CPT: Pharmacometrics & Systems Pharmacology最新文献

In this second part of a case study on the practical use of model-informed drug development (MIDD), we describe the clinical development of AZD8233, a novel proprotein convertase subtilisin/kexin type 9 (PCSK9) antisense oligonucleotide, from phase 2b to the start of phase 3. The case study exemplifies the use of MIDD to answer key design questions for the phase 3 program, including the design of a pivotal phase 3 study, a head-to-head study, and a cardiovascular outcome study informed by model-averaging analysis. Extensive phase 3 study simulations assessed the impact of drop-out, readout timing, dose frequency, and analysis method on study outcomes. The final phase 3 design assumed around 1% monthly drop-out (based on other PCSK9 inhibitor trials), used an EMA/FDA-approved analysis method, and set the primary readout at week 16. A simulated study predicted a reduction in low-density lipoprotein cholesterol (LDL-C) by week 16 of −69% with AZD8233 60 mg every 4 weeks. A virtual head-to-head study showed AZD8233 lowered LDL-C by 27% more than an active competitor (inclisiran) at day 270. Predicted cardiovascular relative risk reduction (RRR) for AZD8233 on top of statins ranged from 24% to 49% based on model choice; a model-averaging approach predicted an RRR of 27% assuming 63% LDL-C reduction from a 130 mg/dL baseline. This case study highlights the importance of cross-functional collaboration and other key MIDD enablers to ensure that MIDD extends beyond a simple simulation exercise and is instead considered an integral part of drug development dedicated to quantitative decision making.

Bosentan is the first approved oral medication for pulmonary arterial hypertension, yet the black-box warning on its labeling implies a substantial risk of liver injury associated with bosentan exposure. The risk assessment of bosentan-induced liver injury requires a thorough understanding of the underlying mechanisms, for which there is accumulating evidence. Integrating these mechanisms with clinical liver bosentan concentration would enable a more dynamic and relevant risk assessment. This study designed a workflow of physiologically−based pharmacokinetic (PBPK) model development to capture bosentan's hepatic disposition and predict the (intra)hepatic bosentan exposure. Specifically, clinical plasma and excretion data of bosentan were used to minimize the uncertainty in estimating the hepatic clearance. The model predictions were well overlapped with observations in the systemic circulation and excretion. Furthermore, the model-derived intrinsic hepatic clearance was comparable with the one derived from a clinical study. These results reflected confidence in the model's capability to predict hepatic bosentan exposure. The model-simulated steady-state unbound exposure to bosentan in hepatocytes and liver tissue ranged from 1.65 to 34.1 ng/mL following twice-daily 125-mg oral doses. The ratio of the simulated unbound concentration between the liver matrices and systemic plasma was between 0.80 and 2.93 across the therapeutic dosing regimens. In summary, a bosentan PBPK model was successfully developed with the designed workflow and was able to predict the hepatic disposition of bosentan. The developed model can be applied to generate hepatic bosentan exposure that bridges the toxicological mechanistic findings from in vitro to in vivo, assisting in risk assessment.

The development of new treatments for rare neurological diseases (RNDs) may be very challenging due to limited natural history data, lack of relevant biomarkers and clinical endpoints, small and heterogeneous patient populations, and other complexities. A systematic approach is needed for comparing various design and analysis strategies to identify “optimal” approaches for a clinical trial in a chosen RND with the given resource constraints. For this purpose, we propose a pharmacometrics-informed clinical scenario evaluation framework (CSE-PMx), which includes some important research hallmarks relevant to RND clinical trials: a disease progression model for simulating individual longitudinal outcomes, the choice of a suitable randomization method for trial design, and an option to perform subsequent statistical analysis with randomization tests. We illustrate the utility of CSE-PMx for an exemplary randomized trial to compare the disease-modifying effect of an experimental treatment versus control in patients with Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). In the considered example, our simulation evidence suggests that a nonlinear mixed-effects model (NLMEM) with a population-based likelihood ratio test analysis is valid, robust, and more powerful than some conventional methods such as two-sample t-test, analysis of covariance (ANCOVA), or a mixed model with repeated measurements (MMRM). Our proposed framework is very flexible and generalizable to clinical research in other rare disease indications.

