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We are analyzing https://www.nature.com/articles/s41573-022-00552-x.

Title:
Predictive validity in drug discovery: what it is, why it matters and how to improve it | Nature Reviews Drug Discovery
Description:
Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which ‘predictive validity’ is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation. The ‘predictive validity’ of decision tools such as disease models that are used in drug research and development (R&D) — the degree to which the output from a tool correlates with clinical utility in people — has a major influence on R&D productivity. This article explains this influence and discusses methods to evaluate and improve the predictive validity of decision tools, with the aim of supporting the application of more effective tools and catalysing investment in their creation.
Website Age:
30 years and 10 months (reg. 1994-08-11).

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pubmed, google, scholar, cas, drug, central, cancer, rev, nat, nature, discov, research, discovery, med, development, models, cell, sci, clinical, animal, article, science, validity, plos, human, preclinical, pharmaceutical, drugs, stroke, information, decision, access, screening, treatment, review, biol, data, predictive, scannell, medicine, hepatitis, studies, efficacy, disease, springer, systematic, res, university, evolution, content,

Topics {✒️}

/content/dam/deloitte/uk/documents/life-sciences-health-care/deloitte-uk-ten-years /wp-content/themes/ncss/pdf/procedures/pass/confidence_intervals_for_pearsons_correlation nature portfolio journals clinical research services nature portfolio advertising financial services sectors sells consulting services privacy policy social media regional kinetics reprints open targets platform basic scientific research high-throughput orthogonal screening effective decision tools simple statistical parameter patient-derived xenograft model springer international publishing evaluating κ-opioid antagonism exemplar model-building workflow patient-derived xenograft models scientific advisory board drug administration–designated breakthrough drug-target interactions prediction general drug-likeness based direct-acting antiviral agents sample size dogma author information authors published research findings bmj open irrelevant cell-based models decision-theoretical analyses shown scientific rigor-survey open sci long-term outcomes high-throughput screening toxicology-related content genome-wide association studies decision tools von winterfeldt permissions health-care providers health-care evaluation open 2 public policy chemically defined conditions /articles/senate-finance-committee publishing agreement accepted manuscript version

Questions {❓}

  • An appraisal of FDA approvals for adult solid tumours in 2017–2021: has the eagle landed?
  • Antibiotic discovery: where have we come from, where do we go?
  • Are drug targets with genetic support twice as likely to be approved?
  • Are ideas getting harder to find?
  • Are some animal models more equal than others?
  • Artificial intelligence in drug discovery: what is realistic, what are illusions?
  • Assessing the translatability of drug projects: what needs to be scored to predict success?
  • Believe it or not: how much can we rely on published data on potential drug targets?
  • Can the flow of medicines be improved?
  • Can the pharmaceutical industry reduce attrition rates?
  • Can we learn to distinguish between “drug-like” and “nondrug-like” molecules?
  • Divide and conquer?
  • Drug innovation — what’s slowing it down?
  • Drug targeting in psychiatric disorders — how to overcome the loss in translation?
  • Expert Political Judgment: how Good is it?
  • Hepatitis C virus replicons: dinosaurs still in business?
  • How were new medicines discovered?
  • Is bigger better for depression trials?
  • Minireview: were the IGF signaling inhibitors all bad?
  • Modern biomedical research: an internally self-consistent universe with little contact with medical reality?
  • Preclinical efficacy studies in investigator brochures: do they enable risk–benefit assessment?
  • Progress against cancer?
  • What does dark matter even do?
  • Which aspects of HTS are empirically correlated with downstream success?
  • Why is it hard to terminate failing projects in pharmaceutical R&D?

Schema {🗺️}

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External Links {🔗}(560)

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