Pharmaceutical Medicine最新文献

Cellular senescence, a hallmark of ageing, contributes to tissue or organ dysfunction and the pathophysiology of diverse age-related diseases (ARD) by various mechanisms. Targeting it by selective elimination of senescent cells (SCs) or blocking senescence-associated secretory phenotypes (SASP) with natural or synthetic compounds has been suggested to improve lifespan. Dietary phytochemicals possess a broad spectrum of biochemical and pharmacological effects that are beneficial to human health. Flavonoids, which are widely consumed in fruits and vegetables worldwide, are emerging as potential therapeutic agents to mitigate senescence. Naringenin, hesperetin, hesperidin, quercetin, fisetin, kaempferol, rutin, apigenin, luteolin, nobiletin, tangeretin, genistein, wogonin, epigallocatechin gallate (EGCG), theaflavin-3-gallate (TF2A), and procyanidin C1 possess potent antisenescence effects. A single biochemical process may not explain their pleiotropic pharmacological impact. Flavonoids directly modulate underlying cellular senescence processes or interact with molecular targets that regulate ageing-related pathways. This review discusses the potential use of flavonoids to mitigate senescence and consequently delay the onset of ageing-related diseases. We also highlight the underlying mechanisms of action of flavonoids as potential senotherapeutics and reflect on future perspectives and possible strategies to optimize and increase the translatability from bench to bedside in senotherapy.

Introduction: Patients and healthcare practitioners are increasingly interested in using cannabis and cannabinoids to address unmet clinical needs. Although we have clinical evidence on the medical use of cannabinoids, a significant portion of the data is not based on randomized clinical trials, which are considered the gold standard in clinical research. We have reviewed the registered clinical trials on cannabis and cannabinoids for therapeutic or drug development purposes to underline the past and current attempts to generate robust clinical evidence and identify existing knowledge gaps.

Methods: We reviewed four clinical trial registries (International Clinical Trials Registry Program [ICTRP], ClinicalTrials.gov, European Clinical Trial Registry [EUCTR], Australian New Zealand Clinical Trial Registry [ANZCTR]) to identify clinical trials on cannabinoids (phyto- or synthetic) or cannabis-based medications between January 1, 2000, and December 31, 2021. All interventional clinical trials on cannabinoids and other compounds interacting with the endocannabinoid system, regardless of the investigated medical condition, assessed health outcomes, or choice of comparator, were included, provided they had a therapeutic or drug development purpose. Data on the primary sponsor, type of sponsor, date of registration, recruitment status, number of participants, study design, the phase of the study, country, medical conditions, investigated cannabinoids, and the route of administration were extracted. The therapeutic area and class of cannabinoids were identified based on the details of each trial.

Results: We included 834 out of 2966 reviewed clinical trials. The number of registered clinical trials has constantly increased from 30 in 2013 to 103 in 2021. More than 40% of registered clinical trials in 2021 were phase II and phase III clinical trials. The mean number of trial enrollments for completed, ongoing, and terminated studies were 128, 156, and 542, respectively. Clinical research on Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), and the oral routes of administration dominate the field. Approximately two-thirds of clinical trials were conducted in five therapeutic areas (i.e., 'Chronic pain,' 'Mental, behavioral or neurodevelopmental disorders,' 'Nervous system diseases,' 'Endocrine, nutritional or metabolic diseases,' and 'Neoplasms'). Pharmaceutical companies sponsored 39% of all clinical trials. However, trial sponsorships vary noticeably in different jurisdictions, likely due to, in part, different regulatory frameworks.

Conclusion: Our review highlights the diversification of clinical trials on cannabinoid-based medications in the past 21 years. This review underlines the increased interest in conducting clinical studies on new cannabinoid administration methods such as topical applications and on the investigation of emerging phyto- and synthetic canna

Background: Legislative changes have fueled the global availability of cannabis and cannabis-derived compounds, such as cannabidiol. Little is known about the effectiveness and safety of cannabidiol for treating health conditions other than seizure disorders.

Objective: A systematic review of the literature was performed to investigate other health conditions, characteristics of the studied populations, and the effectiveness of cannabidiol in randomized clinical trials.

