CPT: Pharmacometrics & Systems Pharmacology最新文献

Treatment of breastfeeding mothers with malaria is challenging due to the lack of information describing drug exposure in milk and the daily dose to the breastfed infant. Physiologically based pharmacokinetic (PBPK) modeling was used to predict milk-to-plasma (M/P) ratios, infant daily doses (IDD) and relative infant doses (RID) for five antimalarials with clinical lactation data (chloroquine, pyrimethamine, piperaquine, mefloquine and primaquine). In all cases, RID was correctly categorized as above or below the WHO proposed cut-off of 10% using two prediction models. Predicted M/P ratios were within 2-fold of observations for 63% of studies using both models (75% and 100% were within 3-fold for Models 1 and 2, respectively). M/P ratios, IDD and RID were predicted prospectively for seven antimalarials. RID was < 10% for amodiaquine, dihydroartemisinin, proguanil, and pyronaridine, and > 10% for lumefantrine and tafenoquine. For atovaquone, RID was > 10% with Model 1 but not Model 2. Predicted IDD were considerably lower than licensed doses for infants except for lumefantrine (Model 2) and tafenoquine (not licensed in < 2 years). Predictions were sensitive to drug properties (plasma protein binding and lipophilicity) and milk properties (creamatocrit and pH). This analysis demonstrates the utility of PBPK to predict milk exposure in the absence of clinical lactation information. These prediction methodologies can be used, alongside any licensed dosing information for < 1 year-olds, to evaluate whether a clinical lactation study is necessary and to inform drug label or policy recommendations. The ultimate goal is to better inform optimal treatment for lactating women supporting malaria eradication.

A variety of classical machine learning (ML) approaches has been developed over the past decade aiming to individualize drug dosages based on measured plasma concentrations. However, the interpretability of these models is challenging as they do not incorporate information on pharmacokinetic (PK) drug disposition. In this work we compare drug plasma concentraton predictions of well-known population PK (PopPK) modeling with classical machine learning models and a newly proposed scientific machine learning (MMPK-SciML) framework. MMPK-SciML allows to estimate PopPK parameters and their inter-individual variability (IIV) using multimodal covariate data of each patient and does not require assumptions about the underlying covariate relationships. A dataset of 541 fluorouracil (5FU) plasma concentrations as example for an intravenously administered drug and a dataset of 302 sunitinib and its active metabolite concentrations each as example for an orally administered drug were used for analysis. Whereas classical ML models were not able to describe the data sufficiently, MMPK-SciML allowed us to obtain accurate drug plasma concentration predictions for test patients. In case of 5FU, goodness-of-fit shows that the MMPK-SciML approach predicts drug plasma concentrations more accurately than PopPK models. For sunitinib, we observed slightly less accurate drug concentration predictions compared to PopPK. Overall, MMPK-SciML has shown promising results and should therefore be further investigated as a valuable alternative to classical PopPK modeling, provided there is sufficient training data.

Grazoprevir (GZR), a direct-acting agent for hepatitis C virus, is recognized as a substrate for organic anion transporting polypeptide 1B (OATP1B), cytochrome P450 3A (CYP3A), and P-glycoprotein (P-gp). The objective of the present study was to elucidate the contribution of these molecules to the nonlinear pharmacokinetics of GZR using a physiologically based pharmacokinetic (PBPK) model. Utilizing plasma concentration-time profiles of GZR derived from reported dose-escalation (50-800 mg) clinical studies and cumulative excretion data, around 10 parameters, including Michaelis constants (K_m) for OATP1B, CYP3A, and P-gp, were estimated via a cluster Gauss-Newton method (CGNM). Parameter combinations that could reproduce the clinical data of GZR were obtained; however, discrepancies were noted between the in vivo estimated K_m and the corresponding in vitro K_m. Next, by incorporating the in vitro K_m values into our PBPK-CGNM analyses utilizing a penalized parameter method, newly obtained parameter combinations appropriately reflected both the in vivo and in vitro observations. Particularly regarding OATP1B, while saturation of uptake was not clearly observed in the in vitro experiments without human serum albumin (HSA), K_m values capable of explaining in vivo saturation were obtained under physiological HSA concentrations. By estimating the extent of saturation for each molecule in the liver and intestine and conducting sensitivity analyses of the K_m values, it was inferred that OATP1B3 contributed the most to the nonlinearity of plasma GZR concentrations, followed by P-gp. In conclusion, the PBPK-CGNM, supplemented by penalized in vitro parameters, was shown to be effective for analyzing complex pharmacokinetics involving drug transporters and enzymes.

