Pharmacy and Pharmaceutical Sciences Commons^™

Editorial, Anne M. Friel, Brigid Hooban, Therese Montgomery, Anne Marie O'Brien, Cormac Quigley, Edel Mcneela, Eva Campion, James Walshe, Sinead Loughran

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Bridging The Gap From Preschool To Primary: From Policy To Practice, Aimee O'Connor, Cóilín O’ Braonáin

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Adipose Tissue Estrogen Receptor-Alpha Overexpression Ameliorates High-Fat Diet–Induced Adipose Tissue Inflammation, Marion C. Hope, Christian A. Unger, M. Chase Kettering, Cassidy E. Socia, Ahmed K. Aladhami, Barton C. Rice, Darya S. Niamira, Ben P. Wiznitzer, Diego Altomare Ph.D., William E. Cotham, Reilly T. Enos

Faculty Publications

Context

We created an innovative mouse model that enables inducible overexpression of estrogen receptor-alpha (ERα), specifically in adipose tissue (Adipo-ERα↑). Objective

We aimed to investigate how elevated Adipo-ERα↑ influences the development of high-fat diet (HFD)-induced obesity in both male and female mice. Methods

Male and female Adipo-ERα↑ mice and littermate controls were fed a low-fat diet (LFD) or HFD for 13 weeks. Adipo-ERα↑ was induced at the initiation of dietary treatment. Body morphology and composition, hepatic lipid accumulation, glucose tolerance, fasting insulin concentrations, and adipose tissue mRNA profiling were assessed. Liquid chromatography–mass spectrometry was used to determine circulating and adipose …

Air Pollution And Diseases: Signaling, G Protein-Coupled And Toll Like Receptors, Isabella Cattani-Cavalieri, Katrina F. Ostrom, Jordyn Margolis, Rennolds S. Ostrom, Martina Schmidt

Pharmacy Faculty Articles and Research

Air pollution is a significant public health issue that impacts lung health, particularly in vulnerable populations such as children, the elderly, and individuals with pre-existing respiratory conditions. Both natural and anthropogenic sources of air pollution give rise to a variety of toxic compounds, including particulate matter (PM), ozone (O₃), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and polycyclic aromatic hydrocarbons (PAHs). Exposure to these pollutants is strongly associated with the development and exacerbation of respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), lung cancer, and idiopathic pulmonary fibrosis (IPF). Notably, early life exposure to pollutants such as …

Enhancing Cataract Surgery Outcomes: Optimal Use Of Pre- And Post-Operative Eye Drops, Keith Skolnick M.D., Anu Valiaveedu

Mako: NSU Undergraduate Student Journal

Many preoperative and postoperative cataract patients struggle with comprehending the use of prescription medication as directed. Language barriers and low health literacy levels are major factors contributing to improper use of prescriptions. To increase patients comprehension, the Fort Lauderdale Eye Institute employed an educational intervention consisting of a live presentation and an instructional video. Results found that 44% of patients were hesitant to ask questions to clinical staff, 32% felt overwhelmed, and nearly 70% lacked confidence in using their prescribed eye drops. Following the intervention, 91% of patients reported increased confidence in their medications, and most indicated that the video …

Techno-Enviro-Economic Approach For Electrification Of Rural And Shrimp Farming Regional Development Of An Isolated Island In Indonesia By Utilizing Hybrid Renewable Energy Systems, Fiqih Akbar Wijaya, Mohammad Akita Indianto

Journal of Materials Exploration and Findings

One of the challenges in developing and archipelagic countries such as Indonesia is maintaining energy demand in rural and isolated areas due to difficulties in electrical distribution. For instance, in areas like Bawean Island, no additional electricity capacity has been introduced in the past year, leading to an unmet potential customer demand. One of the possible options is by utilizing Hybrid Renewable Energy Systems (HRES) that are integrated with existing fossil fuel-based energy systems to support the growing energy demand in the remote island. A case study in Bawean Island is conducted with the projected energy demand covers the energy …

