DeepTraSynergy: drug combinations using multimodal deep learning with transformers.

MOTIVATION: Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. RESULTS: Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.

CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction.

Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor. In this work, a novel approach, called CFSSynergy, that combines these two viewpoints is proposed. First, a discriminative representation is extracted for paired drugs and cell lines as input. We have utilized transformer-based architecture for drugs. For cell lines, we have created a similarity matrix between proteins using the Node2Vec algorithm. Then, the new cell line representation is computed by multiplying the protein-protein similarity matrix and the initial cell line representation. Next, we compute the similarity between unique drugs and unique cells using the learned representation for paired drugs and cell lines. Then, we compute a new representation for paired drugs and cell lines based on the similarity-based features and the learned features. Finally, these features are fed to XGBoost as a task predictor. Two well-known data sets were used to evaluate the performance of our proposed method: DrugCombDB and OncologyScreen. The CFSSynergy approach consistently outperformed existing methods in comparative evaluations. This substantiates the efficacy of our approach in capturing complex synergistic interactions between drugs and cell lines, setting it apart from conventional similarity-based or feature-based methods.

A new fluorescence probe for detection of Cu+2 in blood samples: Circuit logic gate.

A Fluorescence probe was designed based on 8-hydroxyquinoline chitosan silica precursor (HQCS) for selective detection of Al3+, Cu2+. The HQCS has no observable fluorescence signal, but after the addition of Al3+, a huge fluorescence signal appeared, and the selective quenching was absorbed after the addition of Cu2+. The effect of other different cations, including Cu2+, Mg2+, Ca2+, Pb2+, Zn2+, Hg2+, Ag+, Fe3+, and K+ was studied. The addition of Cu2+ to the probe (HQCSAL) decreased the fluorescence very repeatable, and the variation of the fluorescence vs. Cu2+ was monotonic and linear. Therefore, the prepared probe was used to determine Cu2+ ions in real samples. The mechanism of fluorescence variation by adding cations to the probe solution was studied using the Stern-Volmer equation. Under the optimum conditions, the linear range and detection limit were 3.5-31 µM and 1 µM, respectively. The probe accuracy on the copper determination in the blood and tap waters was comparable to the ICP-OES results. The circuit logic gate mimic was designed for the fluorescence behavior of the probe constituents.

Multicomponent Synthesis and Investigations Fluorescence Activity of Chromenone-Pyrazole Compounds.

A synthetic method is described to produce some chromenone-pyrazole derivatives through a one-pot multicomponent reaction using SrFe12O19 as a magnetic catalyst. This method provides quite a few merits, including the use of an effective and easy separable nanocatalyst, high yields of products, short reaction time, and easy work-up. Two of the products showed fluorescence properties, which have detected mercury ions without any interference with other ions. They can detect a tiny amount of mercury ions, which were comparable with other chemosensors. The detection limit is 4 × 10-7 and 3 × 10-8 M, respectively, for the compound I and II, respectively, which were considered very low amounts. The effect of mercury on health and environmental pollution is essential in medical science.

An On-off Supramolecular Fluorescence Switch for Detection of Pb2+ Ions and Vitamin C.

ß-cyclodextrin-hydroxyquinoline functionalized graphene oxide (GO-CD-HQ) was facilely fabricated to monitor and quantitatively analyze cations in aqueous media. The optical probe was notably selective enhanced toward Pb2+ ions over the other tested ions like Cu2+, Hg2+, Ca2+, Na+, K+, Zn2+, Fe2+, Fe3+, Ag+, Mg2+, and Cd2+ at 468 nm as an emission wavelength. The probe was shown the best performance in pH value, 5, and optimum time 1 min. Absorption spectra have clearly confirmed the static type fluorescence enhancement mechanism of GO-CD-HQ. Under the optimal conditions, the detection limit of it and linear concentration range for Pb2+ ions were obtained as 3.72 × 10-5 M and (5-60) × 10-5 M, respectively. Additionally, the developed assay exhibited logic gate behavior with Pb2+ ions and vitamin C as a masking agent for cited ions.

In vitro and in silico pharmaceutical activities of the methylated cyclic pentapeptide, persipeptides.

