Abstract B26: NSCLC personalized medicine via integration into biomathematical model individual clinical data and prognostic/predictive biomarkers
Authors: Zohar Assaf , Peled Nir , Amir Orna , Alumot-Yehoshua Michal , Kleiman Marina 
Published: March 2013

Abstract
Introduction: Over the last decades many new chemo and targeted therapies were developed and approved to treat patients with non-small-cell lung cancer (NSCLC). Nevertheless, little progress has been achieved in improving the survival of these patients. Considering the heterogeneity of human cancer, it is well accepted that the ‘one-size-fits-all’ approach is suboptimal, and the need of personalized cancer therapy has been recognized.
In light of these needs, PrediCare™ is medical software that provides personalized predictions of efficacy and drug induced toxicity for NSCLC patients. Accordingly, PrediCare™ is a supportive system for Oncologists in choosing the optimal tailored treatment for a patient considering the balance between efficacy, toxicity, and quality of life, given the approved treatment options. PrediCare™ is based on Optimata Virtual Patient® (OVP™) technology, a set of mechanistic and statistical bio-mathematical models that simulate the progression of solid tumor and effect of treatment. Integrating individual patient data (imaging, histopathology, blood analysis, urine analysis & genotype) into PrediCare™ will generate personal set of parameters that will be utilized to simulate tumor progression and effect of treatment.
Methods: PrediCare™ is composed of the following main components:
Drug Pharmacokinetics/Pharmacodynamics Module: models of drugs, as mono or combination therapies.Efficacy Module: simulates the predicted effect of different drugs on tumor size and subsequently on response and/or time to progression (TTP).Toxicity module: simulates the predicted likelihood for adverse events following administration of different drugs.Database: storage of PK/PD, Efficacy and Toxicity module background data as well as personal patient data.Engine: handles, executes, and integrate the different processes (models) and connect them with the database.Results Analyzer: Interpret the simulation results into clinical standards based on RECIST criteria and Common Toxicity Criteria.
Personalizing the biomathematical models will be achieved in collaboration with Sheba Medical Center. Specifically, retrospective clinical data from 100 of NSCLC patients will be collected. The models will be fitted to the individual clinical outcomes and correlations between the fitted parameters and the individual data will be analyzed. Cross validation, predictions accuracy, bias and relative error will be performed to guarantee rigorous and quantitative personalized predictions.
Summary: Developing PrediCare™ will address the major unmet need in personalized oncology, particularly: 1/Predicting optimal treatment for individual patients, 2/Developing individual screening programs based on best practices, technologies and research, and 3/Facilitating and accelerating oncology drug development through patient stratification and recommended treatment strategies.
Conclusions: Implementing PrediCare™ in Medical Centers offers integration of a broad range of medical examinations and treatment options with unique predictive technology. This will assist health care providers in treating patients on an individual basis, insurance companies for efficient resources allocation and pharmaceutical companies for targeted clinical trials and informed Go/No-Go decision, ultimately leading to significant improvements of the treatment provided to the patients.