Predictive Modeling of Drug Treatment in the Area of Personalized Medicine

Abstract

Despite a growing body of knowledge on the mechanisms underlying the onset and progression of cancer, treatment success rates in oncology are at best modest. Current approaches use statistical methods that fail to embrace the inherent and expansive complexity of the tumor/patient/drug interaction. Computational modeling, in particular mechanistic modeling, has the power to resolve this complexity. Using fundamental knowledge on the interactions occurring between the components of a complex biological system, large-scale in silico models with predictive capabilities can be generated. Here, we describe how mechanistic virtual patient models, based on systematic molecular characterization of patients and their diseases, have the potential to shift the theranostic paradigm for oncology, both in the fields of personalized medicine and targeted drug development. In particular, we highlight the mechanistic modeling platform ModCell™ for individualized prediction of patient responses to treatment, emphasizing modeling techniques and avenues of application.

Keywords: cancer; drug development; mechanistic models; ordinary differential equations; virtual clinical trials; virtual patient models.

Figure 1

Overview of the ModCell™ predictive modeling approach in oncology. ModCell™ uses publicly available resources, representing the sum of knowledge on cancer, cell signaling, and drug action (eg, dissociation constants and molecular targets), to construct a large-scale mechanistic model of cellular signaling. A generic large-scale signaling network is established, which can be personalized with omics data (eg, transcriptome/exome/proteome) from individual patient tumors/cell lines/experimental tissues (public and/or private data resources). The effects of identified molecular alterations on pathway function and cross-talk can then be simulated using the mechanistic modeling approach implemented by ModCell™ and the underlying PyBioS modeling framework. Response to molecularly targeted drugs (single or in combination) can be predicted through establishment of a molecular readout (eg, MYC levels, phosphorylation status of TP53, cleavage of PARP1, and GTP loading status of RAC1 and CDC42) as a proxy for phenotypic effects (eg, cell proliferation, senescence, apoptosis induction and cell migration), allowing identification of the optimal treatment.

Figure 2

Applications of virtual patient modeling in oncology. The ability to predict the effects of drugs in silico opens up numerous avenues of application, from personalized medicine in the clinic to virtual clinical trial scenarios, enabling in silico testing of drug effects (single or combination) and potential side effects on individual or large patient (or preclinical model) cohorts. In virtual clinical trial scenarios, the patients who are most likely to benefit from a particular drug/drug combination can be selected, based on biomarkers identified, for inclusion in smaller, less risky, and less-expensive real-life clinical trials. A test bed is also created for assessing the efficacy of existing (drug repurposing) or failed drugs (‘fallen angels’), again providing a low risk and cost-effective route for further development. For early drug development, in silico models can be deployed for selecting the most relevant drugs/models for further development.