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Carmentix announces milestone publication in peer-reviewed journal AJOG-MFM

Carmentix Announces Publication of Peer-Reviewed Study in the American Journal of Obstetrics & Gynecology MFM (Maternal-Fetal-Medicine) for Identification of Novel Protein Biomarkers in Cervicovaginal Fluid for Early Prediction of Preterm Birth.


Preterm birth (PTB) is the birth of infant before the 37th week of gestation. In 2018, preterm birth affected 1 of every 10 infants born in the United States.[i] Across the globe, preterm birth is the leading cause of perinatal morbidity and mortality. In 2015, preterm birth complications resulted in approximately 1 million deaths among children under 5 years of age.[ii] Across 184 countries, the rate of preterm birth ranges from 5% to 18% of babies born.[iii]

To address the global issue of preterm birth, Carmentix has collaborated with the University of Melbourne to conduct a clinical study to validate protein biomarkers that could accurately identify asymptomatic women who are at risk of preterm birth. The results of the study were recently published in AJOG-MFM, titled “Preterm birth prediction in asymptomatic women at mid-gestation using a panel of novel protein biomarkers: the Prediction of PreTerm Labor (PPeTaL) study”.

The PPeTaL study, conducted in Mercy Hospital for Women and the Royal Women’s Hospital, evaluated cervicovaginal fluid (CVF) samples from 286 asymptomatic pregnant women at early stage of pregnancy of 16-24 weeks of gestation. The women were divided into two cohorts: training cohort (n=136) and validation cohort (n=150). CVF samples collected from the women were subsequently tested against a panel of seven protein biomarkers. These biomarkers have been earlier on identified through high throughput bioinformatic search to be closely related to biological mechanisms leading to labor.

Based on the notion that preterm birth is a multifactorial disease, all seven biomarkers, involved in different biological mechanisms and pathways, were combined into a multiplex panel to develop a predictive algorithm to distinguish term and preterm birth. In the training cohort, the algorithm has achieved a striking predictive accuracy of 100% sensitivity and 74% specificity, identifying 12 of 12 preterm births. These results were further validated in the validation cohort and achieved consistent accuracy. The algorithm accurately identified 10 of 11 preterm birth samples (91% sensitivity) with a specificity of 78% in the validation cohort.



“The results published exceed our initial expectations. Challenging ourselves and succeeding in an all-comers study set a high bar for ourselves and our competitors. Our focus now is to make our test accessible in order to impact preterm birth rates,” commented Dr. Nir Arbel, Chief Executive Officer and Co-founder of Carmentix.

Carmentix plans to validate its findings further in upcoming multi-centred clinical studies, in collaboration with universities and hospitals in Hong Kong, mainland China and the United Kingdom.

To find out more about our publication, visit: https://www.sciencedirect.com/science/article/pii/S2589933319301247

[i] https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pretermbirth.htm

[ii] https://www.who.int/news-room/fact-sheets/detail/preterm-birth

[iii] https://www.who.int/news-room/fact-sheets/detail/preterm-birth

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