Abstract: The environmental fate of many functional molecules that are produced on a large scale as precursors or as additives to specialty goods (plastics, fibers, construction materials, etc.), let alone those synthesized by the pharmaceutical industry, is generally unknown. Assessing their environmental fate is crucial when taking decisions on the manufacturing, handling, usage, and release of these substances, as is the evaluation of their toxicity in humans and other higher organisms. While this data are often hard to come by, the experimental data already available on the biodegradability and toxicity of many unusual compounds (including genuinely xenobiotic molecules) make it possible to develop machine learning systems to predict these features. As such, we have created a predictor of the "risk" associated with the use and release of any chemical. This new system merges computational methods to predict biodegradability with others that assess biological toxicity. The combined platform, named BiodegPred (https://sysbiol.cnb.csic.es/BiodegPred/), provides an informed prognosis of the chance a given molecule can eventually be catabolized in the biosphere, as well as of its eventual toxicity, all available through a simple web interface. While the platform described does not give much information about specific degradation kinetics or particular biodegradation pathways, BiodegPred has been instrumental in anticipating the probable behavior of a large number of new molecules (e.g. antiviral compounds) for which no biodegradation data previously existed.

Abstract: Herbal drugs have drawn substantial interest as effective analgesic agents; however, their therapeutic mechanisms remain to be fully understood. To address this question, we performed a network pharmacology study to explore the system-level mechanisms that underlie the analgesic activity of Jakyak-Gamcho decoction (JGd; Shaoyao-Gancao-Tang in Chinese and Shakuyaku-Kanzo-To in Japanese), an herbal prescription consisting of Paeonia lactiflora Pallas and Glycyrrhiza uralensis Fischer. Based on comprehensive information regarding the pharmacological and chemical properties of the herbal constituents of JGd, we identified 57 active chemical compounds and their 70 pain-associated targets. The JGd targets were determined to be involved in the regulation of diverse biological activities as follows: calcium- and cytokine-mediated signalings, calcium ion concentration and homeostasis, cellular behaviors of muscle and neuronal cells, inflammatory response, and response to chemical, cytokine, drug, and oxidative stress. The targets were further enriched in various pain-associated signalings, including the PI3K-Akt, estrogen, ErbB, neurotrophin, neuroactive ligand-receptor interaction, HIF-1, serotonergic synapse, JAK-STAT, and cAMP pathways. Thus, these data provide a systematic basis to understand the molecular mechanisms underlying the analgesic activity of herbal drugs.

Abstract: Viroporins are oligomeric, pore forming, viral proteins that play critical roles in the life cycle of pathogenic viruses. Viroporins like HIV-1 Vpu, Alphavirus 6 K, Influenza M2, HCV p7, and Picornavirus 2B, form discrete aqueous passageways which mediate ion and small molecule transport in infected cells. The alterations in host membrane structures induced by viroporins is essential for key steps in the virus life cycle like entry, replication and egress. Any disruption in viroporin functionality severely compromises viral pathogenesis. The envelope (E) protein encoded by coronaviruses is a viroporin with ion channel activity and has been shown to be crucial for the assembly and pathophysiology of coronaviruses. We used a combination of virtual database screening, molecular docking, all-atom molecular dynamics simulation and MM-PBSA analysis to test four FDA approved drugs - Tretinoin, Mefenamic Acid, Ondansetron and Artemether - as potential inhibitors of ion channels formed by SARS-CoV-2 E protein. Interaction and binding energy analysis showed that electrostatic interactions and polar solvation energy were the major driving forces for binding of the drugs, with Tretinoin being the most promising inhibitor. Tretinoin bound within the lumen of the channel formed by E protein, which is lined by hydrophobic residues like Phe, Val and Ala, indicating its potential for blocking the channel and inhibiting the viroporin functionality of E. In control simulations, tretinoin demonstrated a lower binding energy with a known target as compared to SARS-CoV-2 E protein. This work thus highlights the possibility of exploring Tretinoin as a potential SARS-CoV-2 E protein ion channel blocker and virus assembly inhibitor, which could be an important therapeutic strategy in the treatment for coronaviruses.

