We investigated savolitinib pharmacokinetics (PK) when administered alone or in combination with rifampicin, itraconazole or famotidine, and investigated midazolam PK when administered with or without savolitinib in healthy males.

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.

Purpose: The drug-drug interactions (DDIs) of tacrolimus greatly contributed to pharmacokinetic variability. Nifedipine, frequently prescribed for hypertension, is a competitive CYP3A5 inhibitor which can inhibit tacrolimus metabolism. The objective of this study was to investigate whether CYP3A5 genotype could influence tacrolimus-nifedipine DDI in Chinese renal transplant patients. Method: All renal transplant patients were divided into CYP3A5*3/*3 homozygotes (group I) and CYP3A5*1 allele carriers (CYP3A5*1/*1 + CYP3A5*1/*3) (group II). Each group was subdivided into patients taking tacrolimus co-administered with nifedipine (CONF) and that administrated with tacrolimus alone (Controls). Tacrolimus trough concentrations (C0) were measured using high performance liquid chromatography. A retrospective analysis compared tacrolimus dose (D)-corrected trough concentrations (C0) (C0/D) between CONF and Controls in group I and II, respectively. At the same time, a multivariate line regression analysis was made to evaluate the effect of variates on C0/D. Results: In this study, a significant DDI between tacrolimus and nifedipine with respect to the CYP3A5*3 polymorphism was confirmed. In group I (n = 43), the C0/D of CONF was significantly higher than in Controls [225.2 ± 66.3 vs. 155.1 ± 34.6 ng/ml/(mg/kg); p = 0.002]. However, this difference was not detected in group II (n = 27) (p = 0.216). The co-administrated nifedipine and CYP3A5*3/*3 homozygotes significantly increased tacrolimus concentrations in multivariate line regression analysis. Discussion: A CYP3A5 genotype-dependent DDI was found between tacrolimus and nifedipine. Therefore, personalized therapy accounting for CYP3A5 genotype detection as well as therapeutic drug monitoring are necessary for renal transplant patients when treating with tacrolimus and nifedipine.

Veterinary Herbal Medicine (VHM) is a comprehensive, current, and informative discipline on the utilization of herbs in veterinary practice. Driven by chemistry but progressively directed by pharmacology and the clinical sciences, drug research has contributed more to address the needs for innovative veterinary medicine for curing animal diseases. However, research into veterinary medicine of vegetal origin in the pharmaceutical industry has reduced, owing to questions such as the short of compatibility of traditional natural-product extract libraries with high-throughput screening. Here, we present a cross-species chemogenomic screening platform to dissect the genetic basis of multifactorial diseases and to determine the most suitable points of attack for future veterinary medicines, thereby increasing the number of treatment options. First, based on critically examined pharmacology and text mining, we build a cross-species drug-likeness evaluation approach to screen the lead compounds in veterinary medicines. Second, a specific cross-species target prediction model is developed to infer drug-target connections, with the purpose of understanding how drugs work on the specific targets. Third, we focus on exploring the multiple targets interference effects of veterinary medicines by heterogeneous network convergence and modularization analysis. Finally, we manually integrate a disease pathway to test whether the cross-species chemogenomic platform could uncover the active mechanism of veterinary medicine, which is exemplified by a specific network module. We believe the proposed cross-species chemogenomic platform allows for the systematization of current and traditional knowledge of veterinary medicine and, importantly, for the application of this emerging body of knowledge to the development of new drugs for animal diseases.

The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention. In this study, we propose an eigenvalue transformation technique and apply this technique to two representative algorithms, the Regularized Least Squares classifier (RLS) and the semi-supervised link prediction classifier (SLP), that have been used to predict drug-target interaction. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better performance on drug-target interaction prediction than did the original algorithms. These findings show that eigenvalue transformation is an efficient technique for improving the performance of methods for predicting drug-target interactions. We further show that, in theory, eigenvalue transformation can be viewed as a feature transformation on the kernel matrix. Accordingly, although we only apply this technique to two algorithms in the current study, eigenvalue transformation also has the potential to be applied to other algorithms based on kernels.

A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale. To address these problems, in this work, a novel algorithm termed weighted ensemble similarity (WES) has been developed to identify drug direct targets based on a large-scale of 98,327 drug-target relationships. WES includes: (1) identifying the key ligand structural features that are highly-related to the pharmacological properties in a framework of ensemble; (2) determining a drug's affiliation of a target by evaluation of the overall similarity (ensemble) rather than a single ligand judgment; and (3) integrating the standardized ensemble similarities (Z score) by Bayesian network and multi-variate kernel approach to make predictions. All these lead WES to predict drug direct targets with external and experimental test accuracies of 70% and 71%, respectively. This shows that the WES method provides a potential in silico model for drug repositioning and discovery.

