The value, qualification, and regulatory use of surrogate end points in drug development

Study protocol: A comparison of mobile and clinic-based spirometry for capturing the treatment effect in moderate asthma

Several inefficiencies in drug development trial implementation may be improved by moving data collection from the clinic to mobile, allowing for more frequent measurements and therefore increased statistical power while aligning to a patient-centric approach to trial design. Sensor-based digital health technologies such as mobile spirometry (mSpirometry) are comparable to clinic spirometry for capturing outcomes, such as forced expiratory volume in 1 s (FEV1); however, the impact of remote spirometry measurements on the detection of treatment effect has not been investigated. A protocol for a multicenter, single-arm, open-label interventional trial of long-acting beta agonist (LABA) therapy among 60 participants with uncontrolled moderate asthma is described. Participants will complete twice-daily mSpirometry at home and clinic spirometry during weekly visits, alongside continuous use of a wrist-worn wearable and regular completion of several diaries capturing asthma symptoms as well as participant- and site-reported satisfaction and ease of use of mSpirometry. The co-primary objectives of this study are (A) to quantify the treatment effect of LABA therapy among participants with moderate asthma, using both clinical spirometry (FEV1c ) and mSpirometry (FEV1m ); and (B) to investigate whether FEV1m is as accurate as FEV1c in detecting the treatment effect using a mixed-effect model for repeated measures. Study results will help inform whether the deployment of mSpirometry and a wrist-worn wearable for remote data collection are feasible in a multicenter setting among participants with moderate asthma, which may then be generalizable to other populations with respiratory disease.

Digital technologies: Innovations that transform the face of drug development

Recently, digital health technologies (DHTs) and digital biomarkers have gained a lot of traction in clinical investigations, motivating sponsors, investigators, and regulators to discuss and implement integrated approaches for deploying DHTs. These new tools present new and unique challenges for optimal technology integration in clinical trial processes, including operational, ethical, and regulatory issues. In this paper, we gathered different perspectives to discuss challenges and perspectives from three different stakeholders: industry, US regulators, and a public-private partnership consortium. The complexities of DHT implementation, which include regulatory definitions, defining the scope of validation experiments, and the need for partnerships between BioPharma and the technology sectors, are highlighted. Most of these challenges are related to translation of DHT-derived measures into endpoints that are meaningful to clinicians and patients, participant safety, training, and retention and privacy of data. The example of the Wearable Assessments in the Clinic and Home in PD (WATCH-PD) study is discussed as an example that demonstrated the advantages of pre-competitive collaborations, which include early regulatory feedback, data sharing, and multistakeholder alignment. Future advances in DHTs are expected to spur device-agnostic measured development and incorporate patient reported outcomes in drug development. More efforts are needed to define validation experiments for a defined context of use, incentivize data sharing and development of data standards. Multistakeholder collaborations via precompetitive consortia will help facilitate broad acceptance of DHT-enabled measures in drug development.

Digital health technology derived measures: Biomarkers or clinical outcome assessments?

Digital health technologies (DHTs) present unique opportunities for clinical evidence generation but pose certain challenges. These challenges stem, in part, from existing definitions of drug development tools, which were not created with DHT-derived measures in mind. DHT-derived measures can be leveraged as either clinical outcome assessments (COAs) or as biomarkers since they share properties with both categories of drug development tools. Examples from the literature indicate a variety of applications for DHT-derived data, including capturing disease physiology, symptom tracking, or response to therapies. The distinction between the categorization of DHT-derived measures as COAs or as biomarkers can be very fine, with terminology variability among regulatory authorities. This has significant implications for integration of DHT-derived measures in clinical trials, leading to confusion regarding the evidence required to support these tools' use in drug development. There is a need to amend definitions and create clear evidentiary requirements to support broad adoption of these new and innovative tools. The biopharma industry, the technology sector, consulting businesses, academic researchers, and regulators need a dialogue via multi-stakeholder collaborations to clarify questions around DHT-derived measures, to unify definitions, and to create the foundations for evidentiary package requirements, providing a path forward to predictable results.

