Clinical Pharmacology & Therapeutics最新文献

Understanding cytokine-related therapeutic protein-drug interactions (TP-DI) is crucial for effective medication management in conditions characterized by elevated inflammatory responses. Recent FDA and ICH guidelines highlight a systematic, risk-based approach for evaluating these interactions, emphasizing the need for a thorough mechanistic understanding of TP-DIs. This study integrates the physiologically based pharmacokinetic (PBPK) model for TP (specifically interleukin-6, IL-6) with small-molecule drug PBPK models to elucidate cytokine-related TP-DI mechanistically. The integrated model successfully predicted TP-DIs across a broad range of both constant and fluctuating IL-6 levels, as observed in patients with rheumatoid arthritis, Crohn's disease, HIV-infection, and those undergoing hip-surgery or bone marrow transplantation (all simulated AUC and Cmax ratios were within a twofold error of the observed data). Constant IL-6 levels that would be associated with mild, moderate, and strong inhibitory interactions were estimated. The time-course and extent of TP-DI potential were also assessed in cytokine storm triggered by SARS-CoV-2 infection (COVID-19) and T-cell engager therapies (blinatumomab, mosunetuzumab, and epcoritamab). Additionally, scenarios involving concurrent CYP enzyme suppression by IL-6 and induction by rifampicin were assessed for the magnitude of drug interaction. By providing a robust mechanistic framework for understanding cytokine-drug interactions and establishing reliable exposure-response relationships, this study enhances predictive accuracy and informs human dosing strategies. It demonstrates the potential of PBPK models to improve therapeutic decision making and patient care, particularly in inflammatory conditions.

A compilation of factors over the past decade-including the availability of increasingly large and rich healthcare datasets, advanced technologies to extract unstructured information from health records and digital sources, advancement of principled study design and analytic methods to emulate clinical trials, and frameworks to support transparent study conduct-has ushered in a new era of real-world evidence (RWE). This review article describes the evolution of the RWE era, including pharmacoepidemiologic methods designed to support causal inferences regarding treatment effects, the role of regulators and other health authorities in establishing distributed real-world data networks enabling analytics at scale, and the many global guidance documents on principled methods of producing RWE. This article also highlights the growing opportunity for RWE to support decision making by regulators, health technology assessment groups, clinicians, patients, and other stakeholders and provides examples of influential RWE studies. RWE holds promise to address important questions that clinical trials typically do not answer about treatment benefits and risks, and to ultimately impact public health by helping to guide decision making across the healthcare ecosystem.

Japan's conditional/time-limited early approval program, initiated in 2014, aimed to advance regenerative medicine by expediting market access. However, the withdrawal of autologous skeletal myoblast sheets (Heartsheet) due to ineffectiveness raises concerns about the balance between rapid approval and scientific integrity. While the program seeks to boost innovation, it risks endorsing costly, unclear treatments under national health care. This case highlights the need to refine regulatory approaches, ensuring clinical efficacy and fiscal responsibility in regenerative therapies.

Rifampicin is a widely employed index inhibitor to assess the impact of organic anion transporting polypeptide 1B (OATP1B) inhibition on investigational drugs. The observation of nitrosamines in certain drug products, including rifampicin, has impacted the conduct of clinical drug-drug interaction (DDI) studies with rifampicin drug products. Cyclosporine is a recommended alternative to assess in vivo OATP1B activity; however, challenges exist in its use due to pharmacokinetic (PK) variability and non-selective inhibition of other drug disposition mechanisms.

