Cts-Clinical and Translational Science最新文献

Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.

Elevated triglyceride levels are associated with an increased risk of cardiovascular events despite guideline-based statin treatment of low-density lipoprotein cholesterol. Peroxisome proliferator-activated receptor α (PPARα) agonists exert a significant triglyceride-lowering effect. However, combination therapy of PPARα agonists with statins poses an increased risk of rhabdomyolysis, which is rare but a major concern of the combination therapy. Pharmacokinetic interaction is suspected to be a contributing factor to the risk. To examine the potential for combination therapy with the selective PPARα modulator (SPPARMα) pemafibrate and statins, drug–drug interaction studies were conducted with open-label, randomized, 6-sequence, 3-period crossover designs for the combination of pemafibrate 0.2 mg twice daily and each of 6 statins once daily: pitavastatin 4 mg/day (n = 18), atorvastatin 20 mg/day (n = 18), rosuvastatin 20 mg/day (n = 29), pravastatin 20 mg/day (n = 18), simvastatin 20 mg/day (n = 20), and fluvastatin 60 mg/day (n = 19), involving healthy male volunteers. The pharmacokinetic parameters of pemafibrate and each of the statins were similar regardless of coadministration. There was neither an effect on the systemic exposure of pemafibrate nor a clinically important increase in the systemic exposure of any of the statins on the coadministration although the systemic exposure of simvastatin was reduced by about 15% and its open acid form by about 60%. The HMG-CoA reductase inhibitory activity in plasma samples from the simvastatin and pemafibrate combination group was about 70% of that in the simvastatin alone group. In conclusion, pemafibrate did not increase the systemic exposure of statins, and vice versa, in healthy male volunteers.

Some anti-mycobacterial drugs are known to cause QT interval prolongation, potentially leading to life-threatening ventricular arrhythmia. However, the highest leprosy and tuberculosis burden occurs in settings where electrocardiographic monitoring is challenging. The feasibility and accuracy of alternative strategies, such as the use of automated measurements or a mobile electrocardiogram (mECG) device, have not been evaluated in this context. As part of the phase II randomized controlled BE-PEOPLE trial evaluating the safety of bedaquiline-enhanced post-exposure prophylaxis (bedaquiline and rifampicin, BE-PEP, versus rifampicin, SDR-PEP) for leprosy, all participants had corrected QT intervals (QTc) measured at baseline and on the day after receiving post-exposure prophylaxis. The accuracy of mECG measurements as well as automated 12L-ECG measurements was evaluated. In total, 635 mECGs from 323 participants were recorded, of which 616 (97%) were of sufficient quality for QTc measurement. Mean manually read QTc on 12L-ECG and mECG were 394 ± 19 and 385 ± 18 ms, respectively (p < 0.001), with a strong correlation (r = 0.793). The mean absolute QTc difference between both modalities was 11 ± 10 ms. Mean manual and automated 12L-ECG QTc were 394 ± 19 and 409 ± 19 ms, respectively (n = 636; p < 0.001), corresponding to moderate agreement (r = 0.655). The use of a mECG device for QT interval monitoring was feasible and yielded a median absolute QTc error of 8 ms. Automated QTc measurements were less accurate, yielding longer QTc intervals.

In the last few decades, developers of new drugs, biologics, and devices have increasingly leveraged digital health technologies (DHTs) to assess clinical trial digital endpoints. To our knowledge, a comprehensive assessment of the financial net benefits of digital endpoints in clinical trials has not been conducted. We obtained data from the Digital Medicine Society (DiMe) Library of Digital Endpoints and the US clinical trials registry, ClinicalTrials.gov. The benefit metrics are changes in trial phase duration and enrollment associated with the use of digital endpoints. The cost metric was obtained from an industry survey of the costs of including digital endpoints in clinical trials. We developed an expected net present value (eNPV) model of the cash flows for new drug development and commercialization to assess financial value. The value measure is the increment in eNPV that occurs when digital endpoints are employed. We also calculated a return on investment (ROI) as the ratio of the estimated increment in eNPV to the mean digital endpoint implementation cost. For phase II trials, the increase in eNPV varied from $2.2 million to $3.3 million, with ROIs between 32% and 48% per indication. The net benefits were substantially higher for phase III trials, with the increase in eNPV varying from $27 million to $40 million, with ROIs that were four to six times the investment. The use of digital endpoints in clinical trials can provide substantial extra value to sponsors developing new drugs, with high ROIs.

