Esters of levonorgestrel and etonogestrel intended as single, subcutaneous-injection, long-lasting contraceptives

Solid implantable devices for sustained drug delivery

Implantable drug delivery systems (IDDS) are an attractive alternative to conventional drug administration routes. Oral and injectable drug administration are the most common routes for drug delivery providing peaks of drug concentrations in blood after administration followed by concentration decay after a few hours. Therefore, constant drug administration is required to keep drug levels within the therapeutic window of the drug. Moreover, oral drug delivery presents alternative challenges due to drug degradation within the gastrointestinal tract or first pass metabolism. IDDS can be used to provide sustained drug delivery for prolonged periods of time. The use of this type of systems is especially interesting for the treatment of chronic conditions where patient adherence to conventional treatments can be challenging. These systems are normally used for systemic drug delivery. However, IDDS can be used for localised administration to maximise the amount of drug delivered within the active site while reducing systemic exposure. This review will cover current applications of IDDS focusing on the materials used to prepare this type of systems and the main therapeutic areas of application.

Non-oral routes, novel formulations and devices of contraceptives: An update

Family planning enables society to prevent unintended pregnancies and helps in attaining desired spacing between the pregnancies. It is done with the use of contraceptive methods and infertility treatments. The use of contraceptives serves to ease maternal ill-health and reduce pregnancy-related deaths and helps to decrease the number of unsafe abortions and HIV transmission from mothers to newborns. The most popular contraception method is a daily dose of combined oral contraceptives pills. However, poor compliance and various adverse effects are common problems of oral contraceptives that considerably reduce their long-term use. Thus, several non-oral contraceptive options have been developed for better compliance, reduced side effects and improved therapeutic efficacy. This review presented the non-oral contraceptive formulations given by different routes such as transdermal, nasal, subcutaneous, intramuscular, intrauterine and vaginal routes. These formulations delivering contraceptives, mainly through devices, include transdermal patches and microneedles, nasal sprays, intrauterine devices and intrauterine systems, vaginal rings, contraceptive implants and contraceptive injections, which are unique in their specific advantages and drawbacks.

DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier

Accurate prediction of drug-target interactions (DTIs), which is often used in the fields of drug discovery and drug repositioning, is regarded a key challenge in the study of drug science. In this paper, a new method called DeepStack-DTIs is proposed to predict DTIs. First, for the target protein, pseudo-position specific score matrix, pseudo amino acid composition and SPIDER3 are used to extract the different feature information of the target protein. Meanwhile, the path-based fingerprint features of each drug are extracted. Then, the synthetic minority oversampling technique (SMOTE) and light gradient boosting machine (LightGBM) are used for data balancing and feature selection, respectively. Finally, the processed features are input to the deep-stacked ensemble classifier composed of gated recurrent unit (GRU), deep neural network (DNN), support vector machine (SVM), eXtreme gradient boosting (XGBoost) and logistic regression (LR) to predict DTIs. Under the five-fold cross-validation and compared with existing methods, the proposed method achieves higher prediction accuracy on the gold standard dataset. To evaluate the predictive power of DeepStack-DTIs, we validate the method on another dataset and predict the drug-target interaction network. The results indicate that DeepStack-DTIs has excellent predictive ability than the other methods, and provides novel insights for the prediction of DTIs. A novel method DeepStack-DTIs for drug-target interactions prediction. PsePSSM, PseAAC, SPIDER3 and FP2 are fused to convert protein sequence and drug molecule information into digital information, respectively. The SMOTE algorithm is used to balance the dataset and LightGBM feature selection algorithm is employed to remove redundant and irrelevant features to select the optimal feature subset. This optimal feature subset is inputted into the deep-stacked ensemble classifier to predict drug-target interactions. The experimental results show DeepStack-DTIs method can significantly improve the prediction accuracy of drug-target interactions.