Computational oncology — mathematical modelling of drug regimens for precision medicine

Is Tumor Growth Influenced by the Bone Remodeling Process?

In this study, we develop a comprehensive model to investigate the intricate relationship between the bone remodeling process, tumor growth, and bone diseases such as multiple myeloma. By analyzing different scenarios within the Basic Multicellular Unit, we uncover the dynamic interplay between remodeling and tumor progression. The model developed developed in the paper are based on the well accepted Komarova's and Ayati's models for the bone remodeling process, then these models were modified to include the effects of the tumor growth. Our in silico experiments yield results consistent with existing literature, providing valuable insights into the complex dynamics at play. This research aims to improve the clinical management of bone diseases and metastasis, paving the way for targeted interventions and personalized treatment strategies to enhance the quality of life for affected individuals.

Insights from a multiscale framework on metabolic rate variation driving glioblastoma multiforme growth and invasion

Non-physiological levels of oxygen and nutrients within the tumors result in heterogeneous cell populations that exhibit distinct necrotic, hypoxic, and proliferative zones. Among these zonal cellular properties, metabolic rates strongly affect the overall growth and invasion of tumors. Here, we report on a hybrid discrete-continuum (HDC) mathematical framework that uses metabolic data from a biomimetic two-dimensional (2D) in-vitro cancer model to predict three-dimensional (3D) behaviour of in-vitro human glioblastoma (hGB). The mathematical model integrates modules of continuum, discrete, and neurons. Results indicated that the HDC model is capable of quantitatively predicting growth, invasion length, and the asymmetric finger-type invasion pattern in in-vitro hGB tumors. Additionally, the model could predict the reduction in invasion length of hGB tumoroids in response to temozolomide (TMZ). This model has the potential to incorporate additional modules, including immune cells and signaling pathways governing cancer/immune cell interactions, and can be used to investigate targeted therapies.

Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management

PROBLEM

Therapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice.

OBJECTIVE

This study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm.

DESIGN

Here, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models).

RESULTS

Our results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics.

CONCLUSIONS

Our findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.

Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework

Patient-derived tumor organoids (PDTOs) are novel cellular models that maintain the genetic, phenotypic and structural features of patient tumor tissue and are useful for studying tumorigenesis and drug response. When integrated with advanced 3D imaging and analysis techniques, PDTOs can be used to establish physiologically relevant high-throughput and high-content drug screening platforms that support the development of patient-specific treatment strategies. However, in order to effectively leverage high-throughput PDTO observations for clinical predictions, it is critical to establish a quantitative understanding of the basic properties and variability of organoid growth dynamics. In this work, we introduced an innovative workflow for analyzing and understanding PDTO growth dynamics, by integrating a high-throughput imaging deep learning platform with mathematical modeling, incorporating flexible growth laws and variable dormancy times. We applied the workflow to colon cancer organoids and demonstrated that organoid growth is well-described by the Gompertz model of growth. Our analysis showed significant intrapatient heterogeneity in PDTO growth dynamics, with the initial exponential growth rate of an organoid following a lognormal distribution within each dataset. The level of intrapatient heterogeneity varied between patients, as did organoid growth rates and dormancy times of single seeded cells. Our work contributes to an emerging understanding of the basic growth characteristics of PDTOs, and it highlights the heterogeneity in organoid growth both within and between patients. These results pave the way for further modeling efforts aimed at predicting treatment response dynamics and drug resistance timing.

Exploring the causal relationship between glutamine metabolism and leukemia risk: a Mendelian randomization and LC-MS/MS analysis

Objective

This investigation sought to delineate the causal nexus between plasma glutamine concentrations and leukemia susceptibility utilizing bidirectional Mendelian Randomization (MR) analysis and to elucidate the metabolic ramifications of asparaginase therapy on glutamine dynamics in leukemia patients.

Methods

A bidirectional two-sample MR framework was implemented, leveraging genetic variants as instrumental variables from extensive genome-wide association studies (GWAS) tailored to populations of European descent. Glutamine quantification was executed through a rigorously validated Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) protocol. Comparative analyses of glutamine levels were conducted across leukemia patients versus healthy controls, pre- and post-asparaginase administration. Statistical evaluations employed inverse variance weighted (IVW) models, MR-Egger regression, and sensitivity tests addressing pleiotropy and heterogeneity.

Results

The MR findings underscored a significant inverse association between glutamine levels and leukemia risk (IVW p = 0.03558833), positing lower glutamine levels as a contributory factor to heightened leukemia susceptibility. Conversely, the analysis disclosed no substantive causal impact of leukemia on glutamine modulation (IVW p = 0.9694758). Notably, post-asparaginase treatment, a marked decrement in plasma glutamine concentrations was observed in patients (p = 0.0068), underlining the profound metabolic influence of the therapeutic regimen.

Conclusion

This study corroborates the hypothesized inverse relationship between plasma glutamine levels and leukemia risk, enhancing our understanding of glutamine's role in leukemia pathophysiology. The pronounced reduction in glutamine levels following asparaginase intervention highlights the critical need for meticulous metabolic monitoring to refine therapeutic efficacy and optimize patient management in clinical oncology. These insights pave the way for more tailored and efficacious treatment modalities in the realm of personalized medicine.

