Drug Repurposing Patent Applications October–December 2015

Tranilast-matrine co-amorphous system: Strong intermolecular interactions, improved solubility, and physiochemical stability

There is a great interest to develop co-amorphous drug delivery systems to enhance the solubility of biopharmaceutics classification system (BCS) class II and IV drugs. However, most reported systems only resulted in severalfold solubility improvement. Tranilast (TRA) is an anti-allergic drug used to treat bronchial asthma and allergic rhinitis. It is a BCS class II drug and its poor aqueous solubility affects its absorption in vivo. To address this issue, a natural alkaloid matrine (MAR) with interesting biological activities was chosen to form a co-amorphous system with TRA, based on the solubility parameter and phase solubility experiment. The TRA-MAR drug-drug co-amorphous system was prepared by the solvent evaporation method, and further characterized by powder X-ray diffraction and modulated temperature differential scanning calorimetry. Fourier transform infrared spectroscopy, FT-Raman, and X-ray photoelectron spectroscopy revealed the formation of salt and the presence of strong intermolecular interactions in the TRA-MAR co-amorphous system, which are also supported by molecular dynamics simulations, showing ionic and hydrogen bonding interactions. This co-amorphous system exhibited excellent physical stability at both 25 °C and 40 °C under anhydrous silica gel condition. Finally, co-amorphous TRA-MAR showed greatly enhanced solubility (greater than 100-fold) and rapid release behavior in the vitro release experiments. NMR spectroscopy revealed the strong intermolecular interactions between TRA and MAR in both DMSO‑d₆ and D₂O. Our study resulted in a TRA-MAR co-amorphous drug system with significant solubility improvement and showcased the great potential to improve the dissolution behaviors of BCS class II and IV drugs through the co-amorphization approach.

Web-based Tools for Drug Repurposing: Successful Examples of Collaborative Research

Computational approaches have been proven to be complementary tools of interest in identifying potential candidates for drug repurposing. However, although the methods developed so far offer interesting opportunities and could contribute to solving issues faced by the pharmaceutical sector, they also come with their constraints. Indeed, specific challenges ranging from data access, standardization and integration to the implementation of reliable and coherent validation methods must be addressed to allow systematic use at a larger scale. In this mini-review, we cover computational tools recently developed for addressing some of these challenges. This includes specific databases providing accessibility to a large set of curated data with standardized annotations, web-based tools integrating flexible user interfaces to perform fast computational repurposing experiments and standardized datasets specifically annotated and balanced for validating new computational drug repurposing methods. Interestingly, these new databases combined with the increasing number of information about the outcomes of drug repurposing studies can be used to perform a meta-analysis to identify key properties associated with successful drug repurposing cases. This information could further be used to design estimation methods to compute a priori assessment of the repurposing possibilities.

Design of efficient computational workflows for in silico drug repurposing

Here, we provide a comprehensive overview of the current status of in silico repurposing methods by establishing links between current technological trends, data availability and characteristics of the algorithms used in these methods. Using the case of the computational repurposing of fasudil as an alternative autophagy enhancer, we suggest a generic modular organization of a repurposing workflow. We also review 3D structure-based, similarity-based, inference-based and machine learning (ML)-based methods. We summarize the advantages and disadvantages of these methods to emphasize three current technical challenges. We finish by discussing current directions of research, including possibilities offered by new methods, such as deep learning.