BCR-ABL1 is a fusion
protein as a result of a unique
chromosomal translocation (producing the so-called
Philadelphia chromosome) that serves as a clinical
biomarker primarily for
chronic myeloid leukemia (CML); the
Philadelphia chromosome also occurs, albeit rather rarely, in other types of
leukemia. This fusion
protein has proven itself to be a promising therapeutic target. Exploiting the natural
vitamin E molecule
gamma-tocotrienol as a BCR-ABL1 inhibitor with deep learning artificial intelligence (AI)
drug design, this study aims to overcome the present toxicity that embodies the currently provided medications for (Ph+)
leukemia, especially
asciminib.
Gamma-tocotrienol was employed in an AI server for
drug design to construct three effective de novo
drug compounds for the BCR-ABL1 fusion
protein. The AIGT's (Artificial Intelligence
Gamma-Tocotrienol)
drug-likeliness analysis among the three led to its nomination as a target possibility. The toxicity assessment research comparing AIGT and
asciminib demonstrates that AIGT, in addition to being more effective nonetheless, is also hepatoprotective. While almost all CML patients can achieve remission with
tyrosine kinase inhibitors (such as
asciminib), they are not cured in the strict sense. Hence it is important to develop new avenues to treat CML. We present in this study new formulations of AIGT. The docking of the AIGT with BCR-ABL1 exhibited a binding affinity of -7.486 kcal/mol, highlighting the AIGT's feasibility as a
pharmaceutical option. Since current medical care only exclusively cures a small number of patients of CML with utter toxicity as a pressing consequence, a new possibility to tackle adverse instances is therefore presented in this study by new formulations of natural compounds of
vitamin E,
gamma-tocotrienol, thoroughly designed by AI. Even though AI-designed AIGT is effective and adequately safe as computed, in vivo testing is mandatory for the verification of the in vitro results.