The human gastrointestinal system undergoes dynamic changes throughout various life stages, leading to variations in intestinal fluid composition. Adjusting dosing and drug delivery systems to specific patient groups is crucial for maximising the efficacy of active pharmaceutical ingredients (APIs) while minimising potential side effects. The physiological and biochemical characteristics of patients vary significantly influencing drug absorption, distribution, metabolism, and excretion. Tailoring drug delivery to these variations ensures that therapeutic concentrations are achieved, optimising the desired pharmacological response. Customising dosing regimens and delivery systems based on patient-specific data allows for precise and targeted drug administration, reducing the risk of under- or overdosing. This personalised approach not only enhances therapeutic outcomes but also minimises the likelihood of adverse effects, as it considers the unique characteristics of each patient group. This project employs molecular dynamics (MD) simulations to investigate the effects of these variabilities, focusing on neonates, toddlers, children, young adults, adults, and the elderly. The primary goal is to understand how differences in bile salts, cholesterol, phospholipids, and lipids impact the formation of colloidal structures within the duodenal environment. In order to run the simulations we will utilise the data on intestinal fluid composition and solubility of several small drug molecules in the fluids available in the literature. Objectives: 1) Simulate the molecular composition of intestinal fluid from different patient groups; analyse the formation of colloidal structures under varying fluid compositions. 2) Investigate drug solubilisation patterns by introducing various drug molecules into simulated intestinal fluid. 3) Validate simulation results by comparing them with experimental drug solubility values in fasted state simulated intestinal fluid from literature sources. 4) Explore interactions between drug molecules and macromolecules present in infant formula fed to neonates. 5) Utilise the data produced by MD simulations to train machine learning models; incorporate experimental drug solubility values for model validation; develop predictive models for drug solubility based on intestinal fluid compositions. Expected Outcomes: Insights into the impact of patient groups on variability of colloidal structures in intestinal fluid. Understanding drug solubilisation patterns in different patient groups, followed by improved formulation strategies. Identification of key interactions between drug molecules and macromolecules in infant formula. Machine learning models predicting drug solubility based on intestinal fluid composition. Future Directions: As a follow-up study, the project aims to simulate the improvement of drug solubility through the introduction of advanced drug delivery formulations. This will provide a foundation for designing targeted drug delivery systems tailored to specific patient groups, enhancing therapeutic outcomes. This multidisciplinary approach combines molecular dynamics simulations, experimental validation, and machine learning, offering a comprehensive understanding of the intricate interactions within the gastrointestinal system and providing valuable insights for personalised medicine and drug development.