Could Twitter Be a Recruitment Tool for Cancer Trials?

twitterAccording to Penn Medicine Researchers study’s, Twitter has the potential to promote patient recruitment into oncology clinical trials and increase the interest of patients [1].

Enrollment into clinical studies is crucial for the development of new treatment options for patients. It is also provides opportunities for those who are not responsive to the previous treatment or who cannot afford it. However, only about five percent of adult cancer patients participate in clinical studies, thus creating a problematic situation for the drug development environment.

According to statistics, approximately 15-20% of all trials never manage to enroll a single patient, 37% of all sites in a given trial fail to meet their enrolment targets. It is also worthy to keep in mind that nearly 30% of the time dedicated to clinical trials is spent on patient recruitment and enrolment [2]. To overcome this problem, companies are trying to reach their potential clinical trial participants via dedicated websites (Novartis), Facebook, Google, YouTube etc. [2].

Physicians from Abramson Cancer Center of the University of Pennsylvania analyzed a number of lung cancer tweets in the social media and found that a great number of posts were about clinical trials. Twitter users were particularly interested in immunotherapy. Surprisingly, only one tweet was used to help recruitment into a clinical study.

“Twitter provides a promising and novel avenue for exploring how cancer patients conceptualize and communicate about their health, and may have the potential to promote much-needed clinical trial recruitment.” said Mina S. Sedrak, MD, MS, a fellow member of the division of Hematology/Oncology at the Perelman School of Medicine at the University of Pennsylvania and first author of the study published online 3 March 2016 as a research letter titled “Cancer Communication in the Social Media Age” in JAMA Oncology.

Nowadays, there are numerous cancer care organizations and centers that use social media, including Twitter, for promotional and educational purposes. Penn Scientists tried to find out to what extent the information about clinical trails for cancer patients present on Twitter are useful.

In the pilot study, Sedrak and his coworkers analyzed a randomly chosen sample of 1,516 tweets out of a total of 15,346 that contained the phrase “lung cancer” from January 5 – 21, 2015, and assessed who read them.

More than half (56%) of the tweets were focused on psychological support and prevention topics. Nevertheless, clinical trials were the topic of almost 18% of analyzed tweets posted by patients, health professionals and other people, making these studies the second largest theme of social communication. Most of the clinical trial tweets (79%) were about immunotherapy studies, and 86% of them contained links directing readers to original websites and articles.

Authors were surprised to find that only one out of one and a half thousand analyzed tweets were linked to a patient enrollment website [1, 3]. According to them, although some more effort is needed to better assess social media involvement in cancer education, prevention and information, it is worthy to start using it as a tool for recruitment for the cancer clinical trials. On the other hand, social media patient enrollment will be the new challenge to institutional review boards with respect to non-coercive content and the assurance of patient’s privacy. New rules and policies may be needed in order to control the social media enrollment campaigns.

Sedrak sums up that “We need to learn more about the ecology of social media because it is clearly not consistently directing patients to the right places (…) social media may provide an infrastructure for cancer centers, researchers, and physicians to interact with the public in new and productive ways, including stimulating interest in new clinical trials with targeted messages that connect patients, caregivers, and families with trial enrollment websites. This potential remains largely untapped” [1].


  1. – assessed 23.06.2016
  2. – assessed 23.06.2016
  3. – assessed 23.06.2016
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We are celebrating an important milestone: – our tenth year of operation!

10yearsWe at Clinical Accelerator are happy to announce the celebration of 10 years successful provision of clinical research services to the biopharma, biotech and medtech industries.

Since our inception back in 2006 as a small provider of clinical trial management services in UK and several then emerging Eastern European countries, we have grown to be the larger organisation we are today. We now have many more established regions of operation and are present in 13 countries of Central and Eastern Europe (listed alphabetically these are: Armenia; Belarus; Estonia; Georgia; Hungary; Latvia; Lithuania; Moldova; Poland; Romania; Russian Federation; Serbia and Ukraine). We also have expanded our valued links with many more highly trained and experienced investigators to whom we are much indebted.

Let us also not forget the support and involvement of all the patients recruited at sites by these investigators, often facilitated by our own in-house patient enrolment support system.  We try to make our patients feel a valued part of the whole process rather than proverbial ‘guinea-pigs’ and their willingness to participate and adhere to sometimes quite challenging protocols (often travelling some distance to trial centres for assessment) always impresses us.

