Jianxin Chen

Special (lipid) delivery: The role of the ionizable lipid pK(a) in the in vivo delivery of siRNA by lipid nanoparticles has been studied with a large number of head group modifications to the lipids. A tight correlation between the lipid pK(a) value and silencing of the mouse FVII gene (FVII ED(50) ) was found, with an optimal pK(a) range of 6.2-6.5. The most potent cationic lipid from this study has ED(50) levels around 0.005 mg kg(-1) in mice and less than 0.03 mg kg(-1) in non-human primates.

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.

MOTIVATION: Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered. RESULTS: We report a systems pharmacology framework to predict drug combinations (PreDCs) on a computational model, termed probability ensemble approach (PEA), for analysis of both the efficacy and adverse effects of drug combinations. First, a Bayesian network integrating with a similarity algorithm is developed to model the combinations from drug molecular and pharmacological phenotypes, and the predictions are then assessed with both clinical efficacy and adverse effects. It is illustrated that PEA can predict the combination efficacy of drugs spanning different therapeutic classes with high specificity and sensitivity (AUC = 0.90), which was further validated by independent data or new experimental assays. PEA also evaluates the adverse effects (AUC = 0.95) quantitatively and detects the therapeutic indications for drug combinations. Finally, the PreDC database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications. AVAILABILITY AND IMPLEMENTATION: The PreDC database is available at http://sm.nwsuaf.edu.cn/lsp/predc.php.

Several pathologies such as neurodegeneration and cancer are associated with aging, which is affected by many genetic and environmental factors. Healthy aging conceives human longevity, possibly due to carrying the defensive genes. For instance, FOXO (forkhead box O) genes determine human longevity. FOXO transcription factors are involved in the regulation of longevity phenomenon via insulin and insulin-like growth factor signaling. Only one FOXO gene (FOXO DAF-16) exists in invertebrates, while four FOXO genes, that is, FOXO1, FOXO3, FOXO4, and FOXO6 are found in mammals. These four transcription factors are involved in the multiple cellular pathways, which regulate growth, stress resistance, metabolism, cellular differentiation, and apoptosis in mammals. However, the accurate mode of longevity by FOXO factors is unclear until now. This article describes briefly the existing knowledge that is related to the role of FOXO factors in human longevity.