Replicated, replicable and relevant-target engagement and pharmacological experimentation in the...

Biochemical Pharmacology xxx (2013) xxx–xxx

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BCP-11804; No. of Pages 14

Review

Replicated, replicable and relevant–target engagement andpharmacological experimentation in the 21st century

Terry Kenakin a, David B. Bylund b, Myron L. Toews b, Kevin Mullane c,Raymond J. Winquist d, Michael Williams e,*a Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USAb Department of Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USAc Profectus Pharma Consulting Inc., San Jose, CA, USAd Department of Integrated Biology, Vertex Pharmaceuticals, Inc., Cambridge, MA, USAe Department of Molecular Pharmacology and Biological Chemistry, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

2. Antecedents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

2.1. Biotech . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

2.2. Bigger challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

2.3. Quantity not quality? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

3. Experimental science—Revisiting the basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

3.1. Hypothesis content and structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

3.1.1. Target-based hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

3.1.2. Compound characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

3.2. Replicating the original data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

3.3. Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

3.4. Execution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

3.4.1. The mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

A R T I C L E I N F O

Article history:

Received 29 October 2013

Accepted 29 October 2013

Available online xxx

Keywords:

Pharmacology

Receptors

Target engagement

Drug discovery

Systems biology

A B S T R A C T

A pharmacological experiment is typically conducted to: i) test or expand a hypothesis regarding the

potential role of a target in the mechanism(s) underlying a disease state using an existing drug or tool

compound in normal and/or diseased tissue or animals; or ii) characterize and optimize a new chemical

entity (NCE) targeted to modulate a specific disease-associated target to restore homeostasis as a

potential drug candidate. Hypothesis testing necessitates an intellectually rigorous, null hypothesis

approach that is distinct from a high throughput fishing expedition in search of a hypothesis. In

conducting an experiment, the protocol should be transparently defined along with its powering, design,

appropriate statistical analysis and consideration of the anticipated outcome (s) before it is initiated.

Compound-target interactions often involve the direct study of phenotype(s) unique to the target at the

cell, tissue or animal/human level. However, in vivo studies are often compromised by a lack of sufficient

information on the compound pharmacokinetics necessary to ensure target engagement and also by the

context-free analysis of ubiquitous cellular signaling pathways downstream from the target. The use of

single tool compounds/drugs at one concentration in engineered cell lines frequently results in

reductionistic data that have no physiologically relevance. This overview, focused on trends in the peer-

reviewed literature, discusses the execution and reporting of experiments and the criteria recommended

for the physiologically-relevant assessment of target engagement to identify viable new drug targets and

facilitate the advancement of translational studies.

� 2013 Elsevier Inc. All rights reserved.

Contents lists available at ScienceDirect

Biochemical Pharmacology

jo u rn al h om epag e: ww w.els evier .c o m/lo cat e/b io c hem p har m

* Corresponding author.

E-mail address: [email protected] (M. Williams).

Please cite this article in press as: Kenakin T, et al. Replicated, replicable and relevant–target engagement and pharmacologicalexperimentation in the 21st century. Biochem Pharmacol (2013), http://dx.doi.org/10.1016/j.bcp.2013.10.024

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3.5. Replication versus pseudo-replication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

3.6. Reporting and publication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

4. Experimental readouts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

4.1. Types of assay system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

4.2. Phenotypic assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

4.3. Biochemical assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

4.3.1. Binding assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

4.3.2. Signal transduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

5. All roads lead to . . . NFkB? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

6. Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 000

1. Introduction

Activities in biomedical research in academia and industry havehistorically provided the means to identify, quantify and imputehierarchical structured alterations in biological systems as theyrelate to both normal cell, tissue and organism homeostasis andputative etiologies of human disease states [1] enabling the searchfor therapeutics to treat disease states. From a pharmacologicalperspective, this typically involves the study of drug targetsincluding receptors, enzymes, transporters, etc. and putative drugtargets in the context of tissue homeostasis and pathophysiology,using existing drugs, tool compounds, including antibodies,various RNAs, and New Chemical Entities (NCEs), to assess theirfunction. Despite an emerging trend in conducting research by therepetitive mining of databases in the absence of active experimen-tation [2], pharmacology, in providing the context and structure fortranslational studies [3,4], is primarily an experimental, integra-tive discipline, the data from which are key in enabling theunderstanding of disease and disease-modifying agents along amolecular–holistic systems continuum.

Research activities by their nature and intent build sequentiallyon existing data sets that are assumed to be based on informed,transparent and documented data generation and interpretationthat fully support the conclusions reported. In the event of new andunexpected findings that question the original premise, it is furtherassumed that the initial data remains accessible, are retrievable intheir raw form and can be reanalyzed with appropriate insightsand context for reconciliation and replication. This, via the peer-reviewed literature, grant review applications, regulatory authori-ty reports and data dissemination at scientific meetings, hastraditionally served as an interactive sharing mechanism that isintended to ensure the robustness and transparency of experi-mental data as well as providing historical venues for clarificationand integration of findings. These premises and assumptions arehowever being questioned due to escalating issues regarding overtreductionism, data irreproducibility and bias in experimentation[5–9]. These have been further challenged by a deluge ofinformation resulting from an exponential increase in papers inthe peer-reviewed literature [7,9], peer commentaries on individ-ual high interest papers and an increase in review-like journals toprovide a means for researchers to keep up to date. To these can beadded more informal information sources that include Internetblogs, webcasts, informed opinion from the mainstream media andventure capitalists as well as Wikipedia which has become the sineque non for scientists (as well as non-scientists) to becomeimmediate ‘‘experts’’ in various research areas without the need todelve into the core literature. Collectively, these sources providevarious precis of research areas, often with unknown provenanceand context, personal insights (and prejudices), advanced notice of‘‘hot topic’’ papers from a variety of journal sources and informedopinion that is frequently accompanied by discussion threads/comments. While the latter can be viewed as dubious with little

Please cite this article in press as: Kenakin T, et al. Replicated, reexperimentation in the 21st century. Biochem Pharmacol (2013), ht

means of validation, often being based on personal or institutionalagendas that do not necessarily involve a transparent scientificmotive, or any particular scientific expertise, they can generateconsiderable interest and debate.

In short, never has so much data been so easily and immediatelyavailable such that many scientists can now plan their researchactivities exclusively around the adage attributed to Einstein in the1920s that ‘‘[I do not] carry such information in my mind since it isreadily available in books’’, an approach which has beensignificantly enabled by Google Scholar and Wikipedia. While thisapproach has its positive aspects, it has led to a decrease in theneed for the individual to assimilate and retain information,instead letting computer algorithms dictate associations amongdata sets. Since the algorithms often vary depending on the precisewording of a query and its sequence within a string of inquiries, theinformation retrieved can be random, varying from one search toanother. This contrasts with the situation where the proactiveremembering of information by an individual to form andconsolidate a memory in the brain together with its recall canfacilitate associations, often intuitive and serendipitous, thatsoftware would not necessarily be capable of mimicking. Similarly,much of the information retrieved via an internet search is bothtopically and temporally biased, reflecting a period of 5–10 yearswhere a discussion with an older colleague on a topic often resultsin a lead to an obscure, yet seminal reference that can inform andredirect a research project.

2. Antecedents

In an ideal world – a period of time often viewed as a ‘‘goldenera’’ – that is removed from the present by 25–50 years andfrequently distorted by the rose colored glasses of time—theconduct of biomedical research was perceived to be free of anyovert influence of any business agenda. Thus in 1955, in responseto a question about patenting parenteral polio vaccine, theinventor, Jonas Salk responded, ‘‘There is no patent. Could youpatent the sun?’’ [10]. Similarly in the ethical pharmaceuticalindustry of the 50s, the head of Merck Research Laboratories,George Merck has been widely quoted as saying, ‘‘We try never toforget that medicine is for the people. It is not for the profits. Theprofits follow, and if we have remembered that, they have neverfailed to appear. The better we have remembered it, the larger theyhave been’’ [11].