Our success is due, in no small measure, to our workforce. We are lucky to have such a dedicated team of clinical research professionals who have medical knowledge and practical experience in many regulatory and therapeutic areas and who work tirelessly to ensure timely delivery of all tasks contracted to us by our clients.  We find that the flat organisational hierarchy enjoyed by our qualified and competent team facilitates a good level of communication, flexibility, adaptability as well as a ‘can-do’ approach, which, we believe, is appreciated both by our team and our clients.

We are, of course, also most grateful and indebted to all our clients and collaborators over the last 10 years who have trusted us with their important projects. Being a part of the process in the development of your products has been most gratifying. The demands, challenges and feedback have encouraged us to evolve and develop new skills to meet varying and exacting needs.

A big ‘thank you’ to all our clients and collaborators for your support and kind words over this last decade.  We look forward to the next 10 years and beyond working with you and welcoming new clients and collaborators into the fold.

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Galmed Collaboration Announcement

Galmed Pharmaceuticals, Ltd. (PRNewsFoto/Galmed Pharmaceuticals, Ltd)

Clinical Accelerator are very pleased to announce collaboration with Galmed Pharmaceuticals Ltd. (NASDAQ: GLMD), on conducting a phase IIB clinical trial for the treatment of patients with overweight or obesity and who are pre diabetic or type II diabetic with the liver condition NASH (Non-Alcoholic Steatohepatitis) at seven sites in Georgia and Lithuania.

This trial is a multi-center randomized, double blind, placebo-controlled, dose-ranging Phase IIB study, which is evaluating the efficacy and safety of two doses of Galmed’s novel drug candidate , AramcholTM, in NASH overweight or obese patients who are pre-diabetic or already have type II diabetes [1].  The trial is already underway in other participating clinical sites in the USA, Israel, Europe and Latin America. Clinical Accelerator aims to add further impetus to recruitment at four sites in Georgia and three in Lithuania.

Eligible subjects are being enrolled into three treatments arms: Aramchol 400 and 600 mg tablets and placebo tablets in ratio 2:2:1.  Subject evaluations occur for 10 scheduled visits over one year. After completion of the treatment period, the subjects will be followed for an additional period of 13 weeks without study medication thus making 11 visits in all.

Aramchol , a conjugate of cholic acid and arachidic acid, is a first-in-class member of a novel family of synthetic Fatty-Acid / Bile-Acid Conjugates (FABACs)  and have been the subject of extensive research over the last 16 years by Galmed’s research team lead by the late Professor Tuvia Gilat.

Aramchol itself has already been shown in a phase IIA clinical study to statistically significantly reduce liver fat content as well as improve metabolic measures associated with fatty liver disease, having a simple once-daily oral dose with no severe adverse effects.

Clinical Accelerator is an independent clinical trial management organisation operating principally in Central and Eastern Europe, Russia, Ukraine and CIS countries.  The organisation offers a broad range of clinical trial services together with dedicated patient enrolment support to worldwide clients in the pharmaceutical, biotechnology, nutraceutical and medical device industries.

Clinical Accelerator always locates GCP-orientated sites with enthusiastic investigators who have the capability to conduct trials to high standards and challenging timelines at competitive rates of remuneration.  Moreover, patients in its areas of operation are usually very happy to participate in clinical trials and research studies, facilitating effective recruitment and retention.

We look forward to a very successful outcome for Galmed’s Phase IIB study and we are delighted to be one of their collaborators in the development of Aramchol, which is showing exciting potential for the treatment of the increasingly common condition of fatty liver disease.


  1. A Clinical Trial to Evaluate the Efficacy and Safety of Two Aramchol Doses Versus Placebo in Patients With NASH (Aramchol_005)



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Boosting patient recruitment and retention in clinical trials – fresh approaches?

Wpatiente hear much about ‘patient centricity’ and ‘empowering patients’ in clinical trials and research as well as everyday clinical practice.  This can be defined loosely as the process of designing a service or solution (e.g. a clinical trial) around the patient.  For clinical trials, this can mean engaging in a dialogue with potential patients or their families about the design of the trial itself including the protocol, trial schedule, number of interventions etc. so that it is considered acceptable and to try and ensure visits and data recording are made as easy as possible.  All this is to be achieved without compromising the scientific integrity and validity of the trial.  So although a good measure of pragmatism is called for one must still not lose sight of the trial’s aims and the necessary information required to achieve them.

