The present study focuses on the development and validation of a simple, rapid, precise, accurate, and stability-indicating reverse-phase high-performance liquid chromatographic (RP-HPLC) method for the quantitative estimation of Tirzepatide. It is a novel dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonist widely used in the management of type 2 diabetes mellitus and obesity. Due to its peptide-based structure, the molecule presents significant analytical challenges, including susceptibility to hydrolytic and oxidative degradation, necessitating a robust and reliable analytical method for routine quality control and stability assessment. Chromatographic separation was achieved using a C18 column (250 mm × 4.6 mm, 5 µm) with a mobile phase consisting of acetonitrile and phosphate buffer (pH 3.0) in the ratio of 45:55 (v/v), delivered at a flow rate of 1.0 mL/min. Detection was performed at 230 nm using a UV detector. The method exhibited a well-resolved and symmetrical peak for Tirzepatide with a retention time of approximately 7.21 minutes. The developed method was validated in accordance with ICH Q2(R1) guidelines for specificity, linearity, precision, accuracy, robustness, limit of detection (LOD), and limit of quantification (LOQ). The method demonstrated excellent linearity over the concentration range of 10–60 µg/mL with a correlation coefficient (r²) of 0.9996. Precision studies showed %RSD values less than 2%, indicating high repeatability and reproducibility. Accuracy was confirmed through recovery studies at 80%, 100%, and 120% levels, yielding mean recoveries within 98–102%. The LOD and LOQ were found to be 0.073 µg/mL and 0.220 µg/mL, respectively, indicating good sensitivity of the method. Robustness evaluation revealed that minor deliberate variations in chromatographic conditions did not significantly affect the analytical performance. Tirzepatide showed maximum degradation under oxidative conditions, followed by alkaline and acidic hydrolysis, while thermal and humidity conditions resulted in comparatively lower degradation. The developed RP-HPLC method is simple, economical, reliable, and highly effective for routine quantitative analysis, quality control, and stability studies of Tirzepatide.

Objective: To formulate a herbal moisturizing cream using Jujube fruit extract and other herbal ingredients. Methods: Jujube extract, Aloe vera, basil, and lavender were combined with excipients such beeswax, liquid paraffin, borax, coconut oil, and vitamin E to create an oil-in-water herbal moisturizing cream. Jujube extract was produced through 48–72 hours of maceration with 70% ethanol. To create a cream, the oil and aqueous phases were heated independently to 75°C and combined while being constantly stirred. The pH, spreadability, viscosity, washability, phase separation, irritancy, and stability of three formulations (F1–F3) were created and assessed. Results: Among the prepared three batches of herbal moisturizing cream, formulation F2 showed optimal physicochemical properties with pH 6.38, good spreadability (28.3 g.cm/s), and viscosity of 39416 cps, which lies within acceptable limits. All formulations showed no phase separation, good washability, and no skin irritation during evaluation. Stability studies indicated that the formulations remained physically stable for 30 days without significant changes in colour, odour, pH, and spreadability. Among them, F2 was found to be the most stable and suitable formulation compared to the other batches. Conclusion: The formulated herbal moisturizing cream containing Jujube extract was found to be stable, safe, and effective. The formulation exhibited good physicochemical properties, skin compatibility, and moisturizing potential. Among all batches, F2 demonstrated better stability and overall performance. The study supports the use of herbal ingredients in cosmetic formulations as a safer alternative to synthetic products.

Vaccination is one of the most effective preventive strategies in modern medicine it plays a crucial role in controlling infectious diseases and reducing modified and mortality rates world vaccines stimulate the immune system to develop protection against specific pathogens without causing the actual disease this article highlights the importance of vaccination, its mechanism of action benefits public health, and its role in disease populations.

Quality by Design (QbD) is a strategic, risk-based approach to development and manufacturing that emphasizes building quality into a product from the very beginning, rather than testing for it after the product is finished. Historically used in the automotive and semiconductor industries, it has become the gold standard in pharmaceutical development.

