Structural investigation and compression of a co-crystal of indomethacin and saccharin

The co-crystalline structure of the non-steroidal, anti-inflammatory indomethacin with the non-toxic, Generally Regarded As Safe (GRAS) sweetener component saccharin was investigated up to 6.33 GPa using a Diamond Anvil Cell (DAC). Single crystal X-ray diffraction measurements show that the co-crystal remains in the same triclinic, P-1, phase throughout the compression with a significant reduction in void space (155.69 to 55.61Å3). Information on the response of different types of intermolecular interactions to external force at the same time is enabled by the use of a co-crystal. We have rationalised that the length and compression rate of the saccharin amide dimer in the co-crystal is caused by the dimer sitting in a ‘pocket’ surrounded by the indomethacin framework. This framework reduces the effects of molecular packing on the dimer allowing for an ideal hydrogen bonding geometry

Unraveling the Impact of High-Order Silk Structures on Molecular Drug Binding and Release Behaviors

Silk continues to amaze: over the past decade, new research threads have emerged that include the use of silk fibroin for advanced pharmaceutics, including its suitability for drug delivery. Despite this ongoing interest, the details of silk fibroin structures and their subsequent drug interactions at the molecular level remain elusive, primarily because of the difficulties encountered in modeling the silk fibroin molecule. Here, we generated an atomistic silk model containing amorphous and crystalline regions. We then exploited advanced well-tempered metadynamics simulations to generate molecular conformations that we subsequently exposed to classical molecular dynamics simulations to monitor both drug binding and release. Overall, this study demonstrated the importance of the silk fibroin primary sequence, electrostatic interactions, hydrogen bonding, and higher-order conformation in the processes of drug binding and release. © Copyright © 2019 American Chemical Society

Unraveling the impact of high-order silk structures on molecular drug binding and release behaviors

Silk continues to amaze: over the past decade, new research threads have emerged that include the use of silk fibroin for advanced pharmaceutics, including its suitability for drug delivery. Despite this ongoing interest, the details of silk fibroin structures and their subsequent drug interactions at the molecular level remain elusive, primarily because of the difficulties encountered in modeling the silk fibroin molecule. Here, we generated an atomistic silk model containing amorphous and crystalline regions. We then exploited advanced well-tempered metadynamics simulations to generate molecular conformations that we subsequently exposed to classical molecular dynamics simulations to monitor both drug binding and release. Overall, this study demonstrated the importance of the silk fibroin primary sequence, electrostatic interactions, hydrogen bonding, and higher order conformation in the processes of drug binding and release

A unified ML framework for solubility prediction across organic solvents

We report a single machine learning (ML)-based model to predict the solubility of drug/drug-like compounds across 49 organic solvents, extensible to more. By adopting a cross-solvent data structure, we enable the exploitation of valuable relational information between systems. The effect is major, with even a single experimental measurement of a solute in a different solvent being enough to significantly improve predictions on it, and successive ones improving them further. Working with a sparse dataset of only 714 experimental data points spanning 75 solutes and 49 solvents (81% sparsity), a ML-based model with a prediction RMSE of 0.75 log S (g/100 g) for unseen solutes was produced. This compares favourably with conductor-like screening model for real solvents (COSMO-RS), an industry-standard model based on thermodynamic laws, which yielded a prediction RMSE of 0.97 for the same dataset. The error for our method reduced to a mean RMSE of 0.65 when one instance of the solute (in a different solvent) was included in the training data; this iteratively reduced further to 0.60, 0.57 and 0.56 when two, three and four instances were available, respectively. This standard of performance not only meets or exceeds those of alternative ML-based solubility models insofar as they can be compared but reaches the perceived ceiling for solubility prediction models of this type. In parallel, we assess the performance of the model with and without the addition of COSMO-RS output as an additional descriptor. We find that a significant benefit is gained from its addition, indicating that mechanistic methods can bring insight that simple molecular descriptors cannot and should be incorporated into a data-driven prediction of molecular properties where possible

