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Page 221 Table 1 New Approaches to Preclinical Biodisposition Study Methods Information QSAR and physicochemistry Regression analysis Neural networks Expert systems Database libraries Data mining Prediction of kinetics and metabolism Candidate choice In vitro metabolism Microsomes, cells, slice Expressed enzymes Rates, routes, and profiles Specific cytochrome involvement Polymorphism, drug interaction, induction, inhibition Scaling In vitro In vivo (allometry) Whole body clearance Clearance, volume, half-life Pharmacology PK/PD Tissue levels, micro-dialysis, PET, NMR Validation of animal model Effect extrapolation to human Validation of plasma/dynamic models Toxicokinetics PK/PD Population kinetics Calculation of safety margins, first dose in man Minimal sampling with fewer animals Preclinical kinetics Limited kinetics and metabolism Limited ADME and distribution Basic IV and PO kinetics in few animals Balance and simple metabolic profiling Radioactive exposure calculation for man This chapter investigates the changing face of preclinical biodisposition studies; the use of predictive artificial intelligence and expert systems; in vitro techniques; imaging to see inside the tissues; and how new statistical procedures, such as population kinetics, can make these investigations more cost effective (Table 1). II. Discovery Although rational drug design for the discovery of new chemicals is still much used, it is being superseded by high throughput screening using combinatorial chemistry, where the emphasis is on producing increasing numbers of molecules
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