Drug development: Raise standards for preclinical cancer research

Lean Big Data integration in systems biology and systems pharmacology

Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs. This approach can lead us to the identification of more relationships between genes, drugs, and phenotypes as well as benchmark computational and experimental methods. Overall, this lean 'Big Data' integration strategy will bring us closer toward the goal of realizing personalized medicine.

Chemical biology approaches to target validation in cancer

Target validation is a crucial element of drug discovery. Especially given the wealth of potential targets emerging from cancer genome sequencing and functional genetic screens, and also considering the time and cost of downstream drug discovery efforts, it is essential to build confidence in a proposed target, ideally using different technical approaches. We argue that complementary biological and chemical biology strategies are essential for robust target validation. We discuss recent progress in the discovery and application of high quality chemical tools and other chemical biology approaches to target validation in cancer. Among other topical examples, we highlight the emergence of designed irreversible chemical tools to study potential target proteins and oncogenic pathways that were hitherto regarded as poorly druggable.

ProtocolNavigator: emulation-based software for the design, documentation and reproduction biological experiments

MOTIVATION

Experimental reproducibility is fundamental to the progress of science. Irreproducible research decreases the efficiency of basic biological research and drug discovery and impedes experimental data reuse. A major contributing factor to irreproducibility is difficulty in interpreting complex experimental methodologies and designs from written text and in assessing variations among different experiments. Current bioinformatics initiatives either are focused on computational research reproducibility (i.e. data analysis) or laboratory information management systems. Here, we present a software tool, ProtocolNavigator, which addresses the largely overlooked challenges of interpretation and assessment. It provides a biologist-friendly open-source emulation-based tool for designing, documenting and reproducing biological experiments.

AVAILABILITY AND IMPLEMENTATION

ProtocolNavigator was implemented in Python 2.7, using the wx module to build the graphical user interface. It is a platform-independent software and freely available from http://protocolnavigator.org/index.html under the GPL v2 license.

Genetically engineered mouse models for drug development and preclinical trials

Drug development and preclinical trials are challenging processes and more than 80% to 90% of drug candidates fail to gain approval from the United States Food and Drug Administration. Predictive and efficient tools are required to discover high quality targets and increase the probability of success in the process of new drug development. One such solution to the challenges faced in the development of new drugs and combination therapies is the use of low-cost and experimentally manageable in vivo animal models. Since the 1980's, scientists have been able to genetically modify the mouse genome by removing or replacing a specific gene, which has improved the identification and validation of target genes of interest. Now genetically engineered mouse models (GEMMs) are widely used and have proved to be a powerful tool in drug discovery processes. This review particularly covers recent fascinating technologies for drug discovery and preclinical trials, targeted transgenesis and RNAi mouse, including application and combination of inducible system. Improvements in technologies and the development of new GEMMs are expected to guide future applications of these models to drug discovery and preclinical trials.

Analysis of the quantitative balance between insulin-like growth factor (IGF)-1 ligand, receptor, and binding protein levels to predict cell sensitivity and therapeutic efficacy

Background

The insulin-like growth factor (IGF) system impacts cell proliferation and is highly activated in ovarian cancer. While an attractive therapeutic target, the IGF system is complex with two receptors (IGF1R, IGF2R), two ligands (IGF1, IGF2), and at least six high affinity IGF-binding proteins (IGFBPs) that regulate the bioavailability of IGF ligands. We hypothesized that a quantitative balance between these different network components regulated cell response.

Results

OVCAR5, an immortalized ovarian cancer cell line, were found to be sensitive to IGF1, with the dose of IGF1 (i.e., the total mass of IGF1 available) a more reliable predictor of cell response than ligand concentration. The applied dose of IGF1 was depleted by both cell-secreted IGFBPs and endocytic trafficking, with IGFBPs sequestering up to 90% of the available ligand. To explore how different variables (i.e., IGF1, IGFBPs, and IGF1R levels) impacted cell response, a mass-action steady-state model was developed. Examination of the model revealed that the level of IGF1-IGF1R complexes per cell was directly proportional to the extent of proliferation induced by IGF1. Model analysis suggested, and experimental results confirmed, that IGFBPs present during IGF1 treatment significantly decreased IGF1-mediated proliferation. We utilized this model to assess the efficacy of IGF1 and IGF1R antibodies against different network compositions and determined that IGF1R antibodies were more globally effective due to the receptor-limited state of the network.

