# COSMOPharm
Welcome to the COSMOPharm package, accompanying [our paper in *Molecular Pharmaceutics*](https://doi.org/10.1021/acs.molpharmaceut.4c00342). This project and its associated publication offer insights and a practical toolkit for researching drug-polymer and drug-solvent systems, aiming to provide the scientific community with the means to reproduce our findings and further the development of COSMO-SAC-based models.
<p align="center">
<!-- <img src="https://github.com/usnistgov/COSMOSAC/raw/master/JCTC2020.PNG" alt="TOC Figure" width="500"> -->
<img src="https://github.com/ivanantolo/cosmopharm/raw/main/TOC.png" alt="TOC Figure">
</p>
## About
COSMOPharm is a Python package designed to streamline the predictive modeling of drug-polymer compatibility, crucial for the development of pharmaceutical amorphous solid dispersions. Apart from that, it can also be used for the miscibility/solubility of drugs with/in common solvents. Leveraging the COSMO-SAC (Conductor-like Screening Model Segment Activity Coefficient) model, COSMOPharm offers a robust platform for scientists and researchers to predict solubility, miscibility, and phase behavior in drug formulation processes.
## Features
- **Compatibility Prediction**: Utilize open-source COSMO-SAC model for prediction of drug-polymer compatibility.
- **Solubility Calculation**: Calculate drug-polymer solubilities to guide the selection of suitable polymers for drug formulations.
- **Miscibility and Phase Behavior Analysis**: Analyze the miscibility of drug-polymer pairs and understand their phase behavior under various conditions.
- **User-friendly Interface**: Easy-to-use functions and comprehensive documentation to facilitate research and development in pharmaceutical sciences.
## Installation
Install COSMOPharm with pip:
`pip install cosmopharm`
Ensure you have installed the `cCOSMO` library as per instructions on the [COSMOSAC GitHub page](https://github.com/usnistgov/COSMOSAC).
## Quick Start
Get started with COSMOPharm using the minimal example below, which demonstrates how to calculate the solubility of a drug in a polymer. This example succinctly showcases the use of COSMOPharm for solubility calculations:
```python
import matplotlib.pyplot as plt
import cCOSMO
from cosmopharm import SLE, COSMOSAC
from cosmopharm.utils import create_components, read_params
# Define components
names = ['SIM','PLGA50']
params_file = "data/sle/table_params.xlsx"
# Load parameters and create components
parameters = read_params(params_file)
mixture = create_components(names, parameters)
# Initialize COSMO-SAC model - replace paths with your local paths to COSMO profiles
db = cCOSMO.DelawareProfileDatabase(
"./profiles/_import_methods/UD/complist.txt",
"./profiles/_import_methods/UD/sigma3/")
for name in names:
iden = db.normalize_identifier(name)
db.add_profile(iden)
COSMO = cCOSMO.COSMO3(names, db)
# Setup the COSMO-SAC model with components
actmodel = COSMOSAC(COSMO, mixture=mixture)
# Calculate solubility (SLE)
sle = SLE(actmodel=actmodel)
solubility = sle.solubility(mix='real')
# Output the solubility
print(solubility[['T', 'w', 'x']].to_string(index=False))
# Plot results
plt.plot(*solubility[['w','T']].values.T,'.-', label='Solubility (w)')
# Settings
plt.xlim(0,1)
plt.ylim(300,500)
# Adding title and labels
plt.title('Solubility vs. Temperature')
plt.ylabel("T / K")
xlabel = {'w':'Weight', 'x':'Mole'}
plt.xlabel(f"Weight fraction {mixture[0].name}")
plt.legend()
# Save the figure to a PNG or PDF file
plt.savefig('solubility_plot.png') # Saves the plot as a PNG file
# plt.savefig('solubility_plot.pdf') # Saves the plot as a PDF file
plt.show()
```
For a more comprehensive demonstration, including advanced functionalities and plotting results, please see the [example_usage.py](https://github.com/ivanantolo/cosmopharm/blob/main/example_usage.py) script in this repository. This detailed example walks through the process of setting up COSMOPharm, initializing models, and visualizing the results of solubility and miscibility calculations.
## Contributing
Contributions are welcome! Please refer to our [GitHub repository](https://github.com/ivanantolo/cosmopharm) for more information.
## Citation
We appreciate citations to our work as they help acknowledge and spread our research contributions. If you use COSMOPharm in your research, please cite the associated paper as follows:
```bibtex
@article{Antolovic2024COSMOPharm,
title={COSMOPharm: Drug--Polymer Compatibility of Pharmaceutical Amorphous Solid Dispersions from COSMO-SAC},
author={Antolovic, Ivan and Vrabec, Jadran and Klajmon, Martin},
journal={Molecular Pharmaceutics},
year={2024},
volume={1}, # Will be adjusted accordingly
issue={1}, # Will be adjusted accordingly
month={3}, # Will be adjusted accordingly
pages={1--10}, # Will be adjusted accordingly
doi={10.1021/acs.molpharmaceut.3c12345} # Will be adjusted accordingly
}
```
## License
COSMOPharm is released under the MIT License. For more details, see the [LICENSE](https://github.com/ivanantolo/cosmopharm/LICENSE) file.
