Structural Alerts Research Articles

QSAR, pharmacophore modeling and molecular docking studies to identify structural alerts for some nitrogen heterocycles as dual inhibitor of telomerase reverse transcriptase and human telomeric G-quadruplex DNA

BackgroundTelomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA are amongst the favorable target for researchers to discover novel and more effective anticancer agents. To understand and elucidate structure activity relationship and mechanism of inhibition of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA, a QSAR modeling and molecular docking were conducted.ResultsTwo robust QSAR model were obtained which consist of full set QSAR model (R2: 0.8174, CCCtr: 0.8995, Q2loo: 0.7881, Q2LMO: 0.7814) and divided set QSAR model (R2: 0.8217, CCCtr: 0.9021, Q2loo: 0.7886, Q2LMO: 0.7783, Q2-F1: 0.7078, Q2-F2: 0.6865, Q2-F3: 0.7346) for envisaging the inhibitory activity of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA. The analysis reveals that carbon atom exactly at 3 bonds from aromatic carbon atom, nitrogen atom exactly at six bonds from planer nitrogen atom, aromatic carbon atom within 2 A0 from the center of mass of molecule and occurrence of element hydrogen within 2 A0 from donar atom are the key pharmacophoric features important for dual inhibition of TERT and human telomeric G-quadruplex DNA. To validate this analysis, pharmacophore modeling and the molecular docking is performed. Molecular docking analysis support QSAR analysis and revealed that, dual inhibition of TERT and human telomeric DNA is mainly contributed from hydrophobic and hydrogen bonding interactions.ConclusionThe findings of molecular docking, pharmacophore modelling, and QSAR are all consistent and in strong agreement. The validated QSAR analyses can detect structural alerts, pharmacophore modelling can classify a molecule's consensus pharmacophore involving hydrophobic and acceptor regions, whereas docking analysis can reveal the mechanism of dual inhibition of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA. The combination of QSAR, pharmacophore modeling and molecular docking may be useful for the future drug design of dual inhibitors to combat the devastating issue of resistance.Graphical abstract

Screening of 6000 Compounds for Uncoupling Activity: A Comparison Between a Mechanistic Biophysical Model and the Structural Alert Profiler Mitotox.

Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes, or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in vitro uncoupling activity, using a biophysical model based on ab initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural alert profiler Mitotox (97%), but much higher sensitivity than Mitotox (47%). The biophysical model thus allows for a more accurate screening for uncoupling activity than existing structural alert profilers. We propose to use our model as a complementary tool to screen large datasets for protonophoric uncoupling activity in drug development and toxicity assessment.

International Consortium to Advance Cross-Species Extrapolation of the Effects of Chemicals in Regulatory Toxicology.

International Consortium to Advance Cross-Species Extrapolation of the Effects of Chemicals in Regulatory Toxicology.

Prediction of Micronucleus Assay Outcome Using In Vivo Activity Data and Molecular Structure Features.

In vivo micronucleus assay is the widely used genotoxic test to determine the extent of chromosomal aberrations caused by the chemicals in human beings, which plays a significant role in the drug discovery paradigm. To reduce the uncertainties of the in vivo experiments and the expenses, we intended to develop novel machine learning-based tools to predict the toxicity of the compounds with high precision. A total of 372 compounds with known toxicity information were retrieved from the PubChem Bioassay database and literature. The fingerprints and descriptors of the compounds were generated using PaDEL and ChemSAR, respectively, for the analysis. The performance of the models was assessed using the three tires of evaluation strategies such as fivefold, tenfold, and validation by external dataset. Further, structural alerts causing genotoxicity of the compounds were identified using SARpy method. Of note, fingerprint-based random forest model built in our analysis is able to demonstrate the highest accuracy of about 0.97 during tenfold cross-validation. In essence, our study highlights that structural alerts such as chlorocyclohexane and trimethylamine are likely to be the leading cause of toxicity in humans. Indeed, we believe that random forest model generated in this study is appropriate for reduction of test animals and should be considered in the future for the good practice of animal welfare.

Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata.

In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC50 for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R2=0.72, Q2LOO=0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.

Identification of structural factors that affect binding to cannabinoid receptor type 1

Similar to the opioid system, the endocannabinoid system which regulates immune response, memory and nociceptive thresholds, cell communication, appetite, and metabolism, also modulates (via Cannabinoid receptor type 1 - CB1R) the rewarding effects of natural and synthetic cannabinoids. Although currently there is significant controversy concerning the (legal) use of cannabis, it has been established that prolonged consumption of a large variety of chemicals from the cannabinoid family can lead to the development of psychiatric and cardiovascular pathologies. In addition to (-)-Δ9-trans-tetrahydrocannabinol (Δ9-THC or simply THC), the main addictive component of cannabis, 43 other synthetic cannabimimetic agents are currently classified as Schedule I drugs and their use in the U.S. is illegal (DEA-DCT-DIR-032-18 [1]). In this study, three-dimensional spectrometric data-activity relationship (3D-SDAR) quantitative and categorical models aimed at better understanding the molecular mechanism of action and the structural features governing binding to CB1R were developed. The performance parameters for a consensus model constructed from composite PLS and KNN quantitative models achieved R2 of 0.703 for an external blind set and an accuracy of 0.846, sensitivity of 0.870, and specificity of 0.840 for binary classification. In addition to the classical A, B, C-tricyclic terpenoid system, other structural alerts were also discovered and reported. We also identified the presence of a hERG linked toxicophore in the molecular structures of a subset of diaryl-(piperidinyl)purines which are known to be selective and potent CB1R binders.

Bacterial mutagenicity test data: collection by the task force of the Japan pharmaceutical manufacturers association

BackgroundAmes test is used worldwide for detecting the bacterial mutagenicity of chemicals. In silico analyses of bacterial mutagenicity have recently gained acceptance by regulatory agencies; however, current in silico models for prediction remain to be improved. The Japan Pharmaceutical Manufacturers Association (JPMA) organized a task force in 2017 in which eight Japanese pharmaceutical companies had participated. The purpose of this task force was to disclose a piece of pharmaceutical companies’ proprietary Ames test data.ResultsAmes test data for 99 chemicals of various chemical classes were collected for disclosure in this study. These chemicals are related to the manufacturing process of pharmaceutical drugs, including reagents, synthetic intermediates, and drug substances. The structure-activity (mutagenicity) relationships are discussed in relation to structural alerts for each chemical class. In addition, in silico analyses of these chemicals were conducted using a knowledge-based model of Derek Nexus (Derek) and a statistics-based model (GT1_BMUT module) of CASE Ultra. To calculate the effectiveness of these models, 89 chemicals for Derek and 54 chemicals for CASE Ultra were selected; major exclusions were the salt form of four chemicals that were tested both in the salt and free forms for both models, and 35 chemicals called “known” positives or negatives for CASE Ultra. For Derek, the sensitivity, specificity, and accuracy were 65% (15/23), 71% (47/66), and 70% (62/89), respectively. The sensitivity, specificity, and accuracy were 50% (6/12), 60% (25/42), and 57% (31/54) for CASE Ultra, respectively. The ratio of overall disagreement between the CASE Ultra “known” positives/negatives and the actual test results was 11% (4/35). In this study, 19 out of 28 mutagens (68%) were detected with TA100 and/or TA98, and 9 out of 28 mutagens (32%) were detected with either TA1535, TA1537, WP2uvrA, or their combination.ConclusionThe Ames test data presented here will help avoid duplicated Ames testing in some cases, support duplicate testing in other cases, improve in silico models, and enhance our understanding of the mechanisms of mutagenesis.