Correlation between pathologic complete response, event-free survival/disease-free survival and overall survival in neoadjuvant and/or adjuvant HR+/HER2-breast cancer

Purpose

The study's purpose was to evaluate the correlation between overall survival (OS) and its potential surrogate endpoints: pathologic complete response (pCR) and event-free survival (EFS)/disease-free survival (DFS) in neoadjuvant and/or adjuvant HR+/HER2- breast cancer.

Methods

Systematic search was performed in MEDLINE, EMBASE, Cochrane Library databases and other relevant sources to identify literature that have reported outcomes of interest in the target setting. The strength of correlation of EFS/DFS with OS, pCR with OS, and pCR with EFS/DFS was measured using Pearson's correlation coefficient (r) based on weighted regression analysis. For Surrogate Endpoint-True Endpoint pairs where correlation was found to be moderate, surrogate threshold effect (STE) was estimated using a mixed-effects model. Sensitivity analyses were conducted on the scale and weights used and removing outlier data.

Results

Moderate correlation was observed of relative measures [log(HR)] of EFS/DFS and OS (r = 0.91; 95% CI: 0.83, 0.96, p < 0.0001). STE for HR_EFS/DFS was estimated to be 0.73. Association between EFS/DFS at 1, 2 and 3 years with OS at 4- and 5-year landmarks was moderate. Relative treatment effects of pCR and EFS/DFS were not strongly associated (r: 0.24; 95% CI: -0.63, 0.84, p = 0.6028). Correlation between pCR and OS was either not evaluated due to inadequate sample size (relative outcomes) or weak (absolute outcomes). Results obtained in the sensitivity analyses were similar to base scenario.

Conclusion

EFS/DFS were moderately correlated with OS in this trial-level analysis. They may be considered as valid surrogates for OS in HR+/HER2- breast cancer.

A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk

A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c-statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a "universal" surrogate end point for cardiovascular risk.

Biomarker qualification at the European Medicines Agency: a review of biomarker qualification procedures from 2008 to 2020

Regulatory qualification of biomarkers facilitates their harmonized use across drug developers, enabling more personalized medicine. This study reviews various aspects of the European Medicines Agency’s (EMA’s) biomarker qualification procedure, including frequency and outcome, common challenges, and biomarker characteristics. Our findings provide insights into the EMA’s biomarker qualification process and will thereby support future applications. All biomarker‐related “Qualification of Novel Methodologies for Medicine Development” procedures that started from 2008 to 2020 were included. Procedural data were extracted from relevant documents and analyzed descriptively. In total, 86 biomarker qualification procedures were identified, of which 13 resulted in qualified biomarkers. Whereas initially many biomarker qualification procedures were linked to a single company and specific drug development program, a shift was observed to qualification efforts by consortia. Most biomarkers were proposed (n = 45) and qualified (n = 9) for use in patient selection, stratification, and/or enrichment, followed by efficacy biomarkers (37 proposed, 4 qualified). Overall, many issues were raised during qualification procedures, mostly related to biomarker properties and assay validation (in 79% and 77% of all procedures, respectively). Issues related to the proposed context of use and rationale were least common yet were still raised in 54% of all procedures. While few qualified biomarkers are currently available, procedures focus increasingly on biomarkers for general use instead of those linked to specific drug compounds. The issues raised during qualification procedures illustrate the thorough discussions taking place between applicants and regulators—highlighting aspects that need careful consideration and underlining the importance of an appropriate validation strategy.

Biomarkers and Surrogate End points in Multiple Sclerosis Trials: Regulatory Issues

SUMMARY

Evoked potentials have assisted in the diagnosis of multiple sclerosis for years, but the potential to demonstrate pathophysiologic change has prompted a reconsideration of their potential role as outcome measures in clinical trials of multiple sclerosis. The use of any surrogate end point or biomarker in clinical trials requires a thorough understanding of that end point's performance characteristics and utility in a particular setting. This article explores regulatory issues regarding the use of biomarkers and surrogate end points in clinical trials of multiple sclerosis with particular emphasis on challenges faced by evoked potential studies.

