Proof of concept: a PhRMA position paper with recommendations for best practice. Clin Pharmacol Ther. 2010;87:278-285. [PMID: 20130568 DOI: 10.1038/clpt.2009.286]

The effect of a combined indomethacin and levonorgestrel-releasing intrauterine system on short-term postplacement bleeding profile: a randomized proof-of-concept trial

BACKGROUND

Long-acting reversible contraceptives, including hormonal levonorgestrel-releasing intrauterine systems, are the most effective methods of reversible contraception. However, unfavorable bleeding, particularly during the first months of use, is one of the most important reasons for discontinuation or avoidance. Minimizing this as early as possible would be highly beneficial. Nonsteroidal anti-inflammatory drugs inhibiting prostaglandin synthesis are known to reduce bleeding and pain at time of menses. A levonorgestrel-releasing intrauterine system has been developed with an additional reservoir containing indomethacin, designed to be released during the initial postplacement period.

OBJECTIVE

This proof-of-concept study aimed to establish whether the addition of indomethacin to the currently available levonorgestrel-releasing intrauterine system (average in vivo levonorgestrel release rate of 8 μg/24 h during the first year of use) reduces the number of bleeding and spotting days during the first 90 days of use compared with the unmodified system. The dose-finding analysis included 3 doses of indomethacin-low (6.5 mg), middle (12.5 mg), and high (15.4 mg)-to determine the ideal dose of indomethacin to reduce bleeding and spotting days with minimal side-effects.

STUDY DESIGN

This was a multicenter, single-blinded, randomized, controlled phase II trial conducted between June 2018 and June 2019 at 6 centers in Europe. Three indomethacin dose-ranging treatment groups (low-, middle-, and high-dose indomethacin/levonorgestrel-releasing intrauterine system) were compared with the unmodified levonorgestrel-releasing intrauterine system group, with participants randomized in a 1:1:1:1 ratio. The primary outcome was the number of uterine bleeding and spotting days over a 90-day reference (treatment) period. Secondary outcomes were the number of women showing endometrial histology expected for intrauterine levonorgestrel application and the frequency of treatment-emergent adverse events. Point estimates and 2-sided 90% credible intervals were calculated for mean and median differences between treatment groups and the levonorgestrel-releasing intrauterine system without indomethacin. Point and interval estimates were determined using a Bayesian analysis.

RESULTS

A total of 174 healthy, premenopausal women, aged 18 to 45 years, were randomized, with 160 women eligible for the per-protocol analysis set. Fewer bleeding and spotting days were observed in the 90-day reference period for the 3 indomethacin/levonorgestrel-releasing intrauterine system dose groups than for the levonorgestrel-releasing intrauterine system without indomethacin group. The largest reduction in bleeding and spotting days was achieved with low-dose indomethacin/levonorgestrel-releasing intrauterine system, which demonstrated a point estimate difference of -32% (90% credible interval, -45% to -19%) compared with levonorgestrel-releasing intrauterine system without indomethacin. Differences for high- and middle-dose indomethacin/levonorgestrel-releasing intrauterine system groups relative to levonorgestrel-releasing intrauterine system without indomethacin were -19% and -16%, respectively. Overall, 97 women (58.1%) experienced a treatment-emergent adverse event considered related to the study drug, with similar incidence across all treatment groups including the unmodified levonorgestrel-releasing intrauterine system. These were all mild or moderate in intensity, with 6 leading to discontinuation. Endometrial biopsy findings were consistent with effects expected for the levonorgestrel-releasing intrauterine system.

CONCLUSION

All 3 doses of indomethacin substantially reduced the number of bleeding and spotting days in the first 90 days after placement of the levonorgestrel-releasing intrauterine system, thus providing proof of concept. Adding indomethacin to the levonorgestrel-releasing intrauterine system can reduce the number of bleeding and spotting days in the initial 90 days postplacement, without affecting the safety profile, and potentially improving patient acceptability and satisfaction.