Raw data
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"description": "# COSMOPharm\r\n\r\nWelcome to the COSMOPharm package, accompanying [our paper in *Molecular Pharmaceutics*](https://doi.org/10.1021/acs.molpharmaceut.4c00342). This project and its associated publication offer insights and a practical toolkit for researching drug-polymer and drug-solvent systems, aiming to provide the scientific community with the means to reproduce our findings and further the development of COSMO-SAC-based models.\r\n\r\n<p align=\"center\">\r\n <!-- <img src=\"https://github.com/usnistgov/COSMOSAC/raw/master/JCTC2020.PNG\" alt=\"TOC Figure\" width=\"500\"> -->\r\n <img src=\"https://github.com/ivanantolo/cosmopharm/raw/main/TOC.png\" alt=\"TOC Figure\">\r\n</p>\r\n\r\n## About \r\n\r\nCOSMOPharm is a Python package designed to streamline the predictive modeling of drug-polymer compatibility, crucial for the development of pharmaceutical amorphous solid dispersions. Apart from that, it can also be used for the miscibility/solubility of drugs with/in common solvents. Leveraging the COSMO-SAC (Conductor-like Screening Model Segment Activity Coefficient) model, COSMOPharm offers a robust platform for scientists and researchers to predict solubility, miscibility, and phase behavior in drug formulation processes.\r\n\r\n## Features\r\n\r\n- **Compatibility Prediction**: Utilize open-source COSMO-SAC model for prediction of drug-polymer compatibility.\r\n- **Solubility Calculation**: Calculate drug-polymer solubilities to guide the selection of suitable polymers for drug formulations.\r\n- **Miscibility and Phase Behavior Analysis**: Analyze the miscibility of drug-polymer pairs and understand their phase behavior under various conditions.\r\n- **User-friendly Interface**: Easy-to-use functions and comprehensive documentation to facilitate research and development in pharmaceutical sciences.\r\n\r\n## Installation\r\n\r\nInstall COSMOPharm with pip:\r\n\r\n`pip install cosmopharm`\r\n\r\nEnsure you have installed the `cCOSMO` library as per instructions on the [COSMOSAC GitHub page](https://github.com/usnistgov/COSMOSAC).\r\n\r\n## Quick Start\r\n\r\nGet started with COSMOPharm using the minimal example below, which demonstrates how to calculate the solubility of a drug in a polymer. This example succinctly showcases the use of COSMOPharm for solubility calculations:\r\n\r\n\r\n```python\r\nimport matplotlib.pyplot as plt\r\nimport cCOSMO\r\nfrom cosmopharm import SLE, COSMOSAC\r\nfrom cosmopharm.utils import create_components, read_params\r\n\r\n# Define components\r\nnames = ['SIM','PLGA50']\r\nparams_file = \"data/sle/table_params.xlsx\"\r\n\r\n# Load parameters and create components\r\nparameters = read_params(params_file)\r\nmixture = create_components(names, parameters)\r\n\r\n# Initialize COSMO-SAC model - replace paths with your local paths to COSMO profiles\r\ndb = cCOSMO.DelawareProfileDatabase(\r\n \"./profiles/_import_methods/UD/complist.txt\",\r\n \"./profiles/_import_methods/UD/sigma3/\")\r\n\r\nfor name in names:\r\n iden = db.normalize_identifier(name)\r\n db.add_profile(iden)\r\nCOSMO = cCOSMO.COSMO3(names, db)\r\n\r\n# Setup the COSMO-SAC model with components\r\nactmodel = COSMOSAC(COSMO, mixture=mixture)\r\n\r\n# Calculate solubility (SLE)\r\nsle = SLE(actmodel=actmodel)\r\nsolubility = sle.solubility(mix='real')\r\n\r\n# Output the solubility\r\nprint(solubility[['T', 'w', 'x']].to_string(index=False))\r\n\r\n# Plot results\r\nplt.plot(*solubility[['w','T']].values.T,'.-', label='Solubility (w)')\r\n\r\n# Settings\r\nplt.xlim(0,1)\r\nplt.ylim(300,500)\r\n# Adding title and labels\r\nplt.title('Solubility vs. Temperature')\r\nplt.ylabel(\"T / K\")\r\nxlabel = {'w':'Weight', 'x':'Mole'}\r\nplt.xlabel(f\"Weight fraction {mixture[0].name}\")\r\nplt.legend()\r\n# Save the figure to a PNG or PDF file\r\nplt.savefig('solubility_plot.png') # Saves the plot as a PNG file\r\n# plt.savefig('solubility_plot.pdf') # Saves the plot as a PDF file\r\nplt.show()\r\n```\r\n\r\nFor a more comprehensive demonstration, including advanced functionalities and plotting results, please see the [example_usage.py](https://github.com/ivanantolo/cosmopharm/blob/main/example_usage.py) script in this repository. This detailed example walks through the process of setting up COSMOPharm, initializing models, and visualizing the results of solubility and miscibility calculations.\r\n\r\n## Contributing\r\n\r\nContributions are welcome! Please refer to our [GitHub repository](https://github.com/ivanantolo/cosmopharm) for more information.\r\n\r\n## Citation\r\n\r\nWe appreciate citations to our work as they help acknowledge and spread our research contributions. 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