AIMS: The persipeptides were recognized as a promising source of multiple pharmaceutical activities which were revealed following structure-activity prediction and examination in experimental analysis. METHODS AND RESULTS: The profile of toxicity, antioxidant, anti-inflammatory, anti-diabetic and anti-ageing activity of persipeptides and the crude extract were evaluated experimentally. The pure Persipeptide A and B revealed a moderate xanthine oxidase inhibition activity at the concentration of 10 µg/ml. Persipeptide exhibited α-glucosidase inhibition activity (~10% inhibition) and less than 2% tyrosinase inhibition activity at the concentration of 10 µg/ml. The extract exhibited the inhibition of less than 2% acetylcholine esterase inhibition activity, but the pure persipeptide showed 6%-14% inhibition activity at the concentration of 10 µg/ml. The molecular docking analysis revealed that the activities of Persipeptide A and B are due to interaction with xanthin oxidase, α-amylase, α-glucosidase, tyrosinase and acetylcholine esterase enzymes. CONCLUSIONS: The persipeptides showed a similar inhibition rate with positive control that might imply its potential as an anti-diabetic and anti-gout compound among. Only acetylcholine esterase inhibition of persipeptide was higher than the extract. The interacting amino acids of the molecules with different targets show that persipeptides might have antioxidant, anti-inflammatory, anti-diabetic, anti-ageing activity and even other potential pharmaceutical activities that were not investigated in this study. SIGNIFICANCE AND IMPACT OF THE STUDY: This report was presented to find some new pharmaceutical activities of Persipeptide A and B including the α-glucosidase inhibition activity as a molecular target of diabetes mellitus. Persipeptides also exhibited an effective inhibition of xanthine oxidase (XO) which can be a drug-like candidate in the treatment of diseases associated with XO like gout. The binding values indicated the interaction of persipeptides with these enzymes.

Gradient Heating Epitaxial Growth Gives Well Lattice-Matched Mo2 C-Mo2 N Heterointerfaces that Boost Both Electrocatalytic Hydrogen Evolution and Water Vapor Splitting.

An optimized approach to producing lattice-matched heterointerfaces for electrocatalytic hydrogen evolution has not yet been reported. Herein, we present the synthesis of lattice-matched Mo2 C-Mo2 N heterostructures using a gradient heating epitaxial growth method. The well lattice-matched heterointerface of Mo2 C-Mo2 N generates near-zero hydrogen-adsorption free energy and facilitates water dissociation in acid and alkaline media. The lattice-matched Mo2 C-Mo2 N heterostructures have low overpotentials of 73 mV and 80 mV at 10 mA cm-2 in acid and alkaline solutions, respectively, comparable to commercial Pt/C. A novel photothermal-electrocatalytic water vapor splitting device using the lattice-matched Mo2 C-Mo2 N heterostructure as a hydrogen evolution electrocatalyst displays a competitive cell voltage for electrocatalytic water splitting.

DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks.

MOTIVATION: An essential part of drug discovery is the accurate prediction of the binding affinity of new compound-protein pairs. Most of the standard computational methods assume that compounds or proteins of the test data are observed during the training phase. However, in real-world situations, the test and training data are sampled from different domains with different distributions. To cope with this challenge, we propose a deep learning-based approach that consists of three steps. In the first step, the training encoder network learns a novel representation of compounds and proteins. To this end, we combine convolutional layers and long-short-term memory layers so that the occurrence patterns of local substructures through a protein and a compound sequence are learned. Also, to encode the interaction strength of the protein and compound substructures, we propose a two-sided attention mechanism. In the second phase, to deal with the different distributions of the training and test domains, a feature encoder network is learned for the test domain by utilizing an adversarial domain adaptation approach. In the third phase, the learned test encoder network is applied to new compound-protein pairs to predict their binding affinity. RESULTS: To evaluate the proposed approach, we applied it to KIBA, Davis and BindingDB datasets. The results show that the proposed method learns a more reliable model for the test domain in more challenging situations. AVAILABILITY AND IMPLEMENTATION: https://github.com/LBBSoft/DeepCDA.

10-(4-Phenylpiperazine-1-carbonyl)acridin-9(10H)-ones and related compounds: Synthesis, antiproliferative activity and inhibition of tubulin polymerization.

