Sponge-derived alkaloid AP-7 as a sensitizer to cisplatin in the treatment of multidrug-resistant NSCLC via Chk1-dependent mechanisms.

Multidrug resistance is a substantial obstacle in treating non-small cell lung cancer (NSCLC) with therapies like cisplatin (DDP)-based adjuvant chemotherapy and EGFR-tyrosine kinase inhibitors (TKIs). Aaptamine-7 (AP-7), a benzonaphthyridine alkaloid extracted from Aaptos aaptos sponge, has been shown to exhibit a broad spectrum of anti-tumor activity. However, the anti-cancer activity of AP-7 in combination with DDP and its molecular mechanisms in multidrug-resistant NSCLC are not yet clear. Our research indicates that AP-7 bolsters the growth inhibition activity of DDP on multidrug-resistant NSCLC cells. AP-7 notably disrupts DDP-induced cell cycle arrest and amplifies DDP-induced DNA damage effects in these cells. Furthermore, the combination of AP-7 and DDP downregulates Chk1 activation, interrupts the DNA damage repair-dependent Chk1/CDK1 pathway, and helps to overcome drug resistance and boost apoptosis in multidrug-resistant NSCLC cells and a gefitinib-resistant xenograft mice model. In summary, AP-7 appears to enhance DDP-induced DNA damage by impeding the Chk1 signaling pathway in multidrug-resistant NSCLC, thereby augmenting growth inhibition, both in vitro and in vivo. These results indicate the potential use of AP-7 as a DDP sensitizer in the treatment of multidrug-resistant NSCLC.

Data considerations for predictive modeling applied to the discovery of bioactive natural products.

Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the large amount of generated data is complex and difficult to analyze effectively. This limitation is increasingly surmounted by artificial intelligence (AI) techniques but more needs to be done. Here, we present and discuss two crucial issues limiting NP-AI drug discovery: the first is on knowledge and resource development (data integration) to bridge the gap between NPs and functional or therapeutic effects. The second issue is on NP-AI modeling considerations, limitations and challenges.