Conclusions

Changes that affect IGF1R occupancy have predictable effects on IGF1-induced proliferation and our model captured these effects. Analysis of this model suggests that IGF1R antibodies will be more effective than IGF1 antibodies, although the difference was minimal in conditions with low levels of IGF1 and IGFBPs. Examining how different components of the IGF system influence cell response will be critical to improve our understanding of the IGF signaling network in ovarian cancer.

Big data and large sample size: a cautionary note on the potential for bias

A number of commentaries have suggested that large studies are more reliable than smaller studies and there is a growing interest in the analysis of "big data" that integrates information from many thousands of persons and/or different data sources. We consider a variety of biases that are likely in the era of big data, including sampling error, measurement error, multiple comparisons errors, aggregation error, and errors associated with the systematic exclusion of information. Using examples from epidemiology, health services research, studies on determinants of health, and clinical trials, we conclude that it is necessary to exercise greater caution to be sure that big sample size does not lead to big inferential errors. Despite the advantages of big studies, large sample size can magnify the bias associated with error resulting from sampling or study design.

The methodological quality of animal research in critical care: the public face of science

BACKGROUND

Animal research (AR) findings often do not translate to humans; one potential reason is the poor methodological quality of AR. We aimed to determine this quality of AR reported in critical care journals.

METHODS

All AR published from January to June 2012 in three high-impact critical care journals were reviewed. A case report form and instruction manual with clear definitions were created, based on published recommendations, including the ARRIVE guidelines. Data were analyzed with descriptive statistics.

RESULTS

Seventy-seven AR publications were reviewed. Our primary outcome (animal strain, sex, and weight or age described) was reported in 52 (68%; 95% confidence interval, 56% to 77%). Of the 77 publications, 47 (61%) reported randomization; of these, 3 (6%) reported allocation concealment, and 1 (2%) the randomization procedure. Of the 77 publications, 31 (40%) reported some type of blinding; of these, disease induction (2, 7%), intervention (7, 23%), and/or subjective outcomes (17, 55%) were blinded. A sample size calculation was reported in 4/77 (5%). Animal numbers were missing in the Methods section in 16 (21%) publications; when stated, the median was 32 (range 6 to 320; interquartile range, 21 to 70). Extra animals used were mentioned in the Results section in 31 (40%) publications; this number was unclear in 23 (74%), and >100 for 12 (16%). When reporting most outcomes, numbers with denominators were given in 35 (45%), with no unaccounted numbers in 24 (31%), and no animals excluded from analysis in 20 (26%). Most (49, 64%) studies reported >40, and another 19 (25%) reported 21 to 40 statistical comparisons. Internal validity limitations were discussed in 7 (9%), and external validity (to humans) discussed in 71 (92%), most with no (30, 42%) or only a vague (9, 13%) limitation to this external validity mentioned.

CONCLUSIONS

The reported methodological quality of AR was poor. Unless the quality of AR significantly improves, the practice may be in serious jeopardy of losing public support.

Phenotypic screening in cancer drug discovery - past, present and future

There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Given that oncology is currently the most active therapeutic area, and also one in which target-focused approaches have been particularly prominent in the past two decades, we investigated the contribution of phenotypic assays to oncology drug discovery by analysing the origins of all new small-molecule cancer drugs approved by the US Food and Drug Administration (FDA) over the past 15 years and those currently in clinical development. Although the majority of these drugs originated from target-based discovery, we identified a significant number whose discovery depended on phenotypic screening approaches. We postulate that the contribution of phenotypic screening to cancer drug discovery has been hampered by a reliance on 'classical' nonspecific drug effects such as cytotoxicity and mitotic arrest, exacerbated by a paucity of mechanistically defined cellular models for therapeutically translatable cancer phenotypes. However, technical and biological advances that enable such mechanistically informed phenotypic models have the potential to empower phenotypic drug discovery in oncology.

