Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia

Tyrosine Kinase Inhibitor Lenvatinib Based Nano Formulations and Cutting-Edge Scale-Up Technologies in revolutionizing Cancer Therapy

Lenvatinib (LEN), a tyrosine kinase inhibitor, has emerged as a promising therapeutic agent for various solid tumors. Nevertheless, a number of constraints, including diminished bioavailability, incapacity to elicit localized inflammation, and inability to selectively accumulate at the tumor site, may impede the comprehensive exploitation of its versatile tyrosine kinase inhibitory capabilities. In order to achieve targeted delivery of LEN while also reducing its high dose used in conventional therapeutics, nanoformulation approaches can be adopted. The integration of LEN into various nanoformulations, such as nanoparticles, nanocrystals, high density lipoproteins (HDLs), liposomes, and micelles, is discussed, highlighting the advantages of these innovative approaches in a comparative manner; however, given that the current methods of nanoformulation synthesis employ toxic organic solvents and chemicals, there is an imperative need for exploring alternative, environmentally friendly approaches. The multifaceted effects of nanocarriers have rendered them profoundly applicable within the biomedical domain, serving as instrumental entities in various capacities such as vehicles for drug delivery and genetic material, diagnostic agents, facilitators of photothermal therapy, and radiotherapy. However, the scalability of these nanotechnological methodologies must be rigorously investigated and addressed to refine drug delivery mechanisms. This endeavor offers promising prospects for revolutionizing strategies in cancer therapeutics, thereby laying the foundation for future research in scale-up techniques in the pursuit of more effective and less toxic therapies for cancer.

Utilization of Machine Learning in the Prediction, Diagnosis, Prognosis, and Management of Chronic Myeloid Leukemia

Chronic myeloid leukemia is a clonal hematologic disease characterized by the presence of the Philadelphia chromosome and the BCR::ABL1 fusion protein. Integrating different molecular, genetic, clinical, and laboratory data would improve the diagnostic, prognostic, and predictive sensitivity of chronic myeloid leukemia. However, without artificial intelligence support, managing such a vast volume of data would be impossible. Considering the advancements and growth in machine learning throughout the years, several models and algorithms have been proposed for the management of chronic myeloid leukemia. Here, we provide an overview of recent research that used specific algorithms on patients with chronic myeloid leukemia, highlighting the potential benefits of adopting machine learning in therapeutic contexts as well as its drawbacks. Our analysis demonstrated the great potential for advancing precision treatment in CML through the combination of clinical and genetic data, laboratory testing, and machine learning. We can use these powerful research instruments to unravel the molecular and spatial puzzles of CML by overcoming the current obstacles. A new age of patient-centered hematology care will be ushered in by this, opening the door for improved diagnosis accuracy, sophisticated risk assessment, and customized treatment plans.

A pectic polysaccharide from Typhonii Rhizoma: Characterization and antiproliferative activity in K562 cells through regulating mitochondrial function and energy metabolism

The pectic polysaccharide WTRP-A0.2b (43 kDa) has been isolated from Typhonii rhizoma and analyzed in terms of its structural features, anti-tumor activities and mechanism of action. NMR, FT-IR, monosaccharide composition, and enzymology demonstrate that WTRP-A0.2b is composed of rhamnogalacturonan I (RG-I), rhamnogalacturonan II (RG-II) and homogalacturonan (HG) domains with mass ratios of 3.7:1:1.7, respectively. The RG-I domains contain a highly branched structure that is substituted primarily with β-D-1,4-galactan, α-L-1,5-arabinan, and AG-II. The HG domains contain un-esterified and methyl-esterified and/or acetyl-esterified oligogalacturonides with a degree of polymerization of 1-8. In vitro experiments demonstrate that WTRP-A0.2b inhibits proliferation of K562 cells by inducing mitochondrial damage and suppressing glycolysis. This activity promotes mitochondrial permeability, increases production of reactive oxygen species (ROS), boosts extracellular oxygen consumption and adenosine triphosphate (ATP) content, while it decreases uncoupling protein-2 (UCP2) expression and lactic acid content. Our results provide valuable insight for screening natural polysaccharide-based anti-tumor effects of polysaccharides from Typhonii rhizoma.

