Forgotten fish habitats: Developing a conceptual framework for evaluating intermittent waterways and flood channels as native fish habitats: Insights from two freshwater catchments in Australia.

The ability to correctly identify Potential Key Fish Habitats (PKFH) before undertaking development assessments or stream restoration projects is a critical step for determining appropriate management interventions and offset strategies to minimize damage to all life stages of fish. However, there are no rapid, low-cost tools that can be universally utilised to identify PKFH in perennial streams and ephemeral streams in particular. We propose a conceptual framework for developing a rapid field appraisal based on a range of physical fish habitat-supporting features. Four steps are involved in developing the framework: 1) understand fish habitat features using published literature and sub-set model selection using actual data, 2) collect field data, 3) develop the conceptual framework, and 4) classify sites. We tested eight fish habitat-supporting features in the assessment and proposed a decision tree that guided the rapid appraisals at 104 ephemeral sites in southeast Australia. Out of 104 sites, 86% were identified as PKFH, 10% as likely key fish habitats and 4% as unlikely key fish habitats. The rapid field appraisal is cost-effective, simple to implement and does not require expert knowledge of fish habitats. Despite some limitations in the conceptual framework, this appraisal has great potential as a screening technique for PKFH in freshwater streams, particularly ephemeral streams in Australia and other overseas jurisdictions.

NERDD: a web portal providing access to in silico tools for drug discovery.

SUMMARY: The New E-Resource for Drug Discovery (NERDD) is a quickly expanding web portal focused on the provision of peer-reviewed in silico tools for drug discovery. NERDD currently hosts tools for predicting the sites of metabolism (FAME) and metabolites (GLORY) of small organic molecules, for flagging compounds that are likely to interfere with biological assays (Hit Dexter), and for identifying natural products and natural product derivatives in large compound collections (NP-Scout). Several additional models and components are currently in development. AVAILABILITY AND IMPLEMENTATION: The NERDD web server is available at https://nerdd.zbh.uni-hamburg.de. Most tools are also available as software packages for local installation.

Morphological predictors of swimming speed performance in river and reservoir populations of Australian smelt Retropinna semoni.

Dam construction is a major driver of ecological change in freshwater ecosystems. Fish populations have been shown to diverge in response to different flow velocity habitats, yet adaptations of fish populations to river and reservoir habitats created by dams remains poorly understood. We aimed to evaluate divergence of morphological traits and prolonged swimming speed performance between lotic and lentic populations of Australian smelt Retropinna semoni and quantify the relationship between prolonged swimming speed performance and morphology. Prolonged swimming speed performance was assessed for 15 individuals from each of three river and two reservoir populations of R. semoni using the critical swimming speed test (Ucrit ). Body shape was characterized using geometric morphometrics, which was combined with fin aspect ratios and standard length to assess morphological divergence among the five populations. Best subsets model-selection was used to identify the morphological traits that best explain Ucrit variation among individuals. Our results indicate R. semoni from river populations had significantly higher prolonged swimming speed performance (Ucrit = 46.61 ± 0.98 cm s-1 ) than reservoir conspecifics (Ucrit = 35.57 ± 0.83 cm s-1 ; F1,74 = 58.624, Z = 35.938, P < 0.001). Similarly, R. semoni sampled from river populations had significantly higher fin aspect ratios (ARcaudal = 1.71 ± 0.04 and 1.29 ± 0.02 respectively; F(1,74) = 56.247, Z = 40.107, P < 0.001; ARpectoral = 1.85 ± 0.03 and 1.33 ± 0.02 respectively; F(1,74) = 7.156, Z = 4.055, P < 0.01). Best-subset analyses revealed Ucrit was most strongly correlated with pectoral and caudal fin aspect ratios (R2 adj = 0.973, AICc = 41.54). Body shape, however, was subject to a three-way interaction among population, habitat and sex effects (F3,74 = 1.038. Z = 1.982; P < 0.05). Thus sexual dimorphism formed a significant component of unique and complex variation in body shape among populations from different habitat types. This study revealed profound effects of human-altered flow environments on locomotor morphology and its functional link to changes in swimming performance of a common freshwater fish. While past studies have indicated body shape may be an important axis for divergence between lotic and lentic populations of several freshwater fishes, fin aspect ratios were the most important predictor of swimming speed in our study. Differences in body morphology here were inconsistent between river and reservoir populations, suggesting this aspect of phenotype may be more strongly influenced by other factors such as predation and sexual dimorphism.

FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes.

In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, druglike compounds, natural products, agrochemicals, and cosmetics. FAME 3 was derived from the MetaQSAR database ( Pedretti et al. J. Med. Chem. 2018 , 61 , 1019 ), a recently published data resource on xenobiotic metabolism that contains more than 2100 substrates annotated with more than 6300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other nonredox reactions, and conjugation reactions. In tests with holdout data, FAME 3 models reached competitive performance, with Matthews correlation coefficients (MCCs) ranging from 0.50 for a global model covering phase 1 and phase 2 metabolism, to 0.75 for a focused model for phase 2 metabolism. A model focused on cytochrome P450 metabolism yielded an MCC of 0.57. Results from case studies with several synthetic compounds, natural products, and natural product derivatives demonstrate the agreement between model predictions and literature data even for molecules with structural patterns clearly distinct from those present in the training data. The applicability domains of the individual models were estimated by a new, atom-based distance measure (FAMEscore) that is based on a nearest-neighbor search in the space of atom environments. FAME 3 is available via a public web service at https://nerdd.zbh.uni-hamburg.de/ and as a self-contained Java software package, free for academic and noncommercial research.

FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity.

We report on the further development of FAst MEtabolizer (FAME; J. Chem. Inf. MODEL: 2013, 53, 2896-2907), a collection of random forest models for the prediction of sites of metabolism (SoMs) of xenobiotics. A broad set of descriptors was explored, from simple 2D descriptors such as those used in FAME, to quantum chemical descriptors employed in some of the most accurate models for SoM prediction currently available. In line with the original FAME approach, our objective was to keep things simple and to come up with accurate and robust models that are based on a small number of 2D descriptors. We found that circular descriptions of atoms and their environments with such descriptors in combination with an extremely randomized trees algorithm can yield models that perform equally well compared to more complex approaches. Thorough evaluation experiments on an independent test set showed that the best of these models obtained a Matthews correlation coefficient, area under the receiver operating characteristic curve, and Top-2 accuracy of 0.57, 0.91 and 94.1%, respectively. Models for the prediction of isoform-specific regioselectivity of CYP 3A4, 2D6, and 2C9 were also developed and showed competitive performance. The best models have been integrated into a newly developed software package (FAME 2), which is available free of charge from the authors.

Bioinformatic analysis of the protein/DNA interface.

To investigate the principles driving recognition between proteins and DNA, we analyzed more than thousand crystal structures of protein/DNA complexes. We classified protein and DNA conformations by structural alphabets, protein blocks [de Brevern, Etchebest and Hazout (2000) (Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Prots. Struct. Funct. Genet., 41:271-287)] and dinucleotide conformers [Svozil, Kalina, Omelka and Schneider (2008) (DNA conformations and their sequence preferences. Nucleic Acids Res., 36:3690-3706)], respectively. Assembling the mutually interacting protein blocks and dinucleotide conformers into 'interaction matrices' revealed their correlations and conformer preferences at the interface relative to their occurrence outside the interface. The analyzed data demonstrated important differences between complexes of various types of proteins such as transcription factors and nucleases, distinct interaction patterns for the DNA minor groove relative to the major groove and phosphate and importance of water-mediated contacts. Water molecules mediate proportionally the largest number of contacts in the minor groove and form the largest proportion of contacts in complexes of transcription factors. The generally known induction of A-DNA forms by complexation was more accurately attributed to A-like and intermediate A/B conformers rare in naked DNA molecules.

MultiSETTER: web server for multiple RNA structure comparison.

BACKGROUND: Understanding the architecture and function of RNA molecules requires methods for comparing and analyzing their tertiary and quaternary structures. While structural superposition of short RNAs is achievable in a reasonable time, large structures represent much bigger challenge. Therefore, we have developed a fast and accurate algorithm for RNA pairwise structure superposition called SETTER and implemented it in the SETTER web server. However, though biological relationships can be inferred by a pairwise structure alignment, key features preserved by evolution can be identified only from a multiple structure alignment. Thus, we extended the SETTER algorithm to the alignment of multiple RNA structures and developed the MultiSETTER algorithm. RESULTS: In this paper, we present the updated version of the SETTER web server that implements a user friendly interface to the MultiSETTER algorithm. The server accepts RNA structures either as the list of PDB IDs or as user-defined PDB files. After the superposition is computed, structures are visualized in 3D and several reports and statistics are generated. CONCLUSION: To the best of our knowledge, the MultiSETTER web server is the first publicly available tool for a multiple RNA structure alignment. The MultiSETTER server offers the visual inspection of an alignment in 3D space which may reveal structural and functional relationships not captured by other multiple alignment methods based either on a sequence or on secondary structure motifs.