Remaining Life Assessment And Fitness For Service Evaluation Of Aging Chemical Reactors In Polyethylene Terephthalate Resin Industry, Aditya Pahlawan Munthe, Donanta Dhaneswara, Wahyuaji Narottama Putra, Gama Widyaputra

Journal of Materials Exploration and Findings

Aging chemical reactors in the polyethylene terephthalate (PET) resin industry require comprehensive evaluation to ensure safe continued operation. This study conducts a remaining life assessment (RLA) and fitness-for-service (FFS) evaluation on five 30-year-old reactors, based on API 510, API 579/ASME FFS-1, and ASME BPVC Section VIII Div. 1 standards. The analysis involves corrosion rate measurement, future corrosion allowance (FCA) projection, and minimum thickness verification. Among the reactors, R-120 was found to have the shortest remaining life less than 15 years. FFS assessments using three criteria Average Measured Thickness, MAWP from Point Thickness Readings, and Minimum Measured Thickness confirm that R-120 …

A Study Of Wear Measurement And Life Prediction Of Insert Chute Liner On Gold Ore Processing, Ferry Ardika Natanael, Nony Maulidya, Dedi Priadi

Journal of Materials Exploration and Findings

Chutes are critical materials handling assets to transport solid particles from one process step to another in mineral processing, coal mining and cement industries. The material transport, because of material characteristics, cause severe wear on internal lining of chutes or bins’ structure. The wear problem on internal lining of bins or chutes needs to be checked to keep production efficiency. Therefore, not checked wear rate causes liner replacement schedule becomes unpredictable. It leads to production loss and highly cost maintenance in industry. This study shows that condition-based maintenance through regular thickness measurement using Ultrasonic Transducer (UT) to predict life service …

Analysis Of Leak Testing Planning For Carbon Dioxide Piping Systems In The Development Of Carbon Capture, Utilization, And Storage Facilities In The Bintuni Basin, Devi Sentiani, Akhmad Herman Yuwono Professor

Journal of Materials Exploration and Findings

Carbon Capture, Utilization, and Storage (CCUS) technologies have become a key strategic response to global efforts to reduce CO₂ emissions. This study presents a leak testing strategy designed for a carbon dioxide (CO₂) piping system as part of a Carbon Capture, Utilization, and Storage (CCUS) project in the Bintuni Basin, West Papua, Indonesia. This study aims to ensure the system is properly prepared before commissioning by designing a leak test plan that is both practical and technical, based on ASME B31.3, ASME PCC-2, and API STD 520 Part 1. We conducted the process, which involves identifying the system boundaries, …

Detection Of Activity Cliffs Produced By Anti-Cancer Drugs And An Algorithm For Reliable Predictions In Affected Areas, Sarah Josephine Aurit

Department of Statistics: Dissertations, Theses, and Student Research

An activity cliff (AC) occurs when drugs close in chemical space produce dissimilar biological results. We focus on developing an inferential procedure to detect the presence of ACs in a chemical landscape. If detected, we provide a distance-based procedure that can be used to identify regions of stability in the chemical landscape of interest and generate prediction with higher precision in those areas of stability. We conceptualize the chemical landscape as a spatial random field and use spatial models for prediction of efficacy for new drugs based on “distance” in chemical space. We argue that an AC manifests itself by …

Combination Of Irreversible Electroporation And Clostridium Novyi-Nt Bacterial Therapy For Colorectal Liver Metastasis, Zigeng Zhang, Guangbo Yu, Qiaoming Hou, Farideh Amirrad, Sha Webster, Surya M. Nauli, Jianhua Yu, Vahid Yaghmai, Aydin Eresen, Zhuoli Zhang

Pharmacy Faculty Articles and Research

Colorectal liver metastasis (CRLM) poses a significant challenge in oncology due to its high incidence and poor prognosis in unresectable cases. Current treatments, including surgical resection, systemic chemotherapy, and liver-directed therapies, often fail to effectively target hypoxic tumor regions, which are inherently more resistant to these interventions. This review examines the potential of a novel therapeutic strategy combining irreversible electroporation (IRE) ablation and Clostridium novyi-nontoxic (C. novyi-NT) bacterial therapy. IRE is a non-thermal tumor ablation technique that uses high-voltage electric pulses to create permanent nanopores in cell membranes, leading to cell death while preserving surrounding structures, and …

