Clustering based drug-drug interaction networks for possible repositioning of drugs against EGFR mutations: Clustering based DDI networks for EGFR mutations

Identification of new potential candidates to inhibit EGF via machine learning algorithm

One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was a different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, the eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs' effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.

Predicting drug-drug interactions based on multi-view and multichannel attention deep learning

Predicting drug-drug interactions (DDIs) has become a major concern in the drug research field because it helps explore the pharmacological function of drugs and enables the development of new therapeutic drugs. Existing prediction methods simply integrate multiple drug attributes or perform tasks on a biomedical knowledge graph (KG). Though effective, few methods can fully utilize multi-source drug data information. In this paper, a multi-view and multichannel attention deep learning (MMADL) model is proposed, which not only extracts rich drug features containing both drug attributes and drug-related entity information from multi-source databases, but also considers the consistency and complementarity of different drug feature representation learning approaches to improve the effectiveness and accuracy of DDI prediction. A single-layer perceptron encoder is applied to encode multi-source drug information to obtain multi-view drug representation vectors in the same linear space. Then, the multichannel attention mechanism is introduced to obtain the attention weight by adaptively learning the importance of drug features according to their contributions to DDI prediction. Further, the representation vectors of multi-view drug pairs with attention weights are used as inputs of the deep neural network to predict potential DDI. The accuracy and precision-recall curves of MMADL are 93.05 and 95.94, respectively. The results indicate that the proposed method outperforms other state-of-the-art methods.

Influence of nitisinone and its metabolites on l-tyrosine metabolism in a model system

Nitisinone (NTBC) is currently used for the treatment of tyrosinemia type 1, a rare disease. It also exhibits potential in the treatment of other orphan diseases as well as nervous system disorders - this is however limited by its side effects. In all living organisms, NTBC inhibits 4-hydroxyphenylpyruvate dioxygenase activity, thereby affecting l-tyrosine (L-TYR) catabolism, which results in the therapeutic effect. The NTBC metabolites formed in patient's body is one of the causes of its side effects. The influence of NTBC and its metabolites; 2-amino-4-(trifluoromethyl)benzoic acid, 2-nitro-4-(trifluoromethyl)benzoic acid, and cyclohexane-1,3-dione on L-TYR catabolism was investigated in Raphanus sativus var. longipinnatus. Based on targeted LC-MS/MS analysis the concentration of NTBC and its metabolites in exposed plant tissues was determined. Based on non-targeted LC-MS/MS analysis the concentrations of products of L-TYR catabolism: levodopa, epinephrine, norepinephrine, normetanephrine, dopamine, tyramine and vitamins C, B5 and B6, additionally leucine and valine were identified as influenced by the NTBC or its metabolites. NTBC and its metabolites influenced L-TYR catabolism differently. Particularly significant changes were found in the content of epinephrine and normetanephrine: in the plant tissues exposed to NTBC, an increase in the content of these neurotransmitters was found (+42%), whereas in the plant treated with 2-amino-4-(trifluoromethyl)benzoic acid or 2-nitro-4-(trifluoromethyl)benzoic acid a decrease in concentration (-39% and 55%, respectively) was observed. Cyclohexane-1,3-dione does not influence epinephrine and normetanephrine concentration. The conclusions of this study provide a platform for expanded research on the causes of side effects of NTBC treatment.

Role of Persistent Organic Pollutants in Breast Cancer Progression and Identification of Estrogen Receptor Alpha Inhibitors Using In-Silico Mining and Drug-Drug Interaction Network Approaches

Simple Summary

The role of persistent organic pollutants (POPs) in breast cancer progression and their bioaccumulation in adipose tissue has been reported. We used a computational approach to study molecular interactions of POPs with breast cancer proteins and identified natural and synthetic compounds to inhibit these interactions. Moreover, for comparative analysis, standard drugs and screened compounds were also docked against estrogen receptor alpha (ERα) and identification of the finest inhibitor was performed using in-silico mining and drug-drug interaction (DDI) network approaches. Based on scoring values, short-chained chlorinated paraffins demonstrated strong interactions with ERα compared to organo-chlorines and PCBs. Synthetic and natural compounds demonstrating strong associations with the active site of the ERα protein could be potential candidates to treat breast cancer specifically caused by POPs and other organic toxins and can be used as an alternative to standard drugs.

Abstract

The strong association between POPs and breast cancer in humans has been suggested in various epidemiological studies. However, the interaction of POPs with the ERα protein of breast cancer, and identification of natural and synthetic compounds to inhibit this interaction, is mysterious yet. Consequently, the present study aimed to explore the interaction between POPs and ERα using the molecular operating environment (MOE) tool and to identify natural and synthetic compounds to inhibit this association through a cluster-based approach. To validate whether our approach could distinguish between active and inactive compounds, a virtual screen (VS) was performed using actives (627 compounds) as positive control and decoys (20,818 compounds) as a negative dataset obtained from DUD-E. Comparatively, short-chain chlorinated paraffins (SCCPs), hexabromocyclododecane (HBCD), and perfluorooctanesulfonyl fluoride (PFOSF) depicted strong interactions with the ERα protein based on the lowest-scoring values of −31.946, −18.916, −17.581 kcal/mol, respectively. Out of 7856 retrieved natural and synthetic compounds, sixty were selected on modularity bases and subsequently docked with ERα. Based on the lowest-scoring values, ZINC08441573, ZINC00664754, ZINC00702695, ZINC00627464, and ZINC08440501 (synthetic compounds), and capsaicin, flavopiridol tectorgenin, and ellagic acid (natural compounds) showed incredible interactions with the active sites of ERα, even more convening and resilient than standard breast cancer drugs Tamoxifen, Arimidex and Letrozole. Our findings confirm the role of POPs in breast cancer progression and suggest that natural and synthetic compounds with high binding affinity could be more efficient and appropriate candidates to treat breast cancer after validation through in vitro and in vivo studies.

In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

BACKGROUND

Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly.

METHODS

To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status.

RESULTS

The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound.

CONCLUSION

It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.

Drug databases and their contributions to drug repurposing

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.