SETTER: web server for RNA structure comparison.

The recent discoveries of regulatory non-coding RNAs changed our view of RNA as a simple information transfer molecule. Understanding the architecture and function of active RNA molecules requires methods for comparing and analyzing their 3D structures. While structural alignment of short RNAs is achievable in a reasonable amount of time, large structures represent much bigger challenge. Here, we present the SETTER web server for the RNA structure pairwise comparison utilizing the SETTER (SEcondary sTructure-based TERtiary Structure Similarity Algorithm) algorithm. The SETTER method divides an RNA structure into the set of non-overlapping structural elements called generalized secondary structure units (GSSUs). The SETTER algorithm scales as O(n(2)) with the size of a GSSUs and as O(n) with the number of GSSUs in the structure. This scaling gives SETTER its high speed as the average size of the GSSU remains constant irrespective of the size of the structure. However, the favorable speed of the algorithm does not compromise its accuracy. The SETTER web server together with the stand-alone implementation of the SETTER algorithm are freely accessible at http://siret.cz/setter.

Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library.

Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized. The composition of the targeted GR ligand library was compared with a reference library that randomly samples chemical space. A random forest model was used to determine the biological activity of ligands, incorporating its applicability domain using conformal prediction. It was demonstrated that the GR library is significantly enriched with GR ligands compared to the random library. Furthermore, a prospective analysis demonstrated that Molpher successfully designed compounds, which were subsequently experimentally confirmed to be active on the GR. A collection of 34 potential new GR ligands was also identified. Moreover, an important contribution of this study is the establishment of a comprehensive workflow for evaluating computationally generated ligands, particularly those with potential activity against targets that are challenging to dock.

Automatic workflow for the classification of local DNA conformations.

BACKGROUND: A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and the enhancement of our understanding of the conformational dependencies in DNA are therefore important for full comprehension of DNA structural polymorphism. RESULTS: A detailed classification of local DNA conformations based on the technique of Fourier averaging was published in our previous work. However, this procedure requires a considerable amount of manual work. To overcome this limitation we developed an automatic classification method consisting of the combination of supervised and unsupervised approaches. A proposed workflow is composed of k-NN method followed by a non-hierarchical single-pass clustering algorithm. We applied this workflow to analyze 816 X-ray and 664 NMR DNA structures released till February 2013. We identified and annotated six new conformers, and we assigned four of these conformers to two structurally important DNA families: guanine quadruplexes and Holliday (four-way) junctions. We also compared populations of the assigned conformers in the dataset of X-ray and NMR structures. CONCLUSIONS: In the present work we developed a machine learning workflow for the automatic classification of dinucleotide conformations. Dinucleotides with unassigned conformations can be either classified into one of already known 24 classes or they can be flagged as unclassifiable. The proposed machine learning workflow permits identification of new classes among so far unclassifiable data, and we identified and annotated six new conformations in the X-ray structures released since our previous analysis. The results illustrate the utility of machine learning approaches in the classification of local DNA conformations.

Efficient RNA pairwise structure comparison by SETTER method.

MOTIVATION: Understanding the architecture and function of RNA molecules requires methods for comparing and analyzing their 3D structures. Although a structural alignment of short RNAs is achievable in a reasonable amount of time, large structures represent much bigger challenge. However, the growth of the number of large RNAs deposited in the PDB database calls for the development of fast and accurate methods for analyzing their structures, as well as for rapid similarity searches in databases. RESULTS: In this article a novel algorithm for an RNA structural comparison SETTER (SEcondary sTructure-based TERtiary Structure Similarity Algorithm) is introduced. SETTER uses a pairwise comparison method based on 3D similarity of the so-called generalized secondary structure units. For each pair of structures, SETTER produces a distance score and an indication of its statistical significance. SETTER can be used both for the structural alignments of structures that are already known to be homologous, as well as for 3D structure similarity searches and functional annotation. The algorithm presented is both accurate and fast and does not impose limits on the size of aligned RNA structures. AVAILABILITY: The SETTER program, as well as all datasets, is freely available from http://siret.cz/hoksza/projects/setter/.

