UGT1A4 Polymorphism is not Associated with a Clinically Relevant Change in Giredestrant Exposure.

PURPOSE: Giredestrant is a potent, orally bioavailable, small-molecule selective estrogen receptor antagonist and degrader (SERD) that is being developed for the treatment of patients with estrogen receptor (ER)-positive breast cancer. In vitro, giredestrant was primarily metabolized by UGT1A4. The goal of this study was to investigate if UGT1A4 polymorphism had a clinically relevant impact on giredestrant exposure. METHODS: Genotyping and pharmacokinetic data were obtained from 118 and 61 patients in two clinical studies, GO39932 [NCT03332797] and acelERA Breast Cancer [NCT04576455], respectively. RESULTS: The overall allelic frequencies of UGT1A4*2 and UGT1A4*3 were 3.3% and 11%, respectively. Giredestrant exposure was consistent between patients with wild-type UGT1A4 and UGT1A4*2 and *3 polymorphisms, with no clinically relevant difference observed. In addition, haplotype analysis indicated that no other UGT1A4 variants were significantly associated with giredestrant exposure. CONCLUSION: Therefore, this study indicates that UGT1A4 polymorphism status is unlikely a clinically relevant factor to impact giredestrant exposure and giredestrant can be administered at the same dose level regardless of patients' UGT1A4 polymorphism status.

Applications of Advanced Natural Language Processing for Clinical Pharmacology.

Natural language processing (NLP) is a branch of artificial intelligence, which combines computational linguistics, machine learning, and deep learning models to process human language. Although there is a surge in NLP usage across various industries in recent years, NLP has not been widely evaluated and utilized to support drug development. To demonstrate how advanced NLP can expedite the extraction and analyses of information to help address clinical pharmacology questions, inform clinical trial designs, and support drug development, three use cases are described in this article: (1) dose optimization strategy in oncology, (2) common covariates on pharmacokinetic (PK) parameters in oncology, and (3) physiologically-based PK (PBPK) analyses for regulatory review and product label. The NLP workflow includes (1) preparation of source files, (2) NLP model building, and (3) automation of data extraction. The Clinical Pharmacology and Biopharmaceutics Summary Basis of Approval (SBA) documents, US package inserts (USPI), and approval letters from the US Food and Drug Administration (FDA) were used as our source data. As demonstrated in the three example use cases, advanced NLP can expedite the extraction and analyses of large amounts of information from regulatory review documents to help address important clinical pharmacology questions. Although this has not been adopted widely, integrating advanced NLP into the clinical pharmacology workflow can increase efficiency in extracting impactful information to advance drug development.

The quest for balance between capturing data and model complexity: A quantitative clinical pharmacology approach applied to monoclonal antibodies.

The main objective of this tutorial is to provide the readers with a roadmap of how to establish increasingly complex target-mediated drug disposition (TMDD) models for monoclonal antibodies. To this end, we built mathematical models, each with a detailed visualization, starting from the basic TMDD model by Mager and Jusko to the well-established, physiologically based model by Li et al. in a step-wise fashion to highlight the relative importance of key physiological processes that impact mAb kinetics and system dynamics. As the models become more complex, the question of structural and parameter identifiability arises. To address this question, we work through a trastuzumab case example to guide the modeler's choice for model and parameter optimization in light of the context of use. We leave the readers of this tutorial with a brief summary of the advantages and limitations of each model expansion, as well as the model source codes for further self-guided exploration and hands-on analysis.

Concentration QTc analysis of giredestrant: Overcoming QT/heart rate confounding in the presence of drug-induced heart rate changes.

Concentration-QTc (C-QTc) analysis has become a common approach for evaluating proarrhythmic risk and delayed cardiac repolarization of oncology drug candidates. Significant heart rate (HR) change has been associated with certain classes of oncology drugs and can result in over- or underestimation of the true QT prolongation risk. Because oncology early clinical trials typically lack a placebo control arm or time-matched, treatment-free baseline electrocardiogram collection, significant HR change brings additional challenges to C-QTc analysis in the oncology setting. In this work, a spline-based correction method (QTcSPL) was explored to mitigate the impact of HR changes in giredestrant C-QTc analysis. Giredestrant is a selective estrogen receptor degrader being developed for the treatment of patients with estrogen receptor-positive (ER+) breast cancer. A dose-related HR decrease has been observed in patients under giredestrant treatment, with significant reductions (>10 bpm) observed at supratherapeutic doses. The QTcSPL method demonstrated superior functionality to reduce the correlation between QTc and HR as compared with the Fridericia correction (QTcF). The effect of giredestrant exposure on QTc was evaluated at the clinical dose of 30 mg and supratherapeutic dose of 100 mg based on a prespecified linear mixed effect model. The upper 90% confidence interval of ΔQTcSPL and ΔQTcF were below the 10 ms at both clinical and supratherapeutic exposures, suggesting giredestrant has a low risk of QT prolongation at clinically relevant concentrations. This work demonstrated the use case of QTcSPL to address HR confounding challenges in the context of oncology drug development for the first time.

From Cold to Hot: Changing Perceptions and Future Opportunities for Quantitative Systems Pharmacology Modeling in Cancer Immunotherapy.

