extfieldsofstudy: Psychology Medicine

text:
Learning from Principles of Evidence-Based Medicine to Optimize Nonclinical Research Practices.

Thousands of pharmacology experiments are performed each day, generating hundreds of drug discovery programs, scientific publications, grant submissions, and other efforts. Discussions of the low reproducibility and robustness of some of this research have led to myriad efforts to increase data quality and thus reliability. Across the scientific ecosystem, regardless of the extent of concerns, debate about solutions, and differences among goals and practices, scientists strive to provide reliable data to advance frontiers of knowledge. Here we share our experience of current practices in nonclinical neuroscience research across biopharma and academia, examining context-related factors and behaviors that influence ways of working and decision-making. Drawing parallels with the principles of evidence-based medicine, we discuss ways of improving transparency and consider how to better implement best research practices. We anticipate that a shared framework of scientific rigor, facilitated by training, enabling tools, and enhanced data sharing, will draw the conversation away from data unreliability or lack of reproducibility toward the more important discussion of how to generate data that advances knowledge and propels innovation.

Introduction
Over the last 10 years, debate has raged about the quality of scientific evidence, expanding from a conversation among experts, amplified by systematic reviews and meta-analyses published in peer-reviewed journals, into a heated discussion splashed across mainstream press and social media. What is widely perceived as a "reproducibility crisis" is the subject of countless, and sometimes inaccurate, statements on the poor "reproducibility," "replicability," insufficient "rigor," "robustness," or "validity" of data and conclusions. In the context of nonclinical pharmacological data, these are cited as foundational for later clinical trial failure. The decision to advance a compound to human testing is based on a substantial body of evidence supporting the efficacy and safety of a therapeutic concept. Nonclinical studies that support, for example, an investigational new drug (IND) filing or a clinical trial application (CTA), which gate studies in humans, are reviewed under quality control procedures; most safety studies must comply with regulations laid out by health authorities, whereas nonclinical efficacy studies are usually performed in a nonregulated environment (see chapter "Quality in Non-GxP Research Environment"). If clinical trial results support both efficacy and safety of the intervention, health authorities review all of the evidence, to determine whether or not to approve a new therapeutic. Once a new therapeutic is made available to patients and their physicians, clinical trial findings and real-world observations contribute to forming a larger body of evidence that can be used for decision-making by a physician considering which treatment option would best benefit a patient. In many countries, medical students are taught to critically appraise all the accessible information in order to choose the "best possible option," based upon the "best possible evidence"; this process is part of evidence-based medicine (EBM), also known as "medicine built on proof." In EBM, clinical evidence is ranked according to the risk of underlying bias, using the available sources of evidence, from case studies through randomized, controlled clinical trials (RCTs) to clinical trial meta-analyses. Well-designed randomized trial results are generally viewed to be of higher reliability, or at least less influenced by internal bias, than observational studies or case reports. Since meta-analysis aims to provide a more trustworthy estimate of the effect and its magnitude (effect size), meta-analyses of RCTs are regarded as the most reliable source for recommending a given treatment, although this can be confounded if the individual RCTs themselves are of low quality.
A well-established framework for rating quality of evidence is the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system (http://www.gradeworkinggroup.org). GRADE takes the EBM process a step further, rating a body of evidence, and considering internal risk of bias, imprecision, inconsistency, indirectness, and publication bias of individual studies as reasons for rating the quality of evidence down, whereas a large effect, or a dose-response relationship, can justify rating it up (Balshem et al. 2011). The Cochrane Collaboration, which produces systematic reviews of health interventions, now requires authors to use GRADE (https://training.cochrane.org/grade-approach). The British Medical Journal has developed a suite of online tools (https://bestpractice.bmj.com/ info/us/toolkit) with a section on how to use GRADE, and various electronic databases and journals that summarize evidence are also available to clinicians. In a recent development, the GRADE Working Group has begun to explore how to rate evidence from nonclinical animal studies, and the first attempt to implement GRADE in the nonclinical space has successfully been performed on a sample of systematic reviews and examples, with further efforts planned (Hooijmans et al. 2018). In contrast, with the exception of those who also have medical training or clinical research experience, most scientists are unaware of the guiding principles of EBM and are unfamiliar with formal decision-enabling algorithms. At least in part due to the diversity of nonclinical experiments, systematic reviews and metaanalyses are far less common in nonclinical phases than in clinical ones, and there are very few broadly accepted tools with which to assess nonclinical data quality (Hooijmans et al. 2018;Sena et al. 2014). Pioneering work in this area came from the stroke field, with nonclinical research guidelines and an assessment tool elaborated by STAIR, the Stroke Therapy Academic Industry Roundtable (Hooijmans et al. 2014) (https://www.thestair.org). The CAMARADES collaboration (originally the "Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Stroke") has now extended its scope to support groups wishing to undertake systematic reviews and meta-analyses of animal studies in research on neurological diseases (http://www.dcn.ed.ac.uk/camarades). The Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) has designed a comprehensive method to systematically review evidence from animal studies (Hooijmans et al. 2014), based on the Cochrane risk of bias tool. SYRCLE's tool covers different forms of bias and several domains of study design, many of which are common to both clinical and nonclinical research ( Table 2 in Hooijmans et al. 2014). As a consequence, measures known to reduce bias in clinical settings, such as randomization and blinding, are recommended for implementation in nonclinical research. Although the tool was primarily developed to guide systematic reviewers, it can also be used to assess the quality of any in vivo experimental pharmacology study. However, these structured approaches have had limited uptake in other fields.
The attention to sources of bias that can influence study conduct, outcomes, and interpretation is an essential element of EBM. A catalog of bias is being collaboratively constructed, to map all the biases that affect health evidence (https:// catalogofbias.org). In the nonclinical space, despite a number of publications and material from training courses and webinars (e.g., http://neuronline.sfn.org/ Collections/Promoting-Awareness-and-Knowledge-to-Enhance-Scientific-Rigor-in-Neuroscience), an equivalent, generalizable framework, or a common standard for rating evidence quality is lacking, as is a unified concept of what constitutes the best possible material for decision-making. Discussions are also limited by confusing and varied terminology, although attempts have been made to clarify and harmonize terms and definitions (Goodman et al. 2016). Here we will use the word "reliability," in its generally accepted sense of accuracy and dependability, of something one expects to be able to rely on to make a decision. As a consequence, reliability also reflects the extent to which something can consistently be repeated. Both meanings apply to experimental pharmacology studies in all parts of the biomedical ecosystem.
The EBM framework is used to find reliable answers to medical questions. Here we will describe the purposes, current practices, and factors that contribute to bias in addressing scientific questions. We will consider which EBM principles can apply to nonclinical pharmacology work and how to strengthen our ability to implement best research practices, without limiting innovation that is urgently needed. Experimental pharmacology studies in biopharma companies and nonclinical contract research organizations (CROs) can have various purposes, such as furthering the understanding of a disease mechanism, developing a model or assay, or characterizing the effects of a novel compound. Such studies can also document a patent application and/or generate data on the efficacy or safety of a compound that is to enter clinical development, in which case the study report may ultimately be part of a regulatory submission to a health authority. In academia, the primary goal is to provide experimental evidence to answer scientific questions and disseminate new knowledge by publishing the findings; academic scientists also occasionally file patents and, in collaboration with biopharma companies, perform studies that may in turn become part of regulatory submission dossiers. Academic drug discovery platforms, which have sprouted in recent years, mainly aim to provide nonclinical data that will be further leveraged in biopharma drug discovery programs, although it is increasingly common that these data are used to advance clinical studies as well. Different business models and end goals across academia and industry, and different outcomes of nonclinical research, imply different processes and deliverables, which can be associated with a step or feature in EBM, as described in Table 1.
Investigating a scientific hypothesis is often done in a stepwise manner; from an initial idea, several questions can be asked in parallel, and answers are generated in both an incremental and iterative manner, by performing additional experiments and repeating cycles. Choices and decisions are made at each step, based on data; if these data are under-or overestimated, their interpretation will be biased, affecting subsequent steps. For example, in a drug discovery project, inaccurate estimates of in vitro potency or in vivo efficacy can skew the doses tested in nonclinical safety experiments, and bias the estimate of the dosage range in which only the desired response is observed in nonclinical species, and, most importantly, affect the subsequent determination of the corresponding dosage range to be tested in humans. As sponsors of clinical trials, among other responsibilities, biopharma companies have an ethical duty to conduct human trials only if there is a solid foundation for a potential clinical benefit with limited safety risks. In academic research, individuals and institutions are accountable to funders and to the community for contributing to the body of scientific knowledge. In all fields and sectors, biased interpretations of experimental data can result in wasted experiments; scientists are therefore responsible for the quality of the evidence generated.