The importance of being able to recruit sufficient patients as well as keep them ‘on study’ for as long as possible (one hopes for the full planned duration….) cannot be over-stated. Thus sponsors and researchers are always looking for ways to achieve this.  We know that it is not uncommon for trials to falter because the expected source of willing patients somehow dries up or, once recruited, they do not manage to stay the course.  This prolongs the timescale and compromises data.

In one such instance of attention to successful recruitment and retention, researchers at Nationwide Children’s Hospital (Columbus, Ohio USA) found ways to increase the number of people recruited and retained in one of their trials quite significantly. This was by seeking the advice of patients, families and other stakeholders in the design of a clinical trial investigating paediatric appendicitis.

The changes were made after the study had been initiated at the recommendation of a group of 20 individuals who are stakeholders on the research team, including (as appropriate to the paediatric study) children 7 to 17 years old, their families, physicians, nurses, patient educators and payers. The stakeholders provided input and advice to the researchers about all phases of the ongoing clinical trial in which the use of a tablet / smartphone app called Patient Activation Tool (PAT) is being investigated [1]. Changes were introduced to the patient information script, which initially simply said that they were ‘investigating a tool designed to improve decision-making about appendicitis treatments’ to a longer two-part message mentioning: i) aim to improve ‘the way in which the medical team communicates with families’ and ii) explanation to families that the study is ‘testing a tool designed to improve both physician-patient communication and promote shared decision-making about treatments’.  Simple changes in wording, but these were to yield impressive results.

To improve retention, the recommendation was to offer an online option via an e-mailed link for participants to complete follow-up questionnaires, to attain preferred times of contact and send out reminder letters about the follow-up assessments.

After these rather more patient/family-centred changes in information and simplifying provision of required data to reduce clinic visits were adopted into the study, rate of enrolment increased from 65 percent to 95 percent and the retention rate increased from 58 percent to 85 percent.  These are indeed encouraging statistics, which are reported in a communication to JAMA surgery [2].

Reportedly, the clinicians involved in the study were themselves surprised at the magnitude of the effect of the changes since recruiting patients for clinical trials, particularly those involving non-elective surgery, is often challenging with recruitment rates for paediatric clinical trials usually being under 50 percent. The study authors made the point that some people just don’t want to be part of research whilst others are afraid of the prospect, perhaps feeling like they are going to be ‘guinea pigs’ [3] . So it is important to approach them in such a way that they understand why it is important and why the results would be important to them or to others just like them.

Retaining those patients who do agree to enrol in a clinical trial is often difficult because it can be time-consuming for patients and their families to fill out the often lengthy and highly detailed follow-up questionnaires. The families of patients treated for conditions such as appendicitis are typically cured or feel better after treatment, so there may be little incentive to take the time to fill out the questionnaires.

The resulting jump in recruitment and retention rates from the study in question underscores the value of involving patients, families and other healthcare professionals when designing and performing clinical trials and making data reporting as patient-friendly as possible.

As we have seen in a previous post on this site [4] there are potential statistical implications in trying to deal with missing data in follow-up analyses when patients ‘drop out’ or do not adhere to the protocol properly – the more patients that are lost for follow-up, the further an analysis deviates from true ‘intention to treat’.  Patient-friendly approaches to data collection therefore aid statistical integrity and accuracy.

It is clear that many research approval Authorities now acknowledge the importance of ‘patient centricity’ and due regard for the role of patients and their families as well as other tangentially involved personnel when considering participation in clinical trials.  Indeed, for clinical trial or research approval applications some reviewing bodies specifically consider whether or not researchers have involved input from patients and stakeholders other than the researchers and sponsors themselves in the trial process.  For instance in the UK’s HRA (Ethics & Management) online application system one question to be addressed is:  In which aspects of the research process have you actively involved, or will you involve, patients, service users, and/or their carers, or members of the public?  Applicants have to tick which of the areas of research to which this applies: Design / Management / Undertaking / Analysis of results, and give details or ‘justify the absence of involvement’.  Although there may be instances where ‘justification’ of no such involvement may not be appropriate, this is a clear message that the authorities are now taking the matter seriously and directing researchers to consider these approaches.

The results of taking advice from a ‘stakeholder’ panel reported by the researchers at Nationwide Children’s Hospital in Ohio show that these considerations can produce dividends.  If you are a sponsor or researcher looking to place responsibility of designing, setting up or managing a clinical study, my advice would be to check with the research organisations being considered to see what ‘patient-centric’ solutions they offer in order to make your study run smoothly and to schedule.

