Computational Toxicology Research Articles

IVIVE-PBPK based new approach methodology for addressing early life toxicity induced by Bisphenol A

Bisphenol A (BPA) is a known endocrine disruptor mimicking natural estrogens with the potential to affect human health, especially during prenatal and postnatal exposure at or below current acceptable daily intake levels. Different adverse effects of BPA are still under investigation, and multiple mechanisms of action remain unexplored. This may be one of the reasons for the continuously changing tolerable daily intake (TDI) of BPA with the emergence of new adverse health effects over time. In addition, translational modelling through in vitro-in vivo extrapolation (IVIVE) can act as prerequisite bridge for translating in-vitro finding into human risk assessment. The objective of this study was to conduct in-vitro experiments and utilize an IVIVE-pregnancy physiologically based pharmacokinetic (P-PBPK) modeling to investigate developmental neurotoxicity and embryotoxicity in humans. The data obtained from human embryonic stem cells-based assays (study conducted between October 2020–2021) were used for the IVIVE-P-PBPK models to obtain the human equivalent doses (HEDs) which were further extrapolated to reference doses (RfDs). The results showed that simulated mean RfDs (μg/kg/day) derived from the HSD3B1 and NFATC2 gene of embryotoxicity and neurodevelopmental toxicity tests, respectively, were 4.94 and 5.18. The simulated RfDs were close to the temporary-tolerable daily intake (t-TDI) recommended by European Food Safety Authority (EFSA) in 2015 (t-TDI: 4 μg/kg·bw) and higher than the TDI of 2023 (0.2 ng/kg·bw). In conclusion, in-vitro toxicogenomics dose-response data combined with PBPK modeling can become a promising alternative new approach methodology (NAM) to support decision-making in chemical risk assessment. Based on the simulated RfDs derived from this NAM, the t-TDI set by EFSA in 2015 may be considered a safe exposure limit for mothers and fetuses at the current BPA intake levels in Chinese mothers. This study provided an animal-free new strategy for NAMs based risk assessment by combining toxicogenomics and computational toxicology.

Tungsten disulfide particles driving ferric ions reduction for the enhancement of calcium peroxide fenton-like reaction

The conventional Fenton method is confined to the inefficient Fe3+/Fe2+ cycle in narrow pH range and disproportionation of H2O2. Herein, we utilized powder tungsten disulfide (WS2) as an assistant catalyst to improve high-low valent iron circulation, and calcium peroxide (CaO2) was used to replace H2O2, thus enhanced the oxidization of typical antibiotic metronidazole (MTZ) in water. The degradation ratio of MTZ achieved 94.3 % after 10 min in the system of WS2/Fe3+/CaO2 with optimum conditions (2 mmol/L CaO2, 1.5 mmol/L Fe3+, 1.5 g/L WS2 and initial pH 7), and the reaction rate constants of two stages (quick and slow) were both higher than that of Fe3+/CaO2. Even after five cycles, more than 80 % of MTZ was still degraded, indicating a benign stability of WS2. And the synergetic system could perform quite well within the wide pH range (3–11). X-ray photoelectron spectroscopy analysis suggested that unsaturated S on the exterior of WS2 exposed reductive W4+, thereby promoting the reduction of Fe3+ and H2O2 decomposition. Hydroxyl radicals (⋅OH) was confirmed as the predominant reactive oxygen species (ROS) through electron paramagnetic resonance and quenching tests, which also certified that the addition of WS2 could accelerate ROS production. The degradation intermediates of MTZ were identified by mass spectrometry, and their biotoxicities were evaluated through computational toxicology software. The proposed WS2/Fe3+/CaO2 process exhibited good performance in different water matrix and real waterbodies, proving a promising potential for improved Fenton technology.

VenomPred 2.0: A Novel In Silico Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules.