Linked Pharmacometric-Pharmacoeconomic Modeling and Simulation in Clinical Drug Development

Market access and pricing of pharmaceuticals are increasingly contingent on the ability to demonstrate comparative effectiveness and cost-effectiveness. As such, it is widely recognized that predictions of the economic potential of drug candidates in development could inform decisions across the product life cycle. This may be challenging when safety and efficacy profiles in terms of the relevant clinical outcomes are unknown or highly uncertain early in product development. Linking pharmacometrics and pharmacoeconomics, such that outputs from pharmacometric models serve as inputs to pharmacoeconomic models, may provide a framework for extrapolating from early-phase studies to predict economic outcomes and characterize decision uncertainty. This article reviews the published studies that have implemented this methodology and used simulation to inform drug development decisions and/or to optimize the use of drug treatments. Some of the key practical issues involved in linking pharmacometrics and pharmacoeconomics, including the choice of final outcome measures, methods of incorporating evidence on comparator treatments, approaches to handling multiple intermediate end points, approaches to quantifying uncertainty, and issues of model validation are also discussed. Finally, we have considered the potential barriers that may have limited the adoption of this methodology and suggest that closer alignment between the disciplines of clinical pharmacology, pharmacometrics, and pharmacoeconomics, may help to realize the potential benefits associated with linked pharmacometric-pharmacoeconomic modeling and simulation.

Biomarkers and Surrogate Endpoints: Lessons Learned From Glaucoma

With the recent progress in imaging technologies for assessment of structural damage in glaucoma, a debate has emerged on whether these measurements can be used as valid surrogate endpoints in clinical trials evaluating new therapies for the disease. A discussion of surrogates should be grounded on knowledge acquired from their use in other areas of medicine as well as regulatory requirements. This article reviews the conditions for valid surrogacy in the context of glaucoma clinical trials and critically evaluates the role of biomarkers such as IOP and imaging measurements as potential surrogates for clinically relevant outcomes. Valid surrogate endpoints must be able to predict a clinically relevant endpoint, such as loss of vision or decrease in quality of life. In addition, the effect of a proposed treatment on the surrogate must capture the effect of the treatment on the clinically relevant endpoint. Despite its widespread use in clinical trials, no proper validation of IOP as a surrogate endpoint has yet been conducted for any class of IOP-lowering treatments. Although strong evidence has accumulated about imaging measurements as predictors of relevant functional outcomes in glaucoma, there is still insufficient evidence to support their use as valid surrogate endpoints. However, imaging biomarkers could potentially be used as part of composite endpoints in glaucoma trials, overcoming weaknesses of the use of structural or functional endpoints in isolation. Efforts should be taken to properly design and conduct studies that can provide proper validation of potential biomarkers in glaucoma clinical trials.

Time to Review the Role of Surrogate End Points in Health Policy: State of the Art and the Way Forward

The efficacy of medicines, medical devices, and other health technologies should be proved in trials that assess final patient-relevant outcomes such as survival or morbidity. Market access and coverage decisions are, however, often based on surrogate end points, biomarkers, or intermediate end points, which aim to substitute and predict patient-relevant outcomes that are unavailable because of methodological, financial, or practical constraints. We provide a summary of the present use of surrogate end points in health care policy, discussing the case for and against their adoption and reviewing validation methods. We introduce a three-step framework for policymakers to handle surrogates, which involves establishing the level of evidence, assessing the strength of the association, and quantifying relations between surrogates and final outcomes. Although the use of surrogates can be problematic, they can, when selected and validated appropriately, offer important opportunities for more efficient clinical trials and faster access to new health technologies that benefit patients and health care systems.

Optimization of human dose prediction by using quantitative and translational pharmacology in drug discovery

In this perspective article, we explain how quantitative and translational pharmacology, when well-implemented, is believed to lead to improved clinical candidates and drug targets that are differentiated from current treatment options. Quantitative and translational pharmacology aims to build and continuously improve the quantitative relationship between drug exposure, target engagement, efficacy, safety and its interspecies relationship at every phase of drug discovery. Drug hunters should consider and apply these concepts to develop compounds with a higher probability of interrogating the clinical biological hypothesis. We offer different approaches to set an initial effective concentration or pharmacokinetic-pharmacodynamic target in man and to predict human pharmacokinetics that determine together the predicted human dose and dose schedule. All concepts are illustrated with ample literature examples.

