Clinical development times for innovative drugs

Brown, Dean G.; Wobst, Heike J.; Kapoor, Anoop; Kenna, Leslie A.; Southall, Noel

doi:10.1038/d41573-021-00190-9

Cited by 81 publications

(89 citation statements)

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“…It has been estimated that the median capitalized development and research cost to bring a new medicine to FDA approval in the United States between 2009 and 2018 was about USD 985 million, with projected costs expected to be in the billions [ 97 ]. The median clinical development time for FDA-approved drugs has remained stable at approximately 8.3 years for the past decade [ 98 ]. The significant time, expertise and cost associated with drug development usually translates into expensive out-of-pocket costs for patients [ 99 , 100 ].…”

Section: The Limitations Of Bottom-up Approachesmentioning

confidence: 99%

Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

Kenny

Antaw

Locke

et al. 2022

Life

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Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered “bottom-up” approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of “big data” and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure—when accompanied by high-quality experimental data for model training—has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.

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Section: The Limitations Of Bottom-up Approachesmentioning

confidence: 99%

Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

Kenny

Antaw

Locke

et al. 2022

Life

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show abstract

“…This is an essential consideration in any creation of definitions because of the very nature of innovation—which is essentially unpredictable. Since it takes, on average, more than a decade to develop a new medicine [ 33 ], along a process that is itself highly unpredictable, with no guarantees that the identified need will be met by any eventual product and with the possibility that another previously unidentified need may indeed be met by the outcome, rigid definitions have limitations. As an example, when research started on mRNA technology, few would have imagined it would play a central role in fighting a pandemic [ 34 ].…”

Section: Devils and Detailsmentioning

confidence: 99%

Meeting the Need for a Discussion of Unmet Medical Need

et al. 2022

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As Europe and the world continue to battle against COVID, the customary complacency of society over future threats is clearly on display. Just 30 months ago, such a massive disruption to global lives, livelihoods and quality of life seemed unimaginable. Some remedial European Union action is now emerging, and more is proposed, including in relation to tackling “unmet medical need” (UMN). This initiative—directing attention to the future of treating disease and contemplating incentives to stimulate research and development—is welcome in principle. But the current approach being considered by EU officials merits further discussion, because it may prove counter-productive, impeding rather than promoting innovation. This paper aims to feed into these ongoing policy discussions, and rather than presenting research in the classical sense, it discusses the key elements from a multistakeholder perspective. Its central concern is over the risk that the envisaged support will fail to generate valuable new treatments if the legislation is phrased in a rigidly linear manner that does not reflect the serpentine realities of the innovation process, or if the definition placed on unmet medical need is too restrictive. It cautions that such an approach presumes that “unmet need” can be precisely and comprehensively defined in advance on the basis of the past. It cautions that such an approach can reinforce the comfortable delusion that the future is totally predictable—the delusion that left the world as easy prey to COVID. Instead, the paper urges reflection on how the legislation that will shortly enter the pipeline can be phrased so as to allow for the flourishing of a culture capable of rapid adaptation to the unexpected.

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“…Drug development is long and expensive process; it is estimated that during the years 2000 to 2015, the average cost of developing a newly approved drug was over USD 2.5 billion per single approved molecule. Commonly, it takes up to 15 years from the design of a single drug to reach the market, and less than 15% of selected compounds that are tested on humans, are later proved as safe and effective and can be finally used [ 5 , 6 ]. It sounds reasonable that humanity can’t wait 15 years for the development of a new molecule for COVID-19 treatment and in this case, the computational chemistry applied in drug design is helping accelerate research and providing stunning results.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

Floresta

Zagni

Gentile

et al. 2022

IJMS

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The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.

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Clinical development times for innovative drugs

Cited by 81 publications

References 0 publications

Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

Meeting the Need for a Discussion of Unmet Medical Need

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

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