Hospital and clinical pharmacists play an essential role in modern healthcare systems by ensuring the safe, effective, and rational use of medicines. Their responsibilities extend beyond dispensing medications to include patient counseling, participation in healthcare teams, monitoring adverse drug reactions, pharmacovigilance, medication therapy management, and clinical decision support. Clinical pharmacy has evolved significantly over the last few decades, improving patient outcomes, reducing medication errors, and enhancing the quality of care. This review discusses the role of pharmacists in hospital and clinical pharmacy, their responsibilities, challenges, and future prospects in healthcare settings.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) mechanisms within the pharmaceutical sector has initiated a critical paradigm shift, modifying traditional experimental approaches into highly accelerated data-driven methodologies. Historically, the pipeline of bringing a novel molecular entity from concept to commercial availability has been characterized by prolonged durations, often spanning 10 to 12 years, with financial outlays exceeding $2.6 billion, and an attrition rate that regularly crosses 90% during clinical trial phases. This comprehensive review highlights the cross-disciplinary applications of sophisticated analytical architectures—including deep neural networks, generative adversarial networks, natural language processing, and deep learning models—across the pharmaceutical value chain. We systematically analyze the implementation of AI frameworks within primary domains: early-stage target discovery and computational virtual screening, de novo chemical synthesis optimization, automated preclinical toxicity assessments, and clinical trial structure redesign, specifically addressing predictive patient enrollment, bio-monitoring, and synthetic control arm creation. Furthermore, this review addresses the technical, operational, and structural limitations slowing absolute integration, emphasizing the 'black box' explainability challenge, data heterogeneity, institutional silos, and the evolving standard regulatory expectations enforced by global bodies like the FDA and EMA. Ultimately, we outline the roadmap towards an autonomous cognitive ecosystem where computational frameworks and experimental pharmaceutical sciences form a symbiotic relationship, drastically lowering cycle times and optimizing translational efficacy.

Nanoparticles play an essential role in biomedical applications, such as drug delivery, bio sensing, and tissue engineering. They guarantee the delivery of drugs to the target tissues. The controlled release mechanisms improve the efficacy and safety of the treatments. Further, Nano-based methods have been developed where cancer therapies are integrated with diagnostic imaging techniques, offering hope for combined or integrated treatments in the future. Over the past decade, tremendous research has revolutionized drug delivery systems by focusing on delivering therapeutic agents and natural active compounds directly to the targeted site for the treatment of various diseases. Although the prevailing drug delivery systems have found immense applications, problems including drug stability, bioavailability, and precise targeting continue to persist. Such limitations open up avenues for more advanced technologies to emerge to discover higher efficacy and safety of the therapy. Nano-based drug delivery systems have emerged as promising options in improving the solubility and controlled-release delivery of drugs and targeted site delivery. By encapsulating therapeutics within Nano carriers, it shields therapeutic agents from degradation while ensuring their delivery to the target site and thereby minimizing the side effects with maximum effectiveness. Nanotechnology is now in the pipeline day by day in search of the precise aspect of its potential in medicine, promising to make a revolution in chronic and complex diseases into more efficient and personalized therapies. This bolds a promising treatment of vast diseases as medicine is destined for further progressions in NDDS. Nanotechnology shall thus be able to revolutionize the delivery method in a way that drugs would be administered in more effective manner, personalized, and therefore reachable to patients all over the world.

Nebivolol According to the most recent international guidelines, ?-blockers maintain a central role in the management of hypertension, being recommended at any treatment step when there is a specific indication, such as heart failure, angina, postacute myocardial infarction, atrial fibrillation or pregnancy. However, ?-blockers are not a homogeneous class: individual molecules differ in terms of pharmacological and clinical profile and are therefore suitable for different patient subtypes. In particular nebivolol, third generation ?1-selective ?-blocker with vasodilating properties, neutral metabolic effects and good tolerability, proved to have advantages over other ?-blockers, which makes the drug suitable in a wide variety of hypertensive patients with or without comorbidities. Lay abstract: ?-blockers are the main class of antihypertensive agents currently available. Nebivolol is one of the most recent ?-blocking agents and it has vasodilating effects which may be Useful in hypertensive patients with heart disease of ischemic (restriction in blood supply) origin or with erectile dysfunction. It has a good tolerability profile which makes it safe to use in patients with metabolic abnormalities (such as diabetes or dyslipidemia) or chronic obstructive pulmonary diseases. First draft submitted: 25 March 2021; Accepted for publication: 7 May 2021; Published online: 1 June 2021.

