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Hande R. Richardson R. This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access. Small cell lung cancer. Oldham There are no affiliations available. Personalised recommendations. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms.

Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments.

However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging.

Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost. We give an overview on in vivo, in vitro and in silico methods used in cancer research.

Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. Underlining and extending the in silico approach with respect to the 3Rs for replacement , reduction and refinement will lead cancer research towards efficient and effective precision medicine.

Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research. Cancer remains to be one of the top causes of disease-related death. Around one out of people will develop cancer each year, and every fourth will die from it [ 2 ]. Despite decades of research [ 3 ], mortality rates and recurrence remain high, and we have limited options for effective therapies or strategies regarding cancer prevention.

Tumor cells exhibit chaotic, heterogeneous and highly differentiated structures, which is determinative to the lack of effective anticancer drugs [ 4 ]. For that matter, predictive preclinical models that integrate in vivo, in vitro and in silico experiments, are rare but necessary for the process of understanding tumor complexity. A biological system comprises a multiplicity of interconnected dynamic processes at different time and spatial range. The complexity often hinders the ability to detail relationships between cause and effect. Model-based approaches help to interprete complex and variable structures of a system and can account for biological mechanisms.

Next to studying pathological processes or molecular mechanisms, they can be used for biomarker discovery, validation, basic approaches to therapy and preclinical testing.

Journal of Cancer Research and Therapeutics : Table of Contents

So far, preclinical research primarily involves in vivo models based on animal experimentation. Intertwining biological experiments with computational analyses and modeling may help to reduce the number of experiments required, and improve the quality of information gained from them [ 5 ].

Instead of broad high-throughput screens, focused screens can lead to increased sensitivity, improved validation rates, and reduced requirements for in vitro and in vivo experiments.

For Austria, the estimated number of laboratory animal kills per year was over [ 6 ]. In Germany the number of animal experiments for research is estimated as 2. Worldwide, the quantity of killed animals for research, teaching, testing and experimentation exceeds per year [ 6 — 14 ], as shown in Fig. Worldwide use of animals for studies. International comparison in numbers of animals used for experimentation, such as toxicology testing for cosmetics, food, drugs, research, teaching and education [ 6 — 14 ].

Principles for humane techniques were classified as replacement, reduction and refinement, also known as the 3Rs [ 15 ]. While most countries follow recommendations of Research Ethics Boards [ 16 ], discussion of ethical issues regarding the use of animals in research continues [ 17 ]. So far, 3R principles have been integrated into legislation and guidelines how to execute experiments using animal models, still, rethinking of refined experimentation will ultimately lead to higher-quality science [ 18 ].

The 3R concept also implies economic, ethical and academic sense behind sharing experimental animal resources, making biomedical research data scientifically easily available [ 19 ]. The idea behind 3R has been implemented in several programs such as Tox21 and ToxCast also offering high throughput assay screening data on several cancer-causing compounds for bioactivity profiles and predictive models [ 20 — 22 ].

It is clear that no model is perfect, and is lacking some aspects of reality. Thus, one has to choose and use appropriate models to advance specific experiments. Cancer research relies on diverse data from clinical trials, in vivo screens and validation studies, and functional studies using diverse in vitro experimental methods, such as cell-based models, spheroid systems, and screening systems for cytotoxicity, mutagenicity and cancerogenesis [ 23 , 24 ].

New technologies will advance in organ-on-a-chip technologies [ 25 ] but also include the in silico branch of systems biology with its goal to create the virtual physiological human [ 26 ]. These computational approaches include storage, exchange and use of information from past in vitro and in vivo experiments, predictions and modeling techniques [ 27 ]. In this regard, the term non-testing methods has been introduced, which summarizes the approach in predictive toxicology using previously given information for risk assessment of chemicals [ 28 ].

Such methods generate non-testing data by the general approach of grouping, quantitative structure-activity relationships QSAR or comprehensive expert systems, which are respectively based on the similarity principle [ 29 — 31 ]. The regulation of the European Union for registration, evaluation, authorisation and restriction of chemicals REACH promotes adaptation of in vivo experimentation under the conditions that non-testing methods or in vitro methods provide valid, reliable, relevant information, adequate for the intended purpose, or in case that testing is technically impossible [ 30 ].

Generally, in vitro and in silico are useful resources for predicting several bio chemical and patho physiological characteristics of likewise potential drugs or toxic compounds, but have not been fit for full pharmacokinetic profiling yet [ 32 ]. In vitro as well as in silico models abound especially in the fields of toxicology and cosmetics, based on cell culture, tissues and simulations [ 33 ]. In terms of 3R, in vitro techniques allow to reduce, refine and replace animal experiments.

