Clinical and population‐based study design considerations to accelerate the investigation of new antiretrovirals during pregnancy
Bibliographic record
Abstract
INTRODUCTION: Pregnant women are routinely excluded from clinical trials, leading to the absence or delay in even the most basic pharmacokinetic (PK) information needed for dosing in pregnancy. When available, pregnancy PK studies use a small sample size, resulting in limited safety information. We discuss key study design elements that may enhance the timely availability of pregnancy data, including the role and timing of randomized controlled trials (RCTs) to evaluate pregnancy safety; efficacy and safety outcome measures; stand-alone protocols, platform trials, single arm studies, sample size and the effect that follow-up time during gestation has on analysis interpretations; and observational studies. DISCUSSION: Pregnancy PK should be studied during drug development, after dosing in non-pregnant persons is established (unless non-clinical or other data raise pregnancy concerns). RCTs should evaluate the safety during pregnancy of priority new HIV agents that are likely to be used by large numbers of females of childbearing age. Key endpoints for pregnancy safety studies include birth outcomes (prematurity, small for gestational age and stillbirth) and neonatal death, with traditional adverse events and infant growth also measured (congenital anomalies are best studied through surveillance). We recommend that viral efficacy be studied as a secondary endpoint of pregnancy RCTs, once PK studies confirm adequate drug exposure in pregnancy. RCTs typically use a stand-alone protocol for new agents. In contrast, master protocols using a platform design can add agents over time, possibly speeding safety data ascertainment. To speed accrual, stand-alone pregnancy trial protocols can include pre-specified starting rules based upon adequate PK levels in pregnancy; and seamless master protocols or platform trials can include a pregnancy PK and safety component. When RCTs are unethical or cost-prohibitive, observational studies should be conducted, preferably using target trial emulation to avoid bias. CONCLUSIONS: Pregnancy PK needs to be obtained earlier in drug evaluation. Timely RCTs are needed to understand safety in pregnancy for high-priority new HIV agents. RCTs that enrol pregnant women should focus on outcomes unique to pregnancy, and observational studies should focus on questions that RCTs are not equipped to answer.
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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 | 0.566 | 0.589 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".