2026-07-29T01:13:43Z https://ijppp.in/index.php/files/oai

oai:ojs2.ijppp.in:article/1 2026-07-23T07:57:49Z files:ART

AI-Guided Molecular Design for Accelerating Small-Molecule Drug Discovery Amit Gupta Eroom's Law, Artificial Intelligence, Drug Discovery, Molecular Design, Chemical Space, Generative AI The pervasive productivity crisis in pharmaceutical R&D, encapsulated by Eroom's Law, demands a fundamental transformation in drug discovery. Artificial intelligence emerges as a pivotal solution, not by replacing human expertise, but by forging a synergistic partnership with medicinal chemists. By leveraging vast, curated biological and chemical data, sophisticated molecular representations, and core AI paradigms— including predictive modeling, unsupervised exploration, and generative design—AI is re-engineering the discovery pipeline. It accelerates target identification, enables billion-compound virtual screening, and drives the de novo design of optimized lead candidates. Critically, AI-powered predictive ADMET and safety profiling frontload risk assessment, aiming to reduce costly late-stage attrition. This integration of AI across the workflow—from target to candidate—promises to compress timelines, lower costs, and enhance the precision of therapeutic design. Ultimately, the AI-guided paradigm represents a necessary and powerful evolution, positioning the field to finally bend the curve of Eroom's Law and deliver innovative medicines to patients with unprecedented efficiency. Sujata Publications 2026-04-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijppp.in/index.php/files/article/view/1 10.62896/ijppp.1.1.01 International Journal of Physiology, Pathophysiology and Pharmacotherapy ; IJPPP: Vol 1, Issue 1, January-June 2026; 1-9 3139-5481 eng https://ijppp.in/index.php/files/article/view/1/1 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijppp.in:article/2 2026-07-23T07:58:44Z files:ART

Predictive Pharmacokinetics Using Artificial Intelligence: A New Frontier in Rational Drug Discovery Sandip Satav Siddharth Verma Pharmacokinetics (PK), Bioavailability (BA), ADME (Absorption, Distribution, Metabolism, Excretion), Drug Development The integration of artificial intelligence and machine learning into pharmacokinetic prediction represents a fundamental advance in the quest to overcome the historical bottleneck of drug development. Current capabilities now extend beyond isolated property prediction, such as solubility or metabolic stability, toward sophisticated, integrated models that forecast comprehensive human PK profiles—including bioavailability, clearance, volume of distribution, and half-life—directly from molecular structure. These data-driven systems learn the complex, non-linear relationships that govern drug disposition, augmenting and often surpassing traditional metho ds like PBPK modeling, especially in early discovery where experimental data are sparse. By enabling de novo molecular design for optimal ADME properties and providing early, accurate human PK forecasts, AI/ML directly addresses the root cause of costly late-stage attrition, shifting the paradigm from reactive optimization to proactive, predictive design Sujata Publications 2026-04-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijppp.in/index.php/files/article/view/2 10.62896/ijppp.1.1.02 International Journal of Physiology, Pathophysiology and Pharmacotherapy ; IJPPP: Vol 1, Issue 1, January-June 2026; 10-22 3139-5481 eng https://ijppp.in/index.php/files/article/view/2/2 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijppp.in:article/3 2026-07-23T07:59:19Z files:ART

Integrating Network Pharmacology and AI for Multi-Target Drug Discovery in Complex Diseases Uriti Sri Venkatesh treating complex, multifactorial diseases, neural networks, network pharmacology The conventional "one drug, one target" paradigm has proven inadequate for treating complex, multifactorial diseases like cancer, Alzheimer's disease, and autoimmune disorders. These conditions arise from dysregulated biological networks, necessitating a paradigm shift toward systems-level therapeutic strategies. This article reviews the transformative integration of network pharmacology and artificial intelligence (AI) for multi-target drug discovery. Network pharmacology provides the foundational framework by constructing and analyzing drugtarget-disease networks to identify key intervention points, such as hub and bottleneck proteins. AI, particularly machine learning and graph neural networks, acts as an indispensable catalyst, enabling the scalable analysis of multi-omics data, the prediction of novel drug-target interactions, and the de novo design of multi-target ligands. We outline the core principles of this integrated workflow, from constructing dynamic network models to employing explainable AI for interpretable predictions. The power of this approach is demonstrated through its application across diverse therapeutic areas: deconvoluting oncogenic signaling networks in pancreatic cancer, elucidating the polypharmacology of natural products in inflammatory diseases, and addressing the shared network perturbations in neurodegenerative and metabolic disorders by integrating gut microbiome data. While challenges in data integration, model interpretability, and translational validation persist, the confluence of network pharmacology and AI charts a new roadmap for drug discovery. This synergy promises to accelerate the development of more effective, resilient, and personalized multi-target therapies, ultimately offering a powerful strategy to restore homeostasis in complex disease networks. Sujata Publications 2026-04-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijppp.in/index.php/files/article/view/3 10.62896/ijppp.1.1.03 International Journal of Physiology, Pathophysiology and Pharmacotherapy ; IJPPP: Vol 1, Issue 1, January-June 2026; 23-31 3139-5481 eng https://ijppp.in/index.php/files/article/view/3/3 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijppp.in:article/4 2026-07-23T07:59:59Z files:ART

