Computational modeling of therapy on pancreatic cancer in its early stages

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.

Mathematical model of mechano-sensing and mechanically induced collective motility of cells on planar elastic substrates

Cells mechanically interact with their environment to sense, for example, topography, elasticity and mechanical cues from other cells. Mechano-sensing has profound effects on cellular behaviour, including motility. The current study aims to develop a mathematical model of cellular mechano-sensing on planar elastic substrates and demonstrate the model's predictive capabilities for the motility of individual cells in a colony. In the model, a cell is assumed to transmit an adhesion force, derived from a dynamic focal adhesion integrin density, that locally deforms a substrate, and to sense substrate deformation originating from neighbouring cells. The substrate deformation from multiple cells is expressed as total strain energy density with a spatially varying gradient. The magnitude and direction of the gradient at the cell location define the cell motion. Cell-substrate friction, partial motion randomness, and cell death and division are included. The substrate deformation by a single cell and the motility of two cells are presented for several substrate elasticities and thicknesses. The collective motility of 25 cells on a uniform substrate mimicking the closure of a circular wound of 200 µm is predicted for deterministic and random motion. Cell motility on substrates with varying elasticity and thickness is explored for four cells and 15 cells, the latter again mimicking wound closure. Wound closure by 45 cells is used to demonstrate the simulation of cell death and division during migration. The mathematical model can adequately simulate the mechanically induced collective cell motility on planar elastic substrates. The model is suitable for extension to other cell and substrates shapes and the inclusion of chemotactic cues, offering the potential to complement in vitro and in vivo studies.

An individualized digital twin of a patient for transdermal fentanyl therapy for chronic pain management

Fentanyl transdermal therapy is a suitable treatment for moderate-to-severe cancer-related pain. The inter-individual variability of the patients leads to different therapy responses. This study aims to determine the effect of physiological features on the achieved pain relief. Therefore, a set of virtual patients was developed by using Markov chain Monte Carlo (MCMC) based on actual patient data. The members of this virtual population differ by age, weight, gender, and height. Tailored digital twins were developed using these correlated, individualized parameters to propose a personalized therapy for each patient. It was shown that patients of different ages, weights, and gender have significantly different fentanyl blood uptake, plasma fentanyl concentration, pain relief, and ventilation rate. In the digital twins, we included the virtual patients' response to the treatment, namely, pain relief. Therefore, the digital twin was able to adjust the therapy in silico to have more efficient pain relief. By implementing digital-twin-assisted therapy, the average pain intensity decreased by 16% compared to conventional therapy. The median time without pain increased by 23 h over 72 h. Therefore, the digital twin can be successfully used in individual control of transdermal therapy to reach higher pain relief and maintain steady pain relief. (Created with BioRender.com).

Photodynamic Therapy and Hyperthermia in Combination Treatment-Neglected Forces in the Fight against Cancer

Cancer is one of the leading causes of death in humans. Despite the progress in cancer treatment, and an increase in the effectiveness of diagnostic methods, cancer is still highly lethal and very difficult to treat in many cases. Combination therapy, in the context of cancer treatment, seems to be a promising option that may allow minimizing treatment side effects and may have a significant impact on the cure. It may also increase the effectiveness of anti-cancer therapies. Moreover, combination treatment can significantly increase delivery of drugs to cancerous tissues. Photodynamic therapy and hyperthermia seem to be ideal examples that prove the effectiveness of combination therapy. These two kinds of therapy can kill cancer cells through different mechanisms and activate various signaling pathways. Both PDT and hyperthermia play significant roles in the perfusion of a tumor and the network of blood vessels wrapped around it. The main goal of combination therapy is to combine separate mechanisms of action that will make cancer cells more sensitive to a given therapeutic agent. Such an approach in treatment may contribute toward increasing its effectiveness, optimizing the cancer treatment process in the future.

Translating complexity and heterogeneity of pancreatic tumor: 3D in vitro to in vivo models

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive type of cancer with an overall survival rate of less than 7-8%, emphasizing the need for novel effective therapeutics against PDAC. However only a fraction of therapeutics which seemed promising in the laboratory environment will eventually reach the clinic. One of the main reasons behind this low success rate is the complex tumor microenvironment (TME) of PDAC, a highly fibrotic and dense stroma surrounding tumor cells, which supports tumor progression as well as increases the resistance against the treatment. In particular, the growing understanding of the PDAC TME points out a different challenge in the development of efficient therapeutics - a lack of biologically relevant in vitro and in vivo models that resemble the complexity and heterogeneity of PDAC observed in patients. The purpose and scope of this review is to provide an overview of the recent developments in different in vitro and in vivo models, which aim to recapitulate the complexity of PDAC in a laboratory environment, as well to describe how 3D in vitro models can be integrated into drug development pipelines that are already including sophisticated in vivo models. Hereby a special focus will be given on the complexity of in vivo models and the challenges in vitro models face to reach the same levels of complexity in a controllable manner. First, a brief introduction of novel developments in two dimensional (2D) models and ex vivo models is provided. Next, recent developments in three dimensional (3D) in vitro models are described ranging from spheroids, organoids, scaffold models, bioprinted models to organ-on-chip models including a discussion on advantages and limitations for each model. Furthermore, we will provide a detailed overview on the current PDAC in vivo models including chemically-induced models, syngeneic and xenogeneic models, highlighting hetero- and orthotopic, patient-derived tissues (PDX) models, and genetically engineered mouse models. Finally, we will provide a discussion on overall limitations of both, in vitro and in vivo models, and discuss necessary steps to overcome these limitations to reach an efficient drug development pipeline, as well as discuss possibilities to include novel in silico models in the process.

Digitally Fabricated and Naturally Augmented In Vitro Tissues

Human in vitro tissues are extracorporeal 3D cultures of human cells embedded in biomaterials, commonly hydrogels, which recapitulate the heterogeneous, multiscale, and architectural environment of the human body. Contemporary strategies used in 3D tissue and organ engineering integrate the use of automated digital manufacturing methods, such as 3D printing, bioprinting, and biofabrication. Human tissues and organs, and their intra- and interphysiological interplay, are particularly intricate. For this reason, attentiveness is rising to intersect materials science, medicine, and biology with arts and informatics. This report presents advances in computational modeling of bioink polymerization and its compatibility with bioprinting, the use of digital design and fabrication in the development of fluidic culture devices, and the employment of generative algorithms for modeling the natural and biological augmentation of in vitro tissues. As a future direction, the use of serially linked in vitro tissues as human body-mimicking systems and their application in drug pharmacokinetics and metabolism, disease modeling, and diagnostics are discussed.

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.

A Cellular Automata Model of Oncolytic Virotherapy in Pancreatic Cancer

Oncolytic virotherapy is known as a new treatment to employ less virulent viruses to specifically target and damage cancer cells. This work presents a cellular automata model of oncolytic virotherapy with an application to pancreatic cancer. The fundamental biomedical processes (like cell proliferation, mutation, apoptosis) are modeled by the use of probabilistic principles. The migration of injected viruses (as therapy) is modeled by diffusion through the tissue. The resulting diffusion–reaction equation with smoothed point viral sources is discretized by the finite difference method and integrated by the IMEX approach. Furthermore, Monte Carlo simulations are done to quantitatively evaluate the correlations between various input parameters and numerical results. As we expected, our model is able to simulate the pancreatic cancer growth at early stages, which is calibrated with experimental results. In addition, the model can be used to predict and evaluate the therapeutic effect of oncolytic virotherapy.

Mathematical Modeling to Address Challenges in Pancreatic Cancer

Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.