Drug development: Raise standards for preclinical cancer research

Murine Models of Inflammatory Bowel Disease (IBD)

Animal models of human disease are a critical tool in both basic research and drug development. The results of preclinical efficacy studies often inform progression of therapeutic candidates through the drug development pipeline; however, the extent to which results in inflammatory bowel disease (IBD) models predict human drug response is an ongoing concern. This review discusses how murine models are currently being used in IBD research. We focus on the considerations and caveats for commonly used models in preclinical efficacy studies and discuss the value of models that utilize specific pathogenic pathways of interest rather than model all aspects of human disease.

Transcriptomic classification of genetically engineered mouse models of breast cancer identifies human subtype counterparts

Background

Human breast cancer is a heterogeneous disease consisting of multiple molecular subtypes. Genetically engineered mouse models are a useful resource for studying mammary cancers in vivo under genetically controlled and immune competent conditions. Identifying murine models with conserved human tumor features will facilitate etiology determinations, highlight the effects of mutations on pathway activation, and should improve preclinical drug testing.

Results

Transcriptomic profiles of 27 murine models of mammary carcinoma and normal mammary tissue were determined using gene expression microarrays. Hierarchical clustering analysis identified 17 distinct murine subtypes. Cross-species analyses using three independent human breast cancer datasets identified eight murine classes that resemble specific human breast cancer subtypes. Multiple models were associated with human basal-like tumors including TgC3(1)-Tag, TgWAP-Myc and Trp53^-/-. Interestingly, the TgWAPCre-Etv6 model mimicked the HER2-enriched subtype, a group of human tumors without a murine counterpart in previous comparative studies. Gene signature analysis identified hundreds of commonly expressed pathway signatures between linked mouse and human subtypes, highlighting potentially common genetic drivers of tumorigenesis.

Conclusions

This study of murine models of breast carcinoma encompasses the largest comprehensive genomic dataset to date to identify human-to-mouse disease subtype counterparts. Our approach illustrates the value of comparisons between species to identify murine models that faithfully mimic the human condition and indicates that multiple genetically engineered mouse models are needed to represent the diversity of human breast cancers. The reported trans-species associations should guide model selection during preclinical study design to ensure appropriate representatives of human disease subtypes are used.

Tumour heterogeneity in the clinic

Recent therapeutic advances in oncology have been driven by the identification of tumour genotype variations between patients, called interpatient heterogeneity, that predict the response of patients to targeted treatments. Subpopulations of cancer cells with unique genomes in the same patient may exist across different geographical regions of a tumour or evolve over time, called intratumour heterogeneity. Sequencing technologies can be used to characterize intratumour heterogeneity at diagnosis, monitor clonal dynamics during treatment and identify the emergence of clinical resistance during disease progression. Genetic interpatient and intratumour heterogeneity can pose challenges for the design of clinical trials that use these data.

No KRAS mutations found in gastrointestinal stromal tumors (GISTs): molecular genetic study of 514 cases

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. A great majority of GISTs is driven by pathological activation of KIT or platelet-derived growth factor receptor-α (PDGFRA), two closely related receptor tyrosine kinases. However, other genetic changes including gain-of-function BRAF mutations and loss of succinate dehydrogenase (SDH) complex activity have been identified in the subsets of KIT-, PDGFRA-wild type tumors. Genetic mutations affecting KIT, PDGFRA, BRAF and SDH complex functions are believed to be mutually exclusive events. Recently, KRAS codon 12 and 13 mutations were reported in a small subset of KIT or PDGFRA mutant GISTs. Moreover, in in vitro experiments, KIT mutants with concurrent KRAS mutation showed resistance to imatinib, a receptor tyrosine kinase inhibitor used in GIST treatment. The aim of this study was to evaluate a large cohort of GISTs to define frequency and clinical significance of KRAS mutations in this type of cancer. A well-characterized cohort of 514 GISTs was screened for KRAS mutations using Sanger sequencing (n=450) and pyrosequencing (n=64). In all, 350 gastric, 100 intestinal and 64 primary disseminated GISTs were analyzed. No KRAS mutations were found. In GIST, KRAS mutations are extremely rare if they exist (<0.2%). Thus, mutational activation of KRAS does not seem to play any significant role in the development and progression of this type of cancer.

