Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes a comprehensive integrated Weight of Evidence (WoE) approach to determine the need for a 2-year rat carcinogenicity study. In the present work, experts from different organizations have joined efforts to standardize as much as possible a procedural framework for the integration of evidence associated with the different ICH S1B(R1) WoE criteria. The framework uses a pragmatic consensus procedure for carcinogenicity hazard assessment to facilitate transparent, consistent, and documented decision-making and it discusses best-practices both for the organization of studies and presentation of data in a format suitable for regulatory review. First, it is acknowledged that the six WoE factors described in the addendum form an integrated network of evidence within a holistic assessment framework that is used synergistically to analyze and explain safety signals. Second, the proposed standardized procedure builds upon different considerations related to the primary sources of evidence, mechanistic analysis, alternative methodologies and novel investigative approaches, metabolites, and reliability of the data and other acquired information. Each of the six WoE factors is described highlighting how they can contribute evidence for the overall WoE assessment. A suggested reporting format to summarize the cross-integration of evidence from the different WoE factors is also presented. This work also notes that even if a 2-year rat study is ultimately required, creating a WoE assessment is valuable in understanding the specific factors and levels of human carcinogenic risk better than have been identified previously with the 2-year rat bioassay alone.

Prediction of Amino Acid Substitutions in ABL1 Protein Leading to Tumor Drug Resistance Based on "Structure-Property" Relationship Classification Models

Drug resistance to anticancer drugs is a serious complication in patients with cancer. Typically, drug resistance occurs due to amino acid substitutions (AAS) in drug target proteins. The study aimed at developing and validating a new approach to the creation of structure-property relationships (SPR) classification models to predict AASs leading to drug resistance to inhibitors of tyrosine-protein kinase ABL1. The approach was based on the representation of AASs as peptides described in terms of structural formulas. The data on drug-resistant and non-resistant variants of AAS for two isoforms of ABL1 were extracted from the COSMIC database. The given training sets (approximately 700 missense variants) were used for the creation of SPR models in MultiPASS software based on substructural atom-centric multiple neighborhoods of atom (MNA) descriptors for the description of the structural formula of protein fragments and a Bayesian-like algorithm for revealing structure-property relationships. It was found that MNA descriptors of the 6th level and peptides from 11 amino acid residues were the best combination for ABL1 isoform 1 with the prediction accuracy (AUC) of resistance to imatinib (0.897) and dasatinib (0.996). For ABL1 isoform 2 (resistance to imatinib), the best combination was MNA descriptors of the 6th level, peptides form 15 amino acids (AUC value was 0.909). The prediction of possible drug-resistant AASs was made for dbSNP and gnomAD data. The six selected most probable imatinib-resistant AASs were additionally validated by molecular modeling and docking, which confirmed the possibility of resistance for the E334V and T392I variants.

Prediction of pathogenic single amino acid substitutions using molecular fragment descriptors

MOTIVATION

Next Generation Sequencing technologies make it possible to detect rare genetic variants in individual patients. Currently, more than a dozen software and web services have been created to predict the pathogenicity of variants related with changing of amino acid residues. Despite considerable efforts in this area, at the moment there is no ideal method to classify pathogenic and harmless variants, and the assessment of the pathogenicity is often contradictory. In this article, we propose to use peptides structural formulas of proteins as an amino acid residues substitutions description, rather than a single-letter code. This allowed us to investigate the effectiveness of chemoinformatics approach to assess the pathogenicity of variants associated with amino acid substitutions.

RESULTS

The structure-activity relationships analysis relying on protein-specific data and atom centric substructural multilevel neighborhoods of atoms (MNA) descriptors of molecular fragments appeared to be suitable for predicting the pathogenic effect of single amino acid variants. MNA-based Naïve Bayes classifier algorithm, ClinVar and humsavar data were used for the creation of structure-activity relationships models for 10 proteins. The performance of the models was compared with 11 different predicting tools: 8 individual (SIFT 4G, Polyphen2 HDIV, MutationAssessor, PROVEAN, FATHMM, MVP, LIST-S2, MutPred) and 3 consensus (M-CAP, MetaSVM, MetaLR). The accuracy of MNA-based method varies for the proteins (AUC: 0.631-0.993; MCC: 0.191-0.891). It was similar for both the results of comparisons with the other individual predictors and third-party protein-specific predictors. For several proteins (BRCA1, BRCA2, COL1A2, and RYR1), the performance of the MNA-based method was outstanding, capable of capturing the pathogenic effect of structural changes in amino acid substitutions.