IMscin001 is a two-part dose-finding (Phase Ib) and -confirmation (Phase III) study to evaluate atezolizumab pharmacokinetics of subcutaneous (SC) compared with intravenous (IV) administration in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC). The objectives of the current analyses were to characterize the population pharmacokinetics (popPK) of atezolizumab and to determine the relationship between atezolizumab exposure and safety and efficacy endpoints in IMscin001. A previously validated IV popPK model was extended to add SC absorption parameters using SC and IV data from Phase Ib and Phase III of IMscin001 (N = 435), and covariate effects were investigated on the SC absorption parameters. The exposure-response (ER) investigation was performed using SC data following 1875 mg every three weeks (q3w) administration in the Phase III portion of IMscin001 (N = 246). The clinical endpoints were objective response rate, progression-free survival, or overall survival for efficacy, serious adverse events, special interest adverse events, Grades 3-5 adverse events, infusion-related reaction, or injection site reactions for safety. Atezolizumab SC absorption was characterized by a first-order absorption with a bioavailability of 71.8% and an absorption rate constant of 0.304 day^-1. The extended popPK model was adequate to predict atezolizumab PK after IV and SC administrations and to predict individual exposure metrics. For all efficacy and safety endpoints, atezolizumab exposure was not statistically significant (p-value > 0.05) in the ER models. The non-inferior popPK exposure and flat ER results supported atezolizumab SC dose at 1875 mg q3w.

Acetaminophen (APAP) has been formulated as immediate-, modified-, and extended-release tablets (APAP-IR, -MR, and -ER, respectively). However, there was concern that APAP-MR previously available in Europe could form a bezoar after a large overdose, leading to delayed absorption and atypical pharmacokinetics (PK) compared to APAP-IR, and that current treatment guidelines developed for APAP overdose to prevent severe hepatotoxicity are inappropriate for APAP-MR. In contrast, APAP-ER caplets available in the United States are designed with an IR layer and an erodible ER layer. Using modeling and simulation, predicted PK and hepatotoxicity biomarkers following various acute overdose and repeated supratherapeutic ingestion (RSTI) scenarios with APAP-IR and APAP-ER were compared to investigate the differences between these two formulations. The existing APAP-IR representation within DILIsym v8A, a quantitative systems toxicology model of drug-induced liver injury, was updated, and an APAP-ER model was developed, using newly acquired in vitro (e.g., tiny-TIMsg) and clinical data. The model and simulated populations (SimPops) representing healthy adults were extensively validated, before simulating PK and three clinically useful hepatic biomarkers after various overdose scenarios. On average, APAP exposure after acute overdose and RSTI in healthy adults was predicted to be slightly lower for APAP-ER compared to APAP-IR, partially due to lower APAP absorption for APAP-ER, while not markedly impacting the expected time course of APAP plasma concentrations. Similar hepatic biomarker profiles were predicted for both APAP formulations. Based on these results, the APAP overdose consensus treatment guidelines updated in 2023 are not further impacted by this report.

Enhanced Recovery After Surgery (ERAS) protocols for cesarean deliveries (CDs) utilize multimodal pain management strategies that often include gabapentin. While gabapentin is excreted in breast milk, its pharmacokinetics in immediately postpartum lactating women are not known. This observational pharmacokinetic study (NCT05099484) enrolled 21 healthy singleton pregnant individuals, ≥ 18 years old, undergoing CD and planning to breastfeed. Participants received 300 mg oral gabapentin before CD and every 6 h for 48 h per hospital protocol. Serial maternal plasma and breast milk samples were collected over a single dosing interval. Gabapentin pharmacokinetics were assessed using two structurally distinct population pharmacokinetic (POPPK) models to describe transfer of drug into breast milk utilizing (A) milk-to-plasma ratio and (B) inter-compartmental rate constants. These models were then used to estimate exposure to breastfed infants. Postpartum gabapentin plasma concentrations fit a 1-compartment model that was adapted to include breast milk concentrations. The two POPPK models both estimated relative infant doses (RID_0-48h) of gabapentin < 0.15% of maternal dose within the first 48 h postpartum. Infant daily dose (IDD) from 24 to 48 h was estimated to be 0.0137 (0.0058-0.0316) mg/kg/day and 0.0139 (0.00041-0.0469) mg/kg/day by models A and B, respectively. These findings indicate limited neonatal exposure to gabapentin administered as part of a postpartum enhanced recovery after surgery protocol.

Sulfonylureas (SU) are commonly prescribed as oral hypoglycemic agents for the management of diabetes mellitus (DM). We postulated that SU possess antimicrobial properties due to their structural resemblance to the antimicrobial agent sulfamethoxazole. Using data from Taiwan's National Health Insurance Research Database, we enrolled patients diagnosed with DM between 2000 and 2013 and followed them for a three-year period. Patients who consistently used SU were categorized into the SU cohort, while those who had never used SU formed the non-sulfonylurea (non-SU) cohort. The primary study endpoints were diagnoses of pneumonia and urinary tract infections (UTIs). Within the database, we identified a total of 15,458,554 patients with DM, with 754,601 (4.88%) in the SU cohort and 2,244,436 (14.52%) in the non-SU cohort. After individual matching based on age, gender, index day, and propensity score of comorbidities, we included 663,056 patients in each cohort. The cumulative incidence of pneumonia and UTI was 29,239 (4.41%) and 60,733 (9.16%) in the SU cohort, respectively, and 24,599 (3.71%) and 56,554 (8.53%) in the non-SU cohort, respectively. Our findings indicated that the use of SU increased the risk of pneumonia (1.26-1.60 times) and UTI (1.13-1.22 times), while also potentially offsetting the protective effects of metformin. This pharmacoepidemiological study represents a concerted effort to assess latent drug properties that may have a significant impact on the clinical management of patients with DM.