Principles for valid histopathologic scoring in research

Histopathologic scoring is a tool by which semiquantitative data can be obtained from tissues. Initially, a thorough understanding of the experimental design, study objectives, and methods is required for the pathologist to appropriately examine tissues and develop lesion scoring approaches. Many principles go into the development of a scoring system such as tissue examination, lesion identification, scoring definitions, and consistency in interpretation. Masking (aka "blinding") of the pathologist to experimental groups is often necessary to constrain bias, and multiple mechanisms are available. Development of a tissue scoring system requires appreciation of the attributes and limitations of the data (eg, nominal, ordinal, interval, and ratio data) to be evaluated. Incidence, ordinal, and rank methods of tissue scoring are demonstrated along with key principles for statistical analyses and reporting. Validation of a scoring system occurs through 2 principal measures: (1) validation of repeatability and (2) validation of tissue pathobiology. Understanding key principles of tissue scoring can help in the development and/or optimization of scoring systems so as to consistently yield meaningful and valid scoring data.

Next-generation biobanking of metastases to enable multidimensional molecular profiling in personalized medicine

Great advances in analytical technology coupled with accelerated new drug development and growing understanding of biological challenges, such as tumor heterogeneity, have required a change in the focus for biobanking. Most current banks contain samples of primary tumors, but linking molecular signatures to therapeutic questions requires serial biopsies in the setting of metastatic disease, next-generation of biobanking. Furthermore, an integration of multidimensional analysis of various molecular components, that is, RNA, DNA, methylome, microRNAome and post-translational modifications of the proteome, is necessary for a comprehensive view of a tumor's biology. While data using such biopsies are now regularly presented, the preanalytical variables in tissue procurement and processing in multicenter studies are seldom detailed and therefore are difficult to duplicate or standardize across sites and across studies. In the context of a biopsy-driven clinical trial, we generated a detailed protocol that includes morphological evaluation and isolation of high-quality nucleic acids from small needle core biopsies obtained from liver metastases. The protocol supports stable shipping of samples to a central laboratory, where biopsies are subsequently embedded in support media. Designated pathologists must evaluate all biopsies for tumor content and macrodissection can be performed if necessary to meet our criteria of >60% neoplastic cells and <20% necrosis for genomic isolation. We validated our protocol in 40 patients who participated in a biopsy-driven study of therapeutic resistance in metastatic colorectal cancer. To ensure that our protocol was compatible with multiplex discovery platforms and that no component of the processing interfered with downstream enzymatic reactions, we performed array comparative genomic hybridization, methylation profiling, microRNA profiling, splicing variant analysis and gene expression profiling using genomic material isolated from liver biopsy cores. Our standard operating procedures for next-generation biobanking can be applied widely in multiple settings, including multicentered and international biopsy-driven trials.

Translational paradigms in pharmacology and drug discovery

The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization.

Appraising iniparib, the PARP inhibitor that never was--what must we learn?

Several drugs targeting poly(ADP-ribose) polymerase (PARP) enzymes are under development. Responses have been observed in patients with germline mutations in BRCA1 and BRCA2, with further data supporting antitumour activity of PARP inhibitors in sporadic ovarian cancer. Strategies to identify other predictive biomarkers remain under investigation. Iniparib was purported to be a PARP inhibitor that showed promising results in randomized phase II trials in patients with triple-negative breast cancer. Negative results from a phase III study in this disease setting, however, tempered enthusiasm for this agent. Recently, data from in vitro experiments suggest that iniparib is not only structurally distinct from other described PARP inhibitors, but is also a poor inhibitor of PARP activity. In this context, the negative iniparib phase III data might have erroneously promulgated the notion that PARP inhibition is not an effective therapeutic strategy. Here, we scrutinize the development of iniparib from preclinical studies to registration trials, and identify and discuss the pitfalls in the development of anticancer drugs to prevent future late-stage trial failures.

Acute alcohol response phenotype in heavy social drinkers is robust and reproducible

BACKGROUND

In 3 previously published works (Brumback et al., 2007, Drug Alcohol Depend 91:10-17; King et al., 2011a, Arch Gen Psychiatry 68:389-399; Roche and King, 2010, Psychopharmacology (Berl) 212:33-44), our group characterized acute alcohol responses in a large group of young, heavy binge drinkers (n = 104) across a variety of subjective, eye-tracking, and psychometric performance measures.

METHODS

The primary goal of the current study was to directly replicate prior findings of alcohol response in heavy social drinkers (HD) in a second independent cohort (n = 104) using identical methodology. A secondary goal was to examine the effects of family history (FH) of alcohol use disorders (AUD) on acute alcohol response in both samples. Participants attended 2 randomized laboratory sessions in which they consumed 0.8 g/kg alcohol or a taste-masked placebo. At pre- and post-drink time points, participants completed subjective scales, psychomotor performance and eye-movement tasks, and provided salivary samples for cortisol determination.

