Physiology-informed pharmacometric models for bacterial infections

Bacterial infections remain a major global health challenge, further exacerbated by the rise of antibiotic resistance. Understanding the interplay between pathogen dynamics, host responses, and drug pharmacokinetics (PK) and pharmacodynamics (PD) is crucial for optimising treatment strategies. Pharmacometric modelling offers a powerful approach to integrating preclinical and clinical data, enhancing predictions of drug efficacy and infection progression. This thesis applies physiology-informed pharmacometric modelling to characterise bacterial infections and antibiotic action under various physiological conditions. Four key studies underpin this work. First, a cytokine response model was developed to describe inflammatory dynamics in a porcine sepsis model, capturing the role of endotoxin in immune activation. The model demonstrated how bacterial exposure patterns influence cytokine release, providing potential for model application in sepsis drug development. Second, physiologically-based pharmacokinetic (PBPK) modelling was employed to investigate meropenem disposition in septic patients, revealing sepsis-induced alterations in drug distribution and elimination. The findings emphasise the need for refined PBPK approaches that incorporate dynamic changes in renal transporter activity and fluid balance in septic patients. Third, plasma effects on bacterial time-kill dynamics were explored, revealing enhanced bacterial suppression in plasma-spiked media compared to traditional broth models. The results suggest that plasma components, possibly immune factors such as the complement system, influence bacterial growth and antibiotic activity. Finally, a lung-mimicking transwell tissue model was utilised to study antibiotic PKPD in lung infections. The developed model captured notable differences in bacterial growth and antibiotic activity between broth and lung tissue models, demonstrating drug-specific interactions with lung-like conditions. This thesis advances the understanding of the dynamics of the host-pathogen interactions, the antibiotic PK and PKPD under physiological conditions, and the utility of novel in vitro models for improved in vivo translation. By bridging gaps between experimental and clinical data, these findings may contribute to the development of more effective, personalised treatment strategies for infectious diseases.