Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

Abstract

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system’s pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.

Looking beyond the cancer cell for effective drug combinations

“…This approach takes into account only the effects of treatment (pharmacodynamics); modeling also the pharmacokinetics (how the organism deals with the therapy) adds yet another level of complexity, particularly as in this case one drug targets the vasculature responsible for delivering the drug and the immune cells to the tumor. A multiscale approach is required to take into account all the molecular, cellular, and physiological layers of processes occurring in an organism with cancer, including the effects of drugs and of the organism’s own immune system [87]. …”

“…For this reason, hybrid strategies that combine different methodologies are likely to be needed to build such models. Indeed, such multiformalism models are becoming increasingly popular [92], and a range of approaches have been reported to link the macroscopic aspects of cancer, such as tumor growth, with the effects of specific therapies [87]. …”

Systems biology for organotypic cell cultures

“…The AOP framework enables a systems-level understanding of exposure to a potentially toxic substance by describing a cascade of multiscale events from exposure to adverse effect, with the goal of a more effective risk assessment. A multiscale computational modeling approach to describing biological processes, which has been proposed by multiple groups (Li et al, 2015), has so far been limited by the availability of in vitro data at the right scales. To date, there has been little effort to model the cell-cell and cell-microenvironment interactions reflected in physiologically relevant, organotypic tissue models.…”

A Better, Faster Road From Biological Data to Human Health: A Systems Biology Approach for Engineered Cell Cultures

“…For such phenomena, modeling using probabilistic, stochastic methods is more predictive than purely deterministic models. 13 The systems biology approach can offer advantages in cases in which purely experimental approaches would take too many resources. This utility is perhaps best demonstrated in combinatorial drug studies.…”