Guidelines for manuscript submission in the peer-reviewed pharmacological literature

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Editorial

Guidelines for manuscript submission in the peer-reviewed pharmacological literature A B S T R A C T

Keywords: Reproducibility Reagent validation Experiment planning Statistics Data reporting

Recent reports have highlighted studies in biomedical research that cannot be reproduced, tending to undermine the credibility, relevance and sustainability of the research process. To address this issue, a number of factors can be monitored to improve the overall probability of reproducibility. These include: (i) shortcomings in experimental design and execution that involve hypothesis conceptualization, statistical analysis, and data reporting; (ii) investigator bias and error; (iii) validation of reagents including cells and antibodies; and (iv) fraud. Historically, research data that have undergone peer review and are subsequently published are then subject to independent replication via the process of self-correction. This often leads to refutation of the original findings and retraction of the paper by which time considerable resources have been wasted in follow-on studies. New NIH guidelines focused on experimental conduct and manuscript submission are being widely adopted in the peer-reviewed literature. These, in their various iterations, are intended to improve the transparency and accuracy of data reporting via the use of checklists that are often accompanied by ‘‘best practice’’ guidelines that aid in validating the methodologies and reagents used in data generation. The present Editorial provides background and context to a newly developed checklist for submissions to Biochemical Pharmacology that is intended to be clear, logical, useful and unambiguous in assisting authors in preparing manuscripts and in facilitating the peer review process. While currently optional, development of this checklist based on user feedback will result in it being mandatory within the next 12 months. ß 2015 Elsevier Inc. All rights reserved.

‘‘Science is a hard taskmaster and its description should not be varied at the whim of the authors’’. Jeff Idle, University of Bern 1. Introduction The outcomes of biomedical research over the past century and a half have contributed immeasurably to individual health, well-being and longevity [1]. Together with improved sanitation, nutrition and surgical procedures numerous generations of medical devices and drugs have led to the eradication and/or prevention of many communicable diseases and transitioned common infections and chronic disease states like diabetes, asthma, coronary heart disease and osteoarthritis from fatal or incapacitating events to conditions with a generally good prognosis with a minimal impact on the quality of life – a shift ‘‘from premature death to years lived with disability’’ [2]. Due to these continued successes, society has come to expect that biomedical researchers in both academia and industry will continue to conduct research that enables the discovery and development of innovative and effective therapeutics to treat both acute and chronic disease states. Funding of the biomedical research enterprise occurs from a number of sources that include: government agencies, e.g., the National Institutes of Health, the Medical Research Council, the Max Planck Institutes; philanthropic organizations including the Wellcome Trust and the Howard Hughes Foundation; notfor-profit organizations like the American Cancer Society, the

American Diabetes Association, the Michael J. Fox Foundation for Parkinson’s Disease; and the biopharmaceutical industry. This support is predicated on the expectation that the role of the biomedical research enterprise, in both the public and private sectors, is focused on improving public health. 2. The peer reviewed biomedical literature – cracks in the firmament? Traditionally, new findings in biomedical research are published as papers in the peer-reviewed literature where their relevance to the advancement of their particular avenue of science can be measured by the body of work that they engender and its relevance, the latter being indicated by the number of times they are cited and for what duration of time. The number of citations for an article represents the basis of its impact factor, a metric that while the subject of considerable debate [3–5], reflects the standing of both the journal and the papers it publishes with both the author and journal being motivated to publish work that will garner the attention of their scientific peers. Once a paper has been published it can then be validated via a process termed selfcorrection where other scientists, using the methods and materials described in the published article can attempt to replicate the original findings. In the majority of instances, this is a relatively straightforward process. However, when the original findings cannot be replicated, the authors of the original paper is typically

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Please cite this article in press as: Mullane K, et al. Guidelines for manuscript submission in the peer-reviewed pharmacological literature. Biochem Pharmacol (2015), http://dx.doi.org/10.1016/j.bcp.2015.06.023

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contacted in order to provide additional insight into possible methodological or reagent differences between the studies to collegially resolve these [6,7]. In other instances, the data cannot be replicated because the original findings had major shortcomings in hypothesis conceptualization, design, execution and analysis [4,8–12] or were fabricated [13–18] confounding the viability of the self-correction process [19]. Given that many thousands of published papers are rarely cited, this has led to comments, again controversial, that ‘‘50% of papers are never read by anyone other than their authors, referees and journal editors’’ [20] and that ‘‘Most papers sit in a wasteland of silence, attracting no attention whatsoever’’ [21], suggesting that the situation regarding data replication may be of greater magnitude than appreciated to date, requiring unambiguous measures to ensure quality, relevance and transparency in manuscripts that are submitted for peer review [11]. In contrast to the ‘‘wasteland of silence’’ are the ‘‘sleeping beauty’’ papers that are ahead of their time. These can go uncited for years but eventually lead to quantal paradigm shifts in their respective field [22] making the citation process an even more uncertain and arbitrary bellwether of data relevance and value [3,5]. Furthermore, while reports of the latest replication scandal can lead to indignation as to the current state of biomedical research, the reader should not lose sight of the fact that studies that cannot be replicated – while potentially flawed in their design or execution – need not be the result of fraudulent activity or incompetence on the part of the investigator. A P value of less than 0.05 (<0.05) is universally accepted as an indication of a statistically significant finding. However, it carries the implicit risk that 5% of studies – however well planned and executed – will be significant solely on the basis of chance. Furthermore, given issues related to inadequate statistical powering [23] and false positives [24], the risk of a finding occurring by chance may be as high as 30–40%. Additionally, the scientific method that is the basis of advancing research [25,26] involves much that is imprecise with many unknowns and disconnects that are only revealed when a published article is subject to independent replication. These caveats are compounded by deficiencies in the training and mentoring of biomedical scientists [4,12] that are reflected in the recent comment – ‘‘I wasn’t trained that you had to validate antibodies; I was just trained that you ordered them’’ [27] – and also by the inherent complexity of some aspects of modern day research [6,7,28] that contributed to considerable confusion in the controversial field of sirtuin research [15,29]. Irrespective of the reasons for irreproducible data being published in the peer reviewed literature, improving the process by which manuscripts are written, submitted and reviewed is an important first step that will enhance transparency, accuracy and quality and begin to address the elements of ‘‘managing change in a well-worn process’’ [30]. 3. The role of pharmacological research in biomedical research Pharmacology represents the core unifying discipline that has underpinned data generation in the biomedical sciences for more than a century and involves the design and execution of a series of experiments that are intended to support or refute a particular biomedical hypothesis [31]. The latter can involve studies related to the function of a cellular target or pathway, the specific and often unique relationship of the latter to a human disease state, or the mechanism of action of a natural product therapeutic, drug or drug candidate (including antibodies, oligonucleotides, RNA interference, etc.). In characterizing the latter, a number of basic properties are mandatory and include potency, efficacy, selectivity and bioavailability that collectively define the PK/PD (pharmacokinetic/pharmacodynamic) profile of a compound [32–34].

