In silico Tools at Early Stage of Pharmaceutical Development: Data Needs and Software Capabilities

In early drug development, the selection of a formulation platform and decisions on formulation strategies have to be made within a short timeframe and often with minimal use of the active pharmaceutical ingredient (API). The current work evaluated the various physicochemical parameters required to improve the prediction accuracy of simulation software for immediate release tablets in early drug development. DDDPlus (TM) was used in simulating dissolution test profiles of immediate release tablets of ritonavir and all simulations were compared with experimental results. The minimum data requirements to make useful predictions were assessed using the ADMET predictor (part of DDDPlus) and Chemicalize (an online resource). A surfactant model was developed to estimate the solubility enhancement in media containing surfactant and the software's transfer model based on the USP two-tiered dissolution test was assessed. One measured data point was shown to be sufficient to make predictive simulations in DDDPlus. At pH 2.0, the software overestimated drug release while at pH 1.0 and 6.8, simulations were close to the measured values. A surfactant solubility model established with measured data gave good dissolution predictions. The transfer model uses a single-vessel model and was unable to predict the two in vivo environments separately. For weak bases like ritonavir, a minimum of three solubility data points is recommended for in silico predictions in buffered media. A surfactant solubility model is useful when predicting dissolution behavior in surfactant media and in silico predictions need measured solubility data to be predictive.