Most attempts to transform exciting findings from laboratories into clinical treatments are unsuccessful. One reason for this may be the failure to consider all of the laboratory work that has been performed before deciding to test a treatment on patients for the first time. In particular, negative findings (that suggest that a potential new treatment is ineffective) may be overlooked.

Stem cells may help to treat life-threatening infections, but this has not been tested in human patients. However, the effectiveness of stem cell treatments has been tested in animals that act as models of human infection.

Before deciding to begin a clinical trial of stem cell therapy for life-threatening infections, Lalu et al. performed an exhaustive search to find all the studies in which stem cells were used to treat animal models of infection. Combining the results of all of these studies using particular analysis techniques revealed that stem cell therapy increased the survival of these animals overall. These positive effects were seen over a range of different experimental conditions (for example, when treating the animals with different doses of stem cells, or giving the doses at different times).

Lalu et al. also identified some limitations with most of the laboratory studies that had tested stem cell therapy for infections. Many of the studies used animal models that may not be the best representations of humans with severe infection. In addition, many of the scientists did not report that they had used methods (such as randomization) that would generate the most confidence in their results. Despite these limitations, there was a lot of consistency in the reported results.

Overall, the results support the decision to proceed to a clinical trial that tests the effectiveness of stem cells for treating human infections. More generally, Lalu et al.’s analysis demonstrates a way of considering all laboratory evidence before deciding to proceed to a first clinical trial in humans. This may help researchers to identify promising therapies to further develop, and also to identify potential failures before they are tested in patients.

The decision to initiate an early phase clinical trial requires careful evaluation of the benefits and risks of a novel intervention. However, for first-in-human studies for which there is no prior clinical experience, the assessment of potential therapeutic efficacy must rely solely on the preclinical investigations. Although regulatory guidance exists for the conduct of preclinical evaluation of novel therapies (U.S. Department of Health and Human services, 2013), there is little guidance to help stakeholders summarize and assess the benefit and risks of novel therapies prior to first-in-human studies. As a result, the evidence from individual preclinical studies is often summarized and described in a non-systematic and potentially biased manner (Food and Drug Administration, 2015). Here, we present an approach to transparently evaluate preclinical evidence of a therapy prior to its potential clinical translation. Our exemplar is mesenchymal stem cell (MSC) therapy for sepsis.

A selective narrative summary of preclinical evidence has significant limitations because the methods used to identify studies are neither comprehensive nor transparent (Sena et al., 2014). This is of particular concern given that studies replicating high profile experiments fail in up to 50–90% of attempts (Begley and Ellis, 2012; Scott et al., 2008; Steward et al., 2012) and significant publication bias results in a skewed representation of effects (Sena et al., 2010). Further, fewer than 5% of high impact preclinical reports are clinically translated (Contopoulos-Ioannidis et al., 2003) and only 11% of clinically tested agents receive licensing (Kola and Landis, 2004). Thus trialists have based predictions of clinical success of novel therapies on flawed data and an inappropriately highly selected and positive preclinical evidence base (Grankvist and Kimmelman, 2016).

Systematic reviews and meta-analyses have become very popular because they can overcome many of these challenges by promoting the transparent evaluation of therapies. Systematic reviews are guided by a protocol with explicit methods to identify, synthesize (which may include meta-analysis), and appraise all investigations pertinent to a particular research question. Similarly, meta-analysis enables pooling of effect sizes across studies and increases statistical power by reducing standard error around the average effect size, providing a more precise estimate of an overall treatment effect (Sena et al., 2014; Cohn and Becker, 2003). Systematic reviews and meta-analyses have long been regarded as essential tools to summarize and evaluate clinical research (Higgins and Green, 2009) and have become a requisite component of grant applications for clinical trials (Canadian Institutes of Health Research, 2016); however, the application of these tools to preclinical studies has been limited.

Preclinical systematic reviews may help predict the magnitude and direction of novel therapeutic effects in high stakes first-in-human trials. For example, preclinical systematic reviews of stroke (Horn et al., 2001) and heart failure (Lee et al., 2003) therapies demonstrated that the resulting negative clinical trials could have been predicted had available preclinical evidence been analyzed in a rigorous manner. Thus, thousands of patients may have avoided exposure to potential risk without any benefit (Kalra et al., 2002; Shuaib et al., 2007). Similarly, previous preclinical systematic reviews have demonstrated that failure to report threats to methodological quality (i.e. internal validity, risk of bias) and construct validity (i.e. extent a model corresponds to the human condition it is intended to represent [Henderson et al., 2013]) influence treatment effect sizes (Crossley et al., 2008; Hirst et al., 2014; Macleod et al., 2008, 2015; Rooke et al., 2011). Unlike this ‘retrospective’ approach that has been described in previous studies, a prospective application of preclinical systematic review methodology may help delineate the limits of a therapy prior to first-in-human application.