The overarching theme of the pharmacological approach is that a biological system can respond to, or a compound produces a specific effect, in a manner that is dose (in vivo) – or concentration (in vitro) – dependent. This should then result in a data set that describes a dose/concentration response curve that follows the Law of Mass Action [35], an intrinsic property of the receptor– ligand interaction that is mandatory for the validation of any biological system and the response, biochemical and/or functional, elicited in the presence of a compound. If data for a system or a compound fails to demonstrate a dose – or concentrationdependent response, then its potential utility and relevance in enhancing understanding of cell and/or tissue function and its relationship to disease causality is questionable and ultimately an exercise in futility. For this reason studies in which single doses or concentrations of a compound are used to define a response are generally suspect unless there is robust evidence in the literature to warrant the doses/concentrations selected. 4. Guidelines and checklists Prompted by an initial checklist developed by Begley ([36]; Table 1) the National Institutes of Health (NIH) held a workshop in June 2014 together with the Nature Publishing Group (NPG) and Science focused on issues of ‘‘reproducibility and rigor of research findings’’ to improve the quality, relevance and transparency in manuscripts submitted for peer review in the biomedical literature. This resulted in the NIH issuing a set of Principles and Guidelines for Reporting Preclinical Research (http://www.nih.gov/ about/reporting-preclinical-research.htm) that compliment and extend existing guidelines for experimentation that include: ARRIVE (Animals in Research: Reporting In Vivo Experiments [30,37,38]), GSPC (Gold Standard Publication Checklist [39,40]) and those from the NINDS (National Institute of Neurological Disorders and Stroke [41]). The NIH guidelines which also includes checklists have been further extended by a comprehensive series of manuscript submission checklists from the NPG ([42] http:// www.nature.com/authors/policies/checklist.pdf) and the British Pharmacological Society (BPS [43]). The NIH guidance has also been adopted by the American Society for Pharmacology and Experimental Therapeutics (ASPET [44]). 5. Guidelines for submissions to Biochemical Pharmacology Based on the NIH guidelines and the existing Guide to Authors used by Biochemical Pharmacology, the editors of the journal have determined that a checklist of 34 questions and two informational points (Table 2) would most appropriately address transparency in manuscripts being submitted for peer review. This checklist has a dual role: firstly as a formal list of items for the author(s) to consider and include in preparing their manuscript for submission and, secondly, to provide critical information to the journal Editors and reviewers regarding key aspects of the manuscript as they proceed with the peer review process.

Table 1 The seminal checklist – Begley’s ‘‘Rule of 6’’. – – – – – –

Were Were Were Were Were Were

studies blinded? all results shown? experiments reported? positive and negative controls shown? reagents validated? statistical tests appropriate?

From Ref. [36].

Please cite this article in press as: Mullane K, et al. Guidelines for manuscript submission in the peer-reviewed pharmacological literature. Biochem Pharmacol (2015), http://dx.doi.org/10.1016/j.bcp.2015.06.023

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Of the 36 items on the checklist only 4 (Q1–Q4; Table 2), approximately 10% of the total, will lead to the mandatory rapid rejection of a manuscript for administrative reasons. The response to the remainder of the questions will be considered individually and are the responsibility of the designated Editor and the selected reviewers with the weight assigned to each of the questions depending on its relevance and importance within the context of the individual study. This provides the necessary flexibility in the review process for each manuscript and avoids a dogmatic ‘‘check the box’’ approach that is anathema to the scientific method. Outlined below are the checklist questions with background information and context as required. 5.1. Formatting – a manuscript will be automatically rejected if any of the first four questions are not marked ‘‘yes’’ 5.1.1. Comment: It should also be noted that a manuscript will be administratively rejected without review if it is evident that authors have ignored the stated journal ‘‘house style’’ or failed to read the Biochemical Pharmacology Guide to Authors (http://www. elsevier.com/journals/biochemical-pharmacology/0006-2952/ guide-for-authors). 5.1.1. Q1. As Biochemical Pharmacology does NOT publish supplemental data with the exception of audio or video files, are all necessary data included in the body of the manuscript? 5.1.1.1. Context/comment: All data the authors consider to be important in supporting their conclusions must be included in the body of the text of the submitted manuscript. With the exception of supplementary videos, all data referenced in the text are considered primary and must be part of the submitted article. 5.1.2. Q2. Are all tables and figures numbered and appropriately titled with descriptive legends that permit stand-alone interpretation? 5.1.2.1. Context/comment: The numbering of figures and tables greatly facilities the peer review process as does numbering the pages and lines within the text. When appropriate, figure and table legends must include ‘n’ and P values, the route of administration as well as compound doses/concentrations that are consistent with those outlined in the text. The notation ‘‘see Methods and Materials’’ in a figure or table legend is inadequate. Many experienced reviewers initially assess the merits of a manuscript by reading the abstract and then reviewing the tables and figures with the text forming the basis of a secondary, more comprehensive discussion of the results and conclusions. In the text, the author can guide the reviewer in his/her appreciation of the relative significance of the data presented to the hypothesis being tested and the conclusions made. If the figures and tables lack data, evidence of data replication, are missing necessary controls, including reference compounds, or evidence of appropriate statistical analysis, this will raise legitimate concerns with the Editor and reviewers, irrespective of how well the manuscript has been written. A table that includes spaces that contain no data, a dash, or the abbreviations ‘‘NT’’ (not tested) or ‘‘ND’’ (not determined – the latter of which is also used to designate ‘‘not detected’’)) will raise concerns as to the rigor of the experimental design and data collection and how the data reported were validated. Inclusion of historical controls or data culled from other publications, either those previously published by the author(s) or from another publication is of additional concern especially as the latter often occurs without due consideration of whether the experimental conditions in the published study were comparable to those used in the manuscript being submitted. Unless the data are derived from the same experiments under the same conditions, they are invalid and undermine the conclusions of a study. This does not however preclude the inclusion of results from previously published studies being included in the Discussion in the context