RESULTS

Results showed that the second cohort of heavy drinkers exhibited a nearly identical pattern of alcohol responses to the original cohort, including sensitivity to alcohol's stimulating and hedonically rewarding effects during the rising breath alcohol content (BrAC) limb, increases in sedation during the declining BrAC limb, a lack of cortisol response, and psychomotor and eye-tracking impairment that was most evident at peak BrAC. The magnitude and temporal pattern of these acute effects of alcohol in the second cohort were similar to the first cohort across all measures, with the exception of 3 eye-movement measures: pro- and antisaccade accuracy and antisaccade velocity. FH of AUD did not affect alcohol response in the first cohort, and this was replicated in the second cohort.

CONCLUSIONS

In sum, in 2 independent samples, we have demonstrated that HD display a consistent and reliable sensitivity to alcohol's subjective effects and impairment of eye-tracking and psychomotor performance, which is not affected by FH status. This acute alcohol response phenotype in heavy, frequent binge drinkers appears to be robust and reproducible.

Reproducibility of research and preclinical validation: problems and solutions

Lack of reproducibility in the scientific and lay literature of many scientific reports is an increasing concern, as are the high rates of failure to validate highly promising preclinical observations in clinical trials. There are many technical reasons why experimental results, particularly in cancer research, cannot be reproduced, including unrecognized variables in the complex experimental model, poor documentation of procedures, selective reporting of the most-positive findings, misinterpretation of technical noise as biological signal and, in the most extreme cases, fabrication of data. We suggest that cognitive biases in research and flaws in the academic incentive system also contribute to the publication of immature results. Recognition of these factors, which are often not discussed, provides additional strategies to improve reproducibility. We suggest that in addition to establishing better standards of data presentation and creating venues for publication of negative results, some changes to the grant submission and funding system could further improve the reproducibility of research findings.

Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches

Innovation and new molecular entity production by the pharmaceutical industry has been below expectations. Surprisingly, more first-in-class small-molecule drugs approved by the U.S. Food and Drug Administration (FDA) between 1999 and 2008 were identified by functional phenotypic lead generation strategies reminiscent of pre-genomics pharmacology than contemporary molecular targeted strategies that encompass the vast majority of lead generation efforts. This observation, in conjunction with the difficulty in validating molecular targets for drug discovery, has diminished the impact of the "genomics revolution" and has led to a growing grassroots movement and now broader trend in pharma to reconsider the use of modern physiology-based or phenotypic drug discovery (PDD) strategies. This "From the Guest Editors" column provides an introduction and overview of the two-part special issues of Journal of Biomolecular Screening on PDD. Terminology and the business case for use of PDD are defined. Key issues such as assay performance, chemical optimization, target identification, and challenges to the organization and implementation of PDD are discussed. Possible solutions for these challenges and a new neoclassic vision for PDD that combines phenotypic and functional approaches with technology innovations resulting from the genomics-driven era of target-based drug discovery (TDD) are also described. Finally, an overview of the manuscripts in this special edition is provided.

How academia and the pharmaceutical industry can work together: the president's lecture, annual meeting of the American Thoracic Society, San Francisco, California

There is a long history of productive collaboration between biomedical scientists in academia and in the pharmaceutical industry. The primary beneficiary of this collaboration has been the public. Since the middle of the last century, marked advances in the treatment and prevention of disease have been driven by the translational research interactions across these two domains. But now, at a time when collaboration between academia and industry should be accelerating based on past success, new technology, and ever-increasing need, numerous obstacles to effective collaboration have appeared. In this analysis, based on experience in both academia and industry, the author provides perspective on current obstacles to academic-industrial collaboration, followed by recommendations on how effective collaboration can be renewed and enhanced.

Discovery and Preclinical Work

In any drug discovery and development effort, we must accomplish a number of critical steps to arrive at a compound that is safe and efficacious, and also exhibits the complex array of desired drug-like behaviors that warrants advancement to the clinic. These tasks include target identification and validation; screening for active compounds; chemical modification of candidate compounds to achieve optimized pharmacology; formulating the final drug product; and establishing safety in preclinical models. “Repurposing” drugs that have previously been approved (or shown to be safe in humans) for new clinical indications can provide a faster, less risky, and more cost-effective route for bringing a new therapy to patients. Such shortcuts in development can be particularly valuable to resource-constrained academicians. When performing drug discovery research, we must be particularly attentive to the robustness of our experiments, because inability to reproduce academic data continues to be a sticking point when projects are transferred to industry. Our experiments must be appropriately blinded, statistically powered, and meticulously documented so that our findings are worthy of the large investment required for their further translation into a drug. This chapter walks through the essential preclinical drug development steps that lead to a clinical drug candidate.

