Box 2.1 The correlation coefficient, r
2.4 HIGH THROUGHPUT EXPERIMENTS
A revolution happened in the pharmaceutical industry in the 1990s that was to have far-reaching consequences. The revolution, called ‘combi- natorial chemistry’, took place in the medicinal chemistry departments
of the research divisions. Hitherto, new compounds for screening as potential new drugs had been synthesized on an individual basis usually based on some design concept such as similarity to existing successful compounds, predictions from a mathematical model or expected inter- action with a biological molecule based on molecular modelling. The ideas behind combinatorial chemistry were inspired by the existence of machines which could automatically synthesize polypeptides (small pro- teins) and latterly sequence them.
These processes were modified so that nonpeptides could be automati- cally synthesized since, although peptides often show remarkable biolog- ical properties, they are poor candidates for new drugs since there are problems with delivery and stability in the body. We routinely destroy proteins as fuel and the body is well prepared to identify and elimi- nate ‘foreign’ molecules such as peptides and proteins. Combinatorial chemistry strategies began with the synthesis of mixtures of compounds, at first a few tens or hundreds but then progressing to millions, but soon developed into parallel synthesis which is capable of producing very large numbers of single compounds. The two approaches, mixtures and singles, are both used today to produce libraries or arrays of com- pounds suitable for testing. But, what about experimental design? At first it was thought that the production and subsequent testing of such large numbers of compounds was bound to produce the required results in a suitable timeframe and thus design would be unnecessary. A little contemplation of the numbers involved, however, soon suggested that this would not be the case. As an example, decapeptides (a very small peptide of 10 residues) built from the 20 naturally occurring amino acids would have 2010 different sequences, in other words 1.024× 1013 dif- ferent molecules [24]. An even more startling example is given by the relatively small protein chymotrypsinogen-B which is composed of 245 amino acid residues. There are 20245 possible sequences−5.65 × 10318 molecules. An estimate of the number of particles in the visible universe is 1088so there isn’t enough available to build even a single molecule of every possible sequence! [25] The result of the realization of the enor- mous potential of combinatorial chemistry soon led to the development of design strategies for ‘diverse’ and ‘focussed’ libraries which, as the names imply, are intended to explore molecular diversity or to home in on a particularly promising range of chemical structures. There is also the question of the size of libraries. At first it was thought that large libraries were best, since this would maximize the chances of finding a useful compound, but it soon became evident that there were associated costs with combinatorial libraries, i.e. the cost of screening, and thus
what was required was a library or screening collection that was large ‘enough’ but not too large [26].
So, what was the effect of this change in compound production on pharmaceutical research? As may be imagined, the effect was quite dra- matic. Biological tests (screens) which were able to handle, at most, a few tens of compounds a day were quite inadequate for the examina- tion of these new compound collections. With great ingenuity screening procedures were automated and designed to operate with small volumes of reagents so as to minimize the costs of testing. The new testing pro- cedures became know as High Throughput Screening (HTS) and even Ultra-HTS. The laboratory instrument suppliers responded with greater automation of sample handling and specialist companies sprang up to supply robotic systems for liquid and solid sample handling. A typical website (http://www.htscreening.net/home/) provides numerous exam- ples in this field.
The ‘far-reaching’ consequences of combinatorial chemistry and HTS are now spreading beyond the pharmaceutical industry. The agrochem- icals industry, which faces similar challenges to pharmaceuticals, was an obvious early adopter and other specialist chemicals businesses are now following suit. Other materials such as catalysts can also benefit from these approaches and academic institutions are now beginning to pursue this approach (e.g. http://www.hts.ku.edu/). The whole process of combinatorial sample production and automated HTS is likely to be an important part of scientific research for the foreseeable future.
2.5 SUMMARY
The concepts underlying experimental design are to a great extent ‘common sense’ although the means to implement them may not be quite so obvious. The value of design, whether applied to an individ- ual experiment or to the construction of a training set, should be clear from the examples shown in this chapter. Failure to apply some sort of design strategy may lead to a set of results which contain suboptimal information, at best, or which contain no useful information at all, at worst. Various design procedures may be applied to individual exper- iments, as indicated in the previous sections, and there are specialist reports which deal with topics such as synthesis [6]. A detailed review of design strategies which may be applied to the selection of compounds has been reported by Pleiss and Unger [27]. The development of combi- natorial chemistry and its effect on compound screening to produce HTS
methods has had a dramatic effect on pharmaceutical and agrochemical research which is now finding its way into many other industries and, eventually, many other areas of scientific research.
In this chapter the following points were covered: 1. the value of experimental design;
2. experimental factors – controlled and uncontrolled;
3. replication, randomization, experimental blocks and balance in design;
4. Latin squares, factorial and fractional factorial design; 5. main effects, interaction effects and the aliasing of effects; 6. variance, covariance and correlation;
7. the balance between maximization of variance and minimization of covariance;
8. D-optimal design and the sequential simplex for compound selec- tion;
9. Combinatorial chemistry and high throughput screening.
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