Translational PK/PD framework for antibody-drug conjugates to inform drug discovery and development
Bibliographic record
Abstract
ADCs represent a transformative class of medicine that combines the specificity of monoclonal antibodies with the potency of highly cytotoxic agents through linkers, aiming to enhance the therapeutic index of cytotoxic drugs. Given the complex molecular structures of ADCs, combining the molecular characteristics of small-molecule drugs and those of large-molecule biotherapeutics, there are several unique considerations when designing nonclinical-to-clinical PK/PD translation strategies.This complexity also demands a thorough understanding of the ADC's components - antibody, linker, and payload - to the overall toxicological, PK/PD, and efficacy profile. ADC development is a multidisciplinary endeavour requiring a strategic integration of nonclinical safety, pharmacology, and PK/PD modelling to translate from bench to bedside successfully.The ADC development underscores the necessity for a robust scientific foundation, leveraging advanced analytical and modelling tools to predict human responses and optimise therapeutic outcomes.This review aims to provide an ADC translational PK/PD framework by discussing unique aspects of ADC nonclinical to clinical PK translation, starting dose determination, and leveraging PK/PD modelling for human efficacious dose prediction and potential safety mitigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".