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Still, wet biomedical research requires numerous resources from a variety of biological sources. In silico methods can further be used to augment and refine in vivo and in vitro models. Validation of computational models will still require results from in vivo and in vitro experiments. Though, in the long run, integrative approaches incorporating computational biology will reduce laboratory work in the first place and effectively succeed in 3R.

Within the next sections, we summarize common methods and novel techniques regarding in vivo, in vitro and in silico cancer research, presented as overview in Fig. Preclinical techniques for cancer research.

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Examples for experiments on the computer in silico , inside the living body in vivo , outside the living body ex vivo as well as in the laboratory in vitro. Animals are the primary resource for research on the pathogenesis of cancer.

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Animal models are commonly used for studies on cancer biology and genetics as well as the preclinical investigation of cancer-therapy and the efficacy and safety of novel drugs [ 34 ]. Animal models represent the in vivo counterpart to cell-lines and suspension culture, while being superior in terms of physiological relevance offering imitation of parental tumors and a heterogeneous microenvironment as part of an interacting complex biochemical system.

In general, animal models primarily based on murine or rodent models can be subdivided into the following groups of I xenograft models, which refer to the heterotopic, subcutaneous intraperitoneal or orthotopic implantation into SCID Severe Combined Immune Deficiency or nude mice, II syngenic models involving the implantation of cells from the same strain into non-immunocompromised mice, and III genetically engineered models, which allow for RNA interference, multigenic mutation, inducible or reversible gene expression [ 35 , 36 ].

Several engineered mouse models on cancer and related diseases have been developed so far [ 37 ]. In case of xenograft models, tumor-specific cells are transplanted into immunocompromised mice. Common tumor xenograft models lack the immune system response that can be crucial in tumor development and progression [ 38 ].

The transplantation of immortalized tumor cell-lines represents a simplified preclinical model with limited clinical application possibilities [ 39 ].

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For these reasons, there is a trend towards genetically engineered animal models, allowing for site-directed mutations on tumor-suppressor genes and proto-oncogenes as the basis for studies on oncogenesis [ 40 ]. Next to the gold standard of murine and rodent models, there are other animal model systems frequently used, such as the Drosophila melanogaster fruit fly or Danio rerio zebra fish [ 41 , 42 ].

The fruit fly offers the advantage of low-cost handling and easy mutant generation while it holds a substantially high conservation of the human cancer-related signaling apparatus [ 41 ]. There are additional animal models, commonly referred to as alternatives, such as zebra fish models for angiogenesis studies and chick embryo CAM chorioallantoic membrane models, offering rapid tumor formation due to the highly vascularized CAM structure [ 40 , 43 , 44 ]. So far, preclinical model systems do not provide sufficient information on target validation, but aid in identifying and selecting novel targets, while new strategies offer a quantitative translation from preclinical studies to clinical applications [ 45 ].

In vitro models offer possibilities for studying several cellular aspects as the tumor microenvironment using specific cell types, extracellular matrices, and soluble factors [ 46 ]. In vitro models are mainly based on either cell cultures of adherent monolayers or free-floating suspension cells [ 47 ]. They can be categorized into: I transwell-based models which include invasion and migration assays [ 48 ], II spheroid-based models involving non-adherent surfaces [ 49 ], hanging droplets and microfluidic devices [ 50 ], III tumor-microvessel models which come with predefined ECM extracellular matrix scaffolds and microvessel self-assemblies [ 51 ], and IV hybrid tumor models including embedded ex vivo tumor sections, 3D invasion through clusters embedded in gel, and 2D vacscular microfluidics [ 52 ].

Generally, such cell culture models focus on key aspects of metabolism, absorption, distribution, excretion of chemicals or other aspects of cell signaling pathways, such as aspects of metastasis under a controlled environment [ 53 ]. Scale-up systems attempt to emulate the physiological variability in order to extrapolate from in vitro to in vivo [ 54 ]. Advanced models as 3D culture systems more accurately represent the tumor environment [ 55 ]. Cell culture techniques include the formation of cell spheroids, which are frequently used in cancer research for approximating in vitro tumor growth as well as tumor invasion [ 56 ].

In particular, multicellular tumor spheroids have been applied for drug screening and studies on proliferation, migration, invasion, immune interactions, remodeling, angiogenesis and interactions between tumor cells and the microenvironment [ 46 ].

An Exploration in New Cancer Research