Pharmaceutical Compounds Research Articles

Metabolomic and sphingolipidomic profiling of human hepatoma cells exposed to widely used pharmaceuticals

Pharmaceutical compounds have become one of the main contaminants of emerging concern (CECs) due to their high usage and increased release into the environment. This study aims to assess the effects caused by three widely consumed hepatotoxic pharmaceutical compounds: an antibiotic (amoxicillin), an antiepileptic (carbamazepine), and an antidepressant (trazodone), on human health when indirectly exposed to toxicologically relevant concentrations (30, 15, and 7.5 μM for amoxicillin and carbamazepine, and 4, 2, and 1 μM for trazodone). A combination of semi-targeted metabolomic and targeted sphingolipid analyses was chosen to unravel the metabolic alterations in human hepatic cells exposed to these CECs at three concentrations for 24 h. HepG2 hepatoma cells were encapsulated in sodium alginate spheroids to improve the physiological relevance of this in vitro approach. Statistical analysis was used to identify the most affected metabolites and sphingolipids for each drug exposure. The results revealed small but significant changes in response to carbamazepine and trazodone exposures, affecting sphingolipid, glycerophospholipid precursors, and amino acid metabolism. Under both drug treatments, a decrease in various ceramide species (related to cell signaling) was observed, along with reduced taurine levels (related to the biosynthesis of bile acid conjugates) and carnitine levels (suggesting an impact on energy production). These and other drug-specific changes indicate that cellular functions in liver cells might be altered under low doses of these CECs, potentially affecting the health of other organs.

Size and polarity fractions of mobile organic matter from manure affect the sorption of sulfadiazine, caffeine and atenolol in soil

Waste-derived organics introduced to soils along with pharmaceutical active compounds (PhAC) are a crude mixture of compounds occurring in various size and polarity fractions. They affect the sorption of PhACs to soil; however, the relevant knowledge is still insufficient. The effects of different size and polarity fractions of manure-derived mobile organic matter (<63 µm) on the sorption of sulfadiazine, caffeine and atenolol to five topsoils were investigated. Mobilization of the PhACs was strongest in the presence of dissolved organic matter (mDOM, <0.45 µm), with a reduction of Kd of sulfadiazine, caffeine and atenolol by mean factors of 0.66, 0.57 and 0.41, respectively. The mobilizing effects of colloidal organic matter (0.45–10 µm) were slightly smaller. Fine particulate organic matter (10–63 µm) reduced the sorption of the PhACs in slightly acidic soils (pH 6.0), but increased it in strongly acidic soil (pH 4.3). Furthermore, hydrophobic (HO-mDOM) and hydrophilic (HI-mDOM) fractions of mDOM reduced the sorption capacity but increased the sorption nonlinearity of PhACs in soils. Effects of HO-mDOM and HI-mDOM were PhAC specific. It is suggested to consider the varying impacts of mobile fractions in animal manure and/or treated wastewater in evaluating the fate and environmental relevance of associated PhACs.

Nanocarbons decorated TiO2 as advanced nanocomposite fabric for photocatalytic degradation of methylene blue dye and ciprofloxacin

In this research, C [graphene (Gr), multi-walled carbon nanotubes (MWCNTs), and single-walled carbon nanotubes (SWCNTs)] decorated TiO2 nanostructures were developed and further utilized to modify textile to achieve high-performance photocatalytic degradation of mixed water pollutants including methylene blue (MB) dye and pharmaceutical active compound, Ciprofloxacin (CPF) on solar radiation exposure (∼105 Lumens). The crystallite sizes of TiO2, TiO2-MWCNT, TiO2-SWCNT, and TiO2-Gr nanocomposites were observed in the 7.2–10.4 nm range. The bandgap, Eg of nanostructured C-TiO2, was observed in the 3.09–3.14 eV range. The photocatalytic performance of C-TiO2 showed almost complete removal of MB under the irradiation of the solar spectrum in just 120 min. Among C-based TiO2 nanocomposites, nanostructured TiO2-SWCNT showed the highest degradation efficiency of ∼98.86 % for MB dye. Further, the textile membrane showed a good degradation efficiency of 99.49 % for MB dye and 95.7 % for CPF within 120 min. An impressive removal efficiency of ∼93 % was observed for the mixture of MB and CPF solution due to a combination of several properties like surface groups (OH− & O2−), low charge recombination rate due to CNTs, and small crystal size. This proof-of-the-concept study can be the potential platform to manage water quality according to sustainability goals.

