Clinical Pharmacology & Therapeutics最新文献

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.

Insulin secretagogues and angiotensin-converting enzyme inhibitors (ACEIs) are commonly co-prescribed for patients with type 2 diabetes (T2D). Case reports suggesting that co-administration of insulin secretagogues with ACEIs is associated with an increased risk of serious hypoglycemia have led to warnings regarding a drug-drug interaction in widely used drug compendia. However, subsequent studies have had inconsistent results. We performed a systematic review to evaluate the evidence that concomitant use of ACEIs and insulin secretagogues increases the risk of serious hypoglycemia. MEDLINE/PubMed and Embase were searched from inception to July 2023 for studies evaluating adults with T2D treated with insulin secretagogues, such as sulfonylureas or meglitinides, and exposed to an ACEI. The primary outcome was serious hypoglycemia. A literature search yielded 472 papers, of which five met the inclusion criteria. The heterogeneity of the studies precluded meta-analysis. Two studies using multiple methods to address bias found no association between hypoglycemia and concomitant use of ACEI and insulin secretagogues. Three studies found potential associations, but only one was statistically significant; these studies were at serious or critical risk of bias due to potential confounding from lack of adjustment for renal dysfunction. The higher quality studies found no association between the concomitant use of insulin secretagogues with ACEI and hypoglycemia. Drug compendia and electronic health records should consider updating and removing alerts warning of a drug-drug interaction between insulin secretagogues as a class and ACEIs.

Enrolling adequate numbers of patients into the control arm of randomized controlled trials (RCTs) often presents barriers. There is interest in leveraging real-world data (RWD) from electronic health records (EHRs) to construct external control (EC) arms to supplement RCT control arms and form hybrid control (HC) arms. This investigation showed the use of an HC arm in second-line metastatic pancreatic ductal adenocarcinoma (PDAC). The RCT experimental arm (atezolizumab + PEGylated recombinant human hyaluronidase (Atezo + PEGPH20)) was compared with an HC arm consisting of patients treated with modified FOLFOX6 or gemcitabine/nab-paclitaxel from the MORPHEUS PDAC internal control arm supplemented with data from a nationwide EHR-derived de-identified database as the EC arm. The EC arm was constructed by applying key inclusion/exclusion criteria from the MORPHEUS PDAC trial to patients from the real-world cohort. Baseline variables were balanced using propensity score matching and covariate adjustment. Three analysis approaches-Cox model with pooled-control data, Cox model with control arm-specific frailty, and Bayesian analysis using a commensurate prior-were assessed. Overall survival was similar between the treatment arms. The direction and magnitude of hazard ratios (HRs) from the multiple HC analyses (HRs ranged from 1.02 to 1.06) were comparable with the reported trial HR (HR 0.91; 95% CI: 0.56, 1.49). This analysis demonstrates the feasibility and applicability of leveraging RWD in clinical trial design to supplement clinical trial control arms.

We evaluated whether drugs approved for other indications that also target metabolic drivers of Alzheimer's disease and related dementia (ADRD) pathogenesis are associated with delayed onset of ADRD. Using routinely collected healthcare data from two population-based data sources from the US (Medicare) and UK (CPRD), we conducted active comparator, new-user cohort studies. Four alternate analytic and design specifications were implemented: (1) an as-treated follow-up approach, (2) an as-started follow-up approach incorporating a 6-month induction period, (3) incorporating a 6-month symptom to diagnosis period to account for misclassification of ADRD onset, and (4) identifying ADRD through symptomatic prescriptions and diagnosis codes. Of the 10 drug pairs evaluated, hydrochlorothiazide vs. dihydropyridine CCBs showed meaningful reductions in 3 out of 4 analyses that addressed specific biases including informative censoring, reverse causality, and outcome misclassification (pooled hazard ratios [95% confidence intervals] across Medicare and CPRD: 0.81 [0.75-0.88] in Analysis 1, 0.98 [0.92-1.06] in Analysis 2, 0.83 [0.75-0.91] in Analysis 3, 0.75 [0.65-0.85] in Analysis 4). Amiloride vs. triamterene, although less precise, also suggested a potential reduction in risk in 3 out of 4 analyses (0.86 [0.66-1.11] in Analysis 1, 0.98 [0.79-1.23] in Analysis 2, 0.74 [0.54-1.00] in Analysis 3, 0.61 [0.36-1.05] in Analysis 4). Other analyses suggested likely no major differences in risk (probenecid, salbutamol, montelukast, propranolol/carvedilol, and anastrozole) or had limited precision precluding a definitive conclusion (semaglutide, ciloztozol, levetiracetam). Future replication studies should be considered to validate our findings.

