Toxicity of the New Psychoactive Substance (NPS) Clephedrone (4-Chloromethcathinone, 4-CMC): Prediction of Toxicity Using In Silico Methods for Clinical and Forensic Purposes
Abstract
:1. Introduction
- (1)
- A basic β-keto phenethylamine structure: 4-CMC possesses a basic β-keto phenethylamine structure; this structure is similar to that of amphetamines and plays a significant role in toxicology due to its impact on the interactions of the substance with various neurotransmitter systems in the body, including dopaminergic, serotonergic and adrenergic systems.
- (2)
- The position of the chlorine atom: the presence of a chlorine atom in the para position (fourth position) of the phenyl ring (hence, ‘4-chloro’) is a key feature; this modification can affect the lipophilicity of the compound, how it crosses the blood–brain barrier, and its overall metabolic stability, which are important for determining its toxicological profile.
- (3)
- A methyl group linked to the nitrogen atom (amino group): the term ‘meth’ in 4-CMC indicates a methyl group attached to the nitrogen atom in the amine group; this modification is common in many stimulants and can influence the pharmacokinetics of the compound, including absorption, distribution, metabolism, and excretion (ADME), often increasing the lipid solubility of the compound and potentially leading to a faster onset of action.
- (4)
- A ketone group: The presence of a ketone group, as indicated by the suffix ‘one’ in the name, is characteristic of cathinones; in 4-CMC, this ketone group is linked to the first carbon of the ethyl chain. The ketone functionality is crucial for the substance’s reactivity and interaction with enzymatic systems in the body, affecting its metabolic pathways and potential toxicity.
- (5)
- An ethyl chain: the ethyl chain that links the amine group and the aromatic ring in 4-CMC affects the spatial configuration; this can influence how the drug binds to neural receptors, affecting its potency and the nature of its toxic effects.
2. Results
2.1. Qualitative In Silico Methods
Acute Toxicity/Eye and Skin Irritation
2.2. Quantitative In Silico Methods
2.2.1. Acute Toxicity
2.2.2. Health Effects
2.2.3. Genotoxicity
2.2.4. Eye and Skin Irritation
2.2.5. Cardiotoxicity
2.2.6. Endocrine System Disruption
3. Discussion
4. Materials and Methods
4.1. Study Design
4.2. Toxicity Endpoints
- Acute toxicity and health effects: the prediction of acute toxicity and health effects is vital in assessing the immediate and long-term risks associated with exposure to 4-CMC; in forensic toxicology, understanding acute toxicity helps determine the cause of acute poisoning incidents, while in clinical toxicology, it guides treatment strategies for overdose cases;
- Genotoxicity: assessing the genotoxic potential of 4-CMC is critical, as genetic mutations or DNA damage can have profound implications for human health; this is particularly relevant in forensic toxicology, to evaluate long-term exposure risks and potential carcinogenicity;
- Eye and skin irritation: the prediction of eye and skin irritation is crucial for understanding the risks of accidental exposure; this information is essential in both forensic and clinical toxicology, to assess cases of dermal or ocular exposure and to provide appropriate medical interventions;
- Cardiotoxicity: given the increasing concern about the cardiovascular effects of NPSs, evaluating the cardiotoxic potential of 4-CMC is essential; this parameter assists in assessing the risk of heart-related complications, which is significant both for emergency medical responses and for forensic investigations;
- Endocrine system disruption: investigating the potential of 4-CMC to disrupt the endocrine system is important due to the critical role that hormones play in bodily functions; endocrine disruption can lead to a range of health issues, making this parameter highly relevant in clinical assessments and forensic analysis.
