Iterative assay

A useful method of weighting is through the use of an iterative reweighted least squares algorithm. The first step in this process is to fit the data to an unweighted model. Table 11.7 shows a set of responses to a range of concentrations of an agonist in a functional assay. The data is fit to a three-parameter model of the form... 

If serum protein or surfactant is added to the acceptor wells, then, in general, p[A l> and P r> are not the same, even under iso-pH conditions. The acceptor-to-donor permeability needs to be solved by performing a separate iso-pH assay, where the serum protein or surfactant is added to the donor side, instead of the acceptor side. The value of Pe is determined, using Eq. (7.20), and used in gradient-pH cases in place of P A /) , as described in the preceding section. The gradient-pH calculation procedure is iterative as well. 

From a DMPK perspective, a common goal is to be able to compare multiple compounds based on their absorption, distribution, metabolism and excretion (ADME) properties as well their preclinical PK properties [8, 12-22]. Therefore, lead optimization typically is performed as an iterative process that uses the DMPK data to select structural modifications that are then tested to see whether the DMPK properties of the series have been improved. This iterative process is shown schematically in Fig. 13.2. Clearly an important element for the successful lead optimization of a series of NCEs is the ability to perform the DMPK assays in a higher throughput manner. The focus of this chapter will be to discuss ways that mass spectrometry (MS), particularly HPLC-MS/MS can be used to support the early PK studies for NCEs in a higher throughput manner. 

Fig. 13.2 Schematic diagram showing the various stages and the iterative steps involved in the lead optimization process from a DMPK perspective. This schematic represents the iterative process that is an important part of the lead optimization process. The in vitro and in vivo screens refer to DMPK assays. Reprinted from [12], with permission from Taylor and Francis Group.

A common problem experienced under the current automation paradigm, with the demand for greater results in shorter iteration cycles (1-2 weeks), is that screeners are required to collect data from several single assay workstations, often run on different software platforms. With the demand for results on more compounds per week, the screeners must process more samples through the assays, each with fewer data points and replicates. This trade off in quality for quantity can result in lower overall data fidelity. 

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