The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particularly in lead optimization and ADMET studies, and to meet the principles of the 3Rs, which calls for the replacement, reduction, and refinement of animal testing. In this context, we herein present the development of VenomPred 2.0 (http://www.mmvsl.it/wp/venompred2/), the new and improved version of our free of charge web tool for toxicological predictions, which now represents a powerful web-based platform for multifaceted and human-interpretable in silico toxicity profiling of chemicals. VenomPred 2.0 presents an extended set of toxicity endpoints (androgenicity, skin irritation, eye irritation, and acute oral toxicity, in addition to the already available carcinogenicity, mutagenicity, hepatotoxicity, and estrogenicity) that can be evaluated through an exhaustive consensus prediction strategy based on multiple ML models. Moreover, we also implemented a new utility based on the Shapley Additive exPlanations (SHAP) method that allows human interpretable toxicological profiling of small molecules, highlighting the features that strongly contribute to the toxicological predictions in order to derive structural toxicophores.

Benzo[b]fluoranthene induces male reproductive toxicity and apoptosis via Akt-Mdm2-p53 signaling axis in mouse Leydig cells: Integrating computational toxicology and experimental approaches.

This study aims to explore the male reproductive toxicity of Benzo[b]fluoranthene (BbF) and related mechanisms. The results of computational toxicology analysis indicated male reproductive toxicity of BbF was related to apoptosis of Leydig cells and that Akt/p53 pathway might play a key role. In experiments, BbF induced testosterone decline, decreased concentration and motility of sperm and aggravated testicular pathological injury in mice. Besides, BbF led to apoptosis in Leydig cells, and decreased expressions of p-Akt and Bcl2, while improving the expressions of p53, Bax and Cleaved Caspase-3 in vivo and in vitro. Further, compared with BbF group, Akt activator SC79 significantly reduced cell apoptosis rate, improved cell viability, promoted the expressions of p-Akt and p-Mdm2, and reversed the above molecular expressions. Similarly, p53 inhibitor Pifithrin-α also significantly enhanced the cell vitality, alleviated the apoptosis of TM3 cells induced by BbF, and decreased the expressions of Bax and Cleaved Caspase-3, with the up-regulation of Bcl2. To sum up, by inhibiting Akt-Mdm2 signaling, BbF activated the p53-mediated mitochondrial apoptosis pathway, further inducing the apoptosis of Leydig cells, therefore resulting in testosterone decline and male reproductive damage. Besides, this study provided a valid mode integrating computational toxicology and experimental approaches in toxicity testing.

Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways.

Failure of animal models to predict hepatotoxicity in humans has created a push to develop biological pathway-based alternatives, such as those that use in vitro assays. Public screening programs (e.g., ToxCast/Tox21 programs) have tested thousands of chemicals using in vitro high-throughput screening (HTS) assays. Developing pathway-based models for simple biological pathways, such as endocrine disruption, has proven successful, but development remains a challenge for complex toxicities like hepatotoxicity, due to the many biological events involved. To this goal, we aimed to develop a computational strategy for developing pathway-based models for complex toxicities. Using a database of 2171 chemicals with human hepatotoxicity classifications, we identified 157 out of 1600+ ToxCast/Tox21 HTS assays to be associated with human hepatotoxicity. Then, a computational framework was used to group these assays by biological target or mechanisms into 52 key event (KE) models of hepatotoxicity. KE model output is a KE score summarizing chemical potency against a hepatotoxicity-relevant biological target or mechanism. Grouping hepatotoxic chemicals based on the chemical structure revealed chemical classes with high KE scores plausibly informing their hepatotoxicity mechanisms. Using KE scores and supervised learning to predict in vivo hepatotoxicity, including toxicokinetic information, improved the predictive performance. This new approach can be a universal computational toxicology strategy for various chemical toxicity evaluations.

Knowledge graph aids comprehensive explanation of drug and chemical toxicity.

In computational toxicology, prediction of complex endpoints has always been challenging, as they often involve multiple distinct mechanisms. State-of-the-art models are either limited by low accuracy, or lack of interpretability due to their black-box nature. Here, we introduce AIDTox, an interpretable deep learning model which incorporates curated knowledge of chemical-gene connections, gene-pathway annotations, and pathway hierarchy. AIDTox accurately predicts cytotoxicity outcomes in HepG2 and HEK293 cells. It also provides comprehensive explanations of cytotoxicity covering multiple aspects of drug activity, including target interaction, metabolism, and elimination. In summary, AIDTox provides a computational framework for unveiling cellular mechanisms for complex toxicity endpoints.

