Current Issue
Volume 3, Issue 8 - 2026 (JPRIMS, Vol-3, Issue-08, August-2026)
Issue Details:
Volume 3 Issue 8 (JPRIMS, Vol-3, Issue-08, August-2026)Issue Description:
Welcome to the 2026 issue of Journal of Pharmaceutical Research and Integrated Medical Sciences. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.
We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.
As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.
Dr. Arpan Kumar Tripathi
Editor-in-Chief
Journal of Pharmaceutical Research and Integrated Medical Sciences
Articles in This Issue
Artificial Intelligence in Drug Discovery and Medicinal Chemistry: A Review
Artificial intelligence (AI) has emerged as one of the most transformative technologies in pharmaceutical research by accelerating drug discovery and medicinal chemistry through machine learning, deep learning, and advanced computational approaches. This review examines the available literature from human clinical studies, computational drug discovery research, systematic reviews, meta-analyses, and clinical investigations, highlighting the applications of AI in target identification, virtual screening, lead optimization, drug repurposing, ADMET prediction, and precision medicine. The review also explores interdisciplinary approaches integrating medicinal chemistry, bioinformatics, structural biology, cheminformatics, and digital healthcare to improve molecular design, reduce research costs, and enhance drug development efficiency. Current evidence indicates that AI significantly improves the accuracy and speed of discovering novel therapeutic compounds while supporting personalized treatment strategies and optimizing clinical trials across diverse therapeutic areas. Despite these advancements, challenges remain regarding data quality, model interpretability, algorithmic bias, regulatory acceptance, and prospective clinical validation. Addressing these limitations through interdisciplinary collaboration and standardized validation frameworks will further strengthen the role of artificial intelligence in advancing drug discovery and medicinal chemistry.
Contributors:
Rare Malignancies: Appendix Cancer: AI- Driven Histopathology and Molecular Biomarker Integration in Biopsy, Chemotherapy, and Immunotherapy Trajectories
Rare appendix malignancies, such as mucinous adenocarcinoma, goblet cell adenocarcinoma, signet-ring cell carcinoma, and appendiceal neuroendocrine tumors, are rare gastrointestinal cancers with diagnostic and treatment challenges due to their histological and molecular diversity. This study presents an explainable Artificial Intelligence (AI)-based approach that integrates digital histological biopsy images, molecular markers, and patient data to enhance disease classification and prediction of response to treatment. A quantitative methodology was used, based on an anonymized dataset of 200 patients with rare appendix malignancies. The performance of machine learning algorithms, such as Logistic Regression, Random Forest, XGBoost, and LightGBM, was compared using Accuracy, Precision, Recall, F1 score, and ROC-AUC metrics, while SHAP and Grad-CAM methods increased model interpretability. LightGBM outperformed all other methods and achieved the highest accuracy (96.2%), F1-score (95.5%), and ROC-AUC (0.987). KRAS, Ki-67, and TP53 were determined to be the most significant predictors from the biomarker analysis and the overall treatment response prediction accuracy of the combined model was found to be 95.1%. These results show that combining digital histopathology, biomarkers, and explainable AI (XAI) can enhance diagnosis accuracy, treatment response predictions, and clinical decision-making in rare appendiceal malignancies.
Contributors:
Artificial Intelligence–Driven Translational Nanomedicine in Oncology: Multistage Targeting Strategies, Clinical Advances, and Future Directions for Liver, Breast, Kidney, and Brain Cancers
Artificial intelligence (AI) and translational nanomedicine are revolutionizing the field of precision oncology through the use of smart nanoparticles, personalization of drug delivery, and evidence-based therapeutic decisions. This review highlights some of the recent developments in AI-driven translational nanomedicine, with special emphasis on multistage targeting approaches and clinical utility in liver, breast, renal, and brain tumors. Machine learning, deep learning, radiomics, multimodal omics, and nanoinformatics are among the areas discussed in this review. Tumor heterogeneity, vascular barrier, immune microenvironment, renal clearance, and blood-brain barrier penetration in organs are considered in order to highlight the necessity for the development of personalized nanomedicine strategies. Despite the advancements that have been made by AI-guided nanomedicine in drug delivery, pharmacokinetics and safety, there are several obstacles that hinder the clinical application of such technology. The above review suggests future strategies which include standardization in nanoinformatics, patient-derived organoids, digital twins, federated learning, adaptive clinical trials based on biomarkers, and explainable AI for accelerating clinical translation. It may be concluded that the combination of AI and translational nanomedicine offers an attractive approach towards the development of safer and effective cancer treatment options.