A Bayesian three-tier quantitative decision-making framework for single arm studies in early phase oncology

In early phase oncology drug development, single arm proof-of-concept (POC) studies are increasingly being used to drive the early decisions for future development of the drug. Decision-makings based on such studies, typically involving small sample size and early surrogate efficacy endpoints, are extremely challenging. In particular, given the tremendous competition in the development of immunotherapies, expedition of the most promising programs is desired. To this end, we have proposed a Bayesian three-tier approach to facilitate the decision-making process, inheriting all the benefits of Bayesian decision-making approaches and formally allowing the option of acceleration. With pre-specified Bayesian decision criteria, three types of decisions regarding the future development of the drug can be made: (1) terminating the program, (2) further investigation, considering totality of evidence or additional POC studies, and (3) accelerating the program. We further proposed a Bayesian adaptive three-tier (BAT) design, extending the decision-making approach to incorporate adaptive thresholds and allow for continuous monitoring of the study. We compare the performance of the proposed methods with some other existing methods through simulations.

Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field

Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.

Challenges of Psychiatry Drug Development and the Role of Human Pharmacology Models in Early Development-A Drug Developer's Perspective

Psychiatric diseases have the lowest probability of success in clinical drug development. This presents not only an issue to address the unmet medical needs of patients, but also a hurdle for pharmaceutical and biotech industry to continue R&D in this disease area. Fundamental pharmacokinetic and pharmacodynamic principles provide an understanding of the drug exposure, target binding and pharmacological activity at the target site of action for a new drug candidate. Collectively, these principles determine the likelihood of testing the mechanism of action and enhancing the likelihood of candidate survival in Phase 2 clinical development, therefore, they are termed as the "three pillars of survival." Human Phase 1 pharmacokinetic and pharmacodynamic studies provide evidence of the three pillars. Electroencephalogram (EEG) assessments and cognitive function tests in schizophrenia patients can provide proof of pharmacology and ensure that a pharmacological active regimen will be tested in Phase 2 proof of concept (POC) studies for the treatment of cognitive impairment associated with schizophrenia (CIAS).

The "rights" of precision drug development for Alzheimer's disease

There is a high rate of failure in Alzheimer's disease (AD) drug development with 99% of trials showing no drug-placebo difference. This low rate of success delays new treatments for patients and discourages investment in AD drug development. Studies across drug development programs in multiple disorders have identified important strategies for decreasing the risk and increasing the likelihood of success in drug development programs. These experiences provide guidance for the optimization of AD drug development. The "rights" of AD drug development include the right target, right drug, right biomarker, right participant, and right trial. The right target identifies the appropriate biologic process for an AD therapeutic intervention. The right drug must have well-understood pharmacokinetic and pharmacodynamic features, ability to penetrate the blood-brain barrier, efficacy demonstrated in animals, maximum tolerated dose established in phase I, and acceptable toxicity. The right biomarkers include participant selection biomarkers, target engagement biomarkers, biomarkers supportive of disease modification, and biomarkers for side effect monitoring. The right participant hinges on the identification of the phase of AD (preclinical, prodromal, dementia). Severity of disease and drug mechanism both have a role in defining the right participant. The right trial is a well-conducted trial with appropriate clinical and biomarker outcomes collected over an appropriate period of time, powered to detect a clinically meaningful drug-placebo difference, and anticipating variability introduced by globalization. We lack understanding of some critical aspects of disease biology and drug action that may affect the success of development programs even when the "rights" are adhered to. Attention to disciplined drug development will increase the likelihood of success, decrease the risks associated with AD drug development, enhance the ability to attract investment, and make it more likely that new therapies will become available to those with or vulnerable to the emergence of AD.

Beyond p-values: A phase II dual-criterion design with statistical significance and clinical relevance

Background

Well-designed phase II trials must have acceptable error rates relative to a pre-specified success criterion, usually a statistically significant p-value. Such standard designs may not always suffice from a clinical perspective because clinical relevance may call for more. For example, proof-of-concept in phase II often requires not only statistical significance but also a sufficiently large effect estimate.

Purpose

We propose dual-criterion designs to complement statistical significance with clinical relevance, discuss their methodology, and illustrate their implementation in phase II.

