Volume 1Issue 1

Background: Naproxen, a propionic-acid-derived non-steroidal anti-inflammatory drug (NSAID), is classified under Biopharmaceutics Classification System (BCS) Class II, meaning it is highly permeable across the intestinal membrane but poorly soluble in aqueous media, so its rate-limiting step for oral absorption is dissolution rather than membrane transport. This solubility bottleneck contributes to slow and variable release from conventional dosage forms, which in clinical practice can encourage higher or more frequent dosing and, in turn, a greater burden of NSAID-related gastrointestinal adverse effects. Objective: The present investigation was undertaken to formulate solid dispersions of naproxen using three hydrophilic natural polysaccharide gums — guar gum (GG), xanthan gum (XG) and gum acacia (GA) — and to comparatively evaluate their capacity to enhance aqueous solubility and in-vitro dissolution relative to one another and to the unprocessed drug. Methods: Twenty-seven formulation batches were generated by fully crossing the three carriers, three preparation techniques (solvent evaporation, fusion/melt, and kneading) and three drug-to-polymer ratios (1:1, 1:2 and 1:3 w/w). Pre-formulation studies — organoleptic evaluation, melting-point verification, equilibrium solubility profiling and FTIR-based drug–polymer compatibility screening — preceded formulation. Every batch was assessed for drug content, equilibrium solubility (in water and pH 6.8 phosphate buffer) and USP Apparatus II dissolution behaviour; the best-performing batch was further characterised in-house by FTIR and differential scanning calorimetry (DSC), compared against powder X-ray diffraction (XRD) data reported in the literature for analogous systems, and subjected to a 90-day accelerated stability study conducted in line with ICH Q1A(R2). Results: Every natural-gum carrier improved both solubility and dissolution relative to pure naproxen (p < 0.001). The guar-gum system prepared by solvent evaporation at a 1:3 ratio (GG-SE3) was clearly the most effective, releasing 94.82 ± 3.1% of the drug within 60 minutes against only 28.6 ± 1.8% for the unprocessed drug, together with a 4.6-fold increase in phosphate-buffer solubility. DSC (this study) and XRD (compared from the literature for analogous systems) confirmed that this improvement coincided with a substantial reduction in naproxen crystallinity, consistent with conversion of the drug to a largely amorphous, molecularly dispersed state within the guar-gum matrix. The optimised batch retained the great majority of its performance across 90 days of accelerated storage (40 °C ± 2 °C / 75% RH ± 5%), with no statistically significant change in drug content, dissolution or solubility (p > 0.05). Conclusion: Natural polysaccharide gums, and guar gum in particular, represent an inexpensive, biocompatible and technically effective platform for improving the dissolution behaviour — and, by extension, the likely oral bioavailability — of naproxen and potentially other BCS Class II NSAIDs.

In recent years, there has been an increasing demand for safe and effective topical formulations for the treatment of dry skin, cracked heels, and other hyperkeratotic skin conditions. Urea is well-known keratolytic and moisturizing agent that improves skin hydration, softens hardened skin, and promotes the removal of dead skin cells. This project focuses on the formulation and evaluation of a urea ointment using kokum butter as a natural ointment base. Kokum butter possesses excellent emollient, moisturizing, and antioxidant properties, making it suitable for topical pharmaceutical formulations. The primary objective of the study is to develop a stable, safe and patient-friendly ointment that provides effective hydration, protects the skin barrier, and enhances the healing of the dry and damaged skin. The formulation was prepared using suitable pharmaceutical excipients to obtain a smooth and stable ointment with good consistency and spreadability. The prepared formulation was evaluated for various physicochemical parameters such as physical appearance, homogeneity, pH, spreadability, viscosity, stability, and the skin irritation to assess its quality and performance. In addition, factors such as patient compliance, ease of application, and cosmetic acceptability were also considered. This project focuses on developing an effective, affordable, and user-friendly topical formulation using urea and kokum butter to moisturize dry skin and treat cracked heels.

Background: Infertility, defined as the inability to conceive after 12 months of  regular unprotected sexual intercourse, impacts millions of humans globally. This  condition not only challenges public health systems but also significantly affects  psychological and social well-being. Current therapeutic approaches vary in  accessibility and effectiveness, necessitating advancements in diagnostic and  treatment modalities. Machine learning (ML), a subset of artificial intelligence,  offers potential benefits in refining diagnostic accuracies and enhancing the  efficacy of infertility treatments through personalized care and predictive  analytics. Methods: This study utilized machine learning algorithms to predict the success  rates of specific infertility treatments, including Intrauterine Insemination (IUI),  In-Vitro Fertilization (IVF), and Intracytoplasmic Sperm Injection (ICSI), among  a sample of primarily infertile patients. Retrospective and prospective data  spanning six months from 567 couples treated at Sunflower Hospital and Gayatri  Hospital were analyzed. ML models such as Decision Trees, Logistic Regression,  and XGBoost were applied, focusing on an array of predictive features including  patient age, infertility duration, previous medical and treatment histories, among  others. These features were rigorously selected based on their importance scores  derived from the ML algorithms to optimize the predictive performance. Results and Discussion: The application of machine learning models  demonstrated significant potential in predicting the outcomes of infertility  treatments. The Decision Tree model exhibited a prediction accuracy of 85.54%  for IUI success rates, which was notably higher than the other models tested. For  IVF and ICSI treatments combined, the XGBoost model achieved an accuracy of  72.09%, indicating robust model performance in these more complex cases.  These models were effective in handling multifaceted data and yielded insights  that could assist in making informed clinical decisions. Notably, the Logistic  Regression model displayed substantial accuracy improvements over traditional  statistical methods, particularly in reducing the likelihood of false negatives,  thereby ensuring that fewer patients undergoing viable treatments were  incorrectly predicted to have poor outcomes. Conclusions: Integrating machine learning into reproductive healthcare can substantially improve the precision of infertility treatment protocols, predict  treatment successes more accurately, and increase the overall efficacy of  interventions. This study underscores the transformative potential of ML in  healthcare, providing a framework for future applications in infertility treatment  and beyond. Continued research and development into these models are  recommended to confirm their effectiveness across different demographic and  geographical contexts and to explore their utility in other medical domains.substantially improve the precision of infertility treatment protocols, predict  treatment successes more accurately, and increase the overall efficacy of  interventions. This study underscores the transformative potential of ML in  healthcare, providing a framework for future applications in infertility treatment  and beyond. Continued research and development into these models are  recommended to confirm their effectiveness across different demographic and  geographical contexts and to explore their utility in other medical domains.

Topical drug delivery has emerged as an effective approach for managing localised pain while minimising the systemic adverse effects associated with oral therapy. This study focuses on the development and evaluation of a medicated roll-on containing ibuprofen and magnesium chloride for the treatment of musculoskeletal pain and inflammation. The formulation was designed using suitable excipients to improve drug solubility, stability, skin compatibility and transdermal penetration. Ibuprofen provides anti-inflammatory and analgesic effects, whereas magnesium chloride promotes muscle relaxation and supports pain relief. The prepared formulation was evaluated for key physicochemical properties, including pH, viscosity, spreadability, drug content, and in vitro drug release. Stability and compatibility studies were also carried out to ensure product quality and performance. The findings indicate that the formulated roll-on is a promising, convenient, and patient-friendly topical system for localised pain management.

Introduction Reactivation of tuberculosis after decades of apparent cure is not unheard of, but multidrug-resistant disease appearing this late — particularly after a central nervous system infection — is rarely documented. Case Presentation We describe a 58-year-old woman who developed fever, weight loss, and a persistent cough twenty-seven years after successful treatment of an intracranial tuberculoma. A cavitary lesion in the right upper lobe led to a diagnosis of multidrug-resistant tuberculosis, with resistance to both rifampicin and isoniazid confirmed on molecular and phenotypic testing. She began a bedaquiline-based regimen in line with current WHO guidance and went on to achieve early culture conversion along with steady clinical and radiological recovery. Conclusions This case is a reminder of how unpredictable tuberculosis latency can be, reinforces the diagnostic value of molecular testing, and confirms the effectiveness of contemporary MDR-TB regimens — while making clear that clinical vigilance should not lapse even decades after apparent cure.

