	Policies to widen access to patient level data from clinical trials are gaining traction, despite strong opposition from research sponsors that such initiatives will undermine patient privacy and incentives for new drug development. There is general agreement that clinical data sharing can improve the efficiency of clinical trials, validate regulatory decisions, and increase public confidence in clinical research. But sponsors are concerned about who controls access to data, the purposes of disclosure, and safeguards to protect all parties. 

	To head off a plan by EMA for public release, or "sharing," of study data in registration dossiers, sponsors are rolling out voluntary data sharing programs, as outlined in a "principles" document issued in July by the Pharmaceutical Research and Manufacturers of America (PhRMA) and the European Federation of Pharmaceutical Industries and Associations. Companies are advised to establish independent scientific review boards to evaluate outside data requests, plus procedures to protect patient privacy. The aim is to delay full data disclosure by EMA in January 2014, so that standards, third-party programs, and voluntary initiatives have more time to demonstrate their ability to enhance research transparency while protecting patients and research companies.

	Industry objects that the EMA plan fails to limit access only to bona fide researchers seeking to address valid scientific questions. And the prospect of releasing full regulatory dossiers could expose proprietary formulation and manufacturing data and information on product development and future indications. Broad release of clinical data, moreover, could undermine product exclusivity in countries such as Australia, Brazil, China, and Korea that link exclusivity to data confidentiality, explained Pfizer Senior Vice President Justin McCarthy at a PhRMA briefing in October. He warned that companies may rethink their development and registration strategies if the EMA proceeds with its plan for full release of regulatory submissions. Sponsors may decide to delay submissions for approval in Europe or seek to eliminate or redact more confidential commercial information in dossiers.

	Sponsors also are concerned about a June 2013 proposal from FDA to make available masked and de-identified data submitted in applications, possibly limited to information aggregated across drug classes. FDA reopened comments to this proposal in October, seeking additional information on what factors to consider in masking study data and if there are ways to identify patients after removing names, birth dates, death dates, and geographic information.

	Industry is more comfortable with FDA's more narrowly focused data sharing plan, although still wary that outside researchers with advanced computer systems and access to extensive genomic information will be able to re-identify patients, particularly those involved in studies on rare diseases or cancers that affect small patient cohorts. And permitting non-experts to gain access to data, comments PhRMA, could result in "unjustified second-guessing of FDA's regulatory decisions" that raises alarm among patients and providers and discourages appropriate treatment.

	These controversies are spurring efforts to reach consensus on achieving data transparency while enhancing biopharmaceutical innovation. An Institute of Medicine committee began deliberations last month on "Responsible Sharing of Clinical Trial Data," with the intent of issuing guiding principles and a framework for such initiatives. An interim report is due in January, and a final report in December 2014 to assess the benefits and risks of data sharing and opportunities for responsible disclosure.

	While more pharma companies will launch data sharing programs in January, these voluntary initiatives have limitations. Firms don't plan to hand over clinical data sets to outsiders, but to run their queries through in-house data systems and provide results. And there are concerns about the real independence of a company-appointed scientific review board.

	Patient representatives at the PhRMA briefing noted problems with informed consent if clinical data is to be used by outside parties for other purposes. FDA requires sponsors to advise clinical trial participants as part of the consent process that a summary of data from the study will be posted on 

, but that this information will not identify specific patients, attorney Mark Barnes pointed out at the October Clinical Trial Disclosure conference sponsored by the Drug Information Association. Weakened privacy safeguards, however, could invalidate that assurance.

	Sponsors insist that they should decide how to share research information-not government regulators. And patient advocates are fearful of questionable secondary statistical analyses that challenge regulatory decisions and sponsor assessments. But there's optimism that the disclosure movement will spur data interoperability standards and open sourcing models, all key to transforming clinical research.

Articles

News

Policies to widen access to patient level data from clinical trials are gaining traction, despite strong opposition from research sponsors that such initiatives will undermine patient privacy and incentives for new drug development.

Sponsors Struggle with Data Disclosure Requirements

In the three-layer approach proposed in this article, how to interpret data in multi-regional trials into three layers is explored: Layer 1 to first look at overall study results, Layer 2 to identify factors which influence overall results from scientific and regulatory perspectives, and Layer 3 to discuss benefit/risk for a specific country/region. 

The International Conference on Harmonization's (ICH) ''E5 Guideline on Ethnic Factors in the Acceptability of Foreign Clinical Data'' (ICH-E5) (R1) was introduced in March 1998.

  Since the ICH-E5 guideline was issued, there has been a lot of discussion on the importance of evaluating the impact of ethnic factors on the ability to extrapolate efficacy and safety data to a new region in order to minimize duplication of clinical trials. Moreover, in recent years, a shift in clinical trials to so-called emerging regions, especially in Eastern European, Latin American, and Asian countries has been noted. This contrasts with traditional clinical trials which have been carried out in relatively affluent countries in North America, Western Europe, and Oceania.

  In other words, in recent years, drug development has become dramatically globalized, and multi-regional trials (MRTs) are now being extensively conducted in both ICH and non-ICH regions.  

In these MRTs, it is import to consider carefully potential regional heterogeneity of treatment effect which may be caused by intrinsic or extrinsic ethnic factors. According to ICH-E5,

  "extrinsic ethnic factors are factors associated with the environment and culture in which a person resides," and "intrinsic ethnic factors are factors that help to define and identify a sub-population and may influence the ability to extrapolate clinical data between regions." A number of MRTs in the past, such as Metoprolol CR/XL Randomized Intervention Trial in Heart Failure (MERIT-HF)

  and Platelet Inhibition and Patient Outcomes (PLATO),

  have suggested the possibility of real regional differences in drug effects. For example, both MERIT-HF and PLATO showed an overall statistically significant efficacy. However, the results in North America, and in particular in the United States, showed a trend in the opposite direction. If a regional heterogeneity is found, the cause of the differences should be thoroughly investigated and its findings should be described in the common technical document (CTD) at the time of the NDA.  

Historically, in a CTD that is first written in a Western country, a description of results of MRTs focused on the overall study results with a limited amount of investigation of study results from various perspectives, including the effect of intrinsic and extrinsic ethnic factors. Therefore, at the time of NDA submission in countries outside of the West, such as in Japan or other Asian countries, additional analyses comparing the results of Western countries with that of Japan or other Asian countries have often been necessary to satisfy requests from the regulatory authorities in these countries. 