Methods

Clinical relevance requires the effect estimate to pass a clinically motivated threshold (the decision value (DV)). In contrast to standard designs, the required effect estimate is an explicit design input, whereas study power is implicit. The sample size for a dual-criterion design needs careful considerations of the study’s operating characteristics (type I error, power).

Results

Dual-criterion designs are discussed for a randomized controlled and a single-arm phase II trial, including decision criteria, sample size calculations, decisions under various data scenarios, and operating characteristics. The designs facilitate GO/NO-GO decisions due to their complementary statistical–clinical criterion.

Limitations

While conceptually simple, implementing a dual-criterion design needs care. The clinical DV must be elicited carefully in collaboration with clinicians, and understanding similarities and differences to a standard design is crucial.

Conclusion

To improve evidence-based decision-making, a formal yet transparent quantitative framework is important. Dual-criterion designs offer an appealing statistical–clinical compromise, which may be preferable to standard designs if evidence against the null hypothesis alone does not suffice for an efficacy claim.

Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development

The failure rate for phase III trials in oncology is high; quantitative predictive approaches are needed. We developed a model‐based meta‐analysis (MBMA) framework to predict progression‐free survival (PFS) from overall response rates (ORR) in relapsed/refractory multiple myeloma (RRMM), using data from seven phase III trials. A Bayesian analysis was used to predict the probability of technical success (PTS) for achieving desired phase III PFS targets based on phase II ORR data. The model demonstrated a strongly correlated (R² = 0.84) linear relationship between ORR and median PFS. As a representative application of the framework, MBMA predicted that an ORR of ∼66% would be needed in a phase II study of 50 patients to achieve a target median PFS of 13.5 months in a phase III study. This model can be used to help estimate PTS to achieve gold‐standard targets in a target product profile, thereby enabling objectively informed decision‐making.

The Burden of the "False-Negatives" in Clinical Development: Analyses of Current and Alternative Scenarios and Corrective Measures

The “false‐negatives” of clinical development are the effective treatments wrongly determined ineffective. Statistical errors leading to “false‐negatives” are larger than those leading to “false‐positives,” especially in typically underpowered early‐phase trials. In addition, “false‐negatives” are usually eliminated from further testing, thereby limiting the information available on them. We simulated the impact of early‐phase power on economic productivity in three developmental scenarios. Scenario 1, representing the current status quo, assumed 50% statistical power at phase II and 90% at phase III. Scenario 2 assumed increased power (80%), and Scenario 3, increased stringency of alpha (1%) at phase II. Scenario 2 led, on average, to a 60.4% increase in productivity and 52.4% increase in profit. Scenario 3 had no meaningful advantages. Our results suggest that additional costs incurred by increasing the power of phase II studies are offset by the increase in productivity. We discuss the implications of our results and propose corrective measures.

Metronomic chemotherapy: A relook at its basis and rationale

Metronomic administration of chemotherapy has long been recognized as having a different biological effect from maximal tolerated dose (MTD) administration. Preclinical studies have demonstrated these differences quite elegantly and many clinical trials have also demonstrated reproducible activity albeit small, in varied solid malignancies even in patients who were heavily pretreated. However, the concept of metronomic chemotherapy has been plagued by lack of a clear definition resulting in the published literature that is rather varied and confusing. There is a need for a definition that is mechanism(s)-based allowing metronomics to be distinguished from standard MTD concept. With significant advances made in understanding cancer biology and biotechnology, it is now possible to attain that goal. What is needed is both a concerted effort and adequate funding to work towards it. This is the only way for the oncology community to determine how metronomic chemotherapy fits in the overall cancer management schema.

Enhancing value of clinical pharmacodynamics in oncology drug development: An alliance between quantitative pharmacology and translational science

Clinical pharmacodynamic evaluation is a key component of the "pharmacologic audit trail" in oncology drug development. We posit that its value can and should be greatly enhanced via application of a robust quantitative pharmacology framework informed by biologically mechanistic considerations. Herein, we illustrate examples of intersectional blindspots across the disciplines of quantitative pharmacology and translational science and offer a roadmap aimed at enhancing the caliber of clinical pharmacodynamic research in the development of oncology therapeutics.

CONSORT 2010 statement: extension to randomised pilot and feasibility trials

The Consolidated Standards of Reporting Trials (CONSORT) statement is a guideline designed to improve the transparency and quality of the reporting of randomised controlled trials (RCTs). In this article we present an extension to that statement for randomised pilot and feasibility trials conducted in advance of a future definitive RCT. The checklist applies to any randomised study in which a future definitive RCT, or part of it, is conducted on a smaller scale, regardless of its design (eg, cluster, factorial, crossover) or the terms used by authors to describe the study (eg, pilot, feasibility, trial, study). The extension does not directly apply to internal pilot studies built into the design of a main trial, non-randomised pilot and feasibility studies, or phase II studies, but these studies all have some similarities to randomised pilot and feasibility studies and so many of the principles might also apply. The development of the extension was motivated by the growing number of studies described as feasibility or pilot studies and by research that has identified weaknesses in their reporting and conduct. We followed recommended good practice to develop the extension, including carrying out a Delphi survey, holding a consensus meeting and research team meetings, and piloting the checklist. The aims and objectives of pilot and feasibility randomised studies differ from those of other randomised trials. Consequently, although much of the information to be reported in these trials is similar to those in randomised controlled trials (RCTs) assessing effectiveness and efficacy, there are some key differences in the type of information and in the appropriate interpretation of standard CONSORT reporting items. We have retained some of the original CONSORT statement items, but most have been adapted, some removed, and new items added. The new items cover how participants were identified and consent obtained; if applicable, the prespecified criteria used to judge whether or how to proceed with a future definitive RCT; if relevant, other important unintended consequences; implications for progression from pilot to future definitive RCT, including any proposed amendments; and ethical approval or approval by a research review committee confirmed with a reference number. This article includes the 26 item checklist, a separate checklist for the abstract, a template for a CONSORT flowchart for these studies, and an explanation of the changes made and supporting examples. We believe that routine use of this proposed extension to the CONSORT statement will result in improvements in the reporting of pilot trials. Editor's note: In order to encourage its wide dissemination this article is freely accessible on the BMJ and Pilot and Feasibility Studies journal websites.

BEACON: A Summary Framework to Overcome Potential Reimbursement Hurdles

OBJECTIVE

To provide a framework for addressing payers' criteria during the development of pharmaceuticals.

METHODS

A conceptual framework was presented to an international health economic expert panel for discussion. A structured literature search (from 2010 to May 2015), using the following databases in Ovid: Medline(®) and Medline(®) In-Process (PubMed), Embase (Ovid), EconLit (EBSCOhost) and the National Health Service Economic Evaluation Database (NHS EED), and a 'grey literature' search, were conducted to identify existing criteria from the payer perspective. The criteria assessed by existing frameworks and guidelines were collated; the most commonly reported criteria were considered for inclusion in the framework. A mnemonic was conceived as a memory aide to summarise these criteria.

RESULTS

Overall, 41 publications were identified as potentially relevant to the objective. Following further screening, 26 were excluded upon full-text review on the basis of no framework presented (n = 13), redundancy (n = 11) or abstract only (n = 2). Frameworks that captured criteria developed for or utilised by the pharmaceutical industry (n = 5) and reimbursement guidance (n = 10) were reviewed. The most commonly identified criteria-unmet need/patient burden, safety, efficacy, quality-of-life outcomes, environment, evidence quality, budget impact and comparator-were incorporated into the summary framework. For ease of communication, the following mnemonic was developed: BEACON (Burden/target population, Environment, Affordability/value, Comparator, Outcomes, Number of studies/quality of evidence).

CONCLUSIONS

The BEACON framework aims to capture the 'essence' of payer requirements by addressing the most commonly described criteria requested by payers regarding the introduction of a new pharmaceutical.