Cilia In The Brain Display Region-Dependent Oscillations Of Length And Orientation, Roudabeh Vakil Monfared, Sherif Abdelkarim, Pieter Derdeyn, Kiki Chen, Hanting Wu, Kenneth Leong, Tiffany Chang, Justine Lee, Sara Versales, Surya M. Nauli, Kevin Beier, Pierre Baldi, Amal Alachkar

Pharmacy Faculty Articles and Research

In this study, we conducted high-throughput spatiotemporal analysis of primary cilia length and orientation across 22 mouse brain regions. We developed automated image analysis algorithms, which enabled us to examine over 10 million individual cilia, generating the largest spatiotemporal atlas of cilia. We found that cilia length and orientation display substantial variations across different brain regions and exhibit fluctuations over a 24-h period, with region-specific peaks during light-dark phases. Our analysis revealed unique orientation patterns of cilia, suggesting that cilia orientation within the brain is not random but follows specific patterns. Using BioCycle, we identified rhythmic fluctuations in cilia length …

Synthesis Of Carboxymethyl Cellulose From Nypa Fruticans For Coating Metformin On Zn-Based Metal-Organic Framework Nanohybrid As A Bionanocomposite Drug Delivery System, Delviani Delviani, Viola Giary Rizkillah Maharani, Putri Nur Shadrina, Tri Warni, Regina Wan Azizah, Indra Lasmana Tarigan, Madyawati Latief

Makara Journal of Science

Diabetes is a common and rapidly spreading disease that affects 6.6% of the world’s population. Metformin hydrochloride (HCl) is an effective oral pharmacological treatment for type 2 diabetes mellitus (T2DM) patients because of its capability to reduce glucose levels and the risk of hypoglycaemia. However, gastrointestinal sensitivities to this drug can cause diarrhoea, nausea, vomiting and stomach ache. An alternative is controlled release technology by coating metformin with carboxymethyl cellulose (CMC) and zinc-metal-organic framework (Zn-MOF) through the encapsulation method. Therefore, this study aimed to coat metformin formulations using CMC in a Zn-MOF to control release into the body and minimise …

Molecular Insights Into The Role Of Estrogen Receptor Beta In Ecdysterone Mediated Anabolic Activity, Syeda Sumayya Tariq, Madiha Sardar, Muhammad Shafiq, Hendrick Heinz, Mohammad Nur-E-Alam, Aftab Ahmad, Zaheer Ul-Haq

Pharmacy Faculty Articles and Research

Ecdysterone, often dubbed a “natural steroid,” has garnered significant attention among athletes for its reputed growth-promoting and anabolic properties. Unlike synthetic anabolic steroids, which are classified as controlled substances, ecdysteroids remain largely unregulated in many countries and are widely marketed as dietary supplements. Notably, ecdysterone has been included in the World Anti-Doping Agency (WADA) monitoring program, highlighting its potential impact on athletic performance and raising questions about its regulation. Emerging evidence indicates that, unlike traditional anabolic steroids that act primarily via the Androgen Receptor (AR), ecdysterone’s anabolic effects may be mediated through Estrogen Receptors (ERs), particularly Estrogen Receptor beta (ERβ). …

Integrative Computational Modeling Of Distinct Binding Mechanisms For Broadly Neutralizing Antibodies Targeting Sars-Cov-2 Spike Omicron Variants: Balance Of Evolutionary And Dynamic Adaptability In Shaping Molecular Determinants Of Immune Escape, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this study, we conducted a comprehensive analysis of the interactions between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and four neutralizing antibodies—S309, S304, CYFN1006, and VIR-7229. Using integrative computational modeling that combined all-atom molecular dynamics (MD) simulations, mutational scanning, and MM-GBSA binding free energy calculations, we elucidated the structural, energetic, and dynamic determinants of antibody binding. Our findings reveal distinct dynamic binding mechanisms and evolutionary adaptation driving the broad neutralization effect of these antibodies. We show that S309 targets conserved residues near the ACE2 interface, leveraging synergistic van der Waals and electrostatic interactions, while S304 focuses on …

