Highlights
- •We present a mathematical model driven framework for virtual clinical trials (phase i trials).
- •The phase i trial paradigm: model development, cohort generation, stratification, and optimizing treatment schedules.
- •Phase i trial in melanoma captured the observed heterogeneity of patient responses to treatment.
- •Phase i trial stratified cohort predicts main differentiators of patient survival.
- •Model predicted optimal treatment schedule not only improved survival but also decreased toxicity.
Abstract
Background
One of major issues in clinical trials in oncology is their high failure rate, despite
the fact that the trials were designed based on the data from successful equivalent
preclinical studies. This is in part due to the intrinsic homogeneity of preclinical
model systems and the contrasting heterogeneity of actual patient responses.
Methods
We present a mathematical model-driven framework, phase i (virtual/imaginary) trials, that integrates the heterogeneity of actual patient responses
and preclinical studies through a cohort of virtual patients. The framework includes
an experimentally calibrated mathematical model, a cohort of heterogeneous virtual
patients, an assessment of stratification factors, and treatment optimisation. We
show the detailed process through the lens of melanoma combination therapy (chemotherapy
and an AKT inhibitor), using both preclinical and clinical data.
Results
The mathematical model predicts melanoma treatment response and resistance to mono
and combination therapies and was calibrated and then validated with in vitro experimental data. The validated model and a genetic algorithm were used to generate
virtual patients whose tumour volume responses to the combination therapy matched
statistically the actual heterogeneous patient responses in the clinical trial. Analyses
on simulated cohorts revealed key model parameters such as a tumour volume doubling
rate and a therapy-induced phenotypic switch rate that may have clinical correlates.
Finally, our approach predicts optimal AKT inhibitor scheduling suggesting more effective
but less toxic treatment strategies.
Conclusion
Our proposed computational framework to implement phase i trials in cancer can readily capture observed heterogeneous clinical outcomes and
predict patient survival. Importantly, phase i trials can be used to optimise future clinical trial design.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to European Journal of CancerAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Targeted cancer therapy.Nature. 2004; 432: 294-297
- Overview of Targeted Therapies for Cancer. My Cancer Genome.2016 (https://www.mycancergenome.org/content/molecular-medicine/overview-of-targeted-therapies-for-cancer/ (Updated August 8))
- Improved survival with vemurafenib in melanoma with BRAF V600E mutation.N Engl J Med. 2011; 364: 2507-2516
- Trial watch: phase III and submission failures: 2007–2010.Nat Rev Drug Discov. 2011; 10: 87
- Assumptions of expected benefits in randomized phase III trials evaluating systemic treatments for cancer.J Natl Cancer Inst. 2012; 104: 590-598
- Oncology clinical trials – secrets of success. BIOtechNOW: Biotechnology Industry Organization, 2012
- Translational research: 4 ways to fix the clinical trial.Nature. 2011; 477: 526-528
- Uncertainty in the translation of preclinical experiments to clinical trials. Why do most phase III clinical trials fail?.Curr Gene Ther. 2009; 9: 368-374
- Lost in translation: animal models and clinical trials in cancer treatment.Am J Transl Res. 2014; 6: 114-118
- Phase 1 trial of the oral AKT inhibitor MK-2206 plus carboplatin/paclitaxel, docetaxel, or erlotinib in patients with advanced solid tumors.J Hematol Oncol. 2014; 7: 1
- Phase i trialist.Lancet Oncol. 2012; 13: 236
- In silico design of clinical trials: a method coming of age.Crit Care Med. 2004; 32: 2061-2070
- Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics.J Clin Oncol. 2009; 27: 4103-4108
- Prediction of early death among patients enrolled in phase I trials: development and validation of a new model based on platelet count and albumin.Br J Cancer. 2012; 107: 1025-1030
- Clinical trial simulation: a review.Clin Pharmacol Ther. 2010; 88: 166-182
- Inhibition of autophagy enhances the effects of the AKT inhibitor MK-2206 when combined with paclitaxel and carboplatin in BRAF wild-type melanoma.Pigment Cell Melanoma Res. 2014; 27: 465-478
- The role of autophagy in cancer development and response to therapy.Nat Rev Cancer. 2005; 5: 726-734
- Deconvoluting the context-dependent role for autophagy in cancer.Nat Rev Cancer. 2012; 12: 401-410
- MK-2206, a novel allosteric inhibitor of Akt, synergizes with gefitinib against malignant glioma via modulating both autophagy and apoptosis.Mol Cancer Ther. 2012; 11: 154-164
- Targeted activation of innate immunity for therapeutic induction of autophagy and apoptosis in melanoma cells.Cancer Cell. 2009; 16: 103-114
- Autosis and autophagic cell death: the dark side of autophagy.Cell Death Differ. 2015; 22: 367-376
- Bcl-2 antiapoptotic proteins inhibit Beclin 1-dependent autophagy.Cell. 2005; 122: 927-939
- Dual functions of autophagy in the response of breast tumor cells to radiation: cytoprotective autophagy with radiation alone and cytotoxic autophagy in radiosensitization by vitamin D 3.Autophagy. 2012; 8: 739-753
- The four faces of autophagy: implications for cancer therapy.Cancer Res. 2014; 74: 647-651
- Clinical pharmacokinetics and pharmacodynamics of paclitaxel: a 3-hour infusion versus a 24-hour infusion.Clin Cancer Res. 1995; 1: 599-606
- A clinical and pharmacokinetic study of the combination of carboplatin and paclitaxel for epithelial ovarian cancer.Br J Cancer. 1997; 75: 287-294
- First-in-man clinical trial of the oral pan-AKT inhibitor MK-2206 in patients with advanced solid tumors.J Clin Oncol. 2011; 29: 4688-4695
- Implicit filtering.Society for Industrial and Applied Mathematics, Philadelphia2011
- Targeting ER stress-induced autophagy overcomes BRAF inhibitor resistance in melanoma.J Clin Invest. 2014; 124: 1406-1417
- Genetic algorithms in search, optimization, and machine learning.Addison-Wesley Pub. Co., Reading, Mass1989
- Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence.University of Michigan Press, Ann Arbor1975
- New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).Eur J Cancer. 2009; 45: 228-247
- The Kolmogorov-Smirnov test for goodness of fit.J Am Stat Assoc. 1951; 46: 68-78
- Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance.Nature. 2013; 494: 251-255
- Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedules.Cell. 2014; 156: 603-616
Article info
Publication history
Published online: September 27, 2016
Accepted:
July 25,
2016
Received in revised form:
July 20,
2016
Received:
April 15,
2016
Identification
Copyright
© 2016 Elsevier Ltd. All rights reserved.
