Voxtalisib Hydrochloride

Osteosarcoma (OS) in pediatric, adolescent, and young adult (AYA) patients is an aggressive bone cancer with limited treatment options. Dysregulation of the CDK4/6-cyclin D axis and the PI3K/mTOR pathway contributes to OS pathogenesis, providing a biological rationale for co-targeting these signaling nodes. However, pharmacologic CDK4/6 inhibition can trigger compensatory activation of the PI3K/mTOR pathway, restoring D-type cyclin expression and partially reactivating CDK4/6 signaling. Thus, dual inhibition of the CDK4/6 and PI3K/mTOR pathways not only addresses two parallel oncogenic drivers but may also prevent potential CDK4/6 inhibitor resistance mediated by feedback activation of PI3K/mTOR. In this study, we tested the hypothesis that coordinated targeting of these pathways would improve tumor control in preclinical OS models. In vitro sensitivity analyses using palbociclib and voxtalisib demonstrated additive to synergistic OS growth suppression, with palbociclib inducing G1 arrest and senescence, and the combination enhancing autophagy. Furthermore, the efficacy, tolerability, and mechanisms of palbociclib and voxtalisib, alone or in combination, were evaluated in molecularly defined primary treatment-naïve, and relapsed/metastatic OS models. In the relapsed/metastatic PDX77-TT2 model, short-term palbociclib exposure activated PI3K/mTOR signaling, whereas the combination of palbociclib and voxtalisib in long-term studies produced marked tumor suppression and extended survival. In the primary treatment-naïve PDX96 model, long-term palbociclib exposure generated a robust CDK4/6 pharmacodynamic response. The addition of voxtalisib reinforced autophagy, sustained CDK pathway inhibition, and improved overall tumor control. In an OS lung-colonization model, CDK4/6 inhibition alone markedly reduced OS lung nodules, with combination therapy providing comparable suppression. Dual CDK4/6-PI3K/mTOR inhibition achieves tumor control across various OS models, supporting the use of genomically guided, pathway-targeted strategies for pediatric and AYA OS.

Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting for 15-20% of breast cancer cases. It is challenging due to low therapeutic response, heterogeneity, and aggressiveness. The PI3Ka isoform is a promising therapeutic target, often hyperactivated in TNBC, contributing to uncontrolled growth and cancer cell formation. We have proposed an interpretable deep convolutional neural network prediction (iDCNNPred) system using 2D molecular images to classify bioactivity and identify potential PI3Ka inhibitors. We built Custom-DCNN models and pre-trained models such as AlexNet, SqueezeNet, and VGG19 by using the Bayesian optimization algorithm, and found that our Custom-DCNN model performed better than a pre-trained model with lower complexity and memory usage. All top-performed models were screened with the Maybridge Chemical library to find predictive hit molecules. The screened molecules were further evaluated for protein-ligand interaction with molecular docking and finally 12 promising hits were shortlisted for biological validation using in-vitro PI3K inhibition studies. After biological evaluation, 4 potent molecules with different structural moieties were identified, and these molecules present new starting scaffolds for further improvement in terms of their potency and selectivity as PI3K inhibitors with the help of medicinal chemistry efforts. Furthermore, we also showed the significance of the interpretation and visualization of the model's predictions by the Grad-CAM technique, enhancing the robustness, transparency, and interpretability of the model's predictions. The data and script files and prediction run of models used for this study to reproduce the experiment are available in the GitHub repository at https://github.com/ravishankar1307/iDCNNPred.git .