CPT: Pharmacometrics & Systems Pharmacology最新文献

Project Optimus is a major FDA initiative aimed at ensuring dose optimization in oncology drug development, moving away from the maximum tolerated dose paradigm and prospectively characterizing dose–response for efficacy and safety for patient-focused maximization of benefit versus risk.^1-3 Mitigating toxicities and enhancing overall benefit versus risk of oncology therapies necessitates dose optimization with commitment to evaluation of innovative dosing paradigms including individualized approaches, where appropriate. This requires the quantitative integration of pharmacological mechanism of action, efficacy, and safety in the context of associated population variability. The problem of dose optimization in the context of mechanism of action, cancer pathophysiology, and associated population variability sits neatly at the intersection of translational/ precision medicine and quantitative clinical pharmacology and is important to approach with a patient-focused mindset.

Forums convened on the topic of oncology dose optimization largely engage scientific leaders primarily working on oncology research and development, and cancer medicine. These include workshops organized by Friends of Cancer Research (FOCR),⁴ American Society of Clinical Oncology (ASCO),^{5, 6} American Association for Cancer Research (AACR),^{7, 8} and the International Society of Pharmacometrics (ISoP)⁹ in partnership with the US Food and Drugs Administration (FDA). Of note, some of these efforts have yielded seminal publications^{1, 2, 10-13} and White Papers¹⁴ offering initial recommendations, including availability of a Draft FDA guidance on the topic.¹⁵ We posited that the American Society for Clinical Pharmacology and Therapeutics (ASCPT) – as a premier scientific and professional organization for clinical pharmacology and translational medicine – is optimally positioned to host a discussion of opportunities for our constituent disciplines (e.g., translational science, clinical pharmacology, pharmacometrics) to synergistically address this problem with a multi-disciplinary approach. To this end, a session was convened at the 2023 ASCPT Annual Meeting bringing together representative scientific leaders from the three scientific journals of the Society – Clinical Pharmacology and Therapeutics (CPT), Clinical and Translational Science (CTS), and CPT: Pharmacometrics and Systems Pharmacology (PSP). These scientific leaders, as at-large representatives of the disciplines of clinical pharmacology and translational medicine, were invited to bring forward their opinions and participate in a fireside chat to identify opportunities for moving the oncology dose optimization needle. This enabled engagement of a broad group of experts without requiring pri

Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure–response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases. On the contrary, common end points such as progression-free survival (PFS) and objective response rate (ORR) are directly derived from longitudinal tumor measurements and are routinely published. To this end, a Bayesian generative model relating underlying tumor dynamics to summary PFS and ORR data is introduced to learn TGI model parameters using only published summary data. The parameterized model can describe the tumor dynamics, quantify treatment effect, and account for differences in the study population. The utility of this model is shown by applying it to several published studies, and learned parameters are combined to simulate an in silico trial of a novel combination therapy in a novel setting.

The Scale for the Assessment and Rating of Ataxia (SARA) is widely used for assessing the severity and progression of genetic cerebellar ataxias. SARA is now considered a primary end point in several ataxia treatment trials, but its underlying composite item measurement model has not yet been tested. This work aimed to evaluate the composite properties of SARA and its items using item response theory (IRT) and to demonstrate its applicability across even ultra-rare genetic ataxias. Leveraging SARA subscores data from 1932 visits from 990 patients of the Autosomal Recessive Cerebellar Ataxias (ARCA) registry, we assessed the performance of SARA using IRT methodology. The item characteristics were evaluated over the ataxia severity range of the entire ataxia population as well as the assessment validity across 115 genetic ARCA subpopulations. A unidimensional IRT model was able to describe SARA item data, indicating that SARA captures one single latent variable. All items had high discrimination values (1.5–2.9) indicating the effectiveness of the SARA in differentiating between subjects with different disease statuses. Each item contributed between 7% and 28% of the total assessment informativeness. There was no evidence for differences between the 115 genetic ARCA subpopulations in SARA applicability. These results show the good discrimination ability of SARA with all of its items adding informational value. The IRT framework provides a thorough description of SARA on the item level, and facilitates its utilization as a clinical outcome assessment in upcoming longitudinal natural history or treatment trials, across a large number of ataxias, including ultra-rare ones.