Artificial Intelligence-Assisted Comparative Analysis of the Overlapping Molecular Pathophysiology of Alzheimer's Disease, Amyotrophic Lateral Sclerosis, and Frontotemporal Dementia

The overlapping molecular pathophysiology of Alzheimer's Disease (AD), Amyotrophic Lateral Sclerosis (ALS), and Frontotemporal Dementia (FTD) was analyzed using relationships from a knowledge graph of 33+ million biomedical journal articles. The unsupervised learning rank aggregation algorithm from SemNet 2.0 compared the most important amino acid, peptide, and protein (AAPP) nodes connected to AD, ALS, or FTD. FTD shared 99.9% of its nodes with ALS and AD; AD shared 64.2% of its nodes with FTD and ALS; and ALS shared 68.3% of its nodes with AD and FTD. The results were validated and mapped to functional biological processes using supervised human supervision and an external large language model. The overall percentages of mapped intersecting biological processes were as follows: inflammation and immune response, 19%; synapse and neurotransmission, 19%; cell cycle, 15%; protein aggregation, 12%; membrane regulation, 11%; stress response and regulation, 9%; and gene regulation, 4%. Once normalized for node count, biological mappings for cell cycle regulation and stress response were more prominent in the intersection of AD and FTD. Protein aggregation, gene regulation, and energetics were more prominent in the intersection of ALS and FTD. Synapse and neurotransmission, membrane regulation, and inflammation and immune response were greater at the intersection of AD and ALS. Given the extensive molecular pathophysiology overlap, small differences in regulation, genetic, or environmental factors likely shape the underlying expressed disease phenotype. The results help prioritize testable hypotheses for future clinical or experimental research.

Asciminib in Newly Diagnosed Chronic Myeloid Leukemia

BACKGROUND

Patients with newly diagnosed chronic myeloid leukemia (CML) need long-term therapy with high efficacy and safety. Asciminib, a BCR::ABL1 inhibitor specifically targeting the ABL myristoyl pocket, may offer better efficacy and safety and fewer side effects than currently available frontline ATP-competitive tyrosine kinase inhibitors (TKIs).

METHODS

In a phase 3 trial, patients with newly diagnosed CML were randomly assigned in a 1:1 ratio to receive either asciminib (80 mg once daily) or an investigator-selected TKI, with randomization stratified by European Treatment and Outcome Study long-term survival score category (low, intermediate, or high risk) and by TKI selected by investigators before randomization (including imatinib and second-generation TKIs). The primary end points were major molecular response (defined as BCR::ABL1 transcript levels ≤0.1% on the International Scale [IS]) at week 48, for comparisons between asciminib and investigator-selected TKIs and between asciminib and investigator-selected TKIs in the prerandomization-selected imatinib stratum.

RESULTS

A total of 201 patients were assigned to receive asciminib and 204 to receive investigator-selected TKIs. The median follow-up was 16.3 months in the asciminib group and 15.7 months in the investigator-selected TKI group. A major molecular response at week 48 occurred in 67.7% of patients in the asciminib group, as compared with 49.0% in the investigator-selected TKI group (difference, 18.9 percentage points; 95% confidence interval [CI], 9.6 to 28.2; adjusted two-sided P<0.001]), and in 69.3% of patients in the asciminib group as compared with 40.2% in the imatinib group within the imatinib stratum (difference, 29.6 percentage points; 95% CI, 16.9 to 42.2; adjusted two-sided P<0.001). The percentage of patients with a major molecular response at week 48 was 66.0% with asciminib and 57.8% with TKIs in the second-generation TKI stratum (difference, 8.2 percentage points; 95% CI, -5.1 to 21.5). Adverse events of grade 3 or higher and events leading to discontinuation of the trial regimen were less frequent with asciminib (38.0% and 4.5%, respectively) than with imatinib (44.4% and 11.1%) and second-generation TKIs (54.9% and 9.8%).

CONCLUSIONS

In this trial comparing asciminib with investigator-selected TKIs and imatinib, asciminib showed superior efficacy and a favorable safety profile in patients with newly diagnosed chronic-phase CML. Direct comparison between asciminib and second-generation TKIs was not a primary objective. (Funded by Novartis; ASC4FIRST ClinicalTrials.gov number, NCT04971226).

Real-World Disproportionality Analysis of the Food and Drug Administration Adverse Event Reporting System Database for Asciminib

INTRODUCTION

Asciminib is primarily utilized for treating Philadelphia chromosome-positive chronic myeloid leukemia in its chronic phase among patients harboring the T315I mutation or those who have been previously treated with at least two tyrosine kinase inhibitors. The safety profile of asciminib across a broad patient population over an extended timeframe remains unverified. This study uses a real-world pharmacovigilance database to evaluate the adverse events (AEs) linked with asciminib, providing valuable insights for clinical drug safety.

