Abstract: Drug combination is now a hot research topic in the pharmaceutical industry, but experiment-based methodologies are extremely costly in time and money. Many computational methods have been proposed to address these problems by starting from existing drug combinations. However, in most cases, only molecular structure information is included, which covers too limited a set of drug characteristics to efficiently screen drug combinations. Here, we integrated similarity-based multifeature drug data to improve the prediction accuracy by using the neighbor recommender method combined with ensemble learning algorithms. By conducting feature assessment analysis, we selected the most useful drug features and achieved 0.964 AUC in the ensemble models. The comparison results showed that the ensemble models outperform traditional machine learning algorithms such as support vector machine (SVM), naive Bayes (NB), and logistic regression (GLM). Furthermore, we predicted 7 candidate drug combinations for a specific drug, paclitaxel, and successfully verified that the two of the predicted combinations have promising effects.

Abstract: Introduction: Establishing efficacy of and molecular pathways for statins has the potential to impact incidence of Alzheimer's and age-related neurodegenerative diseases (NDD). Methods: This retrospective cohort study surveyed US-based Humana claims, which includes prescription and patient records from private-payer and Medicare insurance. Claims from 288,515 patients, aged 45 years and older, without prior history of NDD or neurological surgery, were surveyed for a diagnosis of NDD starting 1 year following statin exposure. Patients were required to be enrolled with claims data for at least 6 months prior to first statin prescription and at least 3 years thereafter. Computational system biology analysis was conducted to determine unique target engagement for each statin. Results: Of the 288,515 participants included in the study, 144,214 patients (mean [standard deviation (SD)] age, 67.22 [3.8] years) exposed to statin therapies, and 144,301 patients (65.97 [3.2] years) were not treated with statins. The mean (SD) follow-up time was 5.1 (2.3) years. Exposure to statins was associated with a lower incidence of Alzheimer's disease (1.10% vs 2.37%; relative risk [RR], 0.4643; 95% confidence interval [CI], 0.44-0.49; P < .001), dementia 3.03% vs 5.39%; RR, 0.56; 95% CI, 0.54-0.58; P < .001), multiple sclerosis (0.08% vs 0.15%; RR, 0.52; 95% CI, 0.41-0.66; P < .001), Parkinson's disease (0.48% vs 0.92%; RR, 0.53; 95% CI, 0.48-0.58; P < .001), and amyotrophic lateral sclerosis (0.02% vs 0.05%; RR, 0.46; 95% CI, 0.30-0.69; P < .001). All NDD incidence for all statins, except for fluvastatin (RR, 0.91; 95% CI, 0.65-1.30; P = 0.71), was reduced with variances in individual risk profiles. Pathway analysis indicated unique and common profiles associated with risk reduction efficacy. Discussion: Benefits and risks of statins relative to neurological outcomes should be considered when prescribed for at-risk NDD populations. Common statin activated pathways indicate overarching systems required for risk reduction whereas unique targets could advance a precision medicine approach to prevent neurodegenerative diseases.

Abstract: When trying to identify new potential therapeutic protein targets, access to data and knowledge is increasingly important. In a field where new resources and data sources become available every day, it is crucial to be able to take a step back and look at the wider picture in order to identify potential drug targets. While this task is routinely performed by bespoke literature searches, it is often time-consuming and lacks uniformity when comparing multiple targets at one time. To address this challenge, we developed TargetDB, a tool that aggregates public information available on given target(s) (links to disease, safety, 3D structures, ligandability, novelty, etc.) and assembles it in an easy to read output ready for the researcher to analyze. In addition, we developed a target scoring system based on the desirable attributes of good therapeutic targets and machine learning classification system to categorize novel targets as having promising or challenging tractrability. In this manuscript, we present the methodology used to develop TargetDB as well as test cases.