Interindividual variability in the expression of drug-metabolizing enzymes and transporters (DMETs) in human liver may contribute to interindividual differences in drug efficacy and adverse reactions. Published studies that analyzed variability in the expression of DMET genes were limited by sample sizes and the number of genes profiled. We systematically analyzed the expression of 374 DMETs from a microarray data set consisting of gene expression profiles derived from 427 human liver samples. The standard deviation of interindividual expression for DMET genes was much higher than that for non-DMET genes. The 20 DMET genes with the largest variability in the expression provided examples of the interindividual variation. Gene expression data were also analyzed using network analysis methods, which delineates the similarities of biological functionalities and regulation mechanisms for these highly variable DMET genes. Expression variability of human hepatic DMET genes may affect drug-gene interactions and disease susceptibility, with concomitant clinical implications.

Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed.

Human sex differences in the gene expression of drug metabolizing enzymes and transporters (DMETs) introduce differences in drug absorption, distribution, metabolism and excretion, possibly affecting drug efficacy and adverse reactions. However, existing studies aimed at identifying dimorphic expression differences of DMET genes are limited by sample size and the number of genes profiled. Focusing on a list of 374 DMET genes, we analyzed a previously published gene expression data set consisting of human male (n=234) and female (n=193) liver samples, and identified 77 genes showing differential expression due to sex. To delineate the biological functionalities and regulatory mechanisms for the differentially expressed DMET genes, we conducted a co-expression network analysis. Moreover, clinical implications of sex differences in the expression of human hepatic DMETs are discussed. This study may contribute to the realization of personalized medicine by better understanding the inter-individual differences between males and females in drug/xenobiotic responses and human disease susceptibilities.

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

Targeting helper T cells, especially Th17 cells, has become a plausible therapy for many autoimmune diseases.

Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as "Acute Effects", "NIOSH Toxicity Data", "Interactions", "Hepatotoxicity", "Carcinogenicity", "Symptoms", and "Human Toxicity Values" sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.

Organic anion transporting polypeptide 1B1 (OATP1B1), a liver-specific uptake transporter, was associated with drug induced liver injury (DILI). Screening and identifying potent OATP1B1 inhibitors with little toxicity is of great value in reducing OATP1B1-mediated DILI. Flavonoids are a group of polyphenols ubiquitously present in vegetables, fruits and herbal products, some of them were reported to produce transporter-mediated DDI. Our objective was to investigate potential inhibitors of OATP1B1 from 99 flavonoids, and to assess the hepatoprotective effects on bosentan induced liver injury. Eight flavonoids, including biochanin A, hispidulin, isoliquiritigenin, isosinensetin, kaempferol, licochalcone A, luteolin and sinensetin exhibited significant inhibition (>50 %) on OATP1B1 in OATP1B1-HEK293 cells, which reduced the OATP1B1-mediated influx of methotrexate, accordingly decreased its cytotoxicity in OATP1B1-HEK293 cells and increased its AUC0-t in different extents in rats, from 28.27%-82.71 %. In bosentan-induced rat liver injury models, 8 flavonoids reduced the levels of serum total bile acid (TBA) and the liver concentration of bosentan in different degrees. Among them, kaempferol decreased the concentration most significantly, by 54.17 %, which indicated that flavonoids may alleviate bosentan-induced liver injury by inhibiting OATP1B1-mediated bosentan uptake. Furthermore, the pharmacophore model indicated the hydrogen bond acceptors and hydrogen bond donors may play critical role in the potency of flavonoids inhibition on OATP1B1. Taken together, our findings would provide helpful information for predicting the potential risks of flavonoid-containing food/herb-drug interactions in humans and alleviating bosentan -induced liver injury by OATP1B1 regulation.

Gestational diabetes mellitus (GDM) is a condition associated with the onset of abnormal glucose tolerance during pregnancy. Long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and genes can form lncRNA-mediated feedforward loops (lnc-FFLs), which are functional network motifs that regulate a wide range of biological processes and diseases. However, lnc-FFL network motifs have not been systematically investigated in GDM, and their role in the disease remains largely unknown. In the present study, a global lnc-FFL network was constructed and analyzed. Glycometabolism- and hormone-related lnc-FFL networks were extracted from the global network. An integrated algorithm was designed to identify dysregulated glycometabolism- and hormone-related lnc-FFLs in GDM. The patterns of dysregulated lnc-FFLs in GDM were complex. Moreover, there were strong associations between dysregulated glycometabolism- and hormone-related lnc-FFLs in GDM. Core modules were extracted from the dysregulated lnc-FFL networks in GDM and showed specific and essential functions. In addition, dysregulated lnc-FFLs could combine with ceRNAs and form more complex modules, which could play novel roles in GDM. Notably, we discovered that the dysregulated lnc-FFLs were enriched in the thyroid hormone signaling pathway. Some drug-repurposing candidates, such as hormonal drugs, could be identified based on lnc-FFLs in GDM. In summary, the present study highlighted the effect of dysregulated glycometabolism- and hormone-related lnc-FFLs in GDM and revealed their potential for the discovery of novel biomarkers and therapeutic targets for GDM.