Probing Binding And Allosteric Mechanisms Of The Kras Interactions With Monobodies And Affimer Proteins : Ensemble-Based Mutational Profiling And Thermodynamic Analysis Of Binding Energetics And Allostery Reveal Diversity Of Functional Hotspots And Cryptic Pockets Linked By Conserved Communication Network, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Guang Hu, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

KRAS, a historically "undruggable" oncogenic driver, has eluded targeted therapies due to its lack of accessible binding pockets in its active state. This study investigates the conformational dynamics, binding mechanisms, and allosteric communication networks of KRAS in complexes with monobodies (12D1, 12D5) and affimer proteins (K6, K3, K69) to characterize the binding and allosteric mechanisms and hotspots of KRAS binding. Through molecular dynamics simulations, mutational scanning, binding free energy analysis and network-based analyses, we identified conserved allosteric hotspots that serve as critical nodes for long-range communication in KRAS. Key residues in β-strand 4 (F78, L80, F82), α-helix 3 (I93, H95, …

Assessment Of H2s-Induced Cracking Susceptibility In Steam Line Pipes And Weld Zones During Geothermal Well Construction, Riene Kaelamanda Pragitta, Yudha Pratesa

Journal of Materials Exploration and Findings

The susceptibility of steam line pipes, especially in the HAZ (heat-affected zone) and weldment areas, to hydrogen sulfide in the geothermal industry is crucial to understand from the early stages, particularly during construction. The combination of tensile stress from residual stresses after welding and metallurgical phase transformation makes the joint areas vulnerable to sulfide stress cracking. This condition becomes even more extreme when the equipment operates during the well stimulation phase. This research assesses the severity of H₂S-induced cracking using NACE MR0175 and ISO 15156-1 standards, focusing on the effects of pH and partial pressure of H₂S (p_{H₂S …}

Advancing Drug-Drug Interaction Prediction Using Multi-Modal Feature Integration With Graph Neural Networks, Ernest C. Chianumba

Theses, Dissertations and Culminating Projects

Pharmaceutical treatments are essential for managing medical conditions, but drug-drug interactions (DDIs) pose significant risks to patient safety and healthcare outcomes. This research integrates Knowledge Graphs and Graph Neural Networks to predict DDIs by exploring complex drug relationships. We construct a comprehensive knowledge graph using DrugBank data (1,000 drugs, 155,774 interactions) enriched with molecular features from PubChem. Our methodology introduces a novel multi-modal approach by integrating transformer-based embeddings (ChemBERTa, SPECTER, and SBERT) to create 1152-dimensional feature vectors that capture structural, biomedical literature, and semantic properties of drugs. Formulating DDI prediction as a link prediction task, we compare three Graph Neural …

Machine Learning And Protein Engineering Approaches To Understanding Kinesin-5 Activity, Jason Eden Sanchez

Open Access Theses & Dissertations

Cancer is a term describing a collection of diseases that result in uncontrolled cell growth. Cancer has manifold etiologies and underlying cancers are rouge biochemical pathways involving many different proteins. In the current work, two approaches are used to enhance knowledge of kinesin-5, a potential cancer target involved in cell division. Kinesin-5 promotes cell division by cross-linking and separating microtubules in dividing cells. The first approach uses machine learning (ML) to identify small molecule inhibitors for kinesin-5. Though decades of research have uncovered classes of small-molecules which inhibit kinesin-5 in vitro and in vivo, no candidates have reached phase III …