AVAILABILITY AND IMPLEMENTATION

The datasets are available as supplemental data at Bioinformatics online. A python script to convert amino acid and nucleotide sequences from single-letter codes to SD files is available at https://github.com/SmirnygaTotoshka/SequenceToSDF. The authors provide trial licenses for MultiPASS software to interested readers upon request.

Identification of novel mycocompounds as inhibitors of PI3K/AKT/mTOR pathway against RCC

PI3K/AKT/mTOR pathway is one of the frequently disrupted signaling pathways in renal cell carcinoma (RCC) that plays a significant role in tumor formation, disease progression and therapeutic resistance. Therefore, novel natural molecules targeting the critical proteins of this pathway will provide the best alternative to existing drugs, which are toxic and develops resistance. Recent studies have recognized the anti-cancer therapeutic potential of mycocompounds. The current study is focused on screening various mycocompounds from Astraeus hygrometricus against key cancer signaling proteins phosphoinositide 3-kinase (PI3K), protein kinase B, PKB (AKT1) and mammalian target of rapamycin (mTOR). We also studied in-silico cancer cells cytotoxicity and ADMET (absorption, distribution, metabolism, excretion and toxicity) profiles to elucidate the molecular mechanism against RCC and also to uncover the pharmacokinetic profile of these compounds. Astrakurkurone and Ergosta-4,6, 8-(14) 22-tetraene-3-one were the two most efficacious compounds with highest interaction scores and bonding. These compounds were both active against RCC4 and VMRC-RCZ cell lines of RCC. The ADME profiles of both were satisfactory based on druglikeness and bioavailability score criteria. Thus, this proposed study identified astrakurkurone and ergosta-4,6, 8-(14) 22-tetraene-3-one as potential anticancer drug candidates, and provides comparative structural insight into their binding to the 3 protein kinases.

Computational Prediction of Inhibitors and Inducers of the Major Isoforms of Cytochrome P450

Human cytochrome P450 enzymes (CYPs) are heme-containing monooxygenases. This superfamily of drug-metabolizing enzymes is responsible for the metabolism of most drugs and other xenobiotics. The inhibition of CYPs may lead to drug–drug interactions and impair the biotransformation of drugs. CYP inducers may decrease the bioavailability and increase the clearance of drugs. Based on the freely available databases ChEMBL and PubChem, we have collected over 70,000 records containing the structures of inhibitors and inducers together with the IC50 values for the inhibitors of the five major human CYPs: 1A2, 3A4, 2D6, 2C9, and 2C19. Based on the collected data, we developed (Q)SAR models for predicting inhibitors and inducers of these CYPs using GUSAR and PASS software. The developed (Q)SAR models could be applied for assessment of the interaction of novel drug-like substances with the major human CYPs. The created (Q)SAR models demonstrated reasonable accuracy of prediction. They have been implemented in the web application P450-Analyzer that is freely available via the Internet.

Palladium(II) Complexes of Substituted Salicylaldehydes: Synthesis, Characterization and Investigation of Their Biological Profile

Five palladium(II) complexes of substituted salicylaldehydes (X-saloH, X = 4-Et2N (for 1), 3,5-diBr (for 2), 3,5-diCl (for 3), 5-F (for 4) or 4-OMe (for 5)) bearing the general formula [Pd(X-salo)2] were synthesized and structurally characterized. The crystal structure of complex [Pd(4-Et2N-salo)2] was determined by single-crystal X-ray crystallography. The complexes can scavenge 1,1-diphenyl-picrylhydrazyl and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radicals and reduce H2O2. They are active against two Gram-positive (Staphylococcus aureus and Bacillus subtilis) and two Gram-negative (Escherichia coli and Xanthomonas campestris) bacterial strains. The complexes interact strongly with calf-thymus DNA via intercalation, as deduced by diverse techniques and via the determination of their binding constants. Complexes interact reversibly with bovine and human serum albumin. Complementary insights into their possible mechanisms of bioactivity at the molecular level were provided by molecular docking calculations, exploring in silico their ability to bind to calf-thymus DNA, Escherichia coli and Staphylococcus aureus DNA-gyrase, 5-lipoxygenase, and membrane transport lipid protein 5-lipoxygenase-activating protein, contributing to the understanding of the role complexes 1–5 can play both as antioxidant and antibacterial agents. Furthermore, in silico predictive tools have been employed to study the chemical reactivity, molecular properties and drug-likeness of the complexes, and also the drug-induced changes of gene expression profile (as protein- and mRNA-based prediction results), the sites of metabolism, the substrate/metabolite specificity, the cytotoxicity for cancer and non-cancer cell lines, the acute rat toxicity, the rodent organ-specific carcinogenicity, the anti-target interaction profiles, the environmental ecotoxicity, and finally the activity spectra profile of the compounds.

