Abstract: Broadly effective antiviral therapies must be developed to be ready for clinical trials, which should begin soon after the emergence of new life-threatening viruses. Here, we pave the way towards this goal by reviewing conserved druggable virus-host interactions, mechanisms of action, immunomodulatory properties of available broad-spectrum antivirals (BSAs), routes of BSA delivery, and interactions of BSAs with other antivirals. Based on the review, we concluded that the range of indications of BSAs can be expanded, and new pan- and cross-viral mono- and combinational therapies can be developed. We have also developed a new scoring algorithm that can help identify the most promising few of the thousands of potential BSAs and BSA-containing drug cocktails (BCCs) to prioritize their development during the critical period between the identification of a new virus and the development of virus-specific vaccines, drugs, and therapeutic antibodies.

Abstract: Drug-drug interaction (DDI) often causes serious adverse reactions and thus results in inestimable economic and social loss. Currently, comprehensive DDI evaluation has become a major challenge in pharmaceutical research due to the time-consuming and costly process of the experimental assessment and it is of high necessity to develop effective in silico methods to predict and evaluate DDIs accurately and efficiently. In this study, based on a large number of substrates and inhibitors related to five important CYP450 isozymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4), a series of high-performance predictive models for metabolic DDIs were constructed by two machine learning methods (random forest and XGBoost) and 4 different types of descriptors (MOE_2D, CATS, ECFP4 and MACCS). To reduce the uncertainty of individual models, the consensus method was applied to yield more reliable predictions. A series of evaluations illustrated that the consensus models were more reliable and robust for the DDI predictions of new drug combination. For the internal validation, the whole prediction accuracy and AUC value of the DDI models were around 0.8 and 0.9, respectively. When it was applied to the external datasets, the model accuracy was 0.793 and 0.795 for multi-level validation and external validation, respectively. Furthermore, we also compared our model with some recently published tools and then applied the final model to predict FDA-approved drugs and proposed 54,013 possible drug pairs with potential DDIs. In summary, we developed a powerful DDI predictive model from the perspective of the CYP450 enzyme family and it will help a lot in the future drug development and clinical pharmacy research.

Abstract: Current environmental monitoring efforts often focus on known, regulated contaminants ignoring the potential effects of unmeasured compounds and/or environmental factors. These specific, targeted approaches lack broader environmental information and understanding, hindering effective environmental management and policy. Switching to comprehensive, untargeted monitoring of contaminants, organism health, and environmental factors, such as nutrients, temperature, and pH, would provide more effective monitoring with a likely concomitant increase in environmental health. However, even this method would not capture subtle biochemical changes in organisms induced by chronic toxicant exposure. Ecosurveillance is the systematic collection, analysis, and interpretation of ecosystem health-related data that can address this knowledge gap and provide much-needed additional lines of evidence to environmental monitoring programs. Its use would therefore be of great benefit to environmental management and assessment. Unfortunately, the science of 'ecosurveillance', especially omics-based ecosurveillance is not well known. Here, we give an overview of this emerging area and show how it has been beneficially applied in a range of systems. We anticipate this review to be a starting point for further efforts to improve environmental monitoring via the integration of comprehensive chemical assessments and molecular biology-based approaches. Bringing multiple levels of omics technology-based assessment together into a systems-wide ecosurveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.

Abstract: The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.

Abstract: Publicly available compound and bioactivity databases provide an essential basis for data-driven applications in life-science research and drug design. By analyzing several bioactivity repositories, we discovered differences in compound and target coverage advocating the combined use of data from multiple sources. Using data from ChEMBL, PubChem, IUPHAR/BPS, BindingDB, and Probes & Drugs, we assembled a consensus dataset focusing on small molecules with bioactivity on human macromolecular targets. This allowed an improved coverage of compound space and targets, and an automated comparison and curation of structural and bioactivity data to reveal potentially erroneous entries and increase confidence. The consensus dataset comprised of more than 1.1 million compounds with over 10.9 million bioactivity data points with annotations on assay type and bioactivity confidence, providing a useful ensemble for computational applications in drug design and chemogenomics.

Abstract: BACKGROUND: In research on new drug discovery, the traditional wet experiment has a long period. Predicting drug-target interaction (DTI) in silico can greatly narrow the scope of search of candidate medications. Excellent algorithm model may be more effective in revealing the potential connection between drug and target in the bioinformatics network composed of drugs, proteins and other related data. RESULTS: In this work, we have developed a heterogeneous graph neural network model, named as HGDTI, which includes a learning phase of network node embedding and a training phase of DTI classification. This method first obtains the molecular fingerprint information of drugs and the pseudo amino acid composition information of proteins, then extracts the initial features of nodes through Bi-LSTM, and uses the attention mechanism to aggregate heterogeneous neighbors. In several comparative experiments, the overall performance of HGDTI significantly outperforms other state-of-the-art DTI prediction models, and the negative sampling technology is employed to further optimize the prediction power of model. In addition, we have proved the robustness of HGDTI through heterogeneous network content reduction tests, and proved the rationality of HGDTI through other comparative experiments. These results indicate that HGDTI can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. CONCLUSIONS: The HGDTI based on heterogeneous graph neural network model, can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. For the convenience of related researchers, a user-friendly web-server has been established at http://bioinfo.jcu.edu.cn/hgdti .