In silico assessment of genotoxicity. Combinations of sensitive structural alerts minimize false negative predictions for all genotoxicity endpoints and can single out chemicals for which experimentation can be avoided

Genotoxicity assessment of chemicals has a crucial role in most regulations. Due to labor, time, cost, and animal welfare issues, attention is being given to (Q)SAR methods. A strategic application of alternative methods is to first use a sequence of conservative (very sensitive) (Q)SARs and/or in vitro models to arrive at the conclusion that no further testing is necessary for negatives, and to use mechanistically based, Weight-Of-Evidence approach to evaluate the chemicals showing positive results. The ICH M7 guideline to detect DNA-reactive impurities in drugs follows these lines (recommending solely (Q)SAR in step 1). However, ICH M7 focuses only on Ames test. Here a large database of more than 6000 chemicals positive in at least one endpoint (in vitro gene mutations or chromosomal aberrations, in vivo micronucleus, aneugenicity) were analyzed with structural alerts implemented in the OECD QSAR Toolbox, resulting in maximum 3% false negatives. These promising results indicate that it may be possible to extend the approach to the whole range of genotoxicity endpoints required by regulations. Since structural alerts may generate false positives, cautious follow-up of positives is recommended (with e.g., statistically based QSARs, read across of similar chemicals, expert judgement, and experimentation when necessary).

Mechanistically driven identification of novel structural alerts for mitochondrial toxicity

Mitochondrial toxicity is a problem of growing concern in modern society, resulting in several serious adverse health effects including cardiac failure, hepatotoxicity, and neurodegenerative disorders. Adverse outcome pathways provide models for structuring a mechanistic understanding of toxicology, but most existing in silico models for prediction of mitochondrial toxicity do not account for the mechanism of action of potential toxicants. In a recent study by Hallinger et al., the Seahorse respirometric assay provides experimental data relating to the mechanism of action of several mitochondrial toxins. This makes possible, for the first time, the development of structural alerts for mitochondrial toxicity linked to mechanisms. By using a maximal common substructure searcher and Bayesian statistical analysis, we have discovered 11 alerts associated with different mechanisms of action. Eight of these are completely novel. By incorporating the mechanisms into the structural alert, more information can be gained about the molecular initiating events involved and build toward more complete adverse outcome pathways for mitochondrial toxicity.

Risk-based prioritization of suspects detected in riverine water using complementary chromatographic techniques

Surface waters are widely used as drinking water sources and hence their quality needs to be continuously monitored. However, current routine monitoring programs are not comprehensive as they generally cover only a limited number of known pollutants and emerging contaminants. This study presents a risk-based approach combining suspect and non-target screening (NTS) to help extend the coverage of current monitoring schemes. In particular, the coverage of NTS was widened by combining three complementary separations modes: Reverse phase (RP), Hydrophilic interaction liquid chromatography (HILIC) and Mixed-mode chromatography (MMC). Suspect lists used were compiled from databases of relevant substances of very high concern (e.g., SVHCs) and the concentration of detected suspects was evaluated based on ionization efficiency prediction. Results show that suspect candidates can be prioritized based on their potential risk (i.e., hazard and exposure) by combining ionization efficiency-based concentration estimation, in vitro toxicity data or, if not available, structural alerts and QSAR.based toxicity predictions. The acquired information shows that NTS analyses have the potential to complement target analyses, allowing to update and adapt current monitoring programs, ultimately leading to improved monitoring of drinking water sources.

Hazard prioritisation of substances in printing inks and adhesives applied to plastic food packaging

