A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge.CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69–79; doi:10.1002/psp4.6; published online 25 February 2015

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient’s uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the “five rights”: the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug–drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Acetaminophen (APAP) is a widely used analgesic and antipyretic drug that undergoes extensive phase I and II metabolism. To better understand the kinetics of this process and to characterize the dynamic changes in metabolism and pharmacokinetics (PK) between children and adults, we developed a physiologically based PK (PBPK) model for APAP integrating in silico, in vitro, and in vivo PK data into a single model. The model was developed and qualified for adults and subsequently expanded for application in children by accounting for maturational changes from birth. Once developed and qualified, it was able to predict clinical PK data in neonates (0–28 days), infants (29 days to <2 years), children (2 to <12 years), and adolescents (12–17 years) following intravenous and orally administered APAP. This approach represents a general strategy for projecting drug exposure in children, in the absence of pediatric PK information, using previous drug‐ and system‐specific information of adults and children through PBPK modeling.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e80; doi:10.1038/psp.2013.55; advance online publication 16 October 2013

Recent clinical trials indicate that the use of azithromycin is associated with the emergence of macrolide resistance. The objective of our study was to simultaneously characterize free target site concentrations and correlate them with the MIC₉₀s of clinically relevant pathogens. Azithromycin (500 mg once daily [QD]) was administered orally to 6 healthy male volunteers for 3 days. The free concentrations in the interstitial space fluid (ISF) of muscle and subcutaneous fat tissue as well as the total concentrations in plasma and polymorphonuclear leukocytes (PMLs) were determined on days 1, 3, 5, and 10. All concentrations were modeled simultaneously in NONMEM 7.2 using a tissue distribution model that accounts for nonlinear protein binding and ionization state at physiological pH. The model performance and parameter estimates were evaluated via goodness-of-fit plots and nonparametric bootstrap analysis. The model we developed described the concentrations at all sampling sites reasonably well and showed that the overall pharmacokinetics of azithromycin is driven by the release of the drug from acidic cell/tissue compartments. The model-predicted unionized azithromycin (AZM) concentrations in the cytosol of PMLs (6.0 ± 1.2 ng/ml) were comparable to the measured ISF concentrations in the muscle (8.7 ± 2.9 ng/ml) and subcutis (4.1 ± 2.4 ng/ml) on day 10, whereas the total PML concentrations were >1,000-fold higher (14,217 ± 2,810 ng/ml). The total plasma and free ISF concentrations were insufficient to exceed the MIC₉₀s of the skin pathogens at all times. Our results indicate that the slow release of azithromycin from low pH tissue/cell compartments is responsible for the long terminal half-life of the drug and thus the extended period of time during which free concentrations reside at subinhibitory concentrations.