Living organisms are incredibly intricate systems, where the genotype—the genetic makeup encoded in DNA—serves as the blueprint for an organism’s development, behaviour, and function. This genotype gives rise to the phenotype, which encompasses the observable traits of a cell or organism, such as receptor expression, protein production, and cell surface structure. The phenotype results from the dynamic interplay between the genotype and environmental factors, creating a unique molecular and structural identity for each cell. Understanding this complexity is crucial for advancing drug and nanomedicine design, as targeting the phenotype offers a more direct and effective way to manipulate biological systems for therapeutic purposes.

The biological complexity of phenotypes arises from their hierarchical organisation, where molecular interactions at the atomic level scale up to dictate cellular and tissue behaviour. For instance, the receptor composition on the cell membrane, the structure of the glycocalyx (the carbohydrate-rich layer surrounding cells), and intracellular signalling pathways all work to determine how a cell interacts with its environment. These interactions are not linear but highly dependent on multivalent processes—where the simultaneous binding of multiple ligands to receptors creates collective effects that exceed the sum of individual interactions.
By translating these biological principles into design rules, we can engineer nanomedicines that exploit these complex interactions to achieve precise control. For example, multivalent drug systems can be designed to bind selectively to phenotypes with specific receptor densities, a principle rooted in Super-Selectivity Theory (SST). This approach allows drugs to differentiate between healthy and diseased cells based on phenotypic signatures, such as the overexpression of certain receptors in cancer cells or the altered glycocalyx in inflamed tissues.
We incorporate statistical physics and computational biophysics into our design framework to create these controlled interactions. Using Phenotypic Association Theory (PAT), we model how multivalent units, such as nanoparticles or polymersomes, interact with the complex phenotypes of target cells. These models capture ligand-receptor binding and the steric and entropic effects imposed by the glycocalyx and surrounding biological environment. For instance, steric barriers created by polymers like PEG can fine-tune the binding properties of ligands, ensuring selective targeting while minimising off-target effects. By leveraging these insights, we have demonstrated the ability to design nanomedicines that achieve super-selective binding, effectively crossing the blood-brain barrier or targeting integrin receptors in cancer cells. Integrating biological complexity into drug design principles allows us to create more effective, safer therapeutics that harmonise with the natural intricacies of living systems. Such innovations mark a paradigm shift in nanomedicine, bridging the gap between genotype-driven understanding and phenotype-targeted intervention, paving the way for precision medicine tailored to the unique molecular landscape of individual diseases.