Heart failure (HF) is a complex, progressive disorder that is associated with substantial morbidity and mortality on a global scale. Relaxin-2 is a naturally occurring hormone that may have potential therapeutic benefit for patients with HF. To investigate the therapeutic potential of relaxin in the treatment of patients with HF, mRNA-0184, a novel, investigational, lipid nanoparticle (LNP)–encapsulated mRNA therapy that encodes for human relaxin-2 fused to variable light chain kappa (Rel2-vlk) was developed. A translational semi-mechanistic population pharmacokinetic (PK)/pharmacodynamic (PD) model was developed using data from non-human primates at dose levels ranging from 0.15 to 1 mg/kg. The PK/PD model was able to describe the PK of Rel2-vlk mRNA and translated Rel2-vlk protein in non-human primates adequately with relatively precise estimates. The preclinical PK/PD model was then scaled allometrically to determine the human mRNA-0184 dose that would achieve therapeutic levels of Rel2-vlk protein expression in patients with stable HF with reduced ejection fraction. Model-based simulations derived from the scaled PK/PD model support the selection of 0.025 mg/kg as an appropriate starting human dose of mRNA-0184 to achieve average trough relaxin levels between 1 and 2.5 ng/mL, which is the potential exposure for cardioprotective action of relaxin.

The University of Florida Health conducted a pragmatic implementation of a pharmacogenetics (PGx) panel-based test to guide medications used for supportive care prescribed to patients undergoing chemotherapy. The implementation was in the context of a pragmatic clinical trial for patients with non-hematologic cancers being treated with chemotherapy. Patients were randomized to either the intervention arm or control arm and received PGx testing immediately or at the end of the study, respectively. Patients completed the MD Anderson Symptom Inventory (MDASI) to assess quality of life (QoL). A total of 150 patients received PGx testing and enrolled in the study. Clinical decision support and implementation infrastructure were developed. While the study was originally planned for 500 patients, we were underpowered in our sample of 150 patients to test differences in the patient-reported MDASI scores. We did observed a high completion rate (92%) of the questionnaires; however, there were few medication changes (n = 6 in the intervention arm) based on PGx test results. Despite this, we learned several lessons through this pragmatic implementation of a PGx panel-based test in an outpatient oncology setting. Most notably, patients were less willing to undergo PGx testing if the cost of the test exceeded $100. In addition, to enhance PGx implementation success, reoccurring provider education is necessary, clinical decision support needs to appear in a more conducive way to fit in with oncologists' workflow, and PGx test results need to be available earlier in treatment planning.

Real-world evidence (RWE) trials have a key advantage over conventional randomized controlled trials (RCTs) due to their potentially better generalizability. High generalizability of study results facilitates new biological insights and enables targeted therapeutic strategies. Random sampling of RWE trial participants is regarded as the gold standard for generalizability. Additionally, the use of sample correction procedures can increase the generalizability of trial results, even when using nonrandomly sampled real-world data (RWD). This study presents descriptive evidence on the extent to which the design of currently planned or already conducted RWE trials takes sampling into account. It also examines whether random sampling or procedures for correcting nonrandom samples are considered. Based on text mining of publicly available metadata provided during registrations of RWE trials on clinicaltrials.gov, EU-PAS, and the OSF-RWE registry, it is shown that the share of RWE trial registrations with information on sampling increased from 65.27% in 2002 to 97.43% in 2022, with a corresponding increase from 14.79% to 28.30% for trials with random samples. For RWE trials with nonrandom samples, there is an increase from 0.00% to 0.95% of trials in which sample correction procedures are used. We conclude that the potential benefits of RWD in terms of generalizing trial results are not yet being fully realized.

Esophageal and gastric varices (EGV) bleeding is a dangerous side effect of liver cirrhosis. Ascites may affect the effectiveness of carvedilol in preventing EGV rebleeding. A retrospective analysis was done on patients with EGV bleeding who visited our gastroenterology department between January 1, 2015, and October 29, 2020, and were given carvedilol therapy again. Patients were classified based on whether they had ascites. The primary outcome was EGV rebleeding. A total of 286 patients were included, with a median follow-up of 24.0 (19.0–42.0) months, comprising those without ascites (N = 155) and those with ascites (N = 131). The mean age of the patients was 55.15 ± 12.44 years, and 177 (61.9%) of them were men. There were 162 (56.6%) Child-Pugh A grades. The etiology of cirrhosis included 135 (47.2%) cases of hepatitis B. After carvedilol therapy, the patient's portal vein diameter (DPV) was widened (p < 0.05), velocity of portal vein (VPV) was slowed (p = 0.001). During the 1-year follow-up, patients with ascites had a substantially higher rebleeding rate than patients without ascites, with 24 (18.3%) versus 13 (8.4%), respectively (p = 0.013). On univariate analysis, ascites was a risk factor for rebleeding (p = 0.015). The multivariate analysis remained significant after adjusting for age, gender, etiology of cirrhosis, and previous endoscopic treatment, with OR of 2.37 (95% CI: 1.12–5.04; p = 0.025). Ascites was a risk factor for EGV rebleeding in patients undergoing carvedilol therapy. After carvedilol therapy, the patient's DPV was widened and VPV was slowed.

Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real-world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI-generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI-generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI-generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI-generated digital twins in clinical practice.