The ultimate goal of a globalized drug development that includes MRTs is to provide a "common" technical document that is common not only in the format, but also in its contents, and therefore can be used to support mutual submissions in multiple countries. 

With the above information as a backdrop, a new approach to presenting data from MRTs in scientific and effective manner in the "common" technical document is proposed. This article will explore bridging studies with historical background; the three-layer approach with examples of MRTs; and a new proposed approach. 

Ethnic insensitivity in bridging strategy

In Japan, data from clinical trials conducted outside of Japan have been accepted in accordance with the ICH-E5 guideline, which proposes that if the health authority in a new region (such as Japan) is presented with a foreign clinical data package that meets the local regulatory requirements, it should ask for only the additional data necessary to evaluate the ability to extrapolate the foreign data to the population of the new region. The additional data can be from a "bridging study," which is often a controlled clinical trial (which could possibly be a dose-response study), carried out in the new region to assess the investigational product's sensitivity to ethnic factors. Typically, a bridging study following completion of Phase II or Phase III studies in the United States or European Union is used to extrapolate the foreign data to construct the full clinical data package in Japan.

  In the bridging strategy, at least one statistically powered study is conducted in both Japan and the foreign countries. Obviously, as each study is sizable, it is possible to compare overall and subgroup analysis results from bridging and the counterpart studies and to evaluate the similarity between the two.   

Recently, countries which used the bridging strategy have been shifting to the global simultaneous drug development by designing or participating in MRTs. How to evaluate data from MRTs is different from that in the bridging strategy which consists of independent studies in each country. Therefore, a new approach to evaluating data of MRTs is proposed. 

Ethnic insensitivity in multi-regional trials

Traditionally, multinational trials had been conducted in limited locations such as in North America, Western Europe, and Oceania as a natural extension of multi-center trials. ICH-E5 Question and Answer No.11 introduced MRTs in 2006, foreseeing the current paradigm of global simultaneous development.

  A shift from multinational trials to MRTs implied an expansion of participating countries in emerging regions to MRT, especially in Asian and Eastern European countries, whose selection is motivated primarily by the ability to reduce operational costs while recruiting a large number of patients.

  Increasing the number of regions in clinical trials results in ethnic and cultural diversity of patients, which in turn leads to a potential for increased variability in treatment response among patients. Therefore, in comparison with traditional multinational trials, data of other regions in MRTs may cause additional issues in terms of ethnic sensitivity/insensitivity. 

A successful MRT requires scientifically valid results from a population with diverse ethnic backgrounds. To assess ethnic sensitivity or insensitivity, the design of an MRT needs to be considered from various perspectives such as medical, statistical, regulatory, commercial, operational, and marketing, encompassing the entire development strategy. Moreover, to generate the data necessary to achieve the trial objectives, the sample size in each country and region should be fully taken into consideration. 

In addition, before conducting an MRT, intrinsic and extrinsic ethnic factors that may affect efficacy and safety of drugs should be assessed and then incorporated into the study design and the analysis plan of MRTs. 

As stated above, how to design and evaluate an MRT is critical to its success. 

Current approach to investigation of clinical data in multi-regional trials. 

As shown in Figure 1, in MRTs the overall results of efficacy and safety are investigated as the first step. As the second step, the current approach tends to give only a cursory examination of data and bypass detailed investigations which may include subgroup analysis from various points of view such as baseline prognostic factors in evaluation of ethnic insensitivity, and proceeds directly to a step where results from subgroup analyses in each country/region are conducted to assess the benefits/risks profile of a drug, when the new drug application is filed in a specific country.

  It is natural for regulatory authorities to be interested in the differences among regions as well as the data for their own people—they have accountability for the benefit/risk profile of the drug which they approve in their respective countries. However, from a scientific point of view, subgroup analyses by demographic and baseline characteristics for overall data should be conducted at the same level as those by country/region factor in the second step to further investigate characteristics of data. Therefore, the term "three-layer approach" is proposed. 

When interpreting results from MRTs, it is natural that one should look at the overall results, and then investigate the subgroup results to identify factor(s) which might influence the interpretation of the overall results.  

Ethnic insensitivity has been evaluated in a similar manner with the bridging strategy as shown in Figure 1. However, it is noted that one should not rush into subgroup analysis within each country/region. Figure 2 schematize the proposed three-layer approach for interpreting the study results from MRTs. 

 In the first step, one looks at the overall results of a study without considering potential differences among subgroups.  

Identifying factors that may influence the overall results (Layer 2). 

Subgroup analyses are typically performed to investigate whether a specific subgroup behaves differently from other subgroups or the overall population, or whether any specific factors exert an influence on the overall results. Such factors may include gender, age, body weight, race, baseline characteristics, and other intrinsic ethnic factors. We refer to the perceptual approach to these factors as a scientific perspective. In contrast, looking at a country or geographical region is a regulatory perspective rather than a scientific one. Each regulatory authority has an accountability to explain the benefit-risk profile of the drug to its public. Therefore, all regulatory authorities are interested in the results from their respective country or region. We note that extrinsic ethnic factors may be confounding the regulatory perspective, and that extrinsic ethnic factors are often not collected in the case report forms and cannot be incorporated into data analyses. 

Observed inconsistencies in responses among the regions/countries, if any, will be investigated from a scientific perspective by examining the distribution of ethnic factors. For example, if a genetic polymorphism in a metabolic enzyme is the most influential factor on the efficacy/safety endpoint, and if the distribution of the polymorphism is different among countries/regions, the observed difference in responses among countries/regions may be attributable to the unequal distribution of the polymorphism.  

As an example of how to investigate data in Layer 2, four patterns of interpretation for results are shown in Figure 3:  								

 There is no factor that affects the overall results from either scientific or regulatory perspectives. In this case the overall results would be very robust.

 There is no factor from regulatory perspectives, but there are some from scientific perspectives. This means intrinsic ethnic factors such as age or polymorphism exist that would have common effect in any countries/regions, but there is no difference among countries/regions in terms of the outcome.  