Brian Cary

[1] Randomized Controlled Trial of a Patient Activation Tool in Pediatric Appendicitis (Antibiotics Alone vs. Appendectomy)

[2]  Minneci PC, et al., Improving Surgical Research by Involving Stakeholders. JAMA Surg. 2016 Feb 10. (doi: 10.1001/jamasurg.2015.4898).


[4] Statistical Controversies in Reporting of Clinical Trials Posted on March 14, 2016

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Statistical Controversies in Reporting of Clinical Trials

statisticsIn one of the recent issues of the Journal of the American College of Cardiology, Professor Stuart Pocock and colleagues discussed controversies in reporting clinical trial results.  Prof. Pocock is a well-known biostatistician from the Department of Medical Statistics at London School of Hygiene and Tropical Medicine who is a widely recognized expert in the field. In the article, the authors list multiplicity of data, composite endpoints, covariate adjustment, subgroups analysis, assessing of individual results, analysis of intention to treat (ITT) population and interpreting surprises as these issues that often are a real pain in the neck for statisticians.

One of the most important statistical issues in reporting of clinical trials is multiplicity of data, i.e. “repeated looks at a data set in different ways, until something statistically significant emerges” [5]. In other words, it seems really hard to validly select data from the numerous variables collected at baseline and during the follow-up, which should be included in major trial publications; just to ensure, that such report is fair to what it includes.

According to authors, the best way to avoid this problem is to have a predefined statistical analysis plan (SAP), that is fully signed off before database locking and study unblinding. Actually, SAP is a regulatory requirement, which must not be overlooked. Furthermore, it is critical to pre-define the primary endpoint (with definition of the endpoint itself), put particular focus should be on the time of follow-up and the precise statistical method for determining its point estimate, confidence interval (CI), and p value.

It is also good practice to have a pre-defined and limited set of secondary endpoints for treatment efficacy. Their results are shown alongside those of the primary endpoint. When the primary endpoint findings are inconclusive, claims of efficacy for any secondary endpoints are more doubtful, like in the PROactive (Prospective pioglitazone clinical trial in macrovascular events) trial [2]. Dormandy et al. put the emphasis on the main secondary endpoint with HR of 0.84 (95% CI: 0.72 to 0.98; p = 0.027) and ignored the lack of statistical significance for the primary endpoint (the HR was 0.90 (95% CI: 0.80 to 1.02; p = 0.095). Such practice from the regulatory point of view, is more than controversial, but as prof. Pocock says: “regulators need to recognize the statistical uncertainties” and interpret the results taking them into considerations.

Composite endpoints are the result of combining two or more outcomes into a single primary endpoint. Anyway, such combination may generate a risk of oversimplifying the evidence by highlighting the composite, without proper assessment of the contribution from each outcome separately. For example in the SYNTAX (Synergy between Percutaneous Coronary Intervention [PCI] with Taxus and Cardiac Surgery) trial of bypass surgery (CABG) versus the TAXUS drug-eluting stent (DES) [3, 4] composite primary endpoint comprising i.a. stroke, and repeat revascularization. It turned out, that more events appeared after DES, what suggests, that DES is inferior to CABG. Anyway, the main difference was in repeat revascularization (majority repeat PCIs). Then, there was a significant excess of strokes after CABG, even though there was no overall difference in the composite endpoint.

The next challenge in reporting clinical trial data is making a decision in terms of whether key results should be adjusted for baseline covariates, and if yes, which ones. Actually, this inconsistency is automatically managed in randomised trials, as randomisation ensures good balance across treatments for baseline variables, and hence, covariate adjustment usually makes little difference. Anyway, to avoid controversies, it is good practice to set-up an appropriate covariate analysis in the SAP. To gain it, first of all, one should specify a limited number of covariates known (on basis of prior knowledge) to have a significant impact on patient prognosis. Then, prepare SAP which contains the clear covariate-adjusted model which is to be fitted. Furthermore, one should avoid post-hoc variable selection- such choices may be used for enhancing the effect of the treatment. Finally- the covariate adjustment can be considered as primary analysis, if the choice of covariate is a generally accepted convention for a specific endpoint.

In many trials, recruited patients do not form a homogenous group. Hence, it may be important to check, if the effects of the treatment apply to the entire study population or depend only on particular baseline characteristic, i.e. age. Despite this fact, usually researchers face problems whilst interpreting the results of subgroup analyses. Trials usually lack power to reliably detect subgroup effects. Moreover, there may be too many subgroups, which one should control. Every additional subgroup analysis may increase the problems with statistical significance as every additional analysis impacts the overall p-value. Without losing in details, in case of several subgroup analysis you only can assure a significance level less than 5% comparison-wise with keeping the overall (study specific) significance level massively under 5% and making subgroup claims (i.e. p value does not reach 5%). To handle the statistical insignificance it is better to use statistical tests of interactions that examine the extent to which the observed difference for example in HRs across subgroups may be attributed to change in explanatory factors.