Environmental computational toxicology for screening persistent, bio-accumulative, and toxic chemicals: Progress and perspectives

Environmental computational toxicology for screening persistent, bio-accumulative, and toxic chemicals: Progress and perspectives

Advancing Computational Toxicology by Interpretable Machine Learning.

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Dissecting the Enantioselective Neurotoxicity of Isocarbophos: Chiral Insight from Cellular, Molecular, and Computational Investigations.

Chiral organophosphorus pollutants are found abundantly in the environment, but the neurotoxicity risks of these asymmetric chemicals to human health have not been fully assessed. Using cellular, molecular, and computational toxicology methods, this story is to explore the static and dynamic toxic actions and its stereoselective differences of chiral isocarbophos toward SH-SY5Y nerve cells mediated by acetylcholinesterase (AChE) and further dissect the microscopic basis of enantioselective neurotoxicity. Cell-based assays indicate that chiral isocarbophos exhibits strong enantioselectivity in the inhibition of the survival rates of SH-SY5Y cells and the intracellular AChE activity, and the cytotoxicity of (S)-isocarbophos is significantly greater than that of (R)-isocarbophos. The inhibitory effects of isocarbophos enantiomers on the intracellular AChE activity are dose-dependent, and the half-maximal inhibitory concentrations (IC50) of (R)-/(S)-isocarbophos are 6.179/1.753 μM, respectively. Molecular experiments explain the results of cellular assays, namely, the stereoselective toxic actions of isocarbophos enantiomers on SH-SY5Y cells are stemmed from the differences in bioaffinities between isocarbophos enantiomers and neuronal AChE. In the meantime, the modes of neurotoxic actions display that the key amino acid residues formed strong noncovalent interactions are obviously different, which are related closely to the molecular structural rigidity of chiral isocarbophos and the conformational dynamics and flexibility of the substrate binding domain in neuronal AChE. Still, we observed that the stable "sandwich-type π-π stacking" fashioned between isocarbophos enantiomers and aromatic Trp-86 and Tyr-337 residues is crucial, which notably reduces the van der Waals' contribution (ΔGvdW) in the AChE-(S)-isocarbophos complexes and induces the disparities in free energies during the enantioselective neurotoxic conjugations and thus elucidating that (S)-isocarbophos mediated by synaptic AChE has a strong toxic effect on SH-SY5Y neuronal cells. Clearly, this effort can provide experimental insights for evaluating the neurotoxicity risks of human exposure to chiral organophosphates from macroscopic to microscopic levels.

Prediction of zebrafish embryonic developmental toxicity by integrating omics with adverse outcome pathway

New approach methodologies (NAMs), especially omics-based high-throughput bioassays have been developed rapidly, providing rich mechanistic information such as molecular initiation events (MIEs) and (sub)cellular key events (KEs) in adverse outcome pathways (AOPs). However, how to apply the knowledge of MIEs/KEs to predict adverse outcomes (AOs) induced by chemicals represents a new challenge for computational toxicology. Here, an integrated method named ScoreAOP was developed and evaluated to predict chemicals’ developmental toxicity for zebrafish embryos by integrating four related AOPs and dose-dependent reduced zebrafish transcriptome (RZT). The rules of ScoreAOP included 1) sensitivity of responsive KEs demonstrated by point of departure of KEs (PODKE), 2) evidence reliability and 3) distance between KEs and AOs. Moreover, eleven chemicals with different modes of action (MoAs) were tested to evaluate ScoreAOP. Results showed that eight of the eleven chemicals caused developmental toxicity at tested concentration in apical tests. All the tested chemicals’ developmental defects were predicted using ScoreAOP, whereas eight out of the eleven chemicals predicted by ScoreMIE which was developed to score MIEs disturbed by chemicals based on in vitro bioassays data. Finally, in terms of mechanism explanation, ScoreAOP clustered chemicals with different MoAs while ScoreMIE failed, and ScoreAOP revealed the activation of aryl hydrocarbon receptor (AhR) plays a significant role in dysfunction of cardiovascular system, resulting in zebrafish developmental defects and mortality. In conclusion, ScoreAOP represents a promising approach to apply mechanism information obtained from omics to predict AOs induced by chemicals.