To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy

Toxicity and emerging drug resistance pose important challenges in poly-adenosine ribose polymerase inhibitor (PARPi) maintenance therapy of ovarian cancer. We propose that adaptive therapy, which dynamically reduces treatment based on the tumor dynamics, might alleviate both issues. Utilizing in vitro time-lapse microscopy and stepwise model selection, we calibrate and validate a differential equation mathematical model, which we leverage to test different plausible adaptive treatment schedules. Our model indicates that adjusting the dosage, rather than skipping treatments, is more effective at reducing drug use while maintaining efficacy due to a delay in cell kill and a diminishing dose-response relationship. In vivo pilot experiments confirm this conclusion. Although our focus is toxicity mitigation, reducing drug use may also delay resistance. This study enhances our understanding of PARPi treatment scheduling and illustrates the first steps in developing adaptive therapies for new treatment settings. A record of this paper's transparent peer review process is included in the supplemental information.

Nucleic acid-based drugs for patients with solid tumours

The treatment of patients with advanced-stage solid tumours typically involves a multimodality approach (including surgery, chemotherapy, radiotherapy, targeted therapy and/or immunotherapy), which is often ultimately ineffective. Nucleic acid-based drugs, either as monotherapies or in combination with standard-of-care therapies, are rapidly emerging as novel treatments capable of generating responses in otherwise refractory tumours. These therapies include those using viral vectors (also referred to as gene therapies), several of which have now been approved by regulatory agencies, and nanoparticles containing mRNAs and a range of other nucleotides. In this Review, we describe the development and clinical activity of viral and non-viral nucleic acid-based treatments, including their mechanisms of action, tolerability and available efficacy data from patients with solid tumours. We also describe the effects of the tumour microenvironment on drug delivery for both systemically administered and locally administered agents. Finally, we discuss important trends resulting from ongoing clinical trials and preclinical testing, and manufacturing and/or stability considerations that are expected to underpin the next generation of nucleic acid agents for patients with solid tumours.

Mathematical model of MMC chemotherapy for non-invasive bladder cancer treatment

Mitomycin-C (MMC) chemotherapy is a well-established anti-cancer treatment for non-muscle-invasive bladder cancer (NMIBC). However, despite comprehensive biological research, the complete mechanism of action and an ideal regimen of MMC have not been elucidated. In this study, we present a theoretical investigation of NMIBC growth and its treatment by continuous administration of MMC chemotherapy. Using temporal ordinary differential equations (ODEs) to describe cell populations and drug molecules, we formulated the first mathematical model of tumor-immune interactions in the treatment of MMC for NMIBC, based on biological sources. Several hypothetical scenarios for NMIBC under the assumption that tumor size correlates with cell count are presented, depicting the evolution of tumors classified as small, medium, and large. These scenarios align qualitatively with clinical observations of lower recurrence rates for tumor size ≤ 30[mm] with MMC treatment, demonstrating that cure appears up to a theoretical x[mm] tumor size threshold, given specific parameters within a feasible biological range. The unique use of mole units allows to introduce a new method for theoretical pre-treatment assessments by determining MMC drug doses required for a cure. In this way, our approach provides initial steps toward personalized MMC chemotherapy for NMIBC patients, offering the possibility of new insights and potentially holding the key to unlocking some of its mysteries.

Morphological Stability for in silico Models of Avascular Tumors

The landscape of computational modeling in cancer systems biology is diverse, offering a spectrum of models and frameworks, each with its own trade-offs and advantages. Ideally, models are meant to be useful in refining hypotheses, to sharpen experimental procedures and, in the longer run, even for applications in personalized medicine. One of the greatest challenges is to balance model realism and detail with experimental data to eventually produce useful data-driven models. We contribute to this quest by developing a transparent, highly parsimonious, first principle in silico model of a growing avascular tumor. We initially formulate the physiological considerations and the specific model within a stochastic cell-based framework. We next formulate a corresponding mean-field model using partial differential equations which is amenable to mathematical analysis. Despite a few notable differences between the two models, we are in this way able to successfully detail the impact of all parameters in the stability of the growth process and on the eventual tumor fate of the stochastic model. This facilitates the deduction of Bayesian priors for a given situation, but also provides important insights into the underlying mechanism of tumor growth and progression. Although the resulting model framework is relatively simple and transparent, it can still reproduce the full range of known emergent behavior. We identify a novel model instability arising from nutrient starvation and we also discuss additional insight concerning possible model additions and the effects of those. Thanks to the framework's flexibility, such additions can be readily included whenever the relevant data become available.

Simulating BRAFV600E-MEK-ERK signalling dynamics in response to vertical inhibition treatment strategies

In vertical inhibition treatment strategies, multiple components of an intracellular pathway are simultaneously inhibited. Vertical inhibition of the BRAFV600E-MEK-ERK signalling pathway is a standard of care for treating BRAFV600E-mutated melanoma where two targeted cancer drugs, a BRAFV600E-inhibitor, and a MEK inhibitor, are administered in combination. Targeted therapies have been linked to early onsets of drug resistance, and thus treatment strategies of higher complexities and lower doses have been proposed as alternatives to current clinical strategies. However, finding optimal complex, low-dose treatment strategies is a challenge, as it is possible to design more treatment strategies than are feasibly testable in experimental settings. To quantitatively address this challenge, we develop a mathematical model of BRAFV600E-MEK-ERK signalling dynamics in response to combinations of the BRAFV600E-inhibitor dabrafenib (DBF), the MEK inhibitor trametinib (TMT), and the ERK-inhibitor SCH772984 (SCH). From a model of the BRAFV600E-MEK-ERK pathway, and a set of molecular-level drug-protein interactions, we extract a system of chemical reactions that is parameterised by in vitro data and converted to a system of ordinary differential equations (ODEs) using the law of mass action. The ODEs are solved numerically to produce simulations of how pathway-component concentrations change over time in response to different treatment strategies, i.e., inhibitor combinations and doses. The model can thus be used to limit the search space for effective treatment strategies that target the BRAFV600E-MEK-ERK pathway and warrant further experimental investigation. The results demonstrate that DBF and DBF-TMT-SCH therapies show marked sensitivity to BRAFV600E concentrations in silico, whilst TMT and SCH monotherapies do not.

