Customized Use of Anti-Tumour Necrosis Factor-α Therapy During Pregnancy
Bibliographic record
Abstract
We read with interest the manuscript by Kanis et al. on the association of anti-tumour necrosis factor-α [TNF] concentrations in cord blood with anti-TNF type.1 It has been well documented that controlling disease activity at the time of conception and during pregnancy results in favourable outcomes for mother and baby.2 However, gastroenterologists continue to question the long-term effects of fetal exposure to such therapies. Kanis et al. studied the effect of withholding anti-TNF therapy during the third trimester to limit fetal exposure by analysing cord blood samples of 52 infliximab- and 42 adalimumab-exposed subjects. Consistent with existing data, cord blood and infant anti-TNF concentrations exceeded that of the mother at term.1,3,4 By using a linear regression model, Kanis et al. found that the timing of last anti-TNF administration during pregnancy and the type of anti-TNF agent significantly influenced cord blood concentrations of infliximab and adalimumab. Other disease and patient demographics including age, body mass index, smoking and concomitant therapies did not improve the model. Consistent with our previous study, Kanis et al. found that serum concentrations increased exponentially during the third trimester for infliximab, yet intrapartum serum concentrations were relative stable for adalimumab.1,5 While both Kanis and Seow concluded that adalimumab can be continued for longer during pregnancy than infliximab without leading to higher anti-TNF concentrations in the newborn, Kanis states that adalimumab may be preferred over infliximab in women with a current or future pregnancy. However, we proposed that infliximab could be safely used during pregnancy, with second-trimester therapeutic drug monitoring informing the need for a third-trimester dose.5 This tailored continuation of therapy reduces potential risks of relapse during pregnancy and the postpartum period, and may also reduce the risk of subsequent loss of response to therapy resulting from low serum trough concentrations and the development of anti-drug antibodies.2,5 A definitive mechanistic explanation for the observed differential disposition of infliximab and adalimumab remains to be elucidated, yet it is highly likely that it involves the neonatal Fc receptor [FcRn] that binds to the Fc region of infliximab and adalimumab. With the exception of certolizumab, which consists of an Fc-free, polyethylene glycol-conjugated antigen-binding fragment, all current commercially available biological therapies for the management of inflammatory bowel disease have an Fc region. This includes the anti-TNFs [infliximab, adalimumab, golimumab], the anti-integrin vedolizumab, and the anti-interleukin 12/23 ustekinumab. Immunoglobulin [Ig] G does not cross the placenta in the first trimester, but is transported efficiently in the subsequent trimesters. Future studies should explore the pharmacokinetics of the other IgG1 biological therapies in pregnancy, to discern if the differences pertain to the class of therapy, mode of administration or other underdetermined factors. Ongoing pharmacovigilance should be conducted to determine the risks of infant exposure. None. CHS: Consultant for Janssen, Abbvie, Shire, Takeda, Actavis, Ferring, Pfizer; Speaker for Janssen, Abbvie, Takeda, Shire. NVC: Consultant for Janssen and Takeda. RP: Consultant: AbbVie, ActoGeniX, AGI Therapeutics, Alba Therapeutics Albireo, Alfa Wasserman, Amgen, AM-Pharma BV, Anaphore, Aptalis, Astellas, Athersys, Atlantic Healthcare, BioBalance, Boehringer-Ingelheim, Bristol-Myers Squibb, Celgene, Celek, Cellerix, Cerimon, ChemoCentryx, CoMentis, Cosmo Technologies, Coronado Biosciences, Cytokine Pharmasciences, Eagle, Eisai Medical Research, Elan, EnGene, Eli Lilly, Enteromedics, Exagen Diagnostics, Ferring, Flexion Therapeutics, Funxional Therapeutics, Genentech, Genzyme, Gilead, Given Imaging, GlaxoSmithKline, Human Genome Sciences, Ironwood, Janssen, KaloBios, Lexicon, Lycera, Meda, Merck & Co., Merck Research Laboratories, MerckSerono, Millennium, Nisshin Kyorin, Novo Nordisk, NPS Pharmaceuticals, Optimer, Orexigen, PDL Biopharma, Pfizer, Procter and Gamble, Prometheus Laboratories, ProtAb, Purgenesis Technologies, Receptos, Relypsa, Salient, Salix, Santarus, Shire Pharmaceuticals, Sigmoid Pharma, Sirtris [a GSK company], S.L.A. Pharma [UK], Targacept, Teva, Therakos, Tillotts, TxCell SA, UCB Pharma, Vascular Biogenics, Viamet and Warner Chilcott UK. Speaker: Abbvie, Aptalis, AstraZeneca, Ferring, Janssen, Merck, Prometheus, Shire, Takeda. Advisory Boards: Abbvie, Abbott, Amgen, Aptalis, AstraZeneca, Baxter, Biogen Idec, Eisai, Ferring, Genentech, Janssen, Merck, Shire, Elan, Glaxo-Smith Kline, Hospira, Pfizer, Bristol-Myers Squibb, Takeda, Cubist, Celgene, Salix. Research/Educational Support: Abbvie, Ferring, Janssen, Shire, Takeda. CS: initiated and wrote the manuscript, content expertise; NVC: content expertise, edited and approved the manuscript; RP: content expertise, edited and approved the manuscript. In response to Kanis SL, de Lima A, van der Ent C. et al. Anti-TNF levels in cord blood at birth are associated with anti-TNF type. J Crohns Colitis 2018;May 15. doi: 10.1093/ecco-jcc/jjy058.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".