In Vitro and Predictive Computational Toxicology Methods for the Neurotoxic Pesticide Amitraz and Its Metabolites

The Varroa destructor parasite is responsible for varroasis in honeybees worldwide, the most destructive disease among parasitic diseases. Thus, different insecticides/acaricides have been widely used within beehives to control these parasitic diseases. Namely, amitraz is the most used acaricide due to its high efficacy shown against Varroa destructor. However, pesticides used for beehive treatments could be incorporated into the honey and accumulate in other hive products. Hence, honeybee health and the impairment of the quality of honey caused by pesticides have gained more attention. Amitraz and its main metabolites, N-(2,4-dimethylphenyl) formamide (2,4-DMF) and 2,4-dimethylaniline (2,4-DMA), are known to be potent neurotoxicants. In this research, the cytotoxicity of amitraz and its metabolites has been assessed by MTT and PC assays in HepG2 cells. In addition, possible target receptors by in silico strategies have been surveyed. Results showed that amitraz was more cytotoxic than its metabolites. According to the in silico ADMEt assays, amitraz and its metabolites were predicted to be compounds that are able to pass the blood-brain barrier (BBB) and induce toxicity in the central and peripheral nervous systems. The main target class predicted for amitraz was the family of A G protein-coupled receptors that comprises responses to hormones and neurotransmitters. This affects, among other things, reproduction, development, locomotion, and feeding. Furthermore, amitraz and its metabolites were predicted as active compounds interacting with diverse receptors of the Tox21-nuclear receptor signaling and stress response pathways.

From Structural Alerts to Signature Fragment Alerts: A Case Study on Pyrrolizidine Alkaloids.

Even though modeling is considered a valid alternative to mutagenicity testing for substances with known structures, it can be applied for mixtures only if all of the single chemical structures are identified. Within the present work, we investigate a new avenue to exploit computational toxicology for mixtures, such as plant-based food ingredients. Indeed, considering that in the absence of toxicological information, an important early consideration is whether any substance may be genotoxic through the mutagenic mechanism of action, we tried to establish a correspondence between genotoxic structural alerts (SAs) and so-called signature fragment alerts (SFAs). Once this correspondence is established, chromatograms could be screened for chemical features associated with genotoxic alerts. Pyrrolizidine alkaloids (PAs), a large group of natural toxins (several of them known as genotoxic) were used as a case study because their early identification would bring significant benefits. The method was built using 56 PA pure standards, resulting in the characterization of signature fragment alerts. Finally, the approach was verified in real plant-based samples such as herbal tea and alfalfa, where the screening of signature fragment alerts allowed highlighting quickly the presence of genotoxic PAs in plant-based mixtures. Therefore, the SFA analysis can be used for risk prioritization of newly identified PAs and for their identification in mixtures, contributing to the unnecessary use of animal experimentation for genotoxicity testing.

Computational toxicology in drug safety research

The progress of computational toxicology (CompTox) in drug safety research is highly anticipated. CompTox provides toxicity screening methods for drug discovery in the early stages. CompTox also contributes to fostering the application of the principles of the 3Rs in toxicity testing by expanding non-animal test methods. The mechanism of toxicity is complex and varied, and drug discovery modalities are becoming more diverse. Consequently, the research is considered necessary to predict toxicity using not only chemical structures but experimental data as well. Additionally, various perspectives, such as interpretation of toxicity mechanisms and species differences, must be considered in risk assessment and management in drug safety research. Therefore, it is important to construct a comprehensive CompTox system that not only presents toxicity prediction results but also provides much information related to the relationship between drug candidate substances and living organisms. In this review paper, CompTox is positioned as a discipline of toxicology that applies computer-based technology, including AI (artificial intelligence). I also introduce toxicity prediction systems based on experimental data and an ontology system that supports the interpretation of toxicity prediction results as examples of research on constructing the foundation of a comprehensive CompTox system.