Scientific Integrity: Responsible Conduct of Research and Awareness of Cognitive Bias
Over the last two decades, many governments and agencies involved in funding and conducting research have taken a strong stance on scientific integrity, issuing policies and charters at international, national, and institutional levels. Compliance with these policies is mandatory for employees and scientists applying for funding (examples: MRC, https://mrc.ukri.org/publications/browse/good-research-practiceprinciples-and-guidelines; NIH, https://grants.nih.gov/policy/research_integrity/ what-is.htm; CNRS, http://www.cnrs.fr/comets/IMG/pdf/guide_2017-en.pdf). Scientific integrity means absolute honesty, transparency, and accountability in the conduct and reporting of research. Responsible research practices encompass the adherence to these principles and the systematic use of measures aiming to reduce cognitive and experimental bias. Training on responsible scientific conduct is now mandatory at masters or PhD level in many universities; at any stage of their career, scientists can access training resources on scientific integrity and responsible research practices (see list made by EMBO, http://www.embo.org/science-policy/research-integrity/resources-on-research-integ rity; NIH, Responsible Conduct of Research Training, https://oir.nih.gov/sourcebook/ ethical-conduct/responsible-conduct-research-training; Mooc, https://www.fun-mooc. fr/courses/course-v1:Ubordeaux+28007EN+session01/about#). The US Department of Health and Human Services Office of Research Integrity has developed responsible conduct of research training courses that incorporate case studies from an academic research context (https://ori.hhs.gov/rcr-casebook-stories-about-researchers-worthdiscussing). Several companies have adopted a similar case-based approach from a biopharma context. Inaccuracy and biased interpretations are not necessarily due to purposeful scientific misconduct; in fact, most of the time, they are inadvertent, as the consequence of poor decision-making, training, or other circumstances. Mistakes can be made and can remain undetected when there is no formal process to critically review study design in advance of execution, an essential step when study outcomes gate decisions with long-term consequences, in particular for human subjects and patients. One aspect of review is to examine the multiple forms of bias that compromise data reliability, confounding evidence, and its analysis and interpretation. Experimental protocols can be biased, as can be experimenters, based on individual perceptions and behaviors: this is known as cognitive bias, i.e., the human tendency to make systematic errors, sometimes without even realizing it. Particularly problematic is confirmation bias, the tendency to seek and find confirmatory evidence for one's beliefs, and to ignore contradictory findings.
Scientists can work to develop evidence to support a hypothesis, rather than evidence to contradict one. Beyond designing and performing experiments to support a hypothesis, confirmation bias can extend to reporting only those experiments that support a particular expectation or conclusion. While confirmation bias is generally subconscious, competitionfor resources, publications, and other recognitionscan obscure good scientific practice. Confirmation bias can be both a cause and a consequence of publication or reporting bias, i.e., omissions and errors in the way results are described in the literature or in reports; it includes "positive" results bias, selective outcome reporting bias, "Hot stuff" bias, "All is well literature" bias, and one-sided reference bias (see definitions in https://catalogofbias.org).
In industry and academia, there are both common and specific risk factors conducive to cognitive bias, and awareness of this bias can be raised with various countermeasures, including those listed in Table 2.

Initiating a Research Project and Documenting Prior Evidence
Scientists running nonclinical pharmacology studies may have different goals, depending on where they work, but initiating a research project or study is driven by questions arising from prior findings in all parts of the biomedical ecosystem. When deciding to test a new hypothesis from emergent science, or when setting up a novel experimental model or assay, scientists generally read a handful of articles or reviews, focusing on the most recent findings. Many scientists methodically formulate an answerable question, weighing the strength of the available evidence and feasibility as primary drivers. Published findings can be weighed heavily as "truth," or disregarded, based on individual scientific judgment and many other factors. When subjective factors, such as journal impact factor, author prominence, or other subjective reasons, are weighed more heavily than the strength of the evidence, a form of bias is embedded from the conception of a research project. Similarly to the flowchart approach used in EBM, where the first step is to frame the clinical question and retrieve all the related evidence, explicitly defining a question and systematically reviewing the literature should be a common practice in nonclinical pharmacology. When deciding to work on a target, biopharma scientists also have to consider whether modulating it could potentially result in adverse effects, so the background evidence to be weighed may have other aspects than for an academic research project. An obstacle to a comprehensive assessment of prior data is that data can be published, unpublished, undisclosed, or inaccessible behind a paywall or another company's firewall or simply out of reach due to past archival practices (see Sect. 2.7). Publication and selective outcome reporting biases will therefore be present in most attempts to review and weigh prior evidence. Thus, in practice, the data a scientist will evaluate at the start of a research project is often incomplete, raising the possibility of flawed experimental design, execution and interpretation, as well as the risk of confirmation and related biases.

Existence and Use of Guidelines
Recommendations on how to design and conduct nonclinical, nonregulated research studies can be found in scientific publications, in scientific society or institution guidelines, and in grant application guidelines. Although recommended "best research practices" have been around for at least a decade, there are no consensus, universal nonclinical pharmacology quality guidelines, but instead a collection of constantly evolving, context, and type-of-experiment-specific suggestions.
Biopharma companies and nonclinical CROs generally have internal guidelines. Scientists are expected to record results in real time in laboratory notebooks, should an organization or individual need to document data and timelines to establish inventorship. Guidelines produced by research quality departments therefore focus on how scientists should record the results of their research, and deviations from standard operating procedures, in order to fulfill legal and regulatory requirements, more than on study design or the use of measures to reduce experimental bias. In the private sector, research quality guidelines and best practice recommendations are generally confidential documents. In publications, research quality guidelines and implementation are rarely mentioned. While indirect, study reporting guidelines (see Sect. 2.7) are slightly more cited, but determining to what extent these were followed is far from trivial.

