Choice of Starting Dose for Biopharmaceuticals in First-in-Human Phase I Cancer Clinical Trials
Bibliographic record
Abstract
BACKGROUND: First-in-human (FIH) trials of low-molecular-weight anticancer agents conventionally derive a safe start dose (SD) from one-tenth the severely toxic dose in 10% of rodents or one-sixth the highest nonseverely toxic dose (HNSTD) in nonrodent species. No consensus has been reached on whether this paradigm can be safely applied to biotechnology-derived products (BDPs). MATERIALS AND METHODS: A comprehensive search was conducted to identify all BDPs (excluding immune checkpoint inhibitors and antibody drug conjugates) with sufficient nonclinical and clinical data to assess the safety of hypothetical use of one-sixth HNSTD in an advanced cancer FIH trial. RESULTS: The search identified 23 BDPs, of which 21 were monoclonal antibodies. The median ratio of the maximum tolerated or maximum administered dose (MTD or MAD) to the actual FIH SD was 36 (range, 8-500). Only 2 BDPs reached the MTD. Hypothetical use of one-sixth HNSTD (allometrically scaled to humans) would not have exceeded the MTD or MAD for all 23 BDPs and would have reduced the median ratio of the MTD or MAD to a SD to 6.1 (range, 3.5-55.3). Pharmacodynamic (PD) markers were included in some animal toxicology studies and were useful to confirm the hypothetical SD of one-sixth HNSTD. CONCLUSION: One-sixth HNSTD would not have resulted in unacceptable toxicities in the data available. Supporting its use could reduce the number of dose escalations needed to reach the recommended dose. A low incidence of toxicities in animals and humans underscores the need to identify the pharmacokinetic and PD parameters to guide SD selection of BDPs for FIH cancer trials. IMPLICATIONS FOR PRACTICE: Start dose (SD) for biotechnology-derived products (BDPs) can be safely derived from one-sixth the highest nonseverely toxic dose in nonrodent species and may reduce the number of dose escalations needed to reach the recommended dose in first-in-human studies while limiting unnecessary exposure to high drug levels in humans. The use of this type of SD could improve the design of phase I studies of BDPs by making them more efficient. The role of preclinical pharmacodynamic markers was useful in confirming the hypothetical SD, and attempts should be explored in future animal studies to identify such parameters.
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".