Identification of medicinal compounds as potential inhibitors for mutated isocitrate dehydrogenases against chondrosarcoma

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Virtual screening of 5000 novel medicinal compounds procured two compounds (5-galloylquinic acid and artocarpetin) capable of establishing interaction with both mutated IDH1 and IDH2 proteins implicated in chondrosarcoma.

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Cell lining prediction studies revealed that both 5-galloylquinic acid and artocarpetin sensitizes chondrosarcoma cell lines and has good cytotoxic influence on CHSA8926 and CHSA0011 cells.

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These compounds possess high acute toxicity values to incite adverse reaction and organ damage in rodents.

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ITGAV, CAPRIN-1, CCL5 COG5 and TNFRSF10B gene are successfully downregulated that are involved in the metastasis inflammation and chondrogenesis by these compounds.

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TP53 expression enhancer, free radical scavenger, MAP kinase stimulant, MM9 expression inhibitor and chemo preventive agent were some biological properties predicted by Prediction of Activity Spectra for substances (PASS) database.

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Artocarpetin had good ADME and druglikness properties as compared to 5-galloylquinic acid, as this compound had low bioavailability score and one lipinski violation for druglikness.

Chondrosarcoma is the third most common cartilaginous bone tumour that is insusceptible to radio- and chemotherapy and it is inclined to metastasis. These resistant qualities are facilitated by mutant variants of isocitrate dehydrogenases (IDH) 1–2 enzyme. These mutant enzymes promote oncogenesis of chondrocytes by changing their epigenetic wardrobe leading to tumour formation. Presently, there are lack of drugs available to be exploited as a remedy for this disease. On the other hand, majority of chemotherapeutic drugs induce cytotoxicity in the cancer cells at the cost of harming surrounding healthy cells, jeopardizing human life. The current study is focused on screening various medicinal compounds against IDH1 and IDH2 combined with insilico gene expression, cancer cells cytotoxicity and ADMET (absorption, distribution, metabolism, excretion and toxicity) studies to elucidate the molecular mechanism against chondrosarcoma and also to uncover pharmacokinetic profile of these compounds. Screening of 5000+ compounds filtered two efficacious compounds (Artocarpetin and 5-Galloylquinic acid) capable of establishing hydrogen bond connections with both IDH variants. Other studies showed that these compounds downregulate ITGAV, CARPIN1, CCL5 and COG5 and TNFRSF10B gene that reduces chondrogenesis and inflammation, Artocarpetin and 5-galloylquinic acid are TP53 expression enhancer and inhibit MM9 expression that promote immunomodulation and apoptosis in these cancers. These compounds are both active against CHSA8926 and CHSA011 cell line of chondrosarcoma. However, the ADME profile of 5-galloylquinic acid is slightly unsatisfactory based on druglikness and bioavailability score criteria as compared to artocarpetin. Both of these compounds are class-5 chemicals and require high doses to elicit adverse response. Our results suggest that artocarpetin and 5-galloylquinic acid are efficacious drug candidates and could be further exploited to validate these findings in vitro.