Benchmark quantum-chemical calculations on a complete set of rotameric families of the DNA sugar-phosphate backbone and their comparison with modern density functional theory.

The DNA sugar-phosphate backbone has a substantial influence on the DNA structural dynamics. Structural biology and bioinformatics studies revealed that the DNA backbone in experimental structures samples a wide range of distinct conformational substates, known as rotameric DNA backbone conformational families. Their correct description is essential for methods used to model nucleic acids and is known to be the Achilles heel of force field computations. In this study we report the benchmark database of MP2 calculations extrapolated to the complete basis set of atomic orbitals with aug-cc-pVTZ and aug-cc-pVQZ basis sets, MP2(T,Q), augmented by ΔCCSD(T)/aug-cc-pVDZ corrections. The calculations are performed in the gas phase as well as using a COSMO solvent model. This study includes a complete set of 18 established and biochemically most important families of DNA backbone conformations and several other salient conformations that we identified in experimental structures. We utilize an electronically sufficiently complete DNA sugar-phosphate-sugar (SPS) backbone model system truncated to prevent undesired intramolecular interactions. The calculations are then compared with other QM methods. The BLYP and TPSS functionals supplemented with Grimme's D3(BJ) dispersion term provide the best tradeoff between computational demands and accuracy and can be recommended for preliminary conformational searches as well as calculations on large model systems. Among the tested methods, the best agreement with the benchmark database has been obtained for the double-hybrid DSD-BLYP functional in combination with a quadruple-ζ basis set, which is, however, computationally very demanding. The new hybrid density functionals PW6B95-D3 and MPW1B95-D3 yield outstanding results and even slightly outperform the computationally more demanding PWPB95 double-hybrid functional. B3LYP-D3 is somewhat less accurate compared to the other hybrids. Extrapolated MP2(D,T) calculations are not as accurate as the less demanding DFT-D3 methods. Preliminary force field tests using several charge sets reveal an almost order of magnitude larger deviations from the reference QM data compared to modern DFT-D3, underlining the challenges facing force field simulations of nucleic acids. As expected, inclusion of the solvent environment approximated by a continuum approach has a large impact on the relative stabilities of different backbone substates and is important when comparing the QM data with structural bioinformatics and other experimental data.

Understanding the role of land use for urban stormwater management in coastal waterways.

A holistic understanding of the quality and quantity of stormwater in the context of catchment land use plays a crucial role in stormwater management. This study investigated the quality and quantity of stormwater from forested, residential, industrial, and mixed land use areas. Water samples were collected from seven sites over two years at different stages of the runoff hydrograph using fixed sampling stations. Analysis of physicochemical and hydrological variables showed different patterns across the four land use types at various flow conditions highlighting the complex nature of stormwater quality influenced by catchment and rainfall characteristics. Mean concentrations of dissolved organic and oxidised nitrogen (DON and NOx-N) and dissolved organic and filterable reactive phosphorus (DOP and FRP) in stormwater from industrial, mixed-use and residential catchment types were statistically different from stormwater originating from a forested catchment. On average, residential, mixed-use and industrial catchments transported over 50 times more NOx-N to the receiving waters compared to forested catchments. Under high flow conditions, total phosphorus, FRP and total suspended solids (TSS) were mobilised, indicating that phosphorous export is directly related to sediment export regardless of the land use. The study outcomes contribute to the formulation of more effective stormwater management strategies to deal with the drivers of nutrients and TSS inputs resulting from modified land use types to minimise the urbanisation impacts on aquatic biota. In particular, the elevated dissolved nitrogen fractions from all the catchment types other than the forested catchment is a concern for receiving waters, as these can potentially impair water quality and impact the ecosystem health of downstream water bodies such as Intermittently Closed and Open Lakes or Lagoons (ICOLL). The stochastic nature of hydrology and corresponding nutrient loads should be prioritised in stormwater management action plans. However, as space limitations hinder the expansion of vegetation cover and retrofitting stormwater management devices, a paradigm shift in stormwater management is required to achieve the desired outcomes. The study outcomes further indicate that a one-size-fits-all approach to stormwater management may not deliver the desired outcomes, and a suite of tailor-made approaches targeting various flow conditions and catchment surface types is needed.