4.3. Utilized In Silico Software
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
4-CMC | 4-Chloromethcathinone |
ADME | Absorption, distribution, metabolism, and excretion |
hERG | Human ether-a-go-go-related gene |
IC50 | Half-maximal inhibitory concentration |
LD50 | Median lethal dose rate 50 |
LogP | Logarithm of the partition coefficient |
LogRBA | Logarithm of relative binding affinity |
NPSs | New psychoactive substances |
pKa | Acid dissociation constant |
RI | Reliability index |
QSAR | Quantitative structure–activity relationships |
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Name(s) | 4-Chloromethcathinone, Clephedrone |
---|---|
IUPAC name | 1-(4-chlorophenyl)-2-(methylamino)-1-propanone |
Acronym | 4-CMC |
Molecular formula | C10H12ClNO |
Structural formula | |
CAS | 1225843-86-6 |
SMILES | ClC1=CC=C(C(C(C)NC)=O)C=C1 |
Molecular weight, g/mol | 197.66 |
Density, g/mL | 1.1 |
Melting point, °C | 198 |
Country/Region | Status |
---|---|
Germany | List I controlled drug |
Sweden | Suggested as illegal narcotic (June 2015) |
China | Controlled substance (as of October 2015) |
Virginia, USA | Schedule 1 substance |
USA | Schedule II (December 2019) |
Poland | Listed among new psychoactive substances (since August 2018) |
Type of Acute Toxicity | STopTox (https://stoptox.mml.unc.edu/, accessed on 30 April 2023) | AdmetSAR 3.0 | ADMETlab 2.0 | ||||
---|---|---|---|---|---|---|---|
Prediction | Confidence (%) | Applicability Domain | Predicted Toxicophore(s) * | Classification | Probability (%) | Probability of Being Toxic | |
Oral acute toxicity | Toxic (+) | 82.0 | III | 85.47 | 0.651 | ||
Dermal acute toxicity | Toxic (+) | 67.0 | NA | NA | NA | ||
Inhalation acute toxicity | Non-Toxic (−) | 50.0 | NA | NA | NA | ||
Eye irritation | Negative (−) | 62.0 | − | 58.38 | NA | ||
Skin irritation | Negative (−) | 80.0 | + | 53.28 | NA |
Software | Species | Administration Route | LD50, mg/kg bw. | Reliability/Similarity Coefficient |
---|---|---|---|---|
Percepta 2023.1.2 | Rat | Oral | 470.00 | Moderate, RI = 0.65 |
Intraperitoneal | 330.00 | Moderate, RI = 0.58 | ||
Mouse | Oral | 620.00 | Moderate, RI = 0.61 | |
Intraperitoneal | 260.00 | High, RI = 0.83 | ||
Intravenous | 120.00 | Moderate, RI = 0.70 | ||
Subcutaneous | 310.00 | Moderate, RI = 0.71 | ||
TEST 5.1.2. (Consensus) | Rat | Oral | 242.07 | SC ≥ 0.9 |
TEST 5.1.2. (Hierarchical clustering) | Rat | Oral | 169.67 | SC ≥ 0.9 |
TEST 5.1.2. (Nearest neighbor) | Rat | Oral | 345.38 | SC ≥ 0.9 |
VEGA QSAR 1.2.3 | Rat | Oral | 201.85 | SC ≥ 0.9 |
ProTox 3.0 | Rat | Oral | 340.00 | SC = 70.97% |
Health Effect | Probability of Health Effects, % | Atomic/Functional Group Contributions to the Calculated Parameter Values | Details |
---|---|---|---|
Gastrointestinal system | 90 | Toxic effects: nausea or vomiting Changes in structure or function of salivary glands Route: oral Species: human, female; rat; dog Studies: acute; chronic | |
Lungs | 80 | Toxic effects: dyspnea Route: oral Species: rat; mouse Studies: acute | |
Cardiovascular system | 39 | Toxic effects: pulse rate increase without fall in blood pressure and other changes Route: oral Species: human, male and female Studies: acute | |
Blood | 33 | No specific values given | |
Liver | 17 | Toxic effects: changes in liver weight Route: oral Species: rat Studies: chronic | |
Kidney | 15 | Toxic effects: changes in bladder weight and other changes Route: oral Species: rat Studies: chronic |
Predicted Ames Test Results | Probability (%) | Structure with Toxicophores | Software |
---|---|---|---|
Mutagen | 45 (Moderate reliability, RI = 0.51) | Percepta 2023.1.2 | |
Inactive | 80 | N/A | OCHEM (https://ochem.eu/predictor/show.do, accessed on 30 April 2023) |