The fall and rise of pharmacology--(re-)defining the discipline?

Pharmacology is an integrative discipline that originated from activities, now nearly 7000 years old, to identify therapeutics from natural product sources. Research in the 19th Century that focused on the Law of Mass Action (LMA) demonstrated that compound effects were dose-/concentration-dependent eventually leading to the receptor concept, now a century old, that remains the key to understanding disease causality and drug action. As pharmacology evolved in the 20th Century through successive biochemical, molecular and genomic eras, the precision in understanding receptor function at the molecular level increased and while providing important insights, led to an overtly reductionistic emphasis. This resulted in the generation of data lacking physiological context that ignored the LMA and was not integrated at the tissue/whole organism level. As reductionism became a primary focus in biomedical research, it led to the fall of pharmacology. However, concerns regarding the disconnect between basic research efforts and the approval of new drugs to treat 21st Century disease tsunamis, e.g., neurodegeneration, metabolic syndrome, etc. has led to the reemergence of pharmacology, its rise, often in the semantic guise of systems biology. Against a background of limited training in pharmacology, this has resulted in issues in experimental replication with a bioinformatics emphasis that often has a limited relationship to reality. The integration of newer technologies within a pharmacological context where research is driven by testable hypotheses rather than technology, together with renewed efforts in teaching pharmacology, is anticipated to improve the focus and relevance of biomedical research and lead to novel therapeutics that will contain health care costs.

Assessing the validity and reproducibility of genome-scale predictions

MOTIVATION

Validation and reproducibility of results is a central and pressing issue in genomics. Several recent embarrassing incidents involving the irreproducibility of high-profile studies have illustrated the importance of this issue and the need for rigorous methods for the assessment of reproducibility.

RESULTS

Here, we describe an existing statistical model that is very well suited to this problem. We explain its utility for assessing the reproducibility of validation experiments, and apply it to a genome-scale study of adenosine deaminase acting on RNA (ADAR)-mediated RNA editing in Drosophila. We also introduce a statistical method for planning validation experiments that will obtain the tightest reproducibility confidence limits, which, for a fixed total number of experiments, returns the optimal number of replicates for the study.

AVAILABILITY

Downloadable software and a web service for both the analysis of data from a reproducibility study and for the optimal design of these studies is provided at http://ccmbweb.ccv.brown.edu/reproducibility.html .

Lessons learned from radiation oncology clinical trials

A workshop entitled "Lessons Learned from Radiation Oncology Trials" was held on December 7-8, 2011, in Bethesda, MD, to present and discuss some of the recently conducted radiation oncology clinical trials with a focus on those that failed to refute the null hypothesis. The objectives of this workshop were to summarize and examine the questions that these trials provoked, to assess the quality and limitations of the preclinical data that supported the hypotheses underlying these trials, and to consider possible solutions to these challenges for the design of future clinical trials. Several themes emerged from the discussions: (i) opportunities to learn from null-hypothesis trials through tissue and imaging studies; (ii) value of preclinical data supporting the design of combinatorial therapies; (iii) significance of validated biomarkers; (iv) necessity of quality assurance in radiotherapy delivery; (v) conduct of sufficiently powered studies to address the central hypotheses; and (vi) importance of publishing results of the trials regardless of the outcome. The fact that well-designed hypothesis-driven clinical trials produce null or negative results is expected given the limitations of trial design and complexities of cancer biology. It is important to understand the reasons underlying such null results, however, to effectively merge the technologic innovations with the rapidly evolving biology for maximal patient benefit through the design of future clinical trials.

