Removal of Trace Organic Contaminants by Parallel Operation of Reverse Osmosis and Granular Activated Carbon for Drinking Water Treatment.

In response to increasingly stringent restrictions for drinking water quality, a parallel operation of two common technologies, low-pressure reverse osmosis (LPRO) and activated carbon filtration (ACF), was investigated in a comprehensive five-month pilot study for the removal of 32 typical trace organic contaminants (TrOCs) from Rhine bank filtrates employing a semi- technical plant. TrOCs have been divided into three groups: polyfluorinated aliphatic compounds; pharmaceuticals, pesticides and metabolites; in addition to volatiles, nitrosamines and aminopolycarboxylic acids, which were also examined. The net pressure behavior, normalized salt passage and rejection of TrOCs by LPRO were investigated and compared with ACF operation. In addition, autopsies from the leading and last membrane modules were performed using adenosine triphosphate (ATP), total organic carbon (TOC), ICP-OES and SEM-EDX techniques. Generally, rather stable LPRO membrane performance with limited membrane fouling was observed. TrOCs with a molecular weight of ≥ 150 Da were completely retained by LPRO, while the rejection of di- and trichloro compounds improved as the filtration progressed. ACF also showed significant removal for most of the TrOCs, but without desalination. Accordingly, the ACF and LPRO can be operated in parallel such that the LPRO permeate and the ACF-treated bypass can be mixed to produce drinking water with adjustable hardness and significantly reduced TrOCs.

Thrombin inhibitors identified by computer-assisted multiparameter design.

Here, we present a series of thrombin inhibitors that were generated by using powerful computer-assisted multiparameter optimization process. The process was organized in design cycles, starting with a set of randomly chosen molecules. Each cycle combined combinatorial synthesis, multiparameter characterization of compounds in a variety of bioassays, and algorithmic processing of the data to devise a set of compounds to be synthesized in the next cycle. The identified lead compounds exhibited thrombin inhibitory constants in the lower nanomolar range. They are by far the most selective synthetic thrombin inhibitors, with selectivities of >100,000-fold toward other proteases such as Factor Xa, Factor XIIa, urokinase, plasmin, and Plasma kallikrein. Furthermore, these compounds exhibit a favorable profile, comprising nontoxicity, high metabolic stability, low serum protein binding, good solubility, high anticoagulant activity, and a slow and exclusively renal elimination from the circulation in a rat model. Finally, x-ray crystallographic analysis of a thrombin-inhibitor complex revealed a binding mode with a neutral moiety in the S1 pocket of thrombin.

Genetic algorithm for the design of molecules with desired properties.

The design of molecules with desired properties is still a challenge because of the largely unpredictable end results. Computational methods can be used to assist and speed up this process. In particular, genetic algorithms have proved to be powerful tools with a wide range of applications, e.g. in the field of drug development. Here, we propose a new genetic algorithm that has been tailored to meet the demands of de novo drug design, i.e. efficient optimization based on small training sets that are analyzed in only a small number of design cycles. The efficiency of the design algorithm was demonstrated in the context of several different applications. First, RNA molecules were optimized with respect to folding energy. Second, a spinglass was optimized as a model system for the optimization of multiletter alphabet biopolymers such as peptides. Finally, the feasibility of the computer-assisted molecular design approach was demonstrated for the de novo construction of peptidic thrombin inhibitors using an iterative process of 4 design cycles of computer-guided optimization. Synthesis and experimental fitness determination of only 600 different compounds from a virtual library of more than 10(17) molecules was necessary to achieve this goal.