Machine learning for the adsorptive removal of ciprofloxacin using sugarcane bagasse as a low-cost biosorbent: comparison of analytic, mechanistic, and neural network modeling.

Contamination with traces of pharmaceutical compounds, such as ciprofloxacin, has prompted interest in their removal via low-cost, efficient biomass-based adsorption. In this study, classical models, a mechanistic model, and a neural network model were evaluated for predicting ciprofloxacin breakthrough curves in both laboratory- and pilot scales. For the laboratory-scale (d = 2.2cm, Co = 5mg/L, Q = 7mL/min, T = 18°C) and pilot-scale (D = 4.4cm, Co = 5mg/L, Q = 28mL/min, T = 18°C) setups, the experimental adsorption capacities were 2.19 and 2.53mg/g, respectively. The mechanistic model reproduced the breakthrough data with high accuracy on both scales (R2 > 0.4 and X2 < 0.15), and its fit was higher than conventional analytical models, namely the Clark, Modified Dose-Response, and Bohart-Adams models. The neural network model showed the highest level of agreement between predicted and experimental data with values of R2 = 0.993, X2 = 0.0032 (pilot-scale) and R2 = 0.986, X2 = 0.0022 (laboratory-scale). This study demonstrates that machine learning algorithms exhibit great potential for predicting the liquid adsorption of emerging pollutants in fixed bed.

Interaction between antidepressant drug trazodone with double-stranded DNA: Multi-spectroscopic and computational analysis

Trazodone (TZD) is an antidepressant drug used to treat major depressive and sleeping disorders. Elevated doses of trazodone are associated with central nervous system depression, which manifests as nausea, drowsiness, confusion, vertigo, exhaustion, etc. To develop a clinically viable active pharmaceutical compound with minimal adverse effects, it is imperative to possess a comprehensive knowledge of the drug's action mechanism on DNA. Hence, we investigate the mode of interaction between trazodone and DNA utilizing various spectroscopic and computational techniques. Studies using UV–vis titration showed that the DNA and trazodone have an effective interaction. The magnitude of the Stern-Volmer constant (KSV) has been calculated to be 5.84 × 106 M−1 by the Lehrer equation from a steady-state fluorescence study. UV–vis absorption, DNA melting, dye displacement, and circular dichroism studies suggested that trazodone binds with DNA in minor grooves. Molecular docking and molecular dynamic simulation demonstrated that the TZD-DNA system was stable, and the mode of binding was minor groove. Furthermore, ionic strength investigation demonstrates that DNA and trazodone do not have a substantial electrostatic binding interaction.

Contemporary Nanoemulsion Research: Extensive Examination of Self-Nanoemulsifying Drug Delivery Systems

Abstract: The administration of new pharmaceutical compounds orally can pose certain challenges in terms of drug absorption, bioavailability, and pharmacokinetic profile. However, a widely recognized method for enhancing bioavailability involves lipid-based drug delivery systems. Lipid-based drug delivery systems (LBDDS) are the most favourable method for formulating medicines that have low solubility in water. Nanotechnology exerts a significant impact on the therapeutic efficacy of hydrophobic medicines and has emerged as a crucial method in the field of drug delivery research. Self-nanoemulsifying drug delivery systems (SNEDDs) are an important approach that combines the advantages of lipid-based drug delivery systems (LBDDS) and nanotechnology. SNEDDs are currently the favoured method for enhancing the formulation of pharmaceuticals that have low solubility in water. SNEDDs are homogenous mixtures that can self-emulsify spontaneously with gentle stirring, forming an oil-in-water emulsion that conveniently protects and creates a pathway for the lipophilic drug. The small particle size of &lt;200nm increases the solubilisation capacity of the drug by increasing its surface area. SNEDDs have demonstrated the ability to enhance the bioavailability of medicines that are not easily soluble in water. SNEDDs stand apart from other solubility enhancement approaches due to their inclusion of biodegradable components, their ease of large-scale manufacture, and their numerous potential for drug targeting. The aim of the present review was to provide basic knowledge about formulation, applications, and benefits of using SNEDDs. A detailed manuscript has been prepared by doing a literature survey on databases like Google Scholar, SCOPUS, and Pubmed to review the current state of nanotechnology applications, industrial developments, and challenges for using SNEDDS as a novel delivery system is provided in this manuscript.