Modeling and Simulation of In Vivo Drug Effects

The concept of a pharmacokinetics-pharmacodynamics (PK/PD) assessment of drug development candidates is well established in pharmaceutical research and development, and PK/PD modeling is common practice in all pharmaceutical companies. A recent analysis (Morgan et al., Drug Discov Today 17(9-10):419-424, 2012) revealed however that insufficient certainty in the integrity of the causal chain of fundamental pharmacological steps from drug dosing through systemic exposure, target tissue exposure, and engagement of molecular target to pharmacological response is still the major driver of failure in phase II of clinical drug development. Despite the rise of molecular biomarkers, ethical, scientific, and practical constraints very often still prevent a direct assessment of each necessary step ultimately leading to an intended drug effect or an unintended adverse reaction. Yet, incomplete investigation of the causality of drug responses is a major risk for translational assessments and the prediction of drug responses in different species or other populations. Mechanism-based modeling and simulation (M&S) offers a means to investigate complex physiological and pharmacological processes and to complement experimental data for non-accessible steps in the pharmacological causal chain. With the help of two examples, it is illustrated, what level of physiological detail, state-of-the-art models can represent, how predictive these models are and how mechanism-based approaches can be combined with empirical correlation-based concepts.

Hollow-Fiber Methodology for Pharmacokinetic/Pharmacodynamic Studies of Antimalarial Compounds

Knowledge of pharmacokinetic/pharmacodynamic (PK/PD) relationships can enhance the speed and economy of drug development by enabling informed and rational decisions at every step, from lead selection to clinical dosing. For anti-infective agents in particular, dynamic in vitro hollow-fiber cartridge experiments permit exquisite control of kinetic parameters and the study of their consequent impact on pharmacodynamic efficacy. Such information is of great interest for the cost-restricted but much-needed development of new antimalarial drugs, especially since the major human pathogen Plasmodium falciparum can be cultivated in vitro but is not readily available in animal models. This protocol describes the materials and procedures for determining the PK/PD relationships of antimalarial compounds.

Recent trends in specialty pharma business model

The recent rise of specialty pharma is attributed to its flexible, versatile, and open business model while the traditional big pharma is facing a challenging time with patent cliff, generic threat, and low research and development (R&D) productivity. These multinational pharmaceutical companies, facing a difficult time, have been systematically externalizing R&D and some even establish their own corporate venture capital so as to diversify with more shots on goal, with the hope of achieving a higher success rate in their compound pipeline. Biologics and clinical Phase II proof-of-concept (POC) compounds are the preferred licensing and collaboration targets. Biologics enjoys a high success rate with a low generic biosimilar threat, while the need is high for clinical Phase II POC compounds, due to its high attrition/low success rate. Repurposing of big pharma leftover compounds is a popular strategy but with limitations. Most old compounds come with baggage either in lackluster clinical performance or short in patent life. Orphan drugs is another area which has gained popularity in recent years. The shorter and less costly regulatory pathway provides incentives, especially for smaller specialty pharma. However, clinical studies on orphan drugs require a large network of clinical operations in many countries in order to recruit enough patients. Big pharma is also working on orphan drugs starting with a small indication, with the hope of expanding the indication into a blockbuster status. Specialty medicine, including orphan drugs, has become the growth engine in the pharmaceutical industry worldwide. Big pharma is also keen on in-licensing technology or projects from specialty pharma to extend product life cycles, in order to protect their blockbuster drug franchises. Ample opportunities exist for smaller players, even in the emerging countries, to collaborate with multinational pharmaceutical companies provided that the technology platforms or specialty medicinal products are what the big pharma wants. The understanding of intellectual properties and international drug regulations are the key for specialty pharma to have a workable strategy for product registration worldwide.

Biomarkers in Pharmaceutical Research

BACKGROUND

Biomarkers are important tools in drug development and are used throughout pharmaceutical research.

