โ Scribed by Daniel R. Pilipauskas
No coin nor oath required. For personal study only.
The traditional way of identifying a potential new drug is to synthesize and test one candidate at a time. Design and optimization of reaction and workup conditions for each new molecule are accomplished through experimentation varying one-variable-at-a-time (OVAT). While this approach of drug discovery has been extremely successful, market pressure to discover and bring new therapies to the customer in half the time is forcing pharmaceutical organizations to look for new ways to find active compounds and optimize series leads. Companies are now using combinatorial chemistry to rapidly synthesize and screen hundreds to thousands of compounds to identify lead candidates and synthesizing hundreds more variations of the lead structure to optimize activity. What has not changed is the need for reliable chemical transformations that will perform for a wide range of compounds. Considerable time is still being expended designing and validating these transformations before the parallel syntheses can begin. The challenge still being faced is reducing the time between synthesis design and validated chemistry. The goal of validated chemistry is achieved when sufficient experimental information is obtained to permit the identification of reaction conditions or variables that have significant influence on yield and purity of the chemical transformation. Reaching this level of understanding may be shortened considerably by using experiment designs that can take advantage of the parallel experimentation capabilities that the combinatorial chemistry field has supplied. Experiment designs that are more suitable for parallel experimentation and provide more information than OVAT experiments are the factorial designs. These designs involve the variation of all of the studied variables in a systematic manner. The outcome of these experiments are quantitative estimates of the influence of each variable, the identification of variable interactions (synergy), the estimation of experimental noise (error estimates), and polynomial models that can be used to optimize the chemical transformation. Because of the structure of these experiment designs, additional experiments run in the future can be added to the original design to extract additional information from the combined set. This last feature removes the need to commit to a large number of runs before sufficient knowledge about the chemistry is known. A recent example from the combinatorial chemistry literature is used to illustrate the features of factorial and fractional factorial designs, and to demonstrate the benefits of using these types of experiments. Graphical analysis of the data is used to illustrate that a formal training in statistics is not needed to take advantage of these designs.
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