Abstract: Linkage disequilibrium and disease-associated variants in the non-coding regions make it difficult to distinguish the truly associated genes from the redundantly associated genes for complex diseases. In this study, we proposed a new conditional gene-based framework called eDESE that leveraged an improved effective chi-squared statistic to control the type I error rates and remove the redundant associations. eDESE initially performed the association analysis by mapping variants to genes according to their physical distance. We further demonstrated that the isoform-level eQTLs could be more powerful than the gene-level eQTLs in the association analysis using a simulation study. Then the eQTL-guided strategies, that is, mapping variants to genes according to their gene/isoform-level variant-gene cis-eQTLs associations, were also integrated with eDESE. We then applied eDESE to predict the potential susceptibility genes of schizophrenia and found that the potential susceptibility genes were enriched with many neuronal or synaptic signaling-related terms in the Gene Ontology knowledgebase and antipsychotics-gene interaction terms in the drug-gene interaction database (DGIdb). More importantly, seven potential susceptibility genes identified by eDESE were the target genes of multiple antipsychotics in DrugBank. Comparing the potential susceptibility genes identified by eDESE and other benchmark approaches (i.e., MAGMA and S-PrediXcan) implied that strategy based on the isoform-level eQTLs could be an important supplement for the other two strategies (physical distance and gene-level eQTLs). We have implemented eDESE in our integrative platform KGGSEE (http://pmglab.top/kggsee/#/) and hope that eDESE can facilitate the prediction of candidate susceptibility genes and isoforms for complex diseases in a multi-tissue context.

Abstract: In silico methods to identify novel drug-target interactions (DTIs) have gained significant importance over conventional techniques owing to their labor-intensive and low-throughput nature. Here, we present a machine learning-based multiclass classification workflow that segregates interactions between active, inactive, and intermediate drug-target pairs. Drug molecules, protein sequences, and molecular descriptors were transformed into machine-interpretable embeddings to extract critical features from standard datasets. Tools such as CHEMBL web resource, iFeature, and an in-house developed deep neural network-assisted drug recommendation (dNNDR)-featx were employed for data retrieval and processing. The models were trained with large-scale DTI datasets, which reported an improvement in performance over baseline methods. External validation results showed that models based on att-biLSTM and gCNN could help predict novel DTIs. When tested with a completely different dataset, the proposed models significantly outperformed competing methods. The validity of novel interactions predicted by dNNDR was backed by experimental and computational evidence in the literature. The proposed methodology could elucidate critical features that govern the relationship between a drug and its target.

Abstract: Bariatric surgery is increasingly applied among people living with HIV to reduce obesity and the associated morbidity and mortality. In people living with HIV, sufficient antiretroviral exposure and activity should always be maintained to prevent development of resistance and disease progression. However, bariatric surgery procedures bring various gastrointestinal modifications including changes in gastric volume, and acidity, gastrointestinal emptying time, enterohepatic circulation and delayed entry of bile acids. These alterations may affect many aspects of antiretroviral pharmacokinetics. Some drug characteristics may result in subtherapeutic exposure and the potential related risk of treatment failure and resistance. Antiretrovirals that require low pH, administration of fatty meals, longer intestinal exposure, and an enterohepatic recirculation for their absorption may be most impacted by bariatric surgery procedures. Additionally, some antiretrovirals can interact with the polyvalent cations in supplements or drugs inhibiting gastric acid, thereby preventing their use as these comedications are commonly prescribed post-bariatric surgery. Predicting pharmacokinetics on the basis of drug characteristics solely proved to be challenging, therefore pharmacokinetic studies remain crucial in this population. Here, we discuss general implications of bariatric surgery on antiretroviral outcomes in people living with HIV as well as drug properties that are relevant for the choice of antiretroviral treatment in this special patient population. Additionally, we summarise studies that evaluated the pharmacokinetics of antiretrovirals post-bariatric surgery. Finally, we performed a comprehensive analysis of theoretical considerations and published pharmacokinetic and pharmacodynamic data to provide recommendations on antiretrovirals for people living with HIV undergoing bariatric surgery.

Abstract: Clinical trials are crucial for drug development but often face uncertain outcomes due to safety, efficacy, or patient-recruitment problems. We propose the Hierarchical Interaction Network (HINT) to predict clinical trial outcomes. First, HINT encodes multi-modal data (drug molecule, target disease, trial eligibility criteria) into embeddings. Then, HINT trains knowledge-embedding modules using drug pharmacokinetic and historical trial data. Finally, a hierarchical interaction graph connects all of the embeddings to capture their interactions and predict trial outcomes. HINT was trained and validated on 1,160 phase I trials, 4,449 phase II trials, and 3,436 phase III trials. It obtained 0.665, 0.620, and 0.847 F1 scores on separate test sets of 627 phase I, 1,653 phase II, and 1,140 phase III trials, respectively. HINT significantly outperforms the best baseline method on most metrics. The benchmark dataset and codes are released at https://github.com/futianfan/clinical-trial-outcome-prediction.