Quantitative modeling of tumor dynamics and development of drug resistance in non-small cell lung cancer patients treated with erlotinib

Insight into the development of treatment resistance can support the optimization of anticancer treatments. This study aims to characterize the tumor dynamics and development of drug resistance in patients with non-small cell lung cancer treated with erlotinib, and investigate the relationship between baseline circulating tumor DNA (ctDNA) data and tumor dynamics. Data obtained for the analysis included (1) intensively sampled erlotinib concentrations from 29 patients from two previous pharmacokinetic (PK) studies, and (2) tumor sizes, ctDNA measurements, and sparsely sampled erlotinib concentrations from 18 patients from the START-TKI study. A two-compartment population PK model was first developed which well-described the PK data. The PK model was subsequently applied to investigate the exposure-tumor dynamics relationship. To characterize the tumor dynamics, models accounting for intra-tumor heterogeneity and acquired resistance with or without primary resistance were investigated. Eventually, the model assumed acquired resistance only resulted in an adequate fit. Additionally, models with or without exposure-dependent treatment effect were explored, and no significant exposure-response relationship for erlotinib was identified within the observed exposure range. Subsequently, the correlation of baseline ctDNA data on EGFR and TP53 variants with tumor dynamics' parameters was explored. The analysis indicated that higher baseline plasma EGFR mutation levels correlated with increased tumor growth rates, and the inclusion of ctDNA measurements improved model fit. This result suggests that quantitative ctDNA measurements at baseline have the potential to be a predictor of anticancer treatment response. The developed model can potentially be applied to design optimal treatment regimens that better overcome resistance.

Overcoming immuno-resistance by rescheduling anti-VEGF/cytotoxics/anti-PD-1 combination in lung cancer model

Background: Many tumors are refractory to immune checkpoint inhibitors, but their combination with cytotoxics is expected to improve sensitivity. Understanding how and when cytotoxics best re-stimulate tumor immunity could help overcome resistance to immune checkpoint inhibitors. Methods: In vivo studies were performed in C57BL/6 mice grafted with immune-refractory LL/2 lung cancer model. A longitudinal immunomonitoring study on tumor, spleen, and blood after multiple treatments including Cisplatin, Pemetrexed, and anti-VEGF, either alone or in combination, was performed, spanning a period of up to 21 days, to determine the optimal time window during which immune checkpoint inhibitors should be added. Finally, an efficacy study was conducted comparing the antiproliferative performance of various schedules of anti-VEGF, Pemetrexed-Cisplatin doublet, plus anti-PD-1 (i.e., immunomonitoring-guided scheduling, concurrent dosing or a random sequence), as well as single agent anti-PD1. Results: Immunomonitoring showed marked differences between treatments, organs, and time points. However, harnessing tumor immunity (i.e., promoting CD8 T cells or increasing the T CD8/Treg ratio) started on D7 and peaked on D14 with the anti-VEGF followed by cytotoxics combination. Therefore, a 14-day delay between anti-VEGF/cytotoxic and anti-PD1 administration was considered the best sequence to test. Efficacy studies then confirmed that this sequence achieved higher antiproliferative efficacy compared to other treatment modalities (i.e., -71% in tumor volume compared to control). Conclusions: Anti-VEGF and cytotoxic agents show time-dependent immunomodulatory effects, suggesting that sequencing is a critical feature when combining these agents with immune checkpoint inhibitors. An efficacy study confirmed that sequencing treatments further enhance antiproliferative effects in lung cancer models compared to concurrent dosing and partly reverse the resistance to cytotoxics and anti-PD1.

Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation

Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Stimuli-sensitive nano-drug delivery with programmable size changes to enhance accumulation of therapeutic agents in tumors

Nano-based drug delivery systems hold significant promise for cancer therapies. Presently, the poor accumulation of drug-carrying nanoparticles in tumors has limited their success. In this study, based on a combination of the paradigms of intravascular and extravascular drug release, an efficient nanosized drug delivery system with programmable size changes is introduced. Drug-loaded smaller nanoparticles (secondary nanoparticles), which are loaded inside larger nanoparticles (primary nanoparticles), are released within the microvascular network due to temperature field resulting from focused ultrasound. This leads to the scale of the drug delivery system decreasing by 7.5 to 150 times. Subsequently, smaller nanoparticles enter the tissue at high transvascular rates and achieve higher accumulation, leading to higher penetration depths. In response to the acidic pH of tumor microenvironment (according to the distribution of oxygen), they begin to release the drug doxorubicin at very slow rates (i.e., sustained release). To predict the performance and distribution of therapeutic agents, a semi-realistic microvascular network is first generated based on a sprouting angiogenesis model and the transport of therapeutic agents is then investigated based on a developed multi-compartment model. The results show that reducing the size of the primary and secondary nanoparticles can lead to higher cell death rate. In addition, tumor growth can be inhibited for a longer time by enhancing the bioavailability of the drug in the extracellular space. The proposed drug delivery system can be very promising in clinical applications. Furthermore, the proposed mathematical model is applicable to broader applications to predict the performance of drug delivery systems.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Computational reactive-diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib

Mathematical models based on partial differential equations (PDEs) can be exploited to handle clinical data with space/time dimensions, e.g. tumor growth challenged by neoadjuvant therapy. A model based on simplified assessment of tumor malignancy and pharmacodynamics efficiency was exercised to discover new metrics of patient prognosis in the OLTRE trial. We tested in a 17-patients cohort affected by early-stage triple negative breast cancer (TNBC) treated with 3 weeks of olaparib, the capability of a PDEs-based reactive-diffusive model of tumor growth to efficiently predict the response to olaparib in terms of SUV_max detected at ¹⁸FDG-PET/CT scan, by using specific terms to characterize tumor diffusion and proliferation. Computations were performed with COMSOL Multiphysics. Driving parameters governing the mathematical model were selected with Pearson's correlations. Discrepancies between actual and computed SUV_max values were assessed with Student's t test and Wilcoxon rank sum test. The correlation between post-olaparib true and computed SUV_max was assessed with Pearson's r and Spearman's rho. After defining the proper mathematical assumptions, the nominal drug efficiency (ε_PD) and tumor malignancy (r_c) were computationally evaluated. The former parameter reflected the activity of olaparib on the tumor, while the latter represented the growth rate of metabolic activity as detected by SUV_max. ε_PD was found to be directly dependent on basal tumor-infiltrating lymphocytes (TILs) and Ki67% and was detectable through proper linear regression functions according to TILs values, while r_c was represented by the baseline Ki67-to-TILs ratio. Predicted post-olaparib SUV*_max did not significantly differ from original post-olaparib SUV_max in the overall, gBRCA-mutant and gBRCA-wild-type subpopulations (p > 0.05 in all cases), showing strong positive correlation (r = 0.9 and rho = 0.9, p < 0.0001 both). A model of simplified tumor dynamics was exercised to effectively produce an upfront prediction of efficacy of 3-week neoadjuvant olaparib in terms of SUV_max. Prospective evaluation in independent cohorts and correlation of these outcomes with more recognized efficacy endpoints is now warranted for model confirmation and tailoring of escalated/de-escalated therapeutic strategies for early-TNBC patients.

Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.

A dynamical systems approach for multiscale synthesis of Alzheimer's pathogenesis

Alzheimer's disease (AD) is a spatially dynamic pathology that implicates a growing volume of multiscale data spanning genetic, cellular, tissue, and organ levels of the organization. These data and bioinformatics analyses provide clear evidence for the interactions within and between these levels. The resulting heterarchy precludes a linear neuron-centric approach and necessitates that the numerous interactions are measured in a way that predicts their impact on the emergent dynamics of the disease. This level of complexity confounds intuition, and we propose a new methodology that uses non-linear dynamical systems modeling to augment intuition and that links with a community-wide participatory platform to co-create and test system-level hypotheses and interventions. In addition to enabling the integration of multiscale knowledge, key benefits include a more rapid innovation cycle and a rational process for prioritization of data campaigns. We argue that such an approach is essential to support the discovery of multilevel-coordinated polypharmaceutical interventions.

Influence of electric pulse characteristics on the cellular internalization of chemotherapeutic drugs and cell survival fraction in electroporated and vasoconstricted cancer tissues using boundary element techniques

Electroporation has emerged as a suitable technique to induce the pore formation in the cell membrane of cancer tissues, facilitating the cellular internalization of chemotherapeutic drugs. An adequate selection of the electric pulse characteristics is crucial to guarantee the efficiency of this technique, minimizing the adverse effects. In the present work, the dual reciprocity boundary element method (DR-BEM) is applied for the simulation of drug transport in the extracellular and intracellular space of cancer tissues subjected to the application of controlled electric pulses, using a continuum tumour cord approach, and considering both the electro-permeabilization and vasoconstriction phenomena. The developed DR-BEM algorithm is validated with numerical and experimental results previously published, obtaining a satisfactory accuracy and convergence. Using the DR-BEM code, a study about the influence of the magnitude of electric field (E) and pulse spacing (dpulses) on the time behavior and spatial distribution of the internalized drug, as well as on the cell survival fraction, is carried out. In general, the change of drug concentration, drug exposure and cell survival fraction with the parameters E and dpulses is ruled by two important factors: the balance between the electro-permeabilization and vasoconstriction phenomena, and the relative importance of the sources of cell death (electric pulses and drug cytotoxicity); these two factors, in turn, significantly depend on the reversible and irreversible thresholds considered for the electric field.

Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling

Toxicity and emerging drug resistance are important challenges in PARP inhibitor (PARPi) treatment of ovarian cancer. Recent research has shown that evolutionary-inspired treatment algorithms which adapt treatment to the tumor's treatment response (adaptive therapy) can help to mitigate both. Here, we present a first step in developing an adaptive therapy protocol for PARPi treatment by combining mathematical modelling and wet-lab experiments to characterize the cell population dynamics under different PARPi schedules. Using data from in vitro Incucyte Zoom time-lapse microscopy experiments and a step-wise model selection process we derive a calibrated and validated ordinary differential equation model, which we then use to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumour growth, even in the absence of any resistance. This is because our model predicts that multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Pilot experiments in vivo confirm this conclusion. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis and showcases some of the challenges involved in developing adaptive therapies for new treatment settings.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models

The field of oncology has transformed with the advent of immunotherapies. The standard of care for multiple cancers now includes novel drugs that target key checkpoints that function to modulate immune responses, enabling the patient's immune system to elicit an effective anti-tumor response. While these immune-based approaches can have dramatic effects in terms of significantly reducing tumor burden and prolonging survival for patients, the therapeutic approach remains active only in a minority of patients and is often not durable. Multiple biological investigations have identified key markers that predict response to the most common form of immunotherapy-immune checkpoint inhibitors (ICI). These biomarkers help enrich patients for ICI but are not 100% predictive. Understanding the complex interactions of these biomarkers with other pathways and factors that lead to ICI resistance remains a major goal. Principles of oncophysics-the idea that cancer can be described as a multiscale physical aberration-have shown promise in recent years in terms of capturing the essence of the complexities of ICI interactions. Here, we review the biological knowledge of mechanisms of ICI action and how these are incorporated into modern oncophysics-based mathematical models. Building on the success of oncophysics-based mathematical models may help to discover new, rational methods to engineer immunotherapy for patients in the future. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods

Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.