Among the myriad carrier technologies developed for pharmaceutical application, liposomes hold a uniquely influential position — they are the first nanomedicine platform to achieve widespread clinical acceptance, combining biocompatible lipid bilayer architecture with the capacity to encapsulate both hydrophilic and hydrophobic drug molecules. This review examines the foundational role of liposomes in modern drug delivery, tracing their evolution from basic vesicle research to today’s ligand-targeted, stimuli-responsive formulations. We systematically address classification by lamellarity, size, surface charge, and preparation methodology, and demonstrate how each design parameter governs pharmacokinetic behaviour, circulation longevity, and tissue distribution. Special attention is given to sterically stabilised (PEGylated) liposomes, actively targeted immunoliposomes, and stimuli-triggerable vesicle systems. Mathematical models of drug release from liposomal bilayers are presented alongside clinical correlates. Therapeutic applications spanning oncology, infectious disease, vaccine delivery, dermatology, and gene therapy are surveyed, and a curated summary of FDA-approved liposomal products is provided. The review concludes with emerging frontiers — including liposome–hydrogel hybrids, theranostic vesicles, and oral liposome technologies — and identifies translational bottlenecks limiting broader patient access.

This review discusses the application of AI in drug safety and efficacy assessment, highlighting its potential to improve clinical trial outcomes. AI techniques analyze vast datasets to predict adverse events, treatment responses, and operational risks. Despite high performance in studies, challenges such as data bias and limited generalizability remain. Addressing these issues can enhance drug development efficiency and patient safety. AI is poised to play a crucial role in future pharmacovigilance and personalized medicine.

Graph Neural Networks (GNNs) are deep learning models that process graph-structured data, which consists of nodes and edges. They capture complex relationships and interaction, making them ideal for tasks like molecular drug discovery, Drug Target Interactions (DTI), and Drug Target (Binding) Affinity (DTA). GNNs revolutionize drug discovery by treating molecules as graphs, with atoms as nodes and chemical bonds as edges. This allows models to understand the complex three-dimensional structures and patterns relevant to biological activity. In poly-pharmacology, GNNs effectively model drug-drug interactions (DDIs) and predict multi-target activities. They improve predictive accuracy, lower development costs, and reduce late-stage failures. This review examines the application of GNNs in various phases of drug discovery, including lead discovery, optimization, synthetic route design, drug-target interaction prediction, and molecular property profiling, while addressing challenges in translational medicine. Computational drug-target affinity prediction has the potential to accelerate drug discovery. Currently, pre-training models have achieved significant success in various fields due to their ability to train the model using vast amounts of unlabelled data. However, given the scarcity of drug-target interaction data, pre-training models can only be trained separately on drug and target data, resulting in features that are insufficient for drug-target affinity prediction. To address this issue, a graph neural pre-training-based drug-target affinity prediction method (GNPDTA). This approach comprises three stages. In the first stage, two pre-training models are utilized to extract low-level features from drug atom graphs and target residue graphs, leveraging a large number of unlabelled training samples. In the second stage, two 2D convolutional neural networks are employed to combine the extracted drug atom features and target residue features into high-level representations of drugs and targets. Finally, in the third stage, a predictor is used to predict the drug-target affinity. This approach fully utilizes both unlabelled and labelled training samples, enhancing the effectiveness of pre-training models for drug-target affinity prediction. In our experiments, GNPDTA outperforms other deep learning methods, validating the efficacy of our approaches.

Among the many biomaterial platforms explored for pharmaceutical use, hydrogels occupy a singularly compelling position — they mimic the water-rich architecture of living tissue while offering a programmable scaffold for drug containment and release. This article examines the growing relevance of hydrogel technology in the design of controlled drug delivery systems (CDDS), tracing its intellectual lineage from early polymer chemistry to present-day stimuli-responsive applications. We systematically address how hydrogels are categorised by their composition, network topology, ionic identity, and cross-link chemistry, and how each of these variables shapes the eventual pharmacokinetic behaviour of the formulation. Special attention is given to injectable and in situ-gelling architectures — including shear-flow-responsive gels and thermoset peptide assemblies — as well as to the continuum from macroscopic implants to colloidal nanogel suspensions. The mathematical underpinnings of drug transport through these matrices are presented alongside illustrative clinical correlates. Delivery applications spanning parenteral, enteral, rectal, and cutaneous routes are surveyed, and a curated summary of authorised hydrogel products on the global market is provided. The review concludes with a forward-looking discussion of intelligent hydrogels, three-dimensional bioprinting, and translational bottlenecks that must be addressed before the next generation of hydrogel therapies reaches patients.