 There is no factor from scientific perspectives, but there is one from the regulatory perspectives. In this case, the difference in outcome among countries/regions might be caused by those factors that cannot be explained with scientific perspectives, such as extrinsic ethnic factors or operational factors. It is likely that causes of the differences are difficult to pinpoint and the results should be interpreted with caution. When such differences are found, we should consider whether they occurred by chance based on statistical calculation with the number of patients for each subgroup, variance, effect size, and so on to evaluate risk and benefit in a given country/region. Causes of the differences might also be difficult to show only based on the trial data. Especially, smaller sample size of the country in question would make such interpretations difficult. Other information or knowledge that is internal or external to the trial including administrative information may help the interpretation of the study results, e.g., possible causes of higher placebo response in some countries or issues with the quality of data or safety reporting in some countries. These explanations may not always be sufficient for some regulatory authorities and their acceptability would be the subject of determination in the Layer 3 discussion with each regulatory authority. 

 There are such factors from both scientific and regulatory perspectives. For example, if a baseline value has an effect on the efficacy outcome, the difference among countries/regions could be explained to some extent by the difference in distributions of the baseline values (i.e., scientific perspective), but not all. The cause of any residual country/region differences may be difficult to explain conclusively, as described in the third case above. 

In order to identify factors that may explain subgroup differences, further analyses could also be conducted in Layer 2. In these analyses, the important way of thinking is that the more clues from various assessments of efficacy and safety pointing the same direction, the greater the likelihood the finding is real and not a false signal. Meanwhile, if one uses hypothetical testing in Layer 2, he/she may consider multiplicity issues not to be deluded by the false signals. 

Because Layer 2 mostly has no pre-specified hypothesis and may have insufficient power to detect any inconsistency, it is important to integrate any available information such as findings from other studies or knowledge of other drugs in the same drug class and to not rely solely on the study results.  

Discussing the benefit/risk for a specific country or region (Layer 3). 

When the new drug application is filed in a specific country/region, benefit-risk balance for that country/region would be explained based on the discussion in Layer 2. No additional extensive discussion on a specific country/region subgroup versus overall population would be needed if analyses in Layer 2 can cover the discussion. If they cannot cover the discussion, in Layer 3, subgroup analyses by baseline prognostic factors in each country or region (such as Japan) would be conducted to explain benefit-risk balance to the regulatory authorities. 

Analyses which are conducted in Layer 2 and Layer 3 could be pre-specified in the statistical analysis plans if some specific factors are known in advance to have influence on the overall results. However, in most cases, subgroup analyses in Layer 2 and Layer 3 would be determined by what is found in the initial planned analyses for Layer 2 and Layer 3; post-hoc analyses would be performed after results from analyses in Layer 2 and Layer 3 are provided. 

The three-layer approach emphasizes the importance of analyses in Layer 2. Discussions in Layer 1 and Layer 2 are not limited to a specific country/region and all countries/regions should be included in the discussion. Such a discussion could be shared in the CTD, which any countries/regions could use when writing the CTD for the countries/regions. 

Recently, more and more clinical trials have been conducted globally. The new approach, the three-layer approach, can be used to analyze and interpret data of MRTs under this new paradigm of drug development. In the three-layer approach, how to interpret data in MRTs into three layers is categorized: Layer 1 to first look through overall study results, Layer 2 to identify factors which influence overall results from scientific and regulatory perspectives, and Layer 3 to discuss benefits/risks for a specific country or region according to the requirements of its regulatory authorities. Discussions in Layer 1 and Layer 2 is not limited to a specific country/region. The importance of the discussions in Layer 2 to be utilized in the new drug application in any country included in the MRTs is emphasized. Such discussions could be used commonly by any country/region, resulting in a more scientific and structured presentation that is more effective in providing evidence to be used commonly and globally. The three-layer approach is also useful from a regulatory point of view in that Layer 3 allows benefits/risks of a drug to be assessed in each country/region. Although subgroup analyses in each country/region may have limitations due to smaller number of subjects in each subgroup, regulatory authorities have often requested subgroup analyses of their own country/region. In the three-layer approach, whether or not there are factors which affect benefits/risks of a drug and to what extent these factors affect results can be firstly evaluated in Layer 2 and then might proceed to Layer 3 to assess the benefits/risks of the drug in each country/region. The important point of the three-layer approach is that no additional extensive discussion on a specific country/region subgroup versus overall population would be needed if analyses in Layer 2 can cover the discussion. 

Moreover, in Layer 2, to investigate factors which would affect efficacy and safety, it would be useful to conduct subgroup analyses. In the case that there are such baseline factors which affect efficacy and safety, in Layer 3, assessing similarity of distribution in such baseline factors among regions or countries could be supportive to explain similarity/difference of regions or countries. However, the limitations of subgroup analysis by baseline factors for specific countries/regions should be taken into consideration.

The proposed three-layer approach may be applied not only to one MRT but also to a set of clinical trials, whose components are conducted in different countries or regions. In this case, as one example, Layer 1 (overall results) could use results from the integrated summary of clinical efficacy and Layer 2 (consistency assessments from various angles) could be evaluated based on the integrated dataset. 

In summary, when the three-layer approach is widely taken into consideration, data from MRTs could be used effectively for global drug development from the perspective of planning development strategies and preparing the CTD. In order to create a CTD following the three-layer approach, sponsors in pharmaceutical industries should prepare a plan to create the CTD which includes globally-shared Layer 2. Especially, it is critical to consider clinical development strategy early in the product development, create a study design, and conduct the study while taking into consideration the evaluation of data by the three-layer approach. 

 The authors would like to thank Yoichi Ii, Christy Chuang-Stein, Zhen Luo, Norisuke Kawai, Peng Qu, and Satoru Mogami for their help with this project. 

Osamu Komiyama, Shintaro Hiro, Naoki Isogawa, Shigeyuki Toyoizumi, Nobushige Matsuoka, Satoshi Hashigaki, Tamotsu Yoshiyama, 

are all Clinical Statisticians at Pfizer Japan Inc., Shinjuku Bunka Quint Building, Shibuya-Ku, Tokyo, Japan, e-mail: 

*To whom all correspondence should be addressed. 