Such altruistic viewpoints have tended to disappear as businessconsiderations, those present in big pharma [12–15] and in thebiotech/venture sector [16,17], together with the nature, politici-zation and quantity of funding in academia [18,19], blended withthe shift from research to entrepreneurial universities [20], haveprogressively encroached on the conduct of science, distortingdecision making that has resulted in financial, corporate andpersonal goals rather than the science prioritizing agendas. Froman historical perspective, scientists were used to discussing

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hypotheses, designing and conducting experiments and sharingthe results with their colleagues via seminars, scientific meetingsand the peer-reviewed literature. In return they advanced theircareers and received appropriate peer acknowledgement for theircontributions in advancing science together with the ability todiscuss their findings with peers. As the business agendaencroached on industrial biomedical research [12,14,15], itdistracted efforts and restricted information flow [1] to thedetriment of progress.

2.1. Biotech

The advent of the biotech revolution in the 1980s and thebeginnings of the US biotechnology industry coincided withUnited States Patent and Trademark Law Amendment, known asthe Bayh-Dole Act [21]. This allowed a university, a not-for-profit, or a small business receiving federal research funding toretain title to any resultant patents, thus enabling the founding ofa biotech company [22]. While many new companies wereformed as a result of the Act, the economic success of many hasbeen questionable [17]. A more concerning consequence fromthe Act was that information that had previously been openlyshared became the subject of secrecy in terms of information,trade secrets or patents. Increasingly, papers that had typicallybeen a complete source for the key information necessary toreplicate an experiment were suddenly lacking key details, e.g.how a reagent was prepared, a missing, key step in a biologicalprotocol, the structure of a NCE, etc., for intellectual propertyreasons. Publishing thus became less about advancing thescience and more about career advancement and gainingpublicity (aided and abetted by the media) to enhance investorinterest in a newly formed company. Additionally, uniqueresearch tools that had previously been shared on a gratis basiswith other investigators become unavailable adding further tothe distortion in intent of biomedical research [23]. In concertwith these changes, the discipline and culture of molecularbiology [24] and the widespread availability and use of thepersonal computer redefined the way in which research wasconducted, moving the latter from a holistic, phenotypically-based approach to one that was overtly reductionistic and target-based [1,25–27].

However, the rise of molecular biology was only one facet of thecult of reductionist science that drove changes in the way researchwas conducted in the 1980s. The advent of high-throughputscreening (HTS) and combinatorial chemistry as two otherreductionist technologies, that were embraced whole-heartedlyby the industry as instruments of fundamental change, no doubtappealing to the mindset of senior management more comfortablerelating to large numbers as a (poor) surrogate for productivitythan scientific complexities (never mind the quality, feel thewidth), have, in the case of HTS, had to undergo majortransformations before being able to provide any value decadeslater, while combinatorial chemistry as originally envisioned hasbeen relegated to the status of a historical quirk.

2.2. Bigger challenges

Additional concerns have been raised regarding both the ethicaland intellectual standards in biomedical research [28,29], coupledwith an increase in irreproducible data, plagiarism, fraud andexperimental bias that is covered in detail elsewhere [1]. Theapparent diminution in intellectual perspective has occurred ashigh throughput methodologies and ‘‘spreadsheet-based’’ sciencehave assumed a greater prominence in research activities andas caution in interpreting data is thrown to the wind with thenew currency being unfettered hyperbole. Examples include


exaggerated reports in the media overstating early stage researchfindings. The first example, now 15 years old, was a report relatedto an early-stage class of anti-angiogenic compounds that wereprojected to ‘‘cure cancer in two years’’ [30] that caused greatcontroversy. The second, more recent, example was based on aninteresting finding [31] using a mouse model of prion-inducedneurodegenerative disease where a selective inhibitor of PERK(protein kinase RNA-like endoplasmic reticulum kinase), GSK2606414 (50 mg/kg po bid), a protein involved in the unfoldedprotein response (UPR) pathway, had neuroprotective activity.While the senior author of the report was quoted as saying that thefinding was ‘‘still a long way from a usable drug for humans—thiscompound had serious side effects’’ [32] among which were weightloss and mild hyperglycemia, perhaps the result of the ubiquitousdistribution of signaling pathways involving PERK, others notedthat the finding would be ‘‘judged by history as a turning point inthe search for medicines to control and prevent Alzheimer’sDisease’’ [32], a rather naive and overoptimistic statement giventhe decades-long challenges and failures in Alzheimer’s diseasedrug development [33].

Such hyperbole, coupled with the changing nature of biomedi-cal research, has tended to promulgate a cavalier attitude toscience that not only dilutes the rigor and objectivity of theresearch but tends to diminish its value in the eye of the public [9].Despite a wealth of new and heuristically compelling researchfindings, especially in the data-rich field of genetics [34], theircontribution to improving success metrics in health care, both interms of better understanding disease causality and morespecifically drug discovery, has been limited to date [35,36]. Thusthere has been a distinct lack of tangible progress in identifyingand validating/quantifying new targets and biomarkers based onthe mapping of the human genome [1,37] while the cost ofdeveloping a new drug is now approaching $5 billion [38].

This outcome has been attributed to any number of causes butis typically highlighted by two major themes: (i) that all the easydrug targets, the ‘‘low hanging fruit’’, have been successfullyexploited with only the ‘‘difficult targets’’ remaining [39] forwhich there is debatable, if any, evidence [40]; and (ii) the clinicaltrial process becoming more complex and expensive withchallenges in terms of patient recruitment and defining efficacyendpoints that has resulted in the FDA, along with otherregulatory authorities, raising the bar for new drug approvals[41]. The latter viewpoint has become quite animated with oneobserver describing a ‘‘bloated process and downright hostile-to-innovation climate for FDA drug approvals’’ [42]. The logic behindthis conclusion was that since: (i) ‘‘it costs 100,000 times less thanit once did to create a three-dimensional map of a disease-causingprotein’’ with ‘‘about 300 times more of these . . . in databases nowthan in times past’’ and, (ii) the ‘‘number of drug-like chemicalsper researcher has increased 800 times’’, the only logicalconclusion for a lack of ‘‘explosion’’ in new drug approvals mustbe the fault of the FDA. This viewpoint was also included in thefindings of the 2012 report from the President’s Council ofAdvisors on Science and Technology (PCAST) [43] on ‘‘propellinginnovation in drug discovery’’ where approximately 5 of the eighthyperbolically-described ‘‘important recommendations to un-leash extraordinary (author italics) innovation and investment’’involved aspects of changing how the FDA conducts the businessof drug approvals.

2.3. Quantity not quality?

However, there are alternative and perhaps more compellingreasons that can account for these shortfalls, which have becomepervasive over time and relate to the way in which preclinicalscience is currently taught and practiced. Thus the efforts to train




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and mentor new scientists have been far less of a priority than thesound bites of ‘‘translational science’’, ‘‘target validation’’ and thevarious ‘‘omic’’ disciplines that include the ‘‘drugome’’ and the‘‘diseaseome’’. Similarly ‘‘big’’ science initiatives (e.g., the NCI EarlyDetection Research Network (EDRN), the Alzheimer’s DiseaseNeuroimaging Initiative (ADNI) [44,45], the National Alzheimer’sProject Act (NAPA), the Therapeutics for Rare and NeglectedDiseases (TRND) program and the National Center for AdvancingTranslational Sciences (NCATS) [46] have tended to absorb allavailable funding while generating meeting schedules rather thanresults. This has resulted in many biomedical researchers not beingadequately trained in the research ethos [47]. The latter runs thespectrum from the cultural aspects of aspiration, mores andmotivation to the more concrete activities of planning, conductingand interpreting actual experiments. Efforts in mentoring haveincreasingly lacked the substance, continuity and altruism thatexemplified research in the past [48].

