Clinical therapeutics最新文献

Purpose

Gastritis, one of the most common clinically diagnosed conditions, is defined as the infiltration of inflammatory cells into the gastric mucosa. Drugs for gastritis include histamine-2 receptor antagonists and proton pump inhibitors (PPIs), which reduce acidity in the stomach, and antacids, which neutralize acid. Esomeprazole is a PPI for gastroesophageal reflux disease and gastric and duodenal ulcers that has been shown to be safe and effective at a 10 mg dose. Dual-release drugs have not yet been approved for the treatment of gastritis domestically or internationally. In this study, a dual delayed-release (DR) esomeprazole (10 mg), was compared to famotidine (20 mg) to determine its effectiveness in the treatment of gastritis.

Methods

This study was a randomized, open-label, multiple-dose, 2-treatment, 2-period, 2-sequence crossover study with a 7-day washout between periods. In each period, the subjects were administered one dose of esomeprazole (10 mg) or famotidine (20 mg) for 7 days at each period. The 24-hour gastric pH was recorded after single and multiple doses. The percentage of time (duration%) that the pH was maintained above 4 in the 24 hours after 7 days of repeated dosing was evaluated.

Findings

The mean percentages of time that the gastric pH was above 4 after multiple doses over 7 days of a dual DR esomeprazole (10 mg) and famotidine (20 mg) was 47.31% ± 14.85% and 23.88% ± 10.73%.

Implications

Multiple doses of a dual DR esomeprazole (10 mg) showed effective gastric acid secretion suppression compared to famotidine with comparable safety and tolerability. These results provide evidence supporting the clinical use of a dual DR esomeprazole (10 mg) to treat gastritis.

ClinicalTrials.gov identifier: NCT04967014.

Purpose

Patients with coronary artery disease (CAD) need to take antiplatelet drugs regularly in order to prevent thrombosis; however, there is existing inter-individual variability in drug response. Pharmacogenomic studies indicate that drug response may also be influenced by genetic variants, and multiple genetic variants may work together. We assumed that patients carrying more risk alleles might have a worse clopidogrel drug response and that a polygenic model integrated different single variants might have the potential to explain clopidogrel drug response variability better. We aimed to investigate whether the polygenic model could be used to predict clopidogrel drug response.

Methods

A total of 935 CAD patients were enrolled in the study. We investigated the association between 19 clopidogrel-related single-nucleotide polymorphisms (SNPs) and the incidence of recurrent ischemic events. Additionally, a polygenic model was constructed to assess the risk of ischemic events.

Findings

There were only 2 SNPs of CYP2C8 gene (rs1934980 and rs17110453) that were nominally associated with incidence of recurrent ischemic events. We constructed a polygenic model integrated with 6 clopidogrel-related SNPs. When compared with patients carrying 6 or fewer risk alleles, patients with 7 or more risk alleles had a higher risk of ischemic events (hazard ratio = 1.87; P = 0.04).

Implications

The polygenetic model may be useful for clopidogrel drug response prediction in patients with CAD.

Purpose

Global fears regarding future epidemics of new and re-emerging infections will prompt clinicians to try out unconventional treatments based on limited evidence, including the repurposing of existing drugs. The dilemma involves balancing clinical intuition with the need to rely on low-quality information because of the scarcity of definitive evidence. An example was ivermectin; with its potential antiviral properties, it was promoted for its efficacy in treating coronavirus disease 2019 despite conflicting outcomes in clinical trials and varying expert opinions. This article describes the development of a decision-making framework to resolve such dilemmas.

Methods

The case study from Sri Lanka illustrates multiple challenges faced by clinicians. As the horrific details of deaths in countries such as Italy spread on social media, there was panic and an unprecedented demand for clinicians and health services to provide effective treatment. This led to the popularity of drugs such as ivermectin and several herbal cures. However, there was no consensus among experts on the efficacy of ivermectin, which eventually led to the authorities to recommend limited approval for use under physician supervision.

Findings

The situation lent itself to a framework with 4 elements: prerequisites, applying an appropriate decision-making tool (eg, multiple criteria decision-making methods), ethical considerations, and sensitive communication.

Implications

We propose this framework for clinicians when they face similar situations with demands to repurpose medicines with inconclusive evidence of efficacy to combat devastating infections from new or re-emerging infections.

