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市場調查報告書

由於醫藥品R&D的資料活用的轉換

Transforming Pharmaceutical R&D with Data

出版商 Datamonitor Healthcare 商品編碼 527414
出版日期 內容資訊 英文 31 Pages
商品交期: 最快1-2個工作天內
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由於醫藥品R&D的資料活用的轉換 Transforming Pharmaceutical R&D with Data
出版日期: 2017年05月16日 內容資訊: 英文 31 Pages
簡介

本報告提供資料活用推動醫藥品R&D的各種方法調查,彙整難以持續的目前R&D之經濟性,藥物研發·藥物開發·核准·打入市場·遵守用藥等各階段資料利用的可能性,資料主導的R&D合理化課題等資料。

摘要整理

目前R&D的經濟學和非永續性

  • 藥物定價環境正在推動更有效的研發
  • 從藥物研發到上市的新工具崛起
  • 推動研發效率需要合作夥伴
  • 從遺忘的資產中創造價值
  • 削減成本·時間等

藥物研發的推動

  • 更快地識別和驗證有希望的藥物靶點和潛在客戶
  • 用人工智能加速發現
  • 大醫藥品經營者:電腦支援藥物研發
  • AI演算法的預測性
  • 精密醫療和生物標記等

開發的推動

  • 臨床試驗的改善·推動
  • 資料主導的選址
  • 加快招聘實驗者
  • 電子化的臨床試驗資料回收等

核准·進入·服藥遵守的推動

  • 推動法律上的流程·藥物引進·服藥遵守
  • 推動法律規章上的檢討
  • 加速商業性引進
  • 轉換期資料:R&D展現更廣泛資料主導的阻礙的一部分等

資料主導的R&D合理化課題

  • 法律規章的不確定性
  • 資料的兼容性
  • 組織性·文化性變化
  • 製藥必須提升其數據技能才能保持競爭力

附錄

圖表

目錄
Product Code: DMKC0172517

In the current drug pricing environment, biopharmaceutical firms cannot afford to continue spending billions of dollars on development programs that are more than 90% likely to fail. Raising prices to compensate for expensive, risky research and development (R&D) is no longer an option amid a global payer backlash against drug costs. Drug R&D needs to become more efficient, faster, and cost-effective in order for biopharma firms to be sustainable and to maintain a supply of innovative treatments.

Fortunately, multiple new tools are emerging to help streamline R&D. Most of these involve more intelligent and targeted use of existing data, and exploiting multiple new kinds of data and analytical methods. They are enabling efforts along the R&D value chain, from discovery through late-stage trials and approval.

Several Big Pharma companies have started to invest in more efficient processes such as e-sourcing clinical data and virtual trial recruitment. Precision medicine, which is growing rapidly in oncology, in theory allows smaller, more targeted trials with a higher chance of success. Meanwhile, technology giants like IBM, as well as a new generation of biotechs, are using artificial intelligence and machine learning to accelerate and improve R&D; many are seeking partners as well as developing their own pipelines. Regulators are very open to new, faster, data-driven approaches to drug development.

Making R&D more efficient will not solve the drug pricing challenge; however, it will help by allowing biopharma to run a wider set of programs and make faster, wiser decisions about when and whether to engage in expensive late-stage trials.

TABLE OF CONTENTS

EXECUTIVE SUMMARY

  • Current R&D economics are unsustainable
  • Accelerating discovery
  • Accelerating development
  • Accelerating approval, access, and adherence
  • Challenges to data-driven R&D streamlining

CURRENT R&D ECONOMICS ARE UNSUSTAINABLE

  • The drug pricing environment is forcing more efficient R&D
  • New tools are emerging from discovery through to commercialization
  • Driving R&D efficiency requires partners
  • Squeezing value out of forgotten assets
  • Cost and time savings may reach 20-50%
  • Bibliography

ACCELERATING DISCOVERY

  • Faster identification and validation of promising drug targets and leads
  • Accelerating discovery with artificial intelligence
  • Augmenting, not replacing, the work of scientists
  • Up-ending drug R&D
  • Big Pharma is signing up for computer-backed discovery
  • How predictive is your AI algorithm?
  • Machine-accelerated drug discovery is still only a promise
  • Precision medicine and biomarkers
  • Bibliography

ACCELERATING DEVELOPMENT

  • Improving and accelerating clinical trials
  • Data driven site-selection
  • Accelerating trial recruitment
  • Electronic trial data capture
  • Bibliography

ACCELERATING APPROVAL, ACCESS, AND ADHERENCE

  • Expediting the regulatory process, drug uptake, and adherence
  • Accelerating regulatory review
  • Faster commercial uptake
  • Outcomes data inform R&D as part of a broader, data-driven disruption
  • Bibliography

CHALLENGES TO DATA-DRIVEN R&D STREAMLINING

  • Regulatory uncertainty
  • Data compatibility
  • Organizational and cultural change
  • Pharma must upgrade its data skills to stay competitive
  • Bibliography

APPENDIX

  • About the author
  • Scope
  • Methodology

LIST OF TABLES

  • Table 1: Selected approaches to accelerating drug discovery
  • Table 2: Selected approaches to accelerating development
  • Table 3: Analytics tools for accelerating approval, access, and adherence
  • Table 4: Challenges to data analytics-driven R&D streamlining
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