In 2014, a career actually spent in generating data andmentoring junior scientists has become increasingly secondaryto the survival of the individual scientist. In academia, scientistsgraduating with a doctorate degree traditionally have focused onobtaining a tenure-track position, the number of which have beenprogressively decreasing over the past 40 years as research fundinghas decreased [49,50]. In 1973, 55% of Ph.D.s in biology were intenure track positions approximately six years after obtaining theirfinal degree. By 2006 this percentage had decreased to 15% [49]. Inthe pharmaceutical industry, continued downsizing of companiesand consolidation has led to well over 300,000 jobs being lost fromthe industry in the decade up to 2012. While the majority(probably greater than 80%) of these positions have been outside ofR&D, nonetheless this has resulted in an excess of extremely wellqualified and experienced researchers for a limited number ofavailable positions [51,52]. While new start-up companies in thebiotechnology space might be anticipated to provide alternativecareer options, especially with pharma companies providingstructure and content [53,54], these are highly unlikely to absorbthe numbers of individuals downsized, particularly since venturecapital for early biotechnology companies has been in decline forthe last six plus years.

An additional facet of this problem is the global trend toencourage students to enter science as a career to increaseeconomic competitiveness [55] with increased funding of federalprograms to promote science and technology, a side effect of whichis presumed (without any tangible evidence) to raise scientificstandards at the high school/secondary school, undergraduate andpostgraduate levels. This has resulted in an excess of universitypositions to train STEM (science, technology, engineering andmathematics) students resulting in a glut of graduates for too fewpositions [56].

In both academia and pharma, individuals with permanentpositions are increasingly preoccupied with justifying theirexistence, often on a monthly basis. In academia, the repetitivewriting of grants, the majority of which do not get funded, hasbecome a primary activity especially when the grants provide agood portion of individual salaries. In industry, whether biotech orestablished pharma, considerable efforts are expended in prepar-ing for upper management to update and review ongoing projects– usually on a quarterly basis, but informally on a monthly basis –and in licensing activities. In biotech, in presenting, and in pharma,reviewing projects for licensing with both parties being involved inrepetitive presentations to one another to the point that typically20% to 40% of time is spent in activities that are tangential togenerating original data. In both academia and industry, thesedistractions can contribute to science that is poorly executed,poorly analyzed and, poorly (peer review literature) or excessively(business-related) reported.


3. Experimental science—Revisiting the basics

The conduct of an experiment remains the fundamental ‘‘unit’’of any type of biomedical research. Once a hypothesis isformulated and refined, it can be tested and then, based on theresults from an experiment, which is then replicated for at least ann value of 3, be accepted, modified or rejected. While a relativelystraightforward process, discussions with editors of mainstreampeer review journals regarding increases in rejection rates [57],have led to a perception that many authors of articles submitted forpeer review, at least in the area of pharmacology, either do notknow, or fail to understand, not only basic pharmacologicalprinciples but also how to conceive, plan, execute, write up andsubmit for publication, a series of experiments focused on adiscrete hypothesis. In an attempt to address these shortcomings,the major issues identified from these ad hoc editorial discussionsare outlined below.

3.1. Hypothesis content and structure

Given current research priorities, which are increasinglyfocused on applied research, modern day experiments inpharmacology can be divided into three major types: (i) thosewhich explore the role of a putative cellular target to assess itsinvolvement in normal tissue function and also in diseasecausality; (ii) those where the beneficial actions of a compound,a biologic, synthetic small molecule or natural product, on cell,tissue or whole organism function are examined and qualified [1]in the context of a discrete mechanism of action (MoA) and (iii), theexamination of both diseased and normal human tissue and animaland cellular counterparts to identify genes that may be associatedwith human disease states/phenotypes. The first two types ofexperiment require the delineation of a hypothesis in order toappropriately design an experiment while the latter, by definition,should ideally be ‘‘hypothesis free’’ to avoid any form of biasthat can influence the results which are by intent, hypothesisseeking [1].

3.1.1. Target-based hypotheses

Experiments of this type are usually based on previous findingsand involve the use of tool compounds, agonists, antagonists(blockers) and modulators of the target that include smallmolecule compounds, antibodies, siRNA, antisense etc., to evaluatethe role of the target by measuring an experimental readout,biochemical or phenotypic. These studies are usually designed tobe hierarchical, sequentially involving cell lines, animals andhuman tissues where appropriate. Data from cell lines, especiallythose that have been engineered by transfecting DNA to introducea normal and/or mutated target, need to be reassessed in native celllines where the target of interest is endogenously expressed in itsnormal milieu [58]. Once consistent data have been obtainedbetween the systems, target function can be assessed in wholeanimals, both wild-type and genetically engineered, via pheno-typic readouts, e.g., behavior, blood pressure changes, tumorgrowth, etc [59].

3.1.1.1. Quantitative versus qualitative assessment. While the dem-onstration that a selective antagonist can block an effect in a cell,tissue or animal (the latter assuming that the pharmacokinetic(PK) properties of the antagonist are consistent with a robustpharmacodynamic (PD) effect [60,61]) is reassuring evidence tosuggest that the target is involved in the response being measured,this should not only be done at a single concentration/dose [62] toyield a qualitative data point. Rather, a full concentration/doseresponse should be conducted to derive a quantitative assessment,e.g., IC50/Ki value [63] which should be replicated for an n value of




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least 3 for an initial dataset to define the parameters for thedefinitive study and more replicates (5–8) for the latter.Additionally, the ability to reverse the blockade with a selectiveagonist should also be explored using a number of concentrations/doses of agonist based on its PK/PD properties [64]. Probably asimportant as demonstrating that an agonist or an antagonistselective for the target under study is effective is duplication of theeffect with a chemically dissimilar agonist or antagonist, andinterrogation of the system with agonists or antagonists that areknown to lack significant activity at the specified target [27], butaffect a related target. This can provide an additional level of nullhypothesis-based support for the primary data and avoid aninherently flawed reductionistic perspective [1].

For example, SB203580 is frequently employed as a selectiveinhibitor of p38a and b MAPK, and its effects in model systems areinterpreted under that assumption. However, SB203580 alsoinhibits GAK, CK1 and RIP2 with a similar or even greater potency[65]. It has been recommended that results obtained withSB203580 be confirmed with another p38MAPK inhibitor,BIRB0796, which acts via a different mechanism and does notinhibit GAK, CK1 or RIP2, but does inhibit JNK2. Another means toconfirm that results with SB203580 are indeed attributable top38MAPK inhibition is to use antisense oligonucleotides or shRNAto knockdown kinase levels, or to convert p38MAPK to aSB203580-insensitive form by replacing the threonine at the‘‘gate-keeper site’’ to an amino acid with a larger side-chain toprevent access of the drug. If all of these approaches yield the sameresult, then a role for p38MAPK can be posited safely.

Current trends to use only a single concentration/dose ofagonist or antagonist in an experiment are therefore fraught withsufficient limitations to render the outcomes of the experimentphysiologically meaningless. Often the concentration/dose select-ed for use is based on reported data that originate from a systemthat is very different to the one being used by the experimenter.Additionally, there can be major differences in compound activitydepending on the laboratory reporting the data, not necessarilybecause of error but because of the inherent variability, e.g., noiseand random fluctuation, in biological data [1,66], such that keydatabases like the IUPHAR Guide to Pharmacology—Ligand list [67]as well as literature from established compound vendors, e.g.Sigma-Aldrich (http://www.sigmaaldrich.com/united-sta-tes.html), Tocris Bioscience (http://www.tocris.com) etc., typicallyprovide a range of values for the activity of a compound that arederived from different systems and sources. Despite this informa-tion, many researchers select an antagonist reported in theliterature as selective with an IC50/Ki value in the 1–10 nM rangeand arbitrarily decide that using a concentration of 1 mM(1000 nM) (or greater) in vitro would more definitively aid indefining target characteristics even when the selectivity is only50–100-fold. This then dilutes the value of the data generated, bydefault potentially invoking additional (and unacknowledged)target actions that could account for any observed responses orside effects.

3.1.2. Compound characterization

Experiments involving compound characterization involvethree main types: (i) to evaluate a compound with an interestingbiological phenotype in various different tissue preparations toidentify its putative MoA [68]; (ii) to identify novel structures thatare active at the target of interest (hit screening); and (iii) toevaluate the potency, efficacy and selectivity of a series ofcompounds as part of a lead optimization campaign in drugdiscovery [62,64].

