A multiscale model to design therapeutic strategies that overcome drug resistance to tyrosine kinase inhibitors in multiple myeloma

Phenotype-structured model of intra-clonal heterogeneity and drug resistance in multiple myeloma

Multiple myeloma (MM) is a genetically complex hematological cancer characterized by the abnormal proliferation of malignant plasma cells in the bone marrow. This disease progresses from a premalignant condition known as monoclonal gammopathy of unknown significance (MGUS) through sequential genetic alterations involving various genes. These genetic changes contribute to the uncontrolled growth of multiple clones of plasma cells. In this study, we present a phenotype-structured model that captures the intra-clonal heterogeneity and drug resistance in multiple myeloma (MM). The model accurately reproduces the branching evolutionary pattern observed in MM progression, aligning with a previously developed multiscale model. Numerical simulations reveal that higher mutation rates enhance tumor phenotype diversity, while access to growth factors accelerates tumor evolution and increases its final size. Interestingly, the model suggests that further increasing growth factor access primarily amplifies tumor size rather than altering clonal dynamics. Additionally, the model emphasizes that higher mutation frequencies and growth factor availability elevate the chances of drug resistance and relapse. It indicates that the timing of the treatment could trajectory of tumor evolution and clonal emergence in the case of branching evolutionary pattern. Given its low computational cost, our model is well-suited for quantitative studies on MM clonal heterogeneity and its interaction with chemotherapeutic treatments.

Chitosan functionalized PCL Nanoparticles Bearing Tyrosine Kinase Inhibitor Osimertinib Mesylate for Effective Lung Cancer Therapy

Lung cancer ranks second position among the cancer-related deaths. Osimertinib mesylate (OSM) is a tyrosine-kinase-inhibitor which can effectively treat NSCLC, but still there are certain limitations and side effects which could be circumvented by polymeric nanoparticles approach. Hence, this research was aimed to develop drug-loaded biodegradable poly caprolactone nanoparticles (PCL-NPs) such as OSM-loaded PCL-NPs (PCL-OSM-NPs) and chitosan fabricated OSM-loaded PCL-NPs (CS-PCL-OSM-NPs) to achieve active-targeting of OSM in the cancerous lung tissue. Thus CS-PCL-OSM-NPs enhances the anticancer efficacy due to active targeting nature and thereby reduces off-target side effects of OSM in the NSCLC treatment.Blank PCL-NPs, PCL-OSM-NPs, and CS-PCL-OSM-NPs were prepared by nanoprecipitation method. Optimized blank PCL-NPs, PCL-OSM-NPs, and CS-PCL-OSM-NPs exhibited the mean particle size of 90.2 ± 4.7nm, 167.7 ± 2.9nm, and 233.7 ± 4.8nm respectively. The %EE of PCL-OSM-NPs was found to be 68.4 ± 3.2%. In-vitro drug release study demonstrated sustained release profile of 69.5 ± 5% and 65.7 ± 1.5% for OSM from both the PCL-OSM-NPs and CS-PCL-OSM-NPs respectively. The PCL-OSM-NPs and CS-PCL-OSM-NPs demonstrated the inhibition of 82.2 ± 0.5% and 81.9 ± 0.2% in A549 cancer cells respectively which clearly signified the improved efficacy. Moreover, the PCL-OSM-NPs and CS-PCL-OSM-NPs exhibited significantly less haemolysis than OSM indicating safety of the formulation.These findings indicate that biohaemocompatibe CS-PCL-OSM-NPs is an attractive option to treat NSCLC with enhanced anticancer activity and reduced side effects.

Improving cancer treatments via dynamical biophysical models

Despite significant advances in oncological research, cancer nowadays remains one of the main causes of mortality and morbidity worldwide. New treatment techniques, as a rule, have limited efficacy, target only a narrow range of oncological diseases, and have limited availability to the general public due their high cost. An important goal in oncology is thus the modification of the types of antitumor therapy and their combinations, that are already introduced into clinical practice, with the goal of increasing the overall treatment efficacy. One option to achieve this goal is optimization of the schedules of drugs administration or performing other medical actions. Several factors complicate such tasks: the adverse effects of treatments on healthy cell populations, which must be kept tolerable; the emergence of drug resistance due to the intrinsic plasticity of heterogeneous cancer cell populations; the interplay between different types of therapies administered simultaneously. Mathematical modeling, in which a tumor and its microenvironment are considered as a single complex system, can address this complexity and can indicate potentially effective protocols, that would require experimental verification. In this review, we consider classical methods, current trends and future prospects in the field of mathematical modeling of tumor growth and treatment. In particular, methods of treatment optimization are discussed with several examples of specific problems related to different types of treatment.

Gefitinib loaded PLGA and chitosan coated PLGA nanoparticles with magnified cytotoxicity against A549 lung cancer cell lines

In the current study, gefitinib loaded PLGA nanoparticles (GFT-PLGA-NPs) and chitosan coated PLGA nanoparticles (GFT-CS-PLGA-NPs) were synthesized to investigate the role of surface charge of NPs for developing drug delivery system for non-small-cell lung cancer (NSCLC). The developed NPs were evaluated for their size, PDI, zeta potential (ZP), drug entrapment, drug loading, DSC, FTIR, XRD, in vitro release profile, and morphology. The anti-cancer activity of GFT loaded PLGA NPs and GFT loaded CS-PLGA-NPs were examined in human A549 lung cancer cell lines. In vitro release studies of GFT-CS-PLGA-NPs showed more sustained release in comparison to GFT-PLGA-NPs due surface charge attraction of chitosan. In addition, viability of A549 cells decreases significantly with the increasing concentration of GFT-PLGA NPs and GFT-CS-PLGA-NPs when compared to that of pure GFT and blank PLGA NPs. In addition, the microscopic analysis and counting of viable cells also validate the cytotoxicity of the developed NPs. This investigation proved that the developed NPs would be efficient carriers to deliver GFT with improved efficacy against NSCLC.

Employment of Artificial Intelligence Based on Routine Laboratory Results for the Early Diagnosis of Multiple Myeloma

Objective

In order to enhance the detection rate of multiple myeloma and execute an early and more precise disease management, an artificial intelligence assistant diagnosis system is developed.

Methods

4,187 routine blood and biochemical examination records were collected from Shengjing Hospital affiliated to China Medical University from January 2010 to January 2020, which include 1,741 records of multiple myeloma (MM) and 2,446 records of non-myeloma (infectious diseases, rheumatic immune system diseases, hepatic diseases and renal diseases). The data set was split into training and test subsets with the ratio of 4:1 while connecting hemoglobin, serum creatinine, serum calcium, immunoglobulin (A, G and M), albumin, total protein, and the ratio of albumin to globulin data. An early assistant diagnostic model of MM was established by Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Deep Neural Networks (DNN), and Random Forest (RF). Out team calculated the precision and recall of the system. The performance of the diagnostic model was evaluated by using the receiver operating characteristic (ROC) curve.

Results

By designing the features properly, the typical machine learning algorithms SVM, DNN, RF and GBDT all performed well. GBDT had the highest precision (92.9%), recall (90.0%) and F1 score (0.915) for the myeloma group. The maximized area under the ROC (AUROC) was calculated, and the results of GBDT (AUC: 0.975; 95% confidence interval (CI): 0.963–0.986) outperformed that of SVM, DNN and RF.

Conclusion

The model established by artificial intelligence derived from routine laboratory results can accurately diagnose MM, which can boost the rate of early diagnosis.