Our preclinical systematic review was conducted prior to the initiation of a Phase 1/2 clinical trial of immunomodulatory cell therapy (mesenchymal stromal cells, mesenchymal stem cells [MSCs], “adult stem cells”) for septic shock (NCT02421484). The specific question addressed was: In preclinical in-vivo animal models of sepsis, what is the effect of MSC administration (compared to control treatment) on death? Septic shock is the result of an overwhelming systemic infection; it is one of the most common and acutely devastating health problems in the intensive care unit with a 90-day mortality rate of approximately 20–30% despite modern therapy (Peake et al., 2014; Mouncey et al., 2015; Stevenson et al., 2014). It is caused by a maladaptive mismatch between host inflammatory response and pathogenic stimuli which leads to organ failure and death. MSCs are ubiquitous cells (da Silva Meirelles et al., 2006) that support tissue repair and are mobilized under inflammatory conditions (Hannoush et al., 2011; Rochefort et al., 2006). Exogenously administered MSCs represent an especially attractive therapeutic for sepsis because they have antibacterial and organ protective effects, in addition to their immune modulatory functions (Walter et al., 2014).

We quantitatively summarized the results of all preclinical studies of MSC therapy for in vivo animal models of sepsis to predict effect size and establish an ethical basis for exposing high-risk patients to this novel therapy. This is the first systematic evaluation of a novel biologic therapy prior to initiating a first-in-human trial. We believe our approach serves as a roadmap to transparently evaluate a preclinical therapy prior to its potential clinical translation. This study has been written in an explicatory manner so that other preclinical and translational researchers not familiar with systematic review methodology may replicate our approach. Readers wishing to replicate our approach for their research agendas are directed to the methods section where explanations are provided in greater depth, and encouraged to contact the authors for further guidance.

Preclinical studies provide necessary justification to conduct a first-in-human clinical trial. Thus, a systematic review approach offers an attractive method to comprehensively synthesize the totality of available evidence. Our systematic review demonstrates that MSC therapy reduces the odds of death in preclinical animal sepsis models. This effect is maintained over a range of time periods (less than two days, between two to four days, and longer than four days). These early outcome windows capture the majority of deaths that occur in these acute models. Moreover, the effect sizes are robustly maintained (replicated) over a variety of experimental conditions, varying models, and differing MSC immunologic compatibility (e.g. allogeneic vs. syngeneic).

It has been suggested that individual study findings have low probability of being ‘true’ (Ioannidis, 2005), however by aggregating results of similar experiments the positive predictive value of a finding dramatically increases (Moonesinghe et al., 2007). Thus, the findings of this systematic review helped support our decision to initiate a Phase 1/2 trial to evaluate the safety of MSC therapy in human patients with septic shock (NCT02421484). We believe our approach of systematically reviewing preclinical evidence is widely applicable for researchers considering first-in-human studies. Although our synthesis suggests MSC treatment of sepsis may be beneficial these results are tempered by the presence of potential threats to validity.

Our preclinical systematic review evaluated internal, external, and construct validity of the data. Methodological weaknesses (i.e. poor internal validity) in clinical trials are associated with an exaggeration of the treatment effect. Similarly, in preclinical studies, failure to address selection bias (through methods such as randomization and allocation concealment) and detection bias (through blinded outcome assessment) results in significantly increased effect sizes (Crossley et al., 2008; Hirst et al., 2014; Rooke et al., 2011). The significance of selection and detection bias has been acknowledged by The National Institutes of Health’s recently issued guidelines for reporting preclinical research. These guidelines have specifically proposed randomization, blinding, and sample size calculations as key methodological information that must be described in preclinical reports (National Institutes of Health, 2015; Landis et al., 2012). In our review, none of the included studies reported randomization or allocation concealment in a manner that could be considered at low risk of bias. Similarly, no studies reported appropriate a priori defined sample sizes. Most of these items were judged as ‘unclear’ in our risk of bias evaluation due to the convention to judge unreported items as ‘unclear’ rather than ‘high risk’. We speculate that many of these ‘unclear’ items were not performed (i.e. they were ‘high risk’) due to a general lack of training of basic scientists in methods to reduce risk of bias (Landis et al., 2012; Collins and Tabak, 2014). This lack of reporting precluded an evaluation of their efforts and points to the need to improve the methodology used in preclinical investigations.

