Chemical-chemical Interactions Research Articles

Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment.

A growing body of literature suggests that developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, investigating the effect of interactions among these ECs can be challenging. We introduced a combination of the classical exposure-mixture Weighted Quantile Sum (WQS) regression and a machine-learning method termed Signed iterative Random Forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In a case-control Childhood Autism Risks from Genetics and Environment (CHARGE) study, we evaluated multiordered synergistic interactions among 62 ECs measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs no). WQS-SiRF identified two synergistic two-ordered interactions between (1) trace-element cadmium (Cd) and the organophosphate pesticide metabolite diethyl-phosphate (DEP); and (2) 2,4,6-trichlorophenol (TCP-246) and DEP. Both interactions were suggestively associated with increased odds of ASD diagnosis in the subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover potentially biologically relevant chemical-chemical interactions associated with ASD.

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations.

Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic functions. Notwithstanding the success of current models, understanding the mechanism of drug synergy from a chemical-gene-tissue interaction perspective lacks study, hindering current algorithms from drug mechanism study. Here, we proposed a deep neural network model termed DTSyn (Dual Transformer encoder model for drug pair Synergy prediction) based on a multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer encoder to capture chemical substructure-gene and gene-gene associations and a coarse-granularity transformer encoder to extract chemical-chemical and chemical-cell line interactions. DTSyn achieved the highest receiver operating characteristic area under the curve of 0.73, 0.78. 0.82 and 0.81 on four different cross-validation tasks, outperforming all competing methods. Further, DTSyn achieved the best True Positive Rate (TPR) over five independent data sets. The ablation study showed that both transformer encoder blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, representing the potential mechanisms of drug action. By leveraging the attention mechanism and pretrained gene embeddings, DTSyn shows improved interpretability ability. Thus, we envision our model as a valuable tool to prioritize synergistic drug pairs with chemical and cell line gene expression profile.

Discovery of new drug indications for COVID-19: A drug repurposing approach.

MotivationThe outbreak of coronavirus health issues caused by COVID-19(SARS-CoV-2) creates a global threat to public health. Therefore, there is a need for effective remedial measures using existing and approved therapies with proven safety measures has several advantages. Dexamethasone (Pubchem ID: CID0000005743), baricitinib(Pubchem ID: CID44205240), remdesivir (PubchemID: CID121304016) are three generic drugs that have demonstrated in-vitro high antiviral activity against SARS-CoV-2. The present study aims to widen the search and explore the anti-SARS-CoV-2 properties of these potential drugs while looking for new drug indications with optimised benefits via in-silico research.MethodHere, we designed a unique drug-similarity model to repurpose existing drugs against SARS-CoV-2, using the anti-Covid properties of dexamethasone, baricitinib, and remdesivir as references. Known chemical-chemical interactions of reference drugs help extract interactive compounds withimprovedanti-SARS-CoV-2 properties. Here, we calculated the likelihood of these drug compounds treating SARS-CoV-2 related symptoms using chemical-protein interactions between the interactive compounds of the reference drugs and SARS-CoV-2 target genes. In particular, we adopted a two-tier clustering approach to generate a drug similarity model for the final selection of potential anti-SARS-CoV-2 drug molecules. Tier-1 clustering was based on t-Distributed Stochastic Neighbor Embedding (t-SNE) and aimed to filter and discard outlier drugs. The tier-2 analysis incorporated two cluster analyses performed in parallel using Ordering Points To Identify the Clustering Structure (OPTICS) and Hierarchical Agglomerative Clustering (HAC). As a result, itidentified clusters of drugs with similar actions. In addition, we carried out a docking study for in-silico validation of top candidate drugs.ResultOur drug similarity model highlighted ten drugs, including reference drugs that can act as potential therapeutics against SARS-CoV-2. The docking results suggested that doxorubicin showed the least binding energy compared to reference drugs. Their practical utility as anti-SARS-CoV-2 drugs, either individually or in combination, warrants further investigation.

Impact of Mixture Effects between Emerging Organic Contaminants on Cytotoxicity: A Systems Biological Understanding of Synergism between Tris(1,3-dichloro-2-propyl)phosphate and Triphenyl Phosphate.

