New designer drug N-(1-phenylcyclohexyl)-3-ethoxypropanamine (PCEPA): studies on its metabolism and toxicological detection in rat urine using gas chromatographic/mass spectrometric techniques

Studies are described on the metabolism and toxicological detection of the phencyclidine-derived designer drug N-(1-phenylcyclohexyl)-3-ethoxypropanamine (PCEPA) in rat urine using gas chromatographic/mass spectrometric techniques. The identified metabolites indicated that PCEPA was metabolized by N-dealkylation, O-deethylation partially followed by oxidation of the resulting alcohol to the corresponding carboxylic acid, hydroxylation of the cyclohexyl ring at different positions of PCEPA, N-dealkyl PCEPA, O-deethyl PCEPA, and of the corresponding carboxylic acids. Finally, aromatic hydroxylation of PCEPA, the corresponding carboxylic acids, and O-deethyl PCEPA, the latter partially followed by oxidation to the corresponding carboxylic acid and hydroxylation of the cyclohexyl ring could be observed. All metabolites were partially excreted in the conjugated form. The authors' systematic toxicological analysis (STA) procedure using full-scan GC/MS after acid hydrolysis, liquid-liquid extraction, and microwave-assisted acetylation allowed the detection in rat urine of an intake of a common drug users' dose of PCEPA. Assuming a similar metabolism in humans, the STA in human urine should be suitable as proof of intake of PCEPA.     

Building addressable libraries: spatially isolated, chip-based reductive amination reactions

A Pd(II) reagent has been generated at preselected sites on an electrochemically addressable chip and used to effect the oxidation of the neighboring alcohols on the polymer coating the chip's surface. The resulting carbonyls were then used to accomplish site-selective reductive amination reactions on the chips. The work demonstrates that the confinement strategy developed for spatially isolated Wacker oxidations to specific sites on the chips is general and can be used for other Pd(II)-based reactions.     

Physically inspired deep learning of molecular excitations and photoemission spectra

Modern functional materials consist of large molecular building blocks with significant chemical complexity which limits spectroscopic property prediction with accurate first-principles methods. Consequently, a targeted design of materials with tailored optoelectronic properties by high-throughput screening is bound to fail without efficient methods to predict molecular excited-state properties across chemical space. In this work, we present a deep neural network that predicts charged quasiparticle excitations for large and complex organic molecules with a rich elemental diversity and a size well out of reach of accurate many body perturbation theory calculations. The model exploits the fundamental underlying physics of molecular resonances as eigenvalues of a latent Hamiltonian matrix and is thus able to accurately describe multiple resonances simultaneously. The performance of this model is demonstrated for a range of organic molecules across chemical composition space and configuration space. We further showcase the model capabilities by predicting photoemission spectra at the level of the GW approximation for previously unseen conjugated molecules.

A physically-inspired machine learning model for orbital energies is developed that can be augmented with delta learning to obtain photoemission spectra, ionization potentials, and electron affinities with experimental accuracy.     

A systematic comparison of four different workup procedures for systematic toxicological analysis of urine samples using gas chromatography–mass spectrometry

Sample preparation for systematic toxicological screening analysis (STA) in urine by gas chromatography–mass spectrometry (GC-MS) generally involves cleavage of conjugates by acid hydrolysis (Hy) or enzymatic hydrolysis (Gluc) followed by liquid–liquid extraction (LLE) or solid-phase extraction (SPE), and derivatization, e.g., acetylation (Ac). LLE and derivatization can be performed simultaneously, e.g., in extractive methylation (ExMe). The work presented consisted of two separate studies. In study I, 350 urine samples from 168 inpatients from an internal medicine ward were worked up by Hy-LLE-Ac, the standard workup in the authors’ laboratory, Gluc-SPE-Ac, and Gluc-ExMe. In study II, 100 urine samples from psychiatric inpatients were worked up by Hy-LLE-Ac and Hy-SPE-Ac. The samples prepared were analyzed by full-scan GC-MS, and the drugs and/or their metabolites/artifacts detected after the different workup procedures were compared. The results obtained after Hy-LLE-Ac and Gluc-SPE-Ac showed only little differences, e.g., salicylic acid not being detectable with the latter. Hy-SPE-Ac covered a similar range of analytes as Hy-LLE-Ac but was much more time-consuming. Comparison of Hy-LLE-Ac and Gluc-ExMe showed that the former was better suited for basic drugs and the latter for acidic drugs, but the overlap was considerable. In conclusion, Hy-LLE-Ac remains the method of choice for STA in clinical toxicology owing to its wide analyte spectrum and short workup time. Gluc-ExMe is an ideal complementary method when acidics need to be covered. Gluc-SPE-Ac can be used as an alternative to Hy-LLE-Ac when turnaround is not critical or when automated analysis is required. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Some of these results were reported at the 46th International TIAFT Meeting, Martinique, 2–8 June 2008.     

