CPT: Pharmacometrics & Systems Pharmacology最新文献

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.

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.

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.

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.

Breastfeeding is the most complete nutritional method of feeding infants, but several impediments affect the decision to breastfeed, including questions of drug safety for medications needed during lactation. Despite recent FDA guidance, few labels provide clear dosing advice during lactation. Physiologically based pharmacokinetic modeling (PBPK) is well suited to mechanistically explore pharmacokinetics and dosing paradigms to fill gaps in the absence of extensive clinical studies and complement existing real-world data. For lactation-focused PBPK (Lact-PBPK) models, information on system parameters (e.g., expression of drug transporters in mammary epithelial cells) is sparse. The breast cancer resistance protein (BCRP) is expressed on the apical side of mammary epithelial cells where it actively transports drugs/substrates into milk (reported milk: plasma ratios range from 2 to 20). A critical review of BCRP and its role in lactation was conducted. Longitudinal changes in BCRP mRNA expression have been identified in women with a maximum reached around 5 months postpartum. Limited data are available on the ontogeny of BCRP in infant intestine; however, data indicate lower BCRP abundance in infants compared to adults. Current status of incorporation of drug transporter information in Lact-PBPK models to predict active secretion of drugs into breast milk and consequential exposure of breast-fed infants is discussed. In addition, this review highlights novel clinical tools for evaluation of BCRP activity, namely a potential non-invasive BCRP biomarker (riboflavin) and liquid biopsy that could be used to quantitatively elucidate the role of this transporter without the need for administration of drugs and to inform Lact-PBPK models.

Multiple myeloma (MM) treatment guidelines recommend waiting for formal progression criteria (FPC) to be met before proceeding to the next line of therapy. As predicting progression may allow early switching to next-line therapy while the disease burden is relatively low, we evaluated the predictive accuracy of a mathematical model to anticipate relapse 180 days before the FPC is met. A subset of 470/1143 patients from the IA16 dataset who were initially treated with VRd (Velcade (bortezomib), Revlimid (lenalidomide), and dexamethasone) in the CoMMpass study (NCT01454297) were randomly split 2:1 into training and testing sets. A model of M-protein dynamics was developed using the training set and used to predict relapse probability in patients in the testing set given their response histories up to 12 or more months of treatment. The predictive accuracy of this model and M-protein "velocity" were assessed via receiver operating characteristics (ROC) analysis. The final model was a two-population tumor growth inhibition model with additive drug effect and transit delay compartments for cell killing. The ROC area under the curve value of relapse prediction 180 days ahead of observed relapse by FPC was 0.828 using at least 360 days of response data, which was superior to the M-protein velocity ROC score of 0.706 under the same conditions. The model can predict future relapse from early M-protein responses and can be used in a future clinical trial to test whether early switching to second-line therapy results in better outcomes in MM.