Predicting pharmaceutical powder flow from microscopy images using deep learning

The powder flowability of active pharmaceutical ingredients and excipients is a key parameter in the manufacturing of solid dosage forms used to inform the choice of tabletting methods. Direct compression is the favoured tabletting method; however, it is only suitable for materials that do not show cohesive behaviour. For materials that are cohesive, processing methods before tabletting, such as granulation, are required. Flowability measurements require large quantities of materials, significant time and human investments and repeat testing due to a lack of reproducible results when taking experimental measurements. This process is particularly challenging during the early-stage development of a new formulation when the amount of material is limited. To overcome these challenges, we present the use of deep learning methods to predict powder flow from images of pharmaceutical materials. We achieve 98.9% validation accuracy using images which by eye are impossible to extract meaningful particle or flowability information from. Using this approach, the need for experimental powder flow characterization is reduced as our models rely on images which are routinely captured as part of the powder size and shape characterization process. Using the imaging method recorded in this work, images can be captured with only 500 mg of material in just 1 hour. This completely removes the additional 30 g of material and extra measurement time needed to carry out repeat testing for traditional flowability measurements. This data-driven approach can be better applied to early-stage drug development which is by nature a highly iterative process. By reducing the material demand and measurement times, new pharmaceutical products can be developed faster with less material, reducing the costs, limiting material waste and hence resulting in a more efficient, sustainable manufacturing process. This work aims to improve decision-making for manufacturing route selection, achieving the key goal for digital design of being able to better predict properties while minimizing the amount of material required and time to inform process selection during early-stage development

Enabling precision manufacturing of active pharmaceutical ingredients: workflow for seeded cooling continuous crystallisations

Continuous manufacturing is widely used for the production of commodity products. Currently, it is attracting increasing interest from the pharmaceutical industry and regulatory agencies as a means to provide a consistent supply of medicines. Crystallisation is a key operation in the isolation of the majority of pharmaceuticals and has been demonstrated in a continuous manner on a number of compounds using a range of processing technologies and scales. Whilst basic design principles for crystallisations and continuous processes are known, applying these in the context of rapid pharmaceutical process development with the associated constraints of speed to market and limited material availability is challenging. A systematic approach for continuous crystallisation process design is required to avoid the risk that decisions made on one aspect of the process conspire to make a later development step or steps, either for crystallisation or another unit operation, more difficult. In response to this industry challenge, an innovative system-wide approach to decision making has been developed to support rapid, systematic, and efficient continuous seeded cooling crystallisation process design. For continuous crystallisation, the goal is to develop and operate a robust, consistent process with tight control of particle attributes. Here, an innovative system-based workflow is presented that addresses this challenge. The aim, methodology, key decisions and output at each at stage are defined and a case study is presented demonstrating the successful application of the workflow for the rapid design of processes to produce kilo quantities of product with distinct, specified attributes suited to the pharmaceutical development environment. This work concludes with a vision for future applications of workflows in continuous manufacturing development to achieve rapid performance based design of pharmaceuticals

A unified AI framework for solubility prediction across organic solvents

We report on the use of a single, unified dataset for machine learning (ML)-driven solubility prediction across the chemical space. This was a departure from the solvent-specific datasets more commonly used

Combined Chemoinformatics Approach to Solvent Library Design Using clusterSim and Multidimensional Scaling

Reported here is a rational approach for the selection of solvents intended for use in physical form screening based on a novel chemoinformatics analysis of solvent properties. A comprehensive assessment of eight clustering methods was carried out on a series of 94 solvents described by calculated molecular descriptors using the clusterSim package in R. The effectiveness of clustering methods was evaluated using a range of statistical measures as well as increasing efficiency of solid form discovery using a cluster-based solvent selection approach. Multidimensional scaling was used to illustrate cluster analysis on a two-dimensional solvent map. The map presented here is a valuable tool to aid efficient solvent selection in physical form screens. This tool is equally applicable to any scientific area which requires a solubility dependent decision on solvent choice