The increasing urgency for standards in basic biologic research

Research advances build upon the validity and reproducibility of previously published data and findings. Yet irreproducibility in basic biologic and preclinical research is pervasive in both academic and commercial settings. Lack of reproducibility has led to invalidated research breakthroughs, retracted articles, and aborted clinical trials. Concerns and requirements for transparent, reproducible, and translatable research are accelerated by the rapid growth of "post-publication peer review," open access publishing, and data sharing that facilitate the identification of irreproducible data/studies; they are magnified by the explosion of high-throughput technologies, genomics, and other data-intensive disciplines. Collectively, these changes and challenges are decreasing the effectiveness of traditional research quality mechanisms and are contributing to unacceptable-and unsustainable-levels of irreproducibility. The global oncology and basic biologic research communities can no longer tolerate or afford widespread irreproducible research. This article discusses (i) how irreproducibility in preclinical research can ultimately be traced to an absence of a unifying life science standards framework, and (ii) makes an urgent case for the expanded development and use of consensus-based standards to both enhance reproducibility and drive innovations in cancer research.

Minimum information about a spinal cord injury experiment: a proposed reporting standard for spinal cord injury experiments

The lack of reproducibility in many areas of experimental science has a number of causes, including a lack of transparency and precision in the description of experimental approaches. This has far-reaching consequences, including wasted resources and slowing of progress. Additionally, the large number of laboratories around the world publishing articles on a given topic make it difficult, if not impossible, for individual researchers to read all of the relevant literature. Consequently, centralized databases are needed to facilitate the generation of new hypotheses for testing. One strategy to improve transparency in experimental description, and to allow the development of frameworks for computer-readable knowledge repositories, is the adoption of uniform reporting standards, such as common data elements (data elements used in multiple clinical studies) and minimum information standards. This article describes a minimum information standard for spinal cord injury (SCI) experiments, its major elements, and the approaches used to develop it. Transparent reporting standards for experiments using animal models of human SCI aim to reduce inherent bias and increase experimental value.

The faces of Big Science

Fifty years ago, academic science was a calling with few regulations or financial rewards. Today, it is a huge enterprise confronted by a plethora of bureaucratic and political controls. This change was not triggered by specific events or decisions but reflects the explosive 'knee' in the exponential growth that science has sustained during the past three-and-a-half centuries. Coming to terms with the demands and benefits of 'Big Science' is a major challenge for today's scientific generation. Since its foundation 50 years ago, the European Molecular Biology Organization (EMBO) has been of invaluable help in meeting this challenge.

Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications

Background

Scientific publications are documentary representations of defeasible arguments, supported by data and repeatable methods. They are the essential mediating artifacts in the ecosystem of scientific communications. The institutional “goal” of science is publishing results. The linear document publication format, dating from 1665, has survived transition to the Web.

Intractable publication volumes; the difficulty of verifying evidence; and observed problems in evidence and citation chains suggest a need for a web-friendly and machine-tractable model of scientific publications. This model should support: digital summarization, evidence examination, challenge, verification and remix, and incremental adoption. Such a model must be capable of expressing a broad spectrum of representational complexity, ranging from minimal to maximal forms.

Results

The micropublications semantic model of scientific argument and evidence provides these features. Micropublications support natural language statements; data; methods and materials specifications; discussion and commentary; challenge and disagreement; as well as allowing many kinds of statement formalization.

The minimal form of a micropublication is a statement with its attribution. The maximal form is a statement with its complete supporting argument, consisting of all relevant evidence, interpretations, discussion and challenges brought forward in support of or opposition to it. Micropublications may be formalized and serialized in multiple ways, including in RDF. They may be added to publications as stand-off metadata.

An OWL 2 vocabulary for micropublications is available at http://purl.org/mp. A discussion of this vocabulary along with RDF examples from the case studies, appears as OWL Vocabulary and RDF Examples in Additional file 1.

Conclusion

Micropublications, because they model evidence and allow qualified, nuanced assertions, can play essential roles in the scientific communications ecosystem in places where simpler, formalized and purely statement-based models, such as the nanopublications model, will not be sufficient. At the same time they will add significant value to, and are intentionally compatible with, statement-based formalizations.

We suggest that micropublications, generated by useful software tools supporting such activities as writing, editing, reviewing, and discussion, will be of great value in improving the quality and tractability of biomedical communications.

Imaging preclinical tumour models: improving translational power

Recent developments and improvements of multimodal imaging methods for use in animal research have substantially strengthened the options of in vivo visualization of cancer-related processes over time. Moreover, technological developments in probe synthesis and labelling have resulted in imaging probes with the potential for basic research, as well as for translational and clinical applications. In addition, more sophisticated cancer models are available to address cancer-related research questions. This Review gives an overview of developments in these three fields, with a focus on imaging approaches in animal cancer models and how these can help the translation of new therapies into the clinic.

