Journal of Pharmacopoeia and Research

A Recent Review on Medicinal Plant: Doronicum hookeri

Ankush Dhiman — 2025-09-11

According to the WHO reports for the primary health care needs of the world's population, most utilise traditional medicines. USM is one of the primary conventional medicine systems. Most lifesaving drugs derive from a medicinal plant. Doronicum hookeri is a medicinal plant (part of the family Compositae/Asteraceae) widely used in the conventional system of medicine. Various parts of the plant are utilized and applied in the manufacturing of herbal medicine. The counteractive and prophylactic activity of the chemicals presents inside these rhizomes strengthens and invigorates the body. Hence, it shows various tonic effects on the nervous and cardiovascular systems. This drug is especially beneficial in treating tachycardia and therefore is applied in multiple cardiac medications. Central purpose of this review is to highlight the values of Doronicum hookeri. The study will provide a clear understanding of the drug profile, its uses, and the potential value of this plant's research to the audience. Various internet databases are used to collect the information regarding Doronicum hookeri, such as Google Scholar, Science direct, Pubmed, and several theses from Sodh-Ganga, Research Gate, and various books. On this plant, minimal evaluation is done due to which most of the plant's properties are unknown, but ancient manuscripts provide fine shreds of evidence regarding its usage as an embryo shielding, an antidote, a tonic effect on the liver, stomach, nerves, and cardiovascular system. Lack of research or preliminary research on its pharmacological and toxicological properties hinders most of this plant's characteristics. Hence, more research is required to prove its medicinal usage and safety.

Formulation and Evaluation of Herbal Facial pack using Syzygium cumini, Punica granatum and Saraca asoka

Deshmukh Kamesh — 2025-12-02

Herbal cosmetic products are becoming increasingly popular as consumers look for natural, safe, and effective alternatives to synthetic formulations. In this study, a new herbal face pack was developed and assessed using extracts from Syzygium cumini (Jamun), Punica granatum (Pomegranate), and Saraca asoca (Ashoka). These plants have long been valued in traditional healing systems, particularly Ayurveda, for their notable astringent, antioxidant, anti-inflammatory, and antimicrobial activities. The formulation aimed to create a product that stimulates blood circulation, cleanses pores, improves skin elasticity, and provides a healthy, rejuvenating glow. The plant materials such as the seeds or leaves of S. cumini, the peels of P. granatum, and the bark or flowers of S. asoca are collected, dried, and processed into fine powders. These herbal powders are then geometrically mixed with a suitable base material, such as Multani Mitti or gram flour, to ensure a uniform mixture. For stability and efficacy, other ingredients like rose water or honey may be added as a vehicle and to enhance beneficial properties.

Fabrication of a Gold Nanoparticle Graphene Enhanced Screen-Printed Electrode for Highly Sensitive Electrochemical Monitoring of Heavy Metal Ions in Water

Geetanjali Yogesh Lubal — 2025-12-06

Rapid, low-cost, in-field detection of heavy metal ions (HMIs) such as Pb(II) and Cd(II) remains a pressing analytical challenge for environmental monitoring. Electrochemical methods particularly anodic stripping voltammetry (ASV) variants offer excellent sensitivity, portability and low reagent use. This work presents a comprehensive research study: (1) a literature synthesis of recent advances in electrochemical HMIs sensing, (2) design and fabrication of a disposable screen-printed carbon electrode (SPCE) modified with a graphene-Au nanoparticle (AuNP) composite, (3) optimization of square-wave anodic stripping voltammetry (SWASV) parameters, and (4) analytical performance evaluation for Pb(II) and Cd(II) in laboratory and spiked real water samples. The proposed sensor shows, under optimized conditions, linear ranges suitable for regulatory limits, low limits of detection (LOD) in the sub-ppb to low ppb range, fast analysis (<10 min per sample), and good selectivity against common interferents. This paper includes an experimentally-oriented methodology, hypothetical but literature-anchored results and a critical discussion of strengths and limitations, with directions for future work toward commercial deployability.

Traditional in Vitro Synergy Models, Like Loewe Additivity or Chou-Talalay, Rely on Dose-Response Curves, which are Often Inapplicable In Vivo Due to Fixed Single-Dose Regimens

P. Supriya — 2025-12-19

Classic methods like Loewe additivity and the Chou–Talalay approach have long been used to study how drugs interact. These models help determine whether combining drugs leads to better, worse, or simply additive effects. Typically, researchers generate dose-response curves in lab settings to analyze these interactions. However, these laboratory-based methods don’t always translate well to real-world scenarios. In clinical and preclinical (animal) studies, it's common to administer fixed doses of each drug rather than exploring a range of doses. This poses a challenge, as many drug interaction models assume a smooth, testable relationship between dose and effect—something that rarely occurs in practical settings. Several real-world factors complicate the interpretation of combination therapies: pharmacokinetics (how drugs move through the body), drug distribution, site-specific activity, and toxicity levels all introduce variability. These complexities often make it difficult for simple mathematical models to accurately reflect what’s happening in the body. For researchers, a key challenge is applying drug interaction models in clinical contexts, where fixed-dose regimens are the norm. This disconnect has sparked growing interest in more advanced approaches, such as:

Modelling tumour growth over time
Using statistical frameworks to analyze biological systems
Simulating drug behavior in the body (pharmacokinetic/pharmacodynamic modeling)

To make meaningful progress, scientists must strike a balance between simplicity and realism—developing models that are both accessible and biologically accurate. This balance is essential because the real value of combination therapies lies not only in theoretical synergy but also in demonstrated practical effectiveness at real-world doses.

Artificial Intelligence, Machine Learning, and Generative AI in Synthetic Planning and Drug Discovery

M. Nigar Fathima — 2025-12-19

Artificial Intelligence (AI) and Machine Learning (ML) have come up as transformative technologies in the pharmaceutical sciences, particularly in synthetic route planning and early-stage drug discovery.ML models can predict retrosynthetic pathways, optimize reaction conditions, and reduce trial-and-error in laboratory synthesis. Beyond their role in synthesis, AI-driven tools are now widely used to analyze complex biological and chemical data, helping researchers identify viable drug candidates at much earlier stages. Techniques such as virtual screening, predictive modeling, and structure–activity analysis allow scientists to make informed decisions before entering costly experimental phases. These methods also support the evaluation of safety, efficacy, and pharmacokinetic behavior, improving the overall quality of candidate selection. At the same time, Generative AI (GAI) is revolutionizing drug discovery by designing novel molecular structures with desired biological properties using deep learning algorithms such as generative adversarial networks (GANs) and transformer-based models. These intelligent systems not only accelerate the drug development timeline but also enhance decision-making in lead optimization and target validation. This integrated AI-driven approach significantly reduces cost, time and resources, enabling the way for faster and more efficient drug innovation.