CPT: Pharmacometrics & Systems Pharmacology最新文献

Quantitative model-based clinical trial simulation tools play a critical role in informing study designs through simulation before actual execution. These tools help drug developers explore various trial scenarios in silico to select a clinical trial design to detect therapeutic effects more efficiently, therefore reducing time, expense, and participants' burden. To increase the usability of the tools, user-friendly and interactive platforms should be developed to navigate various simulation scenarios. However, developing such tools challenges researchers, requiring expertise in modeling and interface development. This tutorial aims to address this gap by guiding developers in creating tailored R Shiny apps, using an example of a model-based clinical trial simulation tool that we developed for Duchenne muscular dystrophy (DMD). In this tutorial, the structural framework, essential controllers, and visualization techniques for analysis are described, along with key code examples such as criteria selection and power calculation. A virtual population was created using a machine learning algorithm to enlarge the available sample size to simulate clinical trial scenarios in the presented tool. In addition, external validation of the simulated outputs was conducted using a placebo arm of a recently published DMD trial. This tutorial will be particularly useful for developing clinical trial simulation tools based on DMD progression models for other end points and biomarkers. The presented strategies can also be applied to other diseases.

Type I interferons contribute to the pathogenesis of several autoimmune disorders, including systemic lupus erythematosus (SLE), systemic sclerosis, cutaneous lupus erythematosus, and myositis. Anifrolumab is a monoclonal antibody that binds to subunit 1 of the type I interferon receptor (IFNAR1). Results of phase IIb and phase III trials led to the approval of intravenous anifrolumab 300 mg every 4 weeks (Q4W) alongside standard therapy in patients with moderate-to-severe SLE. Here, we built a population physiology-based pharmacokinetic (PBPK) model of anifrolumab by utilizing the physiochemical properties of anifrolumab, binding kinetics to the Fc gamma neonatal receptor, and target-mediated drug disposition properties. A novel relative transcriptomics approach was employed to determine IFNAR1 expression in tissues (blood, skin, gastrointestinal tract, lungs, and muscle) using mRNA abundances from bioinformatic databases. The IFNAR1 expression and PBPK model were validated by testing their ability to predict clinical pharmacokinetics over a large dose range from different clinical scenarios after subcutaneous and intravenous anifrolumab dosing. The validated PBPK model predicted high unbound local concentrations of anifrolumab in blood, skin, gastrointestinal tract, lungs, and muscle, which exceeded its IFNAR1 dissociation equilibrium constant values. The model also predicted high IFNAR1 occupancy with subcutaneous and intravenous anifrolumab dosing. The model predicted more sustained IFNAR1 occupancy ≥90% with subcutaneous anifrolumab 120 mg once-weekly dosing vs. intravenous 300 mg Q4W dosing. The results informed the dosing of phase III studies of anifrolumab in new indications and present a novel approach to PBPK modeling coupled with relative transcriptomics in simulating pharmacokinetics of therapeutic monoclonal antibodies.

Pharmacokinetic studies are important for understanding drug disposition in the human body. However, pregnant and lactating women are often excluded from primary pharmacokinetic studies and as such there is often limited dosing information regarding drug use in pregnant and/or lactating women. The objectives of this interim analysis were to define the transfer of rifampicin to a breastfed infant and to determine the area under the concentration-time curve of rifampicin in maternal plasma, breastmilk and infant plasma. Performing this interim analysis enabled us to substantiate whether prior assumptions we made on several study design issues including patient sample size and pharmacokinetic sampling times held and whether we needed to amend our protocol or not. We enrolled lactating mothers on treatment for tuberculosis with their breastfeeding infants (below 12 months of age), performed intensive pharmacokinetic sampling (0-24 h post-dose) on plasma samples from both the mother, infant(s) and breastmilk samples from the mother on two separate occasions (once during the initiation phase and another during the continuation phase of tuberculosis treatment). The initial study design, including sampling times, was informed by a stochastic simulation and estimation exercise, with very limited prior breastmilk data. An interim analysis after recruiting 6 mother-infant pairs ascertained that our initial assumptions were ideal for achieving our study objectives and no amendments to the sampling times were necessary. Initial data from 6 mother-infant pairs show that rifampicin penetrates breastmilk with an approximate milk-to-plasma ratio of 0.169 and 0.189 on two separate visits. However, it was undetectable in most infants.