Use of Experimental Bias Reduction Measures in Study Design and Execution
The core principle of EBM is that the most reliable evidence comes from clinical studies with the lowest risk of bias and typically those that are designed with adequate power, randomization, blinding, and a pre-specified endpoint, in a clinically relevant patient population. There are many resources to help investigators plan human studies, such as the SPIRIT statement (http://www.spirit-statement.org), an evidence-based guideline for designing clinical trial protocols, which is being developed into a web-based protocol building tool. There are fewer resources to assist scientists in designing nonclinical studies; an example is the NC3Rs' Experimental Design Assistant (EDA, https://www.nc3rs.org.uk/experimental-design-assis tant-eda) for in vivo animal studies. Experimental protocols can be found in publications or online, but they are primarily written to provide details on technical aspects, and do not always explicitly address the different sources of experimental bias. In biopharma research, study plans which describe the study design and experimental methods in full detail, including the planned statistical methods and analyses, and any deviations to these plans as the study progresses, are usually mandatory for studies that are critical for decision-making. Study plans are more rarely written for exploratory, pilot studies. Nonclinical CROs use study plan templates that include statistical analysis methodologies, which are generally shared with customers. In our experience, CROs and academic drug discovery centers are very willing to discuss and adapt study designs to suit customer needs. Collaboratively building a study plan is a good opportunity to share knowledge, ensure that a study is conducted and reported according to expectations, and work to identify and reduce conscious and unconscious biases. Across all sectors, planning ahead for in vivo pharmacology studies is more elaborate than for in vitro experiments, due to animal ethics requirements and the logistics of animal care and welfare. However, nonclinical study plans are not normally published, whereas clinical trial protocols are available in online databases such as the EU (https://www.clinicaltrialsregister.eu) and US (https://clinicaltrials.gov/) registers. A few initiatives, such as OSF's "preregistration challenge" (Open Science Foundation, Preregistration Challenge, Plan, Test, Discover, https://osf.io/x5w7h), have begun to promote formal preregistration of nonclinical study protocols, as a means to improve research quality (Nosek et al. 2018). However, preregistering every single nonclinical pharmacological study protocol in a public register would be difficult in practice, for confidentiality considerations, but also due to a perceived incompatibility with the pace of research in all sectors.
Overall, our experience in the field of neuroscience is that the implementation of experimental bias reduction measures is highly variable, within and across sectors, and meta-analyses of scientific publications have shown that there is clearly room for improvement, at least in the reporting of these measures (van der Worp et al. 2010;Egan et al. 2016).
Different fieldand sector-related practices and weights on bias reduction measures, such as blinding and randomization (see chapter "Blinding and Randomization"), can be expected. In the clinical setting, blinding is a means to reduce observer bias, which, along with randomization to reduce selection bias, underlies the higher ranking of RCTs over, for example, open-label trials. Both blinding and randomization are relevant to nonclinical studies because the awareness of treatment or condition allocation can produce observer bias in study conduct and data analysis. Neurobehavioral measures are among the most incriminated for their susceptibility to observer bias. But even automated data capture can be biased if there are no standards for threshold and cutoff values. Observer bias is also a risk, for example, when visually counting immunolabeled cells, selecting areas for analysis in brain imaging data, and choosing recording sites or cells in manual electrophysiology experiments. Blinding has its limitations; blinding integrity may lost, such as when using transgenic mice (which are often noticeably different in appearance or behavior compared to wild-type littermates) or in pathological settings that induce visible body changes, and the experimenter's unawareness of group allocation will not be sufficient to limit the effect observing animals can have on their behavior (analogous to the Hawthorne effect in social sciences, see https://catalogofbias.org/biases/ hawthorne-effect/). Differences in resource availability will influence practices, since training experimenters, standardizing animal handling and husbandry, and earmarking suitable lab space and equipment, among other considerations, are contingent upon funding. Nonclinical CROs are most likely to have strong guidelines, or at least evidence-based standard operating procedures, and to follow them, since credibility, transparency, and customer satisfaction are business-critical. The systematic use of inclusion/exclusion criteria and blinding should be implemented as standard practice in all sectors of the biomedical ecosystem. However, while in the industry there is a tendency to optimize workflows through standardization, and similarly in academia, strong lab "traditions," one size does not necessarily fit all. Specific technical constraints may apply, in particular for randomization. For instance, in some in vitro experiments, features such as "edge effect" or "plate effect" need to be factored into the randomization procedure (https://paasp.net/simple-randomisation); liquid chromatography-coupled mass spectrometry experiments require additional caution, since randomizing the order in which samples from different groups or conditions are tested may be counterproductive if the risk of potential crosscontamination is not addressed. Randomizing the order of procedures, while often a sound measure to prevent procedural bias, may actually increase the risk of bias, if animals behave differently depending on prior procedures or paradigms. While randomization and blinding will generally be effective in reducing risks of selection and observer bias, they have no effect on non-contemporaneous bias, when control groups or samples are tested or analyzed at a separate time from treated ones.
Thus, both in EBM and in nonclinical research, high-quality designs aim to take into account all of the known sources of bias and employ the best available countermeasures. Among these, there are two universally critical items, a pre-specified endpoint with an estimate of the predicted effect size and the corresponding adequate statistical power to detect the predicted effect, given the sample size, all of which require a prior statistical plan.

2.6
Biostatistics: Access and Use to Enable Appropriate Design of Nonclinical Pharmacology Studies Establishing an a priori statistical plan, as part of the study design, remains far from customary in nonclinical pharmacology, mainly because scientists can lack the adequate awareness and knowledge to do so. The latest Research Integrity report by the Science and Technology Committee in the UK (https://publications.parlia ment.uk/pa/cm201719/cmselect/cmsctech/350/350.pdf) emphasized that scientists need to learn and understand the principles of statistics, rather than simply being told of a list of statistical tests and software that does the analyses. In our experience, biologists' statistical proficiency appears to mostly be based on local custom and varies widely even in the same field of biology. This is illustrated by misleading phrases in methods sections of publications, such as "the number of animals used was the minimum required for statistical analysis," or "post hoc comparisons were carried out between means as appropriate," or "animals were randomly assigned to 4 groups," or "the experiments were appropriately randomized" (sic). A side effect of this phenomenon is that it hampers critical assessments of published papers; biologists confronted with unfamiliar terms may struggle to capture which study designs and analyses were actually conducted. In practice, more attention is paid to statistics once the data have been generated. In nonclinical CROs the statistical analyses are provided to the customer in the full study reports. In biopharma companies, for clinical development candidate compounds, it is generally mandatory that the proposed statistical analyses are developed and/or validated by a statistician. Many companies have developed robust proprietary statistics software, with specific wording and a selection of internally approved tests and analysis tools. Although in-house applications are validated and updated, they are not ideal for sharing results and analyses with external partners. Overall, and despite a call for cultural change in the interactions between scientists and nonclinical statisticians (Peers et al. 2012), it seems that the nonclinical pharmacology community remains under-resourced in this area. Insight gained through discussions on data quality among partners of several European initiatives suggests that there are too few research biostatisticians in all biomedical arenas.
When a thorough process is established beforehand, choosing a pre-specified endpoint to test a hypothesis and estimating an effect size for this endpoint are essential. While both are required in EBM, these are less common in nonclinical research. Clinical studies aim to detect a predetermined effect size, or a clinically relevant direction and effect magnitude, based on prior knowledge. In contrast, scientists generally have a rough idea of values that would be negligible, due to biological variation or to inaccuracy or imprecision, but considering which values are biologically meaningful tends to be done after, rather than before, running an experiment. When generating a hypothesis, i.e., in exploratory or pilot studies, it may be possible to choose an endpoint of interest, without necessarily defining its direction and amplitude. In contrast, prior estimates of effect size are essential when the aim is to demonstrate a pharmacological effect in confirmatory studies upon which decisions about next steps are based. This distinction between exploratory and confirmatory studies (Kimmelman et al. 2014 and chapter "Resolving the Tension Between Exploration and Confirmation in Preclinical Biomedical Research") is a determining factor in study design, but remains an underused concept in nonclinical work.
Arguably the most serious consequence of insufficient planning is that nonclinical studies are too often underpowered ( Table 2 in Button et al. 2013) or are of unknown power, when publications fail to reveal how sample sizes were chosen (Carter et al. 2017). Despite its central role in the null hypothesis significance testing framework, which remains the most used in nonclinical pharmacology, for many scientists, statistical power is one of the least well-understood aspects of statistics. This may be because it is generally explained using abstract mathematical terms, and its role more extensively discussed in clinical research, or in psychology, than in biology. However, recognizing that inadequately powered studies can lead to unreliable conclusions on the direction and magnitude of an effect in a sample of the whole population is just as important in nonclinical pharmacology as it is in EBM. Assay development is by definition exploratory in initial attempts; but when the assay is going to be used routinely, sample sizes to achieve a desired statistical power need to be determined. Unfortunately, this is not yet the norm in nonclinical pharmacology, where decisions are often made on so-called converging evidence from several underpowered studies with different endpoints or on a single published study of unknown power, offering little confidence that the same effect(s) would be seen in the whole population from which the sample was taken.
As discussed above (see Sect. 2.5), randomization is essential to prevent selection bias across all sectors of research. Randomization can be achieved even with limited resources and applied in many nonclinical pharmacology studies regardless of their purpose and type, without necessarily involving statistical expertise. The randomization procedure must however be part of the study design, and statistical evaluation before a study is conducted can help determine which procedure is best suited.