Understanding the role of base stacking in nucleic acids. MD and QM analysis of tandem GA base pairs in RNA duplexes.

Preceding NMR experiments show that the conformation of tandem GA base pairs, an important recurrent non-canonical building block in RNA duplexes, is context dependent. The GA base pairs adopt "sheared" N3(G)-N6(A), N2(G)-N7(A) geometry in the r(CGAG)(2) and r(iGGAiC)(2) contexts while switching to "imino" N1(G)-N1(A), O6(G)-N6(A) geometry in the r(GGAC)(2) and r(iCGAiG)(2) contexts (iC and iG stand for isocytosine and isoguanine, respectively). As base stacking is likely to be one of the key sources of the context dependence of the conformation of GA base pairs, we calculated base stacking energies in duplexes containing such base pairs, to see if this dependence can be predicted by stacking energy calculations. When investigating the context dependence of the GA geometry two different conformations of the same duplex were compared (imino vs. sheared). The geometries were generated via explicit solvent MD simulations of the respective RNA duplexes, while the subsequent QM energy calculations focused on base stacking interactions of the four internal base pairs. Geometrical relaxation of nucleobase atoms prior to the stacking energy computations has a non-negligible effect on the results. The stacking energies were derived at the DFT-D/6-311++G(3df,3pd) level. We show a rather good correspondence between the intrinsic gas-phase stacking energies and the NMR-determined GA geometries. The conformation with more favorable gas-phase stacking is in most cases the one observed in experiments. This correlation is not improved when including solvent effects via the COSMO method. On the other side, the stacking calculations do not predict the relative thermodynamic stability of duplex formation for different sequences.

The DNA and RNA sugar-phosphate backbone emerges as the key player. An overview of quantum-chemical, structural biology and simulation studies.

Knowledge of geometrical and physico-chemical properties of the sugar-phosphate backbone substantially contributes to the comprehension of the structural dynamics, function and evolution of nucleic acids. We provide a side by side overview of structural biology/bioinformatics, quantum chemical and molecular mechanical/simulation studies of the nucleic acids backbone. We highlight main features, advantages and limitations of these techniques, with a special emphasis given to their synergy. The present status of the research is then illustrated by selected examples which include classification of DNA and RNA backbone families, benchmark structure-energy quantum chemical calculations, parameterization of the dihedral space of simulation force fields, incorporation of arsenate into DNA, sugar-phosphate backbone self-cleavage in small RNA enzymes, and intricate geometries of the backbone in recurrent RNA building blocks. Although not apparent from the current literature showing limited overlaps between the QM, simulation and bioinformatics studies of the nucleic acids backbone, there in fact should be a major cooperative interaction between these three approaches in studies of the sugar-phosphate backbone.

BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning.

Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert knowledge. Although automation efforts are advancing, existing methods are either demonstrated on a small scale or are entirely proprietary. To predict the immunogenicity risk, the human-likeness of sequences can be evaluated using existing humanness scores, but these lack diversity, granularity or interpretability. Meanwhile, immune repertoire sequencing has generated rich antibody libraries such as the Observed Antibody Space (OAS) that offer augmented diversity not yet exploited for antibody engineering. Here we present BioPhi, an open-source platform featuring novel methods for humanization (Sapiens) and humanness evaluation (OASis). Sapiens is a deep learning humanization method trained on the OAS using language modeling. Based on an in silico humanization benchmark of 177 antibodies, Sapiens produced sequences at scale while achieving results comparable to that of human experts. OASis is a granular, interpretable and diverse humanness score based on 9-mer peptide search in the OAS. OASis separated human and non-human sequences with high accuracy, and correlated with clinical immunogenicity. BioPhi thus offers an antibody design interface with automated methods that capture the richness of natural antibody repertoires to produce therapeutics with desired properties and accelerate antibody discovery campaigns. The BioPhi platform is accessible at https://biophi.dichlab.org and https://github.com/Merck/BioPhi.

Novel D2/5-HT receptor modulators related to cariprazine with potential implication to schizophrenia treatment.

