An 19F NMR fragment-based approach for the discovery and development of BRCA2-RAD51 inhibitors to pursuit synthetic lethality in combination with PARP inhibition in pancreatic cancer.

The BRCA2-RAD51 interaction remains an intriguing target for cancer drug discovery due to its vital role in DNA damage repair mechanisms, which cancer cells become particularly reliant on. Moreover, RAD51 has many synthetically lethal partners, including PARP1-2, which can be exploited to induce synthetic lethality in cancer. In this study, we established a 19F-NMR-fragment based approach to identify RAD51 binders, leading to two initial hits. A subsequent SAR program identified 46 as a low micromolar inhibitor of the BRCA2-RAD51 interaction. 46 was tested in different pancreatic cancer cell lines, to evaluate its ability to inhibit the homologous recombination DNA repair pathway, mediated by BRCA2-RAD51 and trigger synthetic lethality in combination with the PARP inhibitor talazoparib, through the induction of apoptosis. Moreover, we further analyzed the 46/talazoparib combination in 3D pancreatic cancer models. Overall, 46 showed its potential as a tool to evaluate the RAD51/PARP1-2 synthetic lethality mechanism, along with providing a prospect for further inhibitors development.

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA.

RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

Identification of RAD51-BRCA2 Inhibitors Using N-Acylhydrazone-Based Dynamic Combinatorial Chemistry.

RAD51 is an ATP-dependent recombinase, recruited by BRCA2 to mediate DNA double-strand breaks repair through homologous recombination and represents an attractive cancer drug target. Herein, we applied for the first-time protein-templated dynamic combinatorial chemistry on RAD51 as a hit identification strategy. Upon design of N-acylhydrazone-based dynamic combinatorial libraries, RAD51 showed a clear templating effect, amplifying 19 N-acylhydrazones. Screening against the RAD51-BRCA2 protein-protein interaction via ELISA assay afforded 10 inhibitors in the micromolar range. Further 19F NMR experiments revealed that 7 could bind RAD51 and be displaced by BRC4, suggesting an interaction in the same binding pocket of BRCA2. These results proved not only that ptDCC could be successfully applied on full-length oligomeric RAD51, but also that it could address the need of alternative strategies toward the identification of small-molecule PPI inhibitors.

Inhibition of Urease, a Ni-Enzyme: The Reactivity of a Key Thiol With Mono- and Di-Substituted Catechols Elucidated by Kinetic, Structural, and Theoretical Studies.

The inhibition of urease from Sporosarcina pasteurii (SPU) and Canavalia ensiformis (jack bean, JBU) by a class of six aromatic poly-hydroxylated molecules, namely mono- and dimethyl-substituted catechols, was investigated on the basis of the inhibitory efficiency of the catechol scaffold. The aim was to probe the key step of a mechanism proposed for the inhibition of SPU by catechol, namely the sulfanyl radical attack on the aromatic ring, as well as to obtain critical information on the effect of substituents of the catechol aromatic ring on the inhibition efficacy of its derivatives. The crystal structures of all six SPU-inhibitors complexes, determined at high resolution, as well as kinetic data obtained on JBU and theoretical studies of the reaction mechanism using quantum mechanical calculations, revealed the occurrence of an irreversible inactivation of urease by means of a radical-based autocatalytic multistep mechanism, and indicate that, among all tested catechols, the mono-substituted 3-methyl-catechol is the most efficient inhibitor for urease.

Synthetic Lethality in Pancreatic Cancer: Discovery of a New RAD51-BRCA2 Small Molecule Disruptor That Inhibits Homologous Recombination and Synergizes with Olaparib.

Synthetic lethality is an innovative framework for discovering novel anticancer drug candidates. One example is the use of PARP inhibitors (PARPi) in oncology patients with BRCA mutations. Here, we exploit a new paradigm based on the possibility of triggering synthetic lethality using only small organic molecules (dubbed "fully small-molecule-induced synthetic lethality"). We exploited this paradigm to target pancreatic cancer, one of the major unmet needs in oncology. We discovered a dihydroquinolone pyrazoline-based molecule (35d) that disrupts the RAD51-BRCA2 protein-protein interaction, thus mimicking the effect of BRCA2 mutation. 35d inhibits the homologous recombination in a human pancreatic adenocarcinoma cell line. In addition, it synergizes with olaparib (a PARPi) to trigger synthetic lethality. This strategy aims to widen the use of PARPi in BRCA-competent and olaparib-resistant cancers, making fully small-molecule-induced synthetic lethality an innovative approach toward unmet oncological needs.

Rad51/BRCA2 disruptors inhibit homologous recombination and synergize with olaparib in pancreatic cancer cells.

Olaparib is a PARP inhibitor (PARPi). For patients bearing BRCA1 or BRCA2 mutations, olaparib is approved to treat ovarian cancer and in clinical trials to treat breast and pancreatic cancers. In BRCA2-defective patients, PARPi inhibits DNA single-strand break repair, while BRCA2 mutations hamper double-strand break repair. Recently, we identified a series of triazole derivatives that mimic BRCA2 mutations by disrupting the Rad51-BRCA2 interaction and thus double-strand break repair. Here, we have computationally designed, synthesized, and tested over 40 novel derivatives. Additionally, we designed and conducted novel biological assays to characterize how they disrupt the Rad51-BRCA2 interaction and inhibit double-strand break repair. These compounds synergized with olaparib to target pancreatic cancer cells with functional BRCA2. This supports the idea that small organic molecules can mimic genetic mutations to improve the profile of anticancer drugs for precision medicine. Moreover, this paradigm could be exploited in other genetic pathways to discover innovative anticancer targets and drug candidates.