Regioselective hydroamination of unactivated olefins with diazirines as a diversifiable nitrogen source

Nitrogen-containing compounds, such as amines, hydrazines, and heterocycles, play an indispensable role in medicine, agriculture, and materials. Alkylated derivatives of these compounds, especially in sterically congested environments, remain a challenge to prepare. Here we report a versatile method for the regioselective hydroamination of readily available unactivated olefins with diazirines. Over fifty examples are reported, including the protecting group-free amination of fourteen different natural products. A broad functional group tolerance includes alcohols, ketones, aldehydes, and epoxides. The proximate products of these reactions are diaziridines, which, under mild conditions, are converted to primary amines, hydrazines, and heterocycles. Five target- and diversity-oriented syntheses of pharmaceutical compounds are shown, along with the preparation of a bis-15N diazirine validated in the late-stage isotopic labeling of an RNA splicing modulator candidate. In this work, we report using diazirine (1) as an electrophilic nitrogen source in a regioselective hydroamination reaction, and the diversification of the resulting diaziridines.

Multicomponent Cross-Dehydrogenative Coupling of Imidazo[1,2-a]pyridine: Access to Abnormal Mannich and Mannich-Type Reaction.

This study showcases successfully switchable approaches to accomplish the C3-aryl methylation and C3- amino methylation of privileged nitrogen-containing pharmaceutical compounds "imidazopyridines" with distinct amines, which surmounts the long-standing requirement for a superfluous directing group. These two transformations manifest pronounced regio- and chemo-divergent behavior, successfully demonstrating unprecedented multicomponent "abnormal Mannich and Mannich-type" reactions. The remarkable environmentally benign protocol has been efficiently extended to concise the synthesis and late-stage derivatization.

Implementation of Spore Display in Paenibacillus polymyxa with Different Hydrolytic Enzymes.

Biotechnological processes are essential for producing climate-friendly high-value chemicals or pharmaceutical compounds, which can include steps catalyzed by enzymes. Therefore, establishing new, robust, and cheap enzyme production processes is desirable. One possible way to enhance processes is through the use of the spore display method. Spore display can present heterologous proteins on the surface of bacterial spores, offering numerous advantages in a range of biotechnological applications. This study demonstrates the implementation of the spore display method in Paenibacillus polymyxa, achieved by modifying the spore surface, incorporating an anchoring protein, and attaching green fluorescent protein to it, allowing the visualization of fluorescent spores. Following the initial experiment, a native lipase (Lip3), a heterologous lipase (LipA) from Bacillus subtilis, a native esterase (PnbA) from P. polymyxa, and a lipoyl synthase were expressed during sporulation and displayed on the spore surface. The activity profiles were determined in the temperature range from 4 °C to 70 °C. The PnbA reached its optimum at 4 °C, whereas the LipA from B. subtilis showed 4.4-fold higher activity at 42 °C compared to the control. Furthermore, we explored a possible new technique for the purification of enzymes with the TEV cleavage site between the anchor and the protein of interest. Finally, we showed a not-yet-described side activity of the lipoyl synthase over a wide temperature range.

Ion Mobility-Mass Spectrometry Strategies to Elucidate the Anhydrous Structure of Noncovalent Guest/Host Complexes.

