CPT: Pharmacometrics & Systems Pharmacology最新文献

Non-alcoholic fatty liver disease and non-alcoholic steatohepatitis describe a collection of liver conditions characterized by the accumulation of liver fat. Despite biopsy being the reference standard for determining the severity of disease, non-invasive measures such as magnetic resonance imaging proton density fat fraction (MRI-PDFF) and FibroScan® controlled attenuation parameter (CAP™) can be used to understand longitudinal changes in steatosis. The aim of this work was to describe the exposure-response relationship of ervogastat with or without clesacostat on steatosis, through population pharmacokinetic/pharmacodynamic (PK/PD) modeling of both liver fat measurements simultaneously. Population pharmacokinetic and exposure-response models using individual predictions of average concentrations were used to describe ervogastat/clesacostat PKPD. Due to both liver fat endpoints being continuous-bounded outcomes on different scales, a dynamic transform-both-sides approach was used to link a common latent factor representing liver fat to each endpoint. Simultaneous modeling of both MRI-PDFF and CAP™ was successful with both measurements being adequately described by the model. The clinical trial simulation was able to adequately predict the results of a recent Phase 2 study, where subjects given ervogastat/clesacostat 300/10 mg BID for 6 weeks had a LS means and model-predicted median (95% confidence intervals) percent change from baseline MRI-PDFF of -45.8% and -45.6% (-61.6% to -31.8%), respectively. Simultaneous modeling of both MRI-PDFF and CAP™ was successful with both measurements being adequately described. By describing the underlying changes of steatosis with a latent variable, this model may be extended to describe biopsy results from future studies.

Due to limited non-clinical and clinical data, European guidance recommends to discontinue breastfeeding when taking albendazole. The aim of this study was to consider the use of PBPK modeling to support the expected exposure in breastfed infants. A fully mechanistic PBPK approach was used to provide quantitative predictions of albendazole and its main active metabolite, albendazole sulfoxide, concentrations in plasma and breast milk of lactating women. The model predicted the exposure in adults and the large food effect, however, it does not predict all the clinical data for the exposure in children. Milk/plasma ratio predictions were also largely over-predicted for this lipophilic compound, but not for the less lipophilic metabolite. Predictions using the observed ratio and a worse-case exposure based on C_max predictions, suggest doses to children through milk will be low. However, more clinical data are required before full exposure predictions can be made to breastfed infants.

The COVID-19 pandemic, caused by SARS-CoV-2, has underscored the urgent need for innovative therapeutic approaches. Recent studies have revealed a complex interplay between the circadian clock and SARS-CoV-2 infection in lung cells, opening new avenues for targeted interventions. This systems pharmacology study investigates this intricate relationship, focusing on the circadian protein BMAL1. BMAL1 plays a dual role in viral dynamics, driving the expression of the viral entry receptor ACE2 while suppressing interferon-stimulated antiviral genes. Its critical position at the host-pathogen interface suggests potential as a therapeutic target, albeit requiring a nuanced approach to avoid disrupting essential circadian regulation. To enable precise tuning of potential interventions, we constructed a computational model integrating the lung cellular clock with viral infection components. We validated this model against literature data to establish a platform for drug administration simulation studies using the REV-ERB agonist SR9009. Our simulations of optimized SR9009 dosing reveal circadian-based strategies that potentially suppress viral infection while minimizing clock disruption. This quantitative framework offers insights into the viral-circadian interface, aiming to guide the development of chronotherapy-based antivirals. More broadly, it underscores the importance of understanding the connections between circadian timing, respiratory viral infections, and therapeutic responses for advancing precision medicine. Such approaches are vital for responding effectively to the rapid spread of coronaviruses like SARS-CoV-2.