In these types of experiment, it is again necessary to ensure thatthe data derived are quantitative rather than qualitative by runningconcentration/dose response curves to derive EC50/IC50/Ki/pA2


values [63]. Additionally, compound characterization, at a mini-mum, requires the measurement of potency, efficacy, selectivity[62,64] and residence time [69,70] and, ideally PK [61], safety [60]and a structure-activity profile of a compound as compared toclosely related chemical analogs so that the best attributes can becombined in a single entity that can be designated as a lead and thenfurther optimized [71].

In some instances, the selectivity of a compound is unknownbecause it has not been extensively evaluated for off-targetactivity, because the targets are not available for screening due totechnical or intellectual property reasons, or because a novel targetsubsequently found to bind the compound was unobvious, e.g.,outside the realm of ‘‘off target’’ activities previously described forcompounds of this class. A case in point is that of the tyrosinekinase/Raf kinase inhibitor, sorafenib, which has been approved forthe treatment of renal cell and hepatocellular carcinomas. Whileoriginally designated as a selective B-Raf kinase (hence sorafenib),subsequent evaluation as additional kinases became available inscreening format [72,73] showed that it was more potent atadditional members of the human kinome. Thus while the in vitroB-Raf activity of sorafenib was 260–540 nM, it was more active atABL1 (T135I) (160 nM); PDGFRb (37 nM); FMS (29 nM); VEFGR (7–28 nM); KIT (16–18 nM); FLT3 (13 nM) and RET (2 nM). Ofadditional interest was the unexpected finding that sorafenibhad 5-HT receptor antagonist activity (Ki 5-HT2B = 56 nM, [74])building on an avenue of research where 5-HT derivatives werefound to be non-competitive tyrosine kinase inhibitors [75].

Searching for the MoA of natural products that have therapeuticutility is a far more complex challenge [1] with 50 or more targetsbeing reported for epigallocatechin-3-gallate (EGCG) [76] andcurcumin [77]. Nonetheless, a broad-based screening profile(Cerep, Eurofins Panlabs, NIMH Psychoactive Drug ScreeningProgram) for a new compound or existing drug can add significantvalue in understanding the selectivity of a compound and thepotential complexity of its MoA(s) [27] although it is probablybeyond the scope of most academic research laboratories.However, selectivity of any tool compound cannot be assumed,and different approaches should be employed to confirm anyproposed MoA, and alternative interpretations acknowledged.

As noted, exceptions to formulating a hypothesis beforeinitiating an experiment include GWAS (Genome Wide AssociationStudies), NGS (Next Generation Sequencing), PheWAS (Phenome-Wide Association Studies) and microarray studies where theabsence of a hypothesis allows an appropriately agnostic approachto eliminate some types of bias [1]. However, these type of studiescan lend themselves to biases involving outcome-related genefinding, class discovery and supervised prediction and requireadditional insight, guidelines for which have been proposed basedon published studies in the cancer area [78].

3.2. Replicating the original data

In constructing a hypothesis on which to base future experimen-tation, it is important that there is data continuity especially whenthe original findings of interest originated in another laboratory. Asnoted there have been many instances, many of which involved bias[37,79], where data cannot be replicated. It is therefore mandatorythat before initiating a research project that the original data set bebenchmarked by replication. This is not limited to external datasince it has been known for key data not to be replicable even in theoriginating laboratory.

3.3. Experimental design

The design of an experiment should be based on the nullhypothesis approach [80] where it is assumed that the two (or


http://www.sigmaaldrich.com/united-states.html

http://www.sigmaaldrich.com/united-states.html

http://www.tocris.com/



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more) measured parameters, control and experimental, within anexperiment are the same unless proven otherwise. Any robust andsignificant deviation from this anticipated outcome would thenindicate that any observed differences are different.

In addition to being reproduced and reproducible, the designof an experiment should ensure appropriate powering, e.g., theinclusion of sufficient numbers of reaction wells or animals ineach treatment group that include appropriate controls to allowa meaningful statistical analysis. Power reflects the probabilitythat the experimental analysis will lead to ‘‘the appropriaterejection of the null hypothesis if it is in fact false’’ [81]. The lattercan be influenced by: the reliability of the data, e.g. the signal tonoise for individual data points; the size of the anticipated effect;the group size (n); the predetermined significance level (usuallyset at 0.05); the statistical power [9] and the selection of anappropriate statistical test (e.g., parametric, non-parametric).The latter should also be predetermined based of the number ofgroups, number of sampling points and consideration of thepoints above rather than selected post hoc on trial or error basis.While it can often irk a researcher, input from a statisticianduring the design of an experiment can markedly enhance thevalue of the data and its relevance to clinical trial design. Asdiscussed elsewhere [1], there are also many ways by which tointroduce bias into the experimental design, its conduct andanalysis, that can distort the outcomes. For instance, underpowering of a study can lead to large effect sizes [82,83] while alack of blinding or randomization can overestimate statisticalsignificance and efficacy [84,85].

3.4. Execution

In conducting an experiment, it is expected that any scientist‘‘familiar in the art’’ should be able to pick up any notebook (orequivalent electronic record) where the details of an experimentare documented and be able to understand what was done, whyand how. This should include active consideration of what havebeen termed Begley’s Six Rules [86] based on recent reproducibilityconcerns [6] to ensure that the final experiment avoids negativeanswers to the Six Rules:

(i) Were studies blinded?

Fig. 1. Flow chart of the key steps in conducting an experiment. See Se


(ii) Were all results shown? (avoid reporting any data asrepresentative and for western blots ensure that thesehave–(a) not been visually manipulated and (b) replicatedwith appropriate analyses and controls [87]).

(iii) Were experiments repeated?(iv) Were positive and negative controls shown?(v) Were reagents validated? (also are they readily available so

that the experiment can be readily repeated by anotherinvestigator?)

(vi) iv) Were the statistical tests appropriate? (and were the testspreselected to be appropriate to the experimental design?[78,81].

While a good start on improving preclinical trial planning andreporting, issues related to powering, inclusion and exclusioncriteria, sample size calculations, randomization as well asconflicts of interest [88] need to be added to the ‘‘Six Rules’’before they can achieve the widely used benchmark of the ‘‘Rule of5’’ in chemistry [89]. The accurate and systematic documentationof all details and steps in an experiment, in addition to havingimplications for assuring the fidelity of the process and in securingintellectual property should, where appropriate, be consistentwith the intent of the FDA’s Good Laboratory Practice (GLP)regulations (21 CRF 58) [89]. A general description of GLP inWikipedia (http://en.wikipedia.org/wiki/Good_Laboratory_Prac-tice updated Sept 2013), is that it is a ‘‘quality system ofmanagement control system for research laboratories to ensurethe uniformity, consistency, reliability, reproducibility, quality,and integrity of data’’. The importance of incorporating many ofthese practices has been highlighted in the ‘‘nuclear winter’’ oftranslational stroke research [85,88] with a major emphasis on‘‘proper reporting’’ [85].

