Mucosal healing effect of nilotinib in indomethacin-induced enterocolitis: A rat model

Recent advance of clinically approved small-molecule drugs for the treatment of myeloid leukemia

Myeloid leukemia denotes a hematologic malignancy characterized by aberrant proliferation and impaired differentiation of blood progenitor cells within the bone marrow. Despite the availability of several treatment options, the clinical outlook for individuals afflicted with myeloid leukemia continues to be unfavorable, making it a challenging disease to manage. Over the past, substantial endeavors have been dedicated to the identification of novel targets and the advancement of enhanced therapeutic modalities to ameliorate the management of this disease, resulting in the discovery of many clinically approved small-molecule drugs for myeloid leukemia, including histone deacetylase inhibitors, hypomethylating agents, and tyrosine kinase inhibitors. This comprehensive review succinctly presents an up-to-date assessment of the application and synthetic routes of clinically sanctioned small-molecule drugs employed in the treatment of myeloid leukemia. Additionally, it provides a concise exploration of the pertinent challenges and prospects encompassing drug resistance and toxicity. Overall, this review effectively underscores the considerable promise exhibited by clinically endorsed small-molecule drugs in the therapeutic realm of myeloid leukemia, while concurrently shedding light on the prospective avenues that may shape the future landscape of drug development within this domain.

SEMCM: A Self-Expressive Matrix Completion Model for Anti-cancer Drug Sensitivity Prediction

Background:

Genomic data sets generated by several recent large scale high-throughput screening efforts pose a thorny computational challenge for anticancer drug sensitivity prediction.

Objective:

We aimed to design an algorithm model that would predict missing elements in incomplete matrices and could be applicable to drug response prediction programs.

Method:

We developed a novel self-expressive matrix completion model to improve the predictive performance of drug response prediction problems. The model is based on the idea of subspace clustering and as a convex problem, it can be solved by alternating direction method of multipliers. The original incomplete matrix can be filled through model training and parameters updated iteratively.

Results:

We applied SEMCM to Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets to predict unknown response values. A large number of experiments have proved that the algorithm has good prediction results and stability, which are better than several existing advanced drug sensitivity prediction and matrix completion algorithms. Without modeling mutation information, SEMCM could correctly predict cell line-drug associations for mutated cell lines and wild cell lines. SEMCM can also be used for drug repositioning. The newly predicted drug responses of GDSC dataset suggest that BL-41 was highly sensitive to Bortezomib. Moreover, the sensitivity of A172 and NCI-H1437 to Paclitaxel was roughly the same.

Conclusion:

We report an efficient anticancer drug sensitivity prediction algorithm which is open-source and can predict the unknown responses of cancer cell lines to drugs. Experimental results prove that our method can not only improve the prediction accuracy but also can be applied to drug repositioning.