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of comparing these with the present findings. Failure to provide the precise concentrations or doses examined of a test agent or its route of administration will invariably result in a rejection of the work as the absence of such information makes it impossible for others to reproduce the study and replicate the findings. Editors and reviewers are skeptical of a statement within the text to the effect that something of interest did or did not happen as part of the studies reported that are followed by the phrase ‘‘data not shown’’ in parentheses, as this is anecdotal information. If such data are worthy of mention, they should be included in the manuscript. Similarly, actual data should not be cited in the text – e.g., compound activity at IC50 Receptor A = 9.6  1.8 nM and at Receptor B = 61.2  7.3 nM (n = 3; P < 0.05) – unless the source of this data can be clearly identified by the reviewer in either the figures or tables. Authors should not assume that the reviewer will search for experimental details in the text that are required to understand the data presented in a table or figure as most have neither the time nor patience for such an effort. Accordingly, the figures and tables together with their legends should be considered by the author as ‘‘stand alone’’ in terms of understanding how the experiment was performed, the number of independent observations and replicate experiments, the method(s) used to analyze the data with the definition of any abbreviations used. 5.1.3. Q3. Are all data shown in the figures and tables described in the text of the Results section and discussed in the Conclusions? 5.1.3.1. Comment: Authors must ensure that there is consistency between the text of the manuscript and the data contained in the figures and tables. 5.1.4. Q4. Does the e-mail address for the corresponding author indicate an affiliation with a research-based institution or has the author provided a separate statement written in English on institutional letterhead and signed by an official responsible for research activities for the institute verifying the affiliation listed by the corresponding author, along with the official’s institutional e-mail contact information? 5.1.4.1. Comment: To minimize the publication of fraudulent and plagiarized research it is mandatory that the affiliation of the corresponding author(s) be clearly stated such that any issues regarding the veracity of a manuscript can be addressed to both the author(s) and if necessary, the appropriate institutional official. The absence of a bona fide institutional email address for the corresponding author(s) or written institutional verification of a personal email address (e.g., gmail.com; outlook.com; mail.ru; 126.com, etc.) will lead to the automatic rejection of a manuscript without further review. 5.2. Introduction 5.2.1. Q5. Is there a clear statement with background describing the hypothesis being tested in the study? 5.2.1.1. Context/comment: The hypothesis on which the data generated in a manuscript is based should be clearly stated in the Introduction to justify and provide context to the experiments being performed. Investigators frequently conduct exploratory or hypothesis-generating experiments [45–47] especially in the areas of ‘‘omics’’ [48,49] that are by their nature hypothesis-generating. These typically involve a limited data set to assess: (a) whether interrogation of a given biological system is capable of producing reliable data, an adequate signal to noise ratio in the data that can be replicated in several experiments and (b) the feasibility of an idea. Examples of exploratory/hypothesis-generating experiments include: high throughout compound screening campaigns where the goal is to find active compounds, using single concentrations from compound libraries, that can perturb a biological system; genome wide association studies (GWAS) to assess whether

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Table 2 Biochemical Pharmacology Checklist – June 2015.

BIOCHEMICAL PHARMACOLOGY Author Checklist for Research Manuscripts This Checklist must be completed, with checkmarks on every question required, before the manuscript will be considered for review. Comments or explanations may be placed in the fields at the end of the checklist. Formatting - The submission will automatically be rejected if these first four questions are not marked “yes’ 1. As Biochemical Pharmacology does NOT publish supplemental data with the exception of audio or video files, are all necessary data included in the body of the manuscript? 2. Are all tables and figures numbered and appropriately titled with descriptive legends that permit stand-alone interpretation? 3. Are all data shown in the figures and tables also shown in the text of the Results section and discussed in the Conclusions? 4. Does the e-mail address for the corresponding author indicate an affiliation with a research-based institution or has the author provided a separate statement written in English on institutional letterhead and signed by an official responsible for research activities for the institute verifying the affiliation listed by the corresponding author, along with the official’s institutional e-mail contact information?

Yes

No

Not applicable

Introduction 5. Is there a clear statement with background describing the hypothesis being tested by this study? 6. Are the primary endpoints clearly described?

