Optimization of lead compounds

Cruciani G, Pastor M and Guba W. VolSurf a new tool for the pharmacokinetic optimization of lead compounds. Eur J Pharm Sci 2000 11 Suppl 2 S29-39. 

Cruciani G, Crivori P, Carrupt PA, Testa B (2000) Molecular fields in quantitative structure-permeation relationships The VolSurf approach. Theochem 503 17-30 Cruciani G, Pastor M, Clementi S (2000) Handling information from 3D GRID maps for QSAR studies. In Gun-dertofte K, Jorgensen FS (eds) Molecular modelling and prediction of bioactivity, proceedings of the 12th European symposium on quantitative structure-activity relationships (QSAR 98). Plenum Press, New York, pp 73-81 Cruciani G, Pastor M, Guba W (2000) VolSurf A new tool for the pharmacokinetic optimization of lead compounds. Eur J Pharm Sd 11 S29-S39... 

Preformulation is a bridge between discovery and development where development scientists participate in selection and optimization of lead compounds. It is very critical at this stage to evaluate the developabUity of potential drug candidates in order to select new chemical entities and decrease the number of failures during future drug development. 

Optimization of Lead Compounds A typical flowchart used for the desired optimization of lead compounds with CADD techniques is depicted in Figure 3.14. 

The techniques described in this chapter can be of use to the optimization of lead compounds during drug discovery processes. However, the link between drug-protein adduct formation and toxicity has not been well defined. Additional considerations should be taken, such as therapeutic area and clinical doses. It has been suggested that a 10 mg daily dose rarely results in drug-related adverse effects (Uetrecht, 1999). Thus, the propensity of bioactivation should only be considered as one of the factors in the selection of developmental drug candidates. 

Ligand- and structure-based approaches are valuable tools for the identification and optimization of lead compounds. Each strategy needs special prerequisites and has strengths and weaknesses. In some cases only the strengths of both methods may be combined for a joint approach, called structure-based pharmacophore alignment. Here, the receptor site serves as a complement to build the pharmacophore model and sophisticated statistical methods from 3D-QSAR (PCA and PLS) are applied for the prediction of activity [19, 20]. 

Knowledge of the macromolecule structure and its interactions with small-molecule ligands can provide useful information in the design and optimization of lead compounds with enhanced binding affinities as the examples in the previous sections of this chapter have illustrated. - s Structure-based ligand optimization requires two pieces of information. 

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