Mathematical modeling of cancer immunotherapy for personalized clinical translation

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.

Synthesis of Silver Nano Particles Using Myricetin and the In-Vitro Assessment of Anti-Colorectal Cancer Activity: In-Silico Integration

The creation of novel anticancer treatments for a variety of human illnesses, including different malignancies and dangerous microbes, also potentially depends on nanoparticles including silver. Recently, it has been successful to biologically synthesize metal nanoparticles using plant extracts. The natural flavonoid 3,3′, 4′, 5,5′, and 7 hexahydroxyflavon (myricetin) has anticancer properties. There is not much known about the regulatory effects of myricetin on the possible cell fate-determination mechanisms (such as apoptosis/proliferation) in colorectal cancer. Because the majority of investigations related to the anticancer activity of myricetin have dominantly focused on the enhancement of tumor cell uncontrolled growth (i.e., apoptosis). Thus, we have decided to explore the potential myricetin interactors and the associated biological functions by using an in-silico approach. Then, we focused on the main goal of the work which involved the synthesis of silver nanoparticles and the labeling of myricetin with it. The synthesized silver nanoparticles were examined using UV-visible spectroscopy, dynamic light scattering spectroscopy, Fourier transform infrared spectroscopy, and scanning electron microscopy. In this study, we have investigated the effects of myricetin on colorectal cancer where numerous techniques were used to show myricetin’s effect on colon cancer cells. Transmission Electron Microscopy was employed to monitor morphological changes. Furthermore, we have combined the results of the colorectal cancer gene expression dataset with those of the myricetin interactors and pathways. Based on the results, we conclude that myricetin is able to efficiently kill human colorectal cancer cell lines. Since, it shares important biological roles and possible route components and this myricetin may be a promising herbal treatment for colorectal cancer as per an in-silico analysis of the TCGA dataset.

Combinatorial approaches for mitigating resistance to KRAS-targeted therapies

Approximately 15% of all cancer patients harbor mutated KRAS. Direct inhibitors of KRAS have now been generated and are beginning to make progress through clinical trials. These include a suite of inhibitors targeting the KRAS^G12C mutation commonly found in lung cancer. We investigated emergent resistance to representative examples of different classes of Ras targeted therapies. They all exhibited rapid reactivation of Ras signaling within days of exposure and adaptive responses continued to change over long-term treatment schedules. Whilst the gene signatures were distinct for each inhibitor, they commonly involved up-regulation of upstream nodes promoting mutant and wild-type Ras activation. Experiments to reverse resistance unfortunately revealed frequent desensitization to members of a panel of anti-cancer therapeutics, suggesting that salvage approaches are unlikely to be feasible. Instead, we identified triple inhibitor combinations that resulted in more durable responses to KRAS inhibitors and that may benefit from further pre-clinical evaluation.

Revisiting metronomic vinorelbine with mathematical modelling: a Phase I trial in lung cancer

BACKGROUND

A phase Ia/Ib trial of metronomic oral vinorelbine (MOV) driven by a mathematical model was performed in heavily pretreated metastatic Non-Small Cell Lung Cancer or Pleural Mesothelioma patients. Disease Control Rate, progression free survival, toxicity and PK/PD were the main endpoints.

METHODS

Best MOV scheduling was selected using a simplified phenomenological, semi-mechanistic model with a total weekly dose of 150-mg vinorelbine. Computation of individual PK parameters was performed using population approach.

RESULTS

The mathematical model proposed the following metronomic schedule for a 150-mg weekly dose of vinorelbine: 60 mg D1, 30 mg D2, 60 mg D4. A total of 37 heavily pre-treated patients (30 evaluable) were enrolled. Grade III/IV neutropenia was observed in 30% patients. Median PFS was 11 weeks. Disease Control Rate was 73% (i.e.; 13% partial response and 60% stable disease). A large variability in drug exposure (AUC_0-24 h: 53%) and PK parameters (Cl: 83%) were observed among patients. Simulated trough levels after D2 and D4 showed similarly 56-73% variability among patients. Drug exposure was not associated with efficacy, but neutropenia was more frequent in patients with AUC > 250 ng/ml.h. Tumor burden, performance status and neutrophils-to-lymphocyte ratio (NLR) were associated with PFS, suggesting that MOV would be indicated in selected patients. We built a composite score to predict efficacy, mixing baseline tumor size and NLR showing 84% selectivity and 75% specificity.

CONCLUSIONS

MOV was characterized by important variability in drug exposure among patients. However, and despite being all heavily pre-treated, 73% of disease control rate and 11 weeks PFS were achieved with manageable toxicities. PK/PD relationships yielded conflicting results depending on the initial tumor burden and BSA, suggesting that patients should be carefully selected prior to be scheduled for metronomic regimen. Possible role NLR could play as a predictive marker suggests immunomodulating features with MOV.

From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers

An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.

Anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance

Quantitative characterization of evolving tumor resistance under targeted treatment could help identify novel treatment schedules, which may improve the outcome of anti-cancer treatment. In this study, a mathematical model which considers various clonal populations and evolving treatment resistance was developed. With parameter values fitted to the data or informed by literature data, the model could capture previously reported tumor burden dynamics and mutant KRAS levels in circulating tumor DNA (ctDNA) of patients with metastatic colorectal cancer treated with panitumumab. Treatment schedules, including a continuous schedule, intermittent schedules incorporating treatment holidays, and adaptive schedules guided by ctDNA measurements were evaluated using simulations. Compared with the continuous regimen, the simulated intermittent regimen which consisted of 8-week treatment and 4-week suspension prolonged median progression-free survival (PFS) of the simulated population from 36 to 44 weeks. The median time period in which the tumor size stayed below the baseline level (T_TS<TS0) was prolonged from 52 to 60 weeks. Extending the treatment holiday resulted in inferior outcomes. The simulated adaptive regimens showed to further prolong median PFS to 56–64 weeks and T_TS<TS0 to 114–132 weeks under different treatment designs. A prospective clinical study is required to validate the results and to confirm the added value of the suggested schedules.