Abstract: Background: Chuanxiong Rhizoma is one of the traditional Chinese medicines which have been used for years in the treatment of diabetic nephropathy (DN). However, the mechanism of Chuanxiong Rhizoma in DN has not yet been fully understood. Methods: We performed network pharmacology to construct target proteins interaction network of Chuanxiong Rhizoma. Active ingredients were acquired from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. DRUGBANK database was used to predict target proteins of Chuanxiong Rhizoma. Gene ontology (GO) biological process analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed for functional prediction of the target proteins. Molecular docking was applied for evaluating the drug interactions between hub targets and active ingredients. Results: Twenty-eight target genes fished by 6 active ingredients of Chuanxiong Rhizoma were obtained in the study. The top 10 significant GO analyses and 6 KEGG pathways were enriched for genomic analysis. We also acquired 1366 differentially expressed genes associated with DN from GSE30528 dataset, including five target genes: KCNH2, NCOA1, KDR, NR3C2 and ADRB2. Molecular docking analysis successfully combined KCNH2, NCOA1, KDR and ADRB2 to Myricanone with docking scores from 4.61 to 6.28. NR3C2 also displayed good docking scores with Wallichilide and Sitosterol (8.13 and 8.34, respectively), revealing good binding forces to active compounds of Chuanxiong Rhizoma. Conclusions: Chuanxiong Rhizoma might take part in the treatment of DN through pathways associated with steroid hormone, estrogen, thyroid hormone and IL-17. KCNH2, NCOA1, KDR, ADRB2 and NR3C2 were proved to be the hub targets, which were closely related to corresponding active ingredients of Chuanxiong Rhizoma.

Abstract: In patients suffering from moderate-to-severe chronic kidney disease (CKD) or end-stage renal disease (ESRD), subjected to hemodialysis (HD), pain is very common, but often underestimated. Opioids are still the mainstay of severe chronic pain management; however, their prescription in CKD and HD patients is still significantly low and pain is often under-treated. Altered pharmacokinetics and the lack of clinical trials on the use of opioids in patients with renal impairment increase physicians' concerns in this specific population. This narrative review focused on the correct and safe use of opioids in patients with CKD and HD. Morphine and codeine are not recommended, because the accumulation of their metabolites may cause neurotoxic symptoms. Oxycodone and hydromorphone can be safely used, but adequate dosage adjustments are required in CKD. In dialyzed patients, these opioids should be considered as second-line agents and patients should be carefully monitored. According to different studies, buprenorphine and fentanyl could be considered first-line opioids in the management of pain in CKD; however, fentanyl is not appropriate in patients undergoing HD. Tapentadol does not need dosage adjustment in mild-to-moderate renal impairment conditions; however, no data are available on its use in ESRD. Opioid-related side effects may be exacerbated by common comorbidities in CKD patients. Opioid-induced constipation can be managed with peripherally-acting-mu-opioid-receptor-antagonists (PAMORA). Unlike the other PAMORA, naldemedine does not require any dose adjustment in CKD and HD patients. Accurate pain diagnosis, opioid titration and tailoring are mandatory to minimize the risks and to improve the outcome of the analgesic therapy.

Abstract: The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M (pro) ), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M (pro) . Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M (pro) . Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M (pro) . Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design.

Abstract: Background: Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide, yet as of currently, there is no disease-modifying therapy that could delay its progression. Paeonia lactiflora Pall. is the most frequently used herb in formulas for PD in Traditional Chinese Medicine and also a potential neuroprotective agent for neurodegenerative diseases, while its mechanisms remain poorly understood. In this study, we aim to explore the underlying mechanism of P. lactiflora in treating PD utilizing a network pharmacology approach. Methods: The protein targets of P. lactiflora ingredients and PD were first obtained from several databases. To clarify the key targets, a Protein-Protein-Interaction (PPI) network was constructed and analyzed on the String database, and then enrichment analysis was performed by the Metascape platform to determine the main Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathways. Finally, the Ingredient-Target-Pathway (I-T-P) network was constructed and analyzed by Cytoscape software. Results: Six active ingredients of P. lactiflora (kaempferol, ss-sitosterol, betulinic acid, palbinone, paeoniflorin and (+)-catechin) as well as six core targets strongly related to PD treatment [AKT1, interleukin-6, CAT, Tumor necrosis factor (TNF), CASP3, and PTGS2] were identified. The main pathways were shown to involve neuroactive ligand-receptor interaction, Calcium signaling pathway, PI3-Akt signaling pathway, TNF signaling pathway, and apoptosis signaling pathway. The main biological process included the regulation of neurotransmitter levels. Conclusion: P. lactiflora may retard neurodegeneration by reducing neuroinflammation, inhibiting intrinsic and extrinsic apoptosis, and may improve motor and non-motor symptoms by regulating the levels of neurotransmitters. Our study has revealed the mechanism of P. lactiflora in the treatment of PD and may contribute to novel drug development for PD.