Do firms underinvest in long-term research? Evidence from cancer clinical trials

We investigate whether private research investments are distorted away from long-term projects. Our theoretical model highlights two potential sources of this distortion: short-termism and the fixed patent term. Our empirical context is cancer research, where clinical trials - and hence, project durations - are shorter for late-stage cancer treatments relative to early-stage treatments or cancer prevention. Using newly constructed data, we document several sources of evidence that together show private research investments are distorted away from long-term projects. The value of life-years at stake appears large. We analyze three potential policy responses: surrogate (non-mortality) clinicaltrial endpoints, targeted R&D subsidies, and patent design.

Biomarker Qualification: Toward a Multiple Stakeholder Framework for Biomarker Development, Regulatory Acceptance, and Utilization

The discovery, development, and use of biomarkers for a variety of drug development purposes are areas of tremendous interest and need. Biomarkers can become accepted for use through submission of biomarker data during the drug approval process. Another emerging pathway for acceptance of biomarkers is via the biomarker qualification program developed by the Center for Drug Evaluation and Research (CDER, US Food and Drug Administration). Evidentiary standards are needed to develop and evaluate various types of biomarkers for their intended use and multiple stakeholders, including academia, industry, government, and consortia must work together to help develop this evidence. The article describes various types of biomarkers that can be useful in drug development and evidentiary considerations that are important for qualification. A path forward for coordinating efforts to identify and explore needed biomarkers is proposed for consideration.

Observations on Three Endpoint Properties and Their Relationship to Regulatory Outcomes of European Oncology Marketing Applications

BACKGROUND

Guidance and exploratory evidence indicate that the type of endpoints and the magnitude of their outcome can define a therapy's clinical activity; however, little empirical evidence relates specific endpoint properties with regulatory outcomes.

MATERIALS AND METHODS

We explored the relationship of 3 endpoint properties to regulatory outcomes by assessing 50 oncology marketing authorization applications (MAAs; reviewed from 2009 to 2013).

RESULTS

Overall, 16 (32%) had a negative outcome. The most commonly used hard endpoints were overall survival (OS) and the duration of response or stable disease. OS was a component of 91% approved and 63% failed MAAs. The most commonly used surrogate endpoints were progression-free survival (PFS), response rate, and health-related quality of life assessments. There was no difference (p = .3801) between the approved and failed MAA cohorts in the proportion of hard endpoints used. A mean of slightly more than four surrogate endpoints were used per approved MAA compared with slightly more than two for failed MAAs. Longer OS and PFS duration outcomes were generally associated with approvals, often when not statistically significant. The approved cohort was associated with a preponderance of statistically significant (p < .05) improvements in primary endpoints (p < .0001 difference between the approved and failed groups).

CONCLUSION

Three key endpoint properties (type of endpoint [hard/surrogate], magnitude of an endpoint outcome, and its statistical significance) are consistent with the European Medicines Agency guidance and, notwithstanding the contribution of unique disease-specific circumstances, are associated with a predictable positive outcome for oncology MAAs.

IMPLICATIONS FOR PRACTICE

Regulatory decisions made by the European Medicines Agency determine which new medicines will be available to European prescribers and for which therapeutic indications. Regulatory success or failure can be influenced by many factors. This study assessed three key properties of endpoints used in preauthorization trials (type of endpoint [hard/surrogate], magnitude of endpoint outcome, and its statistical significance) and whether they are associated with a positive regulatory outcome. Clinicians can use these properties, which are described in the publicly available European public assessment reports, to help guide their understanding of the clinical effect of new oncologic therapies.