3.4.1. The mechanics

The documentation of an experiment requires that theexperimenter accurately record all the details for planning,running and recording the experiment. Key elements of theprocess are outlined in Fig. 1 and include:

� Title—This should succinctly describe what was done in theexperiment and whether it is related to other experiments in a

ction 3.4.1 for additional details. Adapted from reference [90,91].


http://en.wikipedia.org/wiki/Good_Laboratory_Practice

http://en.wikipedia.org/wiki/Good_Laboratory_Practice



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series, e.g., ‘‘Part 2’’, ‘‘replicate of experiment XYZ’’, etc. The dateon which the experiment was run must be included. It is alsohelpful to create an index of experiments especially when using ahard format notebook rather than an electronic database.� Rationale for experiment—Outline the hypothesis being tested

and describe what possible outcomes can be anticipated.� Experimental design—Following from the rationale and a

consideration of the possible outcomes, document the numberand type (control, reference control, experimental variable, e.g.,compound concentration response) of groups, sample sizecalculations, powering of the groups, the steps taken torandomize the data and to blind treatments, selection of anappropriate statistical test for data analysis and inclusion andexclusion criteria. Exploratory studies to develop the finalprotocol, generate preliminary data to support power calcula-tions, and resolve potential confounding factors, should beclearly defined as such, and clearly separated from the definitivestudy.� Reagent details—This should include recording batch numbers of

the reagents, their actual weights, the volume of solvent used toput them in solution and how they were subsequently diluted.The number of times that an unusual experimental outcome hasbeen explained by the wrong decimal point being read on abalance or the volume for a dilution being off by a factor of 10 farexceeds the fingers on the hands of many 100s of researchers.The importance of formally recording such data is highlighted bythe fact that GLP guidelines typically require the use of aweighing balance that can print out the tare and compoundweights to ensure accuracy and consistency as contrasted to ahandwritten notation on an easily discarded paper towel.Additional information on reagents includes their source andtheir validation, the latter being of especial importance forantibodies supporting their selectivity.� Protocol—The steps for conducting the experiment should be

listed, either as part of the experiment or by reference to astandard procedure that has already been developed and is inroutine use for the experiment being conducted. While thisensures that all the reagents necessary to conduct the experi-ment are available in sufficient quantities together with anoutline of the steps to successfully complete the experiment, arecent survey [29] suggests that scientists are becoming lesswilling to share information on protocol details, with thesuggested reason being ‘‘lack of incentives’’. Some investigatorspurposely avoid providing critical details to ‘maintain acompetitive edge’ by making it harder to replicate the workabsent an appropriate roadmap, in the mistaken belief that theyare unique and the only laboratory qualified to conduct theresearch. Since replication and confirmation of results is criticalto the path forward, and an important basis for publishing thework, such an egotistical, self-serving position should be vilified.One may wonder whether this attitude is a consequence of theinadequate training e.g., in altruistic scientific principles, notedabove.� Data collection, archiving and analysis—Depending on its origin,

data can be manually recorded during the experiment (certainbehavioral tests, e.g. Irwin test) or directly collected via video oronto spreadsheet software using a computer-enabled device. Inthe latter instance, the data can be analyzed independently ofinvestigator input such that the output is first encountered as afinal product. While this is an efficient process, it is imperativethat the raw data are readily accessible and that the investigatorreviews it so that unexpected results can be acknowledged andacted upon. The data should be archived in a retrievable formatthat is clearly marked (notebook, page number or computer file)and linked to any additional material. Any manipulation of thedata and its statistical analysis should be clearly indicated


together with any notations regarding reasons for the exclusionof data with the final outcome being quantitative rather thanqualitative.� Summary of experimental outcomes—It is imperative that the

investigator concludes the experiment with a written discussion,however brief, of what could be deduced from the datagenerated, how it relates to the original intent of the experimentand what next steps would be. This allows a colleague to be ableto fully comprehend the parameters of the experiment and therationale behind the investigator’s conclusions. For this reason, itis important that even experimental failures are acknowledgedas such that rather than leaving colleagues to work this out forthemselves. Additionally, for a series of experiments that areconducted to replicate data, it is important to provide a summarytable that collates the information from the experiments withinthe group as this is usually the data set used as the basis for apublication. It is important that the collated dataset has links (inthe form of notebook or computer file citations and notations forexample) back to the primary data so the source is readilyidentified and fully transparent. In many instances it has beenfound that the data set from an experimental series used toreport the outcomes in a peer reviewed publication or equivalentcannot be reconstructed from the data in the original experi-ments.

For additional detail on experimentation the reader is referredto Table 1 and to Refs. [83,92,93].

3.5. Replication versus pseudo-replication

A major problem with many of the manuscripts that arerejected is an absence and/or a lack of clarity on whether the datahave been appropriately replicated. Data are often presented as‘‘representative’’ leaving the reviewer to guess as to whether thedata presented are the totality of the data generated yielding anunacceptable n value of 1 or has been selected from a series ofexperiments. In the latter instance, the data may not be necessarilyconsistent with one another and thus have not been adequatelyreported [6,88]. Another confound is that of pseudo-replication[93,94] that often leads to semantic arguments regarding: (i) whatprecisely has been replicated to provide an ‘n’ value; (ii) theprecision of the data collected and; (iii) whether the wronghypothesis was being tested [93,94].

With replication, all the data points should be derived fromstatistically independent observations that involve the assessmentof treatment effects on experimental ‘units’; thus, the number ofindependent experimental units to which the treatment is appliedto is the number of replicates. A simple example of independentobservations, which exemplifies replication versus pseudo-repli-cation, would be a behavioral test in rats to assess the effects of acompound, X, that may have antidepressant activity. Thisexperiment is designed to assess the effect of three doses ofcompound X and a single dose of a reference antidepressant, e.g.desimpramine (DMI). The experimental design thus involves 5groups (which for the sake of argument each include 5 animals):control, the DMI control group and three groups of compound Xtreated rats at three different doses. Thus each group (assumingappropriate blinding and randomization) is independent from theother groups in the experiment with the animals within each groupalso being independent of one another. The results from thisexperiment showing whether or not compound X has antidepres-sant activity would, in reality, provide an n’ value (i.e., replicate) of1. For valid replication analysis, obtaining an n value of 3 wouldrequire that the same experiment be repeated a further two times,preferably on separate days using fresh solutions of DMI andcompound X. To most authors, the first experiment may be argued



Table 1Experimental planning and reporting standards.

Study intent � Elaboration of hypothesis under evaluation

� Context of hypothesis referring to or replicating previous experiments or literature findings

� Validation of test methods/assays

Study design � Null hypothesis based

� Number of groups—vehicle and treatment controls + reference standard + experimental groups with concentration/dose response

� Written plan for study including detail on materials sources, solution preparation etc

� Experimental readout—e.g., biochemical signal, gel, animal phenotype/behavior

� Ethical approval for animal usage

� Protocol for randomization to avoid subjective bias established and incorporated into experimental design

� Blinding—experimenter unaware of which assay well or animals are in which group

� Powering—appropriate sample size controlling for effect size including power calculation and prospective selection

of statistical analysis

� Prospective criteria for data inclusion/exclusion

� Control of physiological variables, e.g., body temperature, blood pressure

� Experimental endpoint

� Replication criteria—avoiding the reporting of single observations and pseudo-replication [93,94] including an n of greater than

5 for a collated data set

Experimental detail � Number of assay wells/animals per group

� Animal source, strain, weight, gender

� In vivo—anesthesia (if used) pharmacokinetic data used dosing of reference standard + experimental

� Compound volume, route, frequency and volume of administration, site, duration of injection, time of day

� Documented detail and validation of all reagents—sources, purity

Data analysis � Data collection and analysis randomized manner

� Post hoc rationale for inclusion/exclusion of data

� Primary endpoint

� Secondary outcomes

� Documentation of missing or excluded data with reasons

Outcomes � Conclusions to experiment

� Next steps

� Unexpected outcomes

Data source � Data file location

� Notebook—page number/electronic file

� Validation including witnessing

Reporting � Publication of all data—not just representative sets [6].

Based on guidelines in Refs. [143,144].


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as having an n of 5 since there were 5 animals in each group- butviewing this as an n = 5 exemplifies pseudo-replication. Thecommon use of pseudo-replication in such in vivo examples,keeping in mind that the rationale for choosing such a Design ofExperiment involves additional ethical concerns [95], mandates acareful prodromal assessment of power analysis calculations andmodel/biological variability to appropriately design the treatmentgroups. Moreover, the data generated need to be assessedcontextually i.e., is the vehicle treatment group similar to pastexperiments, is there a dose-response relationship for compound X,does the active comparator (DMI in this example) perform asexpected. A far more egregious and frequent example would be anexperiment involving cell culture where a single vial of cells isthawed, divided in half and allowed to grow in two vessels. One ofthese halves is treated with a compound to assess whether it is acytotoxic agent with the other half serving as control. Three samplesare taken from each of the cell cultures and assessed for the presenceof apoptosis e.g., using an Annexin V assay. Despite three samplesbeing taken from each, the n value of replicates for the assay is not 3,but 1, and in this case pseudo-replicate analysis is unwarranted.