Even though there are no efficacy and safety differences between subgroups, there may be important differences between individuals. Therefore, one needs to determine the individuals’ risks and benefits in order to check if studied treatment is efficient and safe in each case. A good way to get appropriate results is by using multivariable logistic models to separately predict any patient’s risks. It aims to estimate on the absolute scale, how the trade-off between treatments differences in some particular parameters is patient specific.

One can also have some doubts about how to deal with missing data and non-adherence during follow-up analysis. Actually, it is almost impossible to have full follow-up data for every patient, because some of them withdraw from the study or are lost for follow-up. The more patients that are lost for follow-up, the further analysis deviates from true ITT; therefore, loss for follow-up should be minimalized. The easiest way to manage this is by improving treatment compliance and the second- even if subject drops out the study, his follow-up should be continued.

In the majority of time-to-event analysis there is a variation in the actual number of observed patients during the follow-up. However, if the patient is withdrawn from the earlier stage of the study, experiencing the primary endpoint cannot be assumed to occur at random (i.e. patient is likely to have higher risk of primary event, which is unrecorded). Consequently a relatively high percentage of early drop-outs might bias the estimation of the end-points. Neglecting this fact could lead to a biased treatment comparison.

The last, but not the least is interpreting unexpected findings which might be related to endpoints, subgroups, or treatment effects. Sometimes it is about an effect inconsistent with the overall treatment effect or an exaggerate effect, that exceeds prior expectations. Small studies can be the subject of this type or bias with a higher probability.

As Prof. Pocock concludes: “Nevertheless, controversies will continue to arise”, and he hopes that his paper “has provided a statistical insight that will help trialists to present and readers to acquire a balanced perspective.”


  1. Statistical Controversies in Reporting of Clinical Trials
  2. Dormandy JA, Charbonnel B, Eckland DJA, et al., for the PROactive Investigators. Secondary prevention of macrovascular events in patients with type 2 diabetes in the ROactive Study (PROspective pioglitazone Clinical Trial In macroVascular Events): a randomised controlled trial. Lancet 2005; 366: 1279–89.
  3. Serruys PW, Morice MC, Kappetein AP, et al., for the SYNTAX Investigators. Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease. N Engl J Med 2009; 360:961–72.
  4. Mohr FW, Morice MC, Kappetein AP, et al. Coronary artery bypass graft surgery versus percutaneous coronary intervention in patients with three-vessel disease and left main coronary disease: 5-year follow-up of the randomised, clinical SYNTAX trial. Lancet 2013; 381:629–38.
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Replicor to present pre-clinical and updated clinical data on REP 2139-Ca based combination therapy in chronic HBV and HBV / HDV co-infection at APASL 2016

liverBelow, we are re-publishing with permission the press-release issued by Replicor on the 16th of February 2016

NEW YORK, February 16, 2016 – Replicor Inc., a privately held biopharmaceutical company targeting a cure for patients with chronic hepatitis B virus (HBV) and chronic HBV and hepatitis delta virus (HDV) co-infection, will present preclinical and updated clinical data on REP 2139-Ca based combination therapies in HBV infection and HBV / HDV co-infection at the 25th Annual Meeting of the Asia Pacific Association for the Study of the Liver to be held from February 20 -24, 2016 in Toyko, Japan. Three presentations on Replicor technology will be made during the meeting:

HBV RNA is emerging as a potential new marker of viremia in patients with HBV infection.  In patients with HBeAg positive HBV infection, treatment with REP 2139-Ca and immunotherapy (in the REP 102 protcol), not only leads to reduction /clearance of HBsAg and HBV DNA and the appearance of anti-HBs but also to reduction of HBV RNA as well.  The characterization of the HBV RNA response in the REP 102 protocol was done in collaboration with the lab of Dr. Hendrik Reesink at the Amsterdam Medical Center, and will be presented by Dr. Reesink in the Presidential Plenary Session on February 22nd.

An update on the clinical response data from HBV /HDV co-infected patients completing REP 2139-Ca / peg-interferon combination therapy and transitioning to peg-interferon monotherapy in the REP 301 protocol will be presented in an oral presentation on Feb 22nd (O-130).