(1) International Conference on Harmonization, "Ethnic Factors in the Acceptability of Foreign Clinical Data E5 (R1)," (1998), 

(2) F. A. Thiers, et al., "Trends in the Globalization of Clinical Trials," 

(3) C. Anello et al., "Multicentre Trials: A US Regulatory Perspective," 

Statistical Methods in Medical Research, 

(4) L. Wallentin et al., "Ticagrelor Versus Clopidogrel in Patients With Acute Coronary Syndromes," 

(5) M. Tanaka and T. Nagata, "Characterization of Clinical Data Packages Using Foreign Data in New Drug Applications in Japan," Clinical Pharmacology & Therapeutics, 84 (3) 340–346 (2008). 

Feature Article

Planning for ethnic sensitivity from a scientific and regulatory perspective.

Evaluation of Data for Multi-Regional Trials: A Three-Layer Approach

Many factors affect a sponsor's decision to pick a region in which to conduct a clinical trial. Clinical grant expenditures represent a significant portion of later phase clinical trial budgets. Therefore, the relative cost per visit can play a large role in which region to select. 

Figure 1. Cost per visit rate trends, 2008-2010 and 2011-2013.			

IMS Health's current analysis demonstrates that the cost per patient visit in the newer, emerging geographies, is growing more rapidly than in more established regions. The established regions (Western Europe, US/Canada, and Australia/New Zealand) have been averaging 8% annual increases from 2011-2013, while grant costs in developing regions (East Asia, Latin America, and Eastern Europe) are growing at two to three times those rates with an average of 23%. Even if these emerging regions are starting off on a lower base as compared to well established regions, the rapid rise in cost in these regions warrant careful consideration in the coming years. 

—IMS Health (for more information contact 

Cost Per Visit Trends Higher for Less Established Regions

The past decade has seen tremendous growth in the use of pharmacometric methods to inform drug development and regulation. Modeling, simulation, and related decision-analysis tools have evolved from academic explorations to become mainstays in US and European regulatory review

 and the subject of industry focus around the globe.

  Current guidances from US and EU regulatory authorities encourage the development of informative drug-disease-patient models, quantitative utility indexes, and strategic dose and trial simulations to explore available knowledge from all sources. These pharmacometric tools enhance communication within a sponsor company, as well as between sponsors and regulators, and provide a fully-informed, scientific basis for decision making. 

Pharmacometric methods are particularly powerful assets when used to build a comprehensive knowledge base of preclinical, early-phase, literature, and competitor data. That knowledge base supports optimization of later-phase trial designs and enables earlier, more confident go/no-go decisions—maximizing the likelihood of Phase III and ultimate commercial success.  

Drug sponsors that invest in pharmacometrics experience fewer surprises and a more certain path to success—as well as early recognition of failures, for quicker no-go decisions. As target indications and drug benefit/risk profiles have increased in complexity, pharmacometric insights have become essential to understanding new drug candidates and deciding whether to proceed with large-scale trials. Pharmacometric methods integrate all available knowledge of the study drug, disease, subjects, and clinical trials at each phase of development. These model-based approaches combine literature, competitor, and trial data, enabling direct, quantitative evaluation of tradeoffs in treatment approach, target population and indication, and trial designs.  

The resulting knowledge framework provides a rich, quantitative basis for development decisions and a platform for earlier and more informative communication with regulators. Regulators have come to expect pharmacometric analyses in submissions, and apply modeling and simulation themselves to evaluate study designs, labeling, and approval decisions, especially for cases that are difficult or impossible to study directly in clinical trials.

As an organization's pharmacometric knowledge base grows in a given therapeutic area, it can improve efficiency across related development programs. Drug sponsors collect the benefits in cost and time savings, reduced Phase III failures, and a faster and more confident path to regulatory approval.  

Pharmacometrics encompasses a variety of mathematical and statistical methods to combine and examine all sources of information about a candidate drug. Probabilistic, Bayesian models of the drug, disease, patients, and trial gather knowledge from disparate sources into a quantitative framework for exploring benefit/risk profiles and potential sources of variability. Monte Carlo simulations using those models facilitate science-based decisions by testing "what if" scenarios for dosing and trial designs, and exploring the impact of assumptions about the drug and patient population on outcomes. Utility indexes capture available knowledge and expert opinion to form objective benefit/cost evaluations.  

Over the course of development, these analyses grow more certain and more precise as new data become available. By the end of a well-designed Phase II program, these analyses can predict Phase III outcomes with high and explicitly quantified certainty,

 including outcomes for doses and populations not yet studied.

  Observed distributions of prior data from preclinical and early clinical trials—as well as the literature on the target indication and competitors—enable creation of drug, disease, and patient models illustrating very complex drug profiles. The data needn't be richly sampled. For example, population pharmacokinetic (PK) methods may be used to estimate a dose-response relationship based on sparse effect data from Phase I and IIb, by pooling data from multiple early-stage trials and literature data on the disease.

  Models of the dose-concentration and exposure-response relationship for biomarkers or clinical outcomes help quantify potential influences of drug/drug and drug/food interactions, age, concomitant disease, and other subject and pathophysiological traits to aid selection of Phase III study populations.  

Dose and trial simulations drawing on this model framework test assumptions and "what if" scenarios for patient and trial outcomes. Highly realistic evaluations of product profile and competing trial designs are possible by including published dropout data, placebo effect models, dose-exposure-response comparisons with competitors, and more. "Intent to treat" simulations include probabilities for patient compliance to estimate the impact on outcomes. By exploring assumptions and options, the simulation team can aid selection of optimal Phase III trial power, dosing and sampling schemes, and adjustments for important subpopulations.  

How pharmacometrics supports Phase III success 

Pharmcometric methods both quantify and help reduce the high uncertainty of early clinical development. This section explores practical examples and regulatory perspectives demonstrating how pharmacometric methods can provide a better-informed, quantitative basis for decisions that will impact costly late-phase trials. 

 Disease-specific, drug-independent models linking biomarker response to specific clinical endpoints can support Phase II study design and evaluation, as well as go/no-go decisions for Phase III.

  Such analysis can be especially useful in bringing together preclinical and clinical data to better predict drug actions based on limited information. As Phase II progresses, models can be updated with newly available data to more precisely estimate the drug's risk/benefit profile. 

For example, modeling and simulation helped to predict Phase III outcomes based on tumor growth from Phase II as a biomarker for outcome.

  Another recent study demonstrated that time to tumor growth in colorectal cancer predicted the long-term clinical endpoint of overall survival better than other measures, and fully described the therapeutic benefit of a new treatment.