Abstract: Background and Aim: Meloxicam (MX) is a potent hydrophobic non-steroidal anti-inflammatory drug used to reduce inflammation and pain. However, its oral dosage form can cause many adverse gastrointestinal effects. In the present study, a poloxamer P407 based hydrogel system containing transfersomes or flavosomes has been prepared as a potential therapeutic vehicle for the topical delivery of MX. Methods: In this study, MX was encapsulated in conventional liposomes, transfersomes, and flavosomes. The obtained liposomal vesicles were characterized in terms of size, drug entrapment efficiency, zeta potential, and stability. These MX-loaded liposomal formulations were further incorporated into a poloxamer P407 gel and evaluated using rheological properties, a stability study and an ex vivo permeation study through human cadaver skin by both HPLC analysis and confocal laser scanning microscopy (CLSM). Results: The developed deformable liposomes exhibited homogeneous vesicle sizes less than 120 nm with a higher entrapment efficiency as compared to conventional liposomes. The deformable liposomal gel formulations showed improved permeability compared to a conventional liposomal gel and a liposome-free gel. The enhancement effect was also clearly visible by CLSM. Conclusion: These deformable liposomal hydrogel formulations can be a promising alternative to conventional oral delivery of MX by topical administration. Notably, flavosome-loaded gel formulations displayed the highest permeability through the deeper layers of the skin and shortened lag time, indicating a potential faster on-site pain relief and anti-inflammatory effect.

Abstract: The accurate predicting of physical properties and bioactivity of drug molecules in deep learning depends on how molecules are represented. Many types of molecular descriptors have been developed for quantitative structure-activity/property relationships quantitative structure-activity relationships (QSPR). However, each molecular descriptor is optimized for a specific application with encoding preference. Considering that standalone featurization methods may only cover parts of information of the chemical molecules, we proposed to build the conjoint fingerprint by combining two supplementary fingerprints. The impact of conjoint fingerprint and each standalone fingerprint on predicting performance was systematically evaluated in predicting the logarithm of the partition coefficient (logP) and binding affinity of protein-ligand by using machine learning/deep learning (ML/DL) methods, including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), long short-term memory network (LSTM), and deep neural network (DNN). The results demonstrated that the conjoint fingerprint yielded improved predictive performance, even outperforming the consensus model using two standalone fingerprints among four out of five examined methods. Given that the conjoint fingerprint scheme shows easy extensibility and high applicability, we expect that the proposed conjoint scheme would create new opportunities for continuously improving predictive performance of deep learning by harnessing the complementarity of various types of fingerprints.

Abstract: Background: As a class of membrane protein receptors, G protein-coupled receptors (GPCRs) are very important for cells to complete normal life function and have been proven to be a major drug target for widespread clinical application. Hence, it is of great significance to find GPCR targets that interact with drugs in the process of drug development. However, identifying the interaction of the GPCR-drug pairs by experimental methods is very expensive and time-consuming on a large scale. As more and more database about GPCR-drug pairs are opened, it is viable to develop machine learning models to accurately predict whether there is an interaction existing in a GPCR-drug pair. Methods: In this paper, the proposed model aims to improve the accuracy of predicting the interactions of GPCR-drug pairs. For GPCRs, the work extracts protein sequence features based on a novel bag-of-words (BOW) model improved with weighted Silhouette Coefficient and has been confirmed that it can extract more pattern information and limit the dimension of feature. For drug molecules, discrete wavelet transform (DWT) is used to extract features from the original molecular fingerprints. Subsequently, the above-mentioned two types of features are contacted, and SMOTE algorithm is selected to balance the training dataset. Then, artificial neural network is used to extract features further. Finally, a gradient boosting decision tree (GBDT) model is trained with the selected features. In this paper, the proposed model is named as BOW-GBDT. Results: D92M and Check390 are selected for testing BOW-GBDT. D92M is used for a cross-validation dataset which contains 635 interactive GPCR-drug pairs and 1,225 non-interactive pairs. Check390 is used for an independent test dataset which consists of 130 interactive GPCR-drug pairs and 260 non-interactive GPCR-drug pairs, and each element in Check390 cannot be found in D92M. According to the results, the proposed model has a better performance in generation ability compared with the existing machine learning models. Conclusion: The proposed predictor improves the accuracy of the interactions of GPCR-drug pairs. In order to facilitate more researchers to use the BOW-GBDT, the predictor has been settled into a brand-new server, which is available at http://www.jci-bioinfo.cn/bowgbdt.