Implications of the Institute of Medicine Report: Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease

The Institute of Medicine (IOM) released a groundbreaking 2010 report, Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease. Key recommendations included a harmonized scientific process and a general framework for biomarker evaluation with three interrelated steps: (1) Analytical validation -- is the biomarker measurement accurate? (2) Qualification -- is the biomarker associated with the clinical endpoint of concern? (3) Utilization -- what is the specific context of the proposed use?

Functional brain imaging in gastroenterology: to new beginnings

With more than 100 studies published over the past two decades, functional brain imaging research in gastroenterology has become an established field; one that has enabled improved insight into the supraspinal responses evoked by gastrointestinal stimulation both in health and disease. However, there remains considerable inter-study variation in the published results, largely owing to methodological differences in stimulation and recording techniques, heterogeneous patient selection, lack of control for psychological factors and so on. These issues with reproducibility, although not unique to studies of the gastrointestinal tract, can lead to unjustified inferences. To obtain consistent and more clinically relevant results, there is a need to optimize and standardize brain imaging studies across different centres. In addition, the use of complementary and more novel brain imaging modalities and analyses, which are now being used in other fields of research, might help unravel the factors at play in functional gastrointestinal disorders. This Review highlights the areas in which functional brain imaging has been useful and what it has revealed, the areas that are in need of improvement, and finally suggestions for future directions.

A novel knowledge representation framework for the statistical validation of quantitative imaging biomarkers

Quantitative imaging biomarkers are of particular interest in drug development for their potential to accelerate the drug development pipeline. The lack of consensus methods and carefully characterized performance hampers the widespread availability of these quantitative measures. A framework to support collaborative work on quantitative imaging biomarkers would entail advanced statistical techniques, the development of controlled vocabularies, and a service-oriented architecture for processing large image archives. Until now, this framework has not been developed. With the availability of tools for automatic ontology-based annotation of datasets, coupled with image archives, and a means for batch selection and processing of image and clinical data, imaging will go through a similar increase in capability analogous to what advanced genetic profiling techniques have brought to molecular biology. We report on our current progress on developing an informatics infrastructure to store, query, and retrieve imaging biomarker data across a wide range of resources in a semantically meaningful way that facilitates the collaborative development and validation of potential imaging biomarkers by many stakeholders. Specifically, we describe the semantic components of our system, QI-Bench, that are used to specify and support experimental activities for statistical validation in quantitative imaging.

Monitoring drug therapy

Drug development has improved over recent decades, with refinements in analytical techniques, population pharmacokinetic-pharmacodynamic (PK-PD) modelling and simulation, and new biomarkers of efficacy and tolerability. Yet this progress has not yielded improvements in individualization of treatment and monitoring, owing to various obstacles: monitoring is complex and demanding, many monitoring procedures have been instituted without critical assessment of the underlying evidence and rationale, controlled clinical trials are sparse, monitoring procedures are poorly validated and both drug manufacturers and regulatory authorities take insufficient account of the importance of monitoring. Drug concentration and effect data should be increasingly collected, analyzed, aggregated and disseminated in forms suitable for prescribers, along with efficient monitoring tools and evidence-based recommendations regarding their best use. PK-PD observations should be collected for both novel and established critical drugs and applied to observational data, in order to establish whether monitoring would be suitable. Methods for aggregating PK-PD data in systematic reviews should be devised. Observational and intervention studies to evaluate monitoring procedures are needed. Miniaturized monitoring tests for delivery at the point of care should be developed and harnessed to closed-loop regulated drug delivery systems. Intelligent devices would enable unprecedented precision in the application of critical treatments, i.e. those with life-saving efficacy, narrow therapeutic margins and high interpatient variability. Pharmaceutical companies, regulatory agencies and academic clinical pharmacologists share the responsibility of leading such developments, in order to ensure that patients obtain the greatest benefit and suffer the least harm from their medicines.

