Computational Approaches in Theranostics: Mining and Predicting Cancer Data.

Tânia F G G Cova,Daniel J Bento,Sandra C C Nunes

doi:10.3390/pharmaceutics11030119

Tânia F G G Cova, Daniel J Bento + Show 1 more

Open Access

https://doi.org/10.3390/pharmaceutics11030119

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Journal: Pharmaceutics	Publication Date: Mar 13, 2019
Citations: 19	License type: CC BY 4.0

Affiliation: University of Coimbra

Abstract

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Highlights

The highly intricate nature of cancer makes the approaches to managing and rationalizing large dimensional cancer data notably different from those used in other types of diseases [1,2,3]
Cancer data have been associated with myriads of parameters and multiple genome variations and analyzed at the cellular, patient, and population levels [2,4,5,6,7,8], which prevents the establishment of a definite, one-size-fits-all treatment solution
This review provides a timely compilation of the key in silico contributions and advances in cancer theranostics technologies

Summary

Introduction

The highly intricate nature of cancer makes the approaches to managing and rationalizing large dimensional cancer data notably different from those used in other types of diseases [1,2,3]. Fundamental aspects on the cellular and molecular basis of cancer have been explored through the establishment of relevant biological networks [9,10,11,12,13,14,15,16,17] This has been facilitated by combining information from cancer genomic, transcriptomic, proteomic, and metabolomic data and computational techniques, aiming at developing non-invasive methods for diagnostic purposes [9]. The plenty ways in which computational models and methods are employed to facilitate research of large-dimensional data found in cancer diagnosis, drug development, formulation and optimization, drug repurposing, tumor imaging, and cancer data analytics applications, are briefly presented

Connecting Computational Approaches and Theranostics

Relating In Silico and In Vivo Models

Different Models and Different Scales

Optimizing Diagnostic and Therapeutic Agents

Mapping Multidimensional Cancer Data