A novel drug prejudice scaffold-imidazopyridine-conjugate can promote cell death in a colorectal cancer model by binding to β-catenin and suppressing the Wnt signaling pathway

INTRODUCTION

Globally, colorectal cancer (CRC) is the third most common type of cancer, and its treatment frequently includes the utilization of drugs based on antibodies and small molecules. The development of CRC has been linked to various signaling pathways, with the Wnt/β-catenin pathway identified as a key target for intervention.

OBJECTIVES

We have explored the impact of imidazopyridine-tethered chalcone-C (CHL-C) in CRC models.

METHODS

To determine the influence of CHL-C on apoptosis and autophagy, Western blot analysis, annexin V assay, cell cycle analysis, acridine orange staining, and immunocytochemistry were performed. Next, the activation of the Wnt/β-catenin signaling pathway and the anti-cancer effects of CHL-C in vivo were examined in an orthotopic HCT-116 mouse model.

RESULTS

We describe the synthesis and biological assessment of the CHL series as inhibitors of the viability of HCT-116, SW480, HT-29, HCT-15, and SNU-C2A CRC cell lines. Further biological evaluations showed that CHL-C induced apoptosis and autophagy in down-regulated β-catenin, Wnt3a, FZD-1, Axin-1, and p-GSK-3β (Ser9), and up-regulated p-GSK3β (Tyr216) and β-TrCP. In-depth analysis using structure-based bioinformatics showed that CHL-C strongly binds to β-catenin, with a binding affinity comparable to that of ICG-001, a well-known β-catenin inhibitor. Additionally, our in vivo research showed that CHL-C markedly inhibited tumor growth and triggered the activation of both apoptosis and autophagy in tumor tissues.

CONCLUSION

CHL-C is capable of inducing apoptosis and autophagy by influencing the Wnt/β-catenin signaling pathway.

Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors

Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients complying with their treatments. Accelerated pharmaceutical development and drug product approval rates can further benefit from the quantum computing (QC) technology, which will ultimately enable larger profits from patent-protected market exclusivity.Key pharma stakeholders are endorsing cutting-edge technologies based on AI and QC , covering drug discovery, preclinical and clinical development, and postapproval activities. Indeed, AI-QC applications are expected to become standard in the pharma operating model over the next 5-10 years. Generalizing scalability to larger pharmaceutical problems instead of specialization is now the main principle for transforming pharmaceutical tasks on multiple fronts, for which systematic and cost-effective solutions have benefited in areas such as molecular screening, synthetic pathway design, and drug discovery and development.The information generated by coupling the life cycle of drugs and AI and/or QC through data-driven analysis, neural network prediction, and chemical system monitoring will enable (1) better understanding of the complexity of process data, (2) streamlining the design of experiments, (3) discovering new molecular targets and materials, and also (4) planning or rethinking upcoming pharmaceutical challenges The power of AI-QC makes accessible a range of different pharmaceutical problems and their rationalization that have not been previously addressed due to a lack of appropriate analytical tools, demonstrating the breadth of potential applications of these emerging multidimensional approaches. In this context, creating the right AI-QC strategy often involves a steep learning path, especially given the embryonic stage of the industry development and the relative lack of case studies documenting success. As such, a comprehensive knowledge of the underlying pillars is imperative to extend the landscape of applications across the drug life cycle.The topics enclosed in this chapter will focus on AI-QC methods applied to drug discovery and development, with emphasis on the most recent advances in this field.