Underexposure to infliximab often leads to loss of response in patients with inflammatory bowel disease (IBD). Model-informed precision dosing (MIPD) offers a superior approach to maintaining target infliximab concentrations compared to empirical dosage adjustment. This study aims to externally validate the population pharmacokinetic (PK) models implemented in TDMx, an online MIPD dashboard system, for adult and pediatric Korean IBD patients before clinical use. This retrospective study included 199 IBD patients (142 adults, 57 children) treated with intravenous infliximab at Seoul National University Hospital (Seoul, Republic of Korea) from 2019 to 2023. Three adult and seven pediatric models were evaluated based on accuracy, precision, goodness of fit plots, prediction-corrected visual predictive checks, and normalized prediction distribution errors. For adults, the Passot model showed the best fit (mean percentage error (MPE) 26.4%, mean absolute error (MAE) 1.1 mg/L, relative root-mean square error (rRMSE) 159.0%), whereas all pediatric models were unsuitable for clinical use (MPE 30.4%–143.4%, MAE 1.4–2.6 mg/L, rRMSE 96.3%–564.0%). Predictive performance was compared between datasets with or without accurate information on antibodies-toward-infliximab (ATI), as well as with and without previous concentrations. Assuming all patients were ATI positive improved predictive performance, likely due to the inherent positive bias of the population PK models. Incorporating previous concentrations improved predictions for adult models, achieving acceptable accuracy and precision (Passot model: MPE 17.5%, MAE 1.8 mg/L, rRMSE 80.3% with one concentration). However, pediatric models remained clinically unacceptable, highlighting the need to develop models specifically tailored for this population.

Drug development in pediatric rare diseases is complicated by practical and ethical constraints on clinical trial design, stemming from small, highly heterogeneous, and vulnerable patient populations. Virtual patients (VPs) created with machine-learning (ML), mechanistically driven computational approaches, or hybrids thereof, have the potential to expedite and maximize the impact of trials. We discuss the potential of VPs to transform the efficiency and impact of clinical trials in pediatric rare diseases, based on adult and pediatric examples.

A physiologically based pharmacokinetic (PBPK) model was developed and verified for dordaviprone, a small molecule with antitumor effects in glioma patients. The model was applied to assess the drug–drug interaction (DDI) potential of dordaviprone as a victim of CYP3A4 inhibitors and inducers, and as a perpetrator of CYP3A4, CYP2C8, CYP2D6 inhibition. A combination of in vitro and clinical data was used to develop a minimal distribution PBPK model with a single adjusting compartment and mechanistic absorption using the Simcyp Population-Based Simulator (V21). Simulated maximum concentration (Cmax) and area under the concentration time curve (AUC) of the 3 clinical studies used to verify the PBPK model were within 1.4-fold of observed exposures. The simulated increase in dordaviprone AUC and Cmax (4.6- and 1.7-fold) following administration of multiple doses of itraconazole was consistent with the observed values (4.4- and 1.9-fold). All PBPK-simulated changes in dordaviprone plasma exposure when administered with CYP3A4 moderate (erythromycin, fluconazole) and weak (cimetidine) inhibitors, and moderate (efavirenz) and strong (rifampicin) inducers were consistent with their CYP3A4 potency classification (AUC ratio = 2.68, 2.48, 1.42, 0.35, and 0.17, respectively). The simulated AUC and Cmax of probe substrates for CYP3A4 (midazolam), CYP2C8 (repaglinide) and CYP2D6 (desipramine) after coadministration with 625 mg dordaviprone were the same as those in the absence of dordaviprone (ratio = 1.0) and remained unchanged after a sensitivity analysis using 10-fold more potent inhibition constants. Due to changes in dordaviprone plasma exposure when co-administered with CYP3A4 inhibitors, dordaviprone dose adjustments may be necessary; CYP3A4 inducers should be avoided.

