AltraBio provides end-to-end medical data analysis and biostatistical support for clinical research, observational studies, and real-world evidence (RWE) projects. Operating across major therapeutic areas—including neurosciences, immunology, oncology, dermatology, cardiology, and rheumatology—our senior statisticians support human and veterinary biopharma, medical device manufacturers, biotechs, and academic medical centers from early design to post-market clinical follow-up.
End-to-End Biostatistical Services for Clinical Research
Study Design & Biostatistical Planning
Precise determination of cohort sizes required to achieve statistical significance and satisfy regulatory constraints.
Methodological drafting of concise study synopses and clinical protocols aligned with statistical best practices.
Development of comprehensive SAPs outlining primary/secondary endpoints, hypothesis testing, and statistical methodology prior to database lock.
Data Management & CDISC Compliance
Structuring data collection workflows, quality controls, and data governance frameworks.
Expertise in choosing and configuring electronic Case Report Forms tailored to your trial design.
Formatting, extraction, and standardization of clinical datasets according to international CDISC guidelines.
Handling atypical data, protocol deviations, and missing data using validated statistical imputation methods.
Data Analysis, Modeling & Regulatory Valorization
Parametric and non-parametric differential analyses for clinical safety and efficacy endpoints.
Building multivariate regression and machine learning models to identify prognostic factors and stratify patient populations.
Fully documented statistical reports (PDF and interactive web dashboards) with traceable processing algorithms.
Expert assistance in drafting abstracts, posters, and peer-reviewed articles to communicate your clinical findings.ports (PDF and interactive web dashboards) with traceable processing algorithms.
Peer-Reviewed Clinical & Medical Publications
2026
Randall, Matthew J.; Andersen, Claus A.; Brown, Kevin K.; de Bernard, Simon; Ford, Paul; Kaminski, Naftali; Kreuter, Michael; Lim, Sharlene; Maher, Toby M.; Prasad, Niyati; Prasse, Antje; Pujuguet, Philippe; Teneggi, Vincenzo; van den Blink, Bernt; Wain, Louise V.; Watkins, Timothy R.; Wuyts, Wim; Bauer, Yasmina
Prognostic biomarkers for idiopathic pulmonary fibrosis: findings from ISABELA clinical trials Journal Article
In: ERJ Open Res, vol. 12, no. 1, pp. 00893–2025, 2026, ISSN: 2312-0541.
@article{Randall2025,
title = {Prognostic biomarkers for idiopathic pulmonary fibrosis: findings from ISABELA clinical trials},
author = {Matthew J. Randall and Claus A. Andersen and Kevin K. Brown and Simon de Bernard and Paul Ford and Naftali Kaminski and Michael Kreuter and Sharlene Lim and Toby M. Maher and Niyati Prasad and Antje Prasse and Philippe Pujuguet and Vincenzo Teneggi and Bernt van den Blink and Louise V. Wain and Timothy R. Watkins and Wim Wuyts and Yasmina Bauer},
doi = {10.1183/23120541.00893-2025},
issn = {2312-0541},
year = {2026},
date = {2026-01-00},
urldate = {2026-01-00},
journal = {ERJ Open Res},
volume = {12},
number = {1},
pages = {00893--2025},
publisher = {European Respiratory Society (ERS)},
abstract = {Background
Idiopathic pulmonary fibrosis (IPF) is characterised by progressive loss of pulmonary function and poor survival. Although biomarkers for disease progression and mortality exist, their reliability in large studies remains unproven. This study investigates prognostic biomarkers from the ISABELA trials, the largest IPF cohort to date, to identify those predicting worse clinical outcomes.
Methods
Plasma from 1280 IPF patients in ISABELA 1 and 2 (NCT03711162, NCT03733444) was analysed for 17 circulating soluble disease-related biomarkers at multiple time-points and for the MUC5B (rs35705950_T) genotype. Statistical learning algorithms investigated biomarker levels/status with disease progression (≥10% decline in forced vital capacity (FVC) or mortality within 1 year) and pharmacotherapy.
Results
Patients with ≥10% annual decline in FVC had higher median baseline of matrix metalloproteinase-7 (MMP-7) versus those with <10% decline (5.5 versus 4.2 µg·L−1; p<0.005). Patients with baseline MMP-7 ≥5.2 μg·L−1 and/or C-C motif chemokine ligand 18 (CCL18) ≥75.2 μg·L−1 had increased risk of mortality (p<0.0001); with patients having both elevated biomarkers at an even greater risk. Machine learning identified CCL18 changes by week 26 as a predictor of disease progression. The rs35705950_T genotype predicted neither mortality nor disease progression.
Conclusions
We provide new insights into the prognostic value of MMP-7 and CCL18 in identifying high-risk IPF patients in the largest cohort to date. The combination of high baseline MMP-7 and CCL18 levels, along with longitudinal changes in CCL18, has the potential to enhance risk stratification and support efficacy assessment and monitoring in clinical trials.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Idiopathic pulmonary fibrosis (IPF) is characterised by progressive loss of pulmonary function and poor survival. Although biomarkers for disease progression and mortality exist, their reliability in large studies remains unproven. This study investigates prognostic biomarkers from the ISABELA trials, the largest IPF cohort to date, to identify those predicting worse clinical outcomes.
Methods
Plasma from 1280 IPF patients in ISABELA 1 and 2 (NCT03711162, NCT03733444) was analysed for 17 circulating soluble disease-related biomarkers at multiple time-points and for the MUC5B (rs35705950_T) genotype. Statistical learning algorithms investigated biomarker levels/status with disease progression (≥10% decline in forced vital capacity (FVC) or mortality within 1 year) and pharmacotherapy.