ABSTRACT Thousands of intentionally added substances can be used in printing inks and adhesives applied to plastic food packaging. Some of them can be transferred to foodstuffs through a phenomenon called migration, arising concerns on the potential adverse health effects derived from the exposure to chemicals that have not yet been assessed for their risks to humans. The large number of the substances concerned and the lack of prioritisation strategies hamper the work of control authorities, since it is not clear which substances should be monitored as first priority. In this study, a hazard prioritisation strategy is proposed. An inventory listing more than 6,000 substances used in inks and adhesives applied to plastic food packaging was compiled and filtered using several exclusion criteria aimed to set apart those substances for which there is no apparent need for further evaluation or because fall into one of the exclusion categories of the Threshold of Toxicological Concern (TTC) approach. Additionally, substances with a molecular weight >1,000 Da were removed. Approximately 2,300 substances were retained, for which a comprehensive hazard profiling was conducted based on the general scheme for the application of the TTC approach. First, structural alerts for genotoxic and non-genotoxic carcinogenicity were investigated. If a substance was neither genotoxic nor belonging to the chemical classes of organophosphates and carbamates, the Cramer classification was used. Furthermore, the substances were searched for their presence in three so-called ‘Substances of Concern’ lists and RASFF notifications. Groups of high, medium and low priority substances were established, resulting in 1,660 substances classified as high and medium priority. A panel of five experts evaluated these substances with respect to their relevance for further risk evaluations. By applying this hazard prioritisation strategy, 696 substances were identified as ‘Very High Priority Substances’ (VHPS) for which further assessments should be performed.

In vitro Measurement and In vivo Prediction of Time-Dependent Inhibitory Effects of Three Tyrosine Kinase Inhibitors on CYP3A Activity.

Imatinib, sunitinib, and gefitinib are the three most common tyrosine kinase inhibitors (TKIs). However, their quantitative drug-drug interaction potentials In vivo and the relationship between their structure and inhibitory activity remain unknown. This study aimed to investigate the potential drug-drug interaction risk of three TKIs based on CYP3A. 6β-Hydroxylated testosterone formation was selected to probe the CYP3A activity in human liver microsomes. A molecular docking simulation was performed to explore the potential structural alerts. Imatinib exhibited the strongest inhibitory effect towards CYP3A, while the inhibitory potential of gefitinib and sunitinib were comparable to each other but weaker than imatinib. IC50 shift assays demonstrated that the inhibitory potential of all three TKIs was significantly increased after a 30-min preincubation with NADPH. The KI and Kinact values of imatinib, sunitinib, and gefitinib were 3.75 μM and 0.055 min-1, 1.96 μM and 0.037 min-1, and 9.94 μM and 0.031 min-1, respectively. IVIVE results showed that there was a 1.3- to 43.1-fold increase in the AUC of CYP3A-metabolizing drugs in the presence of the TKIs. All three TKIs exhibited a typical irreversible inhibitory effect towards CYP3A. The presence of more N-heterocycles and the resulting better binding confirmation of imatinib may have been responsible for its stronger inhibitory effect than sunitinib and gefitinib. Therefore, caution should be taken when CYP3A-metabolizing drugs are co-administrated with imatinib, sunitinib, or gefitinib.

Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose

An effective approach for assessing a drug’s potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD. We constructed a database containing 171 RM-related structural alerts, generated a dataset of 407 AD- and non-AD-associated drugs, and performed statistical analysis. The nitrogen-containing benzene substituent alerts were found to be significantly associated with the risk of drug-induced ADs (odds ratio = 2.95, p = 0.0036). Furthermore, we developed a machine-learning-based predictive model by using daily dose and nitrogen-containing benzene substituent alerts as the top inputs and achieved the predictive performance of area under curve (AUC) of 70%. Additionally, we confirmed the reactivity of the nitrogen-containing benzene substituent aniline and related metabolites using quantum chemistry analysis and explored the underlying mechanisms. These identified structural alerts could be helpful in identifying drug candidates that carry a potential risk of drug-induced ADs to improve their safety profiles.