2.7
Data Integrity, Reporting, and Sharing Notwithstanding the existence of vast amounts of electronic storage space and sophisticated software to ensure file integrity, retaining, and potentially sharing, original datasets and protocols is not yet straightforward. Barriers to widespread data sharing are slowly being overcome, but there remains a need for long-term funding, and the ability to browse data long after the software used to generate or store them has become obsolete.
In biopharma companies and CROs, it is customary to retain all original individual and transformed data, with information on how a study was performed, in laboratory notebooks and annexes. Scientists working in industry are all aware that the company owns the data; one does not lose or inadvertently misplace or destroy the company's property, and in audits, quality control procedures, preparation for regulatory filings, or patent litigation cases, to name a few, original data must often be produced. This also applies to studies conducted by external collaborators. For compounds that are tested in human trials (including compounds that reach the market), all data and metadata must be safely stored and retrievable for 30 years after the last administration in humans. It is thus common practice to keep the records decades after they were generated (see item GRS023 in https://india-pharma.gsk. com/media/733695/records-retention-policy-and-schedule.pdf). Such durations exceed by far the life of the software used to generate or store the data and require machine-readable formats. Paper laboratory notebooks are also stored for the duration; their contents are notoriously difficult to retrieve as time passes, and teams or companies disperse. Electronic source data in FDA-regulated clinical investigations are expected to be attributable, legible, contemporaneous, original, and accurate (ALCOA). This expectation is also applied to nonregulated nonclinical data in many biopharma companies and in nonclinical CROs. The recent FAIR (findable, accessible, interoperable, reusable) guiding principles for scientific data management and stewardship (Wilkinson et al. 2016) are intended to facilitate data access and sharing while maintaining confidentiality if needed. To this date, broadly sharing raw data and protocols from biopharma research remains rare (but see Sect. 3.1).
Generally speaking, data generated in academia destined for publication are not as strictly managed. Institutional policies (see examples of data retention policies: Harvard, https://vpr.harvard.edu/files/ovpr-test/files/research_records_and_data_ retention_and_maintenance_guidance_rev_2017.pdf; MRC, https://mrc.ukri.org/ documents/pdf/retention-framework-for-research-data-and-records/) may state that data should be retained for a minimum of 3 years after the end of a research project, a period of 7-10 years or more, or as long as specified by research funder, patent law, legislative, and other regulatory requirements. Effective record-keeping and retention is limited by funding and by the rapid turnover of the scientists performing most of the experiments; a classic problem is the struggle to find the data generated by the now long-gone postdoctoral associate. Access to original, individual data can be requested by other scientists or required by journals and funding agencies or, on rare occasions, for investigations of scientific misconduct. Although academic data and metadata sharing is improving (Wallach et al. 2018), with extended supplementary materials and checklists, preprint servers, data repositories (Figshare, https://figshare.com; OSF, https://osf.io; PRIDE, https:// www.ebi.ac.uk/pride/archive), and protocol sharing platforms (https://experiments. springernature.com; https://www.protocols.io), universal open access to data is yet to be achieved.
In biopharma companies, there is an enormous amount of early discovery studies, including but not limited to assay development and screening campaigns, with both "positive" and "negative" data, that are not intended per se for publication, even though many could be considered precompetitive. A relatively small proportion of conducted studies is eventually published. However, for each compound entering clinical development, all the results that are considered relevant are documented in the nonclinical pharmacology study reports that support IND and CTA filings. A summary of the data is included in the nonclinical overview of the application dossiers and in the Investigator's Brochure. From these documents it is often difficult to assess the quality of the evidence, since they contain relatively little experimental or study information ; study design features are more likely to be found in the study reports, although there are no explicit guidelines for these (Langhof et al. 2018). The study reports themselves are confidential documents that are usually only disclosed to health authorities; they are intended to be factual and include study plans and results, statistical plans and analyses, and individual data.
In academia, publishing is the primary goal; publication standards and content are set by guidelines from funders, institutions, partners, peer reviewers, and most importantly by journals and editorial policies. In recent years, journal guidelines to authors have increasingly focused on good reporting practices, implementing recommendations from landmark publications and work shepherded by institutions such as the NC3Rs with the ARRIVE guidelines (Kilkenny et al. 2010), and the NIH (Landis et al. 2012), mirroring coordinated initiatives to improve clinical trial reporting guidelines, such as the EQUATOR network (https://www.equator-net work.org). Yet despite the impressive list of journals and institutions that have officially endorsed the ARRIVE guidelines, meta-research shows that there is much to be improved in terms of compliance (Jin et al. 2018;Hair et al. 2019). Moreover, there is no obligation to publish every single study performed or to report all experiments of a study in peer-reviewed journals; an important amount, possibly as much as 50%, remain unpublished (ter Riet et al. 2012).

3
Overcoming Obstacles and Further Learning from Principles of Evidence-Based Medicine

Working Together to Improve Nonclinical Data Reliability
Many conversations among researchers, basic and clinical, resemble the one between Professor Benchie and Doctor Athena (Macleod 2015), in which Athena concludes that they should be able to improve reliability and translatability, at least a little, by learning from the strengths and weaknesses of their respective backgrounds. Strictly following the EBM and GRADE rules would require that scientists appraise all the available nonclinical evidence with relevance to the question being asked. This should be the case when deciding whether to take a compound to the clinic, but is unlikely to happen for other purposes. Scientists would nevertheless benefit from a basic understanding of the methodology, strengths and weaknesses of systematic review and meta-analysis. Meta-analyses are often performed in collaborations, and a recent feasibility study using crowd-sourcing for clinical study quality assessment suggests that this could be a way forward, since experts and novices obtained the same results (Pianta et al. 2018). Combined with recently developed and highly promising machine learning algorithms (Bannach-Brown et al. 2019), collaborative efforts could increase the pace and reduce human error in systematic reviews and meta-analysis.
In recent years, private sector organizations, academic institutions, disease foundations, patient associations, and government bodies have formed consortia to tackle a wide variety of complex questions, in a precompetitive manner. Many of these partnerships bring together basic and clinical researchers and also aim to share experimental procedures and unpublished findings. Collective efforts have produced consensus recommendations, based on the critical appraisal of published and unpublished data, in fields such as stroke (Macleod et al. 2009) and pain (Knopp et al. 2015;Andrews et al. 2016). In IMI Europain (home page: www.imieuropain.org), the group of scientists and clinicians working on improving and refining animal models of chronic pain, addressing the clinical relevance of endpoints used in animal models and methodologies to reduce experimental bias, held teleconference meetings roughly 10 times a year over 5 years, which represents a substantial amount of shared data and expertise. Leveraging this combined expertise and aiming to develop a novel, non-evoked outcome measure of pain-related behavior in rodents, IMI Europain partners from both academia and industry accomplished a multicenter nonclinical study (Wodarski et al. 2016), in the spirit of a phase 3 multicenter clinical trial. One of the important lessons learned during this study was that absolute standardization should not be the goal, since circumstantial differences such as site location cannot be erased, leading to pragmatic accommodations for local variations in laboratory practice and procedures. An effort to uncover evidence-based drivers of reliability in other subfields of neuroscience is ongoing in IMI EQIPD (home page: https://quality-preclinical-data.eu), with the overarching goal of building broadly applicable tools for managing nonclinical data quality. Discussions on emerging pathways of neurodegenerative disease within the IMI neurodegeneration strategic governance group led to a single, collectively written article describing independent attempts that failed to reproduce or extend the findings of a prominent publication (Latta-Mahieu et al. 2018). A culture of collaboration is thus growing, and not only in large consortia. Co-designing nonclinical studies is now the preferred practice in bilateral partnerships or when studies are outsourced by biopharma companies to nonclinical CROS or academic drug discovery centers.

3.2
Enhancing Capabilities, from Training to Open Access to Data Research quality training should aim to provide the ability to recognize the different forms of bias and how to minimize risks, covering the full scope of data reliability, rather than solely focusing on compliance or on scientific integrity. In the private sector, laboratory notebook compliance audits are routinely performed; checklists are used to assess whether scientists have correctly entered information in laboratory notebooks. When releasing individual audit results to scientists, these compliance checklists or, in all sectors, the Nature Reporting Summary (https://www.nature. com/documents/nr-reporting-summary.pdf) checklist can also be used as tools for continuing training. Initial and continuing training in statistics should be an absolute priority for all biologists. Those who are privileged to work closely with biostatisticians should aim to establish a common language, and a meaningful engagement of both parties from the start, to be able to translate the scientific question to a statistical one and co-build study designs, with the most stringent criteria for confirmatory studies.
Learning to read a paper and to critically appraise evidence and keeping in mind that low-quality reporting can confound the appraisal and that even high-profile publications may have shortcomings should also be part of training, continued in journal clubs, and carried over to post-publication peer review (e.g., PubPeer, https:// pubpeer.com). Paying particular attention to the methods section and any supplementary methods information, searching for sample size considerations, randomization, and blinding, before interpreting data presented in figures, is an effective way to remember that independent evaluation of the data, with its strengths and limitations, is the core responsibility of scientists in all research endeavors.
The fact that many clinical trial findings remain unpublished is still a major roadblock for EBM, which various organizations have been tackling in recent years (see links in https://www.eupati.eu/clinical-development-and-trials/clinicalstudy-results-publication-and-application). In biopharma companies, proprietary nonclinical data include a considerable amount of study replicates, sometimes spread over several years. Many attempts are also made to reproduce data reported in the literature (Begley and Ellis 2012;Prinz et al. 2011;Djulbegovic and Guyatt 2017), but most of these remain undisclosed. In recent years, several independent groups have been instrumental in coordinating and publishing reproducibility studies, such as the Reproducibility Initiative collaboration between Science Exchange, PLOS, figshare, and Mendeley (http://validation.scienceexchange.com/#/reproducibility-ini tiative), the Center for Open Science (The Reproducibility Project, a collaborative effort by the Center for Open Science: https://cos.io), and a unique nonprofit-driven initiative in amyotrophic lateral sclerosis (Scott et al. 2008). In sectors of the biomedical ecosystem where the focus is more on exploring new ideas, generating and testing hypotheses, or confirming and extending a team's own work rather than replicating that of others, a substantial amount of work, possibly as much as 50% (ter Riet et al. 2012), remains unpublished. Thus, in the nonclinical space, the obstacles to widespread open access to data have yet to be overcome.