There is a scarcity of information related to the financial impact of acute kidney injury (AKI), and even more so the economics of drug-associated AKI (D-AKI). Our goal was to provide a comprehensive summary of the economic burden of D-AKI by evaluating the costs of D-AKI compared to not developing AKI and cost savings associated with nephrotoxin stewardship approaches. Following the PRISMA guidelines, a literature search was conducted using PubMed to identify articles from database inception through November 2023. The main outcomes included AKI incidence, resource use, and cost of nephrotoxin stewardship programs/D-AKI event or no event. Key findings were summarized based on whether the study compared the cost of D-AKI vs. no AKI or identified potential cost savings associated with a nephrotoxin stewardship method to prevent D-AKI or worsening D-AKI. All costs were adjusted to USD2023. Twenty-five studies met the inclusion criteria. Eight studies compared the cost of D-AKI to no AKI. Total admission costs of patients who developed D-AKI ranged from $47,696 to $173,569. Nineteen studies implemented nephrotoxin stewardship with 12 substituting a less nephrotoxic drug; five using therapeutic drug monitoring and two altering drug dosing to limit exposure. Overall, these prevention strategies ranged from $5,171 to $364,973 in total medical cost savings and $17 to $942 in total cost savings per patient-day. The in-hospital economic impact of D-AKI is substantial. Implementing nephrotoxin stewardship strategies to reduce D-AKI is associated with cost savings. Institutions should adopt strategic and efficient nephrotoxin stewardship programs to optimize patient care and reduce costs.

Insights into the time needed for evaluation of risk minimization measures' (RMMs) effectiveness might identify areas for improvement. We assessed the duration of time intervals between regulatory milestones for RMM effectiveness studies assessed by the Pharmacovigilance Risk Assessment Committee (PRAC) of the European Medicines Agency (EMA). We included completed RMM effectiveness post-authorization safety studies (PASSs) assessed by PRAC between 2016 and 2022. Regulatory documents submitted by marketing authorization holders and assessment reports were extracted from non-public EMA databases. To calculate the duration of time intervals, we collected the dates of study request, protocol assessment start, protocol approval, study start, final study report assessment start, and final study report PRAC outcome. We identified 98 PASSs. The median duration from study request to final study report PRAC outcome was 52 months (Q1-Q3: 40-70). The median duration from study request to study start was 21 months (Q1-Q3: 15-30; n = 95) and from study start to final study report assessment start was 21 months (Q1-Q3: 13-36; n = 95). The final study report assessment often comprised <6 months (median: 4; Q1-Q3: 1-6). For PASSs with a PRAC-approved protocol (n = 80, 81.6%), the median duration of protocol assessment was 7 months (Q1-Q3: 4-10). Concluding, the median duration from study request to RMM effectiveness PASS completion exceeded 4 years. Next to the study conduct duration, the period from study request until study start was the most time-consuming. The duration of this period might be minimized by improved guidance on RMM effectiveness PASSs and encouraging timely protocol submission.

