Clinical Pharmacology & Therapeutics最新文献

In the past decade, pharmacogenomic (PGx) testing to predict drug response have emerged into clinical care. Clinical decision support (CDS) has and continues to play a key role in educating prescribers and facilitating the integration of pharmacogenomic results into routine clinical practice. The Epic Genomics module, an add-on to Epic's base clinical software, allows for storage of structured genomic data and provides electronic heath record tools designed with PGx CDS implementation in mind. In early 2022, the University of Florida Health deployed the Genomics module. This tutorial outlines the steps taken by the University of Florida Health Precision Medicine Program to implement Epic's Genomic Module at University of Florida Health and identifies key factors for a successful implementation.

Technological developments and innovations in regulatory pathways have meant medicinal products are increasingly associated with substantial clinical and economic uncertainties at launch. This has increased the focus on continuous evidence generation to assess the real-world value of new medicines post-launch. This paper examines Post-Launch Evidence Generation (PLEG) systems in France, Spain, and England, drawing on insights from a series of multistakeholder roundtables hosted by RWE4Decisions. These discussions provided a platform to compare national approaches to PLEG considering PLEG planning and operationalization. The roundtable events included presentations by representatives of the HTA bodies and payers in France, Spain, and England, an industry response, and multistakeholder discussions. The events highlighted that while there are differences in the products to which PLEG is applied and the way it is operationalized, there are many common challenges experienced across systems and by all stakeholders. First, there is a recognition that evidentiary needs must be anticipated earlier to avoid PLEG where possible and better plan for PLEG where needed. Second, there is a need to streamline data collection. This includes trying to make greater use of existing data sources vs. primary data collection, prioritizing collection of a small number of outcomes that directly address key uncertainties, and by improving international collaborations to streamline data collection and evidence generation across borders. Our findings suggest value in improving scientific advice processes and international collaboration to discuss key data gaps early and ensure efficient and effective evidence collection that improves the speed and quality of reimbursement and pricing decisions.

Cytokine release syndrome (CRS) is a common, acute adverse event associated with T-cell redirecting therapies such as bispecific antibodies (BsAbs). The nature of CRS events data makes it challenging to capture an unbiased exposure-response relationship with commonly used models. For example, simple logistic regression models cannot handle traditional time-varying exposure, and static exposure metrics chosen at early time points and with lower priming doses may underestimate the incidence of CRS. Therefore, more advanced modeling techniques are needed to adequately describe the time course of BsAb-induced CRS. Herein, we present a two-part mixture model that describes the population incidence and time course of CRS following various dose-priming regimens of elranatamab, a humanized BsAb that targets the B-cell maturation antigen on myeloma cells and CD3 on T cells, where the conditional time-evolution of CRS was described with a two-state (i.e., CRS-yes or no) Markov model. In the first part, increasing elranatamab exposure (maximum elranatamab concentration at first CRS event time (C_max,event)) was associated with an increased CRS incidence probability. Similarly, in the second part, increased early elranatamab exposure (C_max,D1) increased the predicted probability of CRS over time, whereas premedication including corticosteroids and IL-6 pathway inhibitors use demonstrated the opposite effect. This is the first reported application of a Markov model to describe the probability of CRS following BsAb therapy, and it successfully explained differences between different dose-priming regimens via clinically relevant covariates. This approach may be useful for the future clinical development of BsAbs.

Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in medical product development and regulatory decision-making, especially for rare diseases. After outlining the challenges and possible strategies to address the challenges in rare disease drug development (see the accompanying paper), the Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section reviews the roles of RWD and RWE in clinical trials for drugs treating rare diseases. This paper summarizes relevant guidance documents and frameworks by selected regulatory agencies and the current practice on the use of RWD and RWE in natural history studies and the design, conduct, and analysis of rare disease clinical trials. A targeted learning roadmap for rare disease trials is described, followed by case studies on the use of RWD and RWE to support a natural history study and marketing applications in various settings.

Conducting clinical studies on drug-drug-gene interactions (DDGIs) and extrapolating the findings into clinical dose recommendations is challenging due to the high complexity of these interactions. Here, physiologically-based pharmacokinetic (PBPK) modeling networks present a new avenue for exploring such complex scenarios, potentially informing clinical guidelines and handling patient-specific DDGIs at the bedside. Moreover, they provide an established framework for drug-drug interaction (DDI) submissions to regulatory agencies. The cytochrome P450 (CYP) 2D6 enzyme is particularly prone to DDGIs due to the high prevalence of genetic variation and common use of CYP2D6 inhibiting drugs. In this study, we present a comprehensive PBPK network covering CYP2D6 drug-gene interactions (DGIs), DDIs, and DDGIs. The network covers sensitive and moderate sensitive substrates, and strong and weak inhibitors of CYP2D6 according to the United States Food and Drug Administration (FDA) guidance. For the analyzed CYP2D6 substrates and inhibitors, DD(G)Is mediated by CYP3A4 and P-glycoprotein were included. Overall, the network comprises 23 compounds and was developed based on 30 DGI, 45 DDI, and seven DDGI studies, covering 32 unique drug combinations. Good predictive performance was demonstrated for all interaction types, as reflected in mean geometric mean fold errors of 1.40, 1.38, and 1.56 for the DD(G)I area under the curve ratios as well as 1.29, 1.43, and 1.60 for DD(G)I maximum plasma concentration ratios. Finally, the presented network was utilized to calculate dose adaptations for CYP2D6 substrates atomoxetine (sensitive) and metoprolol (moderate sensitive) for clinically untested DDGI scenarios, showcasing a potential clinical application of DDGI model networks in the field of model-informed precision dosing.