Conclusion and Perspectives
Although the term evidence-based medicine was first introduced almost 30 years ago, building upon efforts over several decades to strengthen a data-driven practice of medicine, there are still misconceptions and resistance to the approach, as well as challenges to its practical implementation, despite a number of striking illustrations of its impact (Djulbegovic and Guyatt 2017). Adapting the conceptual toolbox of EBM and using it to optimize nonclinical research practices and decision-making will likely also require time, and most importantly, strong commitment and welltargeted, well-focused advocacy from all stakeholders. Several lessons from EBM particularly deserve the attention of nonclinical scientists, such as the importance of framing a question, critically appraising prior evidence, carefully designing a study that addresses that question, and assessing the quality of the data before moving to the next step (Fig. 1). In medicine, reviewing the evidence aims to inform the decision about how to treat a patient; in science, the decision can be about whether or not to pursue a project, about which experiment to do next, which assay to develop, whether the work is sufficient for publication, or whether the aggregated evidence supports testing a compound in humans. In all sectors, a universal framework, with customizable tools, such as those available in the clinical setting, higher standards in data, and metadata management practices and sharing would help scientists assess and generate more reliable data.
Adapting EBM principles to nonclinical research need not undermine the freedom to explore. Assessing the quality of prior work should not paralyze scientists or prevent them from thinking out of the box, and the effective implementation of measures, such as blinding and randomization, to reduce bias should not produce a bias against novelty. Exploratory studies aiming to generate new hypotheses may follow less strict designs and statistical approaches, but when they are followed by confirmatory studies, a novel body of evidence and knowledge is formed, which can propel innovation through significance and impact. Indeed, "Innovative research projects are expected to generate data that is reproducible and provides a foundation for future studies" (http://grants.nih.gov/reproducibility/faqs.htm#4831). In other words, to be truly innovative, novel findings should endure beyond the initial excitement they create. If publications were collaboratively appraised using an adaptation of GRADE ratings, journals could develop novel impact metrics to reflect these ratings and the endurance of the findings.
In drug discovery and development, the significance and reliability, or lack thereof, of experimental data have immediate consequences. Biopharma companies need to be able to rely on the data to determine a course of action in a research project, to shape the future of a drug discovery program, and to extrapolate doses that will be administered to humans. There is thus both a financial interest and an ethical imperative, and from the patient's perspective, an absolute requirement, to base a decision to test a compound in humans on reliable data. When the overarching goal of nonclinical pharmacology research is to bring a compound to the clinic, transitioning to an evidence-based model, using and generating evidence rated in the upper levels of the pyramid to inform decisions, would benefit discovery, and at the very least, reduce the amount of wasted experiments.
Even with high quality of evidence and better informed decision-making, it remains to be seen whether the approaches discussed here will effectively decrease the attrition rate of drug candidates and lead to more success in translating findings from nonclinical to clinical studies. There are many reasons for "failure," and only some are related to scientific rigor, reproducibility, and robustness. However, progress in understanding disease mechanisms and target tractability (https://docs. targetvalidation.org/faq/what-is-target-tractability) is linked to the ability to design experiments and clinical trials that provide reliable information. In the near future, as solutions for enhancing data access emerge and stringent reporting standards become mandatory, scientists of all sectors should be encouraged to adapt and adopt EBM principles, to better enable reliable data-driven decisions. Assess and rank quality of prior evidence as reported; determine overall reliability for decisionmaking Decide on next steps, based on assessment; e.g., design study, including statistical plan Implement decisions; e.g., generate new data, reproduce prior study, move to next question or next stage Fig. 1 Adapting the five evidence-based medicine steps to nonclinical pharmacology research


extfieldsofstudy: Chemistry Medicine

s2fieldsofstudy: Biology Medicine

text:
Resistance Modulation Action, Time-Kill Kinetics Assay, and Inhibition of Biofilm Formation Effects of Plumbagin from Plumbago zeylanica Linn

Antimicrobial resistance (AMR) is a threat to the prevention and treatment of the increasing range of infectious diseases. There is therefore the need for renewed efforts into antimicrobial discovery and development to combat the menace. The antimicrobial activity of plumbagin isolated from roots of Plumbago zeylanica against selected organisms was evaluated for resistance modulation antimicrobial assay, time-kill kinetics assay, and inhibition of biofilm formation. The minimum inhibitory concentrations (MICs) of plumbagin and standard drugs were determined via the broth microdilution method to be 0.5 to 8 μg/mL and 0.25–128 μg/mL, respectively. In the resistance modulation study, MICs of the standard drugs were redetermined in the presence of subinhibitory concentration of plumbagin (4 μg/mL), and plumbagin was found to either potentiate or reduce the activities of these standard drugs with the highest potentiation recorded up to 12-folds for ketoconazole against Candida albicans. Plumbagin was found to be bacteriostatic and fungistatic from the time-kill kinetics study. Plumbagin demonstrated strong inhibition of biofilm formation activity at concentrations of 128, 64, and 32 μg/mL against the test microorganisms compared with ciprofloxacin. Plumbagin has been proved through this study to be a suitable lead compound in antimicrobial resistance drug development.

Introduction
Occurrence of infectious diseases in recent times is attributed mostly to bacteria; a result of prominent resistance strains of organisms to available antibiotics. ese strains include drug-resistant Shigella, multidrug-resistant tuberculosis (MDR-TB), multidrug-resistant Escherichia coli and Neisseria gonorrheae, methicillin-resistant Staphylococcus aureus, and extensively resistant Gram-negative organisms like Pseudomonas aeruginosa and Acinetobacter baumannii [1]. Antimicrobial resistance stems from multifactorial causes which include irrational drug use, failure to complete prescribed dosage, prolonged drug use, and therapy duration [2,3]. Infections become difficult to treat due to AMR, while surgical and other medical procedures become highrisk intervention causing disability, prolonged sickness, and death [4]. Other consequences of AMR include passage of antibiotic resistant diseases to others and increase in economic burden on families, societies, and healthcare systems [4]. Modulation of the activities of commercial antimicrobials by phytochemicals may reverse the mechanism of resistance developed by the organisms [5]. Synergism of conventional antibiotics is being researched into in recent times to establish possible candidates for the discovery and development of new drugs to tackle the menace of antimicrobial resistance [6].
Time-kill kinetics assays help understand interactions that exist between microbial strains and antimicrobial agents. e assay shows a concentration or time-dependent test effect of antimicrobial agents on strains of microorganisms. It determines antimicrobial agents as bacteriostatic/fungistatic or bactericidal/fungicidal [7]. Bacteria develop resistance to drugs in different ways including formation of biofilms, active efflux of drugs, drug inactivation caused by enzyme secretion, and drug target site alteration [5]. Biofilm formation reduces the penetrating abilities, and as such bacteria producing biofilms are not affected by the mode of action of antibiotics [8]. Biofilms refer to cells of microbes embedded in selfproduced matrix of extrapolymeric substances attached irreversibly to a surface. Biofilms constitute 65% of microbial infections, and bacteria living in them develop resistance to antimicrobial agents a thousand times than those existing in free-living forms (planktonic forms) [9]. Potential agents to be considered in antimicrobial resistant drug development should therefore demonstrate strong biofilm reduction or inhibition activity [9].
Plumbago zeylanica Linn (Plumbaginaceae) is a bushy and straggling perennial shrub that grows between 1.5-3.0 m in height. It is distributed throughout tropical Africa with collection of the plant reported in Senegal, Guinea, Sierra Leone, Liberia, Ghana, Togo, Nigeria, Zambia, and Equatorial Guinea. Its roots are used traditionally to treat tuberculosis, gonorrhoea, diarrhoea, syphilis, wounds, and rheumatic pains and swellings [10,11]. e major component of the roots of the plant is plumbagin (5-hydroxy-2-methyl-1,4-naphthoquinone), a yellowish pigment also found naturally in plants of the Droseraceae, Ebenaceae, Iridaceae, Ancestrocladaceae, Drosophyllaceae, and Nepenthaceae families [12]. It has been reported to possess several biological activities including anticancer, antioxidant, antibacterial, antifungal, anti-inflammatory, antimalarial, and antiparasitic [13][14][15][16][17][18][19]. Plumbagin isolated from Plumbago scandens was evaluated for antibacterial activity by Paiva et al. using the broth macrodilution method. It exhibited a minimum inhibitory concentration (MIC) of 1.56 μg/mL against Staphylococcus aureus [18]. e resistance modulation action, time-kill kinetics, and effects on biofilm formation of plumbagin isolated from Plumbago zeylanica plant were investigated in this study and are hereby reported as our findings.