In the pharmaceutical industry, making high-quality medicine is not just about the final product; it is about ensuring that every machine and every process works perfectly every single time.[1] This review project focuses on the essential steps of Equipment Qualification (EQ) and Process Validation (PV), which are the "gold standards" for safety and consistency in manufacturing.[2] The project explains the three main stages of checking equipment: Installation Qualification (IQ), which ensures the machine is set up correctly; Operational Qualification (OQ), which checks if the machine runs properly at different speeds or temperatures; and Performance Qualification (PQ), which proves the machine consistently produces the right results during actual production. Together, these steps make sure that the hardware is reliable.[3] Beyond the machines, the project looks at Process Validation, which is the overall "recipe" for success. Following guidelines from regulatory bodies like the FDA and EMA, the industry is moving away from just testing a few batches and moving toward Continuous Monitoring. This means using modern technology to watch the production process in real-time.[4] We also discuss the Risk-Based Approach, where companies focus most of their energy on the parts of the process that are most likely to affect the patient's health. By looking at current trends for 2026, this project shows how these rules help factories stay compliant, reduce waste, and, most importantly, keep medicine safe for everyone.[5] Emerging Trends, the Digitalization, real-time monitoring, and continuous manufacturing are transforming validation practices. With tools like Process Analytical Technology (PAT) and artificial intelligence, the focus is shifting from static batch-based checks to dynamic, lifecycle-driven approaches that enhance efficiency, transparency, and sustainability.

Analytical Method Validation (AMV) is an essential process in pharmaceutical analysis that ensures analytical procedures are suitable, reliable, reproducible, and accurate for their intended use. Validation provides scientific evidence that the analytical method consistently produces acceptable results and complies with regulatory requirements. The International Council for Harmonisation (ICH) established globally accepted guidelines for analytical validation, particularly ICH Q2(R1) and the recently updated ICH Q2(R2), to harmonize pharmaceutical quality standards among different regulatory authorities. The present review focuses on a comparative study of analytical method validation parameters including specificity, accuracy, precision, linearity, range, robustness, ruggedness, limit of detection (LOD), and limit of quantitation (LOQ). The review also discusses lifecycle management, Quality by Design (QbD), risk-based analytical approaches, and modern trends incorporated in ICH Q2(R2). In addition, applications of validated analytical methods in pharmaceutical quality control, dissolution testing, impurity profiling, bioanalysis, and stability studies are highlighted. The review concludes that implementation of updated ICH validation principles enhances scientific understanding, method reliability, regulatory compliance, and patient safety.

Atopic dermatitis (AD) is a chronic, relapsing inflammatory skin illness characterized by immunological dysregulation, skin barrier dysfunction, oxidative stress, and microbial colonization, leading to erythema, pruritus, and reduced quality of life. Conventional treatments, such as calcineurin inhibitors and corticosteroids, alleviate symptoms but are linked to poor patient compliance, immunosuppression, and long-term side effects. Curcuma longa is the source of curcumin, a naturally occurring polyphenolic chemical with strong anti-inflammatory, antioxidant, immunomodulatory, and antibacterial properties that make it a prospective treatment for AD. However, poor water solubility, chemical instability, low skin penetration, and formulation issues including stains and odor hinder its clinical translation. Nano-enabled topical delivery methods, including nanoemulsions, solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs), polymeric nanoparticles, nanogels, and vesicular carriers (liposomes, ethosomes, transfersomes), has been developed to address these restrictions. These nano-systems increase the solubility of curcumin, shield it from deterioration, promote epidermal and dermal penetration, offer regulated and prolonged release, and lessen local discomfort and systemic exposure. Evaluation factors such as particle size, zeta potential, encapsulation efficiency, in vitro release, skin permeability, stability, and in vivo efficacy confirm the higher performance of nano-curcumin formulations compared to conventional preparations. This thesis addresses the therapeutic potential of curcumin in AD, stressing the role of nano-enabled administration in increasing bioavailability, skin retention, and patient compliance. Future research ought to concentrate on well-designed clinical trials, combination therapy with existing anti-AD medications, targeted and personalized nano-delivery techniques, and long-term safety assessments to assist regulatory approval and clinical translation.

Tetralogy of Fallot is a common congenital heart condition characterized by four primary features and several minor associated findings. The four primary elements consist of aortic override, a membranous ventricular septal defect, right ventricular hypertrophy, and obstruction of the right ventricular outflow tract. The patient's prognosis is determined by the severity of the obstruction in the right ventricular outflow tract. Literature documents cases of survival past the fifth decade, whether or not correction was applied. Cyanosis emerges during the first year of life if it is not evident at birth. When mild right ventricular outflow tract obstruction and a balanced ventricular septal defect prevent cyanotic spells, the condition is known as “Pink Tetralogy of Fallot.” We report a 12-year-old Indian girl with acyanotic pink tetralogy of Fallot, characterized by typical echocardiographic findings with mild aortic override.