CONTENT

This review focuses on molecular biomarkers in drug development. It contains sections on how biomarkers are used to assess target engagement, pharmacodynamics, safety, and proof-of-concept. It also covers the use of biomarkers as surrogate end points and patient selection/companion diagnostics and provides insights into clinical biomarker discovery and biomarker development/validation with regulatory implications. To survey biomarkers used in drug development--acknowledging that many pharmaceutical development biomarkers are not published--we performed a focused PubMed search employing "biomarker" and the names of the largest pharmaceutical companies as keywords and filtering on clinical trials and publications in the last 10 years. This yielded almost 500 entries, the majority of which included disease-related (approximately 60%) or prognostic/predictive (approximately 20%) biomarkers. A notable portion (approximately 8%) included HER2 (human epidermal growth factor receptor 2) testing, highlighting the utility of biomarkers for patient selection. The remaining publications included target engagement, safety, and drug metabolism biomarkers. Oncology, cardiovascular disease, and osteoporosis were the areas with the most citations, followed by diabetes and Alzheimer disease.

SUMMARY

Judicious biomarker use can improve pharmaceutical development efficiency by helping to select patients most appropriate for treatment using a given mechanism, optimize dose selection, and provide earlier confidence in accelerating or discontinuing compounds in clinical development. Optimal application of biomarker technology requires understanding of candidate drug pharmacology, detailed modeling of biomarker readouts relative to pharmacokinetics, rigorous validation and qualification of biomarker assays, and creative application of these elements to drug development problems.

Investigation of the Safety Profiles of Japanese Clinical Trials

BACKGROUND

The increasing attrition rates of new drug research and development have become a global problem. To tackle this problem as well as the problem of "drug lag" in Japan, strategies utilizing multiregional clinical trials (MRCTs) are now being commonly applied. It is important to determine whether clinical data in a specific country and region have tendencies or patterns that will help us to consider an appropriate strategy for drug development in the specific region as well as worldwide. However, little has been studied on strategies and methods for drug development to pursue simultaneous development taking into account these characteristics. It would be valuable to determine and characterize the safety profile of Japanese clinical trial data.

METHODS

To characterize the overall safety profile of Japanese data in terms of the frequency of adverse events (AEs), serious AEs, and discontinuation due to AEs compared with non-Japanese data, 73 pharmaceutical products recently approved in Japan were selected. Their clinical trial safety data, derived from comparable studies conducted in Japan and Western countries using the bridging strategy and MRCTs, were reviewed and analyzed.

RESULTS

Japanese data are similar to non-Japanese data in terms of overall frequency of AEs; however, the sample size of Japanese patients in the bridging studies and MRCTs was generally smaller than that in non-Japanese data.

CONCLUSIONS

The safety profile in Japanese clinical data was shown to be similar to that of non-Japanese data from the standpoint of overall frequency of AEs. This finding should be encouraging to pharmaceutical companies and the health authority in Japan to accelerate participation in MRCTs.

A decade of innovation in pharmaceutical R&D: the Chorus model

Chorus is a small, operationally independent clinical development organization within Eli Lilly and Company that specializes in drug development from candidate selection to clinical proof of concept. The mission of Chorus is to achieve proof of concept rapidly and at a low cost while positioning successful projects for 'pharma-quality' late-stage development. Chorus uses a small internal staff of experienced drug developers and a network of external vendors to design and implement chemistry, manufacturing and control processes, preclinical toxicology and biology, and Phase I/II clinical trials. In the decade since it was established, Chorus has demonstrated substantial productivity improvements in both time and cost compared to traditional pharmaceutical research and development. Here, we describe its development philosophy, organizational structure, operational model and results to date.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

Conducting clinical research in community mental health settings: Opportunities and challenges