Abstract 278: Artificial intelligence accelerate drug discovery

Abstract Drug discovery is resource intensive, and involves typical timelines of 10-20 years and costs that range from US$0.5 billion to US$2.6 billion. Artificial intelligence (AI) has recently started to gear-up its application in various sectors of the society and the pharmaceutical industry as a frontrunner beneficiary.Artificial intelligence can accelerate drug discovery and reduce costs by facilitating the rapid screening and identification of compounds. We have developed DM-AI drug discovery platform, including convolutional neural networks, decision treealgorithm, reinforcement learning, generative adversarial networks, big data, and knowledge graphs, along with structure and ligand-based high-throughput virtual screening , for new drug discovery and development. DM-AI optimizes biological activity,toxicity,physicochemical property. We used DM-AI to discover potent inhibitors of SHP2, PIM1, DNA-PK, kinases target implicated in solid tumor and other diseases.We started to train a biological activity prediction model on a database of the given target kinase inhibitors (positive set) and non-kinase targets molecules (negative set), and then predicted the activity of existing million data sets, obtained an initial output of thousands structures. We then evaluated these structures using a pharmacophore reward model on the basis of virtual chemical spaces of kinase inhibitors in complex with target protein. To narrow our focus to a smaller set of molecules for analysis, compounds with higher score were filtered to remove patents and applications molecules, also remove molecules bearing structural alerts and reactive groups.By day 7 after target selection, We had selected dozens structures with structural diversity for experimental validation. and by day 28, they were tested for in vitro inhibitory activity in an enzymatic kinase assay, active compounds accounted for up to 65% in some target models. This illustrates the utility of our DM-AI drug discovery platform for the successful, rapid discovery of drug candidates. Citation Format: Weidong Xie, Xing Cheng, Zhengfang Ding, Riqiang Deng, Dawei Gu. Artificial intelligence accelerate drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 278.

In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.

Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149 , respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota-Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment.

In-Silico Validation and Fabrication of Matrix Diffusion-Based Polymeric Transdermal Patches for Repurposing Gabapentin Hydrochloride in Neuropathic Pain.

Gabapentin (GBP) is an FDA-approved drug for the treatment of partial and secondary generalized seizures, apart from being used for diabetic neuropathic pain. GBP displays a highly intricate mechanism of action and its inhibitory response in elevated antagonism of NMDA (N-methyl-D-aspartate receptor) receptor and thus, can be repurposed for controlling neuropathic pain. Therefore, in the present study, we have selected hBCATc (humanPyridoxal 5'-phosphate- dependent branched-chain aminotransferase cytosolic) gene that is highly expressed in silico validation through neuropathic stressed conditions. Thereafter, have analysed the GBP as its competitive inhibitor by in silico validation through homology modelling, molecular docking, also predicting its structural alerts and pharmacokinetic suitability through ADMET. However, and GBP was found to be a potential drug in controlling neuropathic pain, still, it has certain critical and pharmacokinetic limitations; therefore, the need for its targeted delivery was required, and the same was attained by designing a GBP loaded transdermal patch (GBP-TDP). A suitable and equally efficacious GBP - TDP was developed by a solvent evaporation method using PVP and HPMC in the ratio of 2:1 as a polymer base for reservoir type of TDP. Also, PEG 400 was used as a plasticizer, and PVA (4%) was taken for backing membrane preparation, and then the optimized GBP-TDP was subjected for physical characterization, optimization and ex vivo release kinetics. The results showed desired specifications with uneven and flaky surface appearance giving an avenue for controlled release of the drugs with 92.34 ± 1.43% of drug release in 10 hours, further suggesting that GBP-TDP can be used as an effective tool against diabetic neuropathy pain. In this study, we have repurposed Gabapentin to treat diabetic neuropathy and validated the same by conducting a detailed in silico evaluation starting from homology Modelling of the target protein hBCATc, cross verified by the Ramachandran plot analysis with the most favoured region of 92.1% (encompassing 303 residues out of 386).

Threshold of Toxicological Concern-An Update for Non-Genotoxic Carcinogens.