Purpose

Niraparib is a poly(adenosine diphosphate [ADP]-ribose) polymerase inhibitor approved for the maintenance treatment of advanced ovarian cancer (OC). Niraparib was originally approved in recurrent OC at a fixed starting dose (FSD) of 300 mg once daily (QD). This analysis characterized the population pharmacokinetics (PK) of niraparib and evaluated the relationships between exposure, efficacy, and safety to support clinical use of an individualized dosing strategy, in which the starting dose of niraparib was adjusted based on patient characteristics to improve the benefit-risk profile.

Methods

A population PK model was developed by pooling data from four niraparib clinical trials (PN001 [n = 104], QUADRA [n = 455], NOVA [n = 403], and PRIMA [n = 480]) in patients with solid tumors, including OC. Exposure-response analyses were conducted to explore the relationships of niraparib exposure with progression-free survival (PFS) and adverse events in the PRIMA study. A multivariate logistic regression model was also developed to estimate the probability of grade ≥3 thrombocytopenia, using data from patients enrolled in PRIMA and NOVA. The impact of an individualized starting dose (ISD) regimen (200 mg QD in patients with body weight [BW] <77 kg or platelet count [PLT] <150,000/µL, or 300 mg QD in patients with BW ≥77 kg and PLT ≥150,000/µL) on systemic exposure, efficacy, and safety was assessed.

Findings

Niraparib disposition was best described by a 3-compartment model with linear elimination. Key covariates included baseline creatinine clearance, BW, albumin, and age, all of which had minor effects on niraparib exposure. Comparable model-predicted exposure up to the time of disease progression/death or censoring in the 300-mg FSD and 200-/300-mg ISD groups was consistent with the lower rate of dose reduction in the ISD groups. No consistent niraparib exposure-response relationship was observed for efficacy in all PRIMA patients (first-line OC), and no statistically significant difference was seen in PFS curves for patients receiving a niraparib dose of 200 mg versus 300 mg. In the multivariate regression model, performed using combined data from PRIMA and NOVA, higher niraparib exposure (area under the concentration-time curve at steady-state [AUC_ss]), lower BW, and lower PLT were associated with an increased risk of grade ≥3 thrombocytopenia.

Implications

Population PK and exposure-response analyses support use of an ISD to improve the safety profile of niraparib, including reducing the rate of grade ≥3 thrombocytopenia, without compromising efficacy.

Clinical trial registration

ClinicalTrials.gov, NCT01847274 (NOVA), NCT00749502 (PN001), NCT02655016 (PRIMA), NCT02354586 (QUADRA), www.clinicaltrials.gov.

Purpose

Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes.

Methods

A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes.

Findings

The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs.

Implications

AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.

Purpose

In 2019, the International Working Group (IWG), focusing on New Developments in Pharmacovigilance, was established. This group is coordinated by the Drug Safety Research Unit in the United Kingdom, and the mission of the IWG is to progress pharmacovigilance methodologies and promote the safe and effective use of medicines and vaccines, thereby further protecting patients. Novel therapeutics are continuously being developed to alleviate medical conditions, but with advancing technologies, innovative pharmacovigilance methodologies need to be developed to effectively monitor the use and safety of these products. With reduced timelines proposed for premarketing clinical trials and increased application of real-world evidence supporting regulatory approvals, products may be used in real-world clinical practice in shorter timeframes than before. Therefore, the need for effective methods of monitoring medicines and collecting safety data in real-time is of paramount importance to public health.

Methods

The IWG aims to advance existing methodologies used in the detection, monitoring, and analysis of safety data in pharmacovigilance and to communicate best practice proposals to support decision making in health care. The IWG will identify areas requiring review of current processes or methodologic research and will communicate the output of the IWG through peer-reviewed publications, reports, and presentation of findings at relevant conferences and scientific meetings.

Findings

The IWG is currently reviewing two areas in pharmacovigilance; case-level causality assessment and the strengths and limitations of data sources. The IWG is advancing these areas by producing two scoping reviews which will be easily accessible to regulatory agencies, industry, academia, and interested persons or organizations.

Implications

The scoping reviews comply with the IWGs mission to progress pharmacovigilance methodologies and promote the safe and effective use of medicines and vaccines. The present article shares details of the objectives of the IWG and provides an overview on the status of IWG activities.