Neural ordinary differential equations (NODEs) are an emerging machine learning (ML) method to model pharmacometric (PMX) data. Combining mechanism-based components to describe "known parts" and neural networks to learn "unknown parts" is a promising ML-based PMX approach. In this work, the implementation of low-dimensional NODEs in two widely applied PMX software packages (Monolix and NONMEM) is explained. Inter-individual variability is introduced to NODEs and proposals for the practical implementation of NODEs in such software are presented. The potential of such implementations is shown on various demonstrational datasets available in the Monolix model library, including pharmacokinetic (PK), pharmacodynamic (PD), target-mediated drug disposition (TMDD), and survival analyses. All datasets were fitted with NODEs in Monolix and NONMEM and showed comparable results to classical modeling approaches. Model codes for demonstrated PK, PKPD, TMDD applications are made available, allowing a reproducible and straight-forward implementation of NODEs in available PMX software packages.

Breakthrough bleeding (BTB) is a common side effect of hormonal contraception and is thought to impact adherence to combined oral contraceptives (COCs) but respective dose–response relationships are not yet fully understood. Therefore, the objective of this model-based meta-analysis (MBMA) was to establish dose–response for COCs containing different progestin/EE combinations using BTB as the pharmacodynamic endpoint. Data from 25 studies containing BTB information of 4 progestins (desogestrel, drospirenone, gestodene, and levonorgestrel) in combination with ethinyl estradiol (EE) at various dose levels was used for this analysis. The results of our MBMA show that BTB is significantly increased upon initiation of COC use but subsides over time. The time needed for BTB to return to baseline depends on the EE dose and differs marginally between progestins during the initial months of use at the same EE dose. BTB typically returns to baseline within 3 months at the highest (30 μg) dose, whereas it can take significantly longer to reestablish a regular bleeding pattern at lower EE doses (15 and 20 μg), irrespective of the progestin used. The dose–response relationships established for BTB across different progestin/EE combinations can now be used to support the selection of optimal COC dosing/treatment regimens and serve as the scientific basis for evaluating the impact of clinically relevant factors, including drug–drug interactions and demographics, on BTB.

Age and aging are important predictors of health status, disease progression, drug kinetics, and effects. The purpose was to develop ensemble learning-based physiological age (PA) models for evaluating drug metabolism. National Health and Nutrition Examination Survey (NHANES) data were modeled with ensemble learning to obtain two PA models, PA-M1 and PA-M2. PA-M1 included body composition, blood and urine biomarkers, and disease variables as predictors. PA-M2 had blood and urine-derived variables as predictors. Activity phenotypes for cytochrome-P450 (CYP) CYP2E1, CYP1A2, CYP2A6, xanthine oxidase (XO), and N-acetyltransferase-2 (NAT-2) and telomere attrition were assessed. Bayesian networks were used to obtain mechanistic systems pharmacology model structures for PA. The study included n = 22,307 NHANES participants (51.5% female, mean age 46.0 years, range: 18-79 years). The PA-M1 and PA-M2 distributions had greater dispersion across age strata with a right skew for younger age strata and a left skew for older age strata. There was no evidence of algorithmic bias based on sex or race/ethnicity. Klotho, lean body mass, glycohemoglobin, and systolic blood pressure were the top four predictors for PA-M1. Glycohemoglobin, serum creatinine, total cholesterol, and urine creatinine were the top four predictors for PA-M2. The models also performed satisfactorily in independent validation. Model-predicted PA was associated with CYP2E1, CYP1A2, CYP2A6, XO, and NAT-2 activity. Telomere attrition was associated with greater PA-M1 and PA-M2. Ensemble learning models provide robust assessments of PA from easily obtained blood and urine biomarkers. PA is associated with Phase I drug-metabolizing enzyme phenotypes.

