Risk of major adverse cardiovascular events of CYP2C19 loss-of-function genotype guided prasugrel/ticagrelor vs clopidogrel therapy for acute coronary syndrome patients undergoing percutaneous coronary intervention: a meta-analysis

The most effective antiplatelet treatments for acute coronary syndrome (ACS) patients carrying CYP2C19 loss-of-function (LoF) alleles undergoing percutaneous coronary intervention (PCI) is still debating and conflicting. It was aimed to compare the efficacy and safety endpoints for these patients treated with alternative P2Y12 receptor blockers (e.g. prasugrel or ticagrelor) against clopidogrel. Literature was searched in PubMed, Cochrane library, Synapse and 1000 Genomes databases following PRISMA guidelines for identifying relevant studies. Aggregated risk was estimated by RevMan software using either fixed/random-effects models where P values<0.05 (two-sided) were considered statistically significant. Nine studies comprising 16,132 ACS patients undergoing PCI were included in this analysis in which 2,746 and 2,640 patients were in the CYP2C19 LoF clopidogrel and alternatives treatment group, respectively. It was demonstrated that patients treated with prasugrel or ticagrelor significantly reduced the risk of MACEs (RR 0.58; 95% CI 0.45–0.76; P<0.0001) as compared to patients with clopidogrel where both groups carrying CYP2C19 LoF alleles. Subgroup analysis showed that prasugrel or ticagrelor significantly reduced the risk of cardiovascular death (RR 0.44; 95% CI: 0.25–0.74; P=0.002) and MI (RR 0.60; 95% CI: 0.44–0.81; P=0.0008) while other clinical outcomes were not found statistically significant between these two groups; stroke (RR 0.77; 95% CI: 0.43–1.38; P =0.39), stent thrombosis (RR 0.67; 95% CI: 0.38–1.18; P =0.17), unstable angina (RR 0.55; 95% CI: 0.13–2.33; P =0.42), revascularisation (RR 0.79; 95% CI: 0.–2.24; P=0.66). Bleeding events were not found significantly different between these groups (RR 1.06; 95% CI: 0.88–1.28; P=0.55). Considering efficacy and safety, alternative antiplatelets (e.g. prasugrel or ticagrelor) may be regarded as better treatment option as compared to clopidogrel for ACS patients undergoing PCI

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches