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Recent Submissions
Intelligence at Scale: Reimagining Pharmaceutical Market Insight in the Age of AI
(Chitkara University Publications, 2026-08-14) Upendra Nagaich
Background: The pharmaceutical industry is experiencing a paradigm shift as artificial intelligence (AI) transforms traditional market intelligence from retrospective analysis to predictive, real-time decision-making. Increasing adoption across healthcare systems, growing regulatory acceptance, and advancements in data analytics have positioned AI as a key driver of innovation in drug development, clinical research, commercialization, and healthcare management.
Purpose: This editorial aims to explore the evolving role of AI in pharmaceutical market intelligence, highlighting its adoption trends, economic impact, emerging applications, future opportunities, and challenges associated with its implementation.
Methods: The editorial is based on a narrative synthesis of evidence from reports, policy documents, regulatory updates, and industry analyses. Data relating to AI adoption, economic outcomes, healthcare applications, and regulatory developments were critically reviewed and contextualized within the pharmaceutical sector.
Results: AI adoption has expanded rapidly across healthcare and pharmaceutical industries, with widespread implementation in predictive analytics, drug discovery, clinical trial optimization, disease surveillance, and market forecasting. AI-driven approaches have demonstrated the potential to reduce drug development timelines, improve patient recruitment and trial design, enhance commercialization strategies, and generate substantial economic value.
Conclusion: AI is redefining pharmaceutical market intelligence by enabling proactive, data-driven decision-making and accelerating innovation across the healthcare value chain. Although challenges related to data governance, interoperability, ethical oversight, and workforce preparedness remain, responsible AI adoption supported by robust regulatory frameworks will be critical for sustaining long-term value. As AI capabilities continue to evolve, market intelligence will increasingly serve as a strategic differentiator for pharmaceutical organizations operating in a competitive and rapidly changing global environment.
References
2D-QSAR Modeling of Quinazoline Derivatives as EGFR Inhibitors for Colorectal Cancer
(Chitkara University Publications, 2026-08-13) Rishab Pathak; Mayank Garhewal; Shani Yadav; Jyoti Prakash Panda; Achal Mishra; Ayush Tiwari; Avantika Agrawal; Shekhar Verma; Yogesh Vaishnav
Background: Globally, colorectal cancer represents one of the most common types of cancer, along with being one of the top 10 causes of cancer-related deaths. Epidermal Growth Factor Receptors (EGFRs) are a major target for colorectal cancer, and quinazoline derivatives have shown anticancer activity as EGFR inhibitors.
Purpose: The objective of this research was the development of a two-dimensional quantitative structure–activity relationship model (2D-QSAR); that is, to develop a simple way to identify the structural components that help predict a potent compound with the highest predicted activity among the series of quinazoline derivatives for EGFR inhibitors for colorectal cancer.
Methods: A dataset of 21 quinazoline derivatives was collected from the literature, molecular descriptors were calculated, and Partial Least Squares (PLS) regression was used to develop statistically significant QSAR models. Generated and validated using internal and external validation parameters.
Results: Model-1 (RANDOM_70_30_SFB_PLS_TRIALS_2) showed the best statistical performance with r² = 0.6538, q² = 0.5314, pred-r² = 0.6940, and an F-test value of 22.6638. The descriptors SsssNcount and SaaCHE-index were identified as significant contributors to EGFR inhibitory activity, indicating that tertiary nitrogen count and charge distribution play crucial roles in determining biological potency.
Conclusion: The developed 2D-QSAR model identified a potent compound with the highest predicted biological activity as an EGFR inhibitor for colorectal cancer.
Integrated Pharmacophore-Guided Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, and ADMET Analysis for the Identification of Novel 1,3,4-Thiadiazole-Based Aldose Reductase Inhibitors
(Chitkara University Publications, 2026-08-13) Priya Devi; Debarshi Mondal; Shalini Sharma; Harmel Singh Chahal
Background: Chronic diabetic complications develop through the important role of aldose reductase (AR), a key enzyme in the polyol pathway. Clearly, it is important to identify potent AR inhibitors with better PK properties for treatment of diabetes-associated complications.
Purpose: The purpose of this study was to discover novel 1,3,4-thiadiazole derivatives as aldose reductase inhibitors using an integrated computational drug discovery approach.
Methods: The dataset consisted of 30 reported 1,3,4-thiadiazole derivatives, which were analysed by the pharmacophore modelling, atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, structure-activity relationship (SAR) analysis, R-group enumeration, virtual screening and ADMET prediction methods. Using the best pharmacophore model (AHHRR_1), a validated 3D-QSAR model was developed, and 1,419 new derivatives were designed. These compounds were then further optimised for binding interactions and pharmacokinetic parameters with the top-ranked ones, including the designed derivative PD01.
Results: The optimised pharmacophore and 3D-QSAR models were able to recognise the crucial structural elements that are essential for AR inhibition. Activity increased with the hydrophobic and electron-withdrawing groups, while the bulky polar groups were responsible for decreased activity. Docking studies showed that compounds 04 (-10.178 kcal/mol), 01 (-10.081 kcal/mol), 02 (-10.050 kcal/mol), 10 (-9.977 kcal/mol), and 11 (-9.672 kcal/mol) exhibited stronger binding than Epalrestat (-8.182 kcal/mol). The highest docking score was obtained for PD01 (-10.605 kcal/mol), which had strong hydrogen-bond, hydrophobic, π-π stacking, π-cation and halogen-bond interactions. The ADMET analysis showed good drug-likeness and good oral absorption.
Conclusion: The integrated computational workflow has concluded that PD01 is the most promising lead candidate with excellent binding affinity, a favourable interaction pattern and desirable ADMET properties. The results suggest that the scaffold 1,3,4-thiadiazole is a promising structural template for designing new generation aldose reductase inhibitors for diabetic complications.
Comprehensive Insights into Lipid Nanocarriers: Preparation, Characterization and Applications
(Chitkara University Publications, 2026-08-04) Ravi Goyal; Gurpreet Kaur; Sumit Sharma; Deepika Raina; Nitin Chitranshi
Background: Lipid nanocarriers have emerged as a novel alternative to traditional drug delivery systems such as emulsions, liposomes, and microparticulate systems. These are spherical nano-sized (1–1000 nm) colloidal carriers dispersed in an aqueous surfactant solution. Their biodegradable, biocompatible, and non-toxic nature has led to extensive exploration in clinical medicine for multiple routes of administration, including oral, parenteral, and topical delivery. They offer high stability, large drug-loading capacity, and the ability to deliver both hydrophilic and lipophilic drugs in a targeted and controlled manner.
Purpose: The purpose of this review is to provide a comprehensive overview of lipid nanocarriers, focusing on their fundamental principles, advantages, limitations, preparation methods, and characterization techniques.
Methods: The review compiles and analyzes literature from relevant publications from the last 15 years on lipid nanocarriers, covering their formulation approaches, different preparation techniques, and evaluation parameters used to assess their physicochemical and functional properties.
Results: Lipid nanocarriers demonstrate significant advantages, including enhanced stability, improved drug loading, controlled and targeted drug release, and adaptability to various formulation requirements. Their versatility makes them suitable for applications in pharmaceuticals, vaccines, nutraceuticals, and diagnostic systems.
Conclusions: A comprehensive understanding of lipid nanocarriers can open new avenues in the treatment of multifactorial disorders. Their safety, efficacy, and flexibility position them as promising candidates for advanced drug delivery systems and future therapeutic innovations.
Global Prevalence and Patterns of Self-Medication Practices among Pharmacy Students: A Comprehensive Literature Review
(Chitkara University Publications, 2026-07-25) Ashutosh Rana; Tushar Kanti Das; Ravikant Gupta
Background: Self-medication involves the application of medicines for treating self-diagnosed conditions without prior consultation with a health professional. Pharmacy students, based on their knowledge of pharmacology, find themselves in a unique position relative to self-medication research. Although the information they possess is viewed as a facilitator in making well-informed drug choices, it may also instil excessive confidence leading to irrational decisions. A comprehensive overview of the global extent of self-medication is a prerequisite for developing effective preventive strategies.
Purpose: To summarize and assess scientific literature regarding the prevalence, trends, determinants, drug categories, risks, and awareness of self-medication behaviors among pharmacy students and other health sciences students across the globe.
Methods: A comprehensive literature review was carried out in PubMed/MEDLINE, Google Scholar, Scopus, and ScienceDirect from January 2003 to December 2024, adhering to the PRISMA 2020 criteria. Specific Boolean search terms were used in all the databases. Selection of the studies was done independently by both the authors using two-step screening. The quality of the studies was assessed using the modified Newcastle-Ottawa scale for cross-sectional studies. Regional weighted prevalence was determined by weighing the prevalence of each included study with their corresponding sample size. Meta-analysis could not be done due to high heterogeneity among the methodology used.
Results: A total of 626 records were identified; after removal of 214 duplicates and two-stage screening, 45 studies met the inclusion criteria. The prevalence of self-medication among pharmacy and health science students ranged from 60.4% (UAE) to 92.1% (India), with regional median estimates of approximately 80% in Asia, 72% in Africa, 75% in the Middle East, and 82% in Europe. The sample-size weighted regional averages were about 78% (Asia), 70% (Africa), 73% (Middle East), and 81% (Europe). Analgesics/antipyretics were the most commonly self-medicated drug class (>90% of studies), followed by antibiotics without prescription (35%–65%). The most frequently cited reason for self-medication was the perception of having a minor, self-limiting illness. Approximately 15%–30% of self-medicating students experienced at least one adverse drug reaction (ADR).
Conclusions: Self-medication is a widespread and multi-faceted phenomenon among pharmacy students globally, with antibiotic misuse as the most significant public health concern. Rational drug use education, antibiotic stewardship, digital health literacy, and pharmacovigilance training should be systematically integrated into pharmacy curriculum worldwide.