CURRENT ISSUE

ABSTRACT: Welcome editorial from Editor in Chief

ABSTRACT: Background: Personalised medicine through pharmacogenomics is revolutionalizing healthcare delivery by encouraging individualized therapy that takes into consideration an individual's genetic profile, environment and lifestyle. Pharmacogenomics is an aspect of pharmacy that studies the relationship between genetic profile and response to therapeutic agents.  However, the application of the concepts of pharmacogenomics in healthcare helps in achieving more effective and safe responses from therapy. This study evaluates the application and benefits of pharmacogenomics in clinical practice based on evidence from current practices in various medical fields.
Methods: In carrying out this review, PubMed database was the primary literature source and we analyzed and synthesized findings from the included literature thematically as it relates to pharmacogenomics applications, benefits and challenges as well as safety and ethical concerns.
Results: Pharmacogenomics has been widely applied in various aspects of healthcare such as in dosing, choice of treatment, reducing and management of adverse reactions, individualization of therapy, optimizing efficacy of therapy. Despite its numerous applications, its adoption faces challenges such as limited clinical evidence, lack of specialized training among healthcare professionals, cost and complexity of genetic mapping as well as ethical concerns. 
Conclusion: With ongoing advances in genomic technologies, pharmacogenomics is becoming an integral aspect of individualization therapy in clinical practice and more widely applied in different healthcare sectors.

ABSTRACT: Hospital-acquired infections (HAIs) continue to be one of the biggest problems for modern healthcare systems, and the problem is getting worse because antimicrobial resistance (AMR) is on the rise. Antibiotics that used to work are quickly losing their effectiveness, which is giving rise to highly adaptable bacteria in clinical settings and turning routine procedures into high-risk situations. This publication examines the intersection of healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) within the framework of smart surveillance—digital, data-driven systems engineered to identify, predict, and disrupt the spread of infections in real time. We examine the historical development of infection surveillance, analyze the epidemiological burden and resistance mechanisms contributing to a covert pandemic, and assess emerging technologies such as electronic health record integration, machine-learning analytics, genomic sequencing, and Internet of Things (IoT) sensor networks. These new ideas give us new ways to prevent infections before they happen, but they also bring up difficult moral, legal, and social problems about privacy, fairness, and governance. We contend that intelligent surveillance should be integrated into comprehensive infection prevention frameworks and antimicrobial stewardship initiatives to establish resilient hospitals. By combining predictive analytics with basic IPC procedures, ethical monitoring, and giving workers more autonomy, healthcare organizations may turn passive surveillance into active defense. In the end, winning the war against HAIs will depend not just on cutting-edge technology, but also on how it is used with care, honesty, and openness.

ABSTRACT: Physician burnout is a persistent global concern, driven by workload pressures, administrative demands, and emotional strain. It undermines both physician well-being and patient care, making early detection and intervention critical. While numerous reviews have examined the prevalence and drivers of burnout, less attention has been given to predictive approaches using electronic health records (EHR) and operational data. This review addresses that gap by evaluating how such data have been incorporated into predictive models, highlighting both their utility and limitations. A narrative review was conducted using PubMed and Google Scholar to identify peer-reviewed studies published between 2014 and June 2025. Ten studies met the inclusion criteria. Findings show that EHR and operational data can capture important predictors of burnout—such as documentation time, after-hours charting, and administrative burden—and are valuable for identifying high-risk clinics. However, current models struggle to achieve accuracy at the individual level because they rely heavily on quantitative workload metrics while neglecting organizational culture, leadership support, and psychosocial factors. This review underscores the need for next-generation predictive models that integrate qualitative and contextual variables with EHR-based measures. By articulating this gap, our contribution lies in reframing EHR and operational data not as standalone predictors but as components of multi-faceted, context-aware models. For healthcare leaders and policymakers, this means investing in tools that combine clinical, organizational, and personal dimensions to better forecast burnout and inform targeted interventions.

ABSTRACT: It takes ten to fifteen years for a compound to progress from its identification to regulatory approval as a drug. Drug discovery is complex and resource-intensive process in which more than 90% of compounds never make it from bench to bedside and eventually get rejected during the development process. Experimental drugs failures often occur due to poor target selection, inadequate preclinical models, unforeseen toxicity, lack of efficacy in human trials, and the complexity of disease mechanisms, which make it difficult to predict drug responses accurately. Additionally, drug discovery is slowed down by a lack of collaboration between academia and industry, limiting the timely exchange of knowledge and expertise. Artificial intelligence (AI) is becoming an important tool in drug discovery, offering new possibilities to overcome existing challenges. It can help researchers identify better drug targets, make the screening process more efficient, and optimize drug design, which could speed up development and improve success rates. However, use of AI is associated with certain drawbacks such as potential exacerbation of healthcare gaps, protection of sensitive patient data and a need for informed consent. This review aims to discuss key challenges that hinder drug development process and explore future directions to enhance the efficiency of drug discovery.