To address external validity (i.e. generalizability) we performed a number of subgroup analyses. Overall, subgroup analyses suggested that MSC effects appeared to be robust over a number of varying experimental conditions and across a number of different laboratories. Results of specific subgroups (e.g. autologous cells, multiple doses, intraperitoneal administration, and adipose tissue source) should be interpreted cautiously as few studies were included in these groups, and the results of one study with differing results (Chang et al., 2012) may have skewed data. The ability of one study to heavily influence overall effect estimates is a short-coming of meta-analyses that include few studies. As such, these subgroup analyses should be treated as exploratory.

Despite the large effect sizes noted, one must bear in mind the potential effect of publication bias (i.e. bias due to the publication of only positive studies). Our funnel plot demonstrated a highly asymmetrical pattern and our trim and fill analysis indicated that a number of unpublished negative studies may exist. This is in keeping with previous analyses of preclinical stroke data that suggested up to one in six animal studies in that field were unreported and unpublished. (Sena et al., 2010) Our inability to analyze these potential studies may have led to an overstatement of effect size.

To evaluate the potential clinical applicability of these results, we examined the construct validity of included studies. This was determined using recommendations that had been developed to improve the clinical generalizability of preclinical sepsis studies (Lamontagne et al., 2010). Animal sepsis models may not be representative of human sepsis because of the timing and severity of sepsis induction, the dose and timing of the treatment in relation to sepsis induction, the use of small/young animals without comorbid illnesses, and lack of administration of standard of care co-interventions such as fluids and antibiotics during the study period. How well animal models of sepsis mimic the pathophysiology of human sepsis has also been a contentious issue (Dyson and Singer, 2009; Osuchowski et al., 2014; Seok et al., 2013). Only two studies incorporated a majority of elements addressing construct validity, thus the effect of construct validity on MSC therapy of sepsis remains to be determined.

There are a number of other issues of note that may impact the translation of MSC therapy to the clinical setting. First, although we did not formally evaluate characterization of cell products, this was variably reported in the included studies. Differences in the quality of cell therapeutics may have accounted for some of the heterogeneity of results observed. Second, dosing of cell products was not equivalent between species, even after adjusting for total cells given. Equivalence dosing of drugs between species is a complex issue and the FDA has endorsed conversion based on body surface area, rather than a dose per weight basis. (Reagan-Shaw et al., 2008) Applying this guidance, 1 million cells in a mouse may be equivalent to 0.5 million in a rat; similarly, this dose in a human would be roughly equivalent to 3 million cells/kg. These equivalencies should be interpreted cautiously given the differences between typical drug therapies and the cellular therapy evaluated in our review. Third, the severity of disease in these animal models at the time of MSC administration is unclear. Based on our experience with endotoxemia and cecal-ligation and puncture models, at 1 hr after disease induction some symptoms may be apparent and after 6 hr most animals have both biochemical and physiological evidence of inflammation and organ-dysfunction. Thus, we performed a subgroup analysis based on timing of administration <1 hr, >1–6 hr, >6 hr as a rough correlate to early and more delayed (intermediate and late) administration of cells in an attempt to simulate the delays in treatment that may be seen in humans who present with severe infection. Of note, no study administered cells at a late time point. A clearer reporting of disease severity at time of cell administration may allow a more precise analysis of when these cells are more (or less) efficacious. A fourth issue is the lack of transparent reporting of risk of bias elements that minimize the ability to evaluate threats to validity in our systematic review. We would suggest that general poor understanding of these core methodological issues may underlie their incomplete reporting. In order to increase the robustness and interpretation of future preclinical systematic review results we submit that authors of primary studies and journal editors should ensure adherence to published reporting guidelines for pre-clinical research studies (National Institutes of Health , 2015; Kilkenny et al., 2010). These guidelines not only detail items relating to risk of bias (e.g. randomization and blinding) but also touch on issues that are very important when primary studies are included in systematic reviews (e.g. differentiating between biological and technical replicates, providing exact n numbers).

The strengths of our systematic review are in the transparent and thorough literature search and an attempt to examine potential for translation by evaluating threats to validity. To date, three clinical trials have been initiated following a systematic review and meta-analysis of animal data (McCann et al., 2014; van der Worp et al., 2007); all have repurposed currently used interventions for neurological conditions and are currently recruiting (NCT01833312, NCT01910259, ISRCTN83290762). To the best of our knowledge ours is the first preclinical systematic review that has evaluated a novel biological therapeutic in preparation for a high risk first-in-human clinical trial.

