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.

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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.

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 19 F 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.

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.

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.

Epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy in the western world, has been historically treated with surgery followed by chemotherapy. Poly (ADP-ribose) polymerase (PARP) inhibitors are one of the most active new targeted therapies for the treatment of EOC. PARPis’ mechanism of action relies on their ability to interfere with DNA repair events leading ultimately to cell death, the biological concept known as synthetic lethality. Initially developed as maintenance therapy in patients with a response after platinum-based chemotherapy in a recurrent setting, PARPis are now approved as the frontline treatment strategy. The aim of this chapter is to examine PARPis’ antineoplastic activity and the clinical development studies that lead to their approval, as well as the safety and the management of adverse events associated with this new class of drugs. Lastly, the rational considerations for the use of PARPis in the frontline setting are discussed.