Phase 2 trials have historically focused on characterizing the dose-exposure-response relationship in relatively homogeneous patient populations before proceeding to confirmatory trials. However, with the rise of multi-regional Phase 2 trials, it is important to strike a balance between this goal and the requirement to make sure that the optimal doses are chosen for patients from various geographic areas. This study uses a dose-ranging trial for an anti-psoriatic drug, featuring a typical design with a total sample size of N = 175, to highlight key considerations regarding sample size in multi-regional exploratory studies. The allocation of sample size to a region of interest (Region X) was evaluated using both a conventional statistical approach and a pharmacometric model-based (PMx) approach, predicated on the assumption of a minimum treatment improvement in Region X. Further evaluation was performed to assess the probability of reaching reliable conclusions regarding clinically relevant inter-regional differences in treatment response. The statistical approach, relying solely on end-of-trial observations from a single dose group, exhibited a maximum power of less than 40% in detecting treatment differences across regions when Region X accounts for 50% of the total sample size. In contrast, the PMx approach, employing data from multiple dose groups across trial duration, demonstrated that 26% of the total sample size yielded over 80% power to identify the inter-regional difference. The PMx approach has also been shown to offer a more efficient characterization of the clinical relevance of inter-regional differences, and has potential to improve decision-making in development progression by integrating prior knowledge.

Optimizing antibiotic therapy requires a holistic bench-to-bedside approach with interdisciplinary collaboration between pharmacologists, clinicians, microbiologists, and computational scientists. Novel experimental models provide insights into drug-pathogen interactions within complex host environments, while multiomics data provide details of the molecular mechanisms shaping bacterial responses. Pharmacometrics and machine learning can be used to integrate these insights into in silico models. This perspective highlights how these approaches—when used effectively and often together to build a systems-level view—can inform drug development and improve clinical decision-making, ensuring the right drug is given to each patient at the right time, at the right dose, and for the right duration.

This year marks the 20th edition of the Basel Modeling and Simulation (M&S) Seminar, an initiative rooted in a commitment to promoting the exchange of the latest advancements in pharmacometrics and related disciplines in the region of Basel, Switzerland. This article provides insight into the history of this event, its operations to the present date, and a glimpse at the future.

Upifitamab rilsodotin—an antibody drug conjugate (ADC)—comprises a NaPi2b-targeted antibody conjugated to an auristatin-based payload (auristatin F-hydroxypropylamide [AF-HPA]). AF-HPA is metabolized by cytochrome P450 3A4 (CYP3A4) and, to a lower extent, by CYP3A5 and demonstrates both reversible and time-dependent inhibition of CYP3A4. AF-HPA is also a P-glycoprotein (P-gp) substrate. A PBPK model was developed using a mixed “bottom-up” and “top-down” modeling approach with a combination of in vitro, nonclinical, and clinical ADME/PK data. The model recapitulated the clinical PK of conjugated and unconjugated AF-HPA. Simulations were used to predict the potential of unconjugated AF-HPA to be a victim or perpetrator of clinical drug–drug interactions (DDI) and predict the impact of hepatic impairment on the exposure to unconjugated AF-HPA. Simulations suggested negligible potential for clinical DDI between unconjugated AF-HPA and CYP3A substrates. Simulations also showed ~30% increase in unconjugated AF-HPA exposure following an IV dose of 36 mg/m² in the presence of itraconazole, an inhibitor of both CYP3A4 and P-gp. A negligible change in the exposure to unconjugated AF-HPA was predicted in patients with mild hepatic impairment, which aligned with observed clinical data. The model predicted a ~1.5-fold increase in unconjugated AF-HPA AUC and negligible change in the C_max in patients with moderate and severe hepatic impairment. Finally, this PBPK model may be applied (with modification to the conjugated drug sub-model parameters) to predict DDI and hepatic impairment potential for other ADCs with the same linker and payload.