Harnessing pain heterogeneity and RNA transcriptome to identify blood-based pain biomarkers: a novel correlational study design and bioinformatics approach in a graded chronic constriction injury model

A quantitative, peripherally accessible biomarker for neuropathic pain has great potential to improve clinical outcomes. Based on the premise that peripheral and central immunity contribute to neuropathic pain mechanisms, we hypothesized that biomarkers could be identified from the whole blood of adult male rats, by integrating graded chronic constriction injury (CCI), ipsilateral lumbar dorsal quadrant (iLDQ) and whole blood transcriptomes, and pathway analysis with pain behavior. Correlational bioinformatics identified a range of putative biomarker genes for allodynia intensity, many encoding for proteins with a recognized role in immune/nociceptive mechanisms. A selection of these genes was validated in a separate replication study. Pathway analysis of the iLDQ transcriptome identified Fcγ and Fcε signaling pathways, among others. This study is the first to employ the whole blood transcriptome to identify pain biomarker panels. The novel correlational bioinformatics, developed here, selected such putative biomarkers based on a correlation with pain behavior and formation of signaling pathways with iLDQ genes. Future studies may demonstrate the predictive ability of these biomarker genes across other models and additional variables.

Bridging the gap: moving predictive and prognostic assays from research to clinical use

The development of clinically useful molecular diagnostics requires validation of clinical assay performance and achievement of clinical qualification in clinical trials. As discussed elsewhere in this Focus section on molecular diagnostics, validation of assay performance must be rigorous, especially when the assay will be used to guide treatment decisions. Here we review some of the problems associated with assay development, especially for academic investigators. These include lack of expertise and resources for analytical validation, lack of experience in designing projects for a specific clinical use, lack of specimens from appropriate patient groups, and lack of access to Clinical Laboratory Improvement Amendments-certified laboratories. In addition, financial support for assay validation has lagged behind financial support for marker discovery or drug development, even though the molecular diagnostic may be considered necessary for the successful use of the companion therapeutic. The National Cancer Institute supports a large number of clinical trials and a significant effort in drug development. In order to address some of these barriers for predictive and prognostic assays that will be used in clinical trials to select patients for a particular treatment, stratify patients into molecularly defined subgroups, or choose between treatments for molecularly defined tumors, the National Cancer Institute has begun a pilot program designed to lessen barriers to the development of validated prognostic and predictive assays.

The role of gene expression profiling in drug discovery

Monitoring gene expression through the dual approaches of transcriptomics (RNA profiling) and proteomics (protein profiling) has become a key component in our efforts to understand complex biological processes. From the molecular stratification of disease states and the selection of potential drug targets, to patient selection and the confirmation of engagement of pharmacology in clinical studies, we are seeing the impact of gene expression profiling across all phases of the drug discovery process. Ongoing technological advances have driven an expansion in the use of these techniques, demonstrated utility in preclinical and clinical settings and increased regulatory and clinical acceptance. As technologies continue to advance apace, gene expression profiling is likely to play an increasingly important role in the future development of drug discovery.

Randomized controlled trials in schizophrenia: opportunities, limitations, and trial design alternatives

State-of-the art clinical trial design and methodology are enormously important for the advancement of the field. In contrast, the critical relevance of trial conduct and implementation have only more recently been the focus of discussion and research. Although randomized controlled trials are generally considered the gold standard for the assessment of pharmacologic and nonpharmacologic interventions in medicine, trials are vulnerable to complications and influences that can seriously compromise their success, Like interventions, trial design and conduct are also contextual. They need to be individualized and adapted to a number of relevant variables, such as setting, population, illness phase, interventions, patient and rater expectations and biases, and the overall aims of the investigation. While this means that there is no unified approach possible, certain general principles and guidelines require careful consideration. Knowledge of basic solutions and alternatives, and the recognition of the complex challenges that need to be addressed proactively can help to minimize unwanted outcomes, including trial failure and uninformative or falsely negative outcomes. Moreover, novel design alternatives need to be explored that target sample enrichment according to the study question and enhancement of precision in the measurement of relevant outcomes. We propose two novel design strategies that take advantage of the recently validated early antipsychotic response paradigm (that has also been observed with antidepressants and mood stabilizers). In the “early responder randomized discontinuation design” all patients are assigned to the active drug, and only those who had at least a minimal response at 2 weeks are enrolled in a double-blind, placebo-controlled discontinuation trial, enriching the placebo controlled trial portion with true drug responders. In the mirror image “early nonresponder randomized dose increase or augmentation design,” early nonresponders at 2 weeks are assigned to staying on the medication or going either to a higher dose or an augmentation agent. It is hoped that through increased attention to the issues raised in this article and further refinement of trial methodology and conduct, the field will make much needed additional progress in the prevention and treatment of schizophrenia.