METHODS

Data from the FDA Adverse Event Reporting System (FAERS) database, spanning from October 2021 to December 2023, served as the basis for this analysis. The extent of disproportional events was assessed using sophisticated metrics such as the reporting odds ratio, proportional reporting ratio, information component, and empirical Bayesian geometric mean.

RESULTS

Within the specified period, the FAERS database documented 3,913,574 AE reports, with asciminib being associated with 966 incidents. Reactions to asciminib spanned 27 system organ categories. Utilizing four distinct analytical algorithms, 663 significant preferred terms exhibiting disproportional frequencies were identified. Notably, this investigation uncovered 26 significant AEs linked to off-label asciminib use, encompassing conditions such as gynecomastia, nephrotic syndrome, orchitis, pyelonephritis, hepatotoxicity, and pancreatitis. The median onset time for asciminib-related AEs was 52.5 days, ranging from 17 to 122.75 days.

CONCLUSION

The study sheds light on additional potential AEs associated with asciminib use, warranting further research to confirm these findings.

Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. This study's goal was to identify the signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via artificial intelligence-enabled literature-based discovery. Cross-domain text mining of 33+ million PubMed articles was performed with SemNet 2.0 to identify and rank multi-scalar and multi-factorial pathophysiological concepts related to DKD. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersected three domains-DKD, the immune response, and glomerular endothelial cells. The top 10% of ranked concepts were mapped to the following biological functions: angiogenesis, apoptotic processes, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, the nitric oxide response, oxidative stress, the cytokine response, macrophage signaling, NFκB factor activity, the TLR pathway, glucose metabolism, the inflammatory response, the ERK/MAPK signaling response, the JAK/STAT pathway, the T-cell-mediated response, the WNT/β-catenin pathway, the renin-angiotensin system, and NADPH oxidase activity. High-ranking genes and proteins were used to generate a protein-protein interaction network. The study results prioritized interactions or molecules involved in dysregulated signaling in DKD, which can be further assessed through biochemical network models or experiments.

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either "high risk" or "low risk" in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.

Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching "chronic myeloid leukemia" and "artificial intelligence". The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the 'human' factor, which remains key in this domain.

Tyrosine Kinase Inhibitors and Thyroid Toxicity

Some multithyrosine kinase inhibitors have been reported to cause changes in thyroid function. For the management of sunitinib-induced hypothyroidism, an evaluation of thyroid hormone and antibody profile is recommended before starting treatment with tyrosine kinase inhibitors. Patients with pre-existing thyroid dysfunction should undergo dose adjustment of L-thyroxine during treatment with tyrosine kinase inhibitors. Thyroid dysfunction is not a reason to discontinue or reduce the dosage of sunitinib. Their occurrence appears to correlate with increased antitumour efficacy of the inhibitor. There are currently no guidelines for monitoring thyroid activity during treatment with TKIs, and the time interval at which TSH should be periodically measured has not yet been determined. A reasonable approach is to monitor thyroid function, both before and during 2-4 weeks after the end of therapy. A comprehensive analysis of adverse events associated with the use of these inhibitors could help clinical monitoring of patients along with the adoption of appropriate management approaches.

Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19

Multiple studies have reported new or exacerbated persistent or resistant hypertension in patients previously infected with COVID-19. We used literature-based discovery to identify and prioritize multi-scalar explanatory biology that relates resistant hypertension to COVID-19. Cross-domain text mining of 33+ million PubMed articles within a comprehensive knowledge graph was performed using SemNet 2.0. Unsupervised rank aggregation determined which concepts were most relevant utilizing the normalized HeteSim score. A series of simulations identified concepts directly related to COVID-19 and resistant hypertension or connected via one of three renin-angiotensin-aldosterone system hub nodes (mineralocorticoid receptor, epithelial sodium channel, angiotensin I receptor). The top-ranking concepts relating COVID-19 to resistant hypertension included: cGMP-dependent protein kinase II, MAP3K1, haspin, ral guanine nucleotide exchange factor, N-(3-Oxododecanoyl)-L-homoserine lactone, aspartic endopeptidases, metabotropic glutamate receptors, choline-phosphate cytidylyltransferase, protein tyrosine phosphatase, tat genes, MAP3K10, uridine kinase, dicer enzyme, CMD1B, USP17L2, FLNA, exportin 5, somatotropin releasing hormone, beta-melanocyte stimulating hormone, pegylated leptin, beta-lipoprotein, corticotropin, growth hormone-releasing peptide 2, pro-opiomelanocortin, alpha-melanocyte stimulating hormone, prolactin, thyroid hormone, poly-beta-hydroxybutyrate depolymerase, CR 1392, BCR-ABL fusion gene, high density lipoprotein sphingomyelin, pregnancy-associated murine protein 1, recQ4 helicase, immunoglobulin heavy chain variable domain, aglycotransferrin, host cell factor C1, ATP6V0D1, imipramine demethylase, TRIM40, H3C2 gene, COL1A1+COL1A2 gene, QARS gene, VPS54, TPM2, MPST, EXOSC2, ribosomal protein S10, TAP-144, gonadotropins, human gonadotropin releasing hormone 1, beta-lipotropin, octreotide, salmon calcitonin, des-n-octanoyl ghrelin, liraglutide, gastrins. Concepts were mapped to six physiological themes: altered endocrine function, 23.1%; inflammation or cytokine storm, 21.3%; lipid metabolism and atherosclerosis, 17.6%; sympathetic input to blood pressure regulation, 16.7%; altered entry of COVID-19 virus, 14.8%; and unknown, 6.5%.

Hematological Adverse Events with Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia: A Systematic Review with Meta-Analysis

Chronic myeloid leukemia (CML) is treated with tyrosine kinase inhibitors (TKI) that target the pathological BCR-ABL1 fusion oncogene. The objective of this statistical meta-analysis was to assess the prevalence of other hematological adverse events (AEs) that occur during or after predominantly first-line treatment with TKIs. Data from seventy peer-reviewed, published studies were included in the analysis. Hematological AEs were assessed as a function of TKI drug type (dasatinib, imatinib, bosutinib, nilotinib) and CML phase (chronic, accelerated, blast). AE prevalence aggregated across all severities and phases was significantly different between each TKI (p < 0.05) for anemia-dasatinib (54.5%), bosutinib (44.0%), imatinib (32.8%), nilotinib (11.2%); neutropenia-dasatinib (51.2%), imatinib (29.8%), bosutinib (14.1%), nilotinib (14.1%); thrombocytopenia-dasatinib (62.2%), imatinib (30.4%), bosutinib (35.3%), nilotinib (22.3%). AE prevalence aggregated across all severities and TKIs was significantly (p < 0.05) different between CML phases for anemia-chronic (28.4%), accelerated (66.9%), blast (55.8%); neutropenia-chronic (26.7%), accelerated (63.8%), blast (36.4%); thrombocytopenia-chronic (33.3%), accelerated (65.6%), blast (37.9%). An odds ratio (OR) with 95% confidence interval was used to compare hematological AE prevalence of each TKI compared to the most common first-line TKI therapy, imatinib. For anemia, dasatinib OR = 1.65, [1.51, 1.83]; bosutinib OR = 1.34, [1.16, 1.54]; nilotinib OR = 0.34, [0.30, 0.39]. For neutropenia, dasatinib OR = 1.72, [1.53, 1.92]; bosutinib OR = 0.47, [0.38, 0.58]; nilotinib OR = 0.47, [0.42, 0.54]. For thrombocytopenia, dasatinib OR = 2.04, [1.82, 2.30]; bosutinib OR = 1.16, [0.97, 1.39]; nilotinib OR = 0.73, [0.65, 0.82]. Nilotinib had the greatest fraction of severe (grade 3/4) hematological AEs (30%). In conclusion, the overall prevalence of hematological AEs by TKI type was: dasatinib > bosutinib > imatinib > nilotinib. Study limitations include inability to normalize for dosage and treatment duration.

Safety and efficacy of Vitamin D3 supplementation with Imatinib in Chronic Phase- Chronic Myeloid Leukaemia: an Exploratory Randomized Controlled Trial

OBJECTIVES

The study aimed to compare early molecular response (EMR) rates at 3 months of imatinib therapy with and without vitamin D3 supplementation in patients newly diagnosed with chronic-phase chronic myeloid leukaemia (CML-CP). The secondary objective was to assess the effects of vitamin D3 on complete haematological response (CHR) and its safety.

DESIGN

Double-blind, placebo-controlled, exploratory randomised trial.

