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From a drug repositioning to a structure-based drug design approach to tackle acute lymphoblastic leukemia.

ABSTRACT: Cancer cells utilize the main de novo pathway and the alternative salvage pathway for deoxyribonucleotide biosynthesis to achieve adequate nucleotide pools. Deoxycytidine kinase is the rate-limiting enzyme of the salvage pathway and it has recently emerged as a target for anti-proliferative therapies for cancers where it is essential. Here, we present the development of a potent inhibitor applying an iterative multidisciplinary approach, which relies on computational design coupled with experimental evaluations. This strategy allows an acceleration of the hit-to-lead process by gradually implementing key chemical modifications to increase affinity and activity. Our lead compound, OR0642, is more than 1000 times more potent than its initial parent compound, masitinib, previously identified from a drug repositioning approach. OR0642 in combination with a physiological inhibitor of the de novo pathway doubled the survival rate in a human T-cell acute lymphoblastic leukemia patient-derived xenograft mouse model, demonstrating the proof-of-concept of this drug design strategy.

SUBMITTER: Saez-Ayala M

PROVIDER: S-EPMC10227015 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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From a drug repositioning to a structure-based drug design approach to tackle acute lymphoblastic leukemia.

Saez-Ayala Magali M Hoffer Laurent L Abel Sébastien S Ben Yaala Khaoula K Sicard Benoit B Andrieu Guillaume P GP Latiri Mehdi M Davison Emma K EK Ciufolini Marco A MA Brémond Paul P Rebuffet Etienne E Roche Philippe P Derviaux Carine C Voisset Edwige E Montersino Camille C Castellano Remy R Collette Yves Y Asnafi Vahid V Betzi Stéphane S Dubreuil Patrice P Combes Sébastien S Morelli Xavier X

Nature communications 20230529 1

Cancer cells utilize the main de novo pathway and the alternative salvage pathway for deoxyribonucleotide biosynthesis to achieve adequate nucleotide pools. Deoxycytidine kinase is the rate-limiting enzyme of the salvage pathway and it has recently emerged as a target for anti-proliferative therapies for cancers where it is essential. Here, we present the development of a potent inhibitor applying an iterative multidisciplinary approach, which relies on computational design coupled with experime ...[more]

PMID: 37248212

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Project description:Noonan and LEOPARD syndromes (NS and LS) belong to a group of related disorders called RASopathies characterized by abnormalities of multiple organs and systems including hypertrophic cardiomyopathy and dysmorphic facial features. There are no approved drugs for these two rare diseases, but it is known that a missense mutation in PTPN11 genes is associated with approximately 50% and 70% of NS and LS cases, respectively. In this study, we implemented a hybrid computational drug repositioning framework by integrating transcriptomic and structure-based approaches to explore potential treatment options for NS and LS. Specifically, disease signatures were derived from the transcriptomic profiles of human induced pluripotent stem cells (iPSCs) from NS and LS patients and reverse correlated to drug transcriptomic signatures from CMap and L1000 projects on the basis that if disease and drug transcriptomic signatures are reversely correlated, the drug has the potential to treat that disease. The compounds that were ranked top based on their transcriptomic profiles were docked to mutated and wild-type 3D structures of PTPN11 by an adjusted Induced Fit Docking (IFD) protocol. In addition, we prioritized repositioned candidates for NS and LS by a consensus ranking strategy. Network analysis and phenotypic anchoring of the transcriptomic data could discriminate the two diseases at the molecular level. Furthermore, the adjusted IFD protocol was able to recapitulate the binding specificity of potential drug candidates to mutated 3D structures, revealing the relevant amino acids. Importantly, a list of potential drug candidates for repositioning was identified including 61 for NS and 43 for LS and was further verified from literature reports and on-going clinical trials. Altogether, this hybrid computational drug repositioning approach has highlighted a number of drug candidates for NS and LS and could be applied to identifying drug candidates for other diseases as well.

Project description:BackgroundDue to advances in next generation sequencing technologies and corresponding reductions in cost, it is now attainable to investigate genome-wide gene expression and variants at a patient-level, so as to better understand and anticipate heterogeneous responses to therapy. Consequently, it is feasible to inform personalized drug treatment decisions using personal genomics data. However, these efforts are limited due to a lack of reliable computational approaches for predicting effective drugs for individual patients. The reverse gene set enrichment analysis (i.e., connectivity mapping) approach and its variants have been widely and successfully used for drug prediction. However, the performance of these methods is limited by undefined mechanism of action (MoA) of drugs and reliance on cohorts of patients rather than personalized predictions for individual patients.ResultsIn this study, we have developed and evaluated a computational approach, known as Mechanism and Drug Miner (MD-Miner), using a network-based computational approach to predict effective drugs and reveal potential drug mechanisms of action at the level of signaling pathways. Specifically, the patient-specific signaling network is constructed by integrating known disease associated genes with patient-derived gene expression profiles. In parallel, a drug mechanism of action network is constructed by integrating drug targets and z-score profiles of drug-induced gene expression (pre vs. post-drug treatment). Potentially effective candidate drugs are prioritized according to the number of common genes between the patient-specific dysfunctional signaling network and drug MoA network. We evaluated the MD-Miner method on the PC-3 prostate cancer cell line, and showed that it significantly improved the success rate of discovering effective drugs compared with the random selection, and could provide insight into potential mechanisms of action.ConclusionsThis work provides a signaling network-based drug repositioning approach. Compared with the reverse gene signature based drug repositioning approaches, the proposed method can provide clues of mechanism of action in terms of signaling transduction networks.

Dataset Information

From a drug repositioning to a structure-based drug design approach to tackle acute lymphoblastic leukemia.

Publications

From a drug repositioning to a structure-based drug design approach to tackle acute lymphoblastic leukemia.

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