The identification of small-molecule modulators of protein function, and the process of transforming these into high-content lead series, are key activities in modern drug discovery. The decisions taken during this process have far-reaching consequences for success later in lead optimization and even more crucially in clinical development. Recently, there has been an increased focus on these activities due to escalating downstream costs resulting from high clinical failure rates. In addition, the vast emerging opportunities from efforts in functional genomics and proteomics demands a departure from the linear process of identification, evaluation and refinement activities towards a more integrated parallel process. This calls for flexible, fast and cost-effective strategies to meet the demands of producing high-content lead series with improved prospects for clinical success.

The normal plasma protein serum amyloid P component (SAP) binds to fibrils in all types of amyloid deposits, and contributes to the pathogenesis of amyloidosis. In order to intervene in this process we have developed a drug, R-1-[6-[R-2-carboxy-pyrrolidin-1-yl]-6-oxo-hexanoyl]pyrrolidine-2-carboxylic acid, that is a competitive inhibitor of SAP binding to amyloid fibrils. This palindromic compound also crosslinks and dimerizes SAP molecules, leading to their very rapid clearance by the liver, and thus produces a marked depletion of circulating human SAP. This mechanism of drug action potently removes SAP from human amyloid deposits in the tissues and may provide a new therapeutic approach to both systemic amyloidosis and diseases associated with local amyloid, including Alzheimer's disease and type 2 diabetes.

A computer-based method was developed for rapid and automatic identification of potential "frequent hitters". These compounds show up as hits in many different biological assays covering a wide range of targets. A scoring scheme was elaborated from substructure analysis, multivariate linear and nonlinear statistical methods applied to several sets of one and two-dimensional molecular descriptors. The final model is based on a three-layered neural network, yielding a predictive Matthews correlation coefficient of 0.81. This system was able to correctly classify 90% of the test set molecules in a 10-times cross-validation study. The method was applied to database filtering, yielding between 8% (compilation of trade drugs) and 35% (Available Chemicals Directory) potential frequent hitters. This filter will be a valuable tool for the prioritization of compounds from large databases, for compound purchase and biological testing, and for building new virtual libraries.

A computer-based method has been developed for prediction of the hERG (human ether-à-go-go related gene) K(+)-channel affinity of low molecular weight compounds. hERG channel blockage is a major concern in drug design, as such blocking agents can cause sudden cardiac death. Various techniques were applied to finding appropriate molecular descriptors for modeling structure-activity relationships: substructure analysis, self-organizing maps (SOM), principal component analysis (PCA), partial least squares fitting (PLS), and supervised neural networks. The most accurate prediction system was based on an artificial neural network. In a validation study, 93 % of the nonblocking agents and 71 % of the hERG channel blockers were correctly classified. This virtual screening method can be used for general compound-library shaping and combinatorial library design.

The GlyT1 transporter has emerged as a key novel target for the treatment of schizophrenia. Herein, we report on the optimization of the 2-alkoxy-5-methylsulfonebenzoylpiperazine class of GlyT1 inhibitors to improve hERG channel selectivity and brain penetration. This effort culminated in the discovery of compound 10a (RG1678), the first potent and selective GlyT1 inhibitor to have a beneficial effect in schizophrenic patients in a phase II clinical trial.

Erythromycin, avermectin and rapamycin are clinically useful polyketide natural products produced on modular polyketide synthase multienzymes by an assembly-line process in which each module of enzymes in turn specifies attachment of a particular chemical unit. Although polyketide synthase encoding genes have been successfully engineered to produce novel analogues, the process can be relatively slow, inefficient, and frequently low-yielding. We now describe a method for rapidly recombining polyketide synthase gene clusters to replace, add or remove modules that, with high frequency, generates diverse and highly productive assembly lines. The method is exemplified in the rapamycin biosynthetic gene cluster where, in a single experiment, multiple strains were isolated producing new members of a rapamycin-related family of polyketides. The process mimics, but significantly accelerates, a plausible mechanism of natural evolution for modular polyketide synthases. Detailed sequence analysis of the recombinant genes provides unique insight into the design principles for constructing useful synthetic assembly-line multienzymes.

Pyrimidine alkynes can be transformed into the corresponding annulated pyridines efficiently in flow. The superheating of organic solvents far beyond their boiling point enables toxic and difficult to workup solvents such as nitrobenzene or chlorobenzene, which are usually employed for these reactions, to be replaced by less harmful ones like toluene. The relative rate of reactivity for a series of structurally close starting materials was investigated and a scalable flow process was developed, providing facile access to a series of novel annulated pyridine building blocks. The effect of thermal volume expansion of solvents under superheated conditions was found to be significant and influenced the residence times considerably. To obtain meaningful and accurate residence times, flow rates need to be corrected for volume expansion. To avoid confusion with residence times, tR, calculated from nominal flow rates, we would propose to use for such corrected residence times the term effective residence time, tR,eff.

Drug discovery is a multifaceted endeavor encompassing as its core element the generation of structure-activity relationship (SAR) data by repeated chemical synthesis and biological testing of tailored molecules. Herein, we report on the development of a flow-based biochemical assay and its seamless integration into a fully automated system comprising flow chemical synthesis, purification and in-line quantification of compound concentration. This novel synthesis-screening platform enables to obtain SAR data on b-secretase (BACE1) inhibitors at an unprecedented cycle time of only 1 h instead of several days. Full integration and automation of industrial processes have always led to productivity gains and cost reductions, and this work demonstrates how applying these concepts to SAR generation may lead to a more efficient drug discovery process.