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Last update 08 May 2025

TDP1

Last update 08 May 2025

Basic Info

Synonyms

FLJ11090, SCAN1, TDP1

+ [2]

Introduction

DNA repair enzyme that can remove a variety of covalent adducts from DNA through hydrolysis of a 3'-phosphodiester bond, giving rise to DNA with a free 3' phosphate. Catalyzes the hydrolysis of dead-end complexes between DNA and the topoisomerase I active site tyrosine residue. Hydrolyzes 3'-phosphoglycolates on protruding 3' ends on DNA double-strand breaks due to DNA damage by radiation and free radicals. Acts on blunt-ended double-strand DNA breaks and on single-stranded DNA. Has low 3'exonuclease activity and can remove a single nucleoside from the 3'end of DNA and RNA molecules with 3'hydroxyl groups. Has no exonuclease activity towards DNA or RNA with a 3'phosphate.

Drugs associated with TDP1

NSC-88915

Target

TDP1

Mechanism

TDP1 inhibitors

Active Org.-

Originator Org.

National Cancer Institute

Active Indication-

Inactive Indication

Neoplasms

Drug Highest PhasePending

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with TDP1

100 Translational Medicine associated with TDP1

0 Patents (Medical) associated with TDP1

401

Literatures (Medical) associated with TDP1

01 Dec 2025·Molecular Biology Reports

Report of a novel missense TDP1 variant in a Pakistani family affected with an extremely rare disorder congenital spinocerebellar ataxia with axonal neuropathy type 1 (SCAN1)

Article

Author: Houlden, Henry ; Naeem, Muhammad ; Sayyad, Filza ; Ahmad, Riaz

AbstractBackgroundSpinocerebellar ataxia with axonal neuropathy type 1 (OMIM: 607250) is an extremely rare autosomal recessive disorder caused by a mutation in the tyrosyl-DNA phosphodiesterase 1 (TDP1) gene. Only a single missense variant (p.His493Arg) in this gene has been reported. This variant was found in three Arab families with a possible common founder effect.Methods and ResultsWe report a female patient born to a consanguineous Pakistani family segregating autosomal recessive spinocerebellar ataxia with axonal neuropathy type 1. The patient presents additional clinical features distinct from previously reported Arab families including congenital onset of the disease. We performed whole exome sequencing with the patient’s DNA and identified a novel missense variant (NC_000014.9:g.89991982C > T; p.His478Tyr) in exon 13 of the TDP1 gene. Sanger sequencing was performed to verify the autosomal recessive segregation of the p.His478Tyr variant in the family.ConclusionThe current study expands both the clinical and mutation spectrum of the TDP1 associated spinocerebellar ataxia with axonal neuropathy type 1 and increases the body of evidence that supports the pathogenic role of TDP1 in cerebellar ataxias with peripheral neuropathy.

01 Jul 2025·European Journal of Medicinal Chemistry

The Groebke–Blackburn–Bienaymé (GBB) reaction: A powerful tool for generating diverse heterocyclic scaffold libraries in anticancer drug discovery

Review

Author: Saquib, Mohammad ; Kumar, Promod ; Hussain, Mohd Kamil ; Khan, Amzad ; Ahamad, Shakir ; Khatoon, Shahnaaz ; Khan, Khizra ; Ali, Rashid

The Groebke-Blackburn-Bienaymé (GBB) reaction is a versatile multi-component (MCR) synthetic methodology that has transformed anticancer drug discovery by enabling the rapid and efficient generation of diverse heterocyclic scaffolds. These scaffolds, such as imidazo[1,2-a]pyridines, imidazo[1,2-a]pyrimidines, and their derivatives, are highly valuable moieties for targeting critical cancer pathways. The modular nature of the GBB reaction, coupled with post-reaction modifications, allows the design of compounds with tailored structures and enhanced pharmacological properties. GBB-derived compounds exhibit broad anticancer potential by modulating diverse molecular targets. These include protein kinases (e.g. Rock2, Gsk3β, B-Raf), microtubule dynamics via tubulin inhibition, and G-quadruplex DNA stabilization in oncogene promoters (e.g., c-MYC, BCL2), disrupting key mechanisms of tumour progression. Moreover, they target epigenetic regulators such as HDACs, CBP/P300 bromodomains, and BET bromodomains, affecting transcriptional regulation and chromatin remodeling. Immune checkpoints (e.g., PD-1/PD-L1), enzymes such as autotaxin, TDP1, and Hsp90, as well as apoptotic regulators (e.g., Bcl-2, BAG3), are also effectively inhibited. More importantly, these compounds address challenging targets, including KRAS G12C mutations and the menin-MLL interaction, offering solutions to previously "undruggable" pathways. The unparalleled efficiency of GBB reaction and its ability to generate structurally diverse, bioactive compounds spanning multiple oncogenic mechanisms highlights its central role in advancing anticancer drug discovery and its transformative impact on therapeutic innovation.

01 Jul 2025·Water Research

Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation

Article

Author: Zhang, Min ; Yin, Zhipeng ; Liu, Runzeng ; Cai, Yong

The extensive use of per- and polyfluoroalkyl substances (PFAS) in industrial and consumer products poses health risks due to their toxicity. Computational toxicology approaches, particularly quantitative structure-activity relationship (QSAR) models are essential for predicting PFAS bioactivity. However, established QSAR models including machine learning-based ones with traditional molecular descriptors such as constitutional, topological, and geometric descriptors, have limited predictive capability and interpretability. Herein, we proposed a novel machine learning approach that leverages quantitative molecular surface analysis (QMSA) of molecular electrostatic potential. Using QMSA descriptors, five machine learning models (e.g., random forest) achieved outstanding performance, with best accuracy of 0.950 ± 0.017, AUC-ROC of 0.938 ± 0.012, F1-score of 0.734 ± 0.024, and MCC of 0.684 ± 0.111 for five targets (tyrosyl-DNA phosphodiesterase 1 in the absence/presence of camptothecin, ATXN2 protein, transcription factor SMAD3, and transcription factor NRF2), which outperform previously reported models. SHAP analyses revealed that estimated density, molecular volume, positive surface area, and nonpolar surface area were the most important descriptors. These descriptors were deeply involved in PFAS binding to target proteins via non-covalent interactions as evidenced by molecular docking and molecular dynamics simulations. Our results demonstrated that QMSA descriptors-based machine learning models are capable of predicting PFAS toxicity with extraordinary performance and interpretability. This study provides a novel machine learning framework for the high-throughput and cost-effective screening of high-risk emerging PFAS in aquatic environments. By identifying the contaminants that should be prioritized for regulation and treatment among the growing number of PFAS, our work aids in water quality monitoring and risk assessment, and guides decision-making in aquatic environmental management. Furthermore, this work enhances our understanding of the molecular mechanisms involved in PFAS bioactivity.

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TDP1

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