The Brain–Heart Axis as a Unified Physiological System: Implications for Disease and Therapy Sravani Boyapati Brain-Heart Axis, Neurocardiology, Autonomic Nervous System, Cardiac Innervation, Neuroinflammation The brain-heart axis constitutes a sophisticated, bidirectional communication network fundamental to cardiovascular homeostasis and a critical determinant of disease pathogenesis. This review synthesizes the integrated multi-level architecture of this axis, from its anatomical foundations to its pathophysiological dysregulation. The dialogue is mediated through neural, hormonal, and immune pathways, orchestrated by a hierarchical innervation system comprising extrinsic central command and an intrinsic cardiac nervous system. Under physiological conditions, a dynamic balance between sympathetic and parasympathetic tones, refined by immune feedback, ensures adaptive responsiveness. However, following injury such as a myocardial infarction, this axis undergoes catastrophic maladaptive remodeling. This is characterized by a triad of peripheral sympathetic hyperinnervation and arrhythmogenic nerve sprouting, central neuroinflammation in autonomic centers, and a loss of protective parasympathetic tone. These processes form a selfperpetuating vicious cycle that drives disease progression toward heart failure and sudden cardiac death. This paradigm shift—from a cardiocentric to a neurocardiac perspective—reveals the brain-heart axis not merely as a communication channel but as an integrated physiological unit whose dysfunction is central to cardiovascular pathology. It consequently illuminates novel therapeutic frontiers, including bioelectronic neuromodulation and targeted anti-inflammatory strategies, aimed at restoring axis harmony. Sujata Publications 2026-04-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijppp.in/index.php/files/article/view/4 10.62896/ijppp.1.1.04 International Journal of Physiology, Pathophysiology and Pharmacotherapy ; IJPPP: Vol 1, Issue 1, January-June 2026; 32-41 3139-5481 eng https://ijppp.in/index.php/files/article/view/4/4 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijppp.in:article/5 2026-07-23T08:01:06Z files:ART

Topical Dual-Drug Delivery Using β-Cyclodextrin Nanosponges: A Targeted and Safer Therapeutic Strategy for Rheumatoid Arthritis Management Subham Mandal Suraj Mandal Rheumatoid arthritis; topical delivery; β-cyclodextrin nanosponges; baricitinib; tenoxicam; dual-drug therapy; sustained release; CFA-induced arthritis; cytokine modulation; localized therapy Rheumatoid arthritis (RA) is a chronic inflammatory disease often associated with limitations of conventional therapies, including systemic toxicity and poor patient compliance. This study presents a novel topical delivery system utilizing β-cyclodextrin nanosponges for the combined administration of baricitinib and tenoxicam. The developed nanosponges exhibited high drug loading, nanoscale size, and improved solubility through amorphization. Incorporation into a carbopol gel enabled sustained and controlled drug release. In vivo evaluation in an arthritis model demonstrated significant reduction in inflammation, decreased proinflammatory cytokines, and improved joint condition. The formulation also showed minimal systemic toxicity, as indicated by normalized biochemical parameters. Overall, this dual-drug nanosponge gel offers a targeted, effective, and safer approach for RA management. Sujata Publications 2026-04-11 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijppp.in/index.php/files/article/view/5 10.62896/ijppp.1.1.05 International Journal of Physiology, Pathophysiology and Pharmacotherapy ; IJPPP: Vol 1, Issue 1, January-June 2026; 42-52 3139-5481 eng https://ijppp.in/index.php/files/article/view/5/5 https://creativecommons.org/licenses/by/4.0