Humans are exposed to many xenobiotics simultaneously, but little is known about the toxic effects based on chemical-chemical interactions. This study aims at evaluating the binary interactions between 13 common environmental organic compounds (resulting in 78 pairs) by observing their cytotoxicity on HepG2 cells. Among all of the tested pairs, the combination of flame-retardant triphenyl phosphate (TPP) and tris(1,3-dichloro-2-propyl)phosphate (TDCPP) exhibited one of the most significant synergistic effects. We further characterized the transcriptome and metabolome after combined exposure to TPP and TDCPP and individual exposure. The results suggested that the coexposure caused many more changes in gene expressions and cellular activities. The transcriptome data showed that the coexposure triggered significant pathway changes including "cholesterol biosynthesis" and "ATF6-Alpha activated chaperone genes", together with distinct gene ontology (GO) terms such as the "negative regulation of the ERK1 and ERK2 cascade". Additionally, coexposure enhanced the biological activity of liver X receptors and nuclear factor erythroid 2-related factor 2 (Nrf2). The metabolome data showed that coexposure significantly elevated oxidative stress and affected the purine and pyrimidine metabolism. Overall, this study showed that interactions, which may enhance or suppress the biological processes, are common among environmental chemicals, although their environmental relevance should be studied in the future.

Relating Substructures and Side Effects of Drugs with Chemical-chemical Interactions.

Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs and most of them used the fact that similar drugs always have similar side effects. However, previous studies did not analyze which substructures are highly related to which kind of side effect. In this study, we conducted a computational investigation. In this regard, we extracted a drug set for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was constructed by picking up drugs owing such substructure. The relationship between one side effect and one substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in an Es value. Then, the statistical significance of Es value was measured by a permutation test. A number of highly related pairs of side effects and substructures were obtained and some were extensively analyzed to confirm the reliability of the results reported in this study.

Effects of sub-lethal concentrations of copper ammonium acetate, pyrethrins and atrazine on the response of Escherichia coli to antibiotics

Background: Antibiotic resistance in human and animal pathogens is mainly the outcome of human use of antibiotics. However, bacteria are also exposed to thousands of other antimicrobial agents. Increasingly those exposures are being investigated as co-selective agents behind the rapid rise and spread of resistance in bacterial pathogens of people and our domesticated animals. Methods: We measured the sub-lethal effects on antibiotic tolerance of the human pathogen/commensal Escherichia coli caused by exposure to three common biocide formulations based on either copper, pyrethrins, or atrazine as active ingredients. The influence of the efflux pump AcrAB-TolC was investigated using deletion strains, and the persistence of observed effects was determined. Results: Some effects were seen for all biocides, but the largest effects were observed with copper in combination with the antibiotic tetracycline. The effect was caused by both the induction of the adaptive efflux system and by chelation of the antibiotic by copper. Finally, persistence of the adaptive response was measured and found to persist for about two generations. Conclusions: Through a combination of microbe-chemical and chemical-chemical interactions, humanity may be creating micro-environments in which resistance evolution is accelerated.

Effects of sub-lethal concentrations of copper ammonium acetate, pyrethrins and atrazine on the response of Escherichia coli to antibiotics.

Background: Antibiotic resistance in human and animal pathogens is mainly the outcome of human use of antibiotics. However, bacteria are also exposed to thousands of other antimicrobial agents. Increasingly those exposures are being investigated as co-selective agents behind the rapid rise and spread of resistance in bacterial pathogens of people and our domesticated animals. Methods: We measured the sub-lethal effects on antibiotic tolerance of the human pathogen/commensal Escherichia coli caused by exposure to three common biocide formulations based on either copper, pyrethrins, or atrazine as active ingredients. The influence of the efflux pump AcrAB-TolC was investigated using deletion strains, and the persistence of observed effects was determined. Results: Some effects were seen for all biocides, but the largest effects were observed with copper in combination with the antibiotic tetracycline. The effect was caused by both the induction of the adaptive efflux system and by chelation of the antibiotic by copper. Finally, persistence of the adaptive response was measured and found to persist for about two generations. Conclusions: Through a combination of microbe-chemical and chemical-chemical interactions, humanity may be creating micro-environments in which resistance evolution is accelerated.