A poster presentation on the effects of combined treatment with REP 2139-Ca and tenofovir disoproxil fumarate and entecavir on serum and liver virema in vivo will be presented on Feb 22nd (P-0329).

These pre-clinical and clinical studies continue to advance Replicor’s understanding of the antiviral effects of REP 2139-Ca based combination therapies and how these can be used to benefit patients with HBV infection or HBV / HDV co-infection.

For the APASL 2016 meeting and preliminary program:

About Replicor

Replicor is a privately held biopharmaceutical company with the most advanced animal and human clinical data in the development of the cure for HBV and HDV. The company is dedicated to accelerating the development of an effective treatment for patients with HBV and HBV/HDV infection. For further information about Replicor please visit our website at

* The study is conducted by Clinical Accelerator

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Quality Drivers in Clinical Trial Conduct – Part 2

Quality drivers 2This study aimed to illustrate how sponsors should measure quality of clinical trials and identify the various drivers of performance.  It was able to actually quantify the magnitude of the relationship between each of the drivers of performance and quality of conduct as well as measure quality directly. Routine trial management measures like ‘number of days to recruit’ and ‘number of data queries’ are simply measures of ‘time’ and ‘defect’, the authors point out, rather than a measure of quality itself – although they must surely be related to quality as performance drivers.

It is important to realise the implications of apparently ‘insignificant’ drivers of quality that were identified (e.g. the negative coefficients for ‘adherence’ and ‘monitoring’) since this is a bit misleading as the authors point out; for it means only that changing the level of the driver does not change quality and not that it is unimportant element of quality in a trial – they called such elements ‘hygiene drivers’ of quality that do not work to increase quality, but will ‘degrade’ quality if they are not present. In this respect, they discuss implications for risk-based monitoring (‘RBM’ – don’t we hear a lot about these days? No post on trials should be without a mention…). They say that using standard operational metrics to guide RBM could lead to a backward view of the trial and ‘gaming’ by the sites. With the obviously positive correlation between maintenance of good site relations and quality, there is complex relationship between attempts to maintain data integrity and quality of trial conduct.

All in all, the authors of this study maintain that the online (cloud-based) clinical trial questionnaire system they have developed is a statistically validated trial assessment platform that uses appropriate scientific measurements and can provide insights and data on quality that are not available from regular clinical trial management systems, which tend to be based on simple operational metrics.

Don’t fall into the ‘metrics trap’ they say. We should look beyond simple metrics to improve the quality of clinical trials and this seems to be sensible advice – despite the fact that it is from people who have a vested interest in a means of doing this…..

  1.…/Guidances/UCM269919.pdf (accessed 20/11/2015)
  2. Lawrence X. Yu, Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control. Pharmaceutical Research, April 2008, Volume 25, Issue 4, pp 781-791
  3. Howley, MJ and Malamis, P. Quality drivers in clinical trial conduct available at: (accessed 25/11/2015)
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Quality Drivers in Clinical Trial Conduct – Part 1

Quality drivers 1“Quality is never an accident. It is always the result of intelligent effort”

(John Ruskin 1819-1900).

Quality is an abstract concept and a term with which we are all familiar and tend to take for granted.  It is something to which all aspire in life and certainly do so in the conduct of clinical research and trials, where Quality Assurance and Quality Control (QA and QC) functions are considered paramount.  However, what does it really mean and, more to the point, how do you measure it validly in this scenario?

In recent years new clinical trial management techniques have become popular and have been advocated as ‘quality drivers’ – for example ‘Risk-based Monitoring’ (1) and ‘Quality by Design’ (2).  Nevertheless, the impact has not been immense and this fact, a study (3) by two founders of a company specialising in pharmaceutical performance analytics, is because we have been using the wrong measures to assess quality all these years.

They focussed the survey-based statistically validated study on clinical trial conduct (as opposed to study start-up or close-down) and aimed to identify key drivers of performance and relate them to quality.  In this way they identified metrics that have the greatest impact on trial quality.

Firstly, through a series of interviews over 18 months with CRO and sponsor trial managers they identified seven broad ‘performance activity’ areas and drivers of quality considered important: adaptability, adherence, enrolment, functions, monitoring, project management, and site relations.  Interestingly, the consensus was that last two were considered to be the most important, carrying the greatest impact.  Each area could, of course, comprise more than one item – for example ‘adaptability’ could refer to adaptability in terms of protocol amendments, change-order processes, protocol violation (PV) management, query resolution.