Yes

No

Not applicable

Materials and Methods 7. Are the sources of all materials clearly indicated? 8. Is (are) the chemical structure(s) of any new compound(s) presented as a figure in the manuscript or referenced to the peer-reviewed literature? 9. Are the source(s), passage number and population doubling time (PDL) of cell lines indicated? 10. Were cell lines authenticated by you or the vendor? 11. If used, has the selectivity of antibodies and/or interference RNA been validated and is their source clearly indicated? 12. If animals are used, has the species, strain, sex, weight and source been provided? 13. Is a statement included in the text indicating compliance with regulations on the ethical treatment of animals including the identification of the institutional committee that approved the experiments? 14. Is the rationale provided for the selection of concentrations, doses, route and frequency of compound administration? 15. Are quantified results (e.g,. IC50 and/or EC50 values) of concentration- and dose-response related experiments included in the manuscript? 16. If used, is the method of anesthesia described? 17. Are all group sizes approximately the same? 18. Are the criteria used for excluding any data from analysis determined prospectively and clearly stated?

Yes

No

Not applicable

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19. Was the investigator responsible for data analysis blinded to which samples/animals represent control and treatment groups? 20. Is the exact sample size (n) for each experimental group/condition clearly indicated in the text and/or in the tables and figures? 21. Are the reported data displayed as the mean ± standard deviation (SD) of three or more independent experimental replications? 22. Is the number of replicates used to generate an indi vidual data point in each of the independent experiments clearly indicated and is it equal to or greater than 3? 23. Were the statistical tests used to analyze the primary endpoints predetermined as part of the experimental design? 24. Is the threshold for statistical significance (P value) clearly indicated? 25. Were the data normalized? 26. Were post hoc tests used to assess the statistical significance among means? 27. Were human tissues or fluids used in this study? Results 28. If western blots are shown, are the following included: i) appropriate loading controls for each western blot, ii) replication data, iii) quantification, and iv) the results of a statistical analysis? 29. If PCR and RT-PCR are included, were MIQE guidelines followed? 30. Was a reference standard(s) (positive or negative controls) included in the study to validate the experiment?

Yes

No

Not applicable

Discussion 31. Are all the findings considered within the context of the hypothesis presented in the Introduction? 32. Are the primary conclusions and their implications clearly stated? 33. Are any secondary endpoints reported and are these sufficiently powered for appropriate statistical analysis? 34. Are the limitations of the current study or alternative interpretations of the findings clearly stated?

Yes

No

Not applicable

Conflict of Interest/Financial Support A. Indicate by checking the box on the right that a conflict of interest statement is included in the manuscript B. Indicate by checking the box at right that all organizations providing funding for this work are listed in the Acknowledgements.

Question Number

Author Comment

genome polymorphisms in a diseased tissue can be associated specifically with the disease; the preliminary assessment of the capabilities of a new technology; or whether a tagged ligand can be used to develop a new binding assay. To a major extent, preliminary experiments like these can be viewed as ‘‘fishing expeditions’’, initiated and guided by the experience and/or intuition of the investigator and more often than not fail to yield useful data and are insufficiently robust to warrant peer review. Such findings can however be used as the basis for additional experiments that are conducted with appropriate powering and endpoint definition [43]. Manuscripts of this type will not be considered for publication in Biochemical Pharmacology. 5.2.2. Q6. Are the primary endpoints clearly described? 5.2.2.1. Context/comment: Authors must clearly indicate in the Methods section the primary endpoints that are being measured in regard to the hypothesis under evaluation and how these are measured. The defined primary endpoints of the study must be

discussed in the results section irrespective of the outcome. Secondary endpoints may also be part of the manuscript as these may provide a basis for subsequent research activities where they become the primary endpoint. 5.3. Materials and methods 5.3.1. Q7. Are the sources of all materials clearly indicated? 5.3.1.1. Context/comment: For a study to be amenable to replication, it is imperative that the source of all materials used is given, either from a commercial source (with catalog, batch number and geographical location) or from another institution or investigator (with their name and affiliation and details on the purity, origin, validation, etc., of reagents; Table 3). This section therefore needs to provide sufficient detail to allow another researcher, one ‘‘skilled in the art’’, an individual with appropriate training and experience, to both gain an in-depth understanding of

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Table 3 Checklist for the selection and use of reference standards/probe/tool compounds (after Ref. [85]). – Is the potency and selectivity of the reference standard/probe compound sufficiently well established to draw logical conclusions from the experiment? – Is the reference standard/probe compound used at a concentration consistent with its IC50/EC50 value at the primary target? – Is this evidence provided or a reference citation given? – Is there evidence that the reference standard/probe compound is engaging its primary target in cells or tissues? – For use in vivo, are the pharmacokinetics/pharmacodynamics of the reference standard/probe compound appropriate to the model being studied, e.g. is the plasma concentration of the doses(s) of the reference standard/probe compound at the time of measuring the assay readout known? – Are control compounds used? – Is an inactive structural analog of the reference standard/probe compound used to validate the observed effects of the reference standard/probe compound? – Are data on the structure activity response (SAR) of any new compound and its analogs presented? – Is the purity and the source of the compounds documented? – Is the chemical structure of the probe compound reported? actual experimental procedures and be able to repeat the study reported. This involves clearly detailing the vehicles used for the various compounds used in a study (which may differ depending on their solubility) as well as the final concentrations of the solutions used in vitro and the doses administered in vivo, the volumes used and the route of administration. 5.3.2. Q8. Is (are) the chemical structure(s) of any novel compound(s) presented as a figure in the manuscript or referenced to the peer-reviewed literature? 5.3.2.1. Context/comment: It is essential that the structure of any new compound be made available to the reader either via a citation to a published paper or by showing the actual structure in the manuscript (Table 3). While the chemical name based on the International Union of Pure and Applied Chemistry (IUPAC) nomenclature, e.g., 1,3,7-trimethylpurine-2,6-dione for caffeine, is generally accurate it is not always free from ambiguity especially when compounds are reported for the first time and there is a possibility of misassignment of structure. A citation to the patent literature is insufficient given the tendency of inventors and patent agents to mislead readers regarding compound structures. 5.3.3. Q9. Are the source(s), passage number and population doubling time (PDL) of cell lines indicated? 5.3.3.1. Context/comment: Given issues with cell line contamination and genetic transformation due to multiple cell passages that that can lead to ‘‘imposter cell lines’’ that have a major negative impact on reproducibility [50], the sources, passage number and population doubling time (PDL) of cell lines used in study should be provided [51,52]. If the source, passage number or PDL is unknown, this should be clearly stated. 5.3.4. Q10. Were cell lines authenticated by you or the vendor? 5.3.4.1. Context/comment: Authors should indicate whether or not the cell lines used in a study have been recently authenticated using short tandem repeat (STR) DNA fingerprinting [52–54]. This is critical for cell-based studies in cancer research where the authenticity of a cell line is critical in interpreting the results obtained and where many studies have been invalidated with ‘‘imposter cell lines’’ [50] that have no relationship to the cell line originally described due to contamination, e.g., bladder cancer cells that were actually HeLa cervical cancer cells (the genome of which differs markedly from that originally isolated due to evolution in cell culture, contamination or misidentification [55]) resulting in nearly 35 years of wasted research in bladder [56] and in thyroid cancer cell lines that were actually melanoma cell lines that were used as the basis of failed clinical trials with bexarotene and vemurafenib [57]. 5.3.5. Q11. If used, has the selectivity of antibodies and interference RNA been validated and is their source clearly indicated? 5.3.5.1. Context/comment: By their very nature, antibodies exhibit high specificity, affinity and avidity, making them uniquely