Mavacamten is a cardiac myosin inhibitor for adults with obstructive hypertrophic cardiomyopathy (HCM). Dose optimization is performed 4 weeks after starting mavacamten, guided by periodic echo measurements of Valsalva left ventricular outflow tract gradient (VLVOTg) and left ventricular ejection fraction (LVEF). Previously, a population pharmacokinetic (PPK) model was developed and exposure-response (E-R) of VLVOTg (efficacy) and LVEF (safety) was used to identify the mavacamten titration regimen with the optimal benefit/risk ratio, now included in the US prescribing information. Mavacamten is metabolized primarily by cytochrome P450 2C19 (CYP2C19) (74%), a highly polymorphic enzyme. China has a higher prevalence of poor CYP2C19 metabolizer phenotype compared with the global population; therefore, a previous model was adapted to include Chinese patients with obstructive HCM to identify the optimal dosing regimen for this population. Data from a phase I (healthy Chinese volunteers) and a phase III (EXPLORER-CN, NCT05174416; Chinese patients with obstructive HCM) trial of mavacamten were added to the previous PPK and E-R models, and the observed VLVOTg and LVEF from EXPLORER-CN were successfully simulated. Next, five echocardiography-guided titration regimens (plus the EXPLORER-CN regimen) using representative or equal CYP2C19 phenotypes were simulated. The final simulated regimen recommended with an optimal benefit/risk profile across CYP2C19 phenotypes included: down-titration at Week 4 (if VLVOTg < 20 mmHg), restart at Week 12, and up-titration at Week 12 (for VLVOTg ≥ 30 mmHg and LVEF ≥ 55%), and every 12 weeks thereafter. This supports the previously recommended regimen for Chinese patients with obstructive HCM, now approved by the National Medicinal Products Administration.

Protein therapeutics have emerged as an exceedingly promising treatment modality in recent times but are predominantly given as intravenous administration. Transitioning to subcutaneous (SC) administration of these therapies could significantly enhance patient convenience by enabling at-home administration, thereby potentially reducing the overall cost of treatment. Approaches that enable sustained delivery of subcutaneously administered biologics offer further advantages in terms of less frequent dosing and better patient compliance. Controlled release technologies, such as hydrogels and subcutaneous implantable technologies, present exciting solutions by enabling the gradual release of biologics from the delivery system. Despite their substantial potential, significant hurdles remain in appropriately applying and integrating these technologies with the ongoing development of complex biologic-based therapies. We evaluate the potential impact of subcutaneously delivered controlled release systems on the downstream pharmacokinetics (PK) of several FDA-approved biologics by employing rigorous mathematical analysis and predictive PK simulations. By leveraging linear time-invariant (LTI) systems theory, we provide a robust framework for understanding and optimizing the release dynamics of these technologies. We demonstrate simple quantitative metrics and approaches that can inform the design and implementation of controlled release technologies. The findings highlight key opportunity areas to reduce dosing frequency, stabilize concentration profiles, and synergize the codelivery of biologics, calling for collaboration between drug delivery and PK scientists to create the most convenient, optimized, and effective precision therapies.

A growing number of covariate modeling methods have been proposed in the field of popPK modeling, but limited information exists on how they all compare. The objective of this study was to perform a systematic review of all popPK covariate modeling methods, focusing on assessing the existing knowledge on their performances. For each method of each article included in this review, evaluation setting, performance metrics along with their associated values, and relative computational times were reported when available. Evaluation settings report was done for uncertainty assessment of communicated results. Results showed that EBEs-based ML methods stood out as the best covariate selection methods. AALASSO, a hybrid genetic algorithm, FREM with a clinical significance criterion and SCM+ with stagewise filtering were the best covariate model selection techniques-AALASSO being the very best one. Results also showed a lack of consensus on how to benchmark simulated datasets of different scenarios when evaluating method performances, but also on which metrics to use for method evaluation. We propose to systematically report TPR (sensitivity), FPR (Type I error), FNR (Type II error), TNR (specificity), covariate parameter error bias (MPE) and precision (RMSE), clinical relevance, and model fitness by means of BIC, concentration prediction error bias (MPE), and precision (RMSE) of new proposed methods and compare them with SCM. We propose to systematically combine covariate selection techniques to SCM or FFEM to allow for comparison with SCM. We also highlight the need for an open-source benchmark of simulated datasets on a representative set of scenarios.