Despite being approved by the US FDA and the EU European Medicines Agency, the performance of esketamine nasal spray as an adjunctive therapy with an antidepressant in major depressive disorder is still controversial. Comprehensive retrieval in Embase, Pubmed, and Web of Science was conducted to identify randomized controlled trials comparing esketamine nasal spray versus control in major depressive disorder or treatment-resistant depression. The primary efficacy outcome was a reduction of the Montgomery-Asberg Depression Rating Scale, from baseline to Day 2 or Day 28 for patients with or without suicidal ideation, respectively. The long-term efficacy outcome was the relapse rate of patients who achieved stable remission. The certainty of evidence was assessed according to the Cochrane recommendation. Esketamine nasal spray was superior to placebo in reduction of Montgomery-Asberg Depression Rating Scale from baseline to Day 28 in patients without suicidal ideation (standardized mean difference: -0.24, 95% confidence interval: -0.38, -0.09, P = 0.001, I² = 24%), and on Day 2 in patients with suicidal ideation (standardized mean difference: -0.30, 95% confidence interval: -0.47, -0.12, P = 0.0008, I² = 0%). The long-term relapse rate was significantly lower in the esketamine nasal spray group than in the placebo/quetiapine group (risk ratio: RR: 0.60, 95% confidence interval: 0.45-0.80, I² = 0%). The rate of suicidal ideation was similar between the two groups. The certainty of evidence was graded as "moderate" or "high" in the abovementioned results. Esketamine nasal spray in conjunction with an antidepressant effectively controls short-term and long-term depressive symptoms in major depressive disorder and treatment-resistant depression, with a manageable trade-off between efficacy and safety.

OATP1B, P-gp, BCRP, and CYP3A are the most contributing drug-metabolizing enzymes or transporters (DMETs) for commonly prescribed medication. Their activities may change in end-stage renal disease (ESRD) patients with large inter-individual variabilities (IIVs), leading to altered substrate drug exposure and ultimately elevated safety risk. However, the changing extent and indictive influencing factors are not quantified so far. Here, a microdose cocktail regimen containing five sensitive substrate drugs (pitavastatin, dabigatran etexilate, rosuvastatin, midazolam, and atorvastatin) for these DMETs was administrated to Chinese healthy volunteers and ESRD patients. Drug pharmacokinetics profiles were determined, together with physiological, pharmacogenetic, and gut microbiome signature. Population pharmacokinetic and machine learning model were established to identify key influencing factors and quantify their contribution to drug exposure change. The exposure of pitavastatin, dabigatran, rosuvastatin, and atorvastatin increased to 1.8-, 3.1-, 1.1-, and 1.3-fold, respectively, whereas midazolam exposure decreased by 72% in ESRD patients. Notably, in addition to disease state, the relative abundance of genus Veillonella and Clostridium_XIVb were firstly identified as significant influencing factors for PTV and RSV apparent clearance, respectively, suggesting their indicative role for OATP and BCRP activity evaluation. Moreover, several genera were found to strongly associate with drug clearance and reduce unexplained IIVs. Accordingly, it was estimated that OATP1B and intestine P-gp activity decreased by 35-75% and 29-44%, respectively, whereas BCRP and CYP3A4 activity may upregulate to some extent. Our study provides a quantitative and mechanistic understanding of individual DMET activity and could support precision medicine of substrate drugs in ESRD patients.

The value of a medicine is defined by its impact on patients, caregivers, health system, and society. A pharmaceutical company will generate evidence to demonstrate this value in various studies, including randomized clinical trials, non-interventional and observational studies, real-world data analyses, modeling, and simulation. The quality and strength of the evidence supporting a medicine's effectiveness, safety and product quality will drive decisions by healthcare system stakeholders for marketing authorization (regulatory authorities). Additional evidence of comparative clinical, humanistic, economic, and societal value of the medicine will be critical for reimbursement coverage by HTA (health technology assessment) bodies and payers, guideline inclusion by clinical societies, and ultimately the treatment decision between a patient and their healthcare provider (HCP). The purpose of this article is to provide practical guidance for an effective approach to evidence planning for pharmaceutical companies. In the first section, we give a brief overview of the requirements for evidence generation from the perspectives of healthcare system decision makers, key functions involved in evidence generation within a pharmaceutical company, and different archetypes of products. We then discuss how a company can implement effective integrated evidence planning across the lifecycle of a product. We also review how requirements are likely to evolve given recent changes in major healthcare system regulations, such as Centers for Medicare & Medicaid Services (CMS) drug price negotiations in the US and EU HTA Regulation (HTAR) in Europe, and finally provide some practical recommendations of how to start implementing a new integrated evidence approach.

