Assay optimization

For pesticide residue immunoassays, matrices may include surface or groundwater, soil, sediment and plant or animal tissue or fluids. Aqueous samples may not require preparation prior to analysis, other than concentration. For other matrices, extractions or other cleanup steps are needed and these steps require the integration of the extracting solvent with the immunoassay. When solvent extraction is required, solvent effects on the assay are determined during assay optimization. Another option is to extract in the desired solvent, then conduct a solvent exchange into a more miscible solvent. Immunoassays perform best with water-miscible solvents when solvent concentrations are below 20%. Our experience has been that nearly every matrix requires a complete validation. Various soil types and even urine samples from different animals within a species may cause enough variation that validation in only a few samples is not sufficient. 

A lead is variously defined in the pharmaceutical industry as a compound derived from a hit with some degree of in vitro optimization (potency in primary assay, activity in functional and/or cellular assay), optimization of physical properties (solubility, permeability), and optimization of in vitro ADME properties (microsomal stability, CYP inhibition). Moreover, a lead must have established SAR/SPR around these parameters such that continued optimization appears possible. A lead may also have preliminary PK and in vivo animal model data. However, it is the task of the lead optimization chemist to improve PK and in vivo activity to the levels needed for identification of a clinical candidate. 

The evaluation of results of assay optimization experiments such as those described above (see Section 6.4.2.1) also provides valuable information about enzymatic kinetic behavior. For example, the results shown in Figures 6.45 and 6.46 already provide information on enzymatic activity at each time point. In general, when evaluating enzyme kinetics, assays are designed to yield a measured conversion close to initial velocity.32... 

Samsonova, J.V., N.A. Uskova, A.N. Andresyuk, et al. 2004. Biacore biosensor immunoassay for 4-nonylphenols Assay optimization and applicability for shellfish analysis. Chemosphere 57 975-985. 

Tschmelak, J., G. Proll, and G. Gauglitz. 2005. Optical biosensor for pharmaceuticals, antibiotics, hormones, endocrine disrupting chemicals and pesticides in water Assay optimization process for estrone as example. Talanta 65 313-323. 

Mercader, J.V. and A. Montoya. 1999. Development of monoclonal ELISAs for azinphos-methyl. II. Assay optimization and water sample analysis. J. Agric. Food Chem. 47 1285-1293. 

The major reaction principles that form the basis of the genotyping technologies available today are primer extension, ligation, and hybridization. Many of the methods for SNP genotyping rely on PCR amplification of the sequence of interest prior to allele determination. Primer design and assay optimization for multiplexed and reproducible genotyping of SNPs in large sample sets have become major bottle necks in most methods used today. 

In the following sections, both membrane preparation and SPA assay optimization for chemokine-chemokine receptor interactions will be described. 

Gain knowledge of kinetic and mechanistic parameters as guidelines for assay optimization. 

FIGURE 1.3 Assay optimization cycle and typical test parameters. 

We found no significant difference in the response ratio between cells plated at 2,500, 5,000 or 10,000 cells per well. However, when the cells were plated at 20,000 cells per well, we observed a lower response ratio due to a higher unstimulated background. We chose a cell density of 10,000 cells for all subsequent assay optimization steps because it revealed the least well-to-well variation compared to the other cell densities. 

Kayyali, U.S., Moore, T.B., Randall, J.C., Richardson, R.J. (1991). Neurotoxic esterase (NTE) assay optimized conditions based on detergent-induced shifts in the phenol/4-aminoantipyrine chromophore spectrum. J. Anal. Toxicol. 15 86-9. 

Peers, S., MiUigan, A., and Harrison, P. (2000). Assay optimization and regulation of urease activity in two marine diatoms. J. Phycol. 36, 523-528. 

Clayton,. R., Jr., and Ahmed, S. I. (1986). Detection of glutamate synthase (GOGAT) activity in phytoplankton Evaluation of cofactors and assay optimization. Mar. Ecol. Prog. Ser. 32, 115—122. 

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