In silico screening combined with bioactivity evaluation to identify AMI-1 as a novel anticancer compound by targeting AXL

Fused in silico and bioactivity evaluation method for drug discovery: T001-10027877 was identified as an antiproliferative agent that targets EGFRT790M/C797S/L858R and EGFRT790M/L858R

BACKGROUND

Facing the significant challenge of overcoming drug resistance in cancer treatment, particularly resistance caused by mutations in epidermal growth factor receptor (EGFR), the aim of our study was to identify potent EGFR inhibitors effective against the T790M/C797S/L858R mutant, a key player in resistance mechanisms.

METHODS

Our integrated in silico approach harnessed machine learning, virtual screening, and activity evaluation techniques to screen 5105 compounds from three libraries, aiming to find candidates capable of overcoming the resistance conferred by the T790M and C797S mutations within EGFR. This methodical process narrowed the search down to six promising compounds for further examination.

RESULTS

Kinase assays identified three compounds to which the T790M/C797S/L858R mutant exhibited increased sensitivity compared to the T790M/L858R mutant, highlighting the potential efficacy of these compounds against resistance mechanisms. Among them, T001-10027877 exhibited dual inhibitory effects, with IC50 values of 4.34 µM against EGFRT790M/C797S/L858R and 1.27 µM against EGFRT790M/L858R. Further investigations into the antiproliferative effects in H1975, A549, H460 and Ba/F3-EGFRL858/T790M/C797S cancer cells revealed that T001-10027877 was the most potent anticancer agent among the tested compounds. Additionally, the induction of H1975 cell apoptosis and cell cycle arrest by T001-10027877 were confirmed, elucidating its mechanism of action.

CONCLUSIONS

This study highlights the efficacy of combining computational techniques with bioactivity assessments in the quest for novel antiproliferative agents targeting complex EGFR mutations. In particular, T001-10027877 has great potential for overcoming EGFR-mediated resistance and merits further in vivo exploration. Our findings contribute valuable insights into the development of next-generation anticancer therapies, demonstrating the power of an integrated drug discovery approach.

In silico method and bioactivity evaluation to discover novel antimicrobial agents targeting FtsZ protein: Machine learning, virtual screening and antibacterial mechanism study

This research paper utilizes a fused-in-silico approach alongside bioactivity evaluation to identify active FtsZ inhibitors for drug discovery. Initially, ROC-guided machine learning was employed to obtain almost 13182 compounds from three libraries. After conducting virtual screening to assess the affinity of 2621 acquired compounds, cluster analysis and bonding model analysis led to the discovery of five hit compounds. Additionally, antibacterial activity assays and time-killing kinetics revealed that T3995 could eliminate Staphylococcus aureus ATCC6538 and Bacillus subtilis ATCC9732, with MIC values of 32 and 2 μg/mL. Further morphology and FtsZ polymerization assays indicated that T3995 could be an antimicrobial inhibitor by targeting FtsZ protein. Moreover, hemolytic toxicity evaluation demonstrated that T3995 is safe at or below 16 ug/mL concentration. Additionally, bonding model analysis explained how the compound T3995 can display antimicrobial activity by targeting the FtsZ protein. In conclusion, this study presents a promising FtsZ inhibitor that was discovered through a fused computer method and bioactivity evaluation.

T6496 targeting EGFR mediated by T790M or C797S mutant: machine learning, virtual screening and bioactivity evaluation study

Acquired resistance to EGFR is a major impediment in lung cancer treatment, highlighting the urgent need to discover novel compounds to overcome EGFR drug resistance. In this study, we utilized in silico methods and bioactivity evaluation for drug discovery to identify novel active anticancer agents targeting EGFRT790M/L858R and EGFRT790M/C797S/L858R. Firstly, we employed ROC-guided machine learning to retrieve nearly 7,765 compounds from a collection of three libraries (comprising over 220,000 compounds). Next, virtual screening, cluster analysis, and binding model analysis were employed to identify six potential compounds. Additionally, the kinase assay revealed that these six compounds demonstrated higher sensitivity to EGFR than c-Met. Among these compounds, T6496 inhibited both EGFRT790M/L858R and EGFRT790M/C797S/L858R kinases, with an IC50 of 3.30 and 8.72 μM. Furthermore, we evaluated the antitumor effects of the six selected compounds, and compound T6496 exhibited the strongest anticancer activity against H1975 cell lines, with an IC50 value of 2.7 μM. These results suggest that T6496 may mitigate EGFR resistance caused by T790M or C797S mutations. Moreover, the AO staining assay, JC-1 staining, ROS experiment and hemolytic toxicity evaluation revealed that T6496 could induce apoptosis in H1975 cell lines in a time-dependent and concentration-dependent manner, and is a potential compound for further structural optimization.Communicated by Ramaswamy H. Sarma.