powerful tools to both modulate proteins of interest and to identify their presence, e.g., in a western blot. Nonetheless, ENCODE (Encyclopedia of DNA Elements) reported that approximately 25% of 246 histone-modifying antibodies failed to show their purported activity, while only 41% exhibited 100% specificity [58]. This has been explained by the presence of ‘‘Hitchhiker antigens’’ expressing antibody complementarity-determining regions (CDRs) that stem from the dead cells and debris in the large-scale bioreactors used to make the antibodies [59]. Like cell lines, there are major concerns regarding antibody characterization with multiple examples of poorly characterized antibodies contributing to a lack of experimental reproducibility [27,60]. Over 2 million antibodies are used in research (http://www. citeab.com) representing some 250,000–300,000 ‘‘core’’ antibodies [60]. The majority of these are supplied by commercial vendors and show marked-batch-to-batch variability, are often inadequately characterized with documentation that refers to previous batches, and are able to recognize proteins distinct from those for which they are being used [60]. These issues are not confined to the antibodies used in cancer research [27] but also to those used in the study of receptors where it has been noted that ‘‘a lack of selectivity appears to be the rule rather than the exception for antibodies’’ [61]. To circumvent these problems, many researchers buy the same antibody from different sources to assess their quality in the particular assay system being studied. Nonetheless, some 25–50% of antibodies fail validation for specificity. An initiative for standardization of protein-binding reagents using sequence-validated recombinant antibodies instead of the current antibodies derived from the immunization of animals is underway and potentially costly [27,60] but in the long run is likely to be far cheaper than the resources wasted on irreproducible experiments [50]. Another tool used to characterize and manipulate key gene products, their relationship to cellular phenotypes and ultimately to the disease process is RNA interference (RNAi) [62–64]. Use of these probes requires at least two controls with one or two nucleotide changes for the same target as well as evidence for specificity when using RNAi mixtures for target inactivation. As with antibodies, authors must provide full details on the RNAi reagent sources and their validation in the Methods section. 5.3.6. Q12. If animals are used has the species, strain, sex, weight and source been provided? 5.3.6.1. Context/comment: For a reported study to be amenable to replication authors must provide full details, including species, sex, strain, age, weight, and the vendor source of the experimental animals employed for the work. Additionally, authors should provide detail on animal housing, feeding etc. as recommended in the ARRIVE [37,38] and GPSC [39,40] guidelines and indicate the animal species used in the title of the submitted manuscript. For

Please cite this article in press as: Mullane K, et al. Guidelines for manuscript submission in the peer-reviewed pharmacological literature. Biochem Pharmacol (2015), http://dx.doi.org/10.1016/j.bcp.2015.06.023

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studies in which mutated rodent strains, knock-out, knock-in, floxed or transgenic animals are used, wild type or other controls should be clearly described. 5.3.7. Q13. Is a statement included in the text indicating compliance with regulations on the ethical treatment of animals including the identification of the institutional committee that approved the experiments? 5.3.7.1. Context/comment: Any studies involving the use of animals must be formally approved by an Institutional or National Animal Care and Use Committee (IACUC) that reviews submitted experimental protocols and approves these based on the guidance contained in The Guide for the Care and Use of Laboratory Animals [65,66]. A statement must be included in the Methods section identifying the IACUC approving the experimental protocol used in the submitted manuscript. 5.3.8. Q14. Is the rationale provided for the selection of concentrations, doses, route and frequency of compound administration? 5.3.8.1. Context/comment: Authors must provide evidence – either from the literature or from exploratory experiments – that the concentrations, doses, route and frequency of administration of a compound are consistent with its known pharmacokinetic and pharmacodynamic properties [32–34] (Table 3). Using a compound at a concentration or dose that is non-selective for its established target or where the pharmacokinetics are inconsistent with either target engagement [67] or residence time [68,69] to evoke a quantifiable response questions the validity and relevance of the data reported. 5.3.9. Q15. Are quantified results (e.g., IC50 and/or EC50 values) from concentration- and dose-response related experiments included in the manuscript? 5.3.9.1. Context/comment: If data on the concentration-/doseresponse of a compound are part of a manuscript these should be quantitatively analyzed to derive IC50/EC50/Ki/pA2 values [31,35] based on three independent experiments (see 5.3.15). 5.3.10. Q16. If used, is the method of anesthesia described? 5.3.10.1. Context/comment: For a study to be replicable, details must be provided regarding the use of anesthetics, including the name of the anesthetic agent, the dose administered, the route and frequency of administration, and the physical signs monitored to ensure the appropriate level of anesthesia. 5.3.11. Q17. Are all group sizes approximately the same? 5.3.11.1. Context/comment: Different treatment groups in an experiment should be of approximately equal size, e.g. have the same number of animals or in vitro assays unless there is a valid, clearly stated reason for unequal sample sizes. Acceptable reasons include the loss or elimination of animals or samples due to defined causes or pre-established criteria that should be stated in the text. Some statisticians have argued that unequal n values between groups can increase the incidence of Type II errors or ‘‘false negatives’’ in hypothesis testing without affecting Type I errors, ‘‘false positives’’. If the experimental design is known to have a high risk of failures that could result in unequal group sizes, investigators should ensure that larger group sizes are used to have adequate numbers for evaluation at the conclusion of the experiment. 5.3.12. Q18. Are the criteria used for excluding any data from analysis determined prospectively and clearly stated? 5.3.12.1. Context/comment: An experiment is conducted within defined parameters such that the conclusions reached are limited to that particular setting, be it a certain cell line, animal strain, transgenic animal, compound concentration range, time-course, compound administration route, etc. Also operating within the experimental paradigm are potential confounding factors that can negatively influence the interpretation of the experiment and these must be considered as part of the experimental design with the investigator prospectively defining the criteria for accepting or rejecting the outcome of each experiment. At the in vitro level it