Collection of Plant Material.
e Plumbago zeylanica roots were collected from the Physic garden in the Faculty of Pharmacy and Pharmaceutical Sciences, Kwame Nkrumah University of Science, and Technology (KNUST) Kumasi, Ghana, in September 2018. e collected sample was authenticated by Mr. Clifford Osafo Asare, the horticulturist with the Department of Pharmacognosy, KNUST, and deposited in the Department's herbarium with a voucher specimen number of (003/10/07).

Isolation of Plumbagin.
e powdered roots (2.4 kg) of Plumbago zeylanica were extracted with ethyl acetate for 5 days, and the extract obtained was concentrated with rotary evaporator (Buchi, Switzerland/R-114) at a temperature of 40°C and controlled vacuum pressure, to obtain a dried mass. Plumbagin (2.7 g) was eluted with a mobile phase composition of ethyl acetate (15% v/v) in petroleum ether (85% v/v) from a silica gel column.

Confirmation of Isolate.
e isolate's identity was established with melting point, 1D-NMR (proton and carbon-13), and 2D-NMR (COSY, HMBC, HSQC, and DEPT-135) spectroscopy, with the support of infrared (IR) and ultraviolet-visible (UV-Vis) spectroscopy. e melting point of the isolate was determined in triplicate with the Stuart melting point apparatus [UK/ R000105350]. e UV-spectrophotometer (Jenway, UK/7315) was calibrated in the range of 200 to 700 nm of wavelength, with methanol as blank. A quantity (4 mg) of the isolate was dissolved completely in methanol and the UV-spectrum of the methanolic solution of the isolate subsequently determined.
e IR spectrum of the isolate was obtained using a Fourier-transform Infra-red (FTIR) (Bruker FTIR spectrometer) with the sample at a wavelength range of 4000 cm − 1 to 400 cm − 1 .
Spectra for 1D-NMR (proton and carbon-13) as well as 2D-NMR techniques like COSY, HMBC, HSQC, and DEPT-135 were obtained with the Bruker Biospin NMR spectrometer (Billerica, US/F/NMR/A 175). e sample was run using deuterated chloroform (CDCl 3 ) at a frequency of 500 MHz and temperature of 298 K.

Determination of Minimum Inhibitory Concentrations of Plumbagin and Standard Drugs.
e minimum inhibitory concentration (MIC) of plumbagin and the reference drugs (ciprofloxacin, amoxicillin, ampicillin, and ketoconazole) were determined with the broth microdilution method. e 96-well microtitre plates were singly filled with 100 μL of double strength nutrient broth and subsequently with different concentrations of plumbagin and reference drugs ranging from 0.25 to 256 μg/mL. Twenty microliters of 10 6 cfu/mL of test organisms were added to each well. Selected wells were filled with nutrient broth only and nutrient broth and organisms only, to serve as negative and positive controls, respectively. e microtitre plates were then incubated at 37°C for 24 h, after which 20 μL of 1.25 mg/mL of 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) was introduced into each well and observations made for a purple coloration after 30 min of incubation, which signifies growth. e minimum concentrations of plumbagin and reference drugs that did not show any colour change in the wells were selected as the MIC. e experiment was carried out in triplicate [5].

Resistance Modulation Studies.
e MICs of the antibiotics and antifungal were determined in the presence of subinhibitory concentration (4 μg/mL) of plumbagin using the broth microdilution technique. e 96-well microtitre plates wells were singly filled with 100 μL of double-strength nutrient broth, appropriate volumes of different concentrations of the drugs, and 20 μL of 10 6 cfu/mL of test organisms. e plates were incubated for 24 h at 37°C, after which 20 μL of MTT was introduced to the wells, and the MICs were determined as the lowest concentration at which no growth was observed by colour change to purple [5,[20][21][22]].

Time-Kill Kinetics Assay of Plumbagin.
Time kill kinetics of plumbagin was carried out using a modified procedure described by Appiah et al. [23]. e organisms employed (Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 27853, Escherichia coli ATCC 25922, and Candida albicans ATCC 10231) were subcultured and diluted to 0.5 McFarland standard. Concentrations equal to MIC, twice the MIC, and four times the MIC of plumbagin were prepared and transferred into sterile broth in test tubes, after which an inoculum size of 1.0 × 10 6 cfu/mL of the standardized organisms was added and the test tubes incubated for 37°C. Aliquots (1.0 mL) of the medium were taken at time intervals of 0, 6, 12, 18, 24, and 30 h for bacteria and 0, 6, 12, 30, 36, 48, 54, and 72 h for fungi and aseptically inoculated into nutrient agar in sterile Petri dishes. e agar was allowed to set, and the Petri dishes containing inoculum were incubated at 37°C for 24 h. A control test was performed alongside for the organisms only. e colony-forming unit (cfu) of the test organisms was determined and the procedure performed in triplicate. A graph of log CFU/mL was plotted against time. e data obtained from the study were analysed using one-way ANOVA followed by Dunnett's post hoc test from Graph Pad Prism Version 5.10 for windows (Graph Pad Software Inc., San Diego, CA, USA) [23].

Formation of Bacteria Biofilm.
Pure cultures of the four bacteria (Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 27853, Klebsiella pneumoniae ATCC 10031, and Escherichia coli ATCC 25922) diluted to 0.5 McFarland standard in Mueller-Hinton broth were employed. Ten microliters (10 μL) of the standardized cultures was added to 100 μL of broth in wells of a 96-well microtitre plate and the plate incubated at 37°C for 24 h. Planktonic cells were aspirated and the wells washed with sterile water to get rid of free floating bacteria after incubation. e bacteria biofilms formed in the wells were dried between 25°C to 28°C and stained with 0.1% crystal violet for 20 min.
e stain was washed with sterile water and dried. e stained bacteria biofilm stuck to the wall of the wells were reconstituted in ethanol/acetic acid (1 : 1), and the absorbance read at 595 nm with a multimode microtitre plate reader. e optical density (OD) of the sterile broth was subtracted from that of the biofilm formed to eliminate background absorbance. e determination was performed in triplicate [24].

Inhibition Effect of Plumbagin on Biofilm Formation.
Plumbagin and reference drug ciprofloxacin were reconstituted in dimethyl sulfoxide (DMSO) (1% v/v). Two-fold dilutions were made to achieve a concentration range of 1 to 128 μg/mL. Aliquots of 100 μL of double-strength Mueller-Hinton broth was added to the 96-well microtitre plate and the sample solution (plumbagin and ciprofloxacin) added. Ten microliters of microorganisms (24 h broth culture diluted to 0.5 McFarland standard and containing 10 6 cells per mL) was added, and the plate was incubated at 37°C for 24 h. e planktonic cells were aspirated and the wells washed, dried, fixed, and stained after incubation. Absorbance was read at 595 nm and the optical density (OD) of the culture media control subtracted to obtain the inhibitory effects of plumbagin and ciprofloxacin. e determination was carried out in triplicate. e ability of the plumbagin and the reference drug (ciprofloxacin) to reduce the optical density (OD) compared to the negative control was considered the biofilm inhibitory activity [24]" % biofilm inhibition � optical density(OD) of control − OD of treatment optical density(OD) of control × 100. (1)

Results and Discussion
3.1. Characterization of Plumbagin. Plumbagin (1.5 g) was obtained (after recrystallization) as yellowish needle-like crystals with a melting point of 78-80°C. e retardation factor (R f ) was obtained on a silica gel coated TLC plate developed with mobile phase of petroleum ether-ethyl acetate (70% : 30%) as 0.82. e melting point of the isolated compound was obtained to be 78-80°C. is closely agrees with 78-79°C reported for plumbagin in the literature [13]. e sharp and undepressed nature of the melting point range obtained for the isolate emphasizes the purity of the compound. e UV-Vis spectrum of the isolate showed absorbance of 0.339 and 0.093 at wavelengths of 265 nm and 410 nm, respectively, indicating the presence of an extended conjugated chromophore system (Figure 1). e IR spectrum showed a broad band at 3293 cm − 1 that pointed to the OH group on the carbon at position 5. Peaks seen at wavenumbers 1161.60 cm − 1 and 1639.66 cm − 1 point to the free and hydrogen-bonded carbonyls at positions 1 and 4, respectively. Aromatic C�C vibrations observed at 1605.48 cm − 1 is attributed to the carbons at positions 2 and 3, 5 and 10, 6 and 7, and 8 and 9. e absorption seen at 3038.4 cm − 1 revealed -C-H stretches of aromatic Sp 2 carbons ( Figure 2). e NMR data obtained further confirmed the isolated compound to be plumbagin. Chemical shifts and peak multiplicities obtained for the compound in the proton NMR analysis in deuterated chloroform (Table 1) bear similarities to the reported values in the literature [13]. e chemical shift integrals revealed a total of 8 protons which is in agreement with the molecular formulae of C 11 H 8 O 3 . e carbon-13 analysis of the compound in deuterated chloroform showed 11 carbons consistent with the molecular formula of plumbagin (Figure 1).