Retractions of scientific publications: responsibility and accountability

This evidence-based opinion piece gives a short overview of the increase in retractions of publications in scientific journals and discusses various reasons for that increase. Also discussed are some of the recent prominent cases of scientific misconduct, the number of authors with multiple retractions, and problems with reproducibility of published research. Finally, some of the effects of faulty research on science and society, as well as possible solutions are discussed.

Effect of initial conditions on reproducibility of scientific research

Background:

It is estimated that about half of currently published research cannot be reproduced. Many reasons have been offered as explanations for failure to reproduce scientific research findings- from fraud to the issues related to design, conduct, analysis, or publishing scientific research. We also postulate a sensitive dependency on initial conditions by which small changes can result in the large differences in the research findings when attempted to be reproduced at later times.

Methods:

We employed a simple logistic regression equation to model the effect of covariates on the initial study findings. We then fed the input from the logistic equation into a logistic map function to model stability of the results in repeated experiments over time. We illustrate the approach by modeling effects of different factors on the choice of correct treatment.

Results:

We found that reproducibility of the study findings depended both on the initial values of all independent variables and the rate of change in the baseline conditions, the latter being more important. When the changes in the baseline conditions vary by about 3.5 to about 4 in between experiments, no research findings could be reproduced. However, when the rate of change between the experiments is ≤2.5 the results become highly predictable between the experiments.

Conclusions:

Many results cannot be reproduced because of the changes in the initial conditions between the experiments. Better control of the baseline conditions in-between the experiments may help improve reproducibility of scientific findings.

Sources of error in the retracted scientific literature

Retraction of flawed articles is an important mechanism for correction of the scientific literature. We recently reported that the majority of retractions are associated with scientific misconduct. In the current study, we focused on the subset of retractions for which no misconduct was identified, in order to identify the major causes of error. Analysis of the retraction notices for 423 articles indexed in PubMed revealed that the most common causes of error-related retraction are laboratory errors, analytical errors, and irreproducible results. The most common laboratory errors are contamination and problems relating to molecular biology procedures (e.g., sequencing, cloning). Retractions due to contamination were more common in the past, whereas analytical errors are now increasing in frequency. A number of publications that have not been retracted despite being shown to contain significant errors suggest that barriers to retraction may impede correction of the literature. In particular, few cases of retraction due to cell line contamination were found despite recognition that this problem has affected numerous publications. An understanding of the errors leading to retraction can guide practices to improve laboratory research and the integrity of the scientific literature. Perhaps most important, our analysis has identified major problems in the mechanisms used to rectify the scientific literature and suggests a need for action by the scientific community to adopt protocols that ensure the integrity of the publication process.

Flaws in animal studies exploring statins and impact on meta-analysis

BACKGROUND

Animal experiments should be appropriately designed, correctly analysed and transparently reported to increase their scientific validity and maximise the knowledge gained from each experiment. This systematic review of animal experiments investigating statins evaluates their quality of reporting and methodological aspects as well as their implications for the conduction of meta-analyses.

METHODS

We searched medline and embase for studies reporting research on statins in mice, rats and rabbits. We collected detailed information about the characteristics of studies, animals and experimental methods.

RESULTS

We retrieved 161 studies. A little over half did not report randomisation (55%) and most did not describe blinding (88%). All studies reported details on the experimental procedure, although many omitted information about animal gender, age or weight. Four percent did not report the number of animals used. None reported the sample size. Fixed- and random-effects models gave different results (ratio of effect size increased by five folds). Heterogeneity was consistently substantial within animal models, for which accounting for covariates had minimal impact. Publication bias is highly suspected across studies.

CONCLUSIONS

Although statins showed efficacy in animal models, preclinical studies highlighted fundamental problems in the way in which such research is conducted and reported. Results were often difficult to interpret and reproduce. Different meta-analytic approaches were highly inconsistent: a reliable approach to estimate the true parameter was imperceptible. Policies that address these issues are required from investigators, editors and institutions that care about the quality standards and ethics of animal research.

