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Pharmacogenetic-Pharmacokinetic Interactions in Drug Marketing Authorization Applications via the European Medicines Agency Between 2014 and 2017.

Marc MaliepaardTimi ToiviainenMarie Louise De BruinDidier Meulendijks
Published in: Clinical pharmacology and therapeutics (2020)
This study aimed to determine to which extent data on potential pharmacogenetic-pharmacokinetic (PG-PK) interactions are provided to, and assessed by, the European Medicines Agency (EMA) in novel drug marketing authorization applications (MAAs), and whether regulatory assessment of PG-PK interactions is adequate or could be optimized. For this purpose, we retrospectively analyzed MAAs of small molecule drugs assessed by the EMA between January 2014 and December 2017. As per two key requirements in the EMA's guideline, we analyzed cases where (i) a single functionally polymorphic drug metabolizing enzyme (DME) metabolizes > 25% of the drug, or (ii) the drug's PK shows high interindividual variability not explained by other factors than PG. Results showed that, of 113 drugs analyzed, 53 (47%) had ≥ 1 functionally polymorphic DME accounting for > 25% of the drug's metabolism, yielding 55 gene-drug pairs. For 36 of 53 (68%) of the products, CYP3A4 was the major DME. Compliance with European Union (EU) guidance on PG-PK issues in drug development was notably different for CYP3A4 substrates vs. non-CYP3A4 substrates. Adequate PG-PK data were provided during registration in 89% (16/18) of cases concerning non-CYP3A4 substrates, compared with 32% (12/37) of cases concerning CYP3A4 substrates. Concluding, PG-PK interactions related to non-CYP3A4 substrate drugs were, in general, addressed adequately in EU MAAs. PG-PK information on CYP3A4 substrates was available less frequently, despite some available evidence on the functional relevance of CYP3A4 polymorphisms. A more harmonized approach toward assessment of PG-PK issues in EU MAAs seems warranted, and a discussion on the relevance of CYP3A4 polymorphisms, such as CYP3A4*22, is recommended.
Keyphrases
  • small molecule
  • drug induced
  • adverse drug
  • healthcare
  • machine learning
  • risk assessment
  • climate change