YZG-331 is a synthetic adenosine analogue which exhibits the sedative and hypnotic effects by binding to the adenosine receptor. The present study was designed to investigate the effects of P-glycoprotein (P-gp) on the intestine and brain distribution of YZG-331 in vitro and in vivo as well as related binding mechanisms. The activity of P-gp ATPase was both induced by YZG-331 and verapamil, a typical P-gp inhibitor, but affinity of YZG-331 for P-gp was lower than that of verapamil. The docking analyses further elucidated the binding relationship of YZG-331 and P-gp. The directional transport of YZG-331 was disappeared in Caco-2 and MDCK-MDR1 cells when the P-gp activity was blocked. However, the penetration of digoxin, a P-gp known substrate, was not change in MDCK-MDR1 cells along with YZG-331. In the everted intestinal sac model, the influx of YZG-331 was significantly reduced in the presence of verapamil in all the segments except for the colon. In the in situ and in vivo study, the brain exposure of YZG-331 was promoted after co-administered of verapamil. Furthermore, the Kp value changed from 0.03 to 0.05 after drug combination. Taken together, these results indicated that YZG-331 is a substrate but may not an inhibitor of P-gp. The intestine and brain permeability of YZG-331 can be restricted, at least in part, by P-gp. The drug interactions should be awarded when YZG-331 and other P-gp-related drugs used together.

Inflammation is a hallmark of many diseases like diabetes, cancers, atherosclerosis and arthritis. Thus, lots of concerns have been raised toward developing novel anti-inflammatory agents. Many alternative herbal medicines possess excellent anti-inflammatory properties, yet their precise mechanisms of action are yet to be elucidated. Here, a novel systems pharmacology approach based on a large number of chemical, biological and pharmacological data was developed and exemplified by a probe herb Folium Eriobotryae, a widely used clinical anti-inflammatory botanic drug. The results show that 11 ingredients of this herb with favorable pharmacokinetic properties are predicted as active compounds for anti-inflammatory treatment. In addition, via systematic network analyses, their targets are identified to be 43 inflammation-associated proteins including especially COX2, ALOX5, PPARG, TNF and RELA that are mainly involved in the mitogen-activated protein kinase (MAPK) signaling pathway, the rheumatoid arthritis pathway and NF-κB signaling pathway. All these demonstrate that the integrated systems pharmacology method provides not only an effective tool to illustrate the anti-inflammatory mechanisms of herbs, but also a new systems-based approach for drug discovery from, but not limited to, herbs, especially when combined with further experimental validations.

Preeclampsia (PE) is a heterogeneous, hypertensive disorder of pregnancy, with no robust biomarkers or effective treatments. PE increases the risk of poor outcomes for both the mother and the baby. Methylation-mediated transcriptional dysregulation motifs (methTDMs) could contribute the PE development. However, precise functional roles of methTDMs in PE have not been globally described.

