Prospective, Registry-Based Observational Cohort Study of Ritlecitinib Safety in Pregnancy

Study type

• Study topic:
  Human medicinal product
• Study type:
  Non-interventional study
• Scope of the study:
  Safety study (incl. comparative)
• Data collection methods:
  Primary data collection

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Name of medicine:
  LITFULO
• Study drug International non-proprietary name (INN) or common name:
  RITLECITINIB
• Anatomical Therapeutic Chemical (ATC) code:
  (L04AF08) ritlecitinib

  ritlecitinib
• Medical condition to be studied:
  Pregnancy

  Alopecia areata

Population studied

• Short description of the study population:
  Two cohorts of pregnant individuals with AA in the US. The study cohorts will include individuals exposed to ritlecitinib during pregnancy and individuals unexposed to ritlecitinib during pregnancy who are enrolled in the registry.
• Age groups:
  • Paediatric Population (< 18 years)

    • Neonate

      • Preterm newborn infants (0 – 27 days)
      • Term newborn infants (0 – 27 days)
    • Children (2 to < 12 years)
    • Adolescents (12 to < 18 years)
  • Adult and elderly population (≥18 years)

    • Adults (18 to < 65 years)

      • Adults (18 to < 46 years)
      • Adults (46 to < 65 years)
    • Elderly (≥ 65 years)

      • Adults (65 to < 75 years)
      • Adults (75 to < 85 years)
      • Adults (85 years and over)
• Special population of interest:
  Pregnant women
• Estimated number of subjects:
  400

Study design details

• Study design:
  This study is a prospective, registry-based observational cohort study. This study will be a new, product-based pregnancy registry. Data will be collected from eligible participants in the US.
• Main study objective:
  Primary objective: To estimate the prevalence of MCM births among pregnant individuals with AA who are (1) exposed to ritlecitnib and (2) unexposed to ritlecitnib.
• Setting:
  The study population will be derived from eligible individuals in the US enrolled in the pregnancy registry.
• Outcomes:
  Outcomes of interest; Primary: Major congenital malformation (MCM); Secondary: Spontaneous abortion (SAB), elective termination, pregnancy complications (pre-eclampsia, eclampsia), stillbirth, preterm birth, SGA, minor congenital malformation, infant postnatal growth deficiency, and infant developmental delay.
• Data analysis plan:
  The analysis population will include valid participants who were prospectively enrolled in the registry, are not exposed to teratogens or investigational medications during pregnancy, and who are not considered lost to follow up. Patient characteristics will be summarized with descriptive statistics for each cohort. The number of observations, median, mean, standard deviation, minimum, and maximum will be reported for each continuous variable. The frequency and percentage per category will be reported for each categorical variable. Prevalence of the outcomes of interest will be calculated according to the conventions described in the protocol. In general, the prevalence of each outcome will be calculated by dividing the number of cases of the outcome by the appropriate denominator for that particular outcome, based on clinical knowledge. For most outcomes, the analysis population (denominator) will be the number of pregnant individuals with pregnancy outcome data, the number of live births, or the number of infants with follow-up data at the timepoint of interest, as appropriate; however, for some outcomes, the analysis population (denominator) will be restricted based on certain relevant factors. Comparative analyses will be conducted for each outcome if sample size permits. Crude (unadjusted) RRs (and corresponding 95% CIs) will be calculated using Exact methods. Adjusted RRs will be calculated using generalized linear models (binomial family) with a log (RR) link and weighted by IPTW. The Clopper-Pearson method will be used to derive 95% CIs. IPTW will be calculated using propensity scores estimated from propensity score models. Each individual’s propensity score will be estimated using a logistic regression model with exposure status as the outcome (dependent variable). Detailed methodology for summary and statistical analysis of the data collected in this study will be documented in a statistical analysis plan