SETTING

Tertiary care hospital in northern India.

PARTICIPANTS

Treatment-naive patients with chronic phase chronic myeloid leukaemia (n=62) aged >12 years were recruited from January 2020 to January 2021. Patients with progressive disease, pregnancy and hypercalcaemia were excluded.

INTERVENTION

Oral vitamin D3 supplementation (60 000 IU) or matched placebo was given once weekly for an initial 8 weeks along with imatinib after randomisation with 1:1 allocation ratio.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was to compare EMR (defined as BCR-ABL1 transcript level ≤10%, international scale) at 3 months. The secondary outcomes were to compare effect of the intervention on CHR, correlation of 25(OH)2D3 levels with treatment response and safety according to Common Terminology Criteria for Adverse Events (CTCAE) version 5.

RESULTS

At baseline, 14.5% of the patients had normal vitamin D3 levels. EMR at 3 months was attained in 24 patients (82.7%) of the vitamin D3 group and 21 (75%) of the placebo group (OR 1.6, 95% CI 0.37 to 7.37, p=0.4). A significant difference in vitamin D3 levels from baseline to the end of study was observed. Patients with vitamin D3 supplementation did not achieve higher CHR in comparison with placebo (OR 1.3, 95% CI 0.25 to 7.23, p=1.0). Vitamin D3 levels were not significantly correlated with BCR-ABL1 levels. No dose-limiting toxicities were observed.

CONCLUSION

Vitamin D3 levels were low among patients with CML-CP in this study. Vitamin D3 supplementation with imatinib therapy did not have significant effect on EMR or CHR. Further clinical trials could be undertaken to assess the effective dosage and duration of vitamin D3 supplementation in these patients.

TRIAL REGISTRATION NUMBER

CTRI/2019/09/021164.

Literature-Based Discovery Predicts Antihistamines Are a Promising Repurposed Adjuvant Therapy for Parkinson's Disease

Parkinson's disease (PD) is a movement disorder caused by a dopamine deficit in the brain. Current therapies primarily focus on dopamine modulators or replacements, such as levodopa. Although dopamine replacement can help alleviate PD symptoms, therapies targeting the underlying neurodegenerative process are limited. The study objective was to use artificial intelligence to rank the most promising repurposed drug candidates for PD. Natural language processing (NLP) techniques were used to extract text relationships from 33+ million biomedical journal articles from PubMed and map relationships between genes, proteins, drugs, diseases, etc., into a knowledge graph. Cross-domain text mining, hub network analysis, and unsupervised learning rank aggregation were performed in SemNet 2.0 to predict the most relevant drug candidates to levodopa and PD using relevance-based HeteSim scores. The top predicted adjuvant PD therapies included ebastine, an antihistamine for perennial allergic rhinitis; levocetirizine, another antihistamine; vancomycin, a powerful antibiotic; captopril, an angiotensin-converting enzyme (ACE) inhibitor; and neramexane, an N-methyl-D-aspartate (NMDA) receptor agonist. Cross-domain text mining predicted that antihistamines exhibit the capacity to synergistically alleviate Parkinsonian symptoms when used with dopamine modulators like levodopa or levodopa-carbidopa. The relationship patterns among the identified adjuvant candidates suggest that the likely therapeutic mechanism(s) of action of antihistamines for combatting the multi-factorial PD pathology include counteracting oxidative stress, amending the balance of neurotransmitters, and decreasing the proliferation of inflammatory mediators. Finally, cross-domain text mining interestingly predicted a strong relationship between PD and liver disease.

Rash with different types of BCR-ABL inhibitors in chronic myelogenous leukemia: a systematic review and meta-analysis

Objective: A meta-analysis was conducted to systematically review the risks of all-grade as well as high-grade rash in chronic myelogenous leukemia (CML) patients using different types of BCR-ABL inhibitors. Methods: Literature published between 2000 and April 2022 were searched using PubMed, Cochrane Library, Embase and ClinicalTrials.gov. Results: A total of 12 studies were included for meta-analysis. The results showed that the incidence of all-grade or high-grade rash associated with new-generation BCR-ABL inhibitors had no significant difference compared with a standard dose of imatinib. Subgroup analysis suggested that, compared with imatinib, the incidence of all grades of rash was higher in the nilotinib, bosutinib and ponatinib groups. Conclusion: For CML patients treated with nilotinib, bosutinib and ponatinib, the occurrence of skin toxicity should not be ignored.