Nontoxic | 88 | ADMETlab 3.0 | |
Non-mutagen | N/A | VEGA QSAR 1.2.3 |
Eye Irritation | Skin Irritation | Software |
---|---|---|
Probability: 57% | Probability: 74% | Percepta 2023.1.2 |
Irritant (out of AD) | Not irritant (possibly out of AD) | VEGA QSAR 1.2.3 |
Cardiotoxicity Predictions | Probability | Reliability | Software |
---|---|---|---|
hERG half-maximal inhibitory concentration | IC50 = 240.2 µM | N/A | Percepta 2023.1.2 |
hERG inhibition (Ki < 10 µM, patch clamp) | 3% | Moderate (RI = 0.55) | Percepta 2023.1.2 |
hERG inhibition (Ki < 40 µM, patch clamp) | 40% | N/A | ADMETlab 3.0 |
Probability of Estrogen Receptor Binding | |||
---|---|---|---|
LogRBA > −3 | Reliability | LogRBA > 0 | Reliability |
0.03 | Moderate (RI = 0.62) | 0.00 | High (RI = 0.79) |
Software | Description | Toxicity Endpoints |
---|---|---|
StopTox (https://stoptox.mml.unc.edu/, accessed on 30 April 2023) | STopTox is a sophisticated software designed to predict human acute toxicity tests known as the ‘6-pack’ [10]. These tests cover various aspects such as oral, dermal, and inhalation toxicity, as well as irritation and sensitization [23]. The predictions are based on quantitative structure–activity relationship (QSAR) models validated using animal experimental data. The machine learning algorithm, random forest, along with MACCS descriptors, is employed for prediction. It offers a swift screening method for assessing chemical toxicity, identifying molecular components that increase or decrease toxicity, and providing fragment contribution predictions. | Qualitative: acute toxicity, skin and eye irritation. |
AdmetSar 3.0 | AdmetSAR has emerged as a comprehensive software solution for predicting the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of chemicals [24,25]. Its latest version, admetSAR 3.0, integrates over 40 predictive models using in silico filtering techniques to evaluate new chemical properties related to ADMET [26,27]. The model uses a dataset of 10,207 molecules with LD50 values determined in rat [28]. | Qualitative: oral toxicity, skin and eye irritation |
ACD/Labs Percepta 2023.1.2 | The ACD/Labs Percepta platform is commercially available scientific software for predicting different toxicological properties using computational methods. It provides insight into how chemical structures correlate with a wide array of ADME, toxicological, and physicochemical characteristics. The platform includes a structure optimization module with comprehensive ADMET filters and takes advantage of various data inputs to generate predictions. The reliability and accuracy of Percepta models are continually evaluated and refined based on new scientific data. Research was carried out in Percepta version 2023.1.2 with a license purchased by the Institute of Medical Expertises in Łódź. | Quantitative: acute toxicity (LD50), health effects (blood, cardiovascular system, gastrointestinal system, kidneys, liver, lungs), genotoxicity (mutagenicity as Ames test), eye and skin irritation, cardiotoxicity (hERG inhibition), endocrine system disruption. |
ProTox 3.0 | ProTox 3.0, updated scientific software released in March 2024, utilizes diverse machine learning models and databases to predict toxicological properties [29,30]. Mainly applied in drug development and chemical safety evaluation, ProTox 3.0 provides valuable insights into the potential toxic effects of novel compounds. Predictions include similarity- and fragment-based approaches, generating alerts for potential toxicity targets, with results provided with confidence scores, a comprehensive toxicity radar chart, and details on analogous known toxic compounds. | Quantitative: acute toxicity |