Pharmacogenetic testing can prevent severe toxicities from several oncology drug therapies; it also has the potential to improve the outcomes from supportive care drugs. Paired tumor and germline sequencing is increasingly common in oncology practice; these include sequencing of pharmacogenes, but the germline pharmacogenetic variants are rarely included in the clinical reports, despite many being clinically actionable. We established an informatics workflow to evaluate the clinical sequencing results for pharmacogenetic variants. We used the Aldy computational tool, which we have previously shown to determine the variant alleles in 14 pharmacogenes in clinical sequencing data with >99% accuracy, to identify pharmacogenetic variants in the clinical whole exome sequencing from our molecular tumor board. Patients with genetic variants that are clinically actionable for their individual therapy programs, including both treatment and supportive care, are referred to a clinical pharmacogenetics testing laboratory for confirmation. Through an evaluation of our weekly informatics workflow, we determined it took approximately 3.25 hours to complete the analysis of the sequencing data from approximately 20 patients. Using a United States pharmacist's median salary, we estimated the incremental added cost of the process to be only ~$15 per patient. This adds only a minor increase to the patient's cost of testing and has the potential to improve the safety and efficacy of their treatment.

Studies with negative results are less likely to be published than others, potentially leading to publication bias. Introduced in 2000, trial registration could have participated in decreasing the proportion of unpublished studies. We assessed the proportion of negative randomized controlled trials (RCT) over the last 20 years. We searched Medline for RCT published in 2000, 2005, 2010, 2015, and 2020 in the British Medical Journal, the Journal of the American Medical Association, the Lancet, and the New England Journal of Medicine. The primary endpoint was the proportion of negative (final comparison on the primary study-endpoint without statistical significance or favoring the control arm) studies published in 2000 and 2020. Factors independently associated with the publication of negative studies were identified using multivariable analysis. A total of 1,542 studies were included. The proportion of negative RCT significantly increased between 2000 and 2020 (from 27.6% to 37.4%; P = 0.01), however, the trend over time was not significant (P = 0.203). In multivariable analysis, the following factors were associated with a higher proportion of published negative studies: superiority (P < 0.001), two-group trials (P < 0.001), number of patients ≥510 (P < 0.001), cardiology trials (P = 0.003), emergency/critical care trials (P < 0.001), obstetrics trials (P = 0.032), surgery trials (P = 0.006), pneumology trials (P = 0.029). Exclusive industry funding was associated with a lower proportion of published negative studies (P < 0.001). The proportion of published negative studies in 2020 was higher only when compared to 2000. During the two decades, no trend was noticeable. There is no clear relationship between trial registration and the publication of negative results over time.

Two observational studies were conducted to support an initiative to qualify translational kidney safety biomarkers as clinical drug development tools that identify tubular injury prior to changes in estimated glomerular filtration rate (eGFR). Normal healthy volunteers provided three morning spot urine collections over 4 weeks. Patients undergoing surgical resection and intrathoracic cisplatin for malignant pleural mesothelioma provided urine samples pre- and postoperatively at 4, 8, and 12 hours and daily for 6 days. Using receiver-operating characteristics curves, "statistically significant thresholds" established peak longitudinal changes for 8 biomarkers to differentiate mesothelioma patients who developed acute kidney injury (AKI) from normal healthy volunteers. We also assessed "medically significant thresholds" to differentiate mesothelioma patients who did vs. did not develop AKI. Statistically and medically significant thresholds for a fold-change from baseline of urine creatinine (UCr)-normalized values were established for 6 biomarkers: clusterin (2.2, 5.1); osteopontin (3.1, 7.1); N-acetyl-ß-D-glucosaminidase (2.7, 8.1); kidney injury molecule-1 (4.3, 7.5); cystatin C (1.8, 4.5); neutrophil gelatinase-associated lipocalin (2.9, 7.8). For urine albumin and total protein, thresholds were established based on UCr-normalized absolute values: (> upper limit normal, > 10× upper limit normal). Statistically significant thresholds for all biomarkers outperformed eGFR at discriminating mesothelioma subjects exposed to cisplatin from healthy volunteers, demonstrating their utility for enhancing safe drug development. Medically significant thresholds provide perspective on when patients begin to exhibit AKI. These studies have established guideposts for confirmatory studies with additional cohorts and nephrotoxicants to formally qualify the selected biomarkers with worldwide regulatory authorities.