Colorectal cancer in Saudi Arabia as the proof-of-principle model for implementing strategies of predictive, preventive, and personalized medicine in healthcare

Colorectal cancer (CRC) is the most commonly diagnosed cancer among Saudi males and ranks third in females with up to 73% of cases diagnosed at late stage. This review provides an analysis of CRC situation in the Kingdom of Saudi Arabia (KSA) from healthcare perspective. A PUBMED (1986-2018) search was done to identify publications focusing on CRC in KSA. Due to reports of increased CRC incidence among young age group (< 50), and given the young population of KSA, the disease may burden the national healthcare system in the next decades. Environmental factors attributed to increasing incidence rates of CRC include red meat consumption, sedentary lifestyle, and increased calorie intake. Despite substantial investment in healthcare, attention to predictive diagnostics and targeted prevention is lacking. There is a need to develop national screening guidelines based on evidence that supports a reduction in incidence and mortality of CRC when screening is implemented. Future approaches are discussed based on multi-level diagnostics, risk assessment, and population screening programs focused on the needs of young populations that among others present the contents of the advanced approach by predictive, preventive, and personalized medicine. Recommendations are provided that could help to develop policies at regional and national levels. Countries with demographics and lifestyle similar to KSA may gain insights from this review to shape their policies and procedures.

Predictive Modeling of Drug Treatment in the Area of Personalized Medicine

Despite a growing body of knowledge on the mechanisms underlying the onset and progression of cancer, treatment success rates in oncology are at best modest. Current approaches use statistical methods that fail to embrace the inherent and expansive complexity of the tumor/patient/drug interaction. Computational modeling, in particular mechanistic modeling, has the power to resolve this complexity. Using fundamental knowledge on the interactions occurring between the components of a complex biological system, large-scale in silico models with predictive capabilities can be generated. Here, we describe how mechanistic virtual patient models, based on systematic molecular characterization of patients and their diseases, have the potential to shift the theranostic paradigm for oncology, both in the fields of personalized medicine and targeted drug development. In particular, we highlight the mechanistic modeling platform ModCell™ for individualized prediction of patient responses to treatment, emphasizing modeling techniques and avenues of application.

Article Commentary: Predictive Modeling of Drug Treatment in the Area of Personalized Medicine

Despite a growing body of knowledge on the mechanisms underlying the onset and progression of cancer, treatment success rates in oncology are at best modest. Current approaches use statistical methods that fail to embrace the inherent and expansive complexity of the tumor/patient/drug interaction. Computational modeling, in particular mechanistic modeling, has the power to resolve this complexity. Using fundamental knowledge on the interactions occurring between the components of a complex biological system, large-scale in silico models with predictive capabilities can be generated. Here, we describe how mechanistic virtual patient models, based on systematic molecular characterization of patients and their diseases, have the potential to shift the theranostic paradigm for oncology, both in the fields of personalized medicine and targeted drug development. In particular, we highlight the mechanistic modeling platform ModCell™ for individualized prediction of patient responses to treatment, emphasizing modeling techniques and avenues of application.

Personalized medicine approaches for colon cancer driven by genomics and systems biology: OncoTrack

The post-genomic era promises to pave the way to a personalized understanding of disease processes, with technological and analytical advances helping to solve some of the world's health challenges. Despite extraordinary progress in our understanding of cancer pathogenesis, the disease remains one of the world's major medical problems. New therapies and diagnostic procedures to guide their clinical application are urgently required. OncoTrack, a consortium between industry and academia, supported by the Innovative Medicines Initiative, signifies a new era in personalized medicine, which synthesizes current technological advances in omics techniques, systems biology approaches, and mathematical modeling. A truly personalized molecular imprint of the tumor micro-environment and subsequent diagnostic and therapeutic insight is gained, with the ultimate goal of matching the "right" patient to the "right" drug and identifying predictive biomarkers for clinical application. This comprehensive mapping of the colon cancer molecular landscape in tandem with crucial, clinical functional annotation for systems biology analysis provides unprecedented insight and predictive power for colon cancer management. Overall, we show that major biotechnological developments in tandem with changes in clinical thinking have laid the foundations for the OncoTrack approach and the future clinical application of a truly personalized approach to colon cancer theranostics.