The Threshold of Toxicological Concern (TTC) concept can be applied to organic compounds with the known chemical structure to derive a threshold for exposure, below which a toxic effect on human health by the compound is not expected. The TTC concept distinguishes between carcinogens that may act as genotoxic and non-genotoxic compounds. A positive prediction of a genotoxic mode of action, either by structural alerts or experimental data, leads to the application of the threshold value for genotoxic compounds. Non-genotoxic substances are assigned to the TTC value of their respective Cramer class, even though it is recognized that they could test positive in a rodent cancer bioassay. This study investigated the applicability of the Cramer classes specifically to provide adequate protection for non-genotoxic carcinogens. For this purpose, benchmark dose levels based on tumor incidence were compared with no observed effect levels (NOELs) derived from non-, pre- or neoplastic lesions. One key aspect was the categorization of compounds as non-genotoxic carcinogens. The recently finished CEFIC LRI project B18 classified the carcinogens of the Carcinogenicity Potency DataBase (CPDB) as either non-genotoxic or genotoxic compounds based on experimental or in silico data. A detailed consistency check resulted in a dataset of 137 non-genotoxic organic compounds. For these 137 compounds, NOEL values were derived from high quality animal studies with oral exposure and chronic duration using well-known repositories, such as RepDose, ToxRef, and COSMOS DB. Further, an effective tumor dose (ETD10) was calculated and compared with the lower confidence limit on benchmark dose levels (BMDL10) derived by model averaging. Comparative analysis of NOEL/EDT10/BMDL10 values showed that potentially bioaccumulative compounds in humans, as well as steroids, which both belong to the exclusion categories, occur predominantly in the region of the fifth percentiles of the distributions. Excluding these 25 compounds resulted in significantly higher but comparable fifth percentile chronic NOEL and BMDL10 values, while the fifth percentile EDT10 value was slightly higher but not statistically significant. The comparison of the obtained distributions of NOELs with the existing Cramer classes and their derived TTC values supports the application of Cramer class thresholds to all non-genotoxic compounds, such as non-genotoxic carcinogens.

Glutathione conjugation and protein modification resulting from metabolic activation of venlafaxine in vitro and in vivo

Venlafaxine (VLF), an antidepressant agent, is widely used to combat major depressive disorders, particularly for the treatment of selective serotonin reuptake inhibitor-resistant depression. VLF has been shown to cause liver injury. The present study aimed to investigate the metabolic activation of VLF and explore the mechanisms of hepatotoxicity induced by VLF. One glutathione (GSH) conjugate and one cysteine conjugate were both detected in mouse and human liver microsomal incubations containing VLF and GSH or cysteine. The two conjugates were also detected in cultured mouse primary hepatocytes and bile of rats after exposure to VLF. The in vitro and in vivo studies demonstrated that VLF was metabolized to a quinone methide intermediate reactive to GSH and cysteine residues of hepatic protein. The observed protein covalent binding revealed dose-dependency. The metabolic activation of VLF was P450-dependent, and CYP3A4 was found as the predominant enzyme involved in the bioactivation process. These findings facilitate better understanding of the metabolic activation-hepatotoxicity relationship of VLF and provide chemists with information about new potential structural alerts during drug design process.

In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities

BackgroundDrug-induced toxicity is one of the problems that have negatively impacted on the well-being of populations throughout the world, including Malawi. It results in unnecessary hospitalizations, retarding the development of the country. This study assessed the Malawi Essential Medicines List (MEML) for structural alerts and reactive metabolites with the potential for drug-induced toxicities.MethodsThis in-silico screening study used StopTox, ToxAlerts and LD-50 values toxicity models to assess the MEML drugs. A total of 296 drugs qualified for the analysis (those that had defined chemical structures) and were screened in each software programme. Each model had its own toxicity endpoints and the models were compared for consensus of their results.ResultsIn the StopTox model, 86% of the drugs had potential to cause at least one toxicity including 55% that had the potential of causing eye irritation and corrosion. In ToxAlerts, 90% of the drugs had the potential of causing at least one toxicity and 72% were found to be potentially reactive, unstable and toxic. In LD-50, 70% of the drugs were potentially toxic.Model consensus evaluation results showed that the highest consensus was observed between ToxAlerts and StopTox (80%). The overall consensus amongst the three models was 57% and statistically significant (p < 0.05).ConclusionsA large number of drugs had the potential to cause various systemic toxicities. But the results need to be interpreted cautiously since the clinical translation of QSAR-based predictions depends on many factors. In addition, inconsistencies have been reported between screening results amongst different models.

In silico prediction of drug-induced ototoxicity using machine learning and deep learning methods.

Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment.