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

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Abstract

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™ 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.

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Acknowledgments

The authors would like to thank Simulations Plus, the Drug Development and Innovation Centre of the University of Alberta and AbbVie for their support.

Juliet Obianuju Njoku, Daniela Amaral Silva are graduate students and Raimar Löbenberg is a professor at the Faculty of Pharmacy and Pharmaceutical Sciences at the University of Alberta. Dwaipayan Mukherjee and Gregory Webster are employees of AbbVie and may hold AbbVie stocks or options. Abbvie jointly participated in the interpretation of data, writing, reviewing and approving the publication. The presentation contains no proprietary AbbVie data.

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Authors and Affiliations

  1. Faculty of Pharmacy and Pharmaceutical Sciences, Katz Group Centre for Pharmacy and Health Research, University of Alberta, Edmonton, Alberta, T6G 2H5, Canada

    Juliet Obianuju Njoku, Daniela Amaral Silva & Raimar Löbenberg

  2. Research and Development, AbbVie Inc., North Chicago, llinois, USA

    Dwaipayan Mukherjee & Gregory K. Webster

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  1. Juliet Obianuju Njoku
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  2. Daniela Amaral Silva
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  3. Dwaipayan Mukherjee
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  5. Raimar Löbenberg
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Correspondence to Raimar Löbenberg.

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Njoku, J.O., Amaral Silva, D., Mukherjee, D. et al. In silico Tools at Early Stage of Pharmaceutical Development: Data Needs and Software Capabilities. AAPS PharmSciTech 20, 243 (2019). https://doi.org/10.1208/s12249-019-1461-5

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