MiR-429-5p attenuates the migration and invasion of malignant melanoma by targeting LIMK1.

OBJECTIVE: To investigate the potential effects of microRNA-429-5p (miR-429-5p) on the development of malignant melanoma (MM) and the relevant mechanism. PATIENTS AND METHODS: Quantitative Real Time-Polymerase Chain Reaction (qRT-PCR) was used to detect the differential expression of miR-429-5p in MM tissues. The relationship between miR-429-5p expression and clinical pathological data of MM patients was analyzed. LIM kinase 1 (LIMK1) was verified as a downstream target of miR-429-5p by online prediction software, and the interaction between LIMK1 and miR-429-5p was verified by Dual-Luciferase reporter assay. RESULTS: Compared with normal skin tissues, miR-429-5p was downregulated in MM tissues. MiR-429-5p expression was correlated with tumor size and stage of MM. Upregulation of miR-429-5p significantly inhibited protein expression of LIMK1 and reduced migration and invasion ability of MM cells. LIMK1 was involved in MM progression regulated by miR-429-5p. CONCLUSIONS: MiR-429-5p attenuates migration and invasion in MM by targeting LIMK1. Hence, miR-429-5p/LIMK1 axis might be a potential therapeutic target for the treatment of MM.

Integrating Multiple Evidence Sources to Predict Adverse Drug Reactions Based on a Systems Pharmacology Model.

Identifying potential adverse drug reactions (ADRs) is critically important for drug discovery and public health. Here we developed a multiple evidence fusion (MEF) method for the large-scale prediction of drug ADRs that can handle both approved drugs and novel molecules. MEF is based on the similarity reference by collaborative filtering, and integrates multiple similarity measures from various data types, taking advantage of the complementarity in the data. We used MEF to integrate drug-related and ADR-related data from multiple levels, including the network structural data formed by known drug-ADR relationships for predicting likely unknown ADRs. On cross-validation, it obtains high sensitivity and specificity, substantially outperforming existing methods that utilize single or a few data types. We validated our prediction by their overlap with drug-ADR associations that are known in databases. The proposed computational method could be used for complementary hypothesis generation and rapid analysis of potential drug-ADR interactions.

In silico toxicity prediction by support vector machine and SMILES representation-based string kernel.

There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals.

[Study on dissolution test in vitro and bioavailability of oral osmotic pump of verapamil hydrochloride].

This paper deals with the evaluation of osmotic pump of verapamil hydrochloride tablet (C) by measuring in vitro/in vivo test. The results showed that the dissolution behaviors were of zero-order kinetic and release constant in vitro (Kr) of C was 9.9450. The plasma levels of Ver.HCl in eight volunteers following single and multiple oral doses of these dosage forms were determined using HPLC method. The pharmacokinetic parameters were fitted by nonlinear least square method with a computer on the basis of two-compartment model. The pharmacokinetic parameters of Cmax, Tmax, t1/2, Ka, K10 and K21 were calculated. The bioavailability of tablet C relative to B and A was 101.7%, 96.16% respectively. A significant correlation was found between in vitro dissolution and in vivo absorption.