Such models prove useful for simulating outcomes for new treatments based on early proof-of-concept (Phase Ib and Phase IIa) data, and for evaluating a candidate versus placebo and standard therapy. In the colorectal cancer example, combining modeling with longitudinal tumor-size data could improve trial design and analysis for more informative early clinical studies, and enable oncology researchers to select the most promising treatments to reduce the high attrition rate in Phase III oncology trials. 

 A well-constructed model can help researchers anticipate drug exposure and response as well as drug/drug and drug/food interactions for unstudied formulations.

In one such case, the development team for a hyperlipidemia agent set Phase III dosing for a new tablet formulation based on early clinical studies of a liquid-filled capsule.

 Population PK models based on three Phase I trials and one Phase IIb study illustrated differences between the two formulations and quantified variability in apparent drug bioavailability with food intake. Complementary pharmacodynamic (PD) modeling captured variability in response with subject characteristics and provided evidence that the new drug acted independently when co-administered with a statin.

Trial simulations based on the models indicated Phase III outcomes would be robust against subject dietary variation as long as subjects adhered to a low-fat regimen. The simulation results supported the choice of Phase III dose regimen using a dosage and formulation not studied in prior trials.

 The value of pharmacometrics is apparent in its impact with regulators during trial design evaluations. FDA design reviews for pediatric and late-phase trials employ quantitative approaches to plan dose selection, justify sample size, and evaluate endpoints to improve chances of trial success.

  Similar practices are encouraged in the EU.

In European Clinical Trial Applications, as in US submissions, modeling can be used as evidence to support proposed doses for clinical study. For later phases, such as dose-response studies, clinical trial simulations based on exposure-response models can justify dose selection. European regulators note this approach is highly encouraged because the analyses provide a rational basis for dose choice and help regulators to better understand safety and effectiveness.

  Simulations can also help to compare trial designs and may reduce failure rates, the regulators note. 

Once dose/response and exposure/response relationships are established, modeling can provide a basis for dose adjustments based on patient-specific characteristics such as age, gender, or concomitant disease or medication.

  Such pharmacometric analyses can provide sufficient evidence for regulatory approval of not only trial designs, but also dosing regimens not studied in Phase III. This was the case for 21 of 198 NDA and BLA submissions reviewed by the FDA pharmacometrics group from 2000-2008.

Simulations can also aid in optimizing sampling schemes. In a published example analysis, FDA pharmacometricians used clinical trial simulations to compare dose-response analyses for a pivotal efficacy trial.

  The group compared placebo-corrected (subtraction of placebo mean from observed treatment value) and placebo-anchored (placebo treated as dose 0) designs across a variety of dose strategies, sample sizes, and exposure-response assumptions—for example, different efficacies. The placebo-anchored analysis proved more powerful to show effectiveness across scenarios. 

Getting a handle on variability and subpopulation effects.

 It is impossible to study every possible combination of subpopulation, drug/drug interaction, food interaction, concomitant disease, or medication. Pharmacometric analysis can provide confidence and convince regulators in instances when trial data are unavailable. Population methods are particularly useful to identify and plan for subpopulations that may process or respond to the drug differently.

  Such analyses can be invaluable to anticipate needed dose adjustments and potential subpopulation effects in large trials, and they have a marked impact on approvals. 

In published cases, exposure-response analysis and/or trial simulations have clarified needed dose adjustments for specific subpopulations, or allowed the sponsor to bridge dosing from one formulation to another with known bioavailability to achieve target concentrations.

These analyses can inform decisions for post-approval study. For example, given a known reduction in drug clearance in patients with renal impairment, dose adjustments for that subgroup were recommended and approved based on calculated target area under the time/plasma concentration curve as a measure of drug exposure, alleviating the need for additional study.

  In another case, FDA scientists applied modeling to identify subpopulation effects while evaluating recommended dosing for a monoclonal antibody in oncology.

  Exposure-response analysis incorporating a case-control comparison identified a subpopulation in the lowest quartile for drug exposure that did not receive a survival benefit from treatment. This analysis identified an important subgroup needing additional study at higher doses. 

Swedish regulators cite such population approaches as essential to extrapolate effectiveness and safety to unstudied subpopulations.

  In addition, they note that physiologically-based PK models (PBPK) can help address regulatory questions about potential influences of subject characteristics, concomitant disease, or drug/drug interactions on drug profile, including safety concerns.

  The FDA pharmacometric group has published information about best practices for such analyses.

In US regulatory submissions, population PK analysis is expected for cases where subpopulations are likely to require altered dosing, the patient population is expected to be highly variable, or where the therapeutic window for exposure-response is narrow.

  Similar methods apply for population PD analyses. The FDA Guidance, Population Pharmacokinetics

  provides recommendations on when to use the methods, and how to use the results in estimating and reporting PK parameters and setting dosage adjustments. 

 Rare diseases or pediatric indications that have small study populations present additional challenges. For example, as much as 50% of pediatric trials fail to show effectiveness.

  Pharmacometrics provides tools that are uniquely able to improve understanding of drug actions based on small studies.  

In submissions for pediatric indications, exposure-response modeling can provide essential evidence of safety and effectiveness even with limited data. PK data alone can support pediatric approval and labeling if it can be clearly demonstrated that disease progression and exposure-response relationships are similar between pediatric and adult patients.

  For example, pediatric dosing for the antimicrobial Zosyn relied on matching drug exposure in children with effective levels in adults, with careful adjustments for differences in drug clearance in younger children.

  In a similar fashion, pediatric dosing adjustments can be estimated by comparing pediatric and adult exposure-response models based on either outcomes or biomarkers. Diseases that progress differently in children compared to adults generally require further clinical study. 

To better account for biological changes, there has been a return to use of PBPK models to improve development efficiency in difficult to study populations, especially in pediatrics. For example, one study used sparse PK measurements in children, combined with known in vitro attributes of the drug and developmental changes in physiology, to model drug exposure in critically-ill children.

  By changing model assumptions and simulating exposure levels, the sponsor was able to explore potential physiological mechanisms underlying altered drug exposure in critically-ill children, and to identify differences in complex drug-drug interactions in pediatric, compared to adult patients, paving the way for improved pediatric dosing.   

EU guidance addresses in general terms the need to use the best available techniques, including PK/PD modeling, in cases where data are scarce to make risk-benefit analysis possible.