A graph model of combination therapies

The simultaneous use of multiple medications causes drug-drug interactions (DDI) that impact therapeutic efficacy. Here, we argue that graph theory, in conjunction with game theory and ecosystem theory, can address this issue. We treat the coexistence of multiple drugs as a system in which DDI is modeled by game theory. We develop an ordinary differential equation model to characterize how the concentration of a drug changes as a result of its independent capacity and the dependent influence of other drugs through the metabolic response of the host. We coalesce all drugs into personalized and context-specific networks, which can reveal key DDI determinants of therapeutical efficacy. Our model can quantify drug synergy and antagonism and test the translational success of combination therapies to the clinic.

Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach

The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches-maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)-as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy.

Optimal Combinations of Chemotherapy and Radiotherapy in Low-Grade Gliomas: A Mathematical Approach

Low-grade gliomas (LGGs) are brain tumors characterized by their slow growth and infiltrative nature. Treatment options for these tumors are surgery, radiation therapy and chemotherapy. The optimal use of radiation therapy and chemotherapy is still under study. In this paper, we construct a mathematical model of LGG response to combinations of chemotherapy, specifically to the alkylating agent temozolomide and radiation therapy. Patient-specific parameters were obtained from longitudinal imaging data of the response of real LGG patients. Computer simulations showed that concurrent cycles of radiation therapy and temozolomide could provide the best therapeutic efficacy in-silico for the patients included in the study. The patient cohort was extended computationally to a set of 3000 virtual patients. This virtual cohort was subject to an in-silico trial in which matching the doses of radiotherapy to those of temozolomide in the first five days of each cycle improved overall survival over concomitant radio-chemotherapy according to RTOG 0424. Thus, the proposed treatment schedule could be investigated in a clinical setting to improve combination treatments in LGGs with substantial survival benefits.

Mathematical model of a personalized neoantigen cancer vaccine and the human immune system

Cancer vaccines are an important component of the cancer immunotherapy toolkit enhancing immune response to malignant cells by activating CD4⁺ and CD8⁺ T cells. Multiple successful clinical applications of cancer vaccines have shown good safety and efficacy. Despite the notable progress, significant challenges remain in obtaining consistent immune responses across heterogeneous patient populations, as well as various cancers. We present a mechanistic mathematical model describing key interactions of a personalized neoantigen cancer vaccine with an individual patient’s immune system. Specifically, the model considers the vaccine concentration of tumor-specific antigen peptides and adjuvant, the patient’s major histocompatibility complexes I and II copy numbers, tumor size, T cells, and antigen presenting cells. We parametrized the model using patient-specific data from a clinical study in which individualized cancer vaccines were used to treat six melanoma patients. Model simulations predicted both immune responses, represented by T cell counts, to the vaccine as well as clinical outcome (determined as change of tumor size). This model, although complex, can be used to describe, simulate, and predict the behavior of the human immune system to a personalized cancer vaccine.

Personalized cancer vaccines have gained attention in recent years due to the advances in sequencing techniques that have facilitated the identification of multiple tumor-specific mutations. This type of individualized immunotherapy has the potential to be specific, efficacious, and safe since it induces an immune response to protein targets not found on normal cells. This work focuses on understanding and analyzing important mechanisms involved in the activity of personalized cancer vaccines using a mechanistic mathematical model. This model describes the interactions of a personalized neoantigen peptide cancer vaccine, the human immune system and tumor cells operating at the molecular and cellular level. The molecular level captures the processing and presentation of neoantigens by dendritic cells to the T cells using cell surface proteins. The cellular level describes the differentiation of dendritic cells due to peptides and adjuvant concentrations in the vaccine, activation, and proliferation of T cells in response to treatment, and tumor growth. The model captures immune response behavior to a vaccine associated with patient-specific factors (e.g., different initial tumor burdens). This model enables the simulation of a complex biological system, the human immune system, by performing in silico experiments that may become the input for further analysis such as the identification of key parameters or mechanisms and/or interpretation of data.

Minimal PK/PD model for simultaneous description of the maximal tolerated dose and metronomic treatment outcomes in mouse tumor models

PURPOSE

Metronomic chemotherapy (MC) is a promising approach where, in contrast to the conventional maximal tolerated dose (MTD) strategy, regular fractionated doses of the drug are used. This approach has proven its efficacy, although drug dosing and scheduling are often chosen empirically. Pharmacokinetic/pharmacodynamic (PK/PD) models provide a way to choose optimal protocols with computational methods. Existing models are usually too complicated and are valid for only a subset of drug schedules. To address this issue, we propose herein a simple model that can describe MC and MTD regimens simultaneously.

METHODS

The minimal model comprises tumor suppression due to antiangiogenic drug effect together with a cell-kill term, responsible for its cytotoxicity. The model was tested on data obtained on tumor-bearing mice treated with gemcitabine in ether MTD, MC, or combined (MTD + MC) regimens.

RESULTS

We conducted a number of tests in which data were divided in various ways into training and validation sets. The model successfully described different trends in the MTD and MC regimens. With parameters obtained by fitting the model to MTD data, the simulations correctly predicted trends in both the MC and combined therapy groups.