As part of our continuing search for potent inhibitors of tubulin polymerization, two novel series of 42 10-(4-phenylpiperazine-1-carbonyl)acridin-9(10H)-ones and N-benzoylated acridones were synthesized on the basis of a retrosynthetic approach. All newly synthesized compounds were tested for antiproliferative activity and interaction with tubulin. Several analogs potently inhibited tumor cell growth. Among the compounds tested, 10-(4-(3-methoxyphenyl)piperazine-1-carbonyl)acridin-9(10H)-one (17c) exhibited excellent growth inhibitory effects on 93 tumor cell lines, with an average GI50 value of 5.4 nM. We were able to show that the strong cytotoxic effects are caused by disruption of tubulin polymerization, as supported by the EBI (N,N'-Ethylenebis(iodoacetamide)) assay and the fact that the most potent inhibitors of cancer cell growth turned out to be the most efficacious tubulin polymerization inhibitors. Potencies were nearly comparable or superior to those of the antimitotic reference compounds. Closely related to this, the most active analogs inhibited cell cycling at the G2/M phase at concentrations down to 30 nM and induced apoptosis in K562 leukemia cells. We believe that our work not only proves the excellent suitability of the acridone scaffold for the design of potent tubulin polymerization inhibitors but also enables synthetic access to further potentially interesting N-acylated acridones.

Design and synthesis of novel quinazolinone-pyrazole derivatives as potential α-glucosidase inhibitors: Structure-activity relationship, molecular modeling and kinetic study.

In this study, a new series of quinazolinone-pyrazole hybrids were designed, synthesized and screened for their α-glucosidase inhibitory activity. The results of the in vitro screening indicated that all the molecular hybrids exhibited more inhibitory activity (IC50 values ranging from 60.5 ± 0.3 µM-186.6 ± 20 µM) in comparison to standard acarbose (IC50 = 750.0 ± 10.0 µM). Limited structure-activity relationship suggested that the variation in the inhibitory activities of the compounds affected by different substitutions on phenyl rings of diphenyl pyrazole moiety. The enzyme kinetic studies of the most potent compound 9i revealed that it inhibited α-glucosidase in a competitive mode with a Ki of 56 µM. Molecular docking study was performed to predict the putative binding interaction. As expected, all pharmacophoric moieties used in the initial structure design playing a pivotal role in the interaction with the binding site of the enzyme. In addition, by performing molecular dynamic investigation and MM-GBSA calculation, we investigated the difference in structural perturbation and dynamic behavior that is observed over α-glycosidase in complex with the most active compound and acarbose relative to unbound α-glycosidase enzyme.

Application of genetic algorithm and multivariate methods for the detection and measurement of milk-surfactant adulteration by attenuated total reflection and near-infrared spectroscopy.

BACKGROUND: The adulteration of milk by hazardous chemicals like surfactants has recently increased. It conceals the quality of the product to gain profit. As milk and milk-based products are consumed by many people, novel analytical procedures are needed to detect these adulterants. This study focused on Fourier-transform infrared (FTIR) spectroscopy equipped with an attenuated total reflection (ATR) accessory, and near-infrared (NIR) spectroscopy for the determination of milk-surfactant adulteration using a genetic algorithm (GA) coupled with multivariate methods. The model surfactant was sodium dodecyl sulfate (SDS), and its concentration varied from 1.94-19.4 gkg-1 in adulterated samples. RESULTS: Prominent peaks in the spectral range of 5500-6400 cm-1 , 1160-1260 cm-1 and 1049-1080 cm-1 may correspond to the sulfonate group in SDS. A genetic algorithm could significantly reduce the number of variables to almost one third by selecting the specific wavenumber region. Principal component analysis (PCA) for ATR and NIR data indicated separate clusters of samples in terms of the concentration level of SDS (P ≤ 0.05). Partial least squares regression (PLSR) was used to determine the maximum R2 value for ATR and NIR data for calibration, cross-validation and prediction, which were 0.980, 0.972, 0.980, and 0.970, 0.937, and 0.956 respectively. The results showed apparent differences between unadulterated and adulterated samples using partial least squares-discriminant analysis (PLS-DA), which was validated by the permutation test. CONCLUSION: The results clearly show the successful application of the proposed methods with multivariate analysis in the selection of variables, classification, clustering, and identification of the adulterant in amounts as low as 1.94 gkg-1 in milk. © 2020 Society of Chemical Industry.