  The guidance encourages careful use of Bayesian approaches, combining a reasonable variety of prior distributions with study data. In European Pediatric Investigation Plan applications, modeling is encouraged to support choice of dose.

  European regulators cite a need to elucidate pediatric exposure/response, to determine whether it is appropriate to aim for the same target exposure as in adults. Population modeling is recommended for products with sparse pediatric data, possibly combined with simulations to predict exposure in children. EMA regulators cite this as an area of change in regulatory thinking, and identify a need to expand the use of pharmacometrics in pediatrics toward PD as well as PK to address safety issues, concomitant disease effects, and provide quality mechanistic models to further research.

FDA regulators have gone so far as to identify pharmacometric methods as not only useful, but necessary, in order to account for maturational changes in physiology when scaling dosing from adults to children and designing effective pediatric trials.

  The FDA also encourages the use of quantitative approaches to plan dose selection, justify sample size, and evaluate endpoints for better chances of trial success in pediatrics.

  Trial design reviews for pediatric and late-phase trials in the FDA employ these methods. FDA pharmacometricians review pediatric trial designs for sufficient number of study subjects and an informed trial design, based on prior exposure-response knowledge in adults and/or children, often as tested in clinical trial simulations. Agency scientists encourage collection of data to enable exposure/response analysis for maturational processes. 

 Pharmacometric analyses can enable earlier and clearer alignment with regulators on development plans, trial designs, and submissions. As many of the preceding examples illustrate, a pharmacometric knowledge base provides a valuable platform to communicate and explore a drug profile with regulators across phases of development, particularly in planning for Phase III and crossing into new indications or dose scenarios. 

End-of-Phase-IIa meetings with the FDA provide an important opportunity to ensure alignment with regulators on dose selection and design for pivotal trials,

  helping to avoid surprises further down the line. The FDA looks to the sponsor's exposure-response models and clinical trial simulations to demonstrate understanding of the drug, and potential sources of variability in subject response as trials move from volunteers to patients and real-world use. The agency may also conduct some modeling in-house to explore specific challenges in the development program, or to validate the sponsor's models. 

The FDA guidance on end-of-Phase-IIa meetings "encourages sponsors to use all prior knowledge (including data and analyses, quantification of disease variability, subgroup heterogeneity, and dose (concentration)­—response models in the development of computer simulations) to make more informed drug development decisions on trial design and dosage regimen selection."

Pharmacometric dose-response studies and analyses can also provide primary evidence of effectiveness for drug registration

  and support approval in unstudied—but well understood—situations. For example, a new antiretroviral gained approval for use in an unstudied three-drug combination on the strength of prior PK data and trials in another indication.

  The new drug was known to produce decreased exposures when given in the three-drug combination studied in registration trials. Early-phase trials and mechanistic models anticipated up to 85% higher exposure when dosing the drug alone, or in combination with other therapies. Evidence of an adequate safety profile was established by evaluating safety outcomes for subjects with the highest Phase III exposure, which modeling indicated would be comparable to that with the non-studied, three-drug drug combination. On that basis, the drug was approved and labeled for use in the non-studied combination. Similarly, modeling based on PK data from three failed Phase III trials provided sufficient dose-exposure-response information to reverse an FDA recommendation for an additional trial. The drug was approved at a lower dosage based on a single trial in a related, but different, indication.

Integrating pharmacometrics into your  development program

Pharmacometric models and simulation results tell a nuanced story about a drug candidate. With increasing frequency, drug sponsors and regulators look to that story to evaluate development plans, dosing recommendations, and target indications, and to make projections to situations that haven't yet or can't be studied in clinical trials.  

Regulators project that their use of pharmacometric information in advice, review, and approvals will continue to increase.

  Areas of particular growth may include physiological modeling to predict exposure with renal impairment or drug-drug interactions, and especially pediatric exposure, based on in vitro data. 

Taken all together, the set of pharmacometric assets for a development program supports strategic, quantitative decision making. The models and simulations serve as a basis for communicating drug knowledge across functional areas and with regulators, across all phases of development—but with particularly important impact bridging Phase II and Phase III. When integrated early into a development program, pharmacometric methods can speed development, manage risks, and provide a quantitative basis for even the most complex development decisions. 

is a Scientific Writer and Contractor to Certara USA, Inc., 9666 Olive Blvd., Suite 425, St. Louis, MO, e-mail: 

(1)  C. E. Garnett, J. Y. Lee, and J. V. S. Gobburu, "Chapter 3: Contribution of Modeling and Simulation in the Regulatory Review and Decision-Making: U.S. FDA Perspective" 

Clinical Trial Simulations: Applications and Trends, Volume 1,

 H. Kimko and C. Peck eds, (AAPS, Publisher Arlington, VA, 2011), 37-57. 

Pharmacometrics optimizes the use of Phase II data to support Phase III success.

Modeling Successful Phase III Trials

Clinical research is becoming increasingly complicated and fraught with risks. In this climate, the European Forum for Good Clinical Practice (EFGCP) has a vital role to play in bringing together all stakeholders to work out how to discharge their responsibilities in the framework of the clinical trial protocol for the protection of each consenting participant, according to two senior EFGCP officials. 

Identifying problems and finding solutions by ensuring collaboration between all stakeholder groups in working parties remains essential to the group's working model, particularly in the important fields of ethics, education, audits, and pediatric and geriatric trials, noted Jean-Pierre Tassignon, MD, former Chairman of the EFGCP, and Board Member Olga Kubar. 

EFGCP celebrates its 20th anniversary this year, and remains a not-for-profit think tank open to anybody who wishes to debate in a multidisciplinary mode real-life issues at the interface between science and ethics concerning the practice of clinical research, they wrote in the Summer issue of 

"By and large, it (clinical research) is practiced for the advancement of medicine and in the public interest," they stated. "It is within the EFGCP structure that multi-stakeholder consensus ripens and is pragmatically formulated. It is vital to continue to reach out to partner organizations outside the EFGCP and to open up the discussion to as many stakeholders as possible. A successful example is the ongoing collaboration with DIA [Drug Information Association] in organizing the Annual Pediatric Conference." 

