Economic Model to Evaluate the Cost-Effectiveness of Second-Line Nilotinib Versus Dasatinib for the Treatment of Philadelphia Chromosome-Positive Chronic Myeloid Leukemia (CML-CP) in Italy
Abstract:Objective The aim of this study was to evaluate the cost effectiveness of second-line nilotinib versus dasatinib for the treatment of Philadelphia chromosome-positive chronic myeloid leukemia (CML-CP) patients who are intolerant or resistant to imatinib and can transition to treatment-free remission (TFR). Methods A partitioned survival model was developed to compare the cost effectiveness of nilotinib versus dasatinib. The model was developed from the Italian healthcare payer perspective and included the foll… Show more
“…Furthermore, dasatinib binds to the kinases and prevents them from stimulating growth and is also administered as a treatment. Dasatinib and bosutinib are both regarded as the second line in therapy [ 19 ]. Additionally, nilotinib treatment is linked to the transitory increase in serum aminotransferase levels and few incidences of clinically evident acute liver damage [ 20 ].…”
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.
“…Furthermore, dasatinib binds to the kinases and prevents them from stimulating growth and is also administered as a treatment. Dasatinib and bosutinib are both regarded as the second line in therapy [ 19 ]. Additionally, nilotinib treatment is linked to the transitory increase in serum aminotransferase levels and few incidences of clinically evident acute liver damage [ 20 ].…”
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.
“…To the best of our knowledge, to date there are no such analyses performed for Italy, as studies are mainly focused on cost-effectiveness analysis estimated by comparing specific TKIs in second or third line based, however, on models and on hypothetical cohorts whose characteristics are assumed by data from clinical trials [ 15 , 16 ].…”
Introduction
Chronic myeloid leukemia (CML) is a hematopoietic myeloproliferative disorder that accounts for 20% of all leukemias of adults. The introduction of tyrosine kinase inhibitors (TKIs) (imatinib, bosutinib, dasatinib, nilotinib, ponatinib) has yielded significant benefits for patients with CML in terms of survival and quality of life. This real-world analysis evaluated the economic burden for managing patients with CML in 2nd or ≥ 3rd TKI lines in Italian settings of clinical practice.
Methods
A retrospective observational analysis was performed exploiting the administrative databases of a sample of entities covering around 15 million inhabitants. From 2015 to 2018, the study included adult patients with at least one prescription for TKIs, (and for some TKI with at least one hospitalization discharge diagnosis for CML, or at least one prescription for BCR–ABL examination). The index date was the first TKI prescription. Healthcare resource consumption and costs for patients with CML in 2nd and ≥ 3rd line treatment with TKIs were analyzed for drug prescriptions, hospitalizations, specialist visits, and diagnostic services.
Results
In total 635 patients were included, 491 in 2nd line and 144 in 3rd line with TKIs. Dasatinib was the most frequently prescribed drug in 2nd line (28.9%) and imatinib in later lines (26.4%). With progressing lines of treatment, healthcare consumption showed a trend towards increased non-TKI prescriptions per patient (8 for 2nd line and 9.7 for ≥ 3rd line). The management of patients with CML in later lines resulted in increased overall healthcare burden, with hospitalizations accounting for about half of total expenditure, whatever the treatment line and type of TKI.
Conclusions
This analysis in Italian real-life clinical practice reported economic expenditure for patients with CML in 2nd or ≥ 3rd lines with TKIs, mostly burdened by hospitalizations. Such clinical complexity suggests that further efforts are needed to improve the therapeutic management of later lines of CML.
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