Background: Obesity is a chronic, relapsing disease associated with cardiometabolic complications and substantial health-system burden. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have transformed pharmacological weight management, but injectable administration, access, cost, and treatment persistence can limit their use. Oral GLP-1 RAs may expand treatment options by combining incretin-based efficacy with tablet administration. Objective: To review the pharmacology, clinical evidence, therapeutic applications, safety, and future directions of oral GLP-1 receptor agonists for obesity pharmacotherapy. Methods: A narrative review was developed from peer-reviewed clinical trials and reviews, regulatory documents, and authoritative prescribing information. Emphasis was placed on oral semaglutide and orforglipron, including phase 3 evidence available through October 2026. This was not a systematic review and no formal risk-of-bias assessment or quantitative synthesis was performed. Results: In the OASIS 4 randomized trial, oral semaglutide 25 mg daily produced an estimated mean body-weight change of −13.6% at week 64, compared with −2.2% with placebo, among adults with overweight or obesity without diabetes. In the 72-week ATTAIN-1 trial, orforglipron produced mean body-weight changes of −7.5%, −8.4%, and −11.2% at the 6-mg, 12-mg, and 36-mg doses, respectively, compared with −2.1% with placebo. Gastrointestinal adverse events were common with both agents. Oral semaglutide and orforglipron received US approval for chronic weight management in 2025 and 2026, respectively; regulatory status differs by jurisdiction. Conclusion: Oral GLP-1 RAs represent a major advance in obesity treatment, potentially improving convenience and expanding patient choice. Their benefits must be considered alongside gastrointestinal tolerability, contraindications, medication administration requirements, cost, access, and the need for long-term follow-up. Comparative effectiveness should not be inferred from cross-trial weight-loss percentages.

Artificial intelligence (AI), including natural language processing, machine learning, and generative models, can assist pharmacovigilance by finding and structuring safety information across individual case safety reports (ICSRs), clinical records, scientific literature, and other sources. This narrative review examines how such tools support adverse drug reaction (ADR) detection and signal management, the evidence for operational use, and the safeguards required for responsible deployment. Current applications include report intake and duplicate detection, extraction of drugs and events from narrative text, case triage, literature screening, coding support, and prioritization of drug-event pairs for expert assessment. Published studies and a systematic review demonstrate technical feasibility, but evidence is heterogeneous and often task-specific; accuracy on curated datasets does not establish generalizability, causal association, or improved patient outcomes. Spontaneous-report disproportionality remains hypothesis-generating and is vulnerable to underreporting, stimulated reporting, missing denominators, and confounding. AI outputs therefore require documented context of use, representative validation, human review proportionate to risk, privacy protection, bias surveillance, change control, and traceable records. Current expectations include EMA’s 2024 lifecycle reflection paper, the 2025 ICH E2D(R1) guideline effective from 18 March 2026, and FDA–EMA Good AI Practice principles issued in 2026. AI is best positioned as governed decision support within the established signal-management process, with pharmacovigilance professionals retaining responsibility for interpretation and action.

Abstract Pharmaceuticals are an essential part of modern health care, but their manufacture, use and disposal generate a growing stream of pharmaceutical waste that, if not properly managed, can raise environmental and public-health concerns. Pharmaceutical waste is defined as expired, unused, damaged, rejected or contaminated medicines and materials generated from households, healthcare facilities, pharmacies, pharmaceutical industries, veterinary services and research laboratories. The review deals with the major sources and improper disposal practices of pharmaceutical waste, especially disposal in household waste, sewer systems, open burning, municipal waste streams, and uncontrolled dumping. Such practices can open the door for pharmaceutical residues to enter soil, groundwater, surface water, wastewater systems and the atmosphere, offering routes for environmental and possible human exposure through drinking water and the food chain. The review also addresses reported environmental concerns, including water and soil contamination, aquatic toxicity, and ecological disruption, and potential human-health concerns, including antimicrobial resistance, endocrine disruption, and toxicity associated with some pharmaceutical compounds. Major regulatory approaches of WHO, FDA, EPA and Indian biomedical-waste management framework are discussed. Current management approaches including segregation, controlled incineration, encapsulation, inertization and drug take-back programs are also discussed. While the problem is increasingly recognized, there are still gaps in public awareness, environmental monitoring, implementation of regulations and evidence from developing countries. Thus, future pharmaceutical-waste management should focus on sustainable disposal technologies, accessible take-back systems, increased monitoring and awareness, and better traceability along the pharmaceutical-waste life cycle.