Strengthening The Paediatric Clinical Trial Ecosystem To Better Inform Policy And Programmes, James A. Berkley, Judd L. Walson, Glenda Gray, Fiona Russell, Zulfiqar Ahmed Bhutta, Per Ashorn, Shane A. Norris, Ebunoluwa A. Adejuyigbe, Rebecca Grais, Bernhards Ogutu

Institute for Global Health and Development

The first WHO Global Clinical Trials Forum was convened in November, 2023 to develop a shared vision of an effective global clinical trial infrastructure. The Paediatric Clinical Trials Working Group was formed to provide perspectives, identify challenges, and propose solutions to strengthen the paediatric clinical trials ecosystem. Participants represented paediatric disciplines, including infectious diseases, nutrition, neonatology, pharmacology, oncology, neurodevelopment, public health, and policy. Childhood diseases have profound lifelong effects on health, livelihoods, and societies. Investment in early childhood results in highly cost-effective changes to lifelong health, productivity, and human capital returns. Yet, there remain substantial gaps in knowledge on the …

Editorial: Machine Learning Advancements In Pharmacology: Transforming Drug Discovery And Healthcare, Moom Rahman Roosan, Ramgopal Mettu

Pharmacy Faculty Articles and Research

"In recent years, the integration of machine learning (ML) into pharmacology has revolutionized how we approach drug discovery, disease modeling, and therapeutic development. By leveraging vast datasets and computational power, ML has enabled researchers to uncover patterns, predict outcomes, and accelerate drug development processes that were previously unimaginable. This Research Topic on 'Machine Learning Advancements in Pharmacology' features five impactful studies that highlight the diverse applications and potential of ML in this field. These contributions, encompassing original research and a systematic review, exemplify the transformative role of ML in addressing some of the most pressing challenges in pharmacology."

Mutational Scanning And Binding Free Energy Computations Of The Sars-Cov-2 Spike Complexes With Distinct Groups Of Neutralizing Antibodies: Energetic Drivers Of Convergent Evolution Of Binding Affinity And Immune Escape Hotspots, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Nishank Raisinghani, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

The rapid evolution of SARS-CoV-2 has led to the emergence of variants with increased immune evasion capabilities, posing significant challenges to antibody-based therapeutics and vaccines. In this study, we conducted a comprehensive structural and energetic analysis of SARS-CoV-2 spike receptor-binding domain (RBD) complexes with neutralizing antibodies from four distinct groups (A–D), including group A LY-CoV016, group B AZD8895 and REGN10933, group C LY-CoV555, and group D antibodies AZD1061, REGN10987, and LY-CoV1404. Using coarse-grained simplified simulation models, rapid energy-based mutational scanning, and rigorous MM-GBSA binding free energy calculations, we elucidated the molecular mechanisms of antibody binding and escape mechanisms, identified key …

Artificial Intelligence (Ai) In Pharmacy, Giang Nguyen, Elizabeth Sartschev, John Reyes, Allie Honigford, Marisa Petrunich, Kiley Devoll, Brianna Lu, Joshua Honaker, T'Bony M. Jewell

Pharmacy and Wellness Review

Artificial Intelligence (AI) has transformed the pharmaceutical field by enabling computer software systems to learn and perform human behavior. Specifically, AI has revolutionized chronic diabetes management through continuous glucose monitoring, showcasing its immense potential in healthcare. However, alongside its transformative impact, AI’s increasing role in healthcare has prompted concerns over privacy and its premature integration. Despite these challenges, AI offers limitless opportunities to improve medication management and treatment regimens, driving advancements across various domains. From improving CT imaging to enhancing adenoma detection in colonoscopies and facilitating medication adherence, AI’s impact on healthcare is profound. Furthermore, AI plays a pivotal role …

Swosu Research And Scholarly Activity Fair 2025, Swosu Office Of Sponsored Programs

SWOSU Research and Scholarly Activity Fair Programs

On behalf of the members of the University Research and Scholarly Activity Committee (USRAC) and the Office of Sponsored Programs (OSP) at Southwestern Oklahoma State University (SWOSU) — welcome to the Thirty-Third SWOSU Research and Scholarly Activity Fair!