Abstract: Objectives: To evaluate the pharmacodynamical effects and pharmacological mechanism of Ginsenoside H dripping pills (GH) in chronic unpredictable mild stress (CUMS) model rats. Methods: First, the CUMS-induced rat model was established to assess the anti-depressant effects of GH (28, 56, and 112 mg/kg) by the changes of the behavioral indexes (sucrose preference, crossing score, rearing score) and biochemical indexes (serotonin, dopamine, norepinephrine) in Hippocampus. Then, the components of GH were identified by ultra-performance liquid chromatography-iron trap-time of flight-mass spectrometry (UPLC/IT-TOF MS). After network pharmacology analysis, the active ingredients of GH were further screened out based on OB and DL, and the PPI network of putative targets of active ingredients of GH and depression candidate targets was established based on STRING database. The PPI network was analyzed topologically to obtain key targets, so as to predict the potential pharmacological mechanism of GH acting on depression. Finally, some major target proteins involved in the predictive signaling pathway were validated experimentally. Results: The establishment of CUMS depression model was successful and GH has antidepressant effects, and the middle dose of GH (56 mg/kg) showed the best inhibitory effects on rats with depressant-like behavior induced by CUMS. Twenty-eight chemical components of GH were identified by UPLC/IT-TOF MS. Subsequently, 20(S)-ginsenoside Rh2 was selected as active ingredient and the PPI network of the 43 putative targets of 20(S)-ginsenoside Rh2 containing in GH and the 230 depression candidate targets, was established based on STRING database, and 47 major targets were extracted. Further network pharmacological analysis indicated that the cAMP signaling pathway may be potential pharmacological mechanism regulated by GH acting on depression. Among the cAMP signaling pathway, the major target proteins, namely, cAMP, PKA, CREB, p-CREB, BDNF, were used to verify in the CUMS model rats. The results showed that GH could activate the cAMP-PKA-CREB-BDNF signaling pathway to exert antidepressant effects. Conclusions: An integrative pharmacology-based pattern was used to uncover that GH could increase the contents of DA, NE and 5-HT, activate cAMP-PKA-CREB-BDNF signaling pathway exert antidepressant effects.

Abstract: Genetic and environmental factors, alone or in combination, contribute to the pathogenesis of autism spectrum disorder (ASD). Although many protein-coding genes have now been identified as disease risk genes for ASD, a detailed illustration of long non-coding RNAs (lncRNAs) associated with ASD remains elusive. In this study, we first identified ASD-related lncRNAs based on genomic variant data of individuals with ASD from a twin study. In total, 532 ASD-related lncRNAs were identified, and 86.7% of these ASD-related lncRNAs were further validated by an independent copy number variant (CNV) dataset. Then, the functions and associated biological pathways of ASD-related lncRNAs were explored by enrichment analysis of their three different types of functional neighbor genes (i.e., genomic neighbors, competing endogenous RNA (ceRNA) neighbors, and gene co-expression neighbors in the cortex). The results have shown that most of the functional neighbor genes of ASD-related lncRNAs were enriched in nervous system development, inflammatory responses, and transcriptional regulation. Moreover, we explored the differential functions of ASD-related lncRNAs in distinct brain regions by using gene co-expression network analysis based on tissue-specific gene expression profiles. As a set, ASD-related lncRNAs were mainly associated with nervous system development and dopaminergic synapse in the cortex, but associated with transcriptional regulation in the cerebellum. In addition, a functional network analysis was conducted for the highly reliable functional neighbor genes of ASD-related lncRNAs. We found that all the highly reliable functional neighbor genes were connected in a single functional network, which provided additional clues for the action mechanisms of ASD-related lncRNAs. Finally, we predicted several potential drugs based on the enrichment of drug-induced pathway sets in the ASD-altered biological pathway list. Among these drugs, several (e.g., amoxapine, piperine, and diflunisal) were partly supported by the previous reports. In conclusion, ASD-related lncRNAs participated in the pathogenesis of ASD through various known biological pathways, which may be differential in distinct brain regions. Detailed investigation into ASD-related lncRNAs can provide clues for developing potential ASD diagnosis biomarkers and therapy.

Abstract: Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is an emerging new viral pathogen that causes severe respiratory disease. SARS-CoV-2 is responsible for the outbreak of COVID-19 pandemic worldwide. As there are no confirmed antiviral drugs or vaccines currently available for the treatment of COVID-19, discovering potent inhibitors or vaccines are urgently required for the benefit of humanity. The glycosylated Spike protein (S-protein) directly interacts with human angiotensin-converting enzyme 2 (ACE2) receptor through the receptor-binding domain (RBD) of S-protein. As the S-protein is exposed to the surface and is essential for entry into the host, the S-protein can be considered as a first-line therapeutic target for antiviral therapy and vaccine development. In silico screening, docking, and molecular dynamics simulation studies were performed to identify repurposing drugs using DrugBank and PubChem library against the RBD of S-protein. The study identified a laxative drug, Bisoxatin (DB09219), which is used for the treatment of constipation and preparation of the colon for surgical procedures. It binds nicely at the S-protein-ACE2 interface by making substantial pi-pi interactions with Tyr505 in the 'Site 1' hook region of RBD and hydrophilic interactions with Glu406, Ser494, and Thr500. Bisoxatin consistently binds to the protein throughout the 100 ns simulation. Taken together, we propose that the discovered molecule, Bisoxatin may be a promising repurposable drug molecule to develop new chemical libraries for inhibiting SARS-CoV-2 entry into the host.

Abstract: BACKGROUND: The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2. METHODS AND FINDINGS: We perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID-DB00701) would probably be the most effective drug based on the selected criterions. CONCLUSIONS: Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.