Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions

Abstract

Several approaches to predict and model drug solubility have been used in the drug discovery and development processes during the last decades. Each of these approaches have their own benefits and place, and are typically used as standalone approaches rather than in concert. The synergistic effects of these are often overlooked, partly due to the need of computational experts to perform the modeling and simulations as well as analyzing the data obtained. Here we provide our views on how these different approaches can be used to retrieve more information on drug solubility, ranging from multivariate data analysis over thermodynamic cycle modeling to molecular dynamics simulations. We are discussing aqueous solubility as well as solubility in more complex mixed solvents and media with colloidal structures present. We conclude that the field of computational pharmaceutics is in its early days but with a bright future ahead. However, education of computational formulators with broad knowledge of modeling and simulation approaches is imperative if computational pharmaceutics is to reach its full potential.

Designing Soluble PROTACs: Strategies and Preliminary Guidelines

“…However, the definition of acceptable solubility is somewhat vague. In the early phase of discovery, where only aqueous solubility is of interest, it has been proposed that a good goal for solubility is >60 μg/mL . More recently some GSK researchers classified compounds into low (<30 μM), intermediate (30–200 μM), or highly soluble molecules (>200 μM) and applied these criteria in many internal drug discovery programs …”

Hydroxypropyl Cellulose for Drug Precipitation Inhibition: From the Potential of Molecular Interactions to Performance Considering Microrheology

“…However, MD simulations have the advantage of providing atomistic details of the molecular association of the drug and polymer. 76 The situation at high aqueous dilution is about drug interactions at the polymer− water interface, which are substantially different from the previously considered molecular interactions in a single phase of drug−polymer. These models provide opposing views with regard to the absence or presence of water in a microscopic environment.…”

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study

“…Finally, as recent expert commentary has emphasised various shortcomings of data-driven modelling [ 11 ], we acknowledge the dataset used in model development here is limited in size ( Supplementary Materials —Modelling Database). As such, this work was essentially a pilot study seeking to investigate the potential of ANNs to improve the accuracy of predictive models.…”

“…However, such classical formulation development is likely to change as different computational tools are already widely used in drug discovery and are gaining momentum in pharmaceutical development. Quantity structure–activity relationships (QSARs) have streamlined the selection of candidates with optimal binding profiles [ 3 ], physiologically based pharmacokinetic (PBPK) models have aided the simulation of pharmacokinetic parameters [ 4 ], while theory- or data-driven modelling applications have improved formulation development [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Using data-driven machine learning (ML) approaches, improved success rates are achievable by ascertaining the statistical relationships between molecular descriptors and the intended response.…”

“…Over the last decade, interest regarding the use of ML algorithms across diverse disciplines in pharmaceutical design and development has grown [ 11 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. While ML models have been produced to optimise lipid-based formulation (LBF) development [ 3 , 22 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], the application of more novel ML approaches for bio-enabling formulations currently focuses on solid dispersions (SDs) [ 21 , 34 , 35 ].…”