Mechanistic Dissolution Modeling of a Poorly Soluble Drug; an Evaluation of Formulation Influence and Simulation Parameters for Enhancing Predictive Capability

• Author / Creator

  Njoku, Obianuju J

• 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). At this stage, there is limited information available about the physicochemical and biopharmaceutical properties of a new drug candidate. The current work evaluated the various physicochemical parameters required to improve dissolution profile prediction accuracy at the early stage of drug development and estimate the effect of formulation strategies on the dissolution profile of immediate release tablets of a poorly soluble drug using in silico tools.

  In the first study, DDDPlusTM (Dose Disintegration and Dissolution Plus) was used in simulating dissolution test profiles of immediate release tablets of ritonavir. The minimum data requirements to make useful predictions were assessed. ADMET predictor (part of DDDPlus) and Chemicalize (an online resource) were used to estimate pKa, logS and molecular charge. A surfactant model was developed to estimate the solubility enhancement in media containing surfactant. The software’s transfer model based on the USP two-tiered dissolution test to mimic the in vivo transfer from stomach to small intestine was assessed. All simulations were compared with experimental results.
  ADMET predictor without any real measurements showed lower drug solubility at pH 1.0 compared to data obtained from Chemicalize, which showed a higher solubility at pH 1.0. One measured data point was shown to be sufficient to make predictive simulations in DDDPlus. However, 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 is at this point not suitable to predict the two in vivo environments separately because the composition of the two media in regard to their surfactant content cannot be differentiated.
  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 behaviour in surfactant media.

  In the second study, solid dispersion of ritonavir was prepared through hot melt extrusion process. Dissolution test results of direct compressed tablets with and without disintegrant in various media with physiologically relevant pH were compared with simulations. Solubilizer and disintegrant effect were evaluated on the DDDPlusTM simulation software using previously published solubility data on ritonavir. Observed and predicted dissolution profiles similarity tests and drug release mechanisms were assessed. Optimization of the Solubilizer Effect Coefficient (SEC) on the program give a good estimation of the effect of copovidone in the extrudate on the dissolution profiles of all tablets. The SEC was dependent on the drug/polymer ratio and was therefore the same for both tablets with and without disintegrant. Disintegrant concentration in the program has no effect on simulations, rather the disintegration time was the main predictive factor. Drug release was formulation controlled in the tablets without disintegrant and in the tablets with disintegrant was via drug diffusion and polymer surface erosion.

  In silico predictions need measured solubility data to be predictive. A combination of minimal experimental data and simulations can support the dissolution development at an early stage. DDDPlusTM has the potential to estimate the effect of excipients in a formulation on in vitro dissolution at an early stage in the drug development process. This could be useful in decisions on formulation strategies to enhance bioavailability in BCS class II and IV drugs.

• Subjects / Keywords

  • Simulation
  • Dissolution
  • Formulation

• Graduation date

  Fall 2019

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-d76v-b473

• License

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