Early-phase drug development requires extensive screening for crystalline forms of potential new active pharmaceutical ingredients (APIs). In this work, researchers evaluate the advantages of knowledge-based approaches in identifying co-crystals for new APIs. They compare two types of analysis to the results of an experimental, systematic crystallization screening:
- An extensive analysis of the Cambridge Structural Database (CSD) to retrieve existing examples of interactions between the functional groups of each drug-coformer pair.
- A computational analysis with Molecular Complementarity.
The Molecular Complementarity component takes into account both molecular energetic and geometrical factors. Of the two approaches, it performed best.
- Molecular Complementarity enabled the identification of all of the observed co-crystals with a 24% reduction of the required experimental attempts.
In early-phase drug discovery, researchers attempt to identify polymorphs with suitable stability and beneficial features, like good solubility. Co-crystallization of multiple molecular components can provide additional positive characteristics, improving the bioavailability of potential APIs. However, testing every potential drug-coformer pair at the benchtop in the hopes of finding an advantageous co-crystal can prove costly. A good computational method can help narrow down the number of required experiments, saving time and resources.
First, the researchers selected five drug molecules and five model co-formers for the study. To identify the drug molecules, they searched the CSD for single-component structures associated with the term “drug bank” that contain 20 or fewer carbons—a common threshold for small molecules. To identify the co-formers, they sought compounds with common functional groups with both hydrogen-bond donors and acceptors that are often used in crystal engineering.
The researchers then predicted likely co-crystals with their two knowledge-based methods: an extensive analysis of the CSD and computational analysis with Molecular Complementarity. For the Molecular Complementarity approach, they used two separate thresholds in the software—a 50% likelihood of co-crystallization and a 33% likelihood of co-crystallization.
To evaluate their predictions, the researchers completed a systematic co-crystallization screening of each drug/co-former pair in a 1:1 ratio. They analyzed the products of each crystalization by powder X-ray diffraction. With the Molecular Complementarity approach (using a threshold of 33% likelihood), they found they could predict 100% of the new crystals while performing only 76% of the experiments—for a 24% reduction in the needed experiments.
Read the full paper: How Many Cocrystals Are We Missing? Assessing Two Crystal Engineering Approaches to Pharmaceutical Cocrystal Screening. Cryst. Growth Des., 2022.
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