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might be that the reference standard shows a substantially reduced potency. Based on prior experiments the quantitative effects and variability of the reference standard can be defined and limits set – e.g., if it shows a 10-fold loss in potency the whole experiment, including the results with the test agent, is rejected. While rejection of the complete experiment would not show up in the data analysis, in some cases only one of a number of experimental plates might be impacted, and lead to differential group sizes that require clarification. There are many confounding factors to be considered for in vivo experiments. For example, changes in blood pressure and/or heart rate can impact the outcome in models of heart failure, myocardial infarction, stroke, arrhythmias, and thrombosis, and might be influenced at the critical time of ‘‘disease’’ development by depth of anesthesia, changes in body temperature, dehydration, stress, infection, and a plethora of other variables. These could vary between individual animals (contributing to variability) as well as between investigators who utilize slightly different techniques (and affecting replication). Consequently, it is important to establish and report the criteria of acceptability a priori and clearly state the reasons when and which experiments were excluded from data analysis. A general concern however, is that experimental replication appears to be a function of the minimum standards required to perform a statistical analysis rather than a prospectively defined power calculation that accounts for the experimental variability to better support the validity of the conclusions made (see 5.3.17). 5.3.13. Q19. Was the investigator responsible for data analysis blinded to which samples/animals represent control and treatment groups? 5.3.13.1. Context/comment: To minimize bias, the investigator conducting the experiments that form the basis of the submitted manuscript and the individual analyzing the data should both be unaware of how the individual designing the experiment randomized the samples and/or animals included in the studies. Additionally, the individual analyzing the data should be unaware of the designation of the groups being compared, in all instances to avoid investigator bias in designing, conducting, analyzing and interpreting the outcomes from an experiment. 5.3.14. Q20. Is the exact sample size (n) for each experimental group/condition clearly indicated in the text and/or in the tables and figures? 5.3.14.1. Context/comment: The inclusion of n values (see 5.3.15) in the text, tables and figures greatly facilitates the understanding and interpretation of the findings by the reviewer (see 5.1.2). 5.3.15. Q21. Are the reported data displayed as the mean  estimate of variability, the standard deviation (SD) of three or more independent experimental replications? 5.3.15.1. Context/comment: While the standard error of the mean, the SEM, measures the precision of the estimate of the population mean, it is highly sensitive to the sample size and does not provide information on the actual variability in the sample set reported [23,70]. As the SEM is derived from the SD by dividing the latter by the square root of the sample size [70] it is always smaller than the SD that while making the data visually more attractive ‘‘. . .provid[e] an illusion that the measurements are more precise than they actually are’’ [23]. For these reasons, the SD is a more accurate and appropriate measure of experimental variability and should be routinely used in manuscript submissions to Biochemical Pharmacology. The term replication in describing experimental findings is frequently misunderstood since it can be used to describe: (i) a set of triplicates in a single experiment, an approach termed ‘‘pseudoreplication’’ that for the purposes of peer review reflects an n value of 1; (ii) data from three or more separate experiments that are independently replicated using new cell/tissue preparations (different sources, passages, etc.), animals that are naı¨ve to the particular experimental protocol, new solutions, etc. that represent