Background: Generative Artificial Intelligence (GenAI) is becoming increasingly popular in healthcare education. Tools, notably ChatGPT, are being utilized by students and educators to support learning, find information, and improve understanding of deep and complex concepts. As the use of AI continues to grow, it is important to understand both its benefits and challenges.    Aims and objectives: This review aimed to explore the use of Generative Artificial Intelligence in healthcare education, with a focus on its applications, advantages, limitations, and future potential. A rigorous literature search was conducted using articles identified from PubMed, Google Scholar, Scopus Studies related to Artificial Intelligence and Generative AI in healthcare education were reviewed and analyzed for the purpose of this paper.     Methods: This narrative review was revised in alignment with the Scale for the Assessment of Narrative Review Articles (SANRA). The authors’ original search covered PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar for English-language literature from January 2023 through June 2026. Of 816 records identified, 765 were screened and 57 were included. A targeted evidence update using SciSpace and Scite on 14 August 2026 added recent, directly relevant physiotherapy studies and current guidance. Evidence was interpreted according to study design, directness to physiotherapy education, comparator, and outcome type no meta analysis was undertaken.    Results: Physiotherapy specific evidence remains limited but expanding. The reviewed studies revealed that GenAI can support learning by helping students access information quickly, grasp complex concepts, prepare study materials, and improve clinical reasoning skills. Transfer evidence from medical education suggests potential for simulated patient encounters and structured feedback. However, concerns were also reported, including inaccurate information, hallucinations, plagiarism, privacy issues, and overreliance on AI tools, data fabrication.    Conclusions: Generative Artificial Intelligence has the potential to become a useful tool in healthcare education. While it offers many benefits for both students and educators, it should be used carefully and responsibly. Policymakers and educators need to critically analyse and set guidelines to ensure ethical use of GenAI. Further research is needed to better understand its long term impact on teaching and learning.

ABSTRACT Objective: To critically examine how artificial intelligence (AI) is being used to identify new therapeutic uses for existing or investigational medicines, with emphasis on validation, reproducibility, clinical translation and regulatory considerations. Methods: A structured narrative literature search was conducted on 18 October 2026 using PubMed/MEDLINE, publisher platforms for peer-reviewed biomedical literature, and U.S. Food and Drug Administration (FDA) regulatory sources. Searches covered drug repurposing/repositioning combined with artificial intelligence, machine learning, deep learning, natural language processing, knowledge graphs, graph neural networks, generative AI, validation and clinical translation. Priority was given to primary studies, comparative benchmarking studies, authoritative reviews and current regulatory documents. Title/abstract screening was followed by full-text assessment for relevance; reference-list citation chasing was used to identify additional key studies. This was a narrative review and was not designed as a PRISMA systematic review or meta-analysis. Findings: AI can integrate heterogeneous molecular, omics, literature and clinical data and can prioritize drug–disease associations. However, reported performance is highly dependent on dataset construction and evaluation design. TxGNN, a graph foundation model, improved zero-shot indication and contraindication prediction relative to benchmark methods, whereas recent benchmarking studies show that random or transductive splits can inflate performance because of similarity, redundancy and information leakage. AI-assisted hypotheses have reached experimental and clinical validation in selected examples, notably baricitinib for COVID-19 and generative-AI prioritization of Alzheimer disease candidates using real-world electronic health records; these examples demonstrate different levels of evidence and should not be interpreted as proof that computational predictions alone establish efficacy. Conclusion: AI-enabled drug repurposing is best viewed as a hypothesis-generation and candidate-prioritization approach. Its translational value will depend on leakage-resistant external validation, mechanistic and pharmacokinetic feasibility, prospective clinical evaluation, transparent reporting, regulatory engagement and multidisciplinary review involving pharmacists, pharmacologists and clinicians.