An Integrated Multi-Label Classifier with Chemical-Chemical Interactions for Prediction of Chemical Toxicity Effects.

Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications.

Identifying Candidates for Breast Cancer Using Interactions of Chemicals and Proteins.

Breast cancer is one of the major cause of cancer death in women worldwide. Therefore, it is urgent to discovery novel drugs or design effective treatments for this disease. However, the research and development of drugs by using only experimental methods is always time-consuming and expensive. With the development of computer science, some advanced computational methods can make full use of known knowledge to design candidate drugs, thereby reducing the cost and time of experimental testing. In this study, a computational method was proposed to identify novel candidates for breast cancer. The approved drugs and genes of breast cancer were taken as the input of the method. The chemical-chemical interactions and chemical-protein interactions were adopted to extract possible candidates from large numbers of existing chemicals. The method included three stages, termed searching stage, filtering stage and selecting stage. In the searching stage, chemicals that have associations with approved drugs were extracted. Then, these chemicals were screened in the filtering stage to discard those that have no relationships with breast cancer related genes. Finally, a clustering algorithm, termed as EM clustering algorithm, was employed to identify the potential candidates in the selecting stage. An extensive analysis by retrieving literature indicated that multiple selected candidates, such as gefitinib, canertinib and sirolimus, that have been approved for other diseases were confirmed to have anti-breast cancer activities. Therefore, this method can provide some valuable instructions for drug repositioning.

Large-Scale Prediction of Drug Targets Based on Local and Global Consistency of Chemical-Chemical Networks.

It is crucial to identify the molecular targets of a compound during the course of the new drug discovery and drug development. Due to the complexity of biological systems, finding drug targets by biological experiments is very tedious and expensive. In the paper, we used chemicalchemical interactions in the STITCH database to construct a network of drug-drug association. Based on the network, a learning method keeping local and global consistency was presented to infer drug targets. We achieved an accuracy of 57.75% in the first order prediction using leave-one-out cross validation, which was higher than the accuracy of 53.77% achieved by the local neighbor model. We manually validated 27 absent drug targets in the crossvalidation using drug-target interactions from other databases. Applying the presented method to large-scale prediction of unknown targets, we manually confirmed 14 pairs of drug-target interactions among the newly predicted drug targets. These results suggested that the presented method was a promising tool for large-scale identification of drug targets.

Predicting the types of metabolic pathway of compounds using molecular fragments and sequential minimal optimization.

A metabolic pathway is a series of biological processes providing necessary molecules and energies for an organism, which could be essential to the lives of the living organisms. Most metabolic pathways require the involvement of compounds and given a compound it is helpful to know what types of metabolic pathways the compound participates in. In this study, compounds are first represented by molecular fragments which are then delivered to a prediction engine called Sequential Minimal Optimization (SMO) for predictions. Maximum relevance and minimum redundancy (mRMR) and incremental feature selection are adopted to extract key features based on which an optimal prediction engine is established. The proposed method is effective comparing to the random forest, Dagging and a popular method that integrating chemical-chemical interactions and chemical-chemical similarities. We also make predictions using some compounds with unknown metabolic pathways and choose 17 compounds for analysis. The results indicate that the method proposed may become a useful tool in predicting and analyzing metabolic pathways.

An Antifungal Combination Matrix Identifies a Rich Pool of Adjuvant Molecules that Enhance Drug Activity against Diverse Fungal Pathogens.

There is an urgent need to identify new treatments for fungal infections. By combining sub-lethal concentrations of the known antifungals fluconazole, caspofungin, amphotericin B, terbinafine, benomyl, and cyprodinil with ∼3,600 compounds in diverse fungal species, we generated a deep reservoir of chemical-chemical interactions termed the Antifungal Combinations Matrix (ACM). Follow-up susceptibility testing against a fluconazole-resistant isolate of C. albicans unveiled ACM combinations capable of potentiating fluconazole in this clinical strain. We used chemical genetics to elucidate the mode of action of the antimycobacterial drug clofazimine, a compound with unreported antifungal activity that synergized with several antifungals. Clofazimine induces a cell membrane stress for which the Pkc1 signaling pathway is required for tolerance. Additional tests against additional fungal pathogens, including Aspergillus fumigatus, highlighted that clofazimine exhibits efficacy as a combination agent against multiple fungi. Thus, the ACM is a rich reservoir of chemical combinations with therapeutic potential against diverse fungal pathogens.

Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach.

Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer.

Machine Learning and Network Methods for Biology and Medicine

Machine Learning and Network Methods for Biology and Medicine

Biotransformation in vitro: An essential consideration in the quantitative in vitro-to-in vivo extrapolation (QIVIVE) of toxicity data.

Early consideration of the multiplicity of factors that govern the biological fate of foreign compounds in living systems is a necessary prerequisite for the quantitative in vitro-in vivo extrapolation (QIVIVE) of toxicity data. Substantial technological advances in in vitro methodologies have facilitated the study of in vitro metabolism and the further use of such data for in vivo prediction. However, extrapolation to in vivo with a comfortable degree of confidence, requires continuous progress in the field to address challenges such as e.g., in vitro evaluation of chemical-chemical interactions, accounting for individual variability but also analytical challenges for ensuring sensitive measurement technologies. This paper discusses the current status of in vitro metabolism studies for QIVIVE extrapolation, serving today's hazard and risk assessment needs. A short overview of the methodologies for in vitro metabolism studies is given. Furthermore, recommendations for priority research and other activities are provided to ensure further widespread uptake of in vitro metabolism methods in 21st century toxicology. The need for more streamlined and explicitly described integrated approaches to reflect the physiology and the related dynamic and kinetic processes of the human body is highlighted i.e., using in vitro data in combination with in silico approaches.

Finding candidate drugs for hepatitis C based on chemical-chemical and chemical-protein interactions.

Hepatitis C virus (HCV) is an infectious virus that can cause serious illnesses. Only a few drugs have been reported to effectively treat hepatitis C. To have greater diversity in drug choice and better treatment options, it is necessary to develop more drugs to treat the infection. However, it is time-consuming and expensive to discover candidate drugs using experimental methods, and computational methods may complement experimental approaches as a preliminary filtering process. This type of approach was proposed by using known chemical-chemical interactions to extract interactive compounds with three known drug compounds of HCV, and the probabilities of these drug compounds being able to treat hepatitis C were calculated using chemical-protein interactions between the interactive compounds and HCV target genes. Moreover, the randomization test and expectation-maximization (EM) algorithm were both employed to exclude false discoveries. Analysis of the selected compounds, including acyclovir and ganciclovir, indicated that some of these compounds had potential to treat the HCV. Hopefully, this proposed method could provide new insights into the discovery of candidate drugs for the treatment of HCV and other diseases.

Interpretable prediction of non-genotoxic hepatocarcinogenic chemicals

Abstract The assessment of non-genotoxic hepatocarcinogenicity of chemicals relies on time-consuming rodent bioassays. The development of alternative methods for non-genotoxic hepatocarcinogenicity could help the identification of potential hepatocarcinogenic chemicals. This study evaluated four types of features for the interpretable prediction of non-genotoxic hepatocarcinogenic chemicals including chemical–chemical interactions (CCI), chemical–protein interactions (CPI), chemical descriptors (QSAR) and gene expression profiles (TGx). Based on the results of decision tree classifiers, the CPI-based features perform best with independent test accuracies of 90% and 86% for interaction scores from combined scores and databases, respectively. Informative features were identified and analyzed to give insights into the non-genotoxic hepatocarcinogenicity of chemicals. The difference between CPI scores and gene expression profiles for the identified important proteins shows that CPI could play more important roles in non-genotoxic hepatocarcinogenicity.

Prediction of Cancer Drugs by Chemical-Chemical Interactions

Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1st order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the ‘wrong’ predicted indications, indicating that some ‘wrong’ drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.

Predicting Chemical Toxicity Effects Based on Chemical-Chemical Interactions

Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1st order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.

Application of Systems Biology and Bioinformatics Methods in Biochemistry and Biomedicine

Application of Systems Biology and Bioinformatics Methods in Biochemistry and Biomedicine