The future of drug development: advancing clinical trial design

Orloff, John J.; Douglas, Frank L.; Pinheiro, José; Levinson, Susan L; Branson, Michael; Chaturvedi, Pravin; Ette, Ene I.; Gallo, Paul; Hirsch, Gigi; Mehta, Cyrus R.; Patel, Nitin R.; Sabir, Sameer; Springs, Stacy L.; Stanski, Donald R.; Evers, Matthias R.; Fleming, Edd; Singh, Navjot; Tramontin, Tony; Golub, Howard

doi:10.1038/nrd3025

Cited by 137 publications

(88 citation statements)

References 26 publications

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“…Since the mid-90s, simulation-based diagnostic principles have grown in popularity (3), and an especially popular diagnostic tool is the so-called Visual Predictive Check (VPC; [4][5][6]. The popularity of VPCs can be attributed to two main advantages of the approach: [1] the principle behind the diagnostic is simple and easily communicated to both modelers and other modeling stakeholders, and [2] by the retention of the original time-course profile and the y-axis units, the VPC graphs are illustrated on a relevant and easily appreciated scale which can be powerful in guiding the modeler to the origin of a potential model misspecification.…”

Section: Introductionmentioning

confidence: 99%

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Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models

Bergstrand

Hooker

Wallin

et al. 2011

AAPS J

1,185

952

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Abstract. Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.

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Section: Introductionmentioning

confidence: 99%

“…Population pharmacokinetic (PK) and pharmacodynamic (PD) models are used increasingly in pharmaceutical research (1). In order to generate reliable conclusions and predictions, good diagnostic tools to evaluate the predictive performance of the models are vital (2).…”

Section: Introductionmentioning

confidence: 99%

Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models

Bergstrand

Hooker

Wallin

et al. 2011

AAPS J

1,185

952

View full text Add to dashboard Cite

show abstract

“…Healthcare expenditure in the US also increased from 27.4 billion USD in 1960 to 2.9 trillion USD in 2013 (Rho and Kim, 2017) [2]. A key part of the cost increase is clinical trial studies; particularly, the cost of performing phase II and phase III is growing rapidly (Orloff et al, 2009) [3]. Clinical trials are not only cost intensive; they are also time-consuming in new drug development.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Collaboration Network Based on ClinicalTrials.gov Database in the Pharmaceutical Industry

Yang

Lee

2018

Sustainability

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Increasing costs, risks, and productivity problems in the pharmaceutical industry are important recent issues in the biomedical field. Open innovation is proposed as a solution to these issues. However, little statistical analysis related to collaboration in the pharmaceutical industry has been conducted so far. Meanwhile, not many cases have analyzed the clinical trials database, even though it is the information source with the widest coverage for the pharmaceutical industry. The purpose of this study is to test the clinical trials information as a probe for observing the status of the collaboration network and open innovation in the pharmaceutical industry. This study applied the social network analysis method to clinical trials data from 1980 to 2016 in ClinicalTrials.gov. Data were divided into four time periods-1980s, 1990s, 2000s, and 2010s-and the collaboration network was constructed for each time period. The characteristic of each network was investigated. The types of agencies participating in the clinical trials were classified as a university, national institute, company, or other, and the major players in the collaboration networks were identified. This study showed some phenomena related to the pharmaceutical industry that could provide clues to policymakers about open innovation. If follow-up studies were conducted, the utilization of the clinical trial database could be further expanded, which is expected to help open innovation in the pharmaceutical industry.

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“…What is more, despite the value of Big Pharma late stage pipelines being historically directly proportional to their R&D spends [12], these companies are experiencing diminishing returns on investment and a broken business model, still to this date too conventionally focused on reaching increasingly elusive blockbuster products using conventional technologies [8,[12][13][14][15][16][17]. In response, beyond merger and acquisition strategies, Big Pharma are currently attempting to become more cost-effective and more efficient at R&D; this includes restructuring internal pharmaceutical workforces [10], targeting emerging or niche markets such as rare diseases [18], implementing personalized medicines [19], and rethinking innovation processes and models [8,[14][15][16][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Syndicate Innovation Venturing: Translating Academic Innovations into Commercial Successes

Vertès

2012

Challenges

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Abstract:Innovations that initiate new technology cycles, i.e., radical innovations, bring tremendous value to Society and build for the companies that deploy them sustainable competitive advantages. However, large firms have typically been relatively inefficient at accessing from academia or technology start-ups such technological leaps. Indeed, most multiyear and multimillion dollar academia-industry partnerships have historically not resulted in any acceleration of the rate of deployment of game-changing innovations, which empirically proceeds in 25 year cycles, such as for example the expansion of the scope of the pharmaceutical industry from small molecules to biologics, or, projecting into the future, to siRNA or therapeutic stem cell technologies. Syndicated innovation venturing is a new strategic partnering concept described here that brings together actors from different economic segments in a non zero-sum game as a means to facilitate seed-funding, with the aim to de-risk technologies while reducing initial financial exposures. A case study in the pharmaceutical industry suggests that alleviating this hurdle may provide an appropriate environment to improve the dynamics of academic technology transfer to the commercial phase. By contributing to the de-risking of the creation of novel biotechnology businesses, this novel mechanism could help speed up the commercialization of emerging technologies on a large scale. At a time when knowledge-based firms such as pharmaceutical companies attempt to revisit their innovation models to advance science, in spite of an environment of increasing risk-aversion, such responses could tilt the balance in favor of disruptive products and sustained corporate financial performance by removing common barriers to radical innovation deployment.

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The future of drug development: advancing clinical trial design

Cited by 137 publications

References 26 publications

Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models

Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models

Long-Term Collaboration Network Based on ClinicalTrials.gov Database in the Pharmaceutical Industry

Syndicate Innovation Venturing: Translating Academic Innovations into Commercial Successes

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