After this initial qualitative fact-finding phase, quantitative statistical analysis was then performed on data from a second phase using an online survey tool (82 usable surveys returned from Phase II – IV trial managers) to distil the previously identified areas down to ‘essential indicator’ items within each broad area and produce figures for the magnitude of their relationship (i.e. importance) to quality of trial conduct.

The authors of the study report do acknowledge that terms they use such regression modelling, predictive or business analytics may be off-putting but emphasise relative underlying basic nature of such techniques, which can be simply performed on an excel spreadsheet. Indeed, some readers of the original ACT article reporting the study may find the statistical analytical techniques just a little bamboozling (and, incidentally, that does include this reader and author of this post, despite having decades of involvement in clinical trials). Suffice to say that this was a sophisticated but entirely appropriate analysis with probabilities of significance being derived from calculations of the standardised regression coefficient beta, indicating the strength of a relationship and gamma statistic, providing a measure of association for ordinal variables. This should not detract from the essential findings and messages of the exercise.

Items found to be significant (t statistic, p<0.05) from those originally considered within each main performance area (italicised below) were as follows:

Adaptability: protocol amendments and PVs

Adherence: to study protocol and to medical management/safety plan

Enrolment: adhering to the timeline for ‘last patient entered’ (only – no other enrolment-related metrics were not found to be significantly related to enrolment performance)

Functions: performance on project management, data management, regulatory, centralised diagnostic service, CRF tracking, and external data sources.

Project Manager, site relations and monitoring: each of these three areas measured as a single global indicator in their own right (i.e. no separate indicator items).

Using the model, beta coefficients were computed to describe the strength of the relationship between the performance activity and quality.

Applying the model for these items, the relationship between the performance drivers and quality are shown in Figure below with histogramatic representation of the regression coefficients (b):


Thus the drivers with the most impact on quality were Project Management performance (b=0.35, p=0.001) and managing Site Relations (b=0.31, p=0.002). Adaptability and enrolment (considered in terms of getting the last patient in on time) were also worth focussing on in terms of trial management.

  1.…/Guidances/UCM269919.pdf (accessed 20/11/2015)
  2. Lawrence X. Yu, Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control. Pharmaceutical Research, April 2008, Volume 25, Issue 4, pp 781-791
  3. Howley, MJ and Malamis, P. Quality drivers in clinical trial conduct available at: (accessed 25/11/2015)
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Replicor discloses continued improvement of the antiviral response in patients with HBV / HDV co-infection receiving REP 2139-Ca based combination therapy

Replicor logoBelow, we are re-publishing with permission the press-release issued by Replicor on the 16th of November 2015

NEW YORK, November 16, 2015 – Replicor Inc., a privately held biopharmaceutical company targeting a cure for patients with chronic hepatitis B virus (HBV) and chronic HBV and hepatitis delta virus (HDV) co-infection, disclosed updated interim safety and efficacy data from its ongoing REP 301 trial (NCT02233075) at the 2015 meeting of the American Association for the Study of Liver Disease (AASLD) being held from November 13-17 in San Francisco, USA.  The REP 301 trial update was presented (Abstract 31) on Sunday November 15th in Parallel Session 4: Hepatitis B: Novel Treatments and Treatment Targets.

Previously reported HBsAg reductions with REP 2139-Ca monotherapy continued to improve during combination therapy with pegylated interferon alpha-2a, becoming > 6 logs in 4 patients (0.01 IU / ml), > 5 logs in 2 patients, > 3 logs in 2 patients and 0.5-2.78 logs in the remaining 4 patients.  HDV RNA continued to decline in all patients and is now currently undetectable in ten patients (~5-8 log reduction from baseline).  Importantly, the addition of pegylated interferon alpha-2a to therapy was associated with dramatic increases in free anti-HBs (to levels as high as 20,665 mIU / ml) and liver flares, but only in those patients who achieved serum HBsAg < 1 IU / ml (> 4 log reduction from baseline) at the start of immunotherapy.  These results continue to demonstrate the clinical potential of REP 2139-Ca in HBV / HDV co-infection and begin to shed light on the importance of achieving multilog reductions in serum HBsAg to improve the antiviral effect of immunotherapy.

A copy of the presentation made will be made available at

About Replicor

Replicor is a privately held biopharmaceutical company with the most advanced animal and human clinical data in the development of the cure for HBV and HDV. The company is dedicated to accelerating the development of an effective treatment for patients with HBV and HBV/HDV infection. For further information about Replicor please visit our website at or follow us on Twitter @replicorinc.