Drug-target binding determines a drug's pharmacodynamics but can also have a profound impact on a drug's pharmacokinetics, known as target-mediated drug disposition (TMDD). TMDD models describe the influence of drug-target binding and target turnover on unbound drug concentrations and are frequently used for biologics and drugs with nonlinear plasma pharmacokinetics. For drug targets expressed in tissues, the effect of TMDD may not be detected when analyzing plasma concentration curves, but it might still affect tissue concentrations and occupancy. This review aimed to investigate the likeliness of such a scenario by reviewing the literature for a typical range of TMDD parameter values and their impact on local drug concentrations and target occupancy in a whole-body PBPK model with TMDD. Our analysis demonstrated that tissue drug concentrations are impacted and significantly depleted in many physiological scenarios. In contrast, the effect on plasma concentrations is much lower, specifically for smaller organs with lower perfusion. Moreover, in scenarios with fast internalization of the drug-target complex, the distribution of large molecules from plasma to tissue interstitial space emerges as a rate-limiting step for the drug-target interaction. These factors may lead to overpredicting local drug concentrations when considering only plasma pharmacokinetics. A sensitivity analysis revealed the high and not always intuitive impact of drug-specific parameters, including the drug molecule hydrodynamic radius, dissociation constant (K_d), drug-target complex internalization rate constant (k_int), and target dissociation rate constant (k_off), on the drug's pharmacokinetics. Our analysis demonstrated that tissue TMDD needs to be considered even if plasma pharmacokinetics are linear.

Edoxaban is an orally active inhibitor of activated factor X (FXa). Population pharmacokinetic (PK) and pharmacodynamic (PD) analyses were performed to characterize the PK and PK-PD relationships of edoxaban in pediatric patients to identify the covariates that may contribute to inter-subject variability in PK and PD of edoxaban in pediatric patients, and to compare the PK and PD data between pediatric and adult patients. The pediatric PK of edoxaban was best described by a two-compartment model with transit compartments, first-order oral absorption, and linear elimination. The estimated glomerular filtration rate (eGFR), body weight, and post-menstrual age were the significant covariates explaining variability in edoxaban PK among pediatric patients. A function based on renal maturation was applied to edoxaban clearance. The clearance for a 70 kg patient with an eGFR of 110 mL/min/1.73 m² was estimated to be 42.9 L/h (CV ~ 31.8%). PK simulation showed that exposures across five pediatric age groups were comparable to that in adult patients receiving 60 mg once daily dose. The PK-PD relationship for anti-factor Xa was best fit with an E_max (8.65 IU/mL) model with an EC₅₀ of 631 ng/mL. The PK-PD relationships for activated partial thromboplastin time and prothrombin time were best fit with linear models (slopes of 0.0467, and 0.0415 s mL/ng, respectively). In addition, due to the small number of efficacy and safety events, an exploratory analysis did not detect a correlation between efficacy events (recurrent venous thromboembolism) or safety events (clinically relevant bleeding) and edoxaban exposure.

Ritonavir-boosted atazanavir (ATV/r) and rifampicin are mainstays of second-line antiretroviral and multiple anti-TB regimens, respectively. Rifampicin induces CYP3A4, a major enzyme involved in atazanavir metabolism, causing a drug–drug interaction (DDI) which might be exaggerated in pregnancy. Having demonstrated that increasing the dose of ATV/r from once daily (OD) to twice daily (BD) in non-pregnant adults can safely overcome this DDI, we developed a pregnancy physiologically based pharmacokinetic (PBPK) model to explore the impact of pregnancy. Predicted pharmacokinetic parameters were validated with separate clinical datasets of ATV/r alone (NCT03923231) and rifampicin alone in pregnant women. The pregnancy model was considered validated when the absolute average fold error (AAFE) for C_trough and AUC_0-24 of both drugs were <2 when comparing predicted vs. observed data. Thereafter, predicted atazanavir C_trough was compared against its protein-adjusted IC₉₀ (14 ng/mL) when simulating the co-administration of ATV/r 300/100 mg OD and rifampicin 600 mg OD. Pregnancy was predicted to increase the rifampicin DDI effect on atazanavir. For the dosing regimens of ATV/r 300/100 mg OD, ATV/r 300/200 mg OD, and ATV/r 300/100 mg BD (all with rifampicin 600 mg OD), predicted atazanavir C_trough was above 14 ng/mL in 29%, 71%, and 100%; and 32%, 73% and 100% of the population in second and third trimesters, respectively. Thus, PBPK modeling suggests ATV/r 300/100 mg BD could maintain antiviral efficacy when co-administered with rifampicin 600 mg OD in pregnancy. Clinical studies are warranted to confirm safety and efficacy in pregnancy.