On the reproducibility of science: unique identification of research resources in the biomedical literature

Scientific reproducibility has been at the forefront of many news stories and there exist numerous initiatives to help address this problem. We posit that a contributor is simply a lack of specificity that is required to enable adequate research reproducibility. In particular, the inability to uniquely identify research resources, such as antibodies and model organisms, makes it difficult or impossible to reproduce experiments even where the science is otherwise sound. In order to better understand the magnitude of this problem, we designed an experiment to ascertain the “identifiability” of research resources in the biomedical literature. We evaluated recent journal articles in the fields of Neuroscience, Developmental Biology, Immunology, Cell and Molecular Biology and General Biology, selected randomly based on a diversity of impact factors for the journals, publishers, and experimental method reporting guidelines. We attempted to uniquely identify model organisms (mouse, rat, zebrafish, worm, fly and yeast), antibodies, knockdown reagents (morpholinos or RNAi), constructs, and cell lines. Specific criteria were developed to determine if a resource was uniquely identifiable, and included examining relevant repositories (such as model organism databases, and the Antibody Registry), as well as vendor sites. The results of this experiment show that 54% of resources are not uniquely identifiable in publications, regardless of domain, journal impact factor, or reporting requirements. For example, in many cases the organism strain in which the experiment was performed or antibody that was used could not be identified. Our results show that identifiability is a serious problem for reproducibility. Based on these results, we provide recommendations to authors, reviewers, journal editors, vendors, and publishers. Scientific efficiency and reproducibility depend upon a research-wide improvement of this substantial problem in science today.

We are not born knowing how to design and analyse scientific experiments

Better training in experimental techniques and changes to the current methods of scientific research funding are both needed to facilitate more-effective improvements in human health

Animal models of human disease: challenges in enabling translation

Animal models have historically played a critical role in the exploration and characterization of disease pathophysiology, target identification, and in the in vivo evaluation of novel therapeutic agents and treatments. In the wake of numerous clinical trial failures of new chemical entities (NCEs) with promising preclinical profiles, animal models in all therapeutic areas have been increasingly criticized for their limited ability to predict NCE efficacy, safety and toxicity in humans. The present review discusses some of the challenges associated with the evaluation and predictive validation of animal models, as well as methodological flaws in both preclinical and clinical study designs that may contribute to the current translational failure rate. The testing of disease hypotheses and NCEs in multiple disease models necessitates evaluation of pharmacokinetic/pharmacodynamic (PK/PD) relationships and the earlier development of validated disease-associated biomarkers to assess target engagement and NCE efficacy. Additionally, the transparent integration of efficacy and safety data derived from animal models into the hierarchical data sets generated preclinically is essential in order to derive a level of predictive utility consistent with the degree of validation and inherent limitations of current animal models. The predictive value of an animal model is thus only as useful as the context in which it is interpreted. Finally, rather than dismissing animal models as not very useful in the drug discovery process, additional resources, like those successfully used in the preclinical PK assessment used for the selection of lead NCEs, must be focused on improving existing and developing new animal models.

Underestimating calorie content when healthy foods are present: an averaging effect or a reference-dependent anchoring effect?

OBJECTIVE

Previous studies have shown that estimations of the calorie content of an unhealthy main meal food tend to be lower when the food is shown alongside a healthy item (e.g. fruit or vegetables) than when shown alone. This effect has been called the negative calorie illusion and has been attributed to averaging the unhealthy (vice) and healthy (virtue) foods leading to increased perceived healthiness and reduced calorie estimates. The current study aimed to replicate and extend these findings to test the hypothesized mediating effect of ratings of healthiness of foods on calorie estimates.

METHODS

In three online studies, participants were invited to make calorie estimates of combinations of foods. Healthiness ratings of the food were also assessed.

RESULTS

The first two studies failed to replicate the negative calorie illusion. In a final study, the use of a reference food, closely following a procedure from a previously published study, did elicit a negative calorie illusion. No evidence was found for a mediating role of healthiness estimates.

CONCLUSION

The negative calorie illusion appears to be a function of the contrast between a food being judged and a reference, supporting the hypothesis that the negative calorie illusion arises from the use of a reference-dependent anchoring and adjustment heuristic and not from an 'averaging' effect, as initially proposed. This finding is consistent with existing data on sequential calorie estimates, and highlights a significant impact of the order in which foods are viewed on how foods are evaluated.

How genes and the social environment moderate each other

Recent research has suggested that the social environment can moderate the expression of genetic influences on health and that genetic influences can shape an individual's sensitivity to the social environment. Evidence supports 4 major mechanisms: genes can influence an individual's response to environmental stress, genes may enhance an individual's sensitivity to both favorable and adverse environments, inherited characteristics may better fit with some environments than with others, and inherited capabilities may only become manifest in challenging or responsive environments. Further progress depends on better recognition of patterns of gene-environment interaction, improved methods of assessing the environment and its impact on genetic mechanisms, the use of appropriately designed laboratory studies, identification of heritable differences in an individual before environmental moderation occurs, and clarification of the timing of the impact of social and genetic moderation.

