Clinical Pharmacology & Therapeutics最新文献

The utilization of lipid nanoparticles (LNP) for encapsulating mRNA has revolutionized the field of therapeutics, enabling the rapid development of COVID-19 vaccines and cancer vaccines. However, the clinical development of mRNA-LNP therapeutics faces numerous challenges due to their complex mechanisms of action and limited clinical experience. To overcome these hurdles, Model-Informed Drug Development (MIDD) emerges as a valuable tool that can be applied to mRNA-LNP therapeutics, facilitating the evaluation of their safety and efficacy through the integration of data from all stages into appropriate modeling and simulation techniques. In this review, we provide an overview of current MIDD applications in mRNA-LNP therapeutics clinical development using in vivo data. A variety of modeling methods are reviewed, including quantitative system pharmacology (QSP), physiologically based pharmacokinetics (PBPK), mechanistic pharmacokinetics/pharmacodynamics (PK/PD), population PK/PD, and model-based meta-analysis (MBMA). Additionally, we compare the differences between mRNA-based therapeutics, small interfering RNA, and adeno-associated virus-based gene therapies in terms of their clinical pharmacology, and discuss the potential for mutual sharing of MIDD knowledge between these therapeutics. Furthermore, we highlight the promising future opportunities for applying MIDD approaches in the development of mRNA-LNP drugs. By emphasizing the importance of applying MIDD knowledge throughout mRNA-LNP therapeutics development, this review aims to encourage stakeholders to recognize the value of MIDD and its potential to enhance the safety and efficacy evaluation of mRNA-LNP therapeutics.

Chimeric antigen receptor (CAR)-T cell and bispecific antibodies (BsAb) have substantially improved outcomes for lymphoid neoplasms (LN); however, a comprehensive analysis regarding the regulatory approval for these products is not available; therefore, we aimed to address this research gap. We identified all indications for CAR-T cell and BsAb products for LN approved in the United States, European Union, or Japan between January 2010 and September 2023 using public databases. The United States was the most frequent region of first approval for both CAR-T (11/12 [92%]) and BsAb groups (6/9 [67%]). Regulatory incentives, such as orphan designation and expedited reviews, were more common in the CAR-T group than in the BsAb group in the United States, the European Union, and Japan. Pivotal trials for approval were generally identical in the United States and the European Union, and all indications, excluding the two indications for CAR-T cell products, were approved based on single-arm trials. The proportion of regular approvals was higher in the CAR-T group than in the BsAb group in both the United States (75% vs. 11%) and the European Union (67% vs. 25%). Unlike in the United States and European Union, all indications were granted regular approval in Japan. In conclusion, most indications for CAR-T cell and BsAb products were approved based on single-arm studies in the United States, European Union, and Japan; however, regulatory incentives and regular approvals were more frequently granted for CAR-T cell products than for BsAb products.

Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phenotypes and genotypes within a disorder, and lack of appropriate surrogate endpoints to measure clinical benefits. The Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section has assembled a research team to assess the landscape including challenges and possible strategies to address these challenges and the role of real-world data (RWD) and RWE in rare disease drug development. This paper first reviews the current regulations by regulatory agencies worldwide and then discusses in more detail the challenges from a statistical perspective in the design, conduct, and analysis of rare disease clinical trials. After outlining an overall development pathway for rare disease drugs, corresponding strategies to address the challenges are presented. Other considerations are also discussed for generating relevant evidence for regulatory decision-making on drugs for rare diseases. The accompanying paper discusses how RWD and RWE can be used to improve the efficiency of rare disease drug development.

Parkinson's Disease (PD) is a neurodegenerative disorder characterized by dopaminergic cell death in the substantia nigra. While the interplay between dopamine loss and symptoms is well-recognized, a respective quantitative link has yet to be established. The objective was to establish a biomarker-directed clinical endpoint model for early-stage PD patients. We developed a disease progression model using DATscan data in 196 healthy subjects and 419 Parkinson's patients to characterize the onset and progression of disease in early-stage PD patients. This disease progression model was then linked to MDS-UPDRS Parts I, II, and III data from the Parkinson's Progression Markers Initiative (PPMI) using a modified item response theory (IRT) analysis to characterize and predict the impact of dopamine loss on motor and non-motor symptoms. Disease onset occurs ~4-15 years pre-diagnosis. There is correlation (Spearman's rank correlation: 0.73-0.78, P < 0.001) between striatal binding ratio values (SBR) and MDS-UPDRS total scores in early-stage PD patients once interindividual differences in age at diagnosis and onset of symptoms are considered. Stratification by degree of damage improved the model's performance for putamen/motor symptoms but not for caudate/cognitive symptoms. The model captured changes in MDS-UPDRS Parts I, II, and III in early-stage, moderately progressing PD patients (60-65% of PPMI patients). In conclusion, we developed an SBR-directed IRT model that characterizes changes in MDS-UPDRS in > 60% of early-stage PPMI patients for ~15 years.

Innovations in digital technologies have emerged digital therapeutics (DTx) as a novel therapeutic intervention. DTx hold potential as novel theragnostics for disorders with broad diagnostic spectra, including neurodevelopmental diseases (NDDs). In this review, we highlight challenging factors in the successful development and deployment of DTx for NDDs with respect to patients, medical professionals, and manufacturers. We also discuss the implications of these factors and future directions for revitalizing DTx development for NDDs.

