark designed for scaffold hopping problems. Komet is available open source at https://komet.readthedocs.io and all datasets, including LCIdb, can be found at https://zenodo.org/records/10731712.

Background: Exhaled breath (EB) aerosol was in principle shown to be a suitable matrix for bioanalysis of volatile but also non-volatile compounds. This attracted particular interest in the field of drug analysis. However, a big gap still exists in the understanding how and which drugs and/or their metabolites are excreted into exhaled breath and could thus actually be detected. The current study aimed to develop an analytical workflow for the qualitative detection of non-volatile drugs in EB aerosol microparticles., Results: The analyte selection covered different drug classes such as antihypertensives, anticonvulsants or opioid analgesics to investigate and understand the excretion of drugs and their metabolites into EB aerosol. A device for collecting aerosol particles from the lung through impaction was used for the non-invasive sampling procedure. Three expiration cycles per participant and device were collected. The sample preparation consisted of a collector extraction with methanol. Qualitative method development and validation were performed using reversed-phase liquid chromatography (LC) coupled to orbitrap-based high-resolution mass spectrometry (HRMS). Qualitative method validation was done according to published recommendations and international guidelines. Parameters such as selectivity, carry-over, limits of detection and identification, recovery, matrix effects, and long-term stability were evaluated. The limits of detection ranged from 100 pg/collector to 10,000 pg/collector. The procedure was finally used to analyze a total of 31 patient EB samples and demonstrated that e.g., tilidine and its metabolite nortilidine as well as tramadol and its active metabolite O-desmethyltramadol can be detected in EB aerosol., Significance and Novelty: The work shows a comprehensive workflow for elucidating drug excretion into exhaled breath aerosol. This bioanalytical strategy and the corresponding novel data from this study are the foundation for further method development and to better understand, which drugs and their metabolites can be addressed by exhaled breath aerosol bioanalysis., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Felix Mahfoud reports financial support was provided by German Research Foundation. Felix Mahfoud reports financial support was provided by German Cardiac Society. Felix Mahfoud reports financial support was provided by German Heart Foundation. Felix Mahfoud reports a relationship with Ablative Solutions, Medtronic, ReCor Medical, Amgen, Astra-Zeneca, Bayer, Boehringer Ingelheim, Inari, Merck, Servier, and Terumo that includes: consulting or advisory and speaking and lecture fees. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Drug overuse harms the biosphere, leading to disturbances in ecosystems' functioning. Consequently, more and more actions are being taken to minimise the harmful impact of xenopharmaceuticals on the environment. One of the innovative solutions is using biosorbents-natural materials such as cells or biopolymers-to remove environmental pollutants; however, this focuses mainly on the removal of metal ions and colourants. Therefore, this study investigated the biosorption ability of selected pharmaceuticals-paracetamol, diclofenac, and ibuprofen-by the biomass of the cyanobacteria Anabaena sp. and Chroococcidiopsis thermalis , using the LC-MS/MS technique. The viability of the cyanobacteria was assessed by determining photosynthetic pigments in cells using a UV-VIS spectrophotometer. The results indicate that both tested species can be effective biosorbents for paracetamol and diclofenac. At the same time, the tested compounds did not have a toxic effect on the tested cyanobacterial species and, in some cases, stimulated their cell growth. Furthermore, the Anabaena sp. can effectively biotransform DCF into its dimer.