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From Molecular Insights to Patient Stratification for Neurological and Psychiatric Disorders Proceedings of a Workshop (2022) / Chapter Skim
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7 Potential Next Steps: Shifting the Trajectory of Treatment Development for Neurological and Psychiatric Disorders
Pages 63-70

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From page 63...
... . • The international scientific community must come together to develop strategies that enable data sharing despite the complex and sometimes conflicting international rules around privacy that present barriers to the goal of building global partnerships (Weninger)
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From page 64...
... The workshop highlighted some of the emerging technologies to b ­ etter define genetic variants, genes linked to these variants, and interactions among these genes and pathways, said Dimitri Krainc. These technologies will make it possible to identify genetically validated targets and demonstrate target engagement in patients, develop a deep mechanistic understanding of disease, produce high-quality and reproducible data, and better stratify diverse patient populations using genetics and biomarkers.
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From page 65...
... Yet she said due to the rarity of the variants, the current dataset only enables identification of low-hanging fruit, and that much larger datasets are nec essary to confirm the promising signals they currently observe beyond the low-hanging fruit. Many of these partnerships encourage data sharing.
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From page 66...
... SHIFTING THE TRAJECTORY OF THERAPEUTIC DEVELOPMENT FOR NEUROLOGICAL AND PSYCHIATRIC DISORDERS: STRATEGIES PROPOSED BY INDIVIDUAL WORKSHOP PARTICIPANTS In the final session of the workshop, participants discussed specific next steps that could shift the trajectory of therapeutic development for neurological and psychiatric disorders. 4 To learn more about PRO-ACT, see https://ncri1.partners.org/ProACT/Document/­ DisplayLatest/9 (accessed December 1, 2021)
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From page 67...
... In support of the idea of bringing people together, Casey said he has been heartened by the emergence of platforms and consortia willing to not only share data, but also to build out the goals of a project that serves the entire community. Examples include GP2, the Global P ­ arkinson's Genetics Program, and the BRAIN Initiative Cell Census Network.5 Reflecting on the workshop discussions, Joshua Gordon, direc tor of the National Institute of Mental Health, challenged work shop participants to develop a vision of what it would take to go from frog to mouse, to non-human primate, to human cells, to a drug for a neurological or psychiatric condition, including the col laborations and scientists needed for this approach to research.
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From page 68...
... Hyman added that biorepositories are needed that are not strictly focused on a single disease and that bring together diverse sources of genetics and diverse ancestries, and collect follow-on materials. For example, he said, 30,000 genetic samples have been collected in East and South Africa to study schizophrenia, but to interpret common variants would also require brain tissue.
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From page 69...
... Hyman added that such natural history phenotyping and biomarker studies that require substantial patient involvement also require the expertise of physician-scientists, who are currently in short supply. He suggested that the need for these studies might motivate NIH to increase support for training physician-scientists.
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From page 70...
... • Integrating biomarkers much earlier into therapeutic development. To support translational research, Tamiz suggested making strati fication a key criterion when creating a target product profile.
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Key Terms

  • therapeutic development
  • clinical trials
  • accessed december
  • brain initiative
  • accessed november

  • international scientific
  • enable data
  • psychiatric disorders
  • census network
  • therapy development

  • john ngai
  • develop targeted
  • ngai added
  • international rules
  • demonstrate efficacy

  • alzheimer center
  • initiative cell
  • international collaboration
  • cell census
  • diverse datasets

  • amsterdam alzheimer
  • weninger noted
  • incentive structures
  • data protection
  • collecting data

  • trials data
  • data banks
  • overfitting data
  • generated data
  • encourage data


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