Next-Generation Medicines for Neurological and Neurodegenerative Disorders: From Discovery to Commercialization
Bibliographic record
Abstract
This book delves deeply into the discovery, preclinical and clinical development, and commercialization of next-generation medicines designed to treat neurological and neurodegenerative disorders. The book also covers pharmacologic targeting of the gut-brain axis in neurological and neurodegenerative disorders, the associated obstacles, challenges, and technologies involved, and the role of nanomedicines in addressing some of those challenges. The editors invited leading scientists and researchers with established credentials in CNS drug development and related technologies to contribute to the writing of the chapters. As a consequence, the editor and the readers are presented with a unique perspective of a selection of novel ideas, technologies, and medicines that have the potential to transform the current therapeutic landscape. The book is aimed primarily toward those in the pharmaceutical industry and academia who are involved in the development of drugs for treating CNS disorders. It will also serve as a valuable resource for technology transfer organizations and venture capital firms for selecting and investing in therapeutic ideas for treating CNS disorders. When we think of neurological and neurodegenerative disorders, we typically think of old age-related diseases such as Alzheimer’s or Parkinson’s disorders. While it is true that these diseases are responsible for the bulk of the societal and economic cost of CNS disorders, it is also true that CNS disorders are typically difficult to treat as a collective group because they have many general challenges in drug development in common, regardless of patient population or phase of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".