In addition, a hierarchical clustering with Pearson correlation distance determined and as potential predictive factors of tumor volume changes (Fig. reactions in the medical trial. Analyses on simulated cohorts exposed key model guidelines such as a tumor volume doubling rate and a therapy-induced phenotypic switch rate that may have medical correlates. Finally, our approach predicts ideal AKT inhibitor scheduling suggesting more effective but less harmful treatment strategies. Summary Our proposed computational platform to implement phase trials in malignancy can readily capture observed heterogeneous medical results and predict patient survival. Importantly, phase trials can be used to optimize long term medical trial design. kinase inhibitors (3)), the majority are not (4-6) despite the fact that such agents possess potent activity in preclinical malignancy cell and animal model studies. The best cause of failure tends to be lack of effectiveness, in part due to lack of powerful predictive models that consider patient heterogeneity, and poorly designed medical tests (6-9). This inconsistency is also partly due to problems in predicting the long-term performance of a tumor therapy using time-limited (typically < one month) or (often < 3 months) model systems. We reasoned that an appropriately defined and parameterized mathematical model, based on observations in cell and animal studies and medical trials, might reveal RAD140 insights concerning the design of improved and educated restorative methods for treating tumor individuals. We consider the recently completed multi-arm phase 1 trial of the MK2206 AKT inhibitor in combination with standard chemotherapy with advanced solid tumors, including melanomas (ClinicalTrials.gov, trial quantity: "type":"clinical-trial","attrs":"text":"NCT00848718","term_id":"NCT00848718"NCT00848718) (10). To investigate potential mechanisms of treatment effectiveness, a mathematical model comprised of a system of regular differential equations was developed to describe the dynamics of melanoma cells exposed to four treatment conditions, no treatment, chemo, AKTi and combination of chemo and RAD140 AKTi. Cell culture experiments were then used to parameterize the model. The calibrated model was further validated using results from an extensive series of cell culture experiments that consider twelve different drug combinations and timings. This validated model was then used to predict the long-term effects of the twelve treatments on melanoma cells, which revealed that all treatments eventually fail, but do so at significantly different rates. To investigate the long-term effects of therapy in a more clinically relevant setting, we varied model parameters to generate virtual patients that experienced a heterogeneous mix of responses much like typical clinical trial outcomes. We employed a genetic algorithm (GA) to generate a diverse virtual patient cohort consisting of over 3,000 patients. Statistical analyses of the simulated RAD140 cohort showed that the treatment responses of 300 virtual patients sampled from your cohort matched actual patient responses in the trial (10). Analyses of total virtual individual cohort defined parameters that discriminated virtual patients having more favorable versus less favorable outcomes. Finally, the model predicts optimal therapeutic methods across all virtual patients. This strategy allowed implementation of a virtual clinical trial (phase trial) (11). Comparable virtual clinical trials have been developed to simulate clinical trials of cardiovascular disease, hypertension, diabetes (www.entelos.com), and acute inflammatory diseases (12). There have also been some previous studies that employed modeling approaches to predict outcomes of clinical trials (13, 14). Statistical methods based on clinical drug metabolism (experiments with clinical studies on melanoma combination therapy, into a phase trial. Results Mathematical Modeling and Underlying Assumptions We reported unexpectedly long-term responses (of up to 15 months) to the combination therapy of chemotherapy (chemo) and AKT inhibitor (AKTi, MK2206) in two studies showed that while AKTi did not augment cell deaths or effectively inhibit melanoma cell growth (16), it did induce autophagy; thus, we assumed that AKTi increases the rate of transitioning to the autophagy phenotypes, and (Fig. S2, black arrows). As combination therapy Gata2 does not augment cell death compared with chemo, nor significantly increase autophagy relative to AKTi, the combination of the two treatments was modeled by adding the effects of chemo and AKTi (16) (Fig. S2, black arrows and crosses). Finally, no cells with a given phenotype can revert to their initial says in the model while any treatment is being applied. The schematic.