drug safety
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2022 ◽  
Vol 12 ◽  
Author(s):  
Zi-Liang Guo ◽  
Mao-Xing Li ◽  
Xiao-Lin Li ◽  
Peng Wang ◽  
Wei-Gang Wang ◽  
...  

Crocetin is an aglycone of crocin naturally occurring in saffron and produced in biological systems by hydrolysis of crocin as a bioactive metabolite. It is known to exist in several medicinal plants, the desiccative ripe fruit of the cape jasmine belonging to the Rubiaceae family, and stigmas of the saffron plant of the Iridaceae family. According to modern pharmacological investigations, crocetin possesses cardioprotective, hepatoprotective, neuroprotective, antidepressant, antiviral, anticancer, atherosclerotic, antidiabetic, and memory-enhancing properties. Although poor bioavailability hinders therapeutic applications, derivatization and formulation preparation technologies have broadened the application prospects for crocetin. To promote the research and development of crocetin, we summarized the distribution, preparation and production, total synthesis and derivatization technology, pharmacological activity, pharmacokinetics, drug safety, drug formulations, and preparation of crocetin.


2022 ◽  
Vol 12 ◽  
Author(s):  
Xiangmin Ji ◽  
Guimei Cui ◽  
Chengzhen Xu ◽  
Jie Hou ◽  
Yunfei Zhang ◽  
...  

Introduction: Improving adverse drug event (ADE) detection is important for post-marketing drug safety surveillance. Existing statistical approaches can be further optimized owing to their high efficiency and low cost.Objective: The objective of this study was to evaluate the proposed approach for use in pharmacovigilance, the early detection of potential ADEs, and the improvement of drug safety.Methods: We developed a novel integrated approach, the Bayesian signal detection algorithm, based on the pharmacological network model (ICPNM) using the FDA Adverse Event Reporting System (FAERS) data published from 2004 to 2009 and from 2014 to 2019Q2, PubChem, and DrugBank database. First, we used a pharmacological network model to generate the probabilities for drug-ADE associations, which comprised the proper prior information component (IC). We then defined the probability of the propensity score adjustment based on a logistic regression model to control for the confounding bias. Finally, we chose the Side Effect Resource (SIDER) and the Observational Medical Outcomes Partnership (OMOP) data to evaluate the detection performance and robustness of the ICPNM compared with the statistical approaches [disproportionality analysis (DPA)] by using the area under the receiver operator characteristics curve (AUC) and Youden’s index.Results: Of the statistical approaches implemented, the ICPNM showed the best performance (AUC, 0.8291; Youden’s index, 0.5836). Meanwhile, the AUCs of the IC, EBGM, ROR, and PRR were 0.7343, 0.7231, 0.6828, and 0.6721, respectively.Conclusion: The proposed ICPNM combined the strengths of the pharmacological network model and the Bayesian signal detection algorithm and performed better in detecting true drug-ADE associations. It also detected newer ADE signals than a DPA and may be complementary to the existing statistical approaches.


2022 ◽  
Author(s):  
Linlin Shi ◽  
Xinkai Wu ◽  
Tongyu Li ◽  
Yuan Wu ◽  
Liwei Song ◽  
...  

Liposomal nanomedicine represents a common and versatile carrier for the delivery of both lipophilic and hydrophilic drugs. However, the direct formulation of many chemotherapeutics into a liposomal system remains an...


2021 ◽  
Vol 15 (2) ◽  
pp. 97-100
Author(s):  
Alexei M. Ovechkin

In the March 2021 issue of the journal Pharmacoepidemiology Drug Safety, an article by K. Bykov et al. was published, which contains an analysis of the use of opioid and non-opioid analgesics in US clinics in the period 20072017. According to the authors, the frequency of use of drugs in this group does not tend to decrease, despite the previously announced opioid epidemic in the USA. In Russia, the problem of the emergence of opioid dependence due to the perioperative use of drugs of this group is of little relevance. The existing legal restrictions on the prescription of opioid analgesics minimize this risk. But these same limitations make the idea of opioid-free analgesia very attractive in our country.


2021 ◽  
Vol 2 (3) ◽  
pp. 109-111
Author(s):  
Jian Di ◽  
Qiqi Zang

Drug safety is related to people's livelihood and public safety. Therefore, we must improve drug supervision and curb the circulation of counterfeit and substandard drugs.At present, most of the centralized drug supervision systems have some problems, such as few participants, easy data tampering, lack of credibility and so on.This paper analyzes the application feasibility of blockchain technology in the field of drug supervision, and designs a decentralized drug supervision system based on Fisco Bcos blockchain, which can solve the problems of data forgery, data tampering, centralization and lack of trust in the current drug supervision system, and provide support and reference for all links of drug circulation.


2021 ◽  
Vol 83 (1) ◽  
Author(s):  
Lauren B. Gerlach ◽  
Tony Van ◽  
Hyungjin Myra Kim ◽  
Ming-Un Myron Chang ◽  
Kipling M. Bohnert ◽  
...  

2021 ◽  
Vol 9 ◽  
Author(s):  
Qier Wu ◽  
Youcef Bagdad ◽  
Olivier Taboureau ◽  
Karine Audouze

Background: The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment.Methods: To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space. Considering the drugs and their associations to biological events, such as molecular initiating event and key event, a bipartite network was developed. This bipartite network was projected into a monopartite network capturing the event–event linkages. Nevertheless, such transformation of a bipartite network to a monopartite network had a huge risk of information loss. A way to solve this problem is to quantify the network reduction. We calculated two scoring systems, one measuring the uncertainty and a second one describing the loss of coverage on the developed event–event network to better investigate events from AOPs linked to drugs.Results: This AON analysis allowed us to identify biological events that are highly connected to drugs, such as events involving nuclear receptors (ER, AR, and PXR/SXR). Furthermore, we observed that the number of events involved in a linkage pattern with drugs is a key factor that influences information loss during monopartite network projection. Such scores have the potential to quantify the uncertainty of an event involved in an AON, and could be valuable for the weight of evidence assessment of AOPs. A case study related to infertility, more specifically to “decrease, male agenital distance” is presented.Conclusion: This study highlights that computational approaches based on network science may help to understand the complexity of drug health effects, with the aim to support drug safety assessment.


2021 ◽  
Author(s):  
Yun Hao ◽  
Phyllis Thangaraj ◽  
Nicholas Tatonetti

Assessing in vivo tissue toxicity of therapeutic targets remains a major challenge in drug development and drug safety research. We developed TissueTox, an algorithm that learns from multi-omic features of a target protein and predicts toxicity in human body systems and tissues. Predicted TissueTox scores accurately differentiate drugs that failed clinical trials from those that succeeded, and, importantly, can be used to identify the tissues where toxic events occurred.


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