Computational toxicology and food safety assessment of Parkia timoriana phytoconstituents using quantitative structure-activity relationship (QSAR) modeling approaches

Computational toxicology and food safety assessment of Parkia timoriana phytoconstituents using quantitative structure-activity relationship (QSAR) modeling approaches

Mechanism and Kinetics of The Degradation of Nitazoxanide and Hydroxychloroquine Drugs by Hydroxyl Radicals: Theoretical Approach to Ecotoxicity

The efforts of contrasting the effects caused by the Covid-19 (coronavirus disease 2019) pandemic increased the disposal of active pharmaceutical ingredients. This paper reports the mechanisms and kinetics of the degradation in aqueous environments induced by  OH of two drugs, among those most widely probed at the outbreak of coronavirus, nitazoxanide and hydroxychloroquine. The investigation exploits quantum chemistry techniques and a reaction rate theory combined with diffusion-controlled processes and quantum mechanical tunneling. The reaction rate constants are obtained in an environmentally relevant temperature range. The results show that (i) the deacetylation of nitazoxanide with formation of tizoxanide is kinetically the most favorable channel, in agreement with experimental work; (ii) for hydroxychloroquine, the present theoretical calculations show that the most favorable channel is the addition of  OH at the aromatic ring. The half-life time degradation products are for both cases in the range between 12 to 138 days. Both drugs presented toxicities between harmful and toxic as obtained by computational toxicology calculations: The toxicity is also calculated for the degradation products: (i) in the nitazoxanide degradation process, tizoxanide was characterized as more toxic, while (ii) in the case of hydroxychloroquine, the major degradation product showed a decrease in the toxicity.

Use of computational toxicology models to predict toxicological points of departure: A case study with triazine herbicides.

Atrazine simazine and propazine, widely used triazine herbicides on food crops and in residential areas, disrupt the neuroendocrine system raising human health concerns. USEPA developed a PBPK model based on triazine common Mode of Action (MOA)-suppression of luteinizing hormone surge in female rats-to generate human regulatory points of departure (POD: mg/kg/day). We compared triazine Human Administered Equivalent Dose (AEDHuman mg/kg/day) predictions from open access computational tools to the PBPK PODs to assess concordance. Computational tools were the following: ToxCast/Tox21 in vitro assays; Toxicogenomic databases to assess concordance with ToxCast/Tox21 targets; integrated chemical environment (ICE) models with ToxCast/Tox21 inputs to predict AEDHuman PODs and population-based age-refined high throughput toxicokinetics (HTTK-Pop) to compare to age-related PBPK PODs. ToxCast/Tox21 assays identified critical targets in the triazine common MOA and gene databases; ICE AEDHuman predictions were mainly concordant with the USEPA PBPK PODs quantitatively. Low fold-differences between PBPK POD and ICE AEDHuman predictions indicated that the ICE models are health-protective. HTTK-Pop age-refinements were within 10-fold of the USEPA PBPK PODs. CompTox tools were used to identify assay targets in the MOA and identify potential molecular initiating targets in the adverse outcome pathway for potential use in risk assessment.

ToxicR: A computational platform in R for computational toxicology and dose–response analyses

The need to analyze the complex relationships observed in high-throughput toxicogenomic and other omic platforms has resulted in an explosion of methodological advances in computational toxicology. However, advancements in the literature often outpace the development of software researchers can implement in their pipelines, and existing software is frequently based on pre-specified workflows built from well-vetted assumptions that may not be optimal for novel research questions. Accordingly, there is a need for a stable platform and open-source codebase attached to a programming language that allows users to program new algorithms. To fill this gap, the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences, in cooperation with the National Toxicology Program (NTP) and US Environmental Protection Agency (EPA), developed ToxicR, an open-source R programming package. The ToxicR platform implements many of the standard analyses used by the NTP and EPA, including dose–response analyses for continuous and dichotomous data that employ Bayesian, maximum likelihood, and model averaging methods, as well as many standard tests the NTP uses in rodent toxicology and carcinogenicity studies, such as the poly-K and Jonckheere trend tests. ToxicR is built on the same codebase as current versions of the EPA’s Benchmark Dose software and NTP’s BMDExpress software but has increased flexibility because it directly accesses this software. To demonstrate ToxicR, we developed a custom workflow to illustrate its capabilities for analyzing toxicogenomic data. The unique features of ToxicR will allow researchers in other fields to add modules, increasing its functionality in the future.