Real-world evidence involving healthcare database studies is well established for making causal inferences in post-market drug safety studies and methods, data, and research infrastructure for evaluating effectiveness have advanced in recent years. The rapidly expanding field of etiologic research using insurance claims and electronic health records databases is being evaluated for supporting effectiveness claims. One such use case to support regulatory decision-making on effectiveness is for expanding indications beyond existing effectiveness claims. Confidence in the validity of findings from cohort studies conducted using databases (hereafter "database study") to support indication expansions could be increased through a structured benchmarking process of an initial database study against RCT evidence followed by calibration of a subsequent database study based on differences in results observed in the initial RCT-database pair. This paper proposes a benchmark, expand, and calibration (BenchExCal) approach to trial emulation and describes the design and process for evaluating the performance of the approach through both simulation studies; five planned empirical examples are also described. The project will provide insights regarding how a first-stage benchmarking emulation of a completed trial for an existing indication can be used to calibrate, increase confidence, and improve interpretation of the results for a second-stage emulation of a hypothetical trial that could potentially provide evidence for an expanded indication. Although the examples have been selected to provide a variety of learnings, five use cases do not address all clinical and data scenarios that may be encountered when seeking a supplemental indication for a marketed drug.

To evaluate the association of sodium-glucose cotransporter 2 inhibitors (SGLT2i) with diabetic ketoacidosis (DKA) in type 2 diabetes mellitus (T2DM) patients across different subgroups, we searched randomized controlled trials (RCTs) comparing SGLT2i with the control groups among T2DM patients and including DKA as a safety outcome. Pooled risk ratios (RRs) were calculated using random or fixed-effects models, as appropriate. An inverse-variance-weighted Mendelian randomization (MR) analysis was performed to estimate the genetic correlation. Twenty-two trials involving 80,235 patients were included. SGLT2i increased the risk of DKA compared to the control groups (RR 2.32, 95% CI 1.64-3.27). The risk was significantly increased in patients with higher HbA1c levels (> 7.9%) (RR 2.24, 95% CI 1.59-3.14), but not in those with lower HbA1c levels (≤ 7.9%) (RR 1.05, 95% CI 0.49-2.26; interaction P = 0.034). SGLT2i increased DKA risk in chronic kidney disease (CKD) (RR 2.70, 95% CI 1.55-4.71) and high atherosclerotic cardiovascular disease (ASCVD) risk trials (RR 2.46, 95% CI 1.47-4.11) but not significantly in heart failure (HF) trials (RR 1.23, 95% CI 0.51-2.96). Moreover, in the HF trials, SGLT2i consistently did not increase the risk of DKA in any clinical subgroups. Nevertheless, MR analysis still confirmed a genetic association between SGLT2i and the risk of DKA among overall T2DM patients. SGLT2i may increase the risk of DKA in T2DM patients, particularly in patients with higher levels of HbA1c and those with comorbid CKD or at high-risk ASCVD. However, the increased risk was not significant in patients with HF.

Real-World Evidence (RWE), as an important pillar of Integrated Evidence Plans (IEP), offers a promising tool to address numerous hurdles impeding patients to receive timely and adequate treatments.

After a medicine has been tested in pivotal trials, regulators, health technology assessment (HTA) organizations, and professional societies make decisions about the patients best served by the medicine. This study assesses how the patient populations for oncology medicines (2010-2023) are defined (1) at trial, (2) regulatory submission, (3) upon approval for marketing authorization, (4) at submission, and (5) recommendation by the HTA, and (6) in clinical guidelines in Australia, Canada, the Netherlands, the United Kingdom, and the United States. Based on 25 populations for oncology medicines, we developed a framework for describing oncology populations consisting of 20 elements in four domains: disease specifications, patient characteristics, treatment position, and exclusion criteria. In exploratory analyses, we tabulated any observed variation in these framework elements throughout the six steps in the lifecycle of a medicine. On average, 10 (95% confidence interval [CI]: 9.2-10.9) potential adjustments were made, 2.3 (95% CI: 2.0-2.5) by each decision-maker. The adjustments by pharmaceutical developers focused mostly on the disease specifications (0.5 of the average 0.8 adjustments, 63%), while adjustments by regulators, HTA organizations, and guideline developers predominantly targeted the treatment's position (range: 0.5/1.3 [36%] in guidelines to 0.6/1.0 [58%] in regulatory approvals). Each decision-maker on average modifies 1.0 element (out of 2.3 [43%]) that was previously adjusted by another decision-maker. The multiple differences observed in the description of patient populations reflect inconsistency in reporting between decision-makers, complicating communication to patients and potentially affecting access to medicines. The developed framework can support consistent reporting across stakeholders and countries.

This perspective focuses on the current and future regulatory landscape in Japan regarding food effect studies of orally administered drugs. Our study of new drugs shows that the drugs classified as Biopharmaceutics Classification System (BCS) Class I exert minimal food effects on systemic pharmacokinetics (C_max and AUC). This work will facilitate discussions to refine drug development and regulatory frameworks regarding the waiver of food effect studies using final formulations.