Abstract: Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.

Abstract: Aconiti Lateralis Radix Praeparata (Fuzi) and Pinelliae Rhizoma (Banxia) are among the 18 incompatible medications that are forbidden from use in one formulation. However, there is increasing evidence implying that this prohibition is not entirely correct. According to the theory of Chinese traditional medicine, they can be used for the treatment of chronic obstructive pulmonary disease (COPD). Thus, we analyzed the possible approaches for the treatment of COPD using network pharmacology. The active compounds of Fuzi and Banxia (FB) were collected, and their targets were identified. COPD-related targets were obtained by analyzing the differentially expressed genes between COPD patients and healthy individuals, which were expressed using a Venn diagram of COPD and FB. Protein-protein interaction data and network regarding COPD and drugs used were obtained. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were conducted. The gene-pathway network was constructed to screen the key target genes. In total, 34 active compounds and 47 targets of FB were identified; moreover, 7,153 differentially expressed genes were identified between COPD patients and healthy individuals. The functional annotations of target genes were found to be related to mechanisms such as transcription, cytosol, and protein binding; furthermore, 68 pathways including neuroactive ligand-receptor interaction, Kaposi sarcoma-associated herpesvirus infection, apoptosis, and measles were significantly enriched. FOS CASP3, VEGFA, ESR1, and PTGS2 were the core genes in the gene-pathway network of FB for the treatment of COPD. Our results indicated that the effect of FB against COPD may involve the regulation of immunological function through several specific biological processes and their corresponding pathways. This study demonstrates the application of network pharmacology in evaluating mechanisms of action and molecular targets of herb-opponents FB.

Abstract: Several clinical therapies such as tissue repair by replacing injured tissues with functional ones have been reported; however, there is great potential for exploring traditional herbal-induced regeneration with good safety. Tongqiao Huoxue Decoction (TQHXD), a well-known classical traditional Chinese medicinal formula, has been widely used for clinical treatment of stroke. However, biological activity and mechanisms of action of its constituents toward conferring protection against cerebral ischemia-reperfusion (I/R) injury remain unclear. In this present study, we evaluated TQHXD quality using HPLC; then, it was screened for its potential active ingredients using a series of indices, such as their drug-likeness and oral bioavailability. Subsequently, we analyzed the potential mechanisms of TQHXD anti-I/R using gene ontology functional enrichment analyses. The network pharmacological approach enabled us to screen 265 common targets associated with I/R, indicating that TQHXD had remarkable protective effects on infarction volume, neurological function scores, and blood-brain barrier (BBB) injury. In addition, TQHXD significantly promoted the recovery of regional cerebral blood flow (rCBF) 7 days after reperfusion compared to rats in the vehicle group. Immunofluorescence results revealed a significantly higher CD34 expression in TQHXD-treated rats 7 days after reperfusion. TQHXD is not merely effective but eventually develops a secretory profile composed of VEGF and cerebral blood flow, a typical signature termed the angiogenesis-associated phenotype. Mechanistically, our data revealed that TQHXD (6 g/kg) treatment resulted in a marked increase in expression of p-focal adhesion kinase (FAK) and p-Paxillin proteins. However, Ki8751-mediated inhibition of VEGFR2 activity repealed its angiogenesis and protective effects and decreased both p-FAK and p-Paxillin protein levels. Taken together, these findings affirmed the potential of TQHXD as a drug for the management of stroke, which might be exerted by increasing the angiogenesis via the VEGF pathway. Therefore, these results provide proof-of-concept evidence that angiogenesis is a major contributor to TQHXD-treated I/R and that TQHXD is a promising traditional ethnic medicine for the management of this condition.