Novel fused 1,2,3-triazolo-benzodiazepine derivatives as potent anticonvulsant agents: design, synthesis, in vivo, and in silico evaluations.

A novel series of 1,2,3-triazolo-benzodiazepine derivatives 6a-o has been synthesized and evaluated in vivo for their anticonvulsant activities using by pentylenetetrazole (PTZ)- and maximal electroshock (MES)-induced seizures in mice. The synthetic approach started with diazotizing 2-aminobenzoic acids 1 to produce 2-azidobenzoic acids 2. Next, reaction of the latter compounds with propargylamine 3, benzaldehyde 4, and isocyanides 5 led to the formation of the title compounds 6a-o, in good yields. All the synthesized compounds exhibited high anticonvulsant activity in the PTZ test, comparable to or better than the standard drug diazepam. Among the tested compounds, N-(tert-butyl)-2-(9-chloro-6-oxo-4H-[1,2,3]triazolo[1,5-a][1,4]benzodiazepin-5(6H)-yl)-2-(3-bromophenyl)acetamide 6h was the most potent compound in this assay. Moreover, compounds 6i and 6k showed excellent activity in MES test. Loss of the anticonvulsant effect of compound 6h in the presence of flumazenil in the PTZ test and appropriate interaction of this compound in the active site of benzodiazepine (BZD)-binding site of GABAA receptor confirm involvement of BZD receptors in the anticonvulsant activity of compound 6h. A novel series of 1,2,3-triazolo-benzodiazepine derivatives 6a-o have been synthesized and evaluated in vivo for their anticonvulsant activities using by pentylenetetrazole (PTZ)- and maximal electroshock (MES)-induced seizures in mice. All the synthesized compounds exhibited high anticonvulsant activity, comparable to or better than the standard drug diazepam in the PTZ test and compounds 6i and 6k showed excellent activity in MES test. Flumazenil test and in silico docking study confirm involvement of benzodiazepine receptors in the anticonvulsant activity of these compounds.

Exploring 3D-QSPR models of human skin permeability for a diverse dataset of chemical compounds.

The control of permeation is vital not only for the topical application of lotions, creams, and ointments but also for the toxicological and risk assessment of materials from environmental and occupational hazards. To understand the effects of physicochemical properties of a variety of 211 compounds on skin permeability, we developed a three-dimensional quantitative structure-property relationship (3 D-QSPR) model. Alignment free GRid-INdependent Descriptors (GRINDs), which were derived from molecular interaction fields (MIFs) contributed to the regression models. Kennard-Stone algorithm was employed to split data set to a training set of 159 molecules and a test set of 52 molecules. Fractional factorial design (FFD), genetic algorithm (GA) and successive projection algorithm (SPA) were applied to select the most relevant 3 D molecular descriptors. The descriptors selected using various feature selection were correlated with skin permeability constants by partial least squares (PLS) and support vector machine (SVM). SPA-SVM model gave prominent statistical values with the correlation coefficient of [Formula: see text]= 0.96, Q2= 0.73 and R2pred=0.76. According to the analysis results, the hydrogen bonding donor and acceptor properties of the investigated compounds can influence the penetration into the human skin. Furthermore, it was found that permeability was enhanced by increasing the hydrophobicity and was diminished by increasing the molecular weight. In addition, the presence of hydrophobic groups in the target molecule, as well as their shape and position, can affect the skin permeability.

An ultrasensitive fluorescence sensor for determination of trace levels of copper in blood samples.