Methods: Seven publication databases were searched from February to March 2021. The inclusion criteria for studies were: (1) utilized a randomized clinical trial design; (2) published in a peer-reviewed journal or thesis/dissertation; (3) published in English; (4) investigated either prescription (i.e., Epidiolex) or non-prescription CBD that was derived from the Cannabis sativa plant with < 3% ∆9-tetrahydrocannabinol; and (5) reported at least one outcome. This review excluded seizure-related disorders as several previous reviews have been done on this topic; it also excluded published protocols, other systematic reviews, or meta-analyses of randomized clinical trials that investigated cannabidiol. Independent reviewing, risk of bias assessment, and data abstraction were performed by two authors.

Results: Fifty-eight studies from eight countries were included in this review. Twenty-seven studies (47%) were conducted in healthy populations, 14% were restricted to male individuals (n = 8), and 72% had sample sizes of fewer than 40 participants. Doses of cannabidiol used in these studies ranged from 400 µg to 6000 mg. The effect of cannabidiol on mental health was the most studied topic (53%), which focused mainly on anxiety, psychosis, schizophrenia, and substance use disorders. The remaining studies investigated neurological conditions (19%) and a myriad of other health conditions or outcomes. While cannabidiol appears to be anxiolytic, its effectiveness for other conditions was highly variable.

Conclusions: This review highlights the inconsistencies of cannabidiol as a treatment for non-seizure-related health conditions or outcomes. Studies incorporating larger sample sizes in more diverse populations are encouraged. While cannabidiol was generally safe and well tolerated even in high doses among the included studies, clearer dosing guidelines and increased regulation of cannabidiol products are also needed.

The COVID-19 pandemic was the first 'stress test' to assess whether the current regulations in the United Kingdom (UK) are fit for purpose to develop novel therapies during pandemics. It saw innovations and collaborations across the spectrum of the drug development and regulatory pathways, including extraordinary collaborations between the various stakeholders involved in the process, the repositioning of medicines, the deployment of multi-arm, multi-interventional adaptive trials, the institution of operational simplicity and flexibility across various trial activities, and regulatory innovations. The question arises whether the innovative flexibilities and the urgency that were instituted could have resulted in compromises to the integrity of the process. An assessment of the conduct of the RECOVERY trial and the speedy approval of dexamethasone by the UK Medicines and Healthcare products Regulatory Agency demonstrates that no compromises were made to the ethical and scientific integrity of the process. Lessons learnt could be applied for future pandemics and to enhance R&D productivity and contribute to global health by improving access to medicines, especially in low- and middle-income countries and for neglected or rare diseases. What is needed is not a major transformation in the process but the flexible adaptation of existing regulations to reduce bureaucracy and handover times. Arriving at an optimal balance between scientific standards, regulations and commercial conflicts of interest will pose considerable challenges but what the COVID-19 pandemic has shown is that where there is will, there is always a way.

Background: The US Food and Drug Administration (FDA) collects and retains several data sets on post-market drugs and associated adverse events (AEs). The FDA Adverse Event Reporting System (FAERS) contains millions of AE reports submitted by the public when a medication is suspected to have caused an AE. The FDA monitors these reports to identify drug safety issues that were undetected during the premarket evaluation of these products. These reports contain patient narratives that provide information regarding the AE that needs to be coded using standardized terminology to enable aggregation of reports for further review. Additionally, the FDA collects structured drug product labels (SPLs) that facilitate standardized distribution of information regarding marketed medical products. Manufacturers are currently not required to code labels with associated AEs.

Objectives: Approaches for automated classification of reports by preferred terminology could enhance regulatory efficiency. The goal of this work was to assess the suitability of manually annotated FDA FAERS and SPL data sets to be subjected to predictive modeling.

Methods: A recurrent neural network (RNN) was proposed as a proof-of-concept model for automated extraction of preferred AE terminology. A separate RNN was fit and cross-validated on two regulatory data sets with varying properties. First, the researchers trained and cross-validated a model on 325 annotated FAERS patient narratives for a sample of AE terms. A model was then trained and validated on a data set of 100 SPLs.

Results: Model cross-validation results for product labels demonstrated that the model performed at least as well as more conventional models for all but one of the terms selected based on F1-score. Model results for the FAERS data set were mixed.

Conclusions: This work successfully demonstrated a proof-of-concept machine learning approach to automatically detect AEs in several textual regulatory data sets to support post-market regulatory activities. Limited instances of each AE class likely prohibited models from generalizing data effectively. Additional data may permit more robust validation.