Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined.
In the present investigation, self-nanoemulsifying drug delivery system (SNEDDS), of Lovastatin is being formulated to increase the solubility and bioavailability. The optimized Lovastatin SNEDDS formulation (F8) has a composition of Acrysol EL 135 as oil phase, Lauro glycol 90 and Capmul MCM as surfactant and co-surfactant respectively. Formulation F8 was found to be best formulation based on evaluation parameters. No drug precipitation or phase separation was observed in the optimized formulation. The particle size of the optimized formulation was found to be 4.9 nm and Z-Average of 71.5 nm indicating all the particles were in the nanometer range. The zeta potential of the optimized SNEDDS formulation was found to be -13.7 mV which comply with the requirement of the zeta potential for stability. Furthermore, pharmacokinetic studies in rats indicated that compared to the pure drug, the optimized SMEDDS formulation significantly improved the oral bioavailability of Lovastatin. Therefore, from our results the study suggests that the Lovastatin loaded self-nanoemulsifying formulation has a great potential for clinical application.
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