Tremendous progress has been made in the past decade surrounding the underlying mechanisms and treatment of neuropsychiatric disease. Technological advancements and a broadened research paradigm have contributed to the understanding of the neurochemistry, brain function and brain circuitry involved in neuropsychiatric disorders. The predominant area of unmet medical need in the United States is major psychiatric disorders, and major depressive disorder is the leading cause of disability for ages 15-44. Total spending on research and development by the pharmaceutical industry has grown exponentially during the past decade, but fewer new molecular entities (NME) for the treatment of major psychiatric disorders have received regulatory approvals compared to other therapeutic areas. Though significant expansion has occurred during the “decade of the brain”, the translation of clinical trials outcomes into the community mental health setting is deficient. Randomized controlled trials (RCTs) have been the standard approach to clinical evaluation of the safety and efficacy of NMEs for the past 60 years; however, there are significant barriers and skepticism in the implementation of evidence-based outcomes into clinical practice. Recruitment of patients, shortages of experienced clinical researchers, regulatory requirements and later translation of outcomes into clinical practice are ever growing problems faced by investigators. The community mental health setting presents particular barriers in the replication of therapeutic outcomes from RCTs. The diagnostic complexity of major psychiatric diseases and the highly selective patient populations involved in clinical trials lend to the gap in translation from the “bench to the bedside”. The community mental health setting lends to a diverse patient population with numerous co-morbidities and environmental factors that are unaccounted in the average RCT. While we acknowledge the enormous complexity in developing novel and innovative treatments for major psychiatric disorders, we must continue to improve the translatability of clinical trials to real world settings. Progress has been rather slow but as the gap in treatment effectiveness is reduced, so will costs and barriers in community mental health.

Building an Integrated Early Clinical Development Platform to Improve the Path to Proof of Concept

Probability of success in phase II dominates the drug development cost calculus, with phase I/II as the critical juncture for proof of concept. Failure to address fundamental pharmacologic questions in early development is alarmingly frequent and a strong predictor of failure. Safety, manufacture, formulation, and commercialization issues are also vital. Systems biology provides a framework to analyze genomic, proteomic, and metabolomic data and construct complex network models of molecular pathophysiology. Biomarkers offer the largest learning opportunity, and combined adaptive protocol designs provide a lean but scientifically robust path to proof of concept. The traditional model of phase I study execution in a clinical pharmacology unit is evolving to a networked model of an integrated early clinical development platform. The power of this platform is enhanced with a proactive multidisciplinary approach to quality and safety, including lean 6 sigma tools and simulations.

Model system to define pharmacokinetic requirements for antimalarial drug efficacy

Malaria presents a tremendous public health burden, and new therapies are needed. Massive compound libraries screened against Plasmodium falciparum have yielded thousands of lead compounds, resulting in an acute need for rational criteria to select the best candidates for development. We reasoned that, akin to antibacterials, antimalarials might have an essential pharmacokinetic requirement for efficacy: action governed either by total exposure or peak concentration (AUC/CMAX), or by duration above a defined minimum concentration [time above minimum inhibitory concentration (TMIC)]. We devised an in vitro system for P. falciparum, capable of mimicking the dynamic fluctuations of a drug in vivo. Using this apparatus, we find that chloroquine is TMIC-dependent, whereas the efficacy of artemisinin is driven by CMAX. The latter was confirmed in a mouse model of malaria. These characteristics can explain the clinical success of two antimalarial drugs with widely different kinetics in humans. Chloroquine, which persists for weeks, is ideally suited for its TMIC mechanism, whereas great efficacy despite short exposure (t1/2 in blood 3 hours or less) is attained by CMAX-driven artemisinins. This validated preclinical model system can be used to select those antimalarial lead compounds whose CMAX or TMIC requirement for efficacy matches pharmacokinetics obtained in vivo. The apparatus can also be used to explore the kinetic dependence of other pharmacodynamic endpoints in parasites.

Mathematical modeling in cancer drug discovery

Mathematical models have the potential to help discover new therapeutic targets and treatment strategies. In this review, we discuss how the latest developments in mathematical modeling can provide useful context for the rational design, validation and prioritization of novel cancer drug targets and their combinations. We give special attention to two modeling approaches: network-based modeling and multiscale modeling, because they have begun to show promise in facilitating the process of effective cancer drug discovery. Both modeling approaches are integrated with a variety of experimental methods to ensure proper parameterization and to maximize their predictive value. We also discuss several challenges faced in modeling-based drug discovery.

Comparisons of Analysis Methods for Proof-of-Concept Trials

Drug development struggles with high costs and time consuming processes. Hence, a need for new strategies has been accentuated by many stakeholders in drug development. This study proposes the use of pharmacometric models to rationalize drug development. Two simulated examples, within the therapeutic areas of acute stroke and type 2 diabetes, are utilized to compare a pharmacometric model-based analysis to a t-test with respect to study power of proof-of-concept (POC) trials. In all investigated examples and scenarios, the conventional statistical analysis resulted in several fold larger study sizes to achieve 80% power. For a scenario with a parallel design of one placebo group and one active dose arm, the difference between the conventional and pharmacometric approach was 4.3- and 8.4-fold, for the stroke and diabetes example, respectively. Although the model-based power depend on the model assumptions, in these scenarios, the pharmacometric model-based approach was demonstrated to permit drastic streamlining of POC trials.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e23; doi:10.1038/psp.2012.24; advance online publication 16 January 2013.

Joint modeling of efficacy, dropout, and tolerability in flexible-dose trials: a case study in depression

Many difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAM D)scores in clinical trials for the evaluation of antidepressant drugs: (i) flexible designs, used to increase the chance of selecting more efficacious doses, (ii) dropout events, and (iii) adverse effects related to the experimental compound.It is crucial to take into account all these factors when designing an appropriate model of the HAM D time course and to obtain a realistic description of the dropout process. In this work, we propose an integrated approach to the modeling of a double-blind, flexible-dose, placebo-controlled, phase II depression trial that comprises response,tolerability, and dropout. We investigate three different dropout mechanisms in terms of informativeness. Goodness of fit is quantitatively assessed with respect to response (HAM D score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAM D evolution, dose changes, and occurrence of drug-related adverse effects.

Preclinical in vivo ADME studies in drug development: a critical review

INTRODUCTION

The last two decades have brought many fundamental changes to the drug development process. One such change is the importance of preclinical pharmacokinetics, which has become an essential part of early drug discovery. Furthermore, bioanalytical methods have become more sensitive and the identification and quantitation of metabolites can now be carried out on limited amount of biological material. There has also been a change in regulatory expectations, which are now particularly focused on the safety of human metabolites.

AREAS COVERED

The focus of this paper is on some 'traditional' in vivo ADME studies: excretion balance, metabolic profile and WBA in the toxicological species. These studies, performed with radiolabeled material, have a long history: and are a regular presence in submission dossiers. This paper reviews their value in the perspective of the contemporary drug development process.

EXPERT OPINION

These experiments may sometimes still be relevant to explain toxicological findings or for other special purposes but should not be considered required pieces of the registration dossiers. An appropriate investigation of samples coming from safety evaluation and human Phase I studies and the knowledge generated during the lead optimization phase provide, in most instances, all the DMPK information needed to take decisions in the drug development process.

Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival

In an effort to uncover systematic learnings that can be applied to improve compound survival, an analysis was performed on data from Phase II decisions for 44 programs at Pfizer. It was found that not only were the majority of failures caused by lack of efficacy but also that, in a large number of cases (43%), it was not possible to conclude whether the mechanism had been tested adequately. A key finding was that an integrated understanding of the fundamental pharmacokinetic/pharmacodynamic principles of exposure at the site of action, target binding and expression of functional pharmacological activity (termed together as the 'three Pillars of survival') all determine the likelihood of candidate survival in Phase II trials and improve the chance of progression to Phase III.

Applications of 'decisionable' biomarkers in cardiovascular drug development

Biomarkers are now increasingly employed in drug development for decision-making. New targets and candidate drugs should not only have drug-like properties (i.e., be 'drugable'), but the supporting biomarker platform should be 'decisionable'. For example, biomarkers for target engagement have supported the biologic plausibility for novel mechanisms and have aided in accelerated proof of concept. In many other circumstances, biomarkers have aided in the elucidation of mechanisms of action and disease progression. In this article, decisonable biomarker principles that aid in decision-making within the realm of early discovery through to clinical proof of concept are discussed. Case studies of applications of both target engagement and disease-related biomarkers are illustrated in the field of cardiovascular drug discovery and translational development. We propose that biomarkers, if prospectively implemented in an early development program, have the potential to accelerate drug development, facilitate the design of informative trials and dose selection for accelerated development, and establish an overall increase in probability of developmental success and efficiency.