Combinatorial Chemistry & High Throughput Screening - Volume 14, Issue 3, 2011

Nanotechnology refers to the creation of functional materials, devices, and systems through the control of matter at the nanometer scale, and the exploitation of novel phenomena and properties at that scale. Advances in nanotechnology will have tremendous impacts on every aspect of our society. The discovery and optimization of novel functional nanomaterials with unique properties require a time-consuming research efforts. Parallel reactions and screenings are deemed to be more efficient than conventional linear operations. Combinatorial chemistry has already revolutionized drug discovery and the discovery of materials, catalysts, and polymers. It has recently also made a significant impact on nanotechnology. In this issue of Combinatorial Chemistry and High Throughput Screening, we provide an overview of research progresses in the powerful combination between combinatorial chemistry and nanotechnology. The discovery of functional nanomaterials has been dramatically improved by introducing combinatorial methods. Such developments are exemplified by catalyst and nanosized catalyst discoveries described by Zhou, Gao and their coworkers. Glycobiology is an important frontier of research, glycosylated nanoparticles have unique biological activities and amenable for high throughput chemistry research. Dong has provided an update in this area. Application of nanoparticles in high throughput separation and analysis, especially in solidphase extraction is reviewed by Liang and Zhang. As in early days of combinatorial chemistry, analytical technology is key to the success of nano-combinatorial chemistry. Liu and Yan provided a timely review on analytical techniques for nanoparticle modifications and a comparison with techniques in solid-phase synthesis. Accompanied these developments, the screening of nanomaterials were facilitated by the development of many parallel screening technologies. These developments are highlighted by articles from Rajan and Milani and coworkers. Computation and modeling are crucial technologies for nanocombinatorial chemistry and associated biological investigations. Tropsa pioneered in this research effort and provided an excellent review on the progresses in this area. With rapid development of nanotechnology, there is rarely an area without being influenced by this technology. We expect that nano-combinatorial chemistry will play an even more important role in future.

Catalyst investigation is a typical “trial and error” process. In order to discover a good catalyst, people usually test a huge number of potential catalyst candidates. High throughput experimentation (HTE) is an efficient methodology for catalyst discovery and optimization. HTE is capable of synthesizing and testing hundreds of catalysts in a short period of time. And it is now becoming a widely applied tool in catalyst discovery and catalyst composition optimization. The development of high throughput experimentation in catalysis includes three steps, which are 1) design and synthesis of catalyst libraries, 2) design of the reactor system for catalytic reactions, and 3) product analysis and data reduction in a high throughput manner. Hence, in this mini-review, the recent developments of high throughput experimentation and these three steps are discussed. The review is focused on the research progress over the last few years.

The last decade has seen significant progresses in the application of combinatorial approaches and high throughput screening in photocatalyst discovery. This paper aims at providing a comprehensive review on the parallel synthesis and high throughput characterization of photocatalysts, including the development of instrumentation, strategy of experiment, preparation of libraries, high throughput screening technique and data analysis. The review ends with a summary of the remaining challenges and prospects on combinatorial photocatalyst discovery.

Over the past two decades, glycosylated nanoparticles (i.e., glyconanoparticles having sugar residues on the surface) received much attention for biomedical applications such as bioassays and targeted drug delivery. This minireview focuses on three aspects: (1) glycosylated gold nanoparticles, (2) glycosylated quantum dots, and (3) glyconanoparticles self-assembled from amphiphilic glycopolymers. The synthetic methods and the multivalent interactions between glyconanoparticles and lectins is shortly illustrated.

Due to the unique properties, such as their large surface to volume ratio and easy modification, nanomaterials have recently been studied as effective sorbents in the field of separation science. It has proven to be more effective and efficient to use nanoparticles (NPs) as a stationary phase in solid-phase extraction separation. In addition, NPs can be also used as buffer additives in capillary electrophoresis separation. This review highlights recent developments in High throughput separation methodologies employing nanomaterials such as carbon nanotubes, gold nanoparticles and magnetic NPs etc.