Evidence of the effectiveness of β-blockers in heart failure (HF) and atrial fibrillation (AF) in a contemporary cohort is controversial. This study investigated the association between the use of β-blockers and prognosis in hospitalized HF patients with and without AF in Japan. Patients hospitalized with the first episode of acute HF were identified from the National Database of Health Insurance Claims and Specific Health Checkups of Japan between April 2014 and March 2021. Associations of β-blocker use and prognosis were compared by propensity score matching among the AF or non-AF group. A mixed-effects survival model was used, and hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Among 428,650 patients discharged with HF in 4,433 hospitals, 175,174 (40.9%) were ≥ 85 years old, 151,873 (35.4%) had complicated AF, and 236,457 (55.2%) were β-blocker users. In a matched AF group, β-blocker use was associated with a lower composite outcome of all-cause mortality or HF rehospitalization (HR [95% CI], 0.95 [0.93-0.97]). A similar result was obtained in a matched non-AF group (0.95 [0.94-0.96]). In addition, the HRs in patients aged ≥ 85 years and female patients were 1.00 [0.98-1.02] and 1.01 [0.98-1.03] in the AF group and 1.03 [1.01-1.05] and 0.98 [0.97-1.00] in the non-AF group, respectively. The favorable prognostic associations of β-blocker use were observed regardless of AF in patients across a broad spectrum of HF in a superaged society.

While drug-drug interactions (DDIs) and their pharmacokinetic (PK) mechanisms are well-studied prior to drug approval, severe adverse drug reactions (SADRs) caused by DDIs often remain underrecognized due to limitations in pre-marketing clinical trials. To address this gap, our study utilized a literature database, applied natural language processing (NLP) techniques, and conducted multi-source electronic health record (EHR) validation to uncover underrecognized DDI-SADR signals that warrant further investigation. PubMed abstracts related to DDIs from January 1962 to December 2023 were retrieved. We utilized PubTator Central for Named Entity Recognition (NER) to identify drugs and SADRs and employed SciFive for Relation Extraction (RE) to extract DDI-SADR signals. The extracted signals were cross-referenced with the DrugBank database and validated using logistic regression, considering risk factors including patient demographics, drug usage, and comorbidities, based on EHRs from Vanderbilt University Medical Center (VUMC) and the All of Us research program. From 160,321 abstracts, we identified 111 DDI-SADR signals. Seventeen were statistically significant (13 by one EHR and 4 by both EHR databases), with 9 being previously not recorded in the DrugBank. These included methadone-ciprofloxacin-respiratory depression, oxycodone-fluvoxamine-clonus, tramadol-fluconazole-hallucination, simvastatin-fluconazole-rhabdomyolysis, ibrutinib-amiodarone-atrial fibrillation, fentanyl-diltiazem-delirium, clarithromycin-voriconazole-acute kidney injury, colchicine-cyclosporine-rhabdomyolysis, and methadone-voriconazole-arrhythmia (odds ratios (ORs) ranged from 1.9 to 35.83, with P-values ranging from < 0.001 to 0.017). Utilizing NLP to extract DDI-SADRs from Biomedical Literature and validating these findings through multiple-source EHRs represents a pioneering approach in pharmacovigilance. This method uncovers clinically relevant SADRs resulting from DDIs that were not evident in pre-marketing trials or the existing DDI knowledge base.

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.

The current standard method for the analysis of potential drug-drug interactions (pDDIs) is time-consuming and includes the use of multiple clinical decision support systems (CDSSs) and the interpretation by healthcare professionals. With the emergence of large language models developed with artificial intelligence, an interesting alternative arose. This retrospective study included 30 patients with polypharmacy, who underwent a pDDI analysis between October 2022 and August 2023, and compared the performance of Chat GPT and established CDSSs (MediQ®, Lexicomp®, Micromedex®) in the analysis of pDDIs. A multidisciplinary team interpreted the obtained results and decided upon clinical relevance and assigned severity grades using three categories: (i) contraindicated, (ii) severe, (iii) moderate. The expert review identified a total of 280 clinically relevant pDDIs (3 contraindications, 13 severe, 264 moderate) using established CDSSs, compared with 80 pDDIs (2 contraindications, 5 severe, 73 moderate) using Chat GPT. Chat GPT almost entirely neglected pDDIs with the risk to QTc prolongation (85 vs. 8), which could also not be sufficiently improved by using a specific prompt. To assess the consistency of the results provided by Chat GPT, we repeated each query and found inconsistent results in 90% of the cases. In contrast, Chat GPT provided acceptable and comprehensible recommendations for specific questions on side effects. The use of Chat GPT for the identification of pDDIs cannot be recommended currently, because clinically relevant pDDIs were not detected, there were obvious errors and results were inconsistent. However, if these limitations are addressed accordingly, it is a promising platform for the future.