Exploring Aromatic Medicinal Compounds for the Treatment of Amyotrophic Lateral Sclerosis

Purpose: Amyotrophic lateral sclerosis (ALS) is a lethal neurodegenerative condition, in which motor neurons start to degenerate due to the accumulation of protein aggregates in the neuron cytoplasm. The formation of aggregates causes neurotoxicity, facilitated by the N-terminal domain (NTD) of the transactive response DNA-binding protein-43 (TDP-43). Therapies used to treat ALS manage secondary symptoms, but do not stop the activity of the rogue NTD domain of TDP-43. Therefore, new drug candidates should be designed to deal efficiently with this disease by inhibiting the domains involved in the development of ALS. This study determined the chemical affinity of aromatic medicinal compounds with NTD. Screening of 1323 medicinal compounds was conducted with PYRX 0.9 software against NTD. Compounds obtained from this analysis were further used to predict absorption, distribution, metabolism, excretion, and toxic (ADMET) properties and their effect on major gene targets of ALS. Results: From 1300 + compounds, acetovanillone showed binding affinity for NTD and had good ADMET and drug likeness attributes. This compound reduced the expression of CXCL2, NOP56, and SOD1 genes implicated in ALS pathogenesis. Conclusion: These results concluded that acetovanillone is a candidate drug for in vitro and clinical studies into the exploitation of drugs within ALS therapeutics.

Targeting FGL2, a molecular drug target for glioblastoma, with natural compounds through virtual screening method

Background: Fibroleukin-2 protein (FGL2) causes redevelopment of brain tumors. Inhibition of these proteins has shown to improve glioblastoma prognosis and treatment efficacy. Aim: The current study gathered recently exploited natural compounds that suppress glioblastoma proliferation in vitro, tested against FGL2 protein. Method: Twenty-five compounds were explored through a virtual screening platform. Results: Three natural compounds (betanine, hesperetin and ovatodiolide) hit the active site of FGL2. Furthermore, the influence of these compounds was also assessed using in silico gene expression, and ADMET tools showed downregulation of some genes, which caused rapid tumor development while possessing a moderate acute toxicity and pharmacokinetic profile. Conclusion: Our study presents three compounds that are good candidates for evaluation in FGL2 mutated glioblastoma animal models.

AntiBac-Pred: A Web Application for Predicting Antibacterial Activity of Chemical Compounds

Discovery of new antibacterial agents is a never-ending task of medicinal chemistry. Every new drug brings significant improvement to patients with bacterial infections, but prolonged usage of antibacterials leads to the emergence of resistant strains. Therefore, novel active structures with new modes of action are required. We describe a web application called AntiBac-Pred aimed to help users in the rational selection of the chemical compounds for experimental studies of antibacterial activity. This application is developed using antibacterial activity data available in ChEMBL and PASS software. It allows users to classify chemical structures of interest into growth inhibitors or noninhibitors of 353 different bacteria strains, including both resistant and nonresistant ones.

[Xenobiotic toxicity prediction combined with xenobiotic metabolism prediction in the human body]

The majority of xenobiotics undergo a number of chemical reactions known as biotransformation in human body. The biological activity, toxicity, and other properties of the metabolites may significantly differ from those of the parent compound. Not only xenobiotic itself and its final metabolites produced in large quantities, but the intermediate and final metabolites that are formed in trace quantities, can cause undesirable effects. We have developed a freely available web resource MetaTox (http://www.way2drug.com/mg/) for integral assessment of xenobiotics toxicity taking into account their metabolism in the humans. The generation of the metabolite structures is based on the reaction fragments. The estimates of the probability of the reaction of a certain class and the probability of site of biotransformation are used at the generation of the xenobiotic metabolism pathways. The web resource MetaTox allows researchers to assess the metabolism of compounds in the humans and to obtain assessment of their acute, chronic toxicity, and adverse effects.

QSAR investigation of acute toxicity of organic compounds during oral administration to mice

The effect of the structure of organic compounds on the acute toxicity upon oral injection in mice was studied using 2D simplex representation of the molecular structure and Random forest (RF) methods. Satisfactory quantitative structure-activity relationship (QSAR) models were constructed (R2 test = 0,61–0,62). The interpretation of the obtained QSAR models was carried out. The contributions of known toxicophores with established mechanisms of action were calculated in order to confirm the ability of the interpretation approach to correctly rank them relative to other structural fragments. The influence of the molecular surroundings of some toxicophores was analyzed. We analyzed the contributions of other highly ranked fragments from the list of common functional groups and ring systems in order to find new potential toxicophores. The on-line version of the expert system “OCHEM” (https://ochem.eu) and Arithmetic Mean Toxicity (AMT) approach were used for a comparative QSAR study.

Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitation

An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes. The web resource Way2Drug is used by over 18,000 researchers from more than 90 countries around the world, which allowed them to obtain over 600,000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra.