Optimization in pharmaceuticals

Other applications of the previously described optimization techniques are beginning to appear regularly in the pharmaceutical literature. A literature search in Chemical Abstracts on process optimization in pharmaceuticals yielded 17 articles in the 1990-1993 time-frame. An additional 18 articles were found between 1985 and 1990 for the same narrow subject. This simple literature search indicates a resurgence in the use of optimization techniques in the pharmaceutical industry. In addition, these same techniques have been applied not only to the physical properties of a tablet formulation, but also to the biological properties and the in-vivo performance of the product [30,31]. In addition to the usual tablet properties the authors studied the following pharmacokinetic parameters (a) time of the peak plasma concentration, (b) lag time, (c) absorption rate constant, and (d) elimination rate constant. The graphs in Fig. 15 show that for the drug hydrochlorothiazide, the time of the plasma peak and the absorption rate constant could, indeed, be... 

G. Szepesi and K. Valko, Predicitin of initial HPLC conditions for selectivity optimizations in pharmaceutical analysis by an expert system approach, J. Chromatogr., 550 87-100 (1991). 

Lead Optimization in Pharmaceutical Development Molecular and Cellular Approaches... 

Lead Optimization in Pharmaceutical Development Molecular and Cellular Approaches Preclinical lead optimization models Sev. 1 or 2... 

Another example that focuses on the use of DSE analysis is to model chemical properties such as predicting the aqueous solubility of compounds." Aqueous solubility provides an example of a physicochemical property that can be addressed at the level of structurally derived chemical descriptors. Because the aqueous solubility of many compounds is known, an accurate and sufficiently large dataset can be accumulated for constructing and evaluating predictive models. In addition, problems surrounding solubility remain a significant issue for lead identification and optimization in pharmaceutical research. ... 

Method development remains the most challenging aspect of chiral chromatographic analysis, and the need for rapid method development is particularly acute in the pharmaceutical industry. To complicate matters, even structurally similar compounds may not be resolved under the same chromatographic conditions, or even on the same CSP. Rapid column equilibration in SFC speeds the column screening process, and automated systems accommodating multiple CSPs and modifiers now permit unattended method optimization in SFC [36]. Because more compounds are likely to be resolved with a single set of parameters in SFC than in LC, the analyst stands a greater chance of success on the first try in SFC [37]. The increased resolution obtained in SFC may also reduce the number of columns that must be evaluated to achieve the desired separation. 

In a tiny fraction of cases, a quick formula can be used. For most cases, the analysis uses an options tree, with one leaf per possible outcome. However, this falls prey to the curse of dimensionality —the number of leaves on the tree grows exponentially in the number of risk and decision dimensions considered. Thus only a limited, simple set of situations can be optimized in this way because one has to severely limit the decisions and risks that are considered. Tools available to help automate and simplify options analysis, widely used in pharmaceutical project evaluation, include Excel addons such as R1SK [11] and more graphically based solutions such as DPL [12]. Both of these support the creation and evaluation of decision trees and of influence diagrams Figure 11.2 shows a simple example of each of these. A primer in applied decision theory is Clemen s book Making Hard Decisions, other sources may be found in the website of James Vornov, Director of Clinical Research at Guildford Pharmaceuticals, a recent convert to decision theory for options analysis [13]. 

Within the realm of physical reality, and most important in pharmaceutical systems, the unconstrained optimization problem is almost nonexistent. There are always restrictions that the formulator wishes to place or must place on a system, and in pharmaceuticals, many of these restrictions are in competition. For example, it is unreasonable to assume, as just described, that the hardest tablet possible would also have the lowest compression and ejection forces and the fastest disintegration time and dissolution profile. It is sometimes necessary to trade off properties, that is, to sacrifice one characteristic for another. Thus, the primary objective may not be to optimize absolutely (i.e., a maxima or minima), but to realize an overall pre selected or desired result for each characteristic or parameter. Drug products are often developed by teaching an effective compromise between competing characteristics to achieve the best formulation and process within a given set of restrictions. 

One of the most widely used methods of experimental optimization in fields other than pharmaceutical technology is the evolutionary operation (EVOP). This technique is especially well suited to a production situation. The basic philosophy is that the production... 

The productivity of pharmaceutical R D in proportion to spend has fallen substantially to the point that its very existence is under threat [1,2]. It has been estimated that 93-96% of nominated candidate drugs fail at some stage in development [3] all attrition, from the start of compound optimization in the discovery phase of a project to launch on the market, is probably closer to 99%. Therefore, there is an urgent imperative to directly address the reasons for attrition, which from candidate drug nomination onwards can be divided into three broad categories [3-5] ... 

O Optimizing LC/MS Equipment to Increase Throughput in Pharmaceutical Analysis... 

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