Structural mass spectrometry (MS) techniques are fast and sensitive analytical methods to identify noncovalent guest/host complexation phenomena for desirable solution-phase properties. Current MS-based studies on guest/host complexes of drug and drug-like molecules are sparse, and there is limited guidance on how to interpret MS information in the context of host nanoencapsulation and inclusion. Here, we use structural MS strategies, combining energy-resolved MS (ERMS), ion mobility-MS (IM-MS), and computational modeling, to characterize 14 chemically distinct drug and drug-like compounds for their propensity to form guest/host complexes with the widely used excipient, beta-cyclodextrin (βCD). The majority (11/14) yielded a 1:1 guest/host complex, and ion mobility collision cross section (CCS) analysis provided subtle evidence of gas-phase compaction of complexes in both polarities. The three distinct dissociation channels observed in ERMS (i.e., charged βCD, charged guest, and partial guest loss) were used to direct charge-site assignments for computational modeling, and structural candidates were prioritized using helium-derived CCS measurements combined with root-mean-square distance analysis. The combined analytical information from ERMS, IM-MS, and computational modeling suggested that the majority of anhydrous complexes are inclusion complexes with βCD. Taken together, this work demonstrates a roadmap for how multiple MS-based analytical measurements can be combined to interpret the structures that guest/host complexes adopt in the absence of water.

Implementation of quantum machine learning in predicting corrosion inhibition efficiency of expired drugs

This study explores the potential of quantum machine learning (QML) 's potential in predicting expired pharmaceutical compounds' corrosion inhibition capacity. This investigation utilized a QSPR model where features derived from density functional theory (DFT) calculations are used as input, while the corrosion inhibition efficiency (CIE) values derived from the experimental study are used as target output. The proposed QML model exhibits varying performance through evaluation metrics, particularly concerning encoding and ansatz design. Notably, the quantum support vector machine (QSVM) demonstrates superior predictive performance compared to the variational quantum circuit (VQC) and quantum neural network (QNN). Specifically, the QSVM model achieves the highest scores in evaluation metrics, with a root mean squared error (RMSE) of 4.36, mean absolute error (MAE) of 3.19, and mean absolute deviation (MAD) of 3.08. The research highlights the importance of larger datasets to improve predictability and emphasizes the potential of QML in investigating anti-corrosion materials. Despite its limitations, this study establishes a foundational framework for utilizing QML to forecast anti-corrosive qualities.

Trifluoromethylation Strategies of Alcohols and Phenols

In this review, we present a comprehensive update on the latest trifluoromethylation protocols for alcohols, highlighting the significant advancements and innovative strategies in this rapidly evolving field. Given the prevalence of hydroxyl groups in pharmaceutical compounds, there is a heightened interest in synthesizing functionalized organic molecules through the ‐CF3 functionalization of parent alcohols. Recent developments have introduced intriguing methods such as O‐trifluoromethylation, dehydroxylative trifluoromethoxylation, deoxytrifluoromethylation, and oxytrifluoromethylation of readily available alcohols. These protocols enable the efficient single‐step construction of diverse structures featuring C‐CF3 and C‐OCF3 bonds. This review aims to encapsulate the significant progress, structural diversity, and mechanistic insights of these transformative reactions, emphasizing their substrate scope and the underlying reaction pathways that drive these advancements.

Equilibrium adsorption, kinetic, thermodynamic, and breakthrough curves modelling of acetaminophen on Elaeagnus Angustifolia L. seeds-based granular activated carbon