Results
Patients with ≥10% annual decline in FVC had higher median baseline of matrix metalloproteinase-7 (MMP-7) versus those with <10% decline (5.5 versus 4.2 µg·L−1; p<0.005). Patients with baseline MMP-7 ≥5.2 μg·L−1 and/or C-C motif chemokine ligand 18 (CCL18) ≥75.2 μg·L−1 had increased risk of mortality (p<0.0001); with patients having both elevated biomarkers at an even greater risk. Machine learning identified CCL18 changes by week 26 as a predictor of disease progression. The rs35705950_T genotype predicted neither mortality nor disease progression.
Conclusions
We provide new insights into the prognostic value of MMP-7 and CCL18 in identifying high-risk IPF patients in the largest cohort to date. The combination of high baseline MMP-7 and CCL18 levels, along with longitudinal changes in CCL18, has the potential to enhance risk stratification and support efficacy assessment and monitoring in clinical trials.
2025
Cognasse, Fabrice; Nguyen, Kim Anh; Heestermans, Marco; Arthaud, Charles-Antoine; Eyraud, Marie-Ange; Prier, Amelie; de Bernard, Simon; Nourikyan, Julien; Duchez, Anne-Claire; Avril, Stephane; Garraud, Olivier; Hamzeh-Cognasse, Hind
Computational modeling of platelet activation signatures in response to diverse immune and hemostatic agonists Journal Article
In: Platelets, vol. 36, no. 1, 2025, ISSN: 1369-1635.
@article{Cognasse2025,
title = {Computational modeling of platelet activation signatures in response to diverse immune and hemostatic agonists},
author = {Fabrice Cognasse and Kim Anh Nguyen and Marco Heestermans and Charles-Antoine Arthaud and Marie-Ange Eyraud and Amelie Prier and Simon de Bernard and Julien Nourikyan and Anne-Claire Duchez and Stephane Avril and Olivier Garraud and Hind Hamzeh-Cognasse},
doi = {10.1080/09537104.2025.2572982},
issn = {1369-1635},
year = {2025},
date = {2025-10-27},
urldate = {2025-10-27},
journal = {Platelets},
volume = {36},
number = {1},
publisher = {Informa UK Limited},
abstract = {Platelets are increasingly recognized as key players not only in hemostasis, but also in immunity and inflammation. However, the mechanisms and markers underlying their activation remain incompletely understood. This study aimed to decipher how platelets respond to different stimuli and to identify specific molecular signatures using computational approaches. Platelets from 10 healthy donors were stimulated under seven conditions, including TRAP (PAR-1), AYPGKF (PAR-4), ADP, collagen, sCD40L, fibrinogen, and a control. A total of 47 markers—encompassing membrane proteins, soluble mediators, and intracellular signals—were analyzed. Statistical and machine learning methods, including hierarchical clustering and random forest algorithms, were used to classify and interpret the data. Distinct activation profiles emerged for each agonist. A reduced panel of six markers (AKT, CD40L, CD62P, PKC, RANTES, and TSLP) enabled identification of the stimulus with 86.8% accuracy. Machine learning further improved classification (87.9% multiclass accuracy). Differences were also observed across donors, highlighting inter-individual variability. This work supports a new paradigm in which platelets act as “biological sensors,” fine-tuning their responses to environmental cues. The identified biomarker panel provides a basis for further investigation into the characterization of platelet activation profiles, with potential relevance for future diagnostic and therapeutic applications in thromboinflammatory and immune-mediated conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ribeiro, Sara; Alves, Karine; Nourikyan, Julien; Lavergne, Jean-Pierre; de Bernard, Simon; Buffat, Laurent
Identifying potential novel widespread determinants of bacterial pathogenicity using phylogenetic-based orthology analysis Journal Article
In: Front. Microbiol., vol. 16, 2025, ISSN: 1664-302X.
@article{Ribeiro2025,
title = {Identifying potential novel widespread determinants of bacterial pathogenicity using phylogenetic-based orthology analysis},
author = {Sara Ribeiro and Karine Alves and Julien Nourikyan and Jean-Pierre Lavergne and Simon de Bernard and Laurent Buffat},
doi = {10.3389/fmicb.2025.1494490},
issn = {1664-302X},
year = {2025},
date = {2025-05-01},
urldate = {2025-05-01},
journal = {Front. Microbiol.},
volume = {16},
publisher = {Frontiers Media SA},
abstract = {<jats:sec><jats:title>Introduction</jats:title><jats:p>The global rise in antibiotic resistance and emergence of new bacterial pathogens pose a significant threat to public health. Novel approaches to uncover potential novel diagnostic and therapeutic targets for these pathogens are needed.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>In this study, we conducted a large-scale, phylogenetic-based orthology analysis (OA) to compare the proteomes of pathogenic to humans (HP) and non-pathogenic to humans (NHP) bacterial strains across 734 strains from 514 species and 91 families.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Using a dedicated workflow, we identified 4,383 hierarchical orthologous groups (HOGs) significantly associated with the HP label, many of which are linked to critical factors such as stress tolerance, metabolic versatility, and antibiotic resistance. Both known virulence factors (VFs) and potential novel widespread pathogenicity determinants were uncovered, supported by both statistical testing and complementary protein domain analysis.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>By integrating curated strain-level pathogenicity annotations from BacSPaD with phylogeny-based OA, we introduce a novel approach and provide a novel resource for bacterial pathogenicity research.</jats:p></jats:sec>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}