Prioritizing Computational Cocrystal Prediction Methods for Experimental Researchers: A Review to Find Efficient, Cost-Effective, and User-Friendly Approaches

Abstract

Pharmaceutical cocrystals offer a versatile approach to enhancing the properties of drug compounds, making them an important tool in drug formulation and development by improving the therapeutic performance and patient experience of pharmaceutical products. The prediction of cocrystals involves using computational and theoretical methods to identify potential cocrystal formers and understand the interactions between the active pharmaceutical ingredient and coformers. This process aims to predict whether two or more molecules can form a stable cocrystal structure before performing experimental synthesis, thus saving time and resources. In this review, the commonly used cocrystal prediction methods are first overviewed and then evaluated based on three criteria: efficiency, cost-effectiveness, and user-friendliness. Based on these considerations, we suggest to experimental researchers without strong computational experiences which methods and tools should be tested as a first step in the workflow of rational design of cocrystals. However, the optimal choice depends on specific needs and resources, and combining methods from different categories can be a more powerful approach.

Keywords: computational cocrystal prediction; drug formulation enhancement; in silico cocrystal prediction methods; pharmaceutical cocrystals; rational cocrystal design.

Figure 1

The workflow of rational cocrystal design.

Figure 2

Evaluation criteria: efficiency, cost-effectiveness, and user-friendliness.

Figure 3

In silico cocrystal screening methods for predicting formation probability and crystal structure.

Figure 4

The MEP map of paracetamol–4,4′-bipyridine cocrystal Reprinted/adapted with permission from Ref. [27]. Copyrigth 2018, copyright MDPI, Basel, Switzerland.

Figure 5

The categories of our scoring model.

Figure 6

In silico screening method comparison across scoring categories.

Figure 7

Clusters of in silico screening methods with similar characteristics.

Figure 8

The flowchart to facilitate the decision-making process to choose the method for cocrystal formation prediction.