Imetelstat is a novel, first-in-class, oligonucleotide telomerase inhibitor in development for the treatment of hematologic malignancies including lower-risk myelodysplastic syndromes and myelofibrosis. A nonlinear mixed-effects model was developed to characterize the population pharmacokinetics of imetelstat and identify and quantify covariates that contribute to its pharmacokinetic variability. The model was developed using plasma concentrations from 7 clinical studies including 424 patients with solid tumors or hematologic malignancies who received single-agent imetelstat via intravenous infusion at various dose levels (0.4–11.7 mg/kg) and schedules (every week to every 4 weeks). Covariate analysis included factors related to demographics, disease, laboratory results, renal and hepatic function, and antidrug antibodies. Imetelstat was described by a two-compartment, nonlinear disposition model with saturable binding/distribution and dose- and time-dependent elimination from the central compartment. Theory-based allometric scaling for body weight was included in disposition parameters. The final covariates included sex, time, malignancy, and dose on clearance; malignancy and sex on volume of the central compartment; and malignancy and spleen volume on concentration of target. Clearance in females was modestly lower, resulting in nonclinically relevant increases in predicted exposure relative to males. No effects on imetelstat pharmacokinetics were identified for mild-to-moderate hepatic or renal impairment, age, race, and antidrug antibody status. All model parameters were estimated with adequate precision (relative standard error < 29%). Visual predictive checks confirmed the capacity of the model to describe the data. The analysis supports the imetelstat body-weight–based dosing approach and lack of need for dose individualizations for imetelstat-treated patients.

Both primary and acquired resistance mechanisms that involve intra-tumoral cell heterogeneity limit the use of BH3-mimetics to trigger tumor cell apoptosis. This article proposes a new quantitative systems pharmacology (QSP)-based methodology in which cell viability assays are used to calibrate virtual tumors (VTs) made of virtual cells whose fate is determined by simulations from an apoptosis QSP model. VTs representing SU-DHL-4 and KARPAS-422 cell lines were calibrated using in vitro data involving venetoclax (anti-BCL2), A-1155463 (anti-BCLXL), and/or A-1210477 (anti-MCL1). The calibrated VTs provide insights into the combination of several BH3-mimetics, such as the distinction between cells eliminated by at least one of the drugs (monotherapies) from the cells eliminated by a pharmacological combination only. Calibrated VTs can also be used as initial conditions in an agent-based model (ABM) framework, and a minimal ABM was developed to bridge in vitro SU-DHL-4 cell viability results to tumor growth inhibition experiments in mice.

Telmisartan, a selective inhibitor of angiotensin II receptor type 1 (AT1), demonstrates nonlinear pharmacokinetics (PK) when orally administered in ascending doses to healthy volunteers, but the underlying mechanisms remain unclear. This study presents a physiologically based pharmacokinetic model integrated with target-mediated drug disposition (TMDD-PBPK model) to explore the mechanism of its nonlinear PK. We employed the Cluster-Gauss Newton method for top-down analysis, estimating the in vivo K_m,OATP1B3 (Michaelis–Menten constant for telmisartan hepatic uptake via Organic Anion Transporting Polypeptide 1B3) to be 2.0–5.7 nM. This range is significantly lower than the reported in vitro value of 810 nM, obtained in 0.3% human serum albumin (HSA) conditions. Further validation was achieved through in vitro assessment in plated human hepatocytes with 4.5% HSA, showing a K_m of 4.5 nM. These results underscore the importance of albumin-mediated uptake effect for the hepatic uptake of telmisartan. Our TMDD-PBPK model, developed through a “middle-out” approach, underwent sensitivity analysis to identify key factors in the nonlinear PK of telmisartan. We found that the nonlinearity in the area under the concentration–time curve (AUC) and/or maximum concentration (C_max) of telmisartan is sensitive to K_m,OATP1B3 across all dosages. Additionally, the dissociation constant (K_d) for telmisartan binding to the AT1 receptor, along with its receptor abundance, notably influences PK at lower doses (below 20 mg). In conclusion, the nonlinear PK of telmisartan appears primarily driven by hepatic uptake saturation across all dose ranges and by AT1-receptor binding saturation, notably at lower doses.