Metabolic reprogramming and validation of hyperpolarized 13C lactate as a prostate cancer biomarker using a human prostate tissue slice culture bioreactor

BACKGROUND

The treatment of prostate cancer has been impeded by the lack of both clinically relevant disease models and metabolic markers that track tumor progression. Hyperpolarized (HP) (13) C MR spectroscopy has emerged as a new technology to investigate the metabolic shifts in prostate cancer. In this study, we investigate the glucose reprogramming using HP (13) C pyruvate MR in a patient-derived prostate tissue slice culture (TSC) model.

METHODS

The steady-state metabolite concentrations in freshly excised human prostate TSCs were assessed and compared to those from snap-frozen biopsy samples. The TSCs were then applied to a perfused cell (bioreactor) platform, and the bioenergetics and the dynamic pyruvate flux of the TSCs were investigated by (31) P and HP (13) C MR, respectively.

RESULTS

The prostate TSCs demonstrated steady-state glycolytic and phospholipid metabolism, and bioenergetics that recapitulate features of prostate cancer in vivo. (13) C spectra following injection of HP (13) C pyruvate showed significantly increased pyruvate to lactate flux in malignant as compared to the benign prostate TSCs. This increased flux in the malignant prostate TSCs correlated with both increased expression of monocarboxylate transporters (MCT) and activity of lactate dehydrogenase (LDH).

CONCLUSIONS

We provide the first mechanistic evidence for HP (13) C lactate as a prostate cancer biomarker in living human tissues, critical for the interpretation of in vivo studies. More broadly, the clinically relevant metabolic model system in combination with HP MR can facilitate the identification of clinically translatable biomarkers of prostate cancer presence, aggressiveness, and treatment response.

Do pluripotent stem cells exist in adult mice as very small embryonic stem cells?

Very small embryonic-like stem cells (VSELs) isolated from bone marrow (BM) have been reported to be pluripotent. Given their nonembryonic source, they could replace blastocyst-derived embryonic stem cells in research and medicine. However, their multiple-germ-layer potential has been incompletely studied. Here, we show that we cannot find VSELs in mouse BM with any of the reported stem cell potentials, specifically for hematopoiesis. We found that: (1) most events within the "VSEL" flow-cytometry gate had little DNA and the cells corresponding to these events (2) could not form spheres, (3) did not express Oct4, and (4) could not differentiate into blood cells. These results provide a failure to confirm the existence of pluripotent VSELs.

Reporting research antibody use: how to increase experimental reproducibility

Research antibodies are used in a wide range of bioscience disciplines, yet it is common to hear dissatisfaction amongst researchers with respect to their quality. Although blame is often attributed to the manufacturers, scientists are not doing all they can to help themselves. One example of this is in the reporting of research antibody use. Publications routinely lack key details, including the host species, code number and even the company who supplied the antibody. Authors also fail to demonstrate that validation of the antibodies has taken place. These omissions make it harder for reviewers to establish the likely reliability of the results and for researchers to reproduce the experiments. The scale of this problem, combined with high profile concerns about experimental reproducibility, has caused the Nature Publishing Group to include a section on antibody information in their recent Reporting Checklist for Life Science Articles. In this commentary we consider the issue of reporting research antibody use and ask what details authors should be including in their publications to improve experimental reproducibility.

Objective assessment of cancer genes for drug discovery

Selecting the best targets is a key challenge for drug discovery, and achieving this effectively, efficiently and systematically is particularly important for prioritizing candidates from the sizeable lists of potential therapeutic targets that are now emerging from large-scale multi-omics initiatives, such as those in oncology. Here, we describe an objective, systematic, multifaceted computational assessment of biological and chemical space that can be applied to any human gene set to prioritize targets for therapeutic exploration. We use this approach to evaluate an exemplar set of 479 cancer-associated genes, reveal the tension between biological relevance and chemical tractability, and describe major gaps in available knowledge that could be addressed to aid objective decision-making. We also propose drug repurposing opportunities and identify potentially druggable cancer-associated proteins that have been poorly explored with regard to the discovery of small-molecule modulators, despite their biological relevance.

Crystallization reports are the backbone of Acta Cryst. F, but do they have any spine?