Finally, the semantics of replication become more complex inexperiments in proteomics, a reductionistic approach to biologicalfunction, where the procedure is often to assay multiple samplesfrom the same column fraction. This is an example of technical orsample replication rather than biological replication and conclu-sions from such studies must be interpreted with caution.

This is also the case with unique clinical samples, e.g., assayingEinstein’s brain to discover the cause of genius. Whatever thefinding, e.g., thicker areas in the corpus callosum than that found ina control group of 15 elderly healthy males [96], the results couldonly be ascribed to Einstein as a biological n value of 1. To assessthe hypothesis that thicker regions of the corpus callosum areassociated with the attribute of genius would require additional


brain samples from other individuals, not additional comparisonsof photographs of Einstein’s brain with different control groups.And if in the hypothetical process of doing this, the brains of SteveJobs or Leonardo DaVinci differed from that of Einstein, theconclusions could be that: (a) different brain regions are associatedwith different types of genius, e.g., thickness in the corpuscallosum is associated with the genius of theoretical physics notthat of painting the Mona Lisa or creating the iPhone; (b) thatneither Jobs nor DaVinci can be qualified as geniuses or; (c)thickness in the corpus callosum has nothing to with genius.

3.6. Reporting and publication

The final step in a series of experiments is to share the findingsin the form of a presentation at a scientific conference, a grantapplication or regulatory filing or in the peer reviewed literature.Each will require a different format with the publication being themost detailed with a presentation or a grant application/regulatoryfiling having limited in depth detail on methods.

For a publication, the manuscript should contain all the dataand the surrounding details and avoid reporting key data on whichmajor conclusions are made as ‘‘data not shown’’. Many journalsalso make it mandatory that the structures of compounds new tothe public domain be included. The conclusions should besupported by the data contained in the paper not by an inferentialrelationship to other work, published or otherwise. In short themanuscript should be a self-contained report. It should also avoidoverstating the data, e.g., as in the hypothetical example ‘‘thiseffect of 1 mM NaCl on neuroblastoma survival in cell culturerepresents a cure for Alzheimer’s disease, cancer and/or Type Idiabetes’’ especially with the possibility of the conclusions beingpicked up by the main stream media and hyped to the nth degree[30,32].




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A multitude of journals exist where an article can be submitted.These differ in terms of the scope of subject matter, quality,audience, house style, stringency (or absence) of peer review, timefor review and publication, cost, the novelty and quantity of thedata and the complexity of the hypothesis being reported. Thus fora paper reporting on a new compound effective in abrogatingtumor growth in an animal model, the authors can submit theirwork to a mainstream high profile journal like Nature, Science orPNAS, a specialized cancer journal, a genomics journal, a clinically-oriented journal, a biochemistry journal, a medicinal chemistryjournal, a drug discovery journal, or a pharmacological journal.With the exception of Nature etc., the different types of journaleach have their own hierarchies in terms of perceived quality andrelevance that are linked to their impact factor. Journals aredivided into subscription-based and open-access formats with theformer representing the majority of the peer reviewed literatureand, for obvious historical reasons, having the greatest archive ofscientific data with many publishers having their origins in the18th century if not sooner. Submission to open access journals canalso undergo peer review although there are journals in this on-line format that allow data to be submitted and published with anad hoc peer review being performed post publication via blogs orcomments from readers. While there has been some controversy inthe quality of the articles published in open access format versusthe traditional subscription-based literature [97], the former hastended to provide an unique format for papers on the conduct andculture of science as in the case of the PLoS (Public Library ofScience) family of journals which have regularly covered topicssuch as bias in both the clinical and preclinical literature and indoing so have tended to influence the way in which traditionaljournals have approached their role to the scientific community.

For an author, once the decision has been made as to whichjournal to submit his/her work, the paper has to be formatted toadhere to the style and content of the particular journal, Thisincludes the type of submission, a brief letter/communication, afull paper, a commentary or review which may have differentcontent in terms of number of figures, tables, references,supplemental information, etc. In addition to these style standards,the journal will adhere to many, if not all, of the conventionsdescribed above to ensure quality and to avoid various forms ofbias, plagiarism and fraud [1]. An interesting, if somewhatdisturbing, development reflective of recent issues resulting fromlax to non-existent standards of mentorship has resulted insubmissions where the author(s) are convinced, irrespective of thewell-established journal guidelines for submission, that they knowbest when it comes to format, grammar, style (why not Tweet yourresults?), interpretation, etc. This makes the peer review processboth challenging and a debatable use of time especially when theauthors refuse to accept feedback and conclude that the journal isunworthy of their contribution. This can result in a demand of ‘‘myway or the highway’’—either the journal accepts what the authorshave submitted or they will take it elsewhere. This has led oneeditor to appropriately comment, ‘‘Science is a hard task masterand its description should not be varied at the whim of theauthors’’ (J. Idle, personal communication). Obviously, with thenumber of available journals authors will inevitably find one thatwill accept their contribution but in the process of finding anappropriate ‘‘level’’ for the article, much time and effort is wasted.

4. Experimental readouts

A key element of experimentation in the biomedical researcharena is what is and what can be measured and its physiologicaland pathophysiological relevance. There are a variety of ways bywhich to assess whether the function of a receptor, enzyme,transporter, gene, ribosome, mitochondrion, signaling pathway


etc. has been altered either as the result of tissue dysfunctionthought to be related to a disease state or due to modulation of anychanges with a tool compound or therapeutic. Some of thesechanges are phenotypic in nature [98] reflecting the integration ofdownstream events distal to the primary mechanistic event, whileothers are immediately proximal to target engagement [99]. Alongwith target qualification and biomarker validation, the ability toreliably measure target engagement is a key pillar of thetranslational medicine process [1].

4.1. Types of assay system

Target engagement events and their readout can be assessed atmany levels in biological systems [100,101]: in vitro in intact,homogenized and fractionated recombinant and native cells; innormal, engineered and diseased tissues, e.g., brain, kidney, heart,in intact tissue and homogenates and; in animals both in vivo andex vivo.

4.2. Phenotypic assays

Phenotypic assays for target engagement involve those at thewhole animal level that measure blood pressure, urine volume,heart rate, muscle tension, stress, body temperature, blood sugar,cognition etc. as well as those at the cellular and tissue level thatinclude neurotransmitter and hormone release, changes in tissuehistology etc., and reflect a composite of integrated responses thatinvolve both the initial molecular event and responses in thetissue/cell/animal that involve compensatory mechanisms. As anexample, the phenotypic assay of blood pressure integrates severalcomponents such as the state of arterial tone, atrial/ventricularrate and contractility, and venous capacitance. As these compo-nents are maintained by autocrine, hormonal/autacoid and neuralsignaling processes [102], blood pressure can be utilized todocument a wide swath of target engagement. Similarly, themechanisms involved in maintaining blood pressure – or theirabsence – must be considered in assessing the effects of othertherapeutic classes of compound. For example, in stroke modelsthat included both spontaneously hypertensive rats (SHR) andnormotensive rats, the spin trap reagent, NXY-059 was onlyeffective in the normotensive rats [85].

Thus phenotypic assays are important for documenting thattarget engagement affects function, e.g. decreases in bloodpressure have long been used as a validation for compoundsinhibiting the renin–angiotensin system [103]. Moreover, devel-opment of more relevant/translational phenotypic assays is animportant pursuit with the goal of screening compound librariesfor discovering novel therapeutics to improve upon currentstandard of care (e.g., development of an in vitro tumorsphereassay for high-throughput screening to identify compounds whichmay be effective against the cancer stem cell/tumorigenic cellpopulation [104].