Contributors:
Artificial Intelligence-Powered Translational Nanomedicine in Oncology: Clinical Developments, Multistage Targeting Techniques, And Prospects for Brain, Liver, Breast, and Kidney Cancers
The emergence of artificial intelligence (AI) has proved to be an important development in the field of translational nanomedicine, as it has made possible intelligent and personalized applications for precision oncology. The integration of machine learning (ML), deep learning, nanoinformatics, predictive modeling, and computational methods with nano delivery systems has improved carrier design, formulation optimization, multiscale targeting, and therapy monitoring using AI. This article discusses some of the latest developments in the area of AI-assisted translational nanomedicine. It also highlights the use of AI in tumor identification, patient stratification, theranostics, and the application of AI in brain, liver, breast, and kidney cancers. Moreover, cutting-edge developments in digital twins, federated learning, explainable AI, large language models, and intelligent nanorobotics are described for their capacity to speed up clinical translation. While much has been accomplished, there are issues regarding data standardization, clinical validation, scalable manufacturing, regulatory clearance, and algorithm transparency that need to be addressed.
Contributors:
ML in Rare Facial Disorders: Hemifacial Microsomia, Parry–Romberg Syndrome, Moebius Syndrome, Treacher Collins Syndrome, Apert Syndrome, and Crouzon Syndrome-From Etiological Mapping and Pathology to AI-Driven Bio-Computational Gene Therapy
These rare facial diseases, which include hemifacial microsomia, Parry–Romberg syndrome, Moebius syndrome, Treacher Collins syndrome, Apert syndrome, and Crouzon syndrome, among others, pose considerable difficulties in diagnosis and treatment due to their clinical variability, rare occurrence, and complex genetic etiology. This review highlights the increasing potential of artificial intelligence (AI) and machine learning (ML) technologies in the better diagnosis, phenotyping, genotyping, and clinical management of these rare disorders. In this review, the applications of AI to facial phenotyping, three-dimensional (3D) imaging, radiomics, multimodal learning, and explainable AI have been highlighted. It also points out some latest developments in the field of bioinformatics, genome editing, RNA therapies, patient-derived models, and digital twin technology for precision medicine and translational research. Presently, there is sufficient evidence indicating the benefits of AI in increasing the accuracy in diagnosis, objective craniofacial evaluation, and customized treatment plans, whereas computational therapy is still experimental. However, various barriers such as data limitations, variability in phenotype, bias in the algorithms, external validation, ethical issues, and regulation prevent its widespread adoption in clinical practice. Future studies need to concentrate on multicenter data sharing, multimodal explainable AI, precision genomics, and translational framework to speed up the adoption of AI in rare craniofacial medicine.
Contributors:
Leptospirosis/Weil's Disease: Pathogenesis, Epidemiology, Bio-Screening, Next-Generation Interventions Powered by AI, ML, Modeling, 3d Fingerprinting, Cover Etiology, Treatment Horizons, Fungal, Mosquito and Airborne Infection
Leptospirosis or commonly referred to as Weil’s disease is an emerging neglected zoonotic bacterial infection that poses great challenges globally due to its complicated epidemiology, varied presentations, and increased prevalence linked with climate change, urbanization, and environmental pollution. Despite advancements in the diagnostic procedures and treatment options with antibiotics, delayed diagnosis and inadequate surveillance systems still hinder disease control efforts. This review provides current information about the disease with regard to the causative agents, pathogenesis, epidemiology, bio-screening techniques, and therapies of leptospirosis together with new developments in artificial intelligence (AI), machine learning (ML), modeling, geospatial analysis, three-dimensional (3D) fingerprinting, digital pathology, and precision medicine. Some of the emerging technologies such as explainable AI, deep learning (DL), Geographic Information System (GIS), Bayesian, and Long Short-Term Memory (LSTM) model, biomarker-based diagnostics, and AI -based drug discovery have demonstrated significant potential in the context of disease surveillance, prediction of outbreaks, prognosis, and personalization of clinical interventions. This review further addresses aspects like vaccine development, digital health, and the difficulties of transitioning these technologies into practice. The integration of intelligent computing techniques and molecular diagnostics in conjunction with One Health surveillance may offer a promising approach to achieve early detection, precise treatment, and preparedness in leptospirosis.