Data-information-knowledge cycle

At the point a compound is recognized and then considered for potential pharmaceutic or therapeutic usefulness, researchers will be both consumers of and contributors to the data-information-knowledge cycle that characterizes science. Initially, in the synthesis and purification phase of drug development, information about the compound s chemistry and physical properties may be both sought and created. Whether or not the compound has been of interest to other researchers may be determined by searching public records of grant and contract awards and also by searching resources that cover preliminary and early research results. The patent status of the compound may need to be established. 

In the introduction to Part A we discussed the arch of knowledge [1] (see Fig. 28.1), which represents the cycle of acquiring new knowledge by experimentation and the processing of the data obtained from the experiments. Part A focused mainly on the first step of the arch a proper design of the experiment based on the hypothesis to be tested, evaluation and optimization of the experiments, with the accent on univariate techniques. In Part B we concentrate on the second and third steps of the arch, the transformation of data and results into information and the combination of information into knowledge, with the emphasis on multivariate techniques. 

A fundamental objective of a computer system applied to automate a pharmaceutical GMP operation is to ensure the quality attributes of the drug product are upheld throughout the manufacturing process. It is therefore important that quality-critical parameters are determined and approved early in the validation life cycle. The exercise should be undertaken to a written procedure with base information from the master product/production record file examined and quality-critical parameter values and limits documented and approved for the process and its operation. In addition, the process and instrument diagrams (P IDs) should be reviewed to confirm the measurement and control components that have a direct impact on the quality-critical parameters and data. This exercise should be carried out by an assessment team made up of user representatives with detailed knowledge of both the computer system application and process, and with responsibility for product quality, system operational use, maintenance, and project implementation. This exercise may be conducted as part of an initial hazard and operability study (HAZOP) and needs to confirm the quality-related critical parameters for use in (or referenced by) the computer control system URS. 

As noted above, step-scan FT-IR can provide a better time resolution than PA-IR spectroscopy for time-resolved studies, as well as full spectra at the desired resolution. On the other hand, its major limitation is that the phenomenon under study must be perfectly repeatable-information which often is not available before an experiment is carried out. Another problematic aspect to consider is that sufficient relaxation time must be allocated for the sample to return to its initial state between consecutive perturbations. Unfortunately, this parameter is also often not known a priori before the experiment is performed, and may risk artifacts appearing in the data. In contrast, a single perturbation is required in a PA-IR experiment to record the time-resolved data, eliminating the requirements of repeatability and an a priori knowledge of the relaxation time. PA-IR spectroscopy was used to assess directly the repeatability of the orientation/reorientation cycles for 5CB [27]. Table 13.1 shows the switch-on and switch-off time constants determined individually for a series of 300 consecutive reorientation cycles. As expected for this well-studied LC, the time constants did not evolve systematically as a function of the number of cycles. In this case, however, the repeatability was demonstrated experimentally and not only assumed, as is often necessary in step-scan studies. 

The Process Data Warehouse (PDW) has been designed to capture and analyze the traces of design processes products, process instantiations and their interdependencies. The artifacts (the technical system) to be designed and modified during the process are traced, and related to the processes which perform these modifications. From these semantically structured product and process traces, the relevant information can be extracted in an analysis step, and then reused in further process executions. This information can be presented to the experts as experience knowledge in order to solve the problems of later development cycles more easily, efficiently, and autonomously. 

A principal factor governing the operating cycle of ethylene steam crackers (ESC) is coke formation on the inside surfaces of the radiantly heated pyrolysis tubes. Steam is used as the carrier for the hydrocarbon feedstock as it is known empirically to minimise this coking. It is probable that the observed deposition is a net process representing the difference between formation and removal, primarily by thermal oxidation. A fundamental requirement of any detailed understanding of the overall processes involved, therefore, is knowledge of the oxidation behaviour of such deposits. Although several studies have been undertaken on various carbons considered to simulate ESC pyrolysis tube coke (e.g. ( )) no relevant information has been published for plant material. To provide these data, therefore, the oxidation behaviour of a coke formed on an ESC tube has now been examined in water vapour. 

Environmental Fate. Since ammonia is a key intermediate in the nitrogen cycle, the environmental fate of ammonia should be interpreted in terms of its involvement in this cycle. Information available on the environmental fate of ammonia is sufficient to define the basic trends, and data are available regarding the direction of changes in these trends resulting from changes in the key variables. There are many subtle facets of the fate of ammonia in the environment that depend on nature and its cycles. Thus, accurately predicting the environmental fate of ammonia is not possible with our present knowledge. 

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