A novel SBA-15-based fluorescent sensor, SBA-PI: mesoporous SBA-15 structure modified with iminostilbene groups, was designed, synthesized, and characterized by Fourier transform-infrared spectroscopy (FT-IR), ultraviolet-visible spectroscopy, field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), thermogravimetric analysis (TGA), low-angle X-ray diffraction techniques (low-angle XRD), and N2 adsorption-desorption techniques. The SBA-PI as a sensor with a selective behavior for detection of Cu2+ comprises iminostilbene carbonyl as the fluorophore group. The SBA-PI sensor displays an excellent fluorescence response in aqueous solutions and the fluorescence intensity quenches remarkably upon addition of Cu2+. Other common interfering ions even at high concentration ratio showed either no or very small changes in the fluorescence intensity of SBA-PI in the absence of Cu2+. A limit of detection of 8.7 × 10-9 M for Cu2+ indicated that this fluorescence sensor has a high sensitivity and selectivity toward the target copper (II) ion. The fabricated Cu2+ sensor was successfully applied for the determination of the Cu2+ in human blood samples without any significant interference. With the selective analysis of Cu2+ ions down to 0.9 nM in blood, the sensor is a promising and a novel detection candidate for Cu2+ and can be applied in the clinical laboratory. A reversibility and accuracy in the fluorescence behavior of the sensor was found in the presence of I¯ that was described as a masking agent for Cu2+. Graphical abstract.

Design and Synthesis of Novel Cytotoxic Indole-Thiosemicarbazone Derivatives: Biological Evaluation and Docking Study.

In this work, two novel series of indole-thiosemicarbazone derivatives were designed, synthesized, and evaluated for their cytotoxic activity against MCF-7, A-549, and Hep-G2 cell lines in comparison to etoposide and colchicine as the reference drugs. Generally, the synthesized compounds showed better cytotoxicity towards A-549 and Hep-G2 than MCF-7. Among them, (2E)-2-{[2-(4-chlorophenyl)-1H-indol-3-yl]methylidene}-N-(4-methoxyphenyl)hydrazinecarbothioamide (8l) was found to be the most potent compound against A-549 and Hep-G2, at least three times more potent than etoposide. The morphological analysis by the acridine orange/ethidium bromide double staining test and flow cytometry analysis indicated that compound 8l induced apoptosis in A-549 cells. Moreover, molecular docking methodology was exploited to elucidate the details of molecular interactions of the studied compounds with putative targets.

Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines.

The presented manuscript reports the simultaneous detection of a ternary mixture of the benzodiazepines diazepam, lorazepam, and flunitrazepam using an array of voltammetric sensors and the electronic tongue principle. The electrodes used in the array were selected from a set of differently modified graphite epoxy composite electrodes; specifically, six electrodes were used incorporating metallic nanoparticles of Cu and Pt, oxide nanoparticles of CuO and WO3, plus pristine electrodes of epoxy-graphite and metallic Pt disk. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Multivariate examination using Principal Component Analysis (PCA) justified the choice of sensors in order to get the proper discrimination of the benzodiazepines. Next, a quantitative model to predict the concentrations of mixtures of the three benzodiazepines was built employing the set of voltammograms, and was first processed with the Discrete Wavelet Transform, which fed an artificial neural network response model. The developed model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 0.034 and 0.106 for the training and test subsets, respectively, and coefficient of correlation R ≥ 0.938 in the predicted vs. expected concentrations comparison graph.

Synthesis and cholinesterase inhibitory activity of new 2-benzofuran carboxamide-benzylpyridinum salts.

A series of benzofuran-2-carboxamide-N-benzyl pyridinium halide derivatives (6a-o) are synthesized as new cholinesterase inhibitors. The synthetic pathway involves the reaction of salicylaldehyde derivatives and ethyl bromoacetate, followed by hydrolysis and amidation with 3- and 4-picolyl amine. Subsequently, N-((pyridin-4-yl) methyl) benzofuran-2-carboxamide and substituted N-((pyridin-3-yl) methyl) benzofuran-2-carboxamides reacts with benzyl halides to afford target compounds (6a-o). The chemical structures of all derivatives were confirmed by spectroscopic methods. The studies reveal that some of the synthesized compounds are potent butyrylcholinesterase inhibitors with IC50 values in the range of 0.054-2.7 µM. In addition, good inhibitory effects on Aß self-aggregation are observed for 6h and 6k (33.1 and 46.4% at 100 µM, respectively).

A detailed structural study of cytotoxicity effect of ionic liquids on the leukemia rat cell line IPC-81 by three dimensional quantitative structure toxicity relationship.