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three (or more) independent experiments – not the same cells, etc., and solutions being used three (or more) times concurrently or simultaneously and; (iii) the reassessment of a single data set to assess the validity and robustness of the methodology used, usually a test of biomarker sensitivity and specificity. The analysis of three or more independent replicates reflects the accuracy of the observation and the likelihood that it can be used inferentially in predicting the relevance of the experimental outcomes as a general phenomenon of the system studied. There has been considerable debate in the pharmacological community on whether n = 3 is a sufficiently large sample set to support inferential statistical outcomes [23,24,43,70,71]. While the BPS Guidance [43] has noted that ‘‘a binomial distribution increasing group size from n = 3 to 5 improves the best attainable P value from P = 0.125 to p = 0.031’’, increasing the sample size is not always feasible due to resource limitations, reagent availability, the ethical demands of the IACUC, or the pressure generated by governmental agencies to reduce animal usage in biomedical research for humane reasons as advocated for example by the National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs) in the UK [72], which can often result in underpowered studies that are essentially ‘‘useless’’ [30]. 5.3.16. Q22. Is the number of replicates used to generate an individual data point in each of the independent experiments clearly indicated and is it equal to or greater than 3? 5.3.16.1. Context/comment: As noted above, a data set generated by running triplicates in a single experiment represents an n of 1, not an n of 3. In a manuscript, the number of replicates used to generate an individual data point should be noted with a minimum of n = 3. 5.3.17. Q23. Were the statistical tests used to analyze the primary endpoints predetermined as part of the experimental design? 5.3.17.1. Context/comment: The statistical test(s) used to analyze a series of data should be decided before the experiment is initiated since this will affect the powering of the study [23,24,43,70,71] 5.3.18. Q24. Is the threshold for statistical significance (P value) clearly indicated? 5.3.18.1. Context/comment: As noted, a P value of less than 0.05 (<0.05) is universally accepted as indicating that a research finding is statistically significant, denoting that there is only a 1 in 20 (5%) probability that the conclusion regarding a difference between groups could be obtained when the null hypothesis is in fact true. A false positive rate - the chance that a "statistically significant" finding with P <0.05 is actually a false positive - does not equal 5%, but is actually much higher [24]. This has become a major point of discussion in biomedical research with proposals that the rigor of the P value should be increased from 0.05 to 0.005 [73] or 0.001 [24], a proposal that may lack relevance in the real world [72]. Until there is a practical and transparent solution to this important issue, Biochemical Pharmacology will continue to require that authors report data significance using a P value of <0.05 as the criterion. In line with the BJP Guidance [43], authors must specifically state the P value used for determining significance in the Methods section and use this value throughout the manuscript. If P < 0.05 is stated as significant, then the use of <0.01 etc. is unnecessary. Authors may however choose to use the actual P values provided these are below the criteria stated for significance, e.g. P = 0.038, etc. For additional insight on this important topic, authors are referred to Marino [23], Colquhoun [24], Motulsky [70], Johnson [73], Nuzzo [74], Simmons et al. [75], and Halsey et al. [76], where theoretical simulations that highlight the errors inherent in the relevance and interpretation of the P value are discussed in reasonably accessible detail for non-statisticians. Additional issues in regard to statistical analysis involve the inappropriate use of data analysis tools and statistical packages, where experimental data is entered into a software program and

different ways of analyzing the data explored, irrespective of their validity, until the desired answer, one congruent with what the investigator was anticipating, is provided (or until time, money, or curiosity run out), a process known as ‘‘P-hacking’’ [70,75]. Additional types of P-hacking include ad hoc sample size collection where data points are added until the results support the hypothesis being tested, the creation of data subgroups that are analyzed to achieve the same outcome [70], and situations where data that is inconsistent with the bulk of the data collected are selected to support an author’s biased self interest [77]. Defining group sizes a priori, e.g., before the experiment is initiated, can avoid many of the issues related to P-Hacking leading to the recommendation to clearly state ‘‘whether the sample size and. . ..[data analysis]. . . were planned as part of the experimental protocol’’ [70]. 5.3.19. Q25. Were the data normalized? 5.3.19.1. Context/comment: Normalizing data to an internal standard, e.g., the use of GAPDH or actin in an experiment using Western blots, is an accepted practice to reduce variance and facilitate comparisons between different data sets that show marked variations in baseline control values, e.g. to eliminate baseline variability especially in in vivo experiments. A major concern in normalizing data is that the control, by default, lacks variability thus leading to the possibility of variance inhomogeneity [78]. Accordingly, when normalization is used, it needs to be justified with the parameters used clearly outlined in the Methods section. 5.3.20. Q26. Were post hoc tests used to assess the statistical significance among means? 5.3.20.1. Context/comment: When making multiple statistical comparisons (e.g. comparing treatments across different groups or time-related changes) an Analysis of Variance (ANOVA) is frequently used. Generally this test will indicate if there is a difference between groups, but not where the difference might lay. That requires a post hoc test (e.g. Dunnett’s or Tukey’s) that should be defined prospectively with the level of significance set (typically at P < 0.05) and only be employed in cases where the ANOVA has indicated a significant difference exists. Subsequent reference to this analysis within the manuscript should then only state if the outcome was, or was not, significant (based on the null hypothesis) and automatically indicates at the P < 0.05 level. Denoting that one component might be significant at the P < 0.01 level, for example, is irrelevant and inaccurate since the post hoc test was predefined at P < 0.05. As the P value is not directly related to effect size, post hoc comparison of P values has little meaning. 5.3.21. Q27. Were human tissues or fluids used in this study? 5.3.21.1. Context/comment: If human tissue or fluids were used, a statement must be included indicating Institutional Review Board (IRB) approval from the institution providing human materials for research purposes (http://www.hhs.gov/ohrp/assurances/irb/). 5.4. Results 5.4.1. Q28. If western blots are shown, are the following included: (i) appropriate loading controls for each western blot, (ii) replication data, (iii) quantification, and (iv) the results of a statistical analysis? 5.4.1.1. Context/comment: Images are routinely used to convey experimental information usually in the form of photographs – the latter derived from a microscope or from the processing of scanned images of polyacrylamide gels. While it has always been possible to inappropriately manipulate images to misrepresent/‘‘improve’’ the original data, this process has become far easier with the advent of digital imaging software and the facile ability of an investigator to alter an image using software like Adobe Photoshop [79–82]. This includes activities such as: deleting a band from a gel blot scan, the author having decided it is irrelevant; adding or duplicating a band because there was an error in conducting the actual experiment;

