Journal of clinical pharmacology最新文献

Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs. Natural history data and real-world data (RWD) play significant roles in defining and characterizing disease progression, final patient populations, novel biomarkers, genetic relationships, and treatment effects. This review provides an introduction to rare diseases, natural history data, RWD, and real-world evidence, the respective sources and applications of these data in several rare diseases. Considerations for data quality and limitations when using natural history and RWD are also elaborated. Opportunities are highlighted for cross-sector collaboration, standardized and high-quality data collection using new technologies, and more comprehensive evidence generation using quantitative approaches such as disease progression modeling, artificial intelligence, and machine learning. Advanced statistical approaches to integrate natural history data and RWD to further disease understanding and guide more efficient clinical study design and data analysis in drug development in rare diseases are also discussed.

Drug development is a highly regulated industry. Therapeutic options for rare diseases must meet the same high standards for the demonstration of safety and efficacy as do those for more common diseases. The approval of the Orphan Drug Act in 1983 has resulted in many more resources for preclinical research, the standardization of patient registries, and the use of real-world data, among other measures, that, along with the advances in drug development, has resulted in the approval of therapies for some of the most unusual diseases. Increased attention to the diagnosis and treatment of rare diseases has also accelerated the development of gene therapies that may offer significant amelioration and even cures for such diseases in the near future. Rare diseases disproportionately affect children, with severe and debilitating effects. Few effective treatments are available for most rare diseases. To avoid the unnecessary waste of data collected in studies of these patients, and to promote efficient drug development, there is a growing collaboration among patient communities, investigators, clinicians, sponsors, and regulatory authorities. All interested parties are working together to identify the most appropriate research questions and move quickly to make available safe and effective treatments. This article is a survey of the most commonly used regulatory remedies that have been put in place to serve as a framework for drug development in rare diseases.

As the temporal, financial, and ethical cost of randomized clinical trials (RCTs) continues to rise, researchers and regulators in drug discovery and development face increasing pressure to make better use of existing data sources. This pressure is especially high in rare disease, where traditionally designed RCTs are often infeasible due to the inability to recruit enough patients or the unwillingness of patients or trial leaders to randomly assign anyone to placebo. Bayesian statistical methods have recently been recommended in such settings for their ability to combine disparate data sources, increasing overall study power. The use of these methods has received a boost in the United States thanks to a new willingness by regulators at the Food and Drug Administration to consider complex innovative trial designs. These designs allow trialists to change the nature of the trial (eg, stop early for success or futility, drop an underperforming trial arm, incorporate data on historical controls, etc) while it is still running. In this article, we review a broad collection of Bayesian techniques useful in rare disease research, indicating the benefits and risks associated with each. We begin with relatively innocuous methods for combining information from RCTs and proceed on through increasingly innovative approaches that borrow strength from increasingly heterogeneous and less carefully curated data sources. We also offer 2 examples from the very recent literature illustrating how clinical pharmacology principles can make important contributions to such designs, confirming the interdisciplinary nature of this work.

Rare diseases are frequently caused by inherited 'monogenic' defects. Treatment interventions that target a specific genetic location or that replaces a specific protein provide rational therapeutic approaches. The current review discusses innovative targeted therapies that act or modulate at the level of DNA, RNA, or protein. They include DNA gene editing, small interference RNA (siRNA), antisense oligonucleotide (ASO), small molecule RNA splicing modifier, and bispecific antibody. With limited numbers of patients, testing multiple dose levels and regimens prior to making an informed dose decision remains one of the major challenges in rare disease drug development. Clinical pharmacology strategically bridges the gap to support drug development and regulatory approvals. Pharmacokinetic drug exposures are driven by absorption, distribution, metabolism, elimination, and in some cases immunogenicity. Drug responses are measured by pharmacodynamic biomarkers that are linked to either short- or long-term clinical outcomes. Understanding the drug exposure-response relationship lies at the heart of bridging the gap that enables a dose decision by balancing effectiveness and safety. Furthermore, and importantly, understanding the influence of intrinsic and extrinsic factors on drug pharmacokinetics enables dose adjustment decisions based on drug exposures. Case examples include the identification of doses and regimens without a formal dose-finding study, the support of new doses and regimens without conducting additional studies, and the extrapolation of adult drug-drug interaction (DDI) studies to pediatrics without performing a pediatric DDI study. With increasing discoveries of innovative treatment modalities, the responsibility of clinical pharmacologists is expected to grow and enhance the development of novel treatments for rare diseases.

