Mathematic Modeling Predicts Drug Response in Virtual Patient

A new theranostic method for prediction of personalized therapy combines tumor xenografts with mathematical models.

There is a paucity of clinical treatment data on rare tumors such as mesenchymal chondrosarcoma (MCS). The new method was validated for determining an improved treatment schedule for an MCS patient suffering from severe myelosuppression with pancytopenia.

Growth curves and gene expression analysis of xenografts in mice, derived from the patient's lung metastasis, served for creating a mathematical model of MCS progression and adapting it to the xenograft setting. The pharmacokinetics (PK) and pharmacodynamics (PD) of six drugs were modeled, and model variables were adjusted by patient-specific chemosensitivity tests.

Simulation of the adapted tumor growth model was performed in conjunction with the relevant human PK/PD models and particular dosing regimens. Where available, patient-specific chemosensitivity information was used to fine-tune the PD models. Other publicly available data were used for the mathematical PK and PD models of the drugs in the xenograft experiments. Following the initial assessment of model prediction accuracy, the variables of several drug models were reevaluated and the accuracy reassessed.

The mathematical model was adjusted to describe the patient's metastatic growth dynamics using gene expression analysis of key proteins in mice and humans, and various treatment regimens were tested. The efficacy of different treatments may be assessed on xenograft models only, but this method is slow, costly, and does not reflect human physiology; it also does not consider patient safety.

The study was carried out by Professor Zvia Agur and her colleagues from Optimata Ltd (Ramat Gan, Israel) together with scientists in the United States. A description of the study was published in the November 1, 2008 issue of the journal Cancer Research.

The MSC patient was treated according to the recommended regimen of drugs based on the simulations. Professor Agur said that after treatment the patient lived for almost a year with a greatly improved quality of life, going back to work and playing tennis. Eventually the patient succumbed to pulmonary progression of the disease.

Prof. Agur is the founder, chairperson, and chief scientific officer of Optimata Ltd and president of the Institute for Biomathematics (IMBM; Bene Ataroth, Israel).

Optimata is a modeling-based biopharmaceutic company, providing comprehensive solutions and navigating drug development through shorter, safer pathways. Expert in predictive biosimulation, Optimata mathematically models patient physiological and pathological processes, along with the dynamics of drug-patient interactions, with a special focus on cancer and oncology drugs.

Simulated by Optimata virtual patient software, these models provide valuable drug effect predictions, with recommendations of optimal treatment regimens per clinical indication and patient population.

IMBM is an independent research institute, which uses analytic and computational approaches for optimizing the treatment of cancer and infectious diseases.

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