Toxicity prediction: An application of alternative testing and computational toxicology in contaminated groundwater sites in Taiwan

Groundwater contamination remains a global threat due to its toxic effects to humans and the environment. The remediation of contaminated groundwater sites can be costly, thus, identifying the priority areas of concern is important to reduce money spent on resources. In this study, we aimed to identify and rank the priority groundwater sites in a contaminated petrochemical district by combining alternative, non-animal approaches – chemical analysis, cell-based high throughput screening (HTS), and Toxicological Priority Index (ToxPi) computational toxicology tool. Groundwater samples collected from ten different sites in a contaminated district showed pollutant levels below the detection limit, however, hepatotoxic bioactivity was demonstrated in human hepatoma HepaRG cells. Integrating the pollutants information (i.e., pollutant characteristics and concentration data) with the bioactivity data of the groundwater samples, an evidence-based ranking of the groundwater sites for future remediation was established using ToxPi analysis. The currently presented combinatorial approach of screening groundwater sites for remediation purposes can further be refined by including relevant parameters, which can boost the utility of this approach for groundwater screening and future remediation.

Updated in Silico Prediction Methods for Fractions Absorbed and Key Input Parameters of 355 Disparate Chemicals for Physiologically Based Pharmacokinetic Models for Time-Dependent Plasma Concentrations after Virtual Oral Doses in Humans.

Human metabolic profiles for substances such as toxic food-derived compounds are usually allometrically extrapolated from traditionally determined in vivo rat concentration profiles. To evaluate internal exposures in humans without any reference to experimental data, physiologically based pharmacokinetic (PBPK) modeling could be used if the model input parameters could be estimated in silico. This approach would simplify the use of PBPK models for forward dosimetry after oral doses. In this study, the in silico estimation of input parameters for PBPK models (i.e., fraction absorbed × intestinal availability, absorption rate constants, and volumes of the systemic circulation) was updated for an panel of 355 chemicals (212 previously analyzed and 143 additional substances) using a light gradient boosting machine learning algorithms (LightGBM) based on between 11 and 29 in silico-calculated chemical descriptors. Simplified human PBPK models were then used to calculate virtual maximum plasma concentrations (Cmax) and areas under the concentration-time curve (AUC) based on two sets of input parameters, i.e., traditionally derived values from in vivo data and those calculated in silico using the current updated systems. Both sets of Cmax and AUC data were well correlated (r = 0.87 and r = 0.73, respectively; p < 0.01, n = 355). Therefore, input parameters for human PBPK models for a diverse range of compounds could be successfully estimated using chemical descriptors and in silico tools. This approach to pharmacokinetic modeling has potential for application in computational toxicology and in the clinical setting for assessing the potential risk of general chemicals.

Novel Cd0.5Zn0.5S/Bi2MoO6 S-scheme heterojunction for boosting the photodegradation of antibiotic enrofloxacin: Degradation pathway, mechanism and toxicity assessment

The artificial S-scheme photocatalysis system, mimicking natural photosynthesis, has shown eminent potential for the photocatalytic destruction of pharmaceuticals and personal care products (PPCPs). Here, S-scheme heterostructures were fabricated by coupling Cd0.5Zn0.5S nanoparticles and Bi2MoO6 microspheres as efficacious photocatalyst for antibiotic oxidation.The optimum Cd0.5Zn0.5S/Bi2MoO6 attains the highest enrofloxacin degradation efficiency of 76.3 % within 40 min, with a significant promotion of 1.8 and 2.6 folds compared to Cd0.5Zn0.5S and Bi2MoO6, respectively. The integration of Cd0.5Zn0.5S with Bi2MoO6 to gain the Cd0.5Zn0.5S/Bi2MoO6 S-scheme heterojunction can availably achieve the effective spatial photo-carrier dissolution and optimize the photo-redox capacity, leading to the significant optimization of photocatalytic performance and stability. The primary reactive species, enrofloxacin degradation pathways and photocatalysis mechanism were illustrated basing on the scavenging tests, ESR characterization, and the HPLC-MS analyses etc. The computational toxicology analysis unravels the weakened bio-toxicity of most intermediates than parent enrofloxacin. Significantly, it also manifests a preeminent catalytic efficacy for annihilating enrofloxacin in authentic water systems, manifesting its vast potential for annihilating PPCPs. This study underlines the impetus of S-scheme heterojunction fabrication and hierarchical heterostructure configuration for the development of high-performance photocatalysts for efficient purification of PPCPs.