Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing

Abstract

In the era of “Bad Bugs, No Drugs,” optimizing antibiotic therapy against multi-drug resistant (MDR) pathogens is crucial. Mathematical modelling has been employed to further optimize dosing regimens. These models include mechanism-based PK/PD models, systems-based models, quantitative systems pharmacology (QSP) and population PK models. Quantitative systems pharmacology has significant potential in precision antimicrobial chemotherapy in the clinic. Population PK models have been employed in model-informed precision dosing (MIPD). Several antibiotics require close monitoring and dose adjustments in order to ensure optimal outcomes in patients with infectious diseases. Success or failure of antibiotic therapy is dependent on the patient, antibiotic and bacterium. For some drugs, treatment responses vary greatly between individuals due to genotype and disease characteristics. Thus, for these drugs, tailored dosing is required for successful therapy. With antibiotics, inappropriate dosing such as insufficient dosing may put patients at risk of therapeutic failure which could lead to mortality. Conversely, doses that are too high could lead to toxicities. Hence, precision dosing which customizes doses to individual patients is crucial for antibiotics especially those with a narrow therapeutic index. In this review, we discuss the various strategies in optimizing antimicrobial therapy to address the challenges in the management of infectious diseases and delivering personalized therapy.

Virtual Patient Simulation Using Copula Modeling

“…We evaluated the performance of copulas compared with other simulation methods. Although performing well in lower dimensions, we observed increasing underestimation in higher dimensions for CD, making the method less suitable for simulations in higher dimension, which is an increasingly important feature, due to the rise in models which include multiple biomarkers and clinical covariates 31 . The MVND showed very promising results in terms of capturing the correlation ( Figure ).…”

“…Although performing well in lower dimensions, we observed increasing underestimation in higher dimensions for CD, making the method less suitable for simulations in higher dimension, which is an increasingly important feature, due to the rise in models which include multiple biomarkers and clinical covariates. 31 The MVND showed very promising results in terms of capturing the correlation (Figure 2). However, this is an inherent feature of how the MVND is estimated, which is based on the mean, standard deviation, and covariance.…”