Background: Antimicrobial resistance (AMR) threatens the effectiveness of antimicrobial therapy and increases the risk of treatment failure, prolonged hospitalization, and mortality. Antimicrobial stewardship programmes (ASPs) seek to optimize antimicrobial selection, dosing, route, and duration. Artificial intelligence (AI), including machine learning (ML) and natural language processing, may strengthen stewardship through resistance prediction, individualized empirical therapy, surveillance, and clinical decision support. Objective: To review current evidence, clinical applications, limitations, and future directions for AI-driven antimicrobial stewardship and AMR prediction. Methods: A narrative review approach was used to synthesize peer-reviewed studies, systematic reviews, clinical guidance, and authoritative surveillance resources on ML-based resistance prediction and stewardship. No formal systematic screening or quantitative synthesis was undertaken. Results: Retrospective studies show that ML models can estimate resistance risk from clinical history, prior microbiology, antimicrobial exposure, and local epidemiology. Personalized antibiogram approaches have demonstrated potential to maintain predicted coverage while reducing broad-spectrum prescribing in retrospective datasets. However, external validation, calibration, workflow integration, and prospective evidence of patient-centred benefit remain essential. Conclusion: AI can augment antimicrobial stewardship by providing timely, patient-specific risk estimates and helping prioritize treatment review. Predictions do not confirm susceptibility, and model performance does not by itself establish clinical benefit. Implementation should include prospective validation, multidisciplinary oversight, transparent uncertainty communication, and ongoing monitoring of clinical and microbiological outcomes.

Rare diseases are individually uncommon but collectively impose substantial clinical, social and economic burdens. Many are monogenic disorders in which a pathogenic variant disrupts a protein, enzyme, structural component or regulatory pathway. Cell and gene therapies (CGTs) offer the possibility of addressing disease mechanisms more directly than conventional symptom-directed pharmacotherapy. Therapeutic strategies include in vivo gene addition using viral or non-viral vectors; ex vivo genetic modification of autologous haematopoietic stem and progenitor cells; genome editing; and cell replacement or engineered-cell approaches. Clinical translation has progressed from proof-of-concept studies to authorised products for selected disorders, including spinal muscular atrophy, inherited retinal dystrophy, haemophilia B, metachromatic leukodystrophy, sickle cell disease and transfusion-dependent β-thalassaemia in certain jurisdictions. However, the evidence, eligibility criteria, durability and benefit-risk profile differ by product and disease. Important constraints include immune responses, off-target or insertional effects, conditioning toxicity, manufacturing complexity, limited long-term data, high costs and unequal access. This narrative review summarises the biological rationale, platforms, representative clinical evidence, clinical implementation, safety, regulatory considerations and emerging directions in rare-disease CGTs, with attention to the Indian context. It emphasises multidisciplinary care, transparent communication and long-term pharmacovigilance.

Pharmacogenomics (PGx) examines how inherited genetic variation influences drug exposure, efficacy, and toxicity. Genetic results can guide treatment when the gene-drug relationship is clinically valid, actionable, and interpreted in the context of the patient and the intended use. This narrative review summarizes the path from testing and genotype interpretation to individualized pharmacotherapy, highlighting implementation evidence, guideline resources, and barriers to equitable routine care. Established examples include HLA-B*57:01 testing to prevent abacavir hypersensitivity; TPMT and NUDT15-guided thiopurine dosing; CYP2C19-guided selection of antiplatelet therapy; DPYD-informed fluoropyrimidine dosing; and CYP2D6-guided use of codeine and tramadol. The FDA’s current pharmacogenetic association table distinguishes associations with therapeutic-management recommendations from those supported only by pharmacokinetic evidence, underscoring that a gene-drug association does not automatically justify testing. The PREPARE implementation trial reported fewer clinically relevant adverse drug reactions among patients with actionable gene-drug pairs receiving a pre-emptive 12-gene panel, while trials in specific settings show that clinical utility varies with population, comparator, outcome, and implementation. Testing alone is insufficient: assay coverage, phenotype translation, ancestry-related allele representation, drug interactions, clinical decision support, result portability, informed consent, and clinician education all influence benefit. PGx is most effective when integrated with medication review, clinical monitoring, patient preferences, and evidence-based guidelines, rather than treated as a stand-alone prescribing algorithm. Further pragmatic research should assess long-term outcomes, costs, equity, and workflow effects across diverse health systems.

