Identification of potential inhibitors for drug resistance in acute lymphoblastic leukemia through differentially expressed gene analysis and in silico screening

Article

Author: Ayna Duran, Gizem ; Kiraz, Yağmur ; Tükel, Ezgi Yağmur ; Özay, Başak

Acute lymphoblastic leukemia (ALL) is a disease of lymphocyte origin predominantly diagnosed in children. While its 5-year survival rate is high, resistance to chemotherapy drugs is still an obstacle. Our aim is to determine differentially expressed genes (DEGs) related to Asparaginase, Daunorubicin, Prednisolone, and Vincristine resistance and identify potential inhibitors via docking. Three datasets were accessed from the Gene Expression Omnibus database; GSE635, GSE19143, and GSE22529. The microarray data was analyzed using R4.2.0 and Bioconductor packages, and pathway and protein-protein interaction analysis were performed. We identified 1294 upregulated DEGs, with 12 genes consistently upregulated in all four resistant groups. KEGG analysis revealed an association with the PI3K-Akt pathway. Among DEGs, 33 hub genes including MDM2 and USP7 were pinpointed. Within common genes, CLDN9 and HS3ST3A1 were subjected to molecular docking against 3556 molecules. Following ADMET analysis, three drugs emerged as potential inhibitors: Flunarizine, Talniflumate, and Eltrombopag. Molecular dynamics analysis for HS3ST3A1 indicated all candidates had the potential to overcome drug resistance, Eltrombopag displaying particularly promising results. This study promotes a further understanding of drug resistance in ALL, introducing novel genes for consideration in diagnostic screening. It also presents potential inhibitor candidates to tackle drug resistance through repurposing.

21 Dec 2023·Journal of biomolecular structure & dynamics

Exploration of the potential neuroprotective compounds targeting GluN1-GluN2B NMDA receptors

Article

Author: Zhang, Xiaozhe ; Zhang, Lihua ; He, Meixi ; Wang, Yi

The N-methyl-d-aspartic acid (NMDA) receptors belongs to the family of ionotropic glutamate receptors, which could mediate most excitatory synaptic transmission in the brain. It is interesting to know if some available drugs have regulatory effects on the NMDARs. Herein, the present study reports the discovery of drugs targeting NMDAR using virtual screening. In this study, talniflumate with the EC50 value at 61.49 nM was successfully screened. The interaction analysis of this compound was further explored through molecular dynamics simulation. It is indicated that talniflumate could form stable interactions with GluN1-GluN2B NMDA receptors. In particular, H-bond interactions with high occupancies between GluN1-GluN2B NMDA receptors and talniflumate were observed. Compared to de novo drug discovery, this approach could be an alternative choice for development of safety and efficiency NMDAR inhibitors from available drugs.Communicated by Ramaswamy H. Sarma.

01 Aug 2023·Clinics in orthopedic surgery

Development of Prediction Model Using Machine-Learning Algorithms for Nonsteroidal Anti-inflammatory Drug-Induced Gastric Ulcer in Osteoarthritis Patients: Retrospective Cohort Study of a Nationwide South Korean Cohort.

Article

Author: Won, Sungho ; Han, Hyuk-Soo ; Jeong, Jaehan ; Ro, Du Hyun ; Han, Hyein

Background:

Nonsteroidal anti-inflammatory drugs (NSAID) are currently among the most prescribed medications worldwide to relieve pain and reduce inflammation, especially in patients suffering osteoarthritis (OA). However, NSAIDs are known to have adverse effects on the gastrointestinal system. If a gastric ulcer occurs, planned OA treatment needs to be changed, incurring additional treatment costs and causing discomfort for both patients and clinicians. Therefore, it is necessary to create a gastric ulcer prediction model that can reflect the detailed health status of each individual and to use it when making treatment plans.

Methods:

Using sample cohort data from 2008 to 2013 from the National Health Insurance Service in South Korea, we developed a prediction model for NSAID-induced gastric ulcers using machine-learning algorithms and investigated new risk factors associated with medication and comorbidities.

Results:

The population of the study consisted of 30,808 patients with OA who were treated with NSAIDs between 2008 and 2013. After a 2-year follow-up, these patients were divided into two groups: without gastric ulcer (n=29,579) and with gastric ulcer (n=1,229). Five machine-learning algorithms were used to develop the prediction model, and a gradient boosting machine (GBM) was selected as the model with the best performance (area under the curve, 0.896; 95% confidence interval, 0.883-0.909). The GBM identified 5 medications (loxoprofen, aceclofenac, talniflumate, meloxicam, and dexibuprofen) and 2 comorbidities (acute upper respiratory tract infection [AURI] and gastroesophageal reflux disease) as important features. AURI did not have a dose-response relationship, so it could not be interpreted as a significant risk factor even though it was initially detected as an important feature and improved the prediction performance.

Conclusions:

We obtained a prediction model for NSAID-induced gastric ulcers using the GBM method. Since personal prescription period and the severity of comorbidities were considered numerically, individual patients' risk could be well reflected. The prediction model showed high performance and interpretability, so it is meaningful to both clinicians and NSAID users.

100 Deals associated with Talniflumate

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KEGG	Wiki	ATC	Drug Bank
D02701	-	-	DB09295

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Indication	Highest Phase	Country/Location	Organization	Date
Cystic Fibrosis	Phase 2	IE	-	-
Cystic Fibrosis	Phase 2	-	Genaera Corp.	-
Cystic Fibrosis	Phase 2	-	Bago sro	-
Asthma	Phase 1	MX	-	-
Gastrointestinal Diseases	Preclinical	US	-	-

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