Application During Lead Identification

The model is only as good as the data used as input. Therefore, it is important to use a model in ways appropriate for the level of data that was available for its development. 

Recent publications discuss the utility of physiologically based pharmacokinetic models to predict the pharmacokinetics of discovery compounds in the rat [7, 9]. The folio vfing examples show the utility of applying these methods at an even earlier stage. 

During lead identification, multiple series are evaluated for the potential to yield drug-like compounds, that is, the appropriate combination of potency, physicochem- 

The extension of PBPK simulation to include pharmacodynamic endpoints is very powerful at this stage to guide the project team with respect to the key properties to consider for optimization, as demonstrated in the example below. 

The analysis was completed for 12 compounds for which protein binding, renal and hepatic clearances and microsomal data were available. Plasma concentration versus time profiles in the rat were also available for these compounds. The approach taken was to simulate the individual processes (metabolic clearance, renal clearance, distribution, pharmacological activity). The ability of the PBPK model to simulate the in vivo behavior of the compound was verified in the rat. Thus, the metabolic clearance of the compounds could be reasonably well simulated, based on microsomal data and assuming no binding to microsomes less than twofold deviation between the observed and predicted clearance was achieved for about eight of the 

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LC/MS Contributions. The use of LC/MS-based approaches has expanded rapidly in drug discovery during the past several years. Applications that range from the quick molecular weight confirmation of synthetic lead compounds (Taylor et al., 1995 Pullen et al., 1995) to novel and highly selective methods for structure identification (Carr et al., 1993) have been demonstrated. Analysis strategies... 

Pyrolysis MS (PyMS) has been applied to the characterisation and identification of a variety of microbial systems over a number of years (for reviews see [25-27]) and, because of its high discriminatory ability [28-30], presents a powerful fingerprinting technique applicable to any organic material. Whilst the pyrolysis mass spectra of complex organic mixtures may be expressed in the simplest terms as sub-patterns of spectra describing the pure components of the mixtures and their relative concentrations [24], this may not always be true because during pyrolysis intermolecular reactions can take place in the pyrolysate [31-33]. This leads to a lack of superposition of the spectral components and to a possible dependence of the mass spectrum on sample size [31]. However, suitable numerical methods (or chemometrics) can still be employed to measure the concentrations of biochemical components from pyrolysis mass spectra of complex mixtures. 

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