For parenteral controlled drug release, the desired zero order release profile with no lag time is often difficult to achieve. To overcome the undesired lag time of the current commercial risperidone controlled release formulation, we developed PLGA–lipid microcapsules (MCs) and PLGA–lipid microgels (MGs). The lipid phase was composed of middle chain triglycerides (MCT) or isopropylmyristate (IPM). Hydroxystearic acid was used as an oleogelator. The three-dimensional inner structure of Risperidone-loaded MCs and MGs was assessed by using the invasive method of electron microscopy with focused ion beam cutting (FIB-SEM) and the noninvasive method of high-resolution nanoscale X-ray computed tomography (nano-CT). FIB-SEM and nano-CT measurements revealed the presence of highly dispersed spherical structures around two micrometres in size. Drug release kinetics did strongly depend on the used lipid phase and the presence or absence of hydroxystearic acid. We achieved a nearly zero order release without a lag time over 60 days with the MC-MCT formulation. In conclusion, the developed lipid-PLGA microparticles are attractive alternatives to pure PLGA-based particles. The advantages include improved release profiles, which can be easily tuned by the lipid composition.
Summary Mass spectrometry is an important analytical technology for the identification of metabolites and small compounds by their exact mass. But dozens or hundreds of different compounds may have a similar mass or even the same molecule formula. Further elucidation requires tandem mass spectrometry, which provides the masses of compound fragments, but in silico fragmentation programs require substantial computational resources if applied to large numbers of candidate structures.We present and evaluate an approach to obtain candidates from a relational database which contains 28 million compounds from PubChem.A training phase associates tandem-MS peaks with corresponding fragment structures. For the candidate search, the peaks in a query spectrum are translated to fragment structures, and the candidates are retrieved and sorted by the number of matching fragment structures. In the cross validation the evaluation of the relative ranking positions (RRP) using different sizes of training sets confirms that a larger coverage of training data improves the average RRP from 0.65 to 0.72. Our approach allows downstream algorithms to process candidates in order of importance.
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