CONCLUSION

Our results demonstrate that the proposed model presents a minimal yet efficient tool for modeling outcomes in different treatment regimens in mice. We hope that this model has the potential for use in clinical practice in the development of patient-specific chemotherapy scheduling protocols based on observed treatment response.

Cancer models in preclinical research: A chronicle review of advancement in effective cancer research

Cancer is a major stress for public well-being and is the most dreadful disease. The models used in the discovery of cancer treatment are continuously changing and extending toward advanced preclinical studies. Cancer models are either naturally existing or artificially prepared experimental systems that show similar features with human tumors though the heterogeneous nature of the tumor is very familiar. The choice of the most fitting model to best reflect the given tumor system is one of the real difficulties for cancer examination. Therefore, vast studies have been conducted on the cancer models for developing a better understanding of cancer invasion, progression, and early detection. These models give an insight into cancer etiology, molecular basis, host tumor interaction, the role of microenvironment, and tumor heterogeneity in tumor metastasis. These models are also used to predict novel cancer markers, targeted therapies, and are extremely helpful in drug development. In this review, the potential of cancer models to be used as a platform for drug screening and therapeutic discoveries are highlighted. Although none of the cancer models is regarded as ideal because each is associated with essential caveats that restraint its application yet by bridging the gap between preliminary cancer research and translational medicine. However, they promise a brighter future for cancer treatment.

Patient-Specific Organoid and Organ-on-a-Chip: 3D Cell-Culture Meets 3D Printing and Numerical Simulation

The last few decades have witnessed diversified in vitro models to recapitulate the architecture and function of living organs or tissues and contribute immensely to advances in life science. Two novel 3D cell culture models: 1) Organoid, promoted mainly by the developments of stem cell biology and 2) Organ-on-a-chip, enhanced primarily due to microfluidic technology, have emerged as two promising approaches to advance the understanding of basic biological principles and clinical treatments. This review describes the comparable distinct differences between these two models and provides more insights into their complementarity and integration to recognize their merits and limitations for applicable fields. The convergence of the two approaches to produce multi-organoid-on-a-chip or human organoid-on-a-chip is emerging as a new approach for building 3D models with higher physiological relevance. Furthermore, rapid advancements in 3D printing and numerical simulations, which facilitate the design, manufacture, and results-translation of 3D cell culture models, can also serve as novel tools to promote the development and propagation of organoid and organ-on-a-chip systems. Current technological challenges and limitations, as well as expert recommendations and future solutions to address the promising combinations by incorporating organoids, organ-on-a-chip, 3D printing, and numerical simulation, are also summarized.

Treatment scheduling effects on the evolution of drug resistance in heterogeneous cancer cell populations

The effect of scheduling of targeted therapy combinations on drug resistance is underexplored in triple-negative breast cancer (TNBC). TNBC constitutes heterogeneous cancer cell populations the composition of which can change dynamically during treatment resulting in the selection of resistant clones with a fitness advantage. We evaluated crizotinib (ALK/MET inhibitor) and navitoclax (ABT-263; Bcl-2/Bcl-xL inhibitor) combinations in a large design consisting of 696 two-cycle sequential and concomitant treatment regimens with varying treatment dose, duration, and drug holiday length over a 26-day period in MDA-MB-231 TNBC cells and found that patterns of resistance depend on the schedule and sequence in which the drugs are given. Further, we tracked the clonal dynamics and mechanisms of resistance using DNA-integrated barcodes and single-cell RNA sequencing. Our study suggests that longer formats of treatment schedules in vitro screening assays are required to understand the effects of resistance and guide more realistically in vivo and clinical studies.

Lung Cancer Radiotherapy: Simulation and Analysis Based on a Multicomponent Mathematical Model

Background

Lung cancer has been one of the most deadly illnesses all over the world, and radiotherapy can be an effective approach for treating lung cancer. Now, mathematical model has been extended to many biomedical fields to give a hand for analysis, evaluation, prediction, and optimization.

Methods

In this paper, we propose a multicomponent mathematical model for simulating the lung cancer growth as well as radiotherapy treatment for lung cancer. The model is digitalized and coded for computer simulation, and the model parameters are fitted with many research and clinical data to provide accordant results along with the growth of lung cancer cells in vitro.

Results

Some typical radiotherapy plans such as stereotactic body radiotherapy, conventional fractional radiotherapy, and accelerated hypofractionated radiotherapy are simulated, analyzed, and discussed. The results show that our mathematical model can perform the basic work for analysis and evaluation of the radiotherapy plan.

Conclusion

It will be expected that in the near future, mathematical model will be a valuable tool for optimization in personalized medical treatment.

Constructing and analysing dynamic models with modelbase v1.2.3: a software update

BACKGROUND

Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies, and disease evolution and transmission. Unfortunately, despite community efforts leading to the development of SBML and the BioModels database, many published models have not been fully exploited, largely due to a lack of proper documentation or the dependence on proprietary software. To facilitate the reuse and further development of systems biology and systems medicine models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent, and reproducible is desired.

RESULTS AND DISCUSSION

We provide an update on the development of modelbase, a free, expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow for convenient analysis of the structural and dynamic properties of models. After specifying reaction stoichiometries and rate equations modelbase can automatically assemble the associated system of differential equations. A newly provided library of common kinetic rate laws reduces the repetitiveness of the computer programming code. modelbase is also fully compatible with SBML. Previous versions provided functions for the automatic construction of networks for isotope labelling studies. Now, using user-provided label maps, modelbase v1.2.3 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in modelbase is growing continuously. Ranging from photosynthesis to tumour cell growth to viral infection evolution, all these models are now available in a transparent, reusable and unified format through modelbase.

