Interrogation of the digital biopharmaceutics toolbox: Predictions of biorelevant solubility and dissolution profiles

Digital tools play a crucial role in various aspects of pharmaceutical research, aiding in data analysis, modelling and simulation. For example, chemoinformatics tools are used to analyse chemical data, including molecular structures, properties, and interactions. These tools are valuable in drug discovery and development, assisting in virtual screening, compound library management, and structure-activity relationship (SAR) analysis. Machine learning and artificial intelligence (AI) algorithms are increasingly used in pharmaceutical research for tasks such as drug discovery, predictive modelling, and data analysis. These tools can analyse large datasets, identify patterns, and make predictions to accelerate drug development processes.

Pharmacokinetic modelling tools are used to predict the absorption, distribution, metabolism, and excretion (ADME) of drugs in the body. These tools help researchers optimize dosing regimens, assess bioequivalence, and understand the pharmacokinetic behaviour of drugs. However, these models still rely in biopharmaceutics inputs for solubility; permeability and dissolution. This project will seek to review existing tools for the prediction of these parameters in the context of oral biopharmaceutics under 3 remits of solubility; dissolution and permeability.

The project will use a combination of experiments, data analytics and simulation to provide more accurate representation of drug solubility and dissolution within biorelevant systems. The predictive models will be validated against large data sets of existing data collected within the University of Strathclyde laboratories. Furthermore, digital twin systems that replicate dissolution apparatus will be developed to further probe the performance of drug products for integration into biopharmaceutics mechanistic modelling systems.

These data streams will inform the development of Machine Learning and Deep Learning models to extract more representative mechanistic understanding of drug-colloid interactions and how these drive solubility and dissolution of drugs. Simulations of the measurement environment will contribute to identifying deviations from ideal scenarios and to providing physical meaning to these anomalies.

Admissions Requirements

At least 2:1 honours degree in Chemistry, Medicinal Chemistry, Pharmacy or related subjects. An MSc in any of these areas is an advantage.