More common are in vitro phenotypic assays, based onfunctional readouts that can be as simple as cell survival. Thisapproach is unbiased with regard to mechanism with a focus onthe key biological event that results from a complex interplay ofmany potential signaling pathways. While a major limitation ofphenotypic screens was the time taken to evaluate outcomes otherthan cell survival, advances in high-throughput, automated,robotic systems coupled with developments in markers of a rangeof cellular functions (e.g., calcium signaling, gene expressionpathways, mitochondrial morphology and trafficking, proteinlocalization and half-life, proteasome and autophagy function,neurite outgrowth, synapse number), enable tens of thousands ofcells to be interrogated daily, assessing multiple phenotypicactivities simultaneously and tracked longitudinally, even for




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several months to follow late onset changes as occur in age-dependent neurodegeneration [105,106]. Since some cellularevents such as cell death are stochastic, and vary in temporalpatterns, these automated, high content evaluations have a 1000fold increase in sensitivity over regular static assays where thesignal is obscured by measuring the mean response of a largenumber of cells, each with its own vulnerability and time-course[105] Such phenotypic assays are also used to assess geneticmodifiers that can highlight other pathways for potentialintervention to correct a disease phenotype, and to helpdeconvolute findings to identify a target [107]. In general, inHTS there has been a call for using assays that have greaterphysiological relevance [108].

4.3. Biochemical assays

Biochemical assays encompass a wide range of assay typesperhaps the best known of which are target binding and signaltransduction.

4.3.1. Binding assays

Binding assays are a cost effective and facile means to directlyassess the interaction of a compound with its target receptor,enzyme, transporter, etc. The traditional approach has been to usea radioactive form of the ligand (a radioligand), agonist, antagonistor allosteric modulator, to provide a readout of the amount ofradioligand associated with the target. This technology wasreduced to practice in the early 1970s with the development ofbinding assays for opioid receptors [109,110] and adrenoceptors[111,112]. Binding assays are relative simple to use to measure theaffinity/potency of a ligand/compound at a target [113], to developa structure activity relationship for a drug discovery project and toexplore the MoA of drugs, or to identify targets for natural productsand NCEs [1,114]. Despite issues with cost, safe handling ofradioactive materials, radioactive waste disposal and the develop-ment of newer non-radioactive techniques for labeling ligands,radioligand binding per se remains in widespread use. Manyligands are now available with fluorescent or antibody epitopetags, which avoid the use of radioactivity and allow bothquantification and localization of ligand binding sites. Otherapproaches include FRET (Fluorescence Resonance Energy Trans-fer; [115]), BRET (Bioluminescence Resonance Energy Transfer[116]) and thermal shift [100] assays that can measure additionalaspects of target modulation, sometimes in greater detail than thatderived from traditional binding assays.

Regardless of the nature of the detectable tag attached to theligand, similar principles, assays, and potential problems areinvolved. A few of the more important and often over-lookedaspects of ligand binding assays are summarized below and arecovered in greater detail in Refs. [113,117]. In general, the first keystep is to establish the time courses for ligand association anddissociation from the receptor to establish the time for which theligand and receptor must be incubated together for binding toreach steady state or equilibrium. They also establish how rapidlythe separation of bound and free ligand must be completed,typically by filtration, to minimize the dissociation of the ligand-receptor complex during its isolation. These time course assaysalso establish the kinetic value (koff/kon) for the equilibriumdissociation constant (KD). A second and equally critical step is toestablish the proper conditions for both determining and reducingnon-specific binding of the tagged ligand (binding to sites otherthan the receptor of interest), to ensure that the binding valuesobtained are truly for interaction with the target of interest. Inaddition to binding to non-target (non-specific) sites in the tissuebeing studied, the tagged ligand may also bind to the tubes/platesor to the filters used to separate bound from free ligand. These not


only contribute to non-specific binding but can also cause a wrongestimation of the concentration of the tagged ligand that is free(e.g., unbound).

After establishing proper binding conditions, complete satura-tion binding assays need to be performed, to determine the totalnumber of receptor binding sites in the preparation (maximalbinding, Bmax) and to establish the equilibrium value for the KD,which should agree with the value obtained from kinetic experi-ments. Saturation assays also allow the determination of anappropriate concentration of labeled ligand to use in competitionbinding assays to assess interaction of other agents with the samereceptor. A frequent problem is the use of too few concentrations ofligand in both saturation and competition binding assays, makingdeterminations of either the KD or the Bmax inadequate.

Attention must be paid to the pharmacological specificity of thetagged ligand for the specific receptor of interest; for example, ifthe tagged ligand interacts with multiple subtypes of the samereceptor (e.g., both b1 and b2 adrenoceptors), then extra care mustbe taken to ensure that only the receptor of interest is being labeledand quantified. Binding to more than one type of receptor can berevealed by ‘‘shallow’’ saturation or competition binding curves,and detailed computerized curve fitting will then be required toseparate the binding parameters for the multiple receptorsinvolved. The tagged ligand may be chemically different fromthe native form of the ligand, by having [125I] or a fluorescent orother tag attached; the attached probe always has the potential toalter the target specificity from that of the native ligand.

A final set of points to remember is that these binding assays donot directly provide information regarding functional responses[62]. The binding KD value does not indicate whether the ligand isan agonist or antagonist; if an agonist ligand, its KD value and itsaffinity do not necessarily correlate with its EC50 value and potencyto elicit a functional response. Similarly, differences in the numberof binding sites do not necessarily equate to differences infunctional responses; for example, desensitization can occurwithout changes in the number of binding sites, and increasesin the number of binding sites may not increase the maximalresponse, depending on the ligand concentration and the possiblepresence of receptor reserve. These many complications andpossible errors highlight the importance of thorough analysis ofligand binding assay parameters and not making conclusionsbased only on a change in binding of a single concentration of aradioligand or similar probe.

4.3.2. Signal transduction

Signal transduction events reflect the consequence of targetengagement and include: alterations in cAMP levels; changes inion flux; modulation of IP3 levels; modulation of membrane andmitochondrial electrical potential; changes in enzyme activity;synthesis and/or secretion of mediators like prostaglandins orcytokines; protein synthesis and degradation; changes in thereceptors themselves, e.g., receptor clustering, desensitization anddown-regulation; depletion of enzyme substrates; increases inmitochondrial respiration and ATP production; induction ofapoptosis, necrosis or autophagy; alterations in gene expressionand so on. These various readouts can aid in the elucidation ofdiscrete steps in the overall response pathway, but are highlyreductionist and limited in scope, with their relevance tophysiology and pathology not always being apparent especiallywhen developed in heterologous expression systems or trans-formed cell lines. A few of the complications and shortcomings inthese types of assays are highlighted below using GPCR signaling asan example.

4.3.2.1. GPCR signaling. Signaling by Gs-coupled receptors is oftenassumed to be exclusively mediated by stimulation of adenylyl




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cyclase and increased cAMP levels. Even when signaling is mediatedvia cAMP, this does not necessarily imply the involvement of PKA,the cAMP-dependent kinase that is a seminal target for cAMP. Gs

proteins can also signal by regulating ion channels, an effectmediated by either a or bg subunits of the G protein complex and byEPAC (exchange protein directly activated by cAMP) [118] and b-arrestin-GPCR kinase (GRK) modulation [119–122].

A frequent problem with signal transduction assays is thefailure to consider the role of the enzymes that terminate theseresponses. An agent that increases cAMP may have no effect onadenylyl cyclase activity but rather inhibit one or more of the manyphosphodiesterases that degrade cAMP. This problem applies toperhaps all other signaling pathways also—changes in tyrosinephosphorylation could be due to effects on tyrosine kinases ortyrosine phosphatases and increases in intracellular Ca2+ could bedue to increased Ca2+ release of or inhibition of Ca2+ reuptake.

A final issue, one that is prominent for signal transductionassays but applies equally to nearly every kind of assay, is thenecessity of including all of the appropriate control conditions. Toofrequently, experiments are reported that include the basal,unstimulated ‘‘control’’ value, the stimulated value using one testagent, and then the decreased effect of that test agent in thepresence of a signaling pathway inhibitor. The critical conditionthat is missing is the effect of the pathway inhibitor alone. It isessential to both assess and present this value in all experiments,as many times the inhibitor alone may have unexpected andperhaps profound effects that would alter the conclusions aboutthe stimulator plus inhibitor condition. If two agents are beingtested, e.g., pathway stimulator and pathway inhibitor, then a2 � 2 matrix of data for all four possible combinations must begenerated and reported—vehicle(s) alone, stimulator alone,inhibitor alone, and stimulator plus inhibitor. It is also essentialto ensure that the solvent for the stimulator is included in all fourassays and that the solvent for the inhibitor is included in all fourassays to ensure that the only variables that are contributing toaltered responses are the absence or presence of the stimulatorand/or the inhibitor. Additionally, the effects of the stimulatorshould be quantitatively assessed using incremental concentra-tions/doses to derive an EC50 value.