Gastrointestinal first-pass metabolism plays an important role in bioavailability and in drug-drug interactions. Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool to integrate these processes mechanistically. However, a correct bottom-up prediction of GI first-pass metabolism is challenging and depends on various model parameters like the level of enzyme expression and the basolateral intestinal mucosa permeability (P_mucosa). This work aimed to investigate if cytochrome P450 (CYP) 3A4 expression could help predict the first-pass effect using PBPK modeling or whether additional factors like P_mucosa do play additional roles using PBPK modeling. To this end, a systematic review of the absolute CYP3A expression in the human gastrointestinal tract and liver was conducted. The resulting CYP3A4 expression profile and two previously published profiles were applied to PBPK models of seven CYP3A4 substrates (alfentanil, alprazolam, felodipine, midazolam, sildenafil, triazolam, and verapamil) built-in PK-Sim®. For each compound, it was assessed whether first-pass metabolism could be adequately predicted based on the integrated CYP3A4 expression profile alone or whether an optimization of P_mucosa was required. Evaluation criteria were the precision of the predicted interstudy bioavailabilities and area under the concentration-time curves. It was found that none of the expression profiles provided upfront an adequate description of the extent of GI metabolism and that optimization of P_mucosa as a compound-specific parameter improved the prediction of most models. Our findings indicate that a pure bottom-up prediction of gastrointestinal first-pass metabolism is currently not possible and that compound-specific features like P_mucosa must be considered as well.

COVID-19 vaccines, including mRNA-1273, have been rapidly developed and deployed. Establishing the optimal dose is crucial for developing a safe and effective vaccine. Modeling and simulation have the potential to play a key role in guiding the selection and development of the vaccine dose. In this context, we have developed an immunostimulatory/immunodynamic (IS/ID) model to quantitatively characterize the neutralizing antibody titers elicited by mRNA-1273 obtained from three clinical studies. The developed model was used to predict the optimal vaccine dose for future pediatric trials. A 25-μg primary vaccine series was predicted to meet non-inferiority criteria in young children (aged 2-5 years) and infants (aged 6-23 months). The geometric mean titers and geometric mean ratios for this dose level predicted using the IS/ID model a priori matched those observed in the pediatric clinical study. These findings demonstrate that IS/ID models represent a novel approach to guide data-driven clinical dose selection of vaccines.

Tuberculosis is the most common opportunistic infection in individuals with HIV, and rifampicin is crucial in the treatment of tuberculosis. Drug-drug interactions complicate the use of DTG in HIV/TB co-infection, which makes drug administration more difficult. This study aimed to develop the population pharmacokinetic model of DTG when co-administered with rifampicin. The developed model was further used to investigate different dosing regimens. Forty HIV/TB-co-infected participants receiving DTG 50 mg once daily (OD) with food or DTG 50 mg twice daily (b.i.d.) without food were included in the analysis. Intensive pharmacokinetic samples were collected. The data were analyzed using a nonlinear mixed-effects modeling approach. A total of 332 DTG concentrations from 40 PLWH were analyzed. The pharmacokinetics of DTG co-administered with rifampicin can be best described by a one-compartment model with first-order absorption (incorporating lag time) and elimination. Total bilirubin was the only covariate that significantly affected CL/F. DTG 50 mg b.i.d. results in the highest proportion of individuals achieving in vitro IC₉₀ of 0.064 mg/L and in vivo EC₉₀ of 0.3 mg/L, while more than 90% of individuals receiving DTG 100 mg OD would achieve the in vitro IC₉₀ target. Therefore, DTG 100 mg OD could serve as an alternative regimen by minimizing the difficulty of drug administration. However, its clinical efficacy requires additional evaluation.

