DryLab is used in a study in which the linear solvent strength model of gradient elution is applied to estimate parameters of lipophilicity and acidity of a series of drugs and model chemicals.

Drylab G software has been used to optimize the reversed-phase HPLC separation of taxoids and co-extracted substances from yew. Two preliminary runs based on a linear gradient from 5 to 100% acetonitrile (ACN) in 20 or 60 min were shown to be satisfactory for optimization of resolution. The optimization experiments were performed on a purified extract fortified with taxoid standards. The identity and purity of the peaks were verified by use of photodiode-array detection. Agreement between simulated and experimental data was good. The optimized chromatographic system can be used for quantitative analysis of paclitaxel, cephalomannine, and 10-deacetylbaccatin in yew extracts.

Reversed-phase columns are widely used in assays based on high-performance liquid chromatography (HPLC). When such assays are repeated over time, it is often necessary to replace the column. In such cases, the selectivity of columns from different production batches may prove sufficiently variable to result in a failed separation. It is possible to compensate for differences in column selectivity by making small changes (adjustments) in separation conditions. The present paper describes an efficient procedure for choosing adjusted conditions and discusses its general applicability.

Two different stationary phases, carbon coated ZrO2 and C18 modified silica were compared. They show very different selectivity. The DryLab software was used to evaluate the different selectivities using a sample of triazine herbicides.

DryLab-Software was used to determine the optimum column temperature and mobile phase pH for the separationof mixtures of roxithromicin and related compounds.

Unusual experiments can provide surprisingly good analytical solutions. When developing chromatographic methods, analysts use in most cases a combination of experience and instinct to choose initial starting conditions. This is often followed by a period of trial-and-error optimization, until the desired method is achieved. The article illustrates, how the process of chromatographic method development can be improved using computer modelling and simulation.