Product-based optimization

Temperature, pH, and feed rate are often measured and controlled. Dissolved oxygen (DO) can be controlled using aeration, agitation, pressure, and/or feed rate. Oxygen consumption and carbon dioxide formation can be measured in the outgoing air to provide insight into the metaboHc status of the microorganism. No rehable on-line measurement exists for biomass, substrate, or products. Most optimization is based on empirical methods simulation of quantitative models may provide more efficient optimization of fermentation. 

The reactions are still most often carried out in batch and semi-batch reactors, which implies that time-dependent, dynamic models are required to obtain a realistic description of the process. Diffusion and reaction in porous catalyst layers play a central role. The ultimate goal of the modehng based on the principles of chemical reaction engineering is the intensification of the process by maximizing the yields and selectivities of the desired products and optimizing the conditions for mass transfer. 

Several simulation-based optimization models in the context of supply chain management can be found e.g. in the area of supply chain network optimization (Preusser et al. 2005) or to simulate rescheduling of production facing demand uncertainty or unplanned shut-downs (Tang/Grubbstrom 2002 Neuhaus/Giinther 2006). A basic approach of simulation-based optimization is presented by Preusser et al. 2005, p. 98 illustrated in fig. 24. 

In literature simulation and simulation-based optimization is focused on supply chain management areas such as production (Smith 2003 Wullink et al. 2004), inventory (Siprelle et al. 2003), transportation or integrated supply chain networks (Preusser et al. 2005). 

These simple examples can only show the opportunity to further extend the value chain planning model usage for decision support integrated in simulation-based optimization architecture. There is an opportunity for further industry-oriented research to better understand production-price dynamics in different types of value chain networks. 

In general, reagent-based selection is much faster and more convenient to execute in the laboratory as compared with the product-based selection. On the other hand, the latter strategy usually provides more accurate results. There exists a potential to combine both approaches to achieve more optimal results, particularly in the case of large exploratory virtual combinatorial libraries, for which mass random synthesis and screening are not economically feasible. In this article, we demonstrated the usefulness of property-based approach for selection of optimal GPCR ligands. 

Natural products continue to play a dominant role in the discovery of leads for the development of drugs to treat human diseases. Such chemical agents have traditionally also played a major role in drug discovery and still constitute a prolific source of novel chemotypes or pharmacophores for medicinal chemistry. Natural product-based scaffolds find key importance in drug discovery as well as in optimizing chemical diversity... 

Arguably, given its immediate and direct impact on public health, the pharmaceutical industry has additional reasons to achieve a higher level of technological execution where product quality is assured by effective automated systems and where variability sources are understood and minimized. Even removing this motivation, this industry should embrace model-based optimization enthusiastically, since it has reduced cost and accelerated product development across many other industries. 

To summarize, library design involves choices of diversity vs. similarity, product based vs. reactant based, and single objective vs. multiobjective optimizations. Chemoinformatics tools, such as various predictive models and chemoinformatics infrastructures, can be utilized to facilitate the selection process of library design. 

The compound selection methods described thus far can be used to select compounds for screening from an in-house collection, or to select which compounds to purchase from an external supplier. In combinatorial library design, however, it is necessary to select subsets of reactants for actual synthesis. The two main strategies for combinatorial library design are reactant-based selection and product-based selection. In reactant-based selection, optimized subsets of reactants are selected without consideration of the products that will result and any of the compound selection methods already identified can be used. An early example of reactant-based design is that already described by Martin and colleagues which is based on experimental design and where diverse subsets of reactants were selected for the synthesis of peptoid libraries [1]. 

Product-based selection is much more computationally demanding than reactant-based selection, however it has been shown that better optimized libraries can result [60, 61], especially when the aim is to optimize the properties of a library as a whole, such as diversity or the distribution of physicochemical properties. In addition, product-based selection is usually more appropriate for focused libraries which require consideration of the properties of the resulting products. 

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