Health-strengthening (Fu-Zheng) herbs is a representative type of traditional Chinese medicine (TCM) widely used for cancer treatment in China, which is in contrast to pathogen eliminating (Qu-Xie) herbs. However, the commonness in the biological basis of health-strengthening herbs remains to be holistically elucidated. In this study, an innovative high-throughput research strategy integrating computational and experimental methods of network pharmacology was proposed, and 22 health-strengthening herbs were selected for the investigation. Additionally, 25 pathogen-eliminating herbs were included for comparison. First, based on network-based, large-scale target prediction, we analyzed the target profiles of 1446 TCM compounds. Next, the actions of 166 compounds on 420 antitumor or immune-related genes were measured using a unique high-throughput screening strategy by high-throughput sequencing, referred to as HTS². Furthermore, the structural information and the antitumor activity of the compounds in health-strengthening and pathogen-eliminating herbs were compared. Using network pharmacology analysis, we discovered that: (1) Functionally, the predicted targets of compounds from health strengthening herbs were enriched in both immune-related and antitumor pathways, similar to those of pathogen eliminating herbs. As a case study, galloylpaeoniflorin, a compound in a health strengthening herb Radix Paeoniae Alba (Bai Shao), was found to exert antitumor effects both in vivo and in vitro. Yet the inhibitory effects of the compounds from pathogen eliminating herbs on tumor cells proliferation as a whole were significantly stronger than those in health-strengthening herbs (p < 0.001). Moreover, the percentage of assay compounds in health-strengthening herbs with the predicted targets enriched in the immune-related pathways (e.g., natural killer cell mediated cytotoxicity and antigen processing and presentation) were significantly higher than that in pathogen-eliminating herbs (p < 0.05). This finding was supported by the immune-enhancing effects of a group of compounds from health-strengthening herbs indicated by differentially expressed genes in the HTS² results. (2) Compounds in the same herb may exhibit the same or distinguished mechanisms in cancer treatment, which was demonstrated as the compounds influence pathway gene expressions in the same or opposite directions. For example, acetyl ursolic acid and specnuezhenide in a health-strengthening herb Fructus Ligustri lucidi (Nv Zhen Zi) both upregulated gene expressions in T cell receptor signaling pathway. Together, this study suggested greater potentials in tumor immune microenvironment regulation and tumor prevention than in direct killing tumor cells of health-strengthening herbs generally, and provided a systematic strategy for unveiling the commonness in the biological basis of health-strengthening herbs in cancer treatment.

Clara cell protein (CC16) is an anti-inflammatory protein, which is expressed in the airway epithelium. It is involved in the development of airway inflammatory diseases, including chronic obstructive pulmonary disease and asthma. However, the exact molecular mechanism underlying its anti‑inflammatory action remains to be fully elucidated. The aim of the present study was to define the protein profiles of the anti‑inflammatory effect of CC16 in lipopolysaccharide (LPS)‑treated rat tracheal epithelial (RTE) cells using shotgun proteomics. Protein extracts were obtained from control RTE cells, RTE cells treated with LPS and RTE cells treated with LPS and recombinant CC16 (rCC16). Subsequent label‑free quantification and bioinformatics analyses identified 12 proteins that were differentially expressed in the three treatment groups as a cluster of five distinct groups according to their molecular functions. Five of the twelve proteins were revealed to be associated with the cytoskeleton: Matrix metalloproteinase‑9, myosin heavy chain 10, actin‑related protein‑3 homolog, elongation factor 1‑α‑1 (EF‑1‑α‑1), and acidic ribosomal phosphoprotein P0. Five of the twelve proteins were associated with cellular proliferation: DNA‑dependent protein kinase catalytic subunit, EF‑1‑α‑1, tyrosine 3‑monooxygenase, caspase recruitment domain (CARD) protein 12 and adenosylhomocysteinase (SAHH) 3. Three proteins were associated with gene regulation: EF‑1‑α‑1, SAHH 3 and acidic ribosomal phosphoprotein P0. Three proteins were associated with inflammation: Tyrosine 3‑monooxygenase, CARD protein 12 and statin‑related protein. ATPase (H+‑transporting, V1 subunit A, isoform 1) was revealed to be associated with energy metabolism, and uridine diphosphate glycosyltransferase 1 family polypeptide A8 with drug metabolism and detoxification. The identified proteins were further validated using reverse transcription‑quantitative polymerase chain reaction. These protein profiles, and their interacting protein network, may facilitate the elucidation of the molecular mechanisms underlying the anti‑inflammatory effects of CC16.

Background. Clinical trials reveal that multiherb prescriptions of herbal medicine often exhibit pharmacological and therapeutic superiority in comparison to isolated single constituents. However, the synergistic mechanisms underlying this remain elusive. To address this question, a novel systems biology model integrating oral bioavailability and drug-likeness screening, target identification, and network pharmacology method has been constructed and applied to four clinically widely used herbs Radix Astragali Mongolici, Radix Puerariae Lobatae, Radix Ophiopogonis Japonici, and Radix Salviae Miltiorrhiza which exert synergistic effects of combined treatment of cardiovascular disease (CVD). Results. The results show that the structural properties of molecules in four herbs have substantial differences, and each herb can interact with significant target proteins related to CVD. Moreover, the bioactive ingredients from different herbs potentially act on the same molecular target (multiple-drug-one-target) and/or the functionally diverse targets but with potentially clinically relevant associations (multiple-drug-multiple-target-one-disease). From a molecular/systematic level, this explains why the herbs within a concoction could mutually enhance pharmacological synergy on a disease. Conclusions. The present work provides a new strategy not only for the understanding of pharmacological synergy in herbal medicine, but also for the rational discovery of potent drug/herb combinations that are individually subtherapeutic.