New therapeutic modalities carry with them great promise for the treatment of rare diseases. They also present unique development challenges including immunogenicity, which can impact the safety and efficacy of those new modalities. In this review, an overview of the basic function of the immune system and its possible interaction with new therapeutic modalities is presented. A juxtaposition of immunogenicity in the rare disease space versus traditional clinical programs is hereby being proposed. A clinical pharmacology viewpoint of immunogenicity, proposed approaches to account for immunogenicity in clinical data, bioanalytical considerations, and effects of route of administration and production changes on immunogenicity are discussed.

Recombinant adeno-associated virus (AAV) is currently the most widely used platform for in vivo gene therapy. Clinical pharmacology is a central field for AAV gene therapy, represented by the pillars of pharmacokinetics, pharmacodynamics/efficacy, and safety. In this review, we provide a comprehensive summary of clinical pharmacology considerations for recombinant AAV. The main topics covered are biodistribution and shedding, dose-exposure-response relationship, safety, immune and stress response, and clinical dose selection strategies. We highlight how the cumulative knowledge of AAV gene therapy could help with guiding clinical trial design and assessing and mitigating risks, as well as planning and executing pharmacokinetic/pharmacodynamic /safety data analyses. In addition, we discuss the major gaps and areas of growth in clinical pharmacology understanding of recombinant AAV. These include the mechanisms of the durability of treatment response and variability in biodistribution, transduction, and immunogenicity, as well as a potential influence on AAV's safety and efficacy profiles by drug product characteristics and patient intrinsic/extrinsic factors.

There are more than 7000 rare diseases affecting approximately 30 million people in the United States. More than 90% of these diseases lack approved therapies. Several challenges face the development of "orphan drugs", such as the small populations of patients, high development costs, and long development timelines. This study evaluates clinical pharmacology assessments conducted during the development of drugs to treat rare diseases approved by the United States Food and Drug Administration in 2020 and 2021. Thirty-nine new drug applications (NDAs) have been identified and the associated regulatory reviews, approved labels, and approval letters were reviewed. Approximately, 95%, 74%, and 77% of these submissions contained at least one type of drug-drug interaction, the effect of organ impairment (hepatic and renal) on drug exposure, and QT liability assessment, respectively. Modeling and simulation approaches were utilized to address many clinical pharmacology questions, with population pharmacokinetic analyses used extensively in the evaluation of the effect of organ impairment on drug exposure and with physiologically based pharmacokinetic analyses used mainly in assessing drug interaction risks. In general, the clinical pharmacology packages in the NDAs of orphan drugs are not optimal and more work is needed to obtain a complete clinical pharmacology package at the time of initial approval to ensure the safe and effective use of these drugs across the spectrum of the target patient population. This study provides insights into the clinical pharmacology studies needed for drugs to treat rare diseases and would help both the regulators and drug developers to identify challenges and opportunities in conducting clinical pharmacology assessments for drugs developed to treat rare diseases.

Rare diseases are affecting 400 million patients worldwide, with 95% of them suffering without treatments. In this article, I make a plea, as a parent of a rare disease kid, and as a drug developer, to turn the attention of pharmacologists to such rare and devastating diseases.

A rare disease is defined as a condition affecting fewer than 200 000 people in the United States by the Orphan Drug Act. For rare diseases, it is challenging to enroll a large number of patients and obtain all critical information to support drug approval through traditional clinical trial approaches. In addition, over half of the population affected by rare diseases are children, which presents additional drug development challenges. Thus, maximizing the use of all available data is in the interest of drug developers and regulators in rare diseases. This brings opportunities for model-informed drug development to use and integrate all available sources and knowledge to quantitatively assess the benefit/risk of a new product under development and to inform dosing. This review article provides an overview of 4 broad categories of use of model-informed drug development in drug development and regulatory decision making in rare diseases: optimizing dose regimen, supporting pediatric extrapolation, informing clinical trial design, and providing confirmatory evidence for effectiveness. The totality of evidence based on population pharmacokinetic simulation as well as exposure-response relationships for efficacy and safety, provides the regulatory ground for the approval of an unstudied dosing regimen in rare diseases without the need for additional clinical data. Given the practical and ethical challenges in drug development in rare diseases, model-informed approaches using all collective information (eg, disease, drug, placebo effect, exposure-response in nonclinical and clinical settings) are powerful and can be applied throughout the drug development stages to facilitate decision making.