ABSTRACT Background: Drug discovery and development are complex, costly, and failure-prone processes that require the integration of biological, chemical, pharmacological, toxicological, and clinical evidence. Artificial intelligence (AI), including machine learning (ML), deep learning, natural language processing, generative models, and foundation models, is increasingly being applied across this pipeline. Objective : This review examines the role of AI from target identification and validation through hit discovery, lead optimization, preclinical evaluation, clinical trial design and conduct, and regulatory oversight. Methods : A narrative review was prepared using peer-reviewed research, major reviews, clinical trial reports, and regulatory guidance identified through biomedical literature resources and targeted searches of journal and official agency websites. Emphasis was placed on evidence published through 30 October 2026. No formal systematic screening or meta-analysis was performed. Results: AI can integrate multi-omics and literature data for target prioritization; support structure prediction, virtual screening, molecular generation, and multi-parameter optimization; and assist toxicity prediction, trial recruitment, protocol feasibility, monitoring, and data analysis. AlphaFold 3 has expanded structural prediction to biomolecular interactions. A notable clinical example is rentosertib (formerly ISM001-055), a generative-AI-discovered TNIK inhibitor evaluated in a randomized phase 2a trial for idiopathic pulmonary fibrosis. The trial provides an important proof-of-concept but does not establish that AI alone caused clinical success or that the candidate is an approved medicine. Conclusion: AI is becoming an enabling technology throughout pharmaceutical research and development, but it does not eliminate the need for biological reasoning, medicinal chemistry, experimental testing, rigorous clinical trials, or regulatory evaluation. Its value depends on high-quality data, prospective validation, transparent context of use, and multidisciplinary human oversight.

Background: Double glucose-dependent insulinotropic polypeptide (GIP)/glucagon-like peptide-1 (GLP-1) receptor stimulation by tirzepatide and the development of triple GIP/GLP-1/glucagon receptor modulation by retatrutide are the latest additions to the category of incretin-based therapies for type 2 diabetes (T2D), obesity and other cardiometabolic conditions. Objective: Here is the objective to critically understand the mechanisms of both hormones, their clinical usage, therapeutic benefits, possible side effects and the role of pharmacy practice from an Indian perspective. Methods: A descriptive account of a study; in Section 5 the authors have clearly explained their methodology, search strategy and its disadvantages. Findings: A major feature of tirzepatide is the fact that it brings about larger glycaemic and weight reductions in head-to-head trials compared to GLP-1 RA's semaglutide and dulaglutide. In T2D with advanced atherosclerotic disease, tirzepatide was no inferior to dulaglutide for major adverse cardiac events. Obesity-related heart failure, which is associated with preserved ejection fraction (HFpEF), and reduced apnoea severity were all improved by it. In a steatohepatitis (NASH) phase 2 trial, it also improved histology in the liver. Retatrutide achieved significant phase 2 and phase 3 weight reductions but is still not a FDA approved drug due partly to gastrointestinal adverse effects at the top doses and a high rate of discontinuation for this reason, and to the absence of outcome or safety data. Gastrointestinal side effects, the need for gradual increase of the dosage (dose-titration), the risk of drug-drug interactions, cost and availability are the prime constraints from a practical viewpoint. Conclusions: In the opinion of the authors, both medicines should be seen as a complement, or part of a wider, coordinated care programme lasting many, or a whole life time where a clinical pharmacist helps with choosing the patient, dose titration, giving patient education, making sure patient remains loyal to therapy, monitoring adverse effects, and designing affordable drug regimen. Comparing one drug with another only based on trial results, and claiming that one is superior to others should be avoided, unless there is direct evidence of better results in head-to-head comparisons. Long-lasting safety, outcome of treatment and actual use of the medications in India are what remain as major gaps in the existing data.

