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.

Keywords: antimicrobial therapy; mechanism-based PK/PD models; multi-omics; pharmacokinetic/pharmacodynamic (PK/PD); precision dosing; systems pharmacology.

FIGURE 1

Antimicrobial pharmacokinetic-pharmacodynamic is affected by three main factors: patient, antimicrobial and microorganism factors. Several approaches are implemented in the clinical practice settings or under the experimental phase to provide individualized dosing and address some degree of variabilities. AUC: area under the curve; MIC: minimum inhibitory concentration; C_max: maximum concentration; fT > MIC: Time that free serum concentration above minimum inhibitory concentration; C_max:MIC: ratio of maximum concentration to minimum inhibitory concentration; AUC:MIC: ratio of area under the concentration-curve to minimum inhibitory concentration.

FIGURE 2

Schematic representation of omics workflow for genomics, transcriptomics, proteomics and metabolomics approaches. Multi-omics data integration can be used for refining and reconciling modelling predictions to construct computational simulations. RNA-Seq: RNA-sequencing; LC-MS: liquid chromatography-mass spectrometry; MS: Mass spectrometry; 2DE: two-dimensional electrophoresis; NMR: nuclear magnetic resonance; GC-MS: gas chromatography coupled to mass spectrometry; UPLC-MS: ultra-high-performance liquid chromatography coupled to mass spectrometry. Created with BioRender.com.