Schizophrenia is a serious mental disorder without a fully understood pathomechanism, but which involves dysregulation of neurotransmitters and their receptors. The best option for the management of schizophrenia comprises so-called multi-target ligands, similar to the third generation of neuroleptics. Dopamine type 2 receptors (D2Rs) are the main target in the treatment of schizophrenia, in particular for mitigation of the positive symptoms. Due to the high expression of 5-hydroxytryptamine type 3 receptors (5-HT3Rs) in human brain areas responsible for emotional behavior, motivation, and cognitive function, 5-HT3Rs represent a potential target for modulating the cognitive and negative symptoms of schizophrenia. Here we present the design, synthesis, and both in vitro and in vivo biological evaluation of 1,4-disubstituted aromatic piperazines. Screening of in vitro properties revealed the two most promising drug candidates (21 and 24) which were found to be potent D2Rs and moderate 5-HT3R antagonists, and which were forwarded to in vivo studies in Wistar rats. Considering toxicity, administration of the maximal feasible dose of 21 (2 mg/kg) did not produce any side effects. By contrast, the higher solubility of 24 led to revelation of mild and temporary side effects at the dose of 20 mg/kg. Importantly, both 21 and 24 showed facile crossing of the blood-brain barrier, even exerting higher levels in the brain in comparison to plasma. In a behavioral study using the acute amphetamine model of psychosis, we showed that compound 24 ameliorated both positive and negative effects of amphetamine including hyperlocomotion, social impairments, and disruption of prepulse inhibition. The effect of the highest dose (10 mg/kg) was comparable to the effect of the reference dose of aripiprazole (1 mg/kg).

Pharmacokinetic, pharmacodynamic, and behavioural studies of deschloroketamine in Wistar rats.

BACKGROUND AND PURPOSE: Deschloroketamine (DCK), a structural analogue of ketamine, has recently emerged on the illicit drug market as a recreational drug with a modestly long duration of action. Despite it being widely used by recreational users, no systematic research on its effects has been performed to date. EXPERIMENTAL APPROACH: Pharmacokinetics, acute effects, and addictive potential in a series of behavioural tests in Wistar rats were performed following subcutaneous (s.c.) administration of DCK (5, 10, and 30 mg·kg-1 ) and its enantiomers S-DCK (10 mg·kg-1 ) and R-DCK (10 mg·kg-1 ). Additionally, activity at human N-methyl-d-aspartate (NMDA) receptors was also evaluated. KEY RESULTS: DCK rapidly crossed the blood brain barrier, with maximum brain levels achieved at 30 min and remaining high at 2 h after administration. Its antagonist activity at NMDA receptors is comparable to that of ketamine with S-DCK being more potent. DCK had stimulatory effects on locomotion, induced place preference, and robustly disrupted PPI. Locomotor stimulant effects tended to disappear more quickly than disruptive effects on PPI. S-DCK had more pronounced stimulatory properties than its R-enantiomer. However, the potency in disrupting PPI was comparable in both enantiomers. CONCLUSION AND IMPLICATIONS: DCK showed similar behavioural and addictive profiles and pharmacodynamics to ketamine, with S-DCK being in general more active. It has a slightly slower pharmacokinetic profile than ketamine, which is consistent with its reported longer duration of action. These findings have implications and significance for understanding the risks associated with illicit use of DCK.

New psychoactive substances on dark web markets: From deal solicitation to forensic analysis of purchased substances.

The dark web scene has been drawing the attention of law enforcement agencies and researchers alike. To date, most of the published works on the dark web are based on data gained by passive observation. To gain a more contextualized perspective, a study was conducted in which three vendors were selected on the "Dream Market" dark web marketplace, from whom subsequently several new psychoactive substances (NPS) were ordered. All transactions were documented from the initial drug deal solicitation to the final qualitative analysis of all received samples. From the selected vendors, a total of nine NPS samples was obtained, all of which were analyzed by NMR, HRMS, LC-UV, and two also by x-ray diffraction. According to our analyses, four of the five substances offered under already known NPS names contained a different NPS. The selected vendors therefore either did not know about their product, or deliberately deceived the buyers. Furthermore, two of three obtained samples of purportedly novel NPS were identified as already documented substances sold under a different name. However, the third characterized substance sold as "MPF-47700" was a novel, yet uncharacterized, NPS. Finally, we received a single undeclared substance, later identified as 5F-ADB. In addition to chemical analysis of the nine obtained NPS samples, the methodology used also yielded contextual information about the accessibility of NPS on the dark web, the associated purchase process, and the modus operandi of three NPS vendors. Direct participation in dark web marketplaces seems to provide additional layers of information useful for forensic studies.