Bibliographic record
Abstract
Abstract Objective : Clinical development of a drug product is a long and complex process, so it is rather impossible to capture all the aspects of this process in details in this article. Accordingly, the key objective of this article is to present an overview of how a new medicinal product undergoes clinical evaluation to ensure that ultimately quality medicine with proven safety and efficacy is delivered to the end user, i.e. patient who consumes it for health benefit. An attempt is also made to briefly cover clinical development of biological drugs, which can require a different approach to supporting the dose justification and assessing immunogenicity risks as compared to chemically synthesized drug products. Method : The information covered in this article is based on personal experience in the area of clinical development for regulatory submissions worldwide, regulatory guidance documents and legislations, as well as extensive literature surveillance using key terms such as clinical research, phases of development, efficacy, safety, new drug product, new chemical entity, new therapeutic entity, generics, biologics and biosimilars, oncology trials, modeling, and simulation. Clinical development is a process driven not only by science and regulations but also significantly by cost and time. The interested reader is encouraged to review clinical trial guidelines on the Food and Drug Administration (FDA) website and websites of other regulatory authorities such as European Medicines Agency (EMA), Therapeutic Goods Administration (TGA), and Health Canada. Those with further interest may also visit Clinical Trial Registry websites including www.clinicaltrials.gov to get familiar to different types of clinical study designs across phases of development carried out by sponsors as research studies or in pursuance of their marketing authorization applications.
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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.020 | 0.021 |
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".