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Understanding the sorption of pharmaceuticals to sewage sludge during waste water treatment processes is important for understanding their environmental fate and in risk assessments. The degree of sorption is defined by the sludge/water partition coefficient (Kd). Experimental Kd values (n = 297) for active pharmaceutical ingredients (n = 148) in primary and activated sludge were collected from literature. The compounds were classified by their charge at pH 7.4 (44 uncharged, 60 positively and 28 negatively charged, and 16 zwitterions). Univariate models relating log Kd to log Kow for each charge class showed weak correlations (maximum R2 = 0.51 for positively charged) with no overall correlation for the combined dataset (R2 = 0.04). Weaker correlations were found when relating log Kd to log Dow. Three sets of molecular descriptors (Molecular Operating Environment, VolSurf and ParaSurf) encoding a range of physico-chemical properties were used to derive multivariate models using stepwise regression, partial least squares and Bayesian artificial neural networks (ANN). The best predictive performance was obtained with ANN, with R2 = 0.62–0.69 for these descriptors using the complete dataset. Use of more complex Vsurf and ParaSurf descriptors showed little improvement over Molecular Operating Environment descriptors. The most influential descriptors in the ANN models, identified by automatic relevance determination, highlighted the importance of hydrophobicity, charge and molecular shape effects in these sorbate-sorbent interactions. The heterogeneous nature of the different sewage sludges used to measure Kd limited the predictability of sorption from physico-chemical properties of the pharmaceuticals alone. Standardization of test materials for the measurement of Kd would improve comparability of data from different studies, in the long-term leading to better quality environmental risk assessments., Graphical abstract Image 1, Highlights • Understanding sorption of pharmaceuticals to sludge is important in risk assessment. • Predicting sorption on molecular properties limited by heterogeneous nature of sludge • Models based only on the partition coefficient gave poor predictive models. • Non-linear artificial neural network models improve predictability. • Descriptors that influenced sorbate-sorbent interactions were identified.

Author(s): Fechner, Uli; de Graaf, Chris; Torda, Andrew E; Gussregen, Stefan; Evers, Andreas; Matter, Hans; Hessler, Gerhard; Richmond, Nicola J; Schmidtke, Peter; Segler, Marwin HS; Waller, Mark P; Pleik, Stefanie; Shea, Joan-Emma; Levine, Zachary; Mullen, Ryan; van den Broek, Karina; Epple, Matthias; Kuhn, Hubert; Truszkowski, Andreas; Zielesny, Achim; Fraaije, Johannes Hans; Gracia, Ruben Serral; Kast, Stefan M; Bulusu, Krishna C; Bender, Andreas; Yosipof, Abraham; Nahum, Oren; Senderowitz, Hanoch; Krotzky, Timo; Schulz, Robert; Wolber, Gerhard; Bietz, Stefan; Rarey, Matthias; Zimmermann, Markus O; Lange, Andreas; Ruff, Manuel; Heidrich, Johannes; Onlia, Ionut; Exner, Thomas E; Boeckler, Frank M; Bermudez, Marcel; Firaha, Dzmitry S; Holloczki, Oldamur; Kirchner, Barbara; Tautermann, Christofer S; Volkamer, Andrea; Eid, Sameh; Turk, Samo; Rippmann, Friedrich; Fulle, Simone; Saleh, Noureldin; Saladino, Giorgio; Gervasio, Francesco L; Haensele, Elke; Banting, Lee; Whitley, David C; Oliveira Santos, Jana Sopkova-de; Bureau, Ronan; Clark, Timothy; Sandmann, Achim; Lanig, Harald; Kibies, Patrick; Heil, Jochen; Hoffgaard, Franziska; Frach, Roland; Engel, Julian; Smith, Steven; Basu, Debjit; Rauh, Daniel; Kohlbacher, Oliver; Boeckler, Frank M; Essex, Jonathan W; Bodnarchuk, Michael S; Ross, Gregory A; Finkelmann, Arndt R; Goller, Andreas H; Schneider, Gisbert; Husch, Tamara; Schutter, Christoph; Balducci, Andrea; Korth, Martin; Ntie-Kang, Fidele; Gunther, Stefan; Sippl, Wolfgang; Mbaze, Luc Meva'a

A 4.1μs molecular dynamics simulation of the NR4A1 (hNur77) apo-protein has been undertaken and a previously undetected druggable pocket has become apparent that is located remotely from the ‘traditional’ nuclear receptor ligand-binding site. A NR4A1/bis-indole ligand complex at this novel site has been found to be stable over 1 μs of simulation and to result in an interesting conformational transmission to a remote loop that has the capacity to communicate with a NBRE within a RXR-α/NR4A1 heterodimer. Several features of the simulations undertaken indicate how NR4A1 can be affected by alternate-site modulators., PLoS ONE, 10 (8), ISSN:1932-6203

TREPAN is an algorithm for the extraction of comprehensible rules from trained neural networks. The method has been applied successfully to biological sequence (bioinformatics) problems. It has now been extended to handle chemoinformatics (QSAR) datasets. The method has been shown to have advantages over traditional symbolic rule induction methods such as C5. Results obtained for bioinformatics and chemoinformatics problems using the TREPAN algorithm are presented., TREPAN je algoritam za izlučivanje razumljivih pravila iz neuronskih mreža nakon provedenoga postupka učenja. Metoda je uspješno primjenjivana na probleme u bioinformatici, za analizu bioloških sekvencija. Primjena TREPAN metode sada se proširuje i na analizu skupova podataka u kemoinformatici (QSAR). Pokazano je da metoda ima prednosti u odnosu na uobičajene postupke koji se rabe za indukciju simboličkih pravila poput metode C5. Prikazani su rezultati koji su dobiveni u analizi bioinformatičkih i kemoinformatičkih problema s pomo}u algoritma TREPAN.