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selectively adjusting/enhancing the intensity of a single band; using historical loading control data; adjusting the contrast to drop the gel blot background which may also delete feint bands deemed by the author to be of no interest; splicing lanes together without inserting lines or a gap in the image to indicate that this has been done; obscuring lanes by pasting other images over them; pseudo-coloring of micrographs; altering brightness or contrast in fluorescence micrographs; altering a microscope field by combining field images; and altering image resolution. Image manipulation and the trend to report image data as a ‘‘representative data sample’’, e.g., an n value of 1, has led to a epidemic of fraudulent reports that have led to high profile retractions of published papers (www.retractionwatch.com). These have led to major efforts to improve author guidelines for the use of gel blot images in the literature [83] and include the use of real time, e.g., simultaneous, rather than historical controls; quantitation of bands with replication to give an n value of at least 3, the latter of which are normalized to the relevant reference protein within the individual experiments; controls for antibody specificity (see 5.3.5) in cardinal coordinate immunoblots; defined quantitative statistical analysis with conventional means and estimates of error and P values and; the description of all modifications in submitted digital images submitted for publication. 5.4.2. Q29. If PCR and RT-PCR are included, were MIQE guidelines followed? 5.4.2.1. Context/comment: The quantification of nucleic acids by real-time quantitative PCR (qPCR) and reverse transcription (RT)-qPCR are widely used techniques in biomedical research. While appearing simple and reliable they are complex leading to data inconsistencies that result from technical, rather than actual, variability that is enabled by a general lack of transparency. Thus the validity of a comparison between mRNA levels in multiple samples cannot be assessed in the absence of data on RNA quantification, purity (quality assessment of RNA templates, i.e. extent of genomic DNA contamination), and determination of PCR efficiencies (2-DDCq method [84]). This has led to the development of the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments [86]) that should be followed by authors and noted in the manuscript. 5.4.3. Q30. Was a reference standard(s) (positive or negative controls) included in the study to validate the experiment? 5.4.3.1. Context/comment: Questions regarding the validity of a study will arise if a known reference standard is not used for validating an assay, or the standard fails to display a response that is consistent with literature values. As an example, if the manuscript describes a new analgesic with a novel mechanism of action, a positive control, an opioid agonist, e.g., morphine and/ or an NSAID (Non-Steroidal Anti-inflammatory Drug), e.g., indomethacin, ketoprofen, must be used. If the new analgesic lacks efficacy in a validated model of pain where morphine or indomethacin work, then it can legitimately be concluded the analgesic does not work. If however the novel analgesic demonstrates efficacy and morphine and/or indomethacin do not work, the study is questionable as the model has not been validated by reference standard(s). Issues with the use of single, high dose/high concentrations of reference or probe compounds that have proven to be of poor quality (e.g., LY294002, a PI3 kinase inhibitor), are promiscuous in their actions (PAINS – pan-assay interfering compounds) or are used incorrectly have been addressed in a Commentary by Arrowsmith et al. [85], authors are encouraged to read this article and refer to the Chemical Probes Portal (http://chemicalprobes.org/) in selecting the most appropriate compound(s) for their study and

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cite this information in the Methods section. The checklist in Table 3 should also be consulted. 5.5. Discussion 5.5.1. Q31. Are all the findings considered within the context of the hypothesis presented in the Introduction? 5.5.1.1. Context/comment: The data reported in each figure and each table should be represented by a paragraph in the results section where the data present in each is clearly and concisely discussed in the context of the original hypothesis and can avoid – or at least set the stage for – HARKing (hypothesizing after the result is known [70,87]) a phenomenon also termed ‘‘double dipping’’ where the same data are used to both generate an hypothesis and to test it [88], although not necessarily in that order. 5.5.2. Q32. Are the primary conclusions and their implications clearly stated? 5.5.3. Q33. Are any secondary endpoints reported and are these sufficiently powered for appropriate statistical analysis? 5.5.4. Q34. Are the limitations of the current study or alternative interpretations of the findings clearly stated? 5.5.4.1. Context/comment: An uncritical assessment of the results without some mention of the potential limitations of the study in the context of other findings in the literature suggests a lack of objectivity in the interpretation of the findings. 5.6. Conflict of interest/financial support 5.6.1. A. Indicate by checking the box on the right that a conflict of interest statement is included in the manuscript. 5.6.1.1. Comment: A conflict of interest reflects a situation where the activities of a researcher in conducting, interpreting and reporting a research study, preclinical and/or clinical, may be potentially affected by some form of material gain that is typically financial or career related. It is important to note that a statement of a conflict of interest does not necessarily imply a conflict but alerts reviewers and readers to a potential for conflict and accompanying bias, overt or unintentional. 5.6.2. B. Indicate by checking the box at right that all organizations providing funding for this work are listed in the Acknowledgements. 5.7. Author comments Authors can add additional detail in this portion of the Checklist to explain more fully any of the responses to the questions. These statements must be placed here rather than in the cover letter to make sure they are seen by the Editors and reviewers during the review process. 6. General comments In providing this guidance and clarifying aspects of the Biochemical Pharmacology review process, it is hoped that by focusing on the end goal – the publication – authors may enhance the planning and execution of their research and in its reporting. In doing so this can aid in reversing the attrition rates of the peer review process which has been estimated to be 70–80% or greater [89,90] and thus reduce the waste represented by meaningless and irreproducible results [91,92]. Acknowledgements The authors would like to thank Michael Curtis and Harvey Motulsky for helpful discussions and feedback and Michael Marino for advice and input on the statistical sections.

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Kevin Mullane Profectus Pharma Consulting Inc., San Jose, CA, United States S.J. Enna Departments of Physiology and of Pharmacology, University of Kansas Medical Center, Kansas City, KS, United States Jacques Piette GIGA (Groupe Interdisciplinaire de Ge´noprote´omique Applique´e), University of Lie`ge, Belgium Michael Williams* Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States *Corresponding author E-mail address: [email protected] (M. Williams). Received 24 June 2015

Please cite this article in press as: Mullane K, et al. Guidelines for manuscript submission in the peer-reviewed pharmacological literature. Biochem Pharmacol (2015), http://dx.doi.org/10.1016/j.bcp.2015.06.023