Challenges in Designing Clinical Trials to Test New Drugs in the Pregnant Woman and Fetus

Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models

This study aimed to develop a model for predicting the completion of clinical trials involving pregnant women using the Cox proportional hazard model and neural network model (DeepSurv) and to compare the predictive performance of both methods. We collected data on 819 clinical trials performed on pregnant women and intervention studies using at least one drug as intervention from 2009 to 2018 from ClinicalTrials.gov. The Cox proportional hazard model and DeepSurv were used to develop models that predict clinical trial completion. The concordance index (C‐index) was used to evaluate the predictive performance. The Cox proportional hazard model revealed that a sample size of n ≥ 329 (hazard ratio [HR] = 0.53), very high human development index (HDI) country (HR = 0.28), abortion (HR = 3.30), labor (HR = 2.16), and iron deficiency anemia (HR = 2.29) were significantly related to the probability of clinical trial completion (all p value < 0.01). The C‐index of the model development dataset and test dataset were 0.72 and 0.73, respectively. DeepSurv model consisted of one hidden layer with 16 nodes. DeepSurv showed the C‐index comparable to the Cox proportional hazard model. The C‐index of the training dataset and test dataset were 0.76 and 0.72, respectively. Further a nomogram that calculate a probability of clinical trial completion at 1 year, 3 years, and 5 years was developed. Both the Cox proportional hazard model and DeepSurv yielded sufficient predicting performance. We hope that this study will contribute to the execution of future clinical trials in pregnant women.

Development of standard definitions and grading for Maternal and Fetal Adverse Event Terminology

OBJECTIVE

Adverse event (AE) monitoring is central to assessing therapeutic safety. The lack of a comprehensive framework to define and grade maternal and fetal AEs in pregnancy trials severely limits understanding risks in pregnant women. We created AE terminology to improve safety monitoring for developing pregnancy drugs, devices and interventions.

METHOD

Existing severity grading for pregnant AEs and definitions/indicators of 'severe' and 'life-threatening' conditions relevant to maternal and fetal clinical trials were identified through a literature search. An international multidisciplinary group identified and filled gaps in definitions and severity grading using Medical Dictionary for Regulatory Activities (MedDRA) terms and severity grading criteria based on Common Terminology Criteria for Adverse Event (CTCAE) generic structure. The draft criteria underwent two rounds of a modified Delphi process with international fetal therapy, obstetric, neonatal, industry experts, patients and patient representatives.

RESULTS

Fetal AEs were defined as being diagnosable in utero with potential to harm the fetus, and were integrated into MedDRA. AE severity was graded independently for the pregnant woman and her fetus. Maternal (n = 12) and fetal (n = 19) AE definitions and severity grading criteria were developed and ratified by consensus.

CONCLUSIONS

This Maternal and Fetal AE Terminology version 1.0 allows systematic consistent AE assessment in pregnancy trials to improve safety.

Anti-Infective Dosing in Special Populations: Pregnancy

Substantial anatomical and physiological changes occur during pregnancy and labor, which impact on drug absorption, distribution, metabolism, and elimination. Reduced maternal concentrations may have a clinically important impact on the efficacy of anti-infectives for mother, fetus, and neonate, with potential dosing implications. However, there is a paucity of pregnancy-specific data examining this. Existing data on the pharmacokinetics of anti-infectives in pregnancy are summarized and evaluated, with emphasis on agents that are used in treatment of HIV, tuberculosis, malaria, and common bacterial infections. Limitations and challenges in achieving ideal study designs in pregnant populations are highlighted, and key quality considerations for the generation of the highest quality evidence are outlined. PubMed was searched for each chosen anti-infective. Pharmacokinetic studies which either compared pharmacokinetics from pregnant women against nonpregnant controls, or which assessed concentrations against a known minimum inhibitory concentration were included. Two independent reviewers extracted data from each study and appraised them using the 24-point ClinPK Checklist. The main finding was that there is a lack of published data for anti-infectives in pregnancy, despite their clinical importance. Of the studies identified, only those investigating cobicistat-boosted antiretroviral regimens firmly concluded that these should not be used in pregnancy. Most studies concluded either that further research was needed, or that there were significant pharmacokinetic differences between pregnant and nonpregnant participants which had uncertain clinical significance. Challenges in applying existing quality grading systems to these studies were noted, suggesting a development of a refined system for appraisal of pharmacokinetic studies in "special populations" may be warranted.

Engineered tissues and strategies to overcome challenges in drug development

Current preclinical studies in drug development utilize high-throughput in vitro screens to identify drug leads, followed by both in vitro and in vivo models to predict lead candidates' pharmacokinetic and pharmacodynamic properties. The goal of these studies is to reduce the number of lead drug candidates down to the most likely to succeed in later human clinical trials. However, only 1 in 10 drug candidates that emerge from preclinical studies will succeed and become an approved therapeutic. Lack of efficacy or undetected toxicity represents roughly 75% of the causes for these failures, despite these parameters being the primary exclusion criteria in preclinical studies. Recently, advances in both biology and engineering have created new tools for constructing new preclinical models. These models can complement those used in current preclinical studies by helping to create more realistic representations of human tissues in vitro and in vivo. In this review, we describe current preclinical models to identify their value and limitations and then discuss select areas of research where improvements in preclinical models are particularly needed to advance drug development. Following this, we discuss design considerations for constructing preclinical models and then highlight recent advances in these efforts. Taken together, we aim to review the advances as of 2020 surrounding the prospect of biological and engineering tools for adding enhanced biological relevance to preclinical studies to aid in the challenges of failed drug candidates and the burden this poses on the drug development enterprise and thus healthcare.

Research in Pregnant Subjects: Increasingly Important, but Challenging

Background: The number of pregnant women with medical comorbidities continues to increase. A large proportion of pregnant women are exposed to medications during pregnancy, but only a fraction of the medications used have been investigated during pregnancy with regard to benefits, risks, and doses.

Methods: This article includes a review of potential deterrents and barriers to pregnant women enrolling in clinical research studies and the federal regulations governing enrollment of pregnant women in research.

Results: Research in pregnant women has been hampered by concerns for liability, the complex physiology of pregnancy with changes related to stage of pregnancy, and federal regulations that deemed pregnant women a vulnerable population. While recent revisions to federal regulations have removed pregnant women from the classification of vulnerable population, regulations regarding consent requirements still limit women's ability to decide on participation in clinical trials. The Department of Health and Human Services established the Task Force on Research Specific to Pregnant Women and Lactating Women to help identify and reduce these barriers.

Conclusion: While recognition of the need for more scientific knowledge on the effects of medications and other interventions in pregnancy is widespread, a number of barriers that hinder enrollment of pregnant women in clinical trials remain.