Minimum Inhibitory Concentration (MIC).
Growth was seen in all the positive controls which indicated that the nutrient broth sustained growth of the organisms employed. No growth was observed in the negative controls, implying that the equipment used and environment did not introduce microorganisms into the nutrient broth. e minimum inhibitory concentration (MIC) of plumbagin and the standard antimicrobials used against the test organisms is shown in Table 2.

Resistance Modulation Studies.
e resistance modulation study involved determination of MICs of standard antimicrobials in the presence of subinhibitory concentrations of the agent(s) under investigation. Subinhibitory concentration is half the MIC of the agent being investigated for modulation effect that ordinarily does not inhibit the growth of microorganisms. MICs of the antimicrobials were redetermined at the concentration range of 0.25-256 μg/mL in the presence of plumbagin's subinhibitory concentration of 4 μg/mL chosen from a range of 0.25-4 μg/mL at 37°C for 24 h. Decrease in MIC obtained implied potentiation of activities and increase in MIC suggested decrease in activities of the antimicrobial agents against the test organisms. Ciprofloxacin's activity was potentiated by 2-folds against Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli. e activity decreased by 2-folds against Klebsiella pneumoniae. Activity of amoxicillin was potentiated by 2-folds, 2-folds, and 6-folds against Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae, respectively. e activity was however decreased by 2-folds against Pseudomonas aeruginosa. Ampicillin's activity was potentiated by 2-folds, 2-folds, and 6-folds against Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae, respectively, and decreased by 2-folds against Pseudomonas aeruginosa. Ketoconazole's activity was potentiated  (Table 1).

Time-Kill Kinetics Assay of Plumbagin.
e time-kill kinetics profile of the isolated plumbagin against Staphylococcus aureus showed a reduction in the number of viable cells between 18 h and 24 h accompanied by an increase up to 30 hrs compared to the control (growth of organism without antimicrobial agents) (Figure 2). Time-kill kinetics profile of plumbagin against Escherichia coli at the test concentrations used showed a reduction in the number of viable cells between 12 h and 18 h, followed by a gradual rise up to the 24 hr and subsequent reduction up to the 30 h against its control (Figure 3). Plumbagin's time-kill kinetics profile against Pseudomonas aeruginosa at the various test concentrations showed a reduction in the number of viable cells between 12 h and 18 h, remained constant till 24 h, and followed by a gradual rise up to 30 h when compared to its control ( Figure 4). Time-kill kinetics profile of plumbagin against Candida albicans at the test concentrations used showed a reduction in the number of viable cells between 30 h and 48 h, followed by a gradual rise up to 54 h and subsequent reduction to 72 h, compared to its control ( Figure 5). e antimicrobial effect of plumbagin was observed to be bacteriostatic against the test bacteria (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and fungistatic against the fungus (Candida albicans) used. e area under the curve (AUC) at the concentrations used showed significant reduction in the number of viable cells (p < 0.0001) compared to the negative controls (Figures 2-5).

Data Availability
e data used to support the findings of this study are available at the Department of Pharmaceutical Chemistry, KNUST, Kumasi.


extfieldsofstudy:
Medicine
Computer Science

text:
The Human Cell Atlas: making ‘cell space’ for disease

ABSTRACT A single change in DNA, RNA, proteins or cellular images can be useful as a biomarker of disease onset or progression. With high-throughput molecular phenotyping of single cells, it is now conceivable that the molecular changes occurring across thousands, or tens of thousands, of individual cells could additionally be considered as a disease biomarker. Transition to a disease state would then be reflected by the shifts in cell numbers and locations across a multidimensional space that is defined by the molecular content of cells. Realising this ambition requires a robust formulation of such a multidimensional ‘cell space’. This is one of the goals of the recently launched Human Cell Atlas project. A second goal is to populate this ‘cell space’ with all cell types in the human body. Here, I consider the potential of the Human Cell Atlas project for improving our description and understanding of the cell-type specificity of disease. Summary: In this Special Article, the author reflects on the potential of the Human Cell Atlas project for improving our understanding of disease.