This year’s event features 99 poster presentations and 9 oral presentations, showcasing the work of over 170 student and faculty researchers, writers, presenters, artists, collaborators, and faculty sponsors. These presentations represent a wide array of scholarly and academic activity from across SWOSU’s academic community, including the Departments of Art, Communication, and Theatre; Allied Health Sciences; Biological Sciences; Business; Chemistry & …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

The Future Of Ai Regulation In Drug Development: A Comparative Analysis, Gabriela Lenarczyk, Timo Minssen, Nicholson Price, Arti Rai

Faculty Scholarship

As artificial intelligence (AI) transforms drug development, regulatory frameworks are evolving to oversee its implementation, particularly at the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This paper makes three contributions to understanding emerging regulatory approaches. First, we offer a comparative analysis of how these agencies have responded to AI-driven advances, incorporating new US executive orders and the European Union (EU)’s AI Act. Second, we propose a novel analytical framework to understand regulatory divergence: the FDA’s flexible, dialog-driven model contrasts with the EMA’s structured, risk-tiered approach, reflecting broader institutional and political-economic differences. While the former encourages …

Rwanda's Natural Remedies: A Fresh Look At The Fight Against Malaria, Leandre Nsabimana Ndisanze

Pomona Senior Theses

Malaria remains one of the most devastating parasitic diseases globally, disproportionately affecting sub-Saharan Africa, where Rwanda continues to face high transmission rates and rising resistance to conventional interventions. Despite decades of control efforts, the emergence of drug-resistant Plasmodium falciparum and insecticide-resistant Anopheles mosquitoes threatens to reverse recent gains. This research responds to the urgent need for sustainable and culturally relevant alternatives by exploring the therapeutic potential of Rwandan traditional medicinal plants. These indigenous remedies, long used by local healers, offer an underexplored reservoir of bioactive compounds that may provide the basis for novel antimalarial drugs.

The central hypothesis of this …

Amorphous Quininium Aspirinate From Neat Mechanochemistry: Diffracting Nanocrystalline Domains And Quick Recrystallization Upon Exposure To Solvent Vapours, Silvina Pagola, James Howard, Johannes Merkelbach, Danny Stam

Chemistry & Biochemistry Faculty Publications

Quininium aspirinate is mechanochemically prepared as a crystalline solid by liquid-assisted grinding, or as an amorphous phase (as determined by X-ray powder diffraction), by neat grinding or neat ball milling. Our previous work demonstrated using FT-IR spectroscopy that a mechanochemical reaction had occurred in the mechanically treated neat mixtures. Herein is reported that microcrystal electron diffraction (microED) afforded the discovery of two diffracting micron-size particles in the amorphous powder synthesized by manual grinding, among a majority of non-diffracting particles. Remarkably, microED data of one of them led to the known lattice parameters of quininium aspirinate. Furthermore, this so-called 'X-ray amorphous' …

Automating The Amino Acid Identification In Elliptical Dichroism Spectrometer With Machine Learning, Ridhanya Sree Balamurugan, Yusuf Asad, Tommy Gao, Dharmakeerthi Nawarathna, Umamaheswara Rao Tida, Dali Sun

Electrical & Computer Engineering Faculty Publications

Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer. Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. The results show that ML can potentially revolutionize the amino acid analysis …

Alpha-Synuclein Interaction With Gedunin, Tony Matundura Nyabayo

Graduate Theses/Dissertations

Parkinson’s disease (PD) and other Proteinopathies develop when α-synuclein misfolds and aggregates into toxic amyloids. While existing treatments for PD are primarily focused on managing its symptoms, a viable solution for slowing down the progress of Parkinson’s disease involves targeting the toxic α-synuclein amyloids. Gedunin, a natural inhibitor of heat shock protein 90, has been extensively used to treat malaria. Also, recent research investigations have shed light on its potential beyond malaria therapy, indicating that Gedunin may offer a possible solution for treating a variety of neurodegenerative diseases. Using plate-based assays, we examined how Gedunin influences α-synuclein fibrillation and its …