In the present study, a very thorough and in-depth three-dimensional quantitative structure-toxicity relationship (3D-QSTR) analysis has been implemented to make a correlation between the structural information of the ionic liquids (ILs) and their cytotoxicity towards Leukemia rat cell line IPC-81, as one of the ILs' toxicological consequences. To do this, alignment free GRid-INdependent Descriptors (GRINDs), which were derived from molecular interaction fields (MIFs), were correlated to the cytotoxicity values by partial least squares (PLS) and support vector regression (SVR). Genetic algorithm (GA), as a powerful linear tool, was used to select the best and interpretative subset of variables for the predictive model building. The selected variables with the capability to screen the effective structural features, showed direct and inverse contribution to the cytotoxicity. In silico modeling can reduce the amount of cellular testing necessary by predicting the toxicological functions of the chemical structures. Acceptable predictions of both internal and external validation sets made it possible to develop the predictive models for a large set of 269 diverse ILs containing 9 cationic cores and 44 types of anions. The constructed 3D-QSTR models use simple and interpretable descriptors to provide an in-depth and mechanistic interpretation of structural characteristics. This helps provide a clear understanding of the cytotoxicity effects of the understudy ILs. The effects of the nature of the cations, anions, and substituents on the cytotoxicities were evaluated and discussed.

Simultaneous determination of binary solution of triphenylmethane dyes in complex matrices onto magnetic amino-rich SWCNT using second-order calibration method.

This study suggested a new method for simultaneous quantification of two dyes in complex matrices using second-order data by spectrophotometry. Second-order data was generated simply without any expensive instrument using two independent variables including wavelength and the monotonic addition of sodium dodecyl sulfate. Solid-phase extraction (SPE) based on amino-rich magnetic single-walled carbon nanotube as an adsorbent was employed prior to second-order data generation. SPE optimization was performed by Box-Behnken design, and parameters and their interaction which were dependent on the simultaneous extraction of dyes were examined. Competitive Langmuir and Freundlich isotherms for a binary system and individual dyes could all represent the equilibrium data well. The second-order data was processed by parallel factor analysis (PARAFAC and PARAFAC2) and multivariate curve resolution-alternating least squares (MCR-ALS). Figures of merit of the model including a limit of detection of 3.0 and 2.5 ng mL-1 for crystal violet and malachite green, respectively, were estimated using the MCR-ALS method. The combination of the second-order calibration and SPE presents an easy and versatile method for determination of the mixture of two dyes in the presence of uncalibrated interferences in environmental water, synthetic, and fish samples with the recoveries of 94-104.

Simultaneous spectrophotometric determination of crystal violet and malachite green in water samples using partial least squares regression and central composite design after preconcentration by dispersive solid-phase extraction.

In this paper, a simple, fast, and inexpensive method is introduced for the simultaneous spectrophotometric determination of crystal violet (CV) and malachite green (MG) contents in aquatic samples using partial least squares regression (PLS) as a multivariate calibration technique after preconcentration by graphene oxide (GO). The method was based on the sorption and desorption of analytes onto GO and direct determination by ultraviolet-visible spectrophotometric techniques. GO was synthesized according to Hummers method. To characterize the shape and structure of GO, FT-IR, SEM, and XRD were used. The effective factors on the extraction efficiency such as pH, extraction time, and the amount of adsorbent were optimized using central composite design. The optimum values of these factors were 6, 15 min, and 12 mg, respectively. The maximum capacity of GO for the adsorption of CV and MG was 63.17 and 77.02 mg g-1, respectively. Preconcentration factors and extraction recoveries were obtained and were 19.6, 98% for CV and 20, 100% for MG, respectively. LOD and linear dynamic ranges for CV and MG were 0.009, 0.03-0.3, 0.015, and 0.05-0.5 (µg mL-1), respectively. The intra-day and inter-day relative standard deviations were 1.99 and 0.58 for CV and 1.69 and 3.13 for MG at the concentration level of 50 ng mL-1, respectively. Finally, the proposed DSPE/PLS method was successfully applied for the simultaneous determination of the trace amount of CV and MG in the real water samples.