Introduction
Can we attribute a disease to a cell type? If so, then what molecular features of a cell type best predict a disease state? Could we pinpoint the origin of a disease both to a particular cell type and to a defined developmental time point? Answers to these questions will help to deliver on the promise of new therapies, specifically those that target disease onset or progression, by taking advantage of the distinctive features of cell types.
An obstacle that needs to be surmounted before this promise is realised is our incomplete knowledge of how to define and distinguish cell types. It is this knowledge gap that the Human Cell Atlas  intends to fill by defining all human cell types according to the molecules that they typically contain. This Atlas, and the information it contains, would then allow researchers to infer a cell type's abundance, physiological states, developmental trajectories and spatiotemporal locations.
The first draft of the Atlas is expected to profile 30-to 100-million cells and their matching tissues. These will be obtained from rapid autopsy or organ donors from deceased, ethnically diverse, adults (20 to 55 years old) of both sexes. In its first draft, information on most cells will be generated by 3′-tag RNA sequencing, which surveys the number of transcripts in each cell's transcriptome. Each tissue's cells will be sampled sufficiently to identify all except the most rare (<1%) of cell types. The consortium has pledged to allow unrestricted access to all the data generated, where consent agreements allow, and as soon as possible. To be fully realised, the lofty ambition of the Human Cell Atlas will need to successfully address a number of technical and analytical challenges (Box 1). Here, I assume that these all come to pass in the near future.
The 'cell space' One expected outcome of the Human Cell Atlas project is the development of a multidimensional representation, a 'cell space' (Trapnell, 2015;Wagner et al., 2016;Clevers et al., 2017), of the molecular similarities and differences among all known types of human cells (Fig. 1). The proximity of cells within this space implies that they are drawn from a population of similar type and state (Box 1). This population need neither to have arisen from a single developmental lineage, nor to have been spatially collocated within the original donor. This cell space would provide a reference against which other cells would be annotated with respect to type or state, simply by virtue of their collocation. Cells that project into unoccupied space could potentially represent novel cell types, although their novelty and distinctive function would require experimental verification (Box 1).
The untested, motivating hypothesis of the Human Cell Atlas is that cells from disease samples consistently project into this space differently to cells from healthy control samples (Fig. 1). Theoretically, such differences could arise from altered cell numbers ( Fig. 1B) or cellular processes (Fig. 1C) for one or more cell populations. It is possible that such a space will not capture all aspects of disease pathophysiology. For example, if an RNA-based atlas does not perfectly reflect cell-cell interactions, then an RNA-defined cell space might not be able to identify the disease states that involve aberrant interactions between cell types (Fig. 1D).
In its first phase, the Human Cell Atlas project will not analyse cells from large disease-case-control cohorts (The Human Cell Atlas Consortium, 2017), so most disease mechanism studies currently lie out of scope . Consequently, we expect its initial importance to stem not from the unbiased molecular definition of disease, but from the construction of a reliable multidimensional reference cell space into which any researcher can project their own single-cell data. Furthermore, the project should deliver standard experimental and analytical protocols for generating single-cell datasets and for projecting them into this common space.
Future studies will likely take advantage of the Human Cell Atlas project's experimental and analytical framework. For example, studies that robustly observe changes in cell populations across casecontrol cohorts could define disease status and quantify disease progression. Metrics for disease progression could be: (1) the rate of change in the size of a disease-predictive cell subpopulation; or, (2) the rate of change of a transcriptomic signature across one or more cell populations; or, (3) a vector representing the shift of a cell population in multidimensional space as the disease progresses.
MRC Human Genetics Unit, University of Edinburgh, Crewe Road, Edinburgh EH4 2XU, UK.
*Author for correspondence (chris.ponting@igmm.ed.ac.uk) C.P.P., 0000-0003-0202-7816 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
Disease status would then be definable by applying a threshold to the predefined disease progression metric. Conversely, drugs that significantly reverse these rates of change or this vector would represent therapeutic candidates.
These are some of the potential future benefits of the Human Cell Atlas project. Are there more near-term advantages for better understanding disease? Two recent studies of the mammalian trachea (Montoro et al., 2018;Plasschaert et al., 2018) exemplify how single-cell studies are enhancing our understanding of Mendelian disease. These studies discovered a previously unknown cell type in which the CFTR (cystic fibrosis transmembrane conductance regulator) gene, which is mutated in cystic fibrosis patients, is highly expressed. In this, as in future examples, the physiological consequences of gene deficiency in these rare cell types will require further detailed experimentation.
Molecular data at the single-cell resolution are also improving our understanding of the cellular response to infectious disease , injury (Arneson et al., 2018) or treatment (Vogel et al., 2016). For example, antibiotic treatment fails to substantially alter the transcriptional identities of innate lymphoid cell populations, yet it perturbs their relative proportions and their transcript abundance for several hundred genes (Gury-BenAri et al., 2016). Similar findings have been reported for mouse intestinal epithelial cells following bacterial or helminth infection (Haber et al., 2017).
Cancer makes only a minor contribution to the future plans of the Human Cell Atlas (The Human Cell Atlas Consortium, 2017). Nevertheless, the Atlas' initial datasets will be invaluable for understanding tumour heterogeneity, initiation and progression. Gene expression profiles of cell types can be used to assess the contributions of stromal cells to bulk tumour transcriptomes (Puram et al., 2017) and to infer the cell of origin in pre-cancerous conditions such as Barrett's oesophagus (Jiang et al., 2017).
Studies on neurological disease are also gaining substantially from an improved definition of cell types and states. Autism spectrum disorders, for example, have been associated with mutations in a set of genes and, because the expression of these genes is enriched in foetal and in adult neurons, in particular in inhibitory neurons, it is proposed that these are the cell types that are most profoundly dysregulated in these neurodevelopmental disorders .
Over the past decade, genomic studies revealed large numbers of associations between genetic variants and diverse complex traits and diseases. These DNA variants tend to lie in close chromosomal proximity to genes and functional elements that are active in the tissues that are relevant to the trait or disease (Boyle et al., 2017). It is reasonable to expect, therefore, that the survey of gene activity provided by the Human Cell Atlas will lead to a more highly resolved understanding of dysregulation at the cell population level, rather than at the heterogeneous tissue level. Nevertheless, most of the heritability of traits and complex diseases can be explained by variants that only indirectly alter the functions of core diseaserelated genes (Boyle et al., 2017). This implies that, even with finely resolved cell-type data, explaining the molecular mechanisms underlying complex disease genetics will remain a substantial challenge. Encouragingly, however, in some instances the expression of many genes implicated in complex diseases is restricted to particular disease-relevant cell types. This allows the candidature of these genes in disease to be further strengthened and cell processes disrupted in genetic disease to be proposed (Smillie et al., 2018).
Not all facets of disease biology will be revealed by studies of human primary cells. Diseases that originate at inaccessible developmental stages, for example, will not be informed by the Human Cell Atlas project because the samples are to be drawn predominantly from adult individuals. To address this shortcoming, organoids, which, by their nature, model organ development, could provide some insight. The Human Cell Atlas refers to a Human Organoid Project within its White Paper (The Human Cell Atlas Consortium, 2017). Nevertheless, insights from human organoids are currently restricted by their irreproducibility and by a limited understanding of the processes that guide their development (Huch et al., 2017).
For these reasons, model organisms are expected to retain their central role in elucidating the mechanisms of human disease. To facilitate comparisons with human cells, there is a need for singlecell atlases from model organisms, such as those being piloted for mouse (Han et al., 2018;Tabula Muris Consortium, 2018), Drosophila (Davie et al., 2018) and the planarian Schmidtea mediterranea (Fincher et al., 2018;Plass et al., 2018). These cell atlases would then need to be compared with the Human Cell Atlas. Comparison would take advantage of orthology relationships and could project cells of one species into the cell space of the other, or Box 1. Current technical and analytical challenges in single-cell biology Efficient isolation Some cell types are robust and remain viable following tissue digestion, cell sorting and/or suspension protocols; others are considerably more fickle and these are at risk of being under-sampled. Cost Large-scale single-cell and spatial transcriptomics or proteomics experiments are not currently routine in most laboratories owing to their high costs, although combinatorial labelling for sequencing experiments has recently reduced these considerably (Cao et al., 2017). Batch effects Technical differences will exist among experiments performed on different platforms, at different times and/or in different laboratories, and these need to be accounted for (Haghverdi et al., 2018).

Cell-type annotation
In general, different cell types are easily discriminated as separate clusters following the clustering of sparse single-cell transcript or epigenetic data (Prakadan et al., 2017;Mezger et al., 2018). The Human Cell Atlas Consortium, however, has not sought to define cell type. Rather, it believes that a robust definition will eventually emerge from empirical observation. Assignment to a type implies that a particular cell shares phenotypic and functional features with other cells of the same type. However, single-cell data, considered alone, are limited to only predicting, rather than demonstrating, cellular functionality. Consequently, independent experimental investigation of cell-type function is necessary. Cell-state inference Cells of a particular type are likely to occupy a continuum of states, owing to the cell cycle, or differentiation, or spatial location, for example (Wagner et al., 2016;Clevers et al., 2017). To assign cell state, therefore, we need to resist being categorical, and instead predict the continuous trajectories of cell-state change. When it is unclear whether these are cell states or types, groups of similar cells may best be described as (sub-) populations. Going beyond measurements of RNA abundance, the rate by which gene expression of these populations changes can be inferred from single samples (La Manno et al., 2018). Multi-omic data integration Increasingly, several different data types will be measured in the same single cell, for example RNA abundance versus spatial location or open chromatin or protein abundance. Maximising the predictive value of such multi-omic data will be a key future challenge (Packer and Trapnell, 2018). project all cell spaces into a common, unifying multidimensional space. Conceptually, this would allow a specific cell type to be annotated across multiple species, and enable researchers to rank cell types by their 'molecular drift', specifically the degree by which each cell type's molecular features have changed since the species' last common ancestor. These analyses thus could predict the degree by which a cell population in one species models the same population in another, a useful metric when justifying the use of models.

Conclusions
In framing this discussion, I have avoided a previous description of the Human Cell Atlas as a periodic table for biology . This is because it is likely that we will eventually conceive of cell states as plastic and part of a continuum in the cell space, rather than being "the 'atomic' units that underlie human life" . Also, while the periodic table of chemical elements is useful in predicting reactions between multiple elements, we are not likely to use cell atlases to predict the details of multicellular function in the near future. The Human Cell Atlas is also different from the Human Genome Project in that it is determining a highly dynamic, rather than a highly stable, system. This means that the completion of any cell atlas will be indeterminate. Rather, the Human Cell Atlas will equip science with the concepts, experimental data and analytical tools required to measure disease states at single-molecule, single-cell, tissue, individual and population levels.
What comes next after the Human Cell Atlas project? By analogy with how the Human Genome Project was followed by surveys of large numbers of genomes, it is likely that there will be a systematic determination of cellular phenotypes across a large number of individuals, presumably when high-throughput assay costs, particularly for single-cell DNA or RNA sequencing, fall further. These cellular phenotypes could also be measured across CRISPR/ Cas9 screens used to disrupt one gene in one cell for all genes and all cell types. The shift in cell space location for a cell's genotype would reflect its contribution to its cell type's specificity.
Another interesting possibility is a cell-space-association study, an extension to existing transcriptome-wide association study approaches (Gusev et al., 2016). The intent of this would be to observe which cell populations are predicted, from their genotypes, to shift in cell space coherently in disease cases compared to the shift in cell space predicted for healthy controls. In such cases, shifts that were discordant between cases and controls would provide robust evidence that a cell type causally contributes to complex disease risk. Such approaches are particularly powerful because their sole reliance on genetic variation makes environmental and other confounding effects irrelevant. Whichever direction this incipient field takes, the Human Cell Atlas project looks set to greatly aid future studies of the cellular basis of disease.