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A new computational model, CCNBR, efficiently screens drug-drug cocrystals (DDCs). It identified promising candidates, including a Furosemide-Telmisartan cocrystal with enhanced therapeutic benefits and bioavailability.

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Area of Science:

  • Computational chemistry and materials science
  • Pharmaceutical sciences and drug discovery

Background:

  • Drug-drug cocrystals (DDCs) offer synergistic therapeutic potential but lack efficient screening methods.
  • Systematic computational approaches are needed to identify promising DDC candidates for drug development.

Purpose of the Study:

  • To introduce CCNBR, a novel multiobjective random walk network model for DDC screening.
  • To evaluate the cocrystal-forming potential of antihypertensive drugs using CCNBR.

Main Methods:

  • Developed CCNBR, integrating cocrystal network topology and molecular structural features.
  • Employed a third-order path-based weighted random walk algorithm to capture supramolecular interactions.
  • Applied CCNBR to 15 antihypertensive drugs, experimentally testing 105 combinations.

Main Results:

  • CCNBR successfully predicted cocrystal formation, identifying two drug pairs.
  • The Furosemide-Telmisartan cocrystal ranked highly and demonstrated improved solubility and bioavailability.
  • Experimental validation confirmed CCNBR's efficacy in identifying viable DDC candidates.

Conclusions:

  • CCNBR provides a rapid and efficient computational method for DDC screening.
  • The identified Furosemide-Telmisartan cocrystal shows significant potential for improved therapeutic outcomes.
  • This approach accelerates the discovery of novel DDCs with enhanced pharmaceutical properties.