Background: The dual GIP/GLP-1 receptor agonist tirzepatide with glucagon-like peptide-1 receptor agonists now include not only reduction of glucose levels but have proved their worth by acting on body weight and heart/kidney benefits. These drugs have shown their efficacy in reducing body weight, heart disease risk, and kidney damage. However, medication safety continues to be an area of concern for the pharmacy because of the nature of the drug absorption through self-injection with stepwise doses increasing. It is, however, expected that in time new safety signals will emerge, and these will need to be monitored in conjunction with known adverse effects. Objective: To describe the role of the pharmacist in the safe and efficient use of GLP-1-based therapeutics, with a focus on Indian practice, and to summarize the pharmacology, formulations, clinical evidence, and safety profile of these treatments. Results: Tirzepatide showed better weight loss than semaglutide at up to -20.2% vs. -13.7% in SURMOUNT-5 and caused a mean weight change of -20.9% after 72 weeks in SURMOUNT-1 versus only -3.1% with placebo. While 1 mg of semaglutide significantly reduced the risk of a composite of kidney events by 24% in the FLOW study, 2.4 mg of semaglutide reduced the occurrence of adverse cardiovascular events by approximately 20% in SELECT. Among the safety issues that pharmacists should be aware of are dose-dependent gastrointestinal effects, gallbladder disease, a possible association with semaglutide and non-arteritized anterior ischemic optic neuropathy (NAION), the risk of procedural aspiration that is still unclear, and administration error with compounded vial products. Certainly pharmacists must keep in mind some major safety issues: Gastrointestinal side effects related to dosage; gallbladder disease; an alleged connection between semaglutide and anterior non-arteritic ischemic optic neuropathy (NAION); uncertainty with procedural aspirations; administration errors of compounded vial products. Conclusion: Pharmacists can assist in bridging the gap between trial efficacy and real-world results by utilizing eligibility screening, titration and injection counseling, error prevention, adherence support, and pharmacovigilance. Controlled research is essential for pharmacist-led interventions especially in India.

Background: Antibody–drug conjugates (ADCs) are an evolving class of targeted anticancer therapeutics that combine monoclonal antibodies, linkers, and potent cytotoxic payloads to selectively deliver drugs to cancer cells expressing specific antigens. Advances in antibody engineering, linker stability, payload development, and conjugation technologies have expanded their therapeutic potential across multiple malignancies. Objective: This review examines the structure, mechanisms of action, clinical applications, safety considerations, emerging developments, and role of pharmacists in the effective use of ADCs in cancer treatment. Methods: A narrative literature review was conducted using PubMed/MEDLINE, Scopus, Embase, and Google Scholar for publications from January 2000 to November 2026. The search included literature on ADCs, therapeutic payloads, linkers, resistance mechanisms, interstitial lung disease, and pharmaceutical care. Following screening, 69 unique articles were selected from 103 identified records, prioritising peer-reviewed reviews and primary research, including phase I–III clinical trials. Results: ADCs have demonstrated clinically meaningful benefits across breast, urothelial, lung, gastric, gynaecological, and haematological cancers. Agents such as trastuzumab deruxtecan, sacituzumab govitecan, enfortumab vedotin, and mirvetuximab soravtansine illustrate the expanding applications of antigen-directed therapy. Their effectiveness is influenced by antigen expression, linker stability, drug-to-antibody ratio, payload activity, and intracellular drug delivery. Important safety concerns include interstitial lung disease, myelosuppression, peripheral neuropathy, ocular toxicity, gastrointestinal disturbances, and hepatotoxicity. Antigen heterogeneity, acquired resistance, manufacturing complexity, treatment costs, and limited predictive biomarkers remain significant challenges. Emerging approaches include bispecific ADCs, novel payloads, site- specific conjugation, and combination regimens. Pharmacists contribute to safe preparation, dispensing, toxicity monitoring, supportive care, patient education, and pharmacovigilance. Conclusion: ADCs represent an important advancement in precision oncology, offering targeted treatment opportunities across diverse cancer types. Further progress depends on optimising ADC design, improving patient selection, managing treatment-related toxicities, overcoming resistance, and enhancing equitable access. Multidisciplinary care, including oncology pharmacy expertise, will remain essential to maximising therapeutic benefit.