This framework is intended to focus humans as model organism on different biological levels rather than whole-animal models [ 95 ]. The International Program on Chemical Safety has also published a framework for analyzing the relevance of a cancer mode of action for humans, formerly assessed for carcinogenesis in animals [ 98 ]. The postulated mode of action comprises a description of critical and measurable key events leading to cancer.
This framework has been integrated into the guidelines on risk assessment by the Environmental Protection Agency to provide a tool for harmonization and transparency of information on carcinogenic effect on humans, likewise intended to support risk assessors and also the research community. Noteworthy, next to frameworks, there are several common toxicological in silico techniques.
Especially similarity methods play a fundamental role in computational toxicology with QSAR modeling as the most prominent example [ 28 , 29 ]. QSARs mathematically relate structure-derived parameters, so-called molecular descriptors, to a measure of property or activity.
Thereby, regression analysis and classification methods are used to generate a continuous or categorical result as qualitative or quantitative endpoint [ 29 , 31 ]. Exemplary, models based on structure and activity data have been used to predict human toxicity endpoints for a number of carcinogens [ 22 , 99 — ]. Still, in order to predict drug efficacy and sensitivity, it is suggested to combine models on chemical features such as structure data with genomic features [ — ].
Combined, in silico methods can be used for both characterization and prediction.
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Thereby, simulations are frequently applied for the systematic analysis of cellular processes. Large-scale models on whole biological systems, including signal-transduction and metabolic pathways, face several challenges of accounted parameters at the cost of computing power [ ]. Still, the complexity and heterogeneity of cancer as well as the corresponding vast amount of available data, asks for a systemic approach such as computational modeling and machine learning [ , ].
Overall, in silico biological systems, especially integrated mathematical models, provide significant link and enrichment of in vitro and in vivo systems [ ]. Oncogenesis and tumor progression of each patient are characterized by multitude of genomic perturbation events, resulting in diverse perturbations of signaling cascades, and thus requiring thorough molecular characterization for designing effective targeted therapies [ ].
Precision medicine customizes healthcare by optimizing treatment to the individual requirements of a patient, often based on the genetic profile or other molecular biomarkers.
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This demands state-of-the-art diagnostic and prognostic tools, comprehensive molecular characterization of the tumor, as well as detailed electronic patient health records [ ]. Computational tools offer the possibility of identifying new entities in signaling cascades as biomarkers and promising targets for anticancer therapy. For example, the Human Protein Atlas provides data on the distribution and the expression of putative gene products in normal and cancer tissues based on immunohistochemical images annotated by pathologists.
This database provides cancer protein signatures to be analysed for potential biomarkers [ , ]. A different approach to the discovery of potential signaling targets is described by metabolomic profiling of biological systems which has been applied to find novel biomarkers for detection and prognosis of the disease [ — ]. Moreover, computational cancer biology and pharmacogenomics have been used for gene targeting by drug repositioning [ , ]. Computational drug repositioning is another example for in silico cancer research, by identifying novel use for FDA-approved drugs, based on available genomic, phenotypic data with the help of bioinformatics and chemoinformatics [ — ].
Cancer systems biology - Wikipedia
Computer-aided drug discovery and development have improved the efficiency of pharmaceutical research and link virtual screening methods, homology and molecular modeling techniques [ , ]. Pharmacological modeling of drug exposures helps to understand therapeutic exposure-response relationships [ ]. Systems pharmacology integrates pharmacokinetic and pharmacodynamic drug relations into the field of systems biology regarding the multiscale physiology [ ]. The discipline of pharmacometrics advances to personalized therapy by linking drug response modeling and health records [ ].
Polypharmacological effects of multi-drug therapies render exclusive wet lab experimentation unfeasible and require modeling frameworks such as system-level networks [ ]. Moreover, the network approach allows to understand variations within a single cancer disease regarding heterogeneous patient profiles, and in the process, to classify cancer subtypes and to identify novel drug targets [ 81 ]. Tumorigenesis is induced by driver mutations and embeds passenger mutations that both can result in upstream or downstream dysregulated signaling pathways [ ].
International Journal of Computational Biology and Drug Design
Computational methods have been used to distinguish driver and passenger mutations in cancer pathways by using public genomic databases available through collaborative projects such as the International Cancer Genome Consortium or The Cancer Genome Atlas TCGA [ 62 ] and others [ ], together with functional network analysis using de novo pathway learning methods or databases on known pathways such as Gene Ontology [ ], Reactome [ ] or the Kyoto Encyclopedia of Genes and Genomes KEGG [ — ].
These primary pathway databases, based on manually curated physical and functional protein interaction data, are essential for annotation and enrichment analysis. To increase proteome coverage of such analyses, pathways can be integrated with comprehensive protein-protein interaction data and data mining approaches to predict novel, functional protein:pathway associations [ ]. Comprehensive preclinical models on molecular features of cancer and diverse therapeutic responses have been built as pharmacogenomic resource for precision oncology [ , ].
Future efforts will need to expand integrative approaches to combine information on multiple levels of molecular aberrations in DNA, RNA, proteins and epigenetic factors [ 62 , ], as well as cellular aspects of the microenvironment and tumor purity [ ], in order to extend treatment efficacy and further refine precision medicine. Informatics in aid to biomedical research, especially in the field of cancer research, faces the challenge of an overwhelming amount of available data, especially in future regards to personalized medicine [ ].
Computational biology provides mathematical models and specialized algorithms to study and predict events in biological systems [ ]. Certainly, biomedical researchers from diverse fields will require computational tools in order to better integrate, annotate, analyze, and extract knowledge from large networks of biological systems. There is an evolution towards computational cancer research.
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In particular, in silico methods have been suggested for refining experimental programs of clinical and general biomedical studies involving laboratory work [ ]. The principles of the 3Rs can be applied to cancer research for the reduction of animal research, saving resources as well as reducing costs spent on clinical and wet lab experiments.
Computational modeling and simulations offer new possibilities for research. Cancer and biomedical science in general will benefit from the combination of in silico with in vitro and in vivo methods, resulting in higher specificity and speed, providing more accurate, more detailed and refined models faster.
In silico cancer models have been proposed as refinement [ ]. We further suggest the combination of in silico modeling and human computer interaction for knowledge discovery, gaining new insights, supporting prediction and decision making [ ]. Here, we provided some thoughts as a motivator for fostering in silico modeling towards 3R, in consideration of refinement of testing methods, and gaining a better understanding of tumorigenesis as tumor promotion, progression and dynamics.
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Whither systems medicine?
Hast Cent Rep. Tannenbaum J, Rowan AN. Rethinking the morality of animal research. Considering aspects of the 3rs principles within experimental animal biology.