Parise-Filho, R. | bioRxiv Science

bioRxiv2024

Dona Flor and her two husbands: Discovery of novel HDAC6/AKT2 inhibitors for myeloid cancer treatment

Hematological cancer treatment with hybrid kinase/HDAC inhibitors is a novel strategy to overcome the challenge of acquired resistance to drugs. We collected IC50 datasets from the ChEMBL database for 13 cancer cell lines (72 h cytotoxicity, measured by MTT), known inhibitors for 38 kinases, and 10 HDACs isoforms, that we identified by target fishing and literature review. The data was subjected to rigorous biological and chemical curation leaving the final datasets ranging from 76 to 8173 compounds depending on the target. We generated Random Forest classification models, whereby 14 showed greater than 80% predictability after 5-fold external cross-validation. We screened 30 hybrid kinase/HDAC inhibitor analogs through each of these models. Fragment-contribution maps were constructed to aid the understanding of SARs and the optimization of these compounds as selective kinase/HDAC inhibitors for cancer treatment. Among the predicted compounds, 9 representative hybrids were synthesized and subjected to biological evaluation to validate the models. We observed high hit rates after biological testing for the following models: K562 (62.5%), MV4-11 (75.0%), MM1S (100%), NB-4 (62.5%), U937 (75.0), and HDAC6 (86.0%). This aided the identification of 6b and 6k as potent anticancer inhibitors with IC50 of 0.2-0.8 {micro}M in three cancer cell lines, linked to HDAC6 inhibition below 2 nM, and blockade of AKT2 phosphorylation at 2 M, validating the ability of our models to predict novel drug candidates. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=47 SRC="FIGDIR/small/626092v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@76705dorg.highwire.dtl.DTLVardef@1cb1d32org.highwire.dtl.DTLVardef@1e9cbdforg.highwire.dtl.DTLVardef@47b819_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LINovel kinase/HDAC inhibitors for cancer treatment were found using machine learning C_LIO_LI61 QSAR models for hematological cancers and its targets were built and validated C_LIO_LIK562, MV4-11, MM1S, NB-4, U937, and HDAC6 models had hit rates above 62.5% in tests C_LIO_LI6b and 6k presented potent IC50 of 0.2-0.8 {micro}M in three cancer cell lines C_LIO_LI6b and 6k inhibited HDAC6 below 2 nM, and blockade of AKT2 phosphorylation at 2 M C_LI

cancer biology↗

bioRxiv2020

Using a fragment-based approach to identify novel chemical scaffolds targeting the dihydrofolate reductase (DHFR) from Mycobacterium tuberculosis

Dihydrofolate reductase (DHFR), a key enzyme involved in folate metabolism, is a widely explored target in the treatment of cancer, immune diseases, bacteria and protozoa infections. Although several antifolates have proved successful in the treatment of infectious diseases, none have been developed to combat tuberculosis, despite the essentiality of M. tuberculosis DHFR (MtDHFR). Herein, we describe an integrated fragment-based drug discovery approach to target MtDHFR that has identified hits with scaffolds not yet explored in any previous drug design campaign for this enzyme. The application of a SAR by catalog strategy of an in house library for one of the identified fragments has led to a series of molecules that bind MtDHFR with low micromolar affinities. Crystal structures of MtDHFR in complex with compounds of this series demonstrated a novel binding mode that differs from other DHFR antifolates, thus opening perspectives for the development of novel and relevant MtDHFR inhibitors.

biochemistry↗