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A predictive PC-SAFT EOS based on COSMO for pharmaceutical compounds.

Samane Zarei Mahmoudabadi1, Gholamreza Pazuki2

  • 1Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

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A new predictive model for the Perturbed Chain Statistical Associating Fluid Theory Equation of State (PC-SAFT EOS) was developed using COSMO computations. This model accurately predicts pharmaceutical compound solubility and octanol/water partition coefficients.

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Area of Science:

  • Physical Chemistry
  • Computational Chemistry
  • Chemical Engineering

Background:

  • The Perturbed Chain Statistical Associating Fluid Theory Equation of State (PC-SAFT EOS) is a powerful tool for thermodynamic modeling.
  • Accurate prediction of solubility and partition coefficients is crucial for pharmaceutical development.
  • Existing models may require extensive parameterization or lack predictive power for certain systems.

Purpose of the Study:

  • To develop a predictive PC-SAFT EOS by integrating COSMO computations.
  • To determine physical adjustable parameters for PC-SAFT EOS using correlations dependent on COSMO results.
  • To validate the predictive capability of the developed model for pharmaceutical compound solubility and octanol/water partition coefficients.

Main Methods:

  • Incorporation of COSMO (COnductor-like Screening MOdel) computations into the PC-SAFT EOS framework.
  • Development of correlations to derive PC-SAFT EOS physical adjustable parameters from COSMO results.
  • Modeling of solubility data for pharmaceutical compounds in pure and mixed solvents.
  • Calculation of octanol/water partition coefficients for pharmaceutical compounds.

Main Results:

  • The developed predictive PC-SAFT EOS achieved a Root Mean Square Error (RMSE) of 1.435 on a logarithmic scale for solubility predictions.
  • The predictive PC-SAFT EOS demonstrated superior performance compared to the COSMO-SAC model (RMSE = 4.385).
  • The RMSE for the predictive PC-SAFT EOS was comparable to the COSMO-RS model (RMSE = 1.412) and showed a standard RMSE of 1.515 for octanol/water partition coefficients.

Conclusions:

  • The proposed predictive PC-SAFT EOS, incorporating COSMO computations, offers a reliable approach for modeling pharmaceutical compound behavior.
  • The model effectively predicts solubility and octanol/water partition coefficients, reducing the need for extensive experimental data.
  • This advancement provides a valuable tool for accelerating pharmaceutical research and development through accurate in silico predictions.