Biomarkers for the early stages of clinical development in Alzheimer's disease

As the failure of several recent Phase III drug development programmes bears witness, the clinical development of "disease-modifying" drugs in Alzheimer's disease has been confronted with challenging methodological difficulties. Taking into account the financial stakes involved taking drug candidates to the Phase III stage of development, and the risk of investing time and resources fruitlessly in the evaluation of poor candidate drugs, the crucial decision remains whether to proceed from Phase II to Phase III (Go/Nogo). The aim of Phase II studies is to select a molecule likely to be effective in Phase III, but also to eliminate candidate-drugs with an inadequate effect. No consensus currently exists on the best possible design of Phase II studies to inform the Go/Nogo decision optimally. The challenges in choosing the best study design relate to the target population, the end-point criteria used, in particular the use of biomarkers, the experimental protocol, and the study duration. The objective of the Round Table (RT) was to gather the opinions of French experts from the academic, industrial, and regulatory world in order to arrive at a consensus recommendation for the best possible design to be used in Phase II studies in Alzheimer's disease.

Recommending flavanols and procyanidins for cardiovascular health: current knowledge and future needs

Data on the potential health benefits of dietary flavanols and procyanidins, especially in the context of cardiovascular health, are considerable and continue to accumulate. Significant progress has been made in flavanol analytics and the creation of phytonutrient-content food databases, and novel data emanated from epidemiological investigations as well as dietary intervention studies. However, a comprehensive understanding of the pharmacological properties of flavanols and procyanidins, including their precise mechanisms of action in vivo, and a conclusive, consensus-based accreditation of a causal relationship between intake and health benefits in the context of primary and secondary cardiovascular disease prevention is still outstanding. Thus, the objective of this review is to identify and discuss key questions and gaps that will need to be addressed in order to conclusively demonstrate whether or not dietary flavanols and procyanidins have a role in preventing, delaying the onset of, or treating cardiovascular diseases, and thus improving human life expectancy and quality of life.

Past and present progress in the pharmacologic treatment of schizophrenia

Despite treatment advances over the past decades, schizophrenia remains one of the most severe psychiatric disorders that is associated with a chronic relapsing course and marked functional impairment in a substantial proportion of patients. In this article, a historical overview of the pharmacologic advances in the treatment of schizophrenia over the past 50 years is presented. This is followed by a review of the current developments in optimizing the treatment and outcomes in patients with schizophrenia. Methodological challenges, potential solutions, and areas of particular need for further research are highlighted. Although treatment goals of response, remission, and recovery have been defined more uniformly, a good "effectiveness" measure mapping onto functional outcomes is still lacking. Moreover, the field must advance in transferring measurement-based approaches from research to clinical practice. There is an ongoing debate regarding whether and which first- or second-generation antipsychotics should be used. However, especially when considering individual adverse effect profiles, the differentiation into first- and second-generation antipsychotics as unified classes cannot be upheld, and a more differentiated view and treatment selection are required. The desired, individualized treatment approach needs to consider current symptoms, comorbid conditions, past therapeutic response, and adverse effects, as well as patient choice and expectations. Acute and long-term goals and effects of medication treatment should be balanced. To date, clozapine is the only evidence-based treatment for refractory patients, and the role of antipsychotic polypharmacy and other augmentation strategies remains unclear, at best. To discover novel treatments with enhanced/broader efficacy and improved tolerability, and to enable personalized treatment, the mechanisms underlying illness development and progression, symptomatic improvement, and side effect development need to be elucidated.