Tassignon and Kubar also looked back at the early days of the forum. Representatives from 25 organizations involved in clinical research in Europe met at a hotel at Brussels airport in November 1991. This group included sponsors, CROs, IT companies, the European Ethics Review Committee (EERC), the UK Medical Research Council, and the Wellcome Foundation. A foundation working group was set up under the chairmanship of Alfred Pauls, Head of Information Systems at Schering AG. He is now senior consultant at the Berlin Center for Epidemiology and Health Research (ZEG Berlin). 

The EFGCP held its constitutional assembly in May 1993, hosted by the European Organization for Research and Treatment of Cancer. Two key people were Josef Hoet, Vice Chairman of the EERC, the first multinational ethics committee in Europe, and Tassignon, Executive Vice President of the largest clinical CRO in Europe at the time: G.H. Besselaar Associates, Inc., Princeton, New Jersey, now part of Covance, Inc. Both men were based in Brussels, and they started brainstorming about the need for a neutral place where professionals involved in clinical trials, including those from the regulatory authorities and patient associations, could meet and debate how to translate moral, scientific, and technical responsibilities into daily practice in different countries. 

The first annual conference took place later in 1993, and the EFGCP has now organized over 150 open, multidisciplinary meetings in Europe and beyond. It has published many position papers, and the ethics, education, and audit working parties, which were created in 1993, remain active. The elaboration of a systematic approach to training of ethics committee members by the ethics working party has been ongoing since 2007. More recently, the EFGCP-EGAN (European Genetics Alliance Network) co-led Patients' Roadmap to Treatment Working Party reflects the absolute necessity of including patient representatives in a very close partnership with clinical research professionals, according to Tassignon and Kubar. 

Tassignon, who is President of Crossover Clinical Research Infrastructures AG, was succeeded as chairman in 2010 by Ingrid Klingmann, MD, President of Pharmaplex bvba. Attention is now focusing on the EFGCP's next annual conference, "Benefits and Risks of Research: How Do We Redress the Current Imbalance?"—to be held on January 28-29 in Brussels. 

"Without evidence, risks are exaggerated and the benefits played down. Medical and drug development is undermined, researchers are cautious, monitors are without clear and appropriate objectives, and as importantly potential research participants ill- or un-informed. We all lose," noted the organizers. "There is therefore an urgent need for an accurate assessment of this balance and a coordinated approach to resolve these problems." 

The event will explore the consequences of research, and look at how to achieve a fair balance that promotes improvements in healthcare and new medicines without diminishing protection of the research participant. It will also address the current proposal for an EU clinical trial regulation and its consequences. 

The European Forum for Good Clinical Practice has a vital role to play in bringing together all stakeholders to work out how to discharge their responsibilities in the framework of the clinical trial protocol for the protection of each consenting participant

GCP's Future: Looking Ahead to the Next 20 Years

What might be the determining phase of discussions on new clinical trials rules for Europe is starting this autumn. 

Talks are scheduled to begin November between the three European Union institutions that must reach a final view on the proposed new rules. This is the stage of EU legislation when members of the European Parliament slug it out with the Council of Ministers­—which consists of national governments—under the watchful gaze of the European Commission, which drafted the initial proposal.  

Compromise is supposed to be the name of the game—and there are plenty of areas where compromise is going to be needed. Divergences of view exist on everything from data transparency to the role of ethics committees, from time-frames for decisions to the requirements for informed consent, and from sponsors' responsibilities to insurance and indemnification.  

But on some of the issues—and notably on data transparency—the positions are so far apart that compromise seems difficult, if not downright unlikely. Add to that the urgency forced onto the talks by a tight legislative calendar, and you have all the ingredients for an explosive rather than an emollient negotiation. 

The urgency springs from the parliament's own timetable. There are now barely six months left in the current EU legislature. The current European Parliament dissolves in April 2014, in time for the May election of a new batch of 751 MEPs for the next five years. Any unfinished legislation may simply lapse. If the talks go well, there is the prospect of a deal without the need for a second reading by the parliament. That could allow the legislation to be signed off—in the EU's elephantine procedures—by everyone by March, just in time to squeeze through before the limbo imposed by elections. But there is little slack in the timetable to accommodate any delay, and without a strong spirit of compromise a second reading could be demanded by the parliament, almost certainly condemning the proposal to death. 

One of the potentially explosive issues is data transparency, and on the eve of the talks, the battle-lines were clearly drawn. On one side was the parliament, whose health committee has unanimously agreed that "in general the data included in clinical-trial study reports should not be considered commercially confidential once a marketing authorization has been granted or the decision-making process on an application for marketing authorization has been completed." 

The amendments sought by the committee go much further than the commission's initial proposal, and it remains to be seen how willing the commission will be to accept any of the changes sought. 

Within the council too, although no public position has been taken yet, there are many national voices reluctant to go as far as the parliament wishes on access to clinical trials data. 

The confrontation is being stoked by advocates from both sides. The European consumers association, BEUC, has urged member states to "stand for transparency and further improve the requirements for the publication of clinical trials results," and the European Patients Forum says it "strongly supports more effective data sharing from clinical trials to enable researchers to revisit and reanalyze clinical trials data." 

But the research-based industry is ferociously opposed to the enthusiasms of the transparency zealots. The European Federation of Pharmaceutical Industries and Associations has attacked "the unbalanced approach adopted by the committee on the issue of confidentiality of clinical data." It has warned that if the committee's view prevails, it "will jeopardize patient privacy, the integrity of regulatory systems, and incentives for investment in biomedical research in Europe." 

The parliament's position is clear, because it results from open public debate. The position of the council is, as ever, much more opaque. National officials have held some 20 meetings so far to examine the proposal—but their meetings take place behind closed doors, and diplomatic reticence and discretion about the proceedings is the rule. 

However, among the hints that have emerged, Poland believes the proposal already goes too far in arrogating what should be done at European level, at the cost of national sovereignty—even before the parliament's more buccaneering approach is taken into account. 

The time limits envisaged for authorization and validation of clinical trials are still too short, say many national governments, who fear they will not be able to cope. There are particular concerns at the provision that trials should receive tacit approval if time limits are not complied with. 

Some national governments are still holding out for an explicit reference in the proposal to the role of ethics committees in the ethical evaluation of clinical trials. And where the proposal deals with the protection of subjects and informed consent, some countries want an explicit reference to national laws on the protection of volunteers. There are also rival schools of thought among member states about the correct approach to the conduct of clinical trials in emergency situations.  