5. All roads lead to . . . NFkB?

In many studies where disease causality or effects of a drug/compound on tissue function are being studied at the molecularlevel, a variety of ubiquitous signaling pathway proteins aresuggested to be involved in a disease and become potential drugtargets. These include NFkB (nuclear factor k-greek kappa light-chain-enhancer of activated B cells) that can control transcriptionand is implicated in cancer, autoimmune diseases, viral infectionand synaptic plasticity [123], Akt (protein kinase B) which has beenreported to be involved in apoptosis, transcription, cell migrationand proliferation, viral infection mechanisms and glucose metab-olism ([124], the transcription factor, AP-1 (activator protein 1)that is involved in apoptosis differentiation and proliferation [125],the STAT (signal transducers and activators of transcription)protein family that mediates the cytokine-mediated signaling thatregulates apoptosis [126,127] and Bcl2 (B-cell lymphoma 2),another regulator of apoptosis [128].

Many of these signaling pathway proteins have formed thebasis of a plethora of disease causality hypotheses and as drugtargets, especially for natural products [1]. Their cellular roleshowever tend to be ubiquitous in nature with an incrediblycomplex repertoire of interactions with other proteins. As anexample, as of October, 2013, Wikipedia (http://en.wikipedia.org/wiki/Bcl-2) listed the following among the signaling/transcriptionproteins interacting with Bcl2—RAD9A, RTN1, BAK1, Reticulon 4,


Bcl-2-associated X protein, IRS-1, Myc, caspase-8, BECN-1, SOD1,BCAP31, BH-3 interacting domain death agonist, RRAS, C-Raf, HRK,PSEN1, PPP2CA Bcl-2-interacting killer, Noxa,CDK-1, NGF, SMN1,CAPN2, VDAC1, TP35BP2, Bcl-2-associated death promoter, BNIPL,APAF1, HRK, IKZF3 and BNIP3. This complexity raises questionsregarding specificity and physiology given that these proteins arecommon to the majority of cells in the body, irrespective ofspecialization. While each protein may represent a viable target formodulation potentially leading to a therapeutic, the questionremains as to specificity of effect. If these targets can, on one hand,be implicated in a variety of different disease states and on anothercan duplicate the effects of one another, e.g., in apoptosis, theirphysiological relevance must be questioned in the context ofsystems redundancy.

A basic premise in defining both disease causality anddeveloping novel therapeutics is the ‘‘lock and key’’ hypothesisof drug action [27] where specificity occurs via the constraints ofthe ‘‘lock’’ and where downstream events, e.g. intracellularsignaling, are a direct consequence of the gatekeeper function ofthe drug target present on the cell surface. With this as context,intracellular targets must be assumed to have major challenges inregard to side effect liabilities until proven otherwise. Thus inselecting a target selection the key points of convergence orregulation in a pathway/process must be identified, such that atherapeutic intervention interceding at that juncture (e.g., recep-tor, rate-limiting enzyme) results in a functional response thatbecomes a multiple of the impact at the selective target. Thechallenge is then to define specific targets unique to thephenotypic change required and identify which of these repre-sented regulatory points to improve activity and selectivity of anyintervention. It now seems that any member of a signaling cascadeis promoted as a viable target, even if it is ubiquitous or selectiveand not a major regulatory step.

One example of the complexity of signaling pathway membersas potential drug targets is that of p38 MAPK [129] where chronicactivation of the signaling role of this protein may contribute toapoptosis in neurodegenerative disease states including Alzhei-mer’s disease (AD) [130]. Conversely, in cancer the increase inapoptosis subsequent to increased p38 MAPK signaling can lead toattenuation of tumor growth and also block tumor initiation [131]making the ‘‘yin-yang’’ nature of p38 signaling a complicationwhere directing the organism away from neurodegeneration viathe use of p38 antagonists facilitates tumor growth and vice versa.The situation becomes more complex since p38 has beenimplicated in chronic inflammatory diseases including rheumatoidarthritis [132] and COPD (Chronic Obstructive Pulmonary Disease[133]) and where some of its actions are anti-apoptotic. A similarmutually opposing role exists for the tumor suppressor protein,p53, ‘‘the guardian of the genome’’, in cancer and neurodegenera-tion [134,135].

Given the complexity of the apoptotic signaling pathway[136,137] with a tendency for certain pathway members tofunction either as feedback or feed forward modulators of thepathway as well as interfacing with and modulating the necrosispathway, the potential impact of therapeutics directed atintracellular target modulation on cellular homeostasis is im-mense, but also raises the potential for significant side effectliabilities as the specificity provided by cell surface targets is lost.The prognosis on whether compounds acting via signalingpathways will prove to be safe and effective as therapeuticsremains to be seen. While novel therapeutics acting via NO (nitricoxide) pathways have encountered many hurdles in clinical trials[138,139], selective phosphodiesterase (PDE) inhibitors, e.g.roflumilast, sildenafil, have been approved for a variety ofindications with tissue specific distribution playing a major rolein their selectivity and safety [140].


http://en.wikipedia.org/wiki/Bcl-2

http://en.wikipedia.org/wiki/Bcl-2



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In the interim, reviewers supporting the peer review processhave to deal with a deluge of articles that document the effects ofdrugs, compounds, NCEs and especially natural products on theexpression and presumed activity of various signaling pathwaymembers that inevitably conclude that NFkB, AP-1, Akt, etc. aretargets for the treatment of Alzheimer’s, cancer, diabetes, obesity,hypertension, atherosclerosis, RA, depression, etc., etc. Given thefinding that the effects of so many different natural productsappear to converge on the very same battery of signaling pathwaytargets [1], it becomes difficult to reconcile a natural and verynecessary level of skepticism with the naive hyperbole surround-ing the reporting of these findings in the context of viable tragets,the majority of which lack any pretense of replicability, specificity,concentration/dose dependence, statistical relevance or contextwithin a null hypothesis approach.

6. Future directions

Well-designed and logical experiments that are executed,analyzed and reported in a rigorously transparent mannerrepresent the bedrock of biomedical research. Those that fail toadhere to these standards undermine and dilute the researchendeavor and in doing so not only waste resources, both time andmoney, but also negatively impact the ability of the researchcommunity to impact societal health.

With an increasing absence of a mentoring function in research,the overwhelming impact of the reductionism originating frommolecular biology and an ‘omics’ culture, many research findingsare lacking in context and thus have little relationship to ‘‘medicalreality’’ [23]. Accordingly, considerable efforts are required toensure the reincorporation of pharmacology as a mainstreamdiscipline in biomedical research and also to reinvigorate thealtruism of mentoring in the research endeavor. This will notaddress funding issues or job availability in the biomedicalsciences but may be anticipated to enhance the quality ofpreclinical research and ensure it can add value to the translationalprocess [1,3,4].

While concerns regarding the fidelity and transparency of thepeer-reviewed publication process have made many researchersskeptical of what they read, there is tremendous value in theprocess not only in being the recipient of constructive peer review,where insightful commentary can improve bodies of work, but alsoin its provision. In the latter context, those that engage in thispublic good have unfortunately become increasingly scarce interms of their number and commitment, as well as expertise andexperience, no doubt due to a self serving ‘‘lack of incentives’’ [29].Nonetheless unless this aspect of the scientific process isappropriately prioritized, instances of irreproducible and irrele-vant research will only continue to increase. Indeed, given that theNIH has suggested that ‘‘researchers would find it hard toreproduce at least (author italics) three-quarters of all publishedbiomedical findings’’ [9], this situation will only contribute furtherto the disconnect between an increasing dysfunctional researchenterprise focused on ‘‘splashy findings. . .[and]. . .press releases’’[141], the improvement of public health [3–9,23] and a re-emergence of innovation [142].

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