The increased incidence of dengue poses a substantially global public health challenge. There are no approved antiviral drugs to treat dengue infections. Ivermectin, an old anti-parasitic drug, had no effect on dengue viremia, but reduced the dengue non-structural protein 1 (NS1) in a clinical trial. This is potentially important, as NS1 may play a causal role in the pathogenesis of severe dengue. This study established an in-host model to characterize the plasma kinetics of dengue virus and NS1 with host immunity and evaluated the effects of ivermectin, using a population pharmacokinetic-pharmacodynamic (PK-PD) modeling approach, based on two studies in acute dengue fever: a placebo-controlled ivermectin study in 250 adult patients and an ivermectin PK-PD study in 24 pediatric patients. The proposed model described adequately the observed ivermectin pharmacokinetics, viral load, and NS1 data. Bodyweight was a significant covariate on ivermectin pharmacokinetics. We found that ivermectin reduced NS1 with an EC₅₀ of 67.5 μg/mL. In silico simulations suggested that ivermectin should be dosed within 48 h after fever onset, and that a daily dosage of 800 μg/kg could achieve substantial NS1 reduction. The in-host dengue model is useful to assess the drug effect in antiviral drug development for dengue fever.

Effective suppression of gastric acid secretion promotes healing of erosive esophagitis. Treatment guidelines recommend proton pump inhibitors (PPIs) and histamine H₂-receptor antagonists (H₂RAs). Emerging evidence also supports potassium-competitive acid blockers (P-CABs). The aim was to construct a mathematical model to examine the relationship between pH holding time ratios (HTRs) and erosive esophagitis healing rates with H₂RAs, PPIs and P-CABs. By literature search, we identified studies of H₂RAs, PPIs or P-CABs that reported mean pH >4 HTRs at steady state (days 5-8) and erosive esophagitis healing rates after 4 and/or 8 weeks. We aggregated treatments by drug class and developed a non-linear, mixed-effects model to explore the relationship between pH >4 HTRs and healing rates. The pH dataset included 82 studies (4297 participants; 201 dosage arms); healing rate data came from 103 studies (43,417 patients; 196 treatment arms). P-CABs achieved the longest periods with intragastric pH >4, and the highest healing rates after 4 and 8 weeks. The predicted probabilities of achieving ≥90% healing rates at 8 weeks were 74.1% for P-CABs, 17.3% for PPIs and 0% for H₂RAs. P-CABs provide the longest duration with intragastric pH >4 and, accordingly, the highest healing rates of erosive esophagitis.

Elinzanetant is a potent and selective dual neurokin-1 (NK-1) and -3 (NK-3) receptor antagonist that is currently developed for the treatment of women with moderate-to-severe vasomotor symptoms (VMS) associated with menopause. Here, we report the development of a population pharmacokinetic (popPK) model for elinzanetant and its principal metabolites based on an integrated dataset from 366 subjects (including 197 women with VMS) collected in 10 phase I or II studies. The pharmacokinetics of elinzanetant and its metabolites could be well described by the popPK model. Within the investigated dose range of 40-160 mg, the oral bioavailability of elinzanetant was dose independent and estimated to be 36.7%. The clearance of elinzanetant was estimated to be 7.26 L/h and the central and peripheral distribution volume were 23.7 and 168 L. No intrinsic or extrinsic influencing factors have been identified in the investigated population other than the effect of a high-fat breakfast on the oral absorption of elinzanetant. The popPK model was then coupled to a pharmacodynamic model to predict occupancies of the NK-1 and NK-3 receptors. After repeated once-daily administration of the anticipated therapeutic dose of 120 mg elinzanetant, the model-predicted median receptor occupancies are >99% for NK-1 and >94.8% for NK-3 during day and night-time, indicating sustained and near-complete inhibition of both target receptors during the dosing interval.