Pharmaceutical Formulation

Schrödinger’s Materials Science software suite offers a range of computational solutions for advancing pharmaceutical formulation, from crystalline or amorphous form characterization, to selection of materials and excipients for processing, to formulations and delivery of active pharmaceutical ingredients (APIs).

Keywords: Crystal structure prediction (CSP), Solubility, Amorphous solid dispersions (ASD), Lipid nanoparticle (LNP), Machine learning, Spectroscopy, Catalysis, API degradation

Background

Due to the accelerating pace of drug discovery, fast and efficient ways to both preformulate and formulate new drugs are critical elements of pharmaceutical development. The latest advancements in molecular modeling and AI/ML are enabling atomistic-level insights to improve drug formulations and the ability to evaluate large numbers of candidate materials and formulations prior to experiments.

Optimizing Drug Formulations with Machine Learning

Mixtures of chemical ingredients, such as formulations, are ubiquitous in materials science, but optimizing their properties remains challenging due to the vast design space. Experimentally fine-tuning formulations for desired properties is expensive because of the large design space of both ingredient structures and compositions. Machine learning (ML) approaches that can accurately map ingredient structure and composition to properties offer a promising solution to rapidly screen formulations for desired target properties. Using Schrödinger’s automated Formulation ML workflow, we demonstrate that formulation-property models can accurately predict temperature-dependent drug solubilities for single or binary solvent systems. The parity plot shows that the Formulation ML workflow achieves a test set R2 of 0.96 (an ideal model would achieve R2 of 1.00), which highlights the accuracy of ML approaches. These tools enable rapid screening capabilities that transform the way we design drugs and take only seconds to generate a prediction, which is orders of magnitude faster than trial-and-error experimental exploration.¹

Example of a drug in single or binary solvent mixtures with compositions in mole percent and temperature that is passed into a formulation machine learning model to predict the drug solubility in grams drug / 100 grams solution using a dataset extracted from Bao, Z, et. al. J Cheminform, 2024, 16, 117.

1. Chew AK, et al. npj Comput Mater, 2025, 11, 72.

Accelerating Amorphous Solid Dispersion Development

Amorphous solid dispersions (ASDs) are widely used to formulate APIs into safe and effective media for human absorption. From screening for compatibility to understanding dissolution mechanisms, the Schrödinger Platform has tools for speeding ASD development.

Complex interactions between an API and key ASD components (e.g., polymers, surfactants, and stabilizers) during dissolution are easily viewed and analyzed with coarse-grained physicsbased simulations such as dispersive particle dynamics (DPD) simulations. The underlying mechanisms governing the overall ASD dissolution process are then accessible, helping to solve practical challenges such as solvent-induced phase separation and the impact of drug load on ASD stability and miscibility. Beyond dissolution, other anhydrous dynamic processes are critical for ASD design. One key factor is the glass transition temperature (T_g), as maintaining the ASD system below T_g prevents excessive polymer mobility that could lead to API recrystallization, ultimately reducing ASD efficiency and shelf-life. Schrödinger’s molecular dynamics workflow provides a reliable method for estimating the T_g of the drug and ASD systems, allowing the targeted design of safe API ASD formulations.^1-2