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein–protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein–protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein–protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

The objective was to characterize the pharmacokinetics (PK) and pharmacodynamics (PD) of glucagon after injectable or nasal administration and confirm the appropriate therapeutic dose of nasal glucagon (NG) for adult patients. Six clinical studies with PK and five clinical studies with PD (glucose) data were included in the analysis. Doses ranging from 0.5 to 6 mg NG, and 0.5 to 1 mg injectable glucagon were studied. A total of 6284 glucagon and 7130 glucose concentrations from 265 individuals (patients and healthy participants) were available. Population PK/PD modeling was performed using NONMEM. Glucagon exposure and glucose response were simulated for patients administered various doses of NG to confirm the optimal dose. Glucagon PK was well-described with a single compartment disposition with first-order absorption and elimination processes. Bioavailability of NG relative to injectable glucagon was 16%. Exposure–response modeling revealed that lower baseline glucose was associated with higher maximum drug effect. The carry-over effect from prior insulin administration was incorporated into the model through a time-dependent increase in elimination rate of glucose. Simulations showed that more than 99% of hypoglycemic adult patients would experience treatment success, defined as an increase in blood glucose to ≥70 mg/dL or an increase of ≥20 mg/dL from nadir within 30 min after administration of NG 3 mg. The population PK/PD model adequately described the PK and PD profiles of glucagon after nasal administration. Modeling and simulations confirmed that NG 3 mg is the most appropriate dose for rescue treatment during hypoglycemia emergencies.

In pharmacometric modeling, it is often important to know whether the data is sufficiently rich to identify the parameters of a proposed model. While it may be possible to assess this based on the results of a model fit, it is often difficult to disentangle identifiability issues from other model fitting and numerical problems. Furthermore, it can be of value to ascertain identifiability beforehand. This paper compares four methods for parameter identifiability, namely Differential Algebra for Identifiability of SYstems (DAISY), the sensitivity matrix method (SMM), Aliasing, and the Fisher information matrix method (FIMM). We discuss the characteristics of the methods and apply them to a set of applications, consisting of frequently used PK model structures, with suitable dosing regimens and sampling times. These applications were selected to validate the methods and demonstrate their usefulness. While traditional identifiability analysis provides a categorical result [PLoS One, 6, 2011, e27755; CPT Pharmacometrics Syst Pharmacol, 8, 2019, 259; Bioinformatics, 30, 2014, 1440], we argue that in practice a continuous scale better reflects the limitations on the data and is more informative. The methods were generally consistent in their evaluation of the applications. The Fisher information matrix method seemed to provide the most consistent answers. All methods provided information on the parameters that were unidentifiable. Some of the results were unexpected, indicating identifiability issues where none were foreseen, but could be explained upon further analysis. This illustrated the usefulness of identifiability assessment.

Parameter identifiability methods assess whether the parameters of a model are uniquely determined by the observations. While the success of a model fit can provide some information on this, it can be valuable to determine identifiability before any fit has been attempted, or to separate identifiability from other issues. Two concepts that lean themselves well for identifiability analysis and have been underutilized are the sensitivity matrix (SM) and the Fisher information matrix (FIM). This paper presents two newly developed methods, one based on the SM and one based on the FIM. Both methods can assess local identifiability for a wide set of models, can be used with limited effort, and are freely available. The methods require the proposed model in the form of a set of differential equations, the parameter values, and the study design as input. They can be used a priori, as they do not need observed values or a successful model fit. Traditional methods provide a single categorical (yes/no) answer to the question of identifiability. In many cases, this is not very informative, and identifiability depends on study design (e.g., dose levels or observation times) and parameter values. Indicators on a continuous scale characterizing the level of identifiability would provide more detailed and relevant information, for example, to guide model development. Our two methods provide both categorical and continuous indicators. Both methods indicate which parameter combinations are difficult to identify by calculating the directions in parameter space that are least identifiable. The methods were validated with an example problem.