* The study is conducted by Clinical Accelerator

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Risk-Based Monitoring in Clinical Trials

paperworkRisk-based monitoring (RBM) is a method that uses risk algorithms to assess the right level of clinical trials monitoring. Food and Drug Administration (FDA) and European Medicines Agency (EMA) currently encourage this approach.

RBM focuses on improving quality of the data and helps to reduce the time consumed and costly on-site 100% source data verification. At times, 25-30% of the whole clinical study budget could be spent on the costs of monitoring (personnel, travel, expenses etc.). Implementing RBM can help to reduce these costs.

There are many other reasons for developing RBM. One of the main reasons is that it could help remove problems that are created through the traditional way of monitoring such as: lack of broadly understood principles; methodology and approach as well as terminology; scope of tasks; deliverables; roles and responsibilities while planning; conducting analysis; reporting and assessment of clinical trials. Additionally, it’s thought that important decisions concerning risks are not based on well-defined and objective criteria but are actually made on the basis of individual or teamwork assessments and opinions. Some observations indicate that there is limited emphasis put on the foundation of RBM with it’s usage restricted to the review of data and ignoring other respects of the study e.g. design of the protocol. It is linked to the fact that often no integrated quality management strategy is implemented. Such strategy should be the foundation of the design of the study, site selection, study management and general oversight aspects. Another problem is lack of sharing risk assessments within and between sponsors. RBM is a risk-based approach developed to manage all these problems.

The application of RBM in clinical trials is growing, especially for Phase II studies. Results of the global survey conducted by the Metrics Champion Consortium  (MCC) in 2013, reveal that all respondents were using some sort of RBM tools. Majority of stake holders (85%) continued traditional on-site monitoring involving 100% source data verification (SDV) activities, while more than a half reported using in the same time some type of RBM program on a pilot basis, or across a full program.

The most popular RBM programs involve on site monitoring with reduced SDV, or remote monitoring with support of the central data analytics (CDA). The methods of remote monitoring include the usage of data analytics reports, remote source data verification and patient profiling, which means monitoring individual study subject reports.

The reasons for adopting RBM varied among different stakeholders. Contract Research Organizations (CROs) and academic research institutes chose to implement RBM to reduce monitoring costs, whereas pharmaceutical and biotech companies chose it to improve quality oversight.

Implementing RBM also has many other advantages. Effective risk management requires a structured approach for risk identification, analysis and control. The system of risk management should be repeatable, reproducible, sustainable and adaptable in meeting regulatory demands and achieving quality outcomes. It outlines a systematic approach for the assessment, communication, control and review of risks related to the respect of quality. Implementing RBM can also help to facilitate many other aspects of the clinical trial, like site selection, qualification, protocol design and subject enrollment.

It seems like implementing RBM strategies would only be possible for big companies and institutions, but the truth is that designing and implementing RBM does not depend on the size of a company. All of interested stakeholders face similar problems e.g. lack of resources, time pressure and growing regulatory requirements and can benefit from developing an effective risk-based approach.

Another myth about RBM is the conviction that it’s implementation can be successful only with the aid of highly sophisticated IT systems. Sometimes, the simplest spreadsheets would be sufficient, but with the growth of study complexity, scope, number of arms and comparators more complex tools that minimize the workload may be needed.  All in all, IT software is not an essential element of the RBM strategy. The basis should be the overall philosophy inclusive identification of risks, analysis of their impact, likelihood and detectability. For such assessments no specific IT systems are necessary.

There is also an opinion that implementing RBM may influence the way of writing protocols and setting up trials. Of course some changes may be needed to improve the overall quality of the study. Indicated fields of improvement are: reaching planned recruitment targets, ending the study on time and on planned budget, launching proper number of amendments, delays and additional costs due to compliance flaws.

The organization, which is interested in developing the Risk-Based Monitoring programs rose as RBM Consortium. It associates quality risk management industry experts, risk-based technology firms, data analysts and biopharmaceutical business strategists. RBM Consortium is a transnational alliance to improve awareness of RBM in clinical trials and give advice to interested stakeholders on the area of quality risk management and methodologies in RBM.

Due to its many advantages and overall interest in ensuring best quality data, RBM seems to be gaining more and more followers and may become the gold standard of risk-monitoring activities in the area of clinical trials.


Risk-Based Monitoring in Clinical Trials, Applied Clinical Trials, May 2015

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