Crystallization of macromolecules is famously difficult. By knowing what has worked for others, researchers can ease the process, both in the case where the protein has already been crystallized and in the situation where more general guidelines are needed. The 264 crystallization communications published in Acta Crystallographica Section F in 2012 have been reviewed, and from this analysis some information about trends in crystallization has been gleaned. More importantly, it was found that there are several ways in which the utility of these communications could be increased: to make each individual paper a more complete crystallization record; and to provide a means for taking a snapshot of what the current `best practices' are in the field.

Fast skeletal muscle troponin activator in the dy2J muscular dystrophy model

INTRODUCTION

Tirasemtiv is a novel small molecule activator of the fast skeletal muscle troponin complex that produces sensitization of the sarcomere to calcium. Tirasemtiv is currently in Phase II clinical trials for neuromuscular disease.

METHODS

We conducted a blinded, randomized, placebo-controlled preclinical study of the effect of tirasemtiv on forearm grip strength, endurance, respiratory physiology, and muscle pathology in adequate sample sizes of the Lama2(dy-2J) mouse model of congenital muscular dystrophy.

RESULTS

Mice receiving a high dose of tirasemtiv had significantly higher muscle fiber cross-sectional area and respiratory response to CO₂ stimulation at 16 weeks than mice on low dose or placebo. There were no changes in muscle pathology, serum creatine kinase, strength, endurance, or respiration following long-term treatment.

CONCLUSIONS

We conclude that tirasemtiv influences the structure of the skeletal muscle fiber in this model of muscular dystrophy but does not impact muscle function, as evaluated in this study.

Deep impact: unintended consequences of journal rank

Most researchers acknowledge an intrinsic hierarchy in the scholarly journals ("journal rank") that they submit their work to, and adjust not only their submission but also their reading strategies accordingly. On the other hand, much has been written about the negative effects of institutionalizing journal rank as an impact measure. So far, contributions to the debate concerning the limitations of journal rank as a scientific impact assessment tool have either lacked data, or relied on only a few studies. In this review, we present the most recent and pertinent data on the consequences of our current scholarly communication system with respect to various measures of scientific quality (such as utility/citations, methodological soundness, expert ratings or retractions). These data corroborate previous hypotheses: using journal rank as an assessment tool is bad scientific practice. Moreover, the data lead us to argue that any journal rank (not only the currently-favored Impact Factor) would have this negative impact. Therefore, we suggest that abandoning journals altogether, in favor of a library-based scholarly communication system, will ultimately be necessary. This new system will use modern information technology to vastly improve the filter, sort and discovery functions of the current journal system.

Biopsies: next-generation biospecimens for tailoring therapy

The majority of samples in existing tumour biobanks are surgical specimens of primary tumours. Insights into tumour biology, such as intratumoural heterogeneity, tumour-host crosstalk, and the evolution of the disease during therapy, require biospecimens from the primary tumour and those that reflect the patient's disease in specific contexts. Next-generation 'omics' technologies facilitate deep interrogation of tumours, but the characteristics of the samples can determine the ultimate accuracy of the results. The challenge is to biopsy tumours, in some cases serially over time, ensuring that the samples are representative, viable, and adequate both in quantity and quality for subsequent molecular applications. The collection of next-generation biospecimens, tumours, and blood samples at defined time points during the disease trajectory--either for discovery research or to guide clinical decisions--presents additional challenges and opportunities. From an organizational perspective, it also requires new additions to the multidisciplinary therapeutic team, notably interventional radiologists, molecular pathologists, and bioinformaticians. In this Review, we describe the existing procedures for sample procurement and processing of next-generation biospecimens, and highlight the issues involved in this endeavour, including the ethical, logistical, scientific, informational, and financial challenges accompanying next-generation biobanking.

Deep impact: unintended consequences of journal rank

Most researchers acknowledge an intrinsic hierarchy in the scholarly journals (“journal rank”) that they submit their work to, and adjust not only their submission but also their reading strategies accordingly. On the other hand, much has been written about the negative effects of institutionalizing journal rank as an impact measure. So far, contributions to the debate concerning the limitations of journal rank as a scientific impact assessment tool have either lacked data, or relied on only a few studies. In this review, we present the most recent and pertinent data on the consequences of our current scholarly communication system with respect to various measures of scientific quality (such as utility/citations, methodological soundness, expert ratings or retractions). These data corroborate previous hypotheses: using journal rank as an assessment tool is bad scientific practice. Moreover, the data lead us to argue that any journal rank (not only the currently-favored Impact Factor) would have this negative impact. Therefore, we suggest that abandoning journals altogether, in favor of a library-based scholarly communication system, will ultimately be necessary. This new system will use modern information technology to vastly improve the filter, sort and discovery functions of the current journal system.