ABSTRACT The widespread presence of pharmaceutical compounds as emerging environmental contaminants in aquatic environments has become a significant concern in recent years. In this study, synthesised granular activated carbon (GAC) derived from Elaeagnus Angustifolia L. seeds was utilised as an adsorbent to remove an emerging contaminant, acetaminophen (commercially known as paracetamol). The operating conditions for the chemical synthesis of the GAC, utilising ZnCl2 as an activating agent, included an impregnation ratio of 1:1, carbonisation at 500°C for 1 hour, and a heating rate of 5 °C/min. The efficacy of this adsorbent was assessed through both batch and fixed-bed column adsorption experiments under various operational parameters, including adsorbent mass (1–6 g/L), contact time, pH (2–10), temperature (20–60°C), inlet acetaminophen concentration (20–150 mg/L), and flow rate (0.5–1.5 mL/min). The results indicate that acetaminophen was better adsorbed in its neutral form rather than in its dissociated state. The findings also revealed that both the Langmuir (two-parameter) and Sips (three-parameter) models provided the best fit for the static adsorption equilibrium data. Furthermore, kinetic analysis indicated that the experimental data were best modelled by the Elovich model and governed by both external diffusion and intraparticle diffusion mechanisms. Therefore, the Yan-Viraraghavan model provided a good fit for the experimental breakthrough curves under various operating conditions. However, the adsorbed amounts of acetaminophen obtained from the column experiment indicated that the high-adsorbed amount of 204.11 mg/g was achieved with an inlet acetaminophen concentration of 150 mg/L, a bed depth of 6 cm, and a flow rate of 0.5 mL/min. These results highlight the potential of the synthesised GAC as a promising adsorbent for pharmaceutical wastewater treatment using fixed-bed column systems.

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds.

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Effect of chemotherapeutic agents on natural transformation frequency in Acinetobacter baylyi.

Natural transformation is the ability of a bacterial cell to take up extracellular DNA which is subsequently available for recombination into the chromosome (or maintenance as an extrachromosomal element). Like other mechanisms of horizontal gene transfer, natural transformation is a significant driver for the dissemination of antimicrobial resistance. Recent studies have shown that many pharmaceutical compounds such as antidepressants and anti-inflammatory drugs can upregulate transformation frequency in the model species Acinetobacter baylyi. Chemotherapeutic compounds have been shown to increase the abundance of antimicrobial resistance genes and increase colonization rates of potentially pathogenic bacteria in patient gastrointestinal tracts, indicating an increased risk of infection and providing a pool of pathogenicity or resistance genes for transformable commensal bacteria. We here test for the effect of six cancer chemotherapeutic compounds on A. baylyi natural transformation frequency, finding two compounds, docetaxel and daunorubicin, to significantly decrease transformation frequency, and daunorubicin to also decrease growth rate significantly. Enhancing our understanding of the effect of chemotherapeutic compounds on the frequency of natural transformation could aid in preventing the horizontal spread of antimicrobial resistance genes.

Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer

ObjectiveBladder cancer (BCa) is a highly lethal urological malignancy characterized by its notable histological heterogeneity. Autophagy has swiftly emerged as a diagnostic and prognostic biomarker in diverse cancer types. Nonetheless, the currently accessible autophagy-related signature specific to BCa remains limited. MethodsA refined autophagy-related signature was developed through a 10-fold cross-validation framework, incorporating 101 combinations of machine learning algorithms. The performance of this signature in predicting prognosis and response to immunotherapy was thoroughly evaluated, along with an exploration of potential drug targets and compounds. In vitro and in vivo experiments were conducted to verify the regulatory mechanism of hub gene. ResultsThe autophagy-related prognostic signature (ARPS) has exhibited superior performance in predicting the prognosis of BCa compared to the majority of clinical features and other developed markers. Higher ARPS is associated with poorer prognosis and reduced sensitivity to immunotherapy. Four potential targets and five therapeutic agents were screened for patients in the high-ARPS group. In vitro and vivo experiments have confirmed that FKBP9 promotes the proliferation, invasion, and metastasis of BCa. ConclusionsOverall, our study developed a valuable tool to optimize risk stratification and decision-making for BCa patients.