Immuno-oncology (IO) is a fast-expanding field due to recent success using IO therapies in treating cancer. As IO therapies do not directly kill tumor cells but rather act upon the patients' own immune cells either systemically or in the tumor microenvironment, new and innovative approaches are required to inform IO therapy research and development. Quantitative systems pharmacology (QSP) modeling describes the biological mechanisms of disease and the mode of action of drugs with mathematical equations, which has significant potential to address the big challenges in the IO field, from identifying patient populations that respond to different therapies to guiding the selection, dosing, and scheduling of combination therapy. To assess the perspectives of the community on the impact of QSP modeling in IO drug development and to understand current applications and challenges, the IO QSP working group-under the QSP Special Interest Group (SIG) of the International Society of Pharmacometrics (ISoP)-conducted a survey among QSP modelers, non-QSP modelers, and non-modeling IO program stakeholders. The survey results are presented here with discussions on how to address some of the findings. One of the findings is the differences in perception among these groups. To help bridge this perception gap, we present several case studies demonstrating the impact of QSP modeling in IO and suggest actions that can be taken in the future to increase the real and perceived impact of QSP modeling in IO drug research and development.

Establishment of a Disease-Drug Trial Model for Postmenopausal Osteoporosis: A Zoledronic Acid Case Study.

Costly and lengthy clinical trials hinder the development of safe and effective treatments for postmenopausal osteoporosis. To reduce the expense associated with these trials, we established a mechanistic disease-drug trial model for postmenopausal osteoporosis that can predict phase 3 trial outcome based on short-term bone turnover marker data. To this end, we applied a previously developed model for tibolone to bisphosphonates using zoledronic acid as paradigm compound by (1) linking the mechanistic bone cell interaction model to bone turnover markers as well as bone mineral density in lumbar spine and total hip, (2) employing a mechanistic disease progression function, and (3) accounting for zoledronic acid's mechanism of action. Once developed, we fitted the model to clinical trial data of 581 postmenopausal women receiving (1) 5-mg zoledronic acid in year 1 and saline in year 2, (2) 5-mg zoledronic acid in year 1 and year 2, or (3) placebo (saline), calcium (500 mg), and vitamin D (400 IU). All biomarker data was fitted reasonably well, with no apparent bias or model misspecification. Age, years since menopause, and body mass index at baseline were identified as significant covariates. In the future, the model can be modified to explore the link between short-term biomarkers and fracture risk.

Physiologically Based Pharmacokinetic Modeling of Monoclonal Antibodies in Pediatric Populations Using PK-Sim.

Physiologically based pharmacokinetic (PBPK) models are increasingly used to support pediatric dose selection for small molecule drugs. In contrast, only a few pediatric PBPK models for therapeutic antibodies have been published recently, and the knowledge on the maturation of the processes relevant for antibody pharmacokinetics (PK) is limited compared to small molecules. The aim of this study was, thus, to evaluate predictions from antibody PBPK models for children which were scaled from PBPK models for adults in order to identify respective knowledge gaps. For this, we used the generic PBPK model implemented in PK-Sim without further modifications. Focusing on general clearance and distribution mechanisms, we selected palivizumab and bevacizumab as examples for this evaluation since they show simple, linear PK which is not governed by drug-specific target mediated disposition at usual therapeutic dosages, and their PK has been studied in pediatric populations after intravenous application. The evaluation showed that the PK of palivizumab was overall reasonably well predicted, while the clearance for bevacizumab seems to be underestimated. Without implementing additional ontogeny for antibody PK-specific processes into the PBPK model, bodyweight normalized clearance increases only moderately in young children compared to adults. If growth during aging at the time of the simulation was considered, the apparent clearance is approximately 20% higher compared to simulations for which growth was not considered for newborns due to the long half-life of antibodies. To fully understand the differences and similarities in the PK of antibodies between adults and children, further research is needed. By integrating available information and data, PBPK modeling can contribute to reveal the relevance of involved processes as well as to generate and test hypothesis.

Vancomycin in peritoneal dialysis: Clinical pharmacology considerations in therapy.

Intraperitoneal vancomycin is the first-line therapy in the management of peritoneal dialysis (PD)-related peritonitis. However, due to the paucity of data, vancomycin dosing for peritonitis in patients on automated peritoneal dialysis (APD) is empiric and based on clinical experience rather than evidence. Studies in continuous ambulatory peritoneal dialysis (CAPD) patients have been used to provide guidelines for dosing and are often extrapolated for APD use, but it is unclear whether this is appropriate. This review summarizes the available pharmacokinetic data used to inform optimal dosing in patients on CAPD or APD. The determinants of vancomycin disposition and pharmacodynamic effects are critically summarized, knowledge gaps explored, and a vancomycin dosing algorithm in PD patients is proposed.

Identification of new oral dosing regimens for the neuraminidase inhibitor oseltamivir in patients with moderate and severe renal impairment.

Availability of lower-dose oseltamivir capsules, an increased pharmacokinetic database, and a desire to align drug exposure across the spectrum of renal function prompted reassessment of oral dosing in patients with renal impairment. The data set comprised 128 subjects (71 with varying degrees of renal impairment) from 8 studies, which included single and multiple doses of 20-1000 mg. Pharmacokinetic profiles of oseltamivir phosphate (OP) and oseltamivir carboxylate (OC) were modeled simultaneously in NONMEM. Exposure metrics of OP and OC (AUC48 h , Cmax , Cmin ) after administration of various dosing regimens were simulated for renal impairment subgroups and compared with exposures in patients with normal renal function receiving approved regimens. For influenza treatment, 30 mg once-daily and twice-daily regimens were selected for severe and moderate impairment, respectively. These regimens provided OC exposures similar or above those of the approved 75-mg twice-daily treatment regimen in subjects with normal renal function. For influenza prophylaxis, 30 mg once every other day and once-daily regimens were selected for severe and moderate impairment, respectively. No dosing adjustments were required for mild impairment. This analysis supported revised labeling in the United States and Europe for oral oseltamivir dosing in patients with moderate and severe renal impairment.