New forms of checks and balances are needed to improve research integrity

Recent attempts at replicating highly-cited peer-reviewed studies demonstrate that the “reproducibility crisis” is indeed upon us. However, punitive measures against individuals committing research misconduct are neither sufficient nor useful because this is a systemic issue stemming from a lack of positive incentive. As an alternative approach, here we propose a system of checks and balances for the publishing process that involves 1) technical review of methodology by publishers, and 2) incentivizing direct replication of key experimental results. Together, these actions will help restore the self-correcting nature of scientific discovery.

Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages--strategies for data mining in support of therapeutic studies

BACKGROUND

Development of cancer therapeutics partially depends upon selection of appropriate animal models. Therefore, improvements to model selection are beneficial.

RESULTS

Forty-nine human tumor xenografts at in vivo passages 1, 4 and 10 were subjected to cDNA microarray analysis yielding a dataset of 823 Affymetrix HG-U133 Plus 2.0 arrays. To illustrate mining strategies supporting therapeutic studies, transcript expression was determined: 1) relative to other models, 2) with successive in vivo passage, and 3) during the in vitro to in vivo transition. Ranking models according to relative transcript expression in vivo has the potential to improve initial model selection. For example, combining p53 tumor expression data with mutational status could guide selection of tumors for therapeutic studies of agents where p53 status purportedly affects efficacy (e.g., MK-1775). The utility of monitoring changes in gene expression with extended in vivo tumor passages was illustrated by focused studies of drug resistance mediators and receptor tyrosine kinases. Noteworthy observations included a significant decline in HCT-15 colon xenograft ABCB1 transporter expression and increased expression of the kinase KIT in A549 with serial passage. These trends predict sensitivity to agents such as paclitaxel (ABCB1 substrate) and imatinib (c-KIT inhibitor) would be altered with extended passage. Given that gene expression results indicated some models undergo profound changes with in vivo passage, a general metric of stability was generated so models could be ranked accordingly. Lastly, changes occurring during transition from in vitro to in vivo growth may have important consequences for therapeutic studies since targets identified in vitro could be over- or under-represented when tumor cells adapt to in vivo growth. A comprehensive list of mouse transcripts capable of cross-hybridizing with human probe sets on the HG-U133 Plus 2.0 array was generated. Removal of the murine artifacts followed by pairwise analysis of in vitro cells with respective passage 1 xenografts and GO analysis illustrates the complex interplay that each model has with the host microenvironment.

CONCLUSIONS

This study provides strategies to aid selection of xenograft models for therapeutic studies. These data highlight the dynamic nature of xenograft models and emphasize the importance of maintaining passage consistency throughout experiments.

Relevance relations for the concept of reproducibility

The concept of reproducibility is widely considered a cornerstone of scientific methodology. However, recent problems with the reproducibility of empirical results in large-scale systems and in biomedical research have cast doubts on its universal and rigid applicability beyond the so-called basic sciences. Reproducibility is a particularly difficult issue in interdisciplinary work where the results to be reproduced typically refer to different levels of description of the system considered. In such cases, it is mandatory to distinguish between more and less relevant features, attributes or observables of the system, depending on the level at which they are described. For this reason, we propose a scheme for a general 'relation of relevance' between the level of complexity at which a system is considered and the granularity of its description. This relation implies relevance criteria for particular selected aspects of a system and its description, which can be operationally implemented by an interlevel relation called 'contextual emergence'. It yields a formally sound and empirically applicable procedure to translate between descriptive levels and thus construct level-specific criteria for reproducibility in an overall consistent fashion. Relevance relations merged with contextual emergence challenge the old idea of one fundamental ontology from which everything else derives. At the same time, our proposal is specific enough to resist the backlash into a relativist patchwork of unconnected model fragments.

Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention

Recent systematic reviews and empirical evaluations of the cognitive sciences literature suggest that publication and other reporting biases are prevalent across diverse domains of cognitive science. In this review, we summarize the various forms of publication and reporting biases and other questionable research practices, and overview the available methods for probing into their existence. We discuss the available empirical evidence for the presence of such biases across the neuroimaging, animal, other preclinical, psychological, clinical trials, and genetics literature in the cognitive sciences. We also highlight emerging solutions (from study design to data analyses and reporting) to prevent bias and improve the fidelity in the field of cognitive science research.