The limitations of our review should be noted. First, we restricted our search to unmodified MSCs since our group was only considering a clinical trial of unmodified cells for sepsis. Although modified cells may be of clinical interest, there are a number of additional regulatory, ethical, and safety issues which significantly increase the complexity of clinical trials using these cells; this is an issue that members of our group have experienced first-hand (NCT00936819). Other limitations of our review relate to the potential methodological limitations of the included studies. None of the included studies were considered low risk of bias across all domains, and their construct validity was highly variable. It is unclear what the influence of these methodological limitations might be in this particular study due to our inability to perform meaningful subgroup analyses. Our evaluation of the methodological aspects of included studies also relied on what the authors reported, and this may have been incomplete in cases. We would suggest however, that similar to other fields, the failure to address threats to internal validity likely contributes to an exaggerated effect size.

Despite the stated limitations of this review, the consistency of the results across the included studies and the large effect size suggest that MSCs reduce the odds of death in preclinical models of sepsis. Moreover, there are a number of studies that have demonstrated biological mechanisms that may underlie the benefits of MSCs in sepsis, including antibacterial, anti-inflammatory, and trophic effects (Spees et al., 2016). These mechanisms do not require engraftment and have been demonstrated to work over thousands of molecular pathways that include improved cellular energetics and activation of macrophages (dos Santos et al., 2012). Given the results of our review along with this biological plausibility, our group gained the support of regulatory agencies, ethics boards, and other stakeholders to proceed to a first-in-human clinical trial. Nonetheless, our efforts to translate this therapy into a clinical trial were tempered by the limitations of the preclinical studies performed to date. If this support was not provided, alternative methods to address efficacy of MSC therapy for sepsis could include conducting a low risk of bias ‘confirmatory’ preclinical study that was informed by the results of this systematic review (Kleikers et al., 2015), or performing a multicenter preclinical study (Llovera et al., 2015). Ultimately, ongoing and future clinical evaluations will determine whether the therapeutic effects of MSCs will translate to the human patient population.

The methods section has been written completely and transparently for researchers unfamiliar with systematic review and meta-analysis methodology. We would encourage readers wishing to replicate our approach for their own research agendas to refer to available resources (Sena et al., 2014; Higgins and Green, (2009)) and/or our group for further guidance.

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Author details

1. Manoj M Lalu

   1. Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, Canada
   2. Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
   3. Regenerative Medicine Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   MML, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-0322-382X

2. Katrina J Sullivan

   Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   KJS, Acquisition of data, Analysis and interpretation of data, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

3. Shirley HJ Mei

   Regenerative Medicine Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   SHJM, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

4. David Moher

   1. Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
   2. School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Canada

   Contribution

   DM, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

5. Alexander Straus

   Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   AS, Acquisition of data, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

6. Dean A Fergusson

   Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   DAF, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

7. Duncan J Stewart

   1. Regenerative Medicine Program, The Ottawa Hospital Research Institute, Ottawa, Canada
   2. Department of Cell and Molecular Medicine, University of Ottawa, Ottawa, Canada

   Contribution

   DJS, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-9113-8691

8. Mazen Jazi

   1. Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
   2. Faculty of Medicine, University of Ottawa, Ottawa, Canada

   Contribution

   MJ, Acquisition of data, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

9. Malcolm MacLeod

   Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   MM, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

10. Brent Winston

    Department of Critical Care Medicine, University of Calgary, Calgary, Canada

    Contribution

    BW, Conception and design, Drafting and revision of article

    Competing interests

    The authors declare that no competing interests exist.

11. John Marshall

    Departments of Surgery and Critical Care Medicine, Keenan Research Centre of the Li KaShing Knowledge Institute, St. Michaels Hospital, The University of Toronto, Toronto, Canada

    Contribution

    JM, Conception and design, Drafting and revision of article

    Competing interests

    The authors declare that no competing interests exist.

12. Brian Hutton

    Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

    Contribution

    BH, Analysis and interpretation of data, Drafting and revision of article

    Competing interests

    The authors declare that no competing interests exist.

13. Keith R Walley

    Department of Medicine, Centre for Heart Lung Innovation, University of British Columbia, Vancouver, Canada

    Contribution

    KRW, Conception and design, Drafting and revision of article

    Competing interests

    The authors declare that no competing interests exist.

14. Lauralyn McIntyre

    1. Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
    2. Department of Medicine, University of Ottawa, Ottawa, Canada

    Contribution

    LM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    lmcintyre@ohri.ca

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0001-7421-1407

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