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.

The use and misuse of statistical methodologies in pharmacology research

Descriptive, exploratory, and inferential statistics are necessary components of hypothesis-driven biomedical research. Despite the ubiquitous need for these tools, the emphasis on statistical methods in pharmacology has become dominated by inferential methods often chosen more by the availability of user-friendly software than by any understanding of the data set or the critical assumptions of the statistical tests. Such frank misuse of statistical methodology and the quest to reach the mystical α<0.05 criteria has hampered research via the publication of incorrect analysis driven by rudimentary statistical training. Perhaps more critically, a poor understanding of statistical tools limits the conclusions that may be drawn from a study by divorcing the investigator from their own data. The net result is a decrease in quality and confidence in research findings, fueling recent controversies over the reproducibility of high profile findings and effects that appear to diminish over time. The recent development of "omics" approaches leading to the production of massive higher dimensional data sets has amplified these issues making it clear that new approaches are needed to appropriately and effectively mine this type of data. Unfortunately, statistical education in the field has not kept pace. This commentary provides a foundation for an intuitive understanding of statistics that fosters an exploratory approach and an appreciation for the assumptions of various statistical tests that hopefully will increase the correct use of statistics, the application of exploratory data analysis, and the use of statistical study design, with the goal of increasing reproducibility and confidence in the literature.

Humanized medium (h7H) allows long-term primary follicular thyroid cultures from human normal thyroid, benign neoplasm, and cancer

CONTEXT

Mechanisms of thyroid physiology and cancer are principally studied in follicular cell lines. However, human thyroid cancer lines were found to be heavily contaminated by other sources, and only one supposedly normal-thyroid cell line, immortalized with SV40 antigen, is available. In primary culture, human follicular cultures lose their phenotype after passage. We hypothesized that the loss of the thyroid phenotype could be related to culture conditions in which human cells are grown in medium optimized for rodent culture, including hormones with marked differences in its affinity for the relevant rodent/human receptor.

OBJECTIVE

The objective of the study was to define conditions that allow the proliferation of primary human follicular thyrocytes for many passages without losing phenotype.

METHODS

Concentrations of hormones, transferrin, iodine, oligoelements, antioxidants, metabolites, and ethanol were adjusted within normal homeostatic human serum ranges. Single cultures were identified by short tandem repeats. Human-rodent interspecies contamination was assessed.

RESULTS

We defined an humanized 7 homeostatic additives medium enabling growth of human thyroid cultures for more than 20 passages maintaining thyrocyte phenotype. Thyrocytes proliferated and were grouped as follicle-like structures; expressed Na+/I- symporter, pendrin, cytokeratins, thyroglobulin, and thyroperoxidase showed iodine-uptake and secreted thyroglobulin and free T3. Using these conditions, we generated a bank of thyroid tumors in culture from normal thyroids, Grave's hyperplasias, benign neoplasms (goiter, adenomas), and carcinomas.

CONCLUSIONS

Using appropriate culture conditions is essential for phenotype maintenance in human thyrocytes. The bank of thyroid tumors in culture generated under humanized humanized 7 homeostatic additives culture conditions will provide a much-needed tool to compare similarly growing cells from normal vs pathological origins and thus to elucidate the molecular basis of thyroid disease.

A proposal regarding reporting of in vitro testing results

The high rate of negative clinical trials and failed drug development programs calls into question the use of preclinical testing as currently practiced. An important issue for the in vitro testing of agents that have advanced into the clinic is the use of clinically irrelevant concentrations in reports making claims for anticancer activity, as illustrated by publications for sorafenib, vorinostat, and metformin. For sorafenib, high protein binding leads to a dichotomy between concentrations active in the 10% serum conditions commonly used for in vitro testing and concentrations active in plasma. Failure to recognize this distinction leads to inappropriate claims of activity for sorafenib based on the micromolar concentrations commonly used for in vitro testing in low serum conditions. For vorinostat and metformin, results using in vitro concentrations higher than those achievable in patients are reported despite the availability of publications describing human pharmacokinetic data for each agent. We encourage journal editors and reviewers to pay greater attention to clinically relevant concentrations when considering reports that include in vitro testing of agents for which human pharmacokinetic data are available. Steps taken to more carefully scrutinize activity claims based on in vitro results can help direct researchers away from clinically irrelevant lines of research and toward lines of research that are more likely to lead to positive clinical trials and to improved treatments for patients with cancer.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.