The meaning of "significance" for different types of research [translated and annotated by Eric-Jan Wagenmakers, Denny Borsboom, Josine Verhagen, Rogier Kievit, Marjan Bakker, Angelique Cramer, Dora Matzke, Don Mellenbergh, and Han L. J. van der Maas]. 1969

Adrianus Dingeman de Groot (1914-2006) was one of the most influential Dutch psychologists. He became famous for his work "Thought and Choice in Chess", but his main contribution was methodological--De Groot co-founded the Department of Psychological Methods at the University of Amsterdam (together with R. F. van Naerssen), founded one of the leading testing and assessment companies (CITO), and wrote the monograph "Methodology" that centers on the empirical-scientific cycle: observation-induction-deduction-testing-evaluation. Here we translate one of De Groot's early articles, published in 1956 in the Dutch journal Nederlands Tijdschrift voor de Psychologie en Haar Grensgebieden. This article is more topical now than it was almost 60years ago. De Groot stresses the difference between exploratory and confirmatory ("hypothesis testing") research and argues that statistical inference is only sensible for the latter: "One 'is allowed' to apply statistical tests in exploratory research, just as long as one realizes that they do not have evidential impact". De Groot may have also been one of the first psychologists to argue explicitly for preregistration of experiments and the associated plan of statistical analysis. The appendix provides annotations that connect De Groot's arguments to the current-day debate on transparency and reproducibility in psychological science.

Hydrogel-based 3D model of patient-derived prostate xenograft tumors suitable for drug screening

The lack of effective therapies for bone metastatic prostate cancer (PCa) underscores the need for accurate models of the disease to enable the discovery of new therapeutic targets and to test drug sensitivities of individual tumors. To this end, the patient-derived xenograft (PDX) PCa model using immunocompromised mice was established to model the disease with greater fidelity than is possible with currently employed cell lines grown on tissue culture plastic. However, poorly adherent PDX tumor cells exhibit low viability in standard culture, making it difficult to manipulate these cells for subsequent controlled mechanistic studies. To overcome this challenge, we encapsulated PDX tumor cells within a three-dimensional hyaluronan-based hydrogel and demonstrated that the hydrogel maintains PDX cell viability with continued native androgen receptor expression. Furthermore, a differential sensitivity to docetaxel, a chemotherapeutic drug, was observed as compared to a traditional PCa cell line. These findings underscore the potential impact of this novel 3D PDX PCa model as a diagnostic platform for rapid drug evaluation and ultimately push personalized medicine toward clinical reality.

Data publication consensus and controversies

The movement to bring datasets into the scholarly record as first class research products (validated, preserved, cited, and credited) has been inching forward for some time, but now the pace is quickening. As data publication venues proliferate, significant debate continues over formats, processes, and terminology. Here, we present an overview of data publication initiatives underway and the current conversation, highlighting points of consensus and issues still in contention. Data publication implementations differ in a variety of factors, including the kind of documentation, the location of the documentation relative to the data, and how the data is validated. Publishers may present data as supplemental material to a journal article, with a descriptive "data paper," or independently. Complicating the situation, different initiatives and communities use the same terms to refer to distinct but overlapping concepts. For instance, the term published means that the data is publicly available and citable to virtually everyone, but it may or may not imply that the data has been peer-reviewed. In turn, what is meant by data peer review is far from defined; standards and processes encompass the full range employed in reviewing the literature, plus some novel variations. Basic data citation is a point of consensus, but the general agreement on the core elements of a dataset citation frays if the data is dynamic or part of a larger set. Even as data publication is being defined, some are looking past publication to other metaphors, notably "data as software," for solutions to the more stubborn problems.

Innovating aging: promises and pitfalls on the road to life extension

One of the main benefits of the dramatic technological progress over the last two centuries is the enormous increase in human life expectancy, which has now reached record highs. After conquering most childhood diseases and a fair fraction of the diseases that plague adulthood, medical technology is now mainly preoccupied by age-related disorders. Further progress is dependent on circumventing the traditional medical focus on individual diseases and instead targeting aging as a whole as the ultimate cause of the health problems that affect humankind at old age. In principle, a major effort to control the gradual accumulation of molecular and cellular damage - considered by many as the ultimate cause of intrinsic aging - may rapidly lead to interventions for regenerating aged and worn-out tissues and organs. While considered impossible by many, there really is no reason to reject this as scientifically implausible. However, as we posit, it is not only scientific progress that is currently a limiting factor, but societal factors that hinder and may ultimately prevent further progress in testing and adopting the many possible interventions to cure aging.