Plans to oblige member states to set up a national indemnification scheme for damage compensation in clinical trials have also come under attack in some countries. The whole question of damage compensation, insurance, and indemnification has raised further concerns in some countries about how these rules should apply to damage that might be suffered by subjects in low-intervention trials.  

Even the concept of a centralized EU portal through which clinical trial applications will be made—widely welcomed by national governments—has prompted questions about how fast this portal will function and how much it will cost, as well as who will pay for it. There is some resentment among member states at the suggestion the commission makes that its own inspections and controls should be conducted on national systems to audit their efficiency. 

One member state which has been less bashful about its position is the United Kingdom. Although it still maintains a "scrutiny reserve" on the proposal (which in effect means that it considers nothing agreed until everything is agreed), it has indicated strong support for the overall concept of the proposed new rules. 

This frank admission emerges from an unlikely source—the recently-published "review of the balance of competences" documents from the British government. This bizarrely-titled exercise is part of the wider UK discussion of whether to stay in the European Union or not. The foreign affairs ministry commissioned an extensive public consultation into what the European Union should be responsible for, and what should be decided at the national level. 

One of the chapters explored health, and the clinical trials rules featured prominently in this. The government's conclusions about EU intervention are not universally favorable, but on this particular issue, the European Union's bid to bring in new rules is hailed as very welcome.  

The 2001 directive that the European Union is aiming to replace is the object of repeated criticism in the UK government paper: "Certain directives have had adverse consequences for the United Kingdom, such as the clinical trials directive," it says. The directive "was having a negative impact on the attractiveness of the European Union as a place to conduct clinical trials." It increased bureaucracy and suffered from inconsistent implementation across member states, with some countries imposing additional requirements due to different national approaches or the lack of detailed guidance. This "made it difficult to conduct cross-border trials and expensive to get approval for a clinical trial in the European Union." As a result, "many researchers have started conducting research outside of the European Union," according to the government's analysis of the current problems. 

Now, the United Kingdom gratefully says, "We recognize the positive steps that have been taken to resolve this particular issue." Following feedback from the UK government, "the recent proposal for a clinical trials regulation to replace the current directive is an excellent example." It could harmonize the currently divergent requirements across the European Union, and ensure consistent implementation across member states. This is because it is couched as a regulation rather than just a directive: a regulation applies directly in national law, removing the scope for member states to transpose the rules into national legislation, and removing the risk of differing interpretations.  

"It is the government's view that it is important that steps are taken across the whole of Europe to harmonize the research environment, remove barriers to transnational research, and ensure quality of conduct and protection of participants in clinical trials and studies," it says. "The new regulation will decrease the burden on researchers whilst patients are protected appropriately. As a result, it will become less burdensome and easier to conduct multi-state trials across the European Union." 

				This, it says, "is an example of the fact that mistakes can be corrected effectively at the European level." And, with a small self-congratulatory pat on the back, it adds that "many of the proposed improvements in the commission proposal reflect UK practice." 

So it can be safely assumed that at least one member state is going to be rooting for an effective compromise as the talks get underway. Whether the views of that particular—and frequently troublesome—member state will help ensure a successful crunch will emerge only later in the autumn. 

is a freelance journalist who specializes in European health affairs and is based in Brussels, Belgium. 

View from Brussels

Compromise is going to play a key role as the clinical trial rules update debate rages on.

Crunch-Time for Europe's Clinical Trials Rules

It is said that the world is getting smaller. A notion that is due to the on-going advances in technology; not just in capability but also in accessibility. Statistics show that there are now over six billion mobile phone subscriptions world-wide and it is this availability and familiarity with technology that has led to an opportunity for the use of a patient's own device in global clinical trials. In turn, this could reduce the need for costly device provision; a model known as: bring your own device (BYOD). 

The rise of BYOD has been remarkable. Some 53% of organizations now let users connect their device to a corporate network. I find this number staggering when you consider the implications of letting employees store confidential information on their personal smartphones, laptop computers, or tablets, and this concern for personally identifiable information is one reason why many working in the healthcare sector believed that BYOD would never transfer successfully to the clinical trial arena when trying to engage patients and capture their data. They have, however, been proved wrong. 

In the past, ePRO data has been captured by provisioning devices, using fixed parameters for data viewing and validation. This has historically brought its own restrictions requiring specific devices regardless of the user population. The use of BYOD or device independent technology has opened up the possibilities of provisioning through the ability to recognize device parameters and optimize the configuration of data according to the device in use. This offers the flexibility to select the provisioned device according to specific populations, as well as ensuring instruments are displayed within validation requirements. Personally identifiable information can be extracted so that specific health information is not linked to an identifiable individual unless the viewer, for example the HCP, has authorization. It also enables physical separation of data in order to comply with local data protection laws. 

Additionally, a device independent approach allows the flexibility to select and provide the most appropriate device for a patient demographic where provisioned devices are preferred. However, the ability to run device diagnostics and identify the specific device in use by each patient opens up the opportunity to significantly reduce and even remove the need for provisioning entirely. Device analysis can easily be integrated into patient enrollment and provisioning can typically be reduced to as little as 10% or 20% of study participants. In some cases the need for provisioning can be removed altogether and the participants' own devices can be employed throughout the study.  

BYOD in clinical trials isn't a concept of the future; it's happening now. For example in large population, multi-country late phase studies the BYOD model has enabled cost-effective deployment of electronic patient communication and data capture. Global cardiovascular outcomes studies have seen an emergence of BYOD, while other studies using a part provisioning model can check a device, then only provision locally when necessary—saving time and money.  

By reducing the cost of full-scale provisioning and significantly reducing the logistical challenge of managing and maintaining large quantities of devices across the globe, a device independent approach delivers an extremely cost effective patient-centric strategy for sponsors, CROs and payers, that both enhances patient engagement and addresses data capture requirements. The train has left the station as far as BYOD use in clinical trials is concerned, and it is for these reasons that device independence makes sense economically and socially and is the future of clinical trials. 

 CEO and Co-Founder  Exco InTouch E-mail: 

Bring Your Own Device (BYOD) makes sense economically and socially and is the future of clinical trials.

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GCP's Future: Looking Ahead to the Next 20 Years

Crunch-Time for Europe's Clinical Trials Rules

Device Independence is the Future of Clinical Trials

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Applied Clinical Trials Digital Edition - November 2013