Biodegradable Nanoparticles Loaded with Levodopa and Curcumin for Treatment of Parkinson’s Disease

Background: Parkinson’s disease (PD) is the second most common age-related neurodegenerative disorder. Levodopa (L-DOPA) remains the gold-standard drug available for treating PD. Curcumin has many pharmacological activities, including antioxidant, anti-inflammatory, antimicrobial, anti-amyloid, and antitumor properties. Copolymers composed of Poly (ethylene oxide) (PEO) and biodegradable polyesters such as Poly (ε-caprolactone) (PCL) can self-assemble into nanoparticles (NPs). This study describes the development of NH₂–PEO–PCL diblock copolymer positively charged and modified by adding glutathione (GSH) on the outer surface, resulting in a synergistic delivery of L-DOPA curcumin that would be able to pass the blood–brain barrier. Methods: The NH₂–PEO–PCL NPs suspensions were prepared by using a nanoprecipitation and solvent displacement method and coated with GSH. NPs were submitted to characterization assays. In order to ensure the bioavailability, Vero and PC12 cells were treated with various concentrations of the loaded and unloaded NPs to observe cytotoxicity. Results: NPs have successfully loaded L-DOPA and curcumin and were stable after freeze-drying, indicating advancing into in vitro toxicity testing. Vero and PC12 cells that were treated up to 72 h with various concentrations of L-DOPA and curcumin-loaded NP maintained high viability percentage, indicating that the NPs are biocompatible. Conclusions: NPs consisting of NH₂–PEO–PCL were characterized as potential formulations for brain delivery of L-DOPA and curcumin. The results also indicate that the developed biodegradable nanomicelles that were blood compatible presented low cytotoxicity.     

Mechanical Enhancement of the Strain-Sensor Response in Dimers of Strongly Coupled Plasmonic Nanoparticles

Due to their particular optical and mechanical properties, plasmomechanical devices have become choice candidates in strain sensing applications. Using numerical simulation, a plasmomechanical system consisting of two gold nanoparticles with different shapes and separated by a small gap, deposited onto a deformable polydimethylsiloxane membrane, is investigated. With the aim of understanding the relationship between the plasmonic behavior of gold nanoparticles and induced mechanical deformations, mechanical extension ranging from 0% to 20% is applied to the polydimethylsiloxane membrane. In a first step, a mechanical calculation based on a hyperelastic model for polydimethylsiloxane shows that the interparticle spacing is enhanced nonlinearly by a percentage greater than the externally applied deformation, depending on the shape and size of the nanoparticles as well as the polydimethylsiloxane membrane thickness. Full optical simulation of the deformed nanosystems demonstrates that the plasmonic resonance wavelength is highly sensitive to the applied displacements and is enhanced compared to a basic approach where the gap deformation is taken as equal to the macroscopic applied deformation. The best figure of merit ($0.022{\%}^{-1}$) is obtained for the disk–rod dimer near the strong coupling regime, larger than the values reported in the literature for localized nanoparticle systems.     

Interfacially induced additional damping peaks in dynamic mechanical spectra – micromechanical transitions in multipolymeric materials

The viscoelastic properties of several hypothetical multiphase polymeric materials were investigated in relation to their phase-property dependencies and microstructures. Theoretical mechanical considerations based on the self-consistent interlayer model were performed to point out that specific geometrical arrangements into phases of a set of properties of the pure constituents can lead to interfacially induced damping peaks in dynamic mechanical spectra (DMS). Such additional contributions in DMS were referred to micromechanical transitions to distinguish them from ordinary molecular transitions.