Introduction: In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after a very long and expensive development period. Alternative methodologies such as computer modeling and Clinical Trial Simulation (CTS) are a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the pre-clinical phase to post-marketing. However, they are seldom used and are poorly regarded for drug approval, despite FDA and EMA recommendations. We propose to discuss the principle behind, and the interest of, CTS approaches in drug development in oncology. Our work is based on a systematic review using two electronic literature databases (Medline and Web of Science).

Key components of CTS: We will explain why and how to successfully develop CTS.

Why developing CTS?

To obtain a better knowledge of pharmacology and/or better prediction of safety and efficacy

To evaluate disease progression dynamics and to test the potential impact of biomarkers

To analyze subpopulations and extend the post-marketing authorization to help in the choice of the best outcome

To optimize experimental designs in order to better anticipate the progress of the study

How to develop CTS?

CTS project must follow three steps:

1/The constitution of pharmacometricians team who elaborates clinical model and simulation plan that must contain three components:

Input/output model: How do covariates impact outcomes?

Covariate distribution Model: How can we generate virtual patients?

Execution model: What can happen during the clinical trial?

2/Obtaining data for simulations. We will present the contribution of machine learning for CTS

3/The use of specialized software to realize simulations

Limitations of CTS: We will present the features that can slow down or accelerate the development of CTS. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by the clinical trials studies and hospital databases. Data-sharing and data-merging raise legal, policy and technical issues that will need to be addressed.

CTS challenges and prospects: Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increase of the computing speed should allow this leap forward. The realization of CTS requires not only the bioinformatics tools to allow interconnection and global integration of all clinical data, but also a universal legal framework to protect the privacy of every patient. CTS will have to be used to provide quantitative support for drug development decisions but will never replace real clinical trials. This in silico medicine opens the way to the 4P medicine: predictive, preventive, personalized and participatory.

Citation Format: Jocelyn Gal, Gerard Milano, Julien Viotti, Renaud Schiappa, Audrey Dugue, Agnes Paquet, Sylvie Chabaud, Jean-Marc Ferrero, Emmanuel Chamorey. Optimizing drug regimens in oncology by clinical trial simulations: Why and how [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 773. doi:10.1158/1538-7445.AM2017-773