Chemical modifications of nanoparticle's (NP's) surfaces can be used to regulate their activities, remove their toxic effects, and enable them to perform desired functions. Similar to SPS samples, modified NPs also have smallmolecules on the surface of a solid support. The need to monitor synthesis, optimize reaction conditions, and characterize the products is quite similar in both situations. FTIR, NMR, MS and other analytical methods have been used as effective methods to analyze surface bound molecules and monitor organic reactions directly, or indirectly, on a solid phase of a resin or a NPs.

The objective of this paper is to examine the challenges and opportunities in high throughput screening of atomic scale chemistry via a technique known as atom probe tomography. While there are numerous methods that exist in the field of throughput screening, even at the nanoscale, most of the effort is on screening properties, rather than chemistry and/or structure. In this overview, we discuss the role atom probe tomography can have as a high throughput screening tool of atomic scale chemistry. Particular emphasis on the study of organic/biological materials is given along with the needs and challenges to make atom probe tomography more widespread in the field of combinatorial chemistry.

The aim of this review is to describe and to analyze the ingredients that are necessary in order to develop a robust and effective experimental approach for the high throughput characterization of protein-nanostructured surface interaction. In the first part of this paper we review the nanostructured surface synthesis methods that are potentially able to create nanostructured inorganic surface libraries. In the second part, we address another fundamental aspect consisting in the availability of high throughput proteins detection methods. We describe in details new emerging analytical tools compatible with nanostructured surfaces, analyzing different possible strategies, depending on the objective of the experiment and on the library format.

Evaluation of desired and undesired, biological effects of Manufactured NanoParticles (MNPs) is of critical importance for the future of nanotechnology. Experimental studies, especially toxicological, are time-consuming and costly, calling for the development of efficient computational tools capable of predicting biological events caused by MNPs from their structures and physical chemical properties. This mini-review assesses the potential of modern cheminformatics methods such as Quantitative Structure - Activity Relationship modeling to develop statistically significant and externally predictive models that can accurately forecast biological effects of MNPs from the knowledge of their physical, chemical, and geometrical properties. We discuss major approaches for model building and validation using both experimental and computed properties of nanomaterials. We consider two different categories of MNP datasets: (i) those comprising MNPs with diverse metal cores and organic decorations, for which experimentally measured properties can be used as particle's descriptors, and (ii) those involving MNPs possessing the same core (e.g., carbon nanotubes), but different surface-modifying organic molecules, for which computational descriptors can be calculated for a single representative of the decorative molecule. We illustrate those concepts with three case studies for which we successfully built and validated predictive models. In summary, this mini-review demonstrates that, analogous to conventional applications of QSAR modeling for the analysis of datasets of bioactive organic molecules, its application to modeling MNPs that we term Quantitative Nanostructure Activity Relationship (QNAR) modeling can be useful for (i) predicting activity profiles of novel MNPs solely from their representative descriptors and (ii) designing and manufacturing safer nanomaterials with desired properties.

Bing Yan got his Ph.D. from Columbia University in 1990 and carried out postdoctoral research at University of Cambridge, U.K. and University of Texas Medical School in Houston from 1990 to 1993. From 1993 to 2005, he worked at Novartis in New Jersey, Discovery Partners International in South San Francisco, and Bristol-Myers Squibb in Connecticut. Now he is a Full Member at Department of Chemical Biology and Therapeutics, St. Jude Children's Research Hospital in Memphis, Tennessee and Professor at Shandong University, China. He serves as Associate Editor for ACS Combinatorial Sciences (formerly Journal of Combinatorial Chemistry), published by American Chemical Society. He is also editor of book series “Critical Reviews in Combinatorial Chemistry” published by Francis & Taylor Group. He is engaged in research on the elucidation and regulation of nanoparticles' biological activities.