Cell penetrating peptoids (CPPos) are potent mimics of the corresponding cell penetrating peptides (CPPs). The synthesis of diverse oligomeric libraries that display a variety of backbone scaffolds and side-chain appendages are a very promising source of novel CPPos, which can be used to either target different cellular organelles or even different tissues and organs. In this study we established the submonomer-based solid phase synthesis of a “proof of principle” peptoid library in IRORI MiniKans to expand the amount for phenotypic high throughput screens of CPPos. The library consisting of tetrameric peptoids [oligo(N-alkylglycines)] was established on Rink amide resin in a split and mix approach with hydrophilic and hydrophobic peptoid side chains. All CPPos of the presented library were labeled with rhodamine B to allow for the monitoring of cellular uptake by fluorescent confocal microscopy. Eventually, all the purified peptoids were subjected to live cell imaging to screen for CPPos with organelle specificity. While highly charged CPPos enter the cells by endocytosis with subsequent endosomal release, critical levels of lipophilicity allow other CPPos to specifically localize to mitochondria once a certain lipophilicity threshold is reached.

2-Mercaptobenzoimidazoles are an important class of heterocycles showing biological activity in diverse therapeutic applications.

Due to the arising resistance of common drugs targeting the Hedgehog signaling pathway, the identification of new compound classes with inhibitory effect is urgently needed. We were able to identify -alkylated 2-mercaptobenzoimidazoles as a new compound class that exhibits Hedgehog signaling activity in a low micromolar range. The scope of the 2-mercaptobenzoimidazole motif has been investigated by the syntheses of diverse derivatives, revealing that the elongation of the linker unit and the exchange of particular substitution patterns are tolerable with respect to the activity of the compound class.

This paper addresses the problems of product lead time (PLT) formulation in the textile industry and proposed a methodology to formulate product lead time of textile fabric production at a textile factory using artificial neural networks. Analysis of the order fulfillment process flow of the textile company was conducted to identify the individual sequential processes that constitute product lead time. Feed forward multilayer perceptron (MLP) neural networks are developed to estimate the lead time of critical PLT processes with incomplete data and various non-linear time affecting factors. The networks are trained in a supervised manner using back propagation algorithm. The finalized neural network lead time estimation models are able to predict the lead time for each process with a good degree of accuracy and can be used as a decision making tool for quoting product lead time to customer.