Currents in Pharmaceutical Research

Publication Bias: A Brief Concept Note

2025-04-22T11:35:29+00:00

Cubosomes in Drug Delivery: Exploring Their Potential for Advanced Therapeutic Applications

2025-04-22T10:42:09+00:00

Cubosomes have become a subject of increasing significance because of their unique three-dimensional nano cubic lattice structure, which is composed of self-assembling lipid molecules like monoolein. A wide range of therapeutic agents, involving both hydrophilic and hydrophobic drugs, can be encapsulated within this stable and water-dispersible structure. Cubosomes' unique cubic lattice also facilitates surface modifications and lipid content adaptation, improving drug stability and resistance to degradation. This enables precise control over the kinetics of drug release and provides an intriguing framework for the development of controlled drug release systems. As a result, cubosomes can improve therapeutic efficacy while minimizing side effects of drugs molecules. In conclusion, this review explores the potential uses of cubosomes and highlights the specifics that emphasize their significance in improving drug delivery methods.

Role of Artificial Intelligence in Drug Discovery and Design: From Foundational Principles to Emerging Applications in Antiviral Therapeutics

2025-10-21T09:32:02+00:00

Artificial Intelligence (AI) has significantly transformed drug discovery by enhancing efficiency, reducing costs, and accelerating timelines, particularly in research related to antiviral drugs. Traditional drug discovery methods are not able to compete with rapidly occurring viral mutations, since these are often time-consuming and labor-intensive. Hence, they have been replaced with AI techniques, capable of handling massive datasets, predicting molecular interactions, and optimizing drug candidates rapidly. AI can be used to identify novel drug molecules, drug targets, and repurposed drugs. Furthermore, it can also be used to predict chemical properties, as well as pharmacokinetic, pharmacodynamic, and toxicology profiles by analyzing large datasets. In the early stages of drug discovery, AI aids in target identification and validation by analyzing the genomic, proteomic, and chemical data to predict disease-relevant proteins. In virtual screening and hit identification, AI replaces high-throughput screening with rapid in silico analysis. Generative chemistry approaches utilize reinforcement learning to design novel, drug-like molecules rapidly. Through off-target profiling using models such as DeepTox, AI reduces adverse effects by forecasting unintended protein interactions and drug-drug interactions, improving safety profiles. Its predictive capabilities at each development stage—from molecular screening to clinical trials—have not only accelerated the pace of antiviral drug discovery but have also reduced overall costs significantly, thus proving essential during global pandemics like COVID-19. AI can be implemented at each step of drug discovery and development, from identifying drug molecules and conducting virtual screening to lead optimization and designing clinical trials, as well as interpreting the data obtained from the trials. Antiviral drugs for viral diseases, such as COVID-19, dengue, influenza, hepatitis, and Ebola, developed using AI are mentioned in this study. It also highlights the significance of AI in healthcare, particularly in novel drug development. There is also a dark side to AI, and concerns are rising about the accuracy and quality, as well as the legal and ethical aspects of fact-driven by datasets.

Cost-Effectiveness Analysis of Antihypertensive Medications Prescribed to Primary Hypertension Patients at District Headquarters Hospital, Kohat

2025-09-30T04:08:30+00:00

Hypertension, a major risk factor for cardiovascular diseases (CVDs), contributes to 9.3 million deaths annually. In low- and middle-income countries (LMICs) like Pakistan, cost-effective treatment is critical due to limited healthcare resources. This study aimed to evaluate the cost-effectiveness of antihypertensive medications prescribed to patients with primary hypertension at the District Headquarters (DHQ) Hospital, KDA, Kohat in the Khyber Pakhtunkhwa province of Pakistan. A cross-sectional retrospective study was conducted over a period of six months, involving 320 patients (55.31% male, 44.69% female) aged 18-60 years. Prescription patterns and costs of antihypertensive medications (monotherapy and combination therapy) were analyzed using hospital records. The total cost of prescribed antihypertensive medications was PKR 261,153.00, with monotherapy accounting for PKR 208170.00 and combination therapy accounting for PKR 52983.00. Among monotherapies, calcium channel blockers (CCBs) were the most frequently prescribed (107 prescriptions) and accounted for the highest cost (PKR 132114.00, 50.58%), followed by angiotensin receptor blockers (ARBs) (PKR 37238.00, 14.25%). In contrast, diuretics were the most cost-effective option, costing only PKR 25.2 per unit dose. For combination therapy, amlodipine + hydrochlorothiazide combination was the most prescribed, while losartan + amlodipine combination incurred the highest cost. Monotherapy was prescribed in 67.18% of cases, with most patients showing a positive response before transitioning to combination therapy. CCBs and diuretics were found to be more cost-effective, whereas ACE inhibitors were among the most expensive options. CCBs were the most frequently prescribed in monotherapy, while the combination of amlodipine and atenolol was commonly used in multidrug regimens. The current study highlights the importance of considering patients’ socioeconomic status when initiating pharmacologic treatment. Cost-effective prescribing is crucial in the resource-limited settings of Pakistan, where balancing clinical efficacy and affordability is essential. The findings support WHO recommendations for the rational use of combination therapy and emphasize the use of low-cost medications to optimize treatment outcomes in low- and middle-income countries.