CONCLUSION

With this new Python software package, which is written in currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others. modelbase enables reproducing and replicating models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their isotopic label-specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.

Signaling pathways in intestinal homeostasis and colorectal cancer: KRAS at centre stage

The intestinal epithelium acts as a physical barrier that separates the intestinal microbiota from the host and is critical for preserving intestinal homeostasis. The barrier is formed by tightly linked intestinal epithelial cells (IECs) (i.e. enterocytes, goblet cells, neuroendocrine cells, tuft cells, Paneth cells, and M cells), which constantly self-renew and shed. IECs also communicate with microbiota, coordinate innate and adaptive effector cell functions. In this review, we summarize the signaling pathways contributing to intestinal cell fates and homeostasis functions. We focus especially on intestinal stem cell proliferation, cell junction formation, remodelling, hypoxia, the impact of intestinal microbiota, the immune system, inflammation, and metabolism. Recognizing the critical role of KRAS mutants in colorectal cancer, we highlight the connections of KRAS signaling pathways in coordinating these functions. Furthermore, we review the impact of KRAS colorectal cancer mutants on pathway rewiring associated with disruption and dysfunction of the normal intestinal homeostasis. Given that KRAS is still considered undruggable and the development of treatments that directly target KRAS are unlikely, we discuss the suitability of targeting pathways downstream of KRAS as well as alterations of cell extrinsic/microenvironmental factors as possible targets for modulating signaling pathways in colorectal cancer.

Precision Oncology, Signaling and Anticancer Agents in Cancer Therapeutics

BACKGROUND

The global alliance for genomics and healthcare facilities provides innovational solutions to expedite research and clinical practices for complex and incurable health conditions. Precision oncology is an emerging field explicitly tailored to facilitate cancer diagnosis, prevention and treatment based on patients' genetic profile. Advancements in "omics" techniques, next-generation sequencing, artificial intelligence and clinical trial designs provide a platform for assessing the efficacy and safety of combination therapies and diagnostic procedures.

METHOD

Data were collected from Pubmed and Google scholar using keywords: "Precision medicine", "precision medicine and cancer", "anticancer agents in precision medicine" and reviewed comprehensively.

RESULTS

Personalized therapeutics including immunotherapy, cancer vaccines, serve as a groundbreaking solution for cancer treatment. Herein, we take a measurable view of precision therapies and novel diagnostic approaches targeting cancer treatment. The contemporary applications of precision medicine have also been described along with various hurdles identified in the successful establishment of precision therapeutics.

CONCLUSION

This review highlights the key breakthroughs related to immunotherapies, targeted anticancer agents, and target interventions related to cancer signaling mechanisms. The success story of this field in context to drug resistance, safety, patient survival and in improving quality of life is yet to be elucidated. We conclude that, in the near future, the field of individualized treatments may truly revolutionize the nature of cancer patient care.

A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients

BACKGROUND AND OBJECTIVES

Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule.

METHODS

We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L₁₆ orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients.

RESULTS

Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.

Monitoring of EGFR mutations in circulating tumor DNA of non-small cell lung cancer patients treated with EGFR inhibitors

PURPOSE

We studied EGFR mutations in circulating tumor DNA (ctDNA) and explored their role in predicting the progression-free survival (PFS) of non-small cell lung cancer (NSCLC) patients treated with erlotinib or gefitinib.

METHODS

The L858R, T790M mutations and exon 19 deletions were quantified in plasma using digital droplet polymerase chain reaction (ddPCR). The dynamics of ctDNA mutations over time and relationships with PFS were explored.

RESULTS

In total, 249 plasma samples (1-13 per patient) were available from 68 NSCLC patients. The T790M and L858R or exon 19 deletion were found in the ctDNA of 49 and 56% patients, respectively. The median (range) concentration in these samples were 7.3 (5.1-3688.7), 11.7 (5.1-12,393.3) and 27.9 (5.9-2896.7) copies/mL, respectively. Using local polynomial regression, the number of copies of EGFR mutations per mL increased several months prior to progression on standard response evaluation.

CONCLUSION

This change was more pronounced for the driver mutations than for the resistance mutations. In conclusion, quantification of EGFR mutations in plasma ctDNA was predictive of treatment outcomes in NSCLC patients. In particular, an increase in driver mutation copy number could predict disease progression.

Development and Validation of a Prediction Model of Overall Survival in High-Risk Neuroblastoma Using Mechanistic Modeling of Metastasis

Prognosis of high-risk neuroblastoma (HRNB) remains poor despite multimodal therapies. Better prediction of survival could help to refine patient stratification and better tailor treatments. We established a mechanistic model of metastasis in HRNB relying on two processes: growth and dissemination relying on two patient-specific parameters: the dissemination rate μ and the minimal visible lesion size S_vis. This model was calibrated using diagnosis values of primary tumor size, lactate dehydrogenase circulating levels, and the meta-iodobenzylguanidine International Society for Paediatric Oncology European (SIOPEN) score from nuclear imaging, using data from 49 metastatic patients. It was able to describe the data of total tumor mass (lactate dehydrogenase, R² > 0.99) and number of visible metastases (SIOPEN, R² = 0.96). A prediction model of overall survival (OS) was then developed using Cox regression. Clinical variables alone were not able to generate a model with sufficient OS prognosis ability (P = .507). The parameter μ was found to be independent of the clinical variables and positively associated with OS (P = .0739 in multivariable analysis). Critically, addition of this computational biomarker significantly improved prediction of OS with a concordance index increasing from 0.675 (95% CI, 0.663 to 0.688) to 0.733 (95% CI, 0.722 to 0.744, P < .0001), resulting in significant OS prognosis ability (P = .0422).