ADMETlab 2.0 | ADMETlab 2.0 is an expansive scientific platform dedicated to predicting the ADMET properties of compounds [31,32]. Designed for user-friendliness and efficiency, ADMETlab 2.0 facilitates batch computation and features an intuitive interface. The prediction models utilize graph-based neural networks with an attention mechanism, producing precise and nuanced predictions across various ADMET-related endpoints. | Qualitative: acute toxicityQuantitative: genotoxicity (mutagenicity by Ames test), cardiotoxicity (hERG inhibition) |
OCHEM (https://ochem.eu/predictor/show.do, accessed on 30 April 2023) | The online chemical modeling environment (OCHEM) is a web-based platform strategically crafted to simplify and automate traditional processes in quantitative structure–activity relationship (QSAR) modeling [33,34]. This database is intricately linked with the modeling framework, which assists users through all necessary steps in developing a predictive model: data retrieval, calculation and selection of various molecular descriptors, application of machine learning methods, validation, model analysis, and assessment of the applicability domain [35,36]. | Quantitative: genotoxicity (mutagenicity as Ames Test) |
TEST 5.1.2 | The toxicity estimation software tool (TEST) was created as an open-source application by the U.S. EPA [37,38,39]. The software utilizes various molecular descriptors sourced from the EPA ECOTOX databases. TEST employs three QSAR approaches: the hierarchical method groups compounds based on structural similarities [21], the nearest-neighbor method averages values from structurally similar chemicals [40], and the consensus method combines predictions from all preceding methods [41]. | Quantitative: acute toxicity |
VEGA QSAR 1.2.3 | VEGA QSAR, a collaborative effort from EU projects, integrates QSAR models and rule-based expert systems to predict various human toxicity endpoints [42]. The platform includes an applicability domain index for reliability assessment and conducts automatic checks to identify potential issues that could compromise prediction accuracy or reliability. These checks include verifying similarity between the target compound and those in the dataset, assessing concordance between experimental values and predictions, evaluating precision, and more. VEGA’s comprehensive approach ensures robust and reliable toxicity predictions, aiding in risk assessment. | Quantitative: acute toxicity, genotoxicity (mutagenicity by Ames test), eye and skin irritation. |
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Jurowski, K.; Niżnik, Ł. Toxicity of the New Psychoactive Substance (NPS) Clephedrone (4-Chloromethcathinone, 4-CMC): Prediction of Toxicity Using In Silico Methods for Clinical and Forensic Purposes. Int. J. Mol. Sci. 2024, 25, 5867. https://doi.org/10.3390/ijms25115867
Jurowski K, Niżnik Ł. Toxicity of the New Psychoactive Substance (NPS) Clephedrone (4-Chloromethcathinone, 4-CMC): Prediction of Toxicity Using In Silico Methods for Clinical and Forensic Purposes. International Journal of Molecular Sciences. 2024; 25(11):5867. https://doi.org/10.3390/ijms25115867
Chicago/Turabian StyleJurowski, Kamil, and Łukasz Niżnik. 2024. "Toxicity of the New Psychoactive Substance (NPS) Clephedrone (4-Chloromethcathinone, 4-CMC): Prediction of Toxicity Using In Silico Methods for Clinical and Forensic Purposes" International Journal of Molecular Sciences 25, no. 11: 5867. https://doi.org/10.3390/ijms25115867
APA StyleJurowski, K., & Niżnik, Ł. (2024). Toxicity of the New Psychoactive Substance (NPS) Clephedrone (4-Chloromethcathinone, 4-CMC): Prediction of Toxicity Using In Silico Methods for Clinical and Forensic Purposes. International Journal of Molecular Sciences, 25(11), 5867. https://doi.org/10.3390/ijms25115867