Assessment of the effectiveness of atmospheric plasma on the removal of selected pharmaceuticals from water

Plasma, next to solids, liquids and gases, is considered the fourth state of matter, occurring in the ground state or in the excited state, having a neutral charge. Its scope of application is wide and constantly expanding. The conducted research aimed to determine the impact of atmospheric plasma for removing selected pharmaceuticals: diclofenac (DFC), sulfamethoxazole (SMX), trimethoprim (TMP), carbamazepine (CBZ) and caffeine (CAF) from water on a laboratory scale. The concentration of the tested pharmaceuticals was 20 mg/L. The analyzed samples were exposed to a continuous stream of cold plasma. The contact time of the sample with the plasma stream ranged from seconds to minutes - depending on the tested pharmaceutical. By examining the degree of reduction of selected pharmaceutical substances before and after the atmospheric plasma treatment process, the samples were subjected to high-performance liquid chromatography analysis. The removal efficiency of individual pharmaceuticals reached up to 98 %. The conducted research confirms the great application potential of using atmospheric plasma as a tool for the elimination of individual pharmaceutical compounds. Cold plasma can be used as a potential tool in water purification and wastewater treatment. Based on the effects of free radical oxidation, UV radiation and pyrolysis, we can eliminate dangerous micropollutants in a more economical and effective way.

Visible‐Light Photocatalytic Cyclopropanation of Alkenes with Dibromomethane

The cyclopropane unit plays a crucial role in pharmaceutical compound design and organic transformation. In this study, we have developed a novel redox‐neutral photocatalytic cyclopropanation method involving dibromomethane and alkenes. This chemistry enables the synthesis of valuable functional group‐substituted cyclopropanes using readily available starting materials and practical reaction conditions. Additionally, we have demonstrated the versatility of the products as precursors for various cyclopropane derivatives with different substituents. Importantly, this protocol facilitates the synthesis of a key unit found in a specific type of T‐type calcium channel blocker. Furthermore, the proposed reaction mechanism is well‐supported by control experiments.

Double Imprinted Nanoparticles for Sequential Membrane-to-Nuclear Drug Delivery.

Efficient and site-specific delivery of therapeutics drugs remains a critical challenge in cancer treatment. Traditional drug nanocarriers such as antibody-drug conjugates are not generally accessible due to their high cost and can lead to serious side effects including life-threatening allergic reactions. Here, these problems are overcome via the engineering of supramolecular agents that are manufactured with an innovative double imprinting approach. The developed molecularly imprinted nanoparticles (nanoMIPs) are targeted toward a linear epitope of estrogen receptor alfa (ERα) and loaded with the chemotherapeutic drug doxorubicin. These nanoMIPs are cost-effective and rival the affinity of commercial antibodies for ERα. Upon specific binding of the materials to ERα, which is overexpressed in most breast cancers (BCs), nuclear drug delivery is achieved via receptor-mediated endocytosis. Consequentially, significantly enhanced cytotoxicity is elicited in BC cell lines overexpressing ERα, paving the way for precision treatment of BC. Proof-of-concept for the clinical use of the nanoMIPs is provided by evaluating their drug efficacy in sophisticated three-dimensional (3D) cancer models, which capture the complexity of the tumor microenvironment in vivo without requiring animal models. Thus, these findings highlight the potential of nanoMIPs as a promising class of novel drug compounds for use in cancer treatment.

Impact of veterinary pharmaceuticals on environment and their mitigation through microbial bioremediation.

Veterinary medications are constantly being used for the diagnosis, treatment, and prevention of diseases in livestock. However, untreated veterinary drug active compounds are interminably discharged into numerous water bodies and terrestrial ecosystems, during production procedures, improper disposal of empty containers, unused medication or animal feed, and treatment procedures. This exhaustive review describes the different pathways through which veterinary medications enter the environment, discussing the role of agricultural practices and improper disposal methods. The detrimental effects of veterinary drug compounds on aquatic and terrestrial ecosystems are elaborated with examples of specific veterinary drugs and their known impacts. This review also aims to detail the mechanisms by which microbes degrade veterinary drug compounds as well as highlighting successful case studies and recent advancements in microbe-based bioremediation. It also elaborates on microbial electrochemical technologies as an eco-friendly solution for removing pharmaceutical pollutants from wastewater. Lastly, we have summarized potential innovations and challenges in implementing bioremediation on a large scale under the section prospects and advancements in this field.