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医薬品R&Dのデータ活用による変容

Transforming Pharmaceutical R&D with Data

発行 Datamonitor Healthcare 商品コード 527414
出版日 ページ情報 英文 31 Pages
納期: 即日から翌営業日
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本日の銀行送金レート: 1USD=113.38円で換算しております。
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医薬品R&Dのデータ活用による変容 Transforming Pharmaceutical R&D with Data
出版日: 2017年05月16日 ページ情報: 英文 31 Pages
概要

当レポートでは、データ活用によって医薬品R&Dを推進する各種アプローチを調査し、持続可能が困難な現在のR&Dの経済性、創薬・薬剤開発・承認・市場アクセス・服薬順守など各段階におけるデータ利用の可能性、データ主導によるR&Dの合理化における課題などをまとめています。

エグゼクティブサマリー

現在のR&Dのエコノミクスと非持続可能性

  • より効率的なR&Dを強いる薬価設定環境
  • 創薬から上市までの新しいツールの台頭
  • R&Dの効率向上:パートナーの必要性
  • 忘れられている資産からの価値の創出
  • コスト・時間の削減、など

創薬の推進

  • 確実な薬剤標的・リードのより迅速な同定およびバリデーション
  • AIによる創薬の加速
  • 大手医薬品事業者:コンピューター支援による創薬
  • AIアルゴリズムの予測性
  • 精密医療とバイオマーカー、など

開発の推進

  • 臨床試験の改善・推進
  • データ主導のサイト選定
  • 試験者採用の推進
  • 電子的な臨床試験データ回収、など

承認・アクセス・服薬遵守の推進

  • 法的プロセス・薬剤導入・服薬遵守の推進
  • 法規制上のレビューの推進
  • 商業的導入の加速
  • 転帰データ:R&Dはより広範なデータ主導のディスラプションの一部であることを示す、など

データ主導によるR&Dの合理化における課題

  • 法規制の不確定性
  • データの互換性
  • 組織的・文化的変化
  • 医薬品事業者:競争力維持のためデータスキルを最新のものにすべき、など

付録

図表

目次
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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