Biomarkers and diagnostics for tuberculosis: progress, needs, and translation into practice

Human infection with Mycobacterium tuberculosis can progress to active disease, be contained as latent infection, or be eradicated by the host response. Tuberculosis diagnostics classify a patient into one of these categories. These are not fixed distinct states, but rather are continua along which patients can move, and are affected by HIV infection, immunosuppressive therapies, antituberculosis treatments, and other poorly understood factors. Tuberculosis biomarkers-host or pathogen-specific-provide prognostic information, either for individual patients or study cohorts, about these outcomes. Tuberculosis case detection remains difficult, partly because of inaccurate diagnostic methods. Investments have yielded some progress in development of new diagnostics, although the existing pipeline is limited for tests for sputum-smear-negative cases, childhood tuberculosis, and accurate prediction of reactivation of latent tuberculosis. Despite new, sensitive, automated molecular platforms for detection of tuberculosis and drug resistance, a simple, inexpensive point-of-care test is still not available. The effect of any new tests will depend on the method and extent of their introduction, the strength of the laboratories, and the degree to which access to appropriate therapy follows access to diagnosis. Translation of scientific progress in biomarkers and diagnostics into clinical and public health programmes is possible-with political commitment, increased funding, and engagement of all stakeholders.

Biomarkers and surrogate end points--the challenge of statistical validation

Biomarkers and surrogate end points have great potential for use in clinical oncology, but their statistical validation presents major challenges, and few biomarkers have been robustly confirmed. Provisional supportive data for prognostic biomarkers, which predict the likely outcome independently of treatment, is possible through small retrospective studies, but it has proved more difficult to achieve robust multi-site validation. Predictive biomarkers, which predict the likely response of patients to specific treatments, require more extensive data for validation, specifically large randomized clinical trials and meta-analysis. Surrogate end points are even more challenging to validate, and require data demonstrating both that the surrogate is prognostic for the true end point independently of treatment, and that the effect of treatment on the surrogate reliably predicts its effect on the true end point. In this Review, we discuss the nature of prognostic and predictive biomarkers and surrogate end points, and examine the statistical techniques and designs required for their validation. In cases where the statistical requirements for validation cannot be rigorously achieved, the biological plausibility of an end point or surrogate might support its adoption. No consensus yet exists on processes or standards for pragmatic evaluation and adoption of biomarkers and surrogate end points in the absence of robust statistical validation.

Proof of concept: a PhRMA position paper with recommendations for best practice

Proof of concept (POC) may be defined as the earliest point in the drug development process at which the weight of evidence suggests that it is "reasonably likely" that the key attributes for success are present and the key causes of failure are absent. POC is multidimensional but is focused on attributes that, if not addressed, represent a threat to the success of the project in crucial areas such as safety, efficacy, pharmaceutics, and commercial and regulatory issues. The appropriate weight of evidence is assessed through the use of mathematical models and by evaluating the consequences of advancing a candidate drug that is not safe, effective, or commercially viable, vs. failing to advance a candidate that possesses these attributes. Tools for POC include biomarkers, targeted populations, pharmacokinetic (PK)/pharmacodynamic (PD) modeling, simulation, and adaptive study designs. Challenges to the success of POCs include a shortage of skilled personnel, failure to integrate multiple disciplines and information, and the demand made by organizations for certainty.

Clinical pharmacology, biomarkers and personalized medicine: education please

Over the past decades, the scope of clinical pharmacology within the pharmaceutical industry has widened considerably. Key growth has been in the area of translational science and exploratory medicine, where clinical pharmacologists are nowadays the mediator between basic research and establishment of clinical usefulness. This role has led to and is supported by the rapid developments in pharmacokinetic–pharmacodynamic modeling and simulation, a strong focus on biomarkers for early informed decision-making, and the advent of pharmacogenomics into safety and efficacy predictions and evaluations. The ultimate goal – safer, more efficacious drug prescription – is shared with that of today’s drive for more personalized medicine. This article reviews the evolution of clinical pharmacology within the industry, the regulatory, clinical and societal drivers for this evolution, and the analogy with the establishment of personalized medicine in clinical practice. Special attention is given to the integration of biomarkers into clinical practice and the concurrent need for education of clinicians, pharmacists, payers and patients.