Computational Method for Structure-Based Analysis of SAR Transfer

Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Prostate cancer is the most common type of cancer in men. The disease presents good survival rates if treated at the early stages. However, the evolution of the disease in its most aggressive variant remains without effective therapeutic answers. Therefore, the identification of novel effective therapeutics is urgently needed. On these premises, we developed a series of machine learning models, based on compounds with reported highly homogeneous cell-based antiproliferative assay data, able to predict the activity of ligands towards the PC-3 and DU-145 prostate cancer cell lines. The data employed in the development of the computational models was finely-tuned according to a series of thresholds for the classification of active/inactive compounds, to the number of features to be implemented, and by using 10 different machine learning algorithms. Models’ evaluation allowed us to identify the best combination of activity thresholds and ML algorithms for the classification of active compounds, achieving prediction performances with MCC values above 0.60 for PC-3 and DU-145 cells. Moreover, in silico models based on the combination of PC-3 and DU-145 data were also developed, demonstrating excellent precision performances. Finally, an analysis of the activity annotations reported for the ligands in the curated datasets were conducted, suggesting associations between cellular activity and biological targets that might be explored in the future for the design of more effective prostate cancer antiproliferative agents.

Targeting Acute Myelogenous Leukemia Using Potent Human Dihydroorotate Dehydrogenase Inhibitors Based on the 2-Hydroxypyrazolo[1,5-a]pyridine Scaffold: SAR of the Aryloxyaryl Moiety

In recent years, human dihydroorotate dehydrogenase inhibitors have been associated with acute myelogenous leukemia as well as studied as potent host targeting antivirals. Starting from MEDS433 (IC50 1.2 nM), we kept improving the structure-activity relationship of this class of compounds characterized by 2-hydroxypyrazolo[1,5-a]pyridine scaffold. Using an in silico/crystallography supported design, we identified compound 4 (IC50 7.2 nM), characterized by the presence of a decorated aryloxyaryl moiety that replaced the biphenyl scaffold, with potent inhibition and pro-differentiating abilities on AML THP1 cells (EC50 74 nM), superior to those of brequinar (EC50 249 nM) and boosted when in combination with dipyridamole. Finally, compound 4 has an extremely low cytotoxicity on non-AML cells as well as MEDS433; it has shown a significant antileukemic activity in vivo in a xenograft mouse model of AML.

Computational method for the systematic alignment of analogue series with structure-activity relationship transfer potential across different targets

Lead optimization focuses on the generation of analogue series (ASs) with sustainable structure-activity relationship (SAR) progression. If roadblocks are encountered during multi-property optimization, it is often desirable to replace an AS with another containing a different core structure but having similar SAR characteristics for a given target. This process represents target-based SAR transfer. A previously unexplored question is to what extent AS-based SAR transfer events might also occur across different targets. To address this question, we have developed and applied a new computational approach to systematically search for ASs with SAR transfer potential and align qualifying series in a chemically intuitive way. The methodology relies on fragment similarity scoring in combination with dynamic programming. Our large-scale analysis has revealed that SAR transfer events across different targets are more frequently observed than one might expect, providing many opportunities for the design of new SAR transfer analogues for evolving series.

Targeting Acute Myelogenous Leukemia Using Potent Human Dihydroorotate Dehydrogenase Inhibitors Based on the 2-Hydroxypyrazolo[1,5-a]pyridine Scaffold: SAR of the Biphenyl Moiety

The connection with acute myelogenous leukemia (AML) of dihydroorotate dehydrogenase (hDHODH), a key enzyme in pyrimidine biosynthesis, has attracted significant interest from pharma as a possible AML therapeutic target. We recently discovered compound 1, a potent hDHODH inhibitor (IC50 = 1.2 nM), able to induce myeloid differentiation in AML cell lines (THP1) in the low nM range (EC50 = 32.8 nM) superior to brequinar's phase I/II clinical trial (EC50 = 265 nM). Herein, we investigate the 1 drug-like properties observing good metabolic stability and no toxic profile when administered at doses of 10 and 25 mg/kg every 3 days for 5 weeks (Balb/c mice). Moreover, in order to identify a backup compound, we investigate the SAR of this class of compounds. Inside the series, 17 is characterized by higher potency in inducing myeloid differentiation (EC50 = 17.3 nM), strong proapoptotic properties (EC50 = 20.2 nM), and low cytotoxicity toward non-AML cells (EC30(Jurkat) > 100 μM).