Biology subjects

Woillard, J.-B.

Publications and source records attributed to Woillard, J.-B..

3 recordsLinked to original sources

bioRxiv2026

A multiscale pharmacokinetic-Boolean modelling framework for ibrutinib resistance in a Chronic Lymphocytic Leukaemia case study

Ibrutinib, a covalent Bruton's tyrosine kinase (BTK) inhibitor, achieves durable remissions in chronic lymphocytic leukaemia (CLL), and yet a substantial proportion of patients relapse. A fundamental question is how the quantitative dynamics of drug exposure relate to the regulatory programmes and cell fate decisions that characterise response and relapse. Addressing it requires a multiscale framework that bridges the quantitative pharmacology of drug exposure with the molecular mechanisms encoded in the gene regulatory network, a coupling that has remained largely unexplored in the context of targeted therapy resistance. Here we present a multi-scale computational framework that addresses this question by coupling continuous patient-specific pharmacokinetic profiles with discrete Boolean dynamics, bridging two modelling paradigms that have so far operated in isolation. Applied to a longitudinal single-cell dataset from one CLL patient treated with ibrutinib across three clinical timepoints (pre-treatment, response, relapse), the framework integrates gene regulatory network inference, cell fate trajectory analysis, and a stochastic Boolean model, whose dynamics is driven by the patient-specific monthly drug exposure. AP-1 and integrated stress response regulons were specific to relapse, and the coupled model reproduced the clinical course. Its perturbation screens identified co-inhibition of BTK with BCL2L1 or NFKB1 as synergistic, in line with the rationale for dual targeting of BTK and the BCL2 family in treatment-naive CLL. These results show how the framework turns single-patient data into mechanistic, testable hypotheses on resistance and combination therapy, which can be evaluated in larger cohorts and in experimental models.

systems biology↗

bioRxiv2026

A unified benchmark of synthetic data generation for clinical transcriptomic cancer cohorts

Achieving a trade-off between biological utility and patient privacy remains a key challenge for secure data sharing when applying transcriptomic clinical datasets to artificial intelligence in precision oncology. Here, we introduce the first benchmarking study tailored to high-dimensional clinical transcriptomic cancer data, comparing synthetic data generation methods across three clinical cancer trials. Our framework, SynOmicsBench, combines standardized preprocessing with multidimensional evaluation, prioritizing downstream biological validation alongside statistical fidelity and attack-based privacy assessment. Results indicate that no single method dominated all dimensions, with Gaussian Copula achieving the most balanced performance, followed by Avatar, demonstrating that metric-based similarity alone is insufficient to ensure preservation of higher-order molecular dependencies. Synthetic data consistently reproduced biomedical signal directionality but with attenuated effect sizes and inter-replicate variability, supporting hypothesis generation when multi-seed synthesis is adopted. Collectively, this framework provides a reproducible decision-support tool for method selection and promotes biologically informed, privacy-aware adoption of synthetic data in precision oncology.

bioinformatics↗

bioRxiv2026

Model Ensembling and Machine Learning Approaches to Predict the First Dose of Amoxicillin in Intensive Care

A priori model informed precision dosing (MIPD) recommends an appropriate first dose based solely on the patients covariates enabling faster target attainment without required concentration measurements. Population pharmacokinetic model ensembling and machine learning (ML) approaches were developed and evaluated to predict a first dose of amoxicillin in intensive care. Following a bibliographic review, a virtual patient population was simulated based on cohorts from four published adult amoxicillin PopPK models. Model-development cohorts were reproduced, and steady-state trough concentrations were simulated using cohort-specific dosing regimens. As reference methods, weighted model ensembling (WME) and classification tree (CT)-informed ensembling were implemented. Two novel ensembling strategies were developed: regression tree (RT)-informed ensembling, using RT to predict the log individual prediction/observation ratio, and factor analysis of mixed data (FAMD), assigning model weights based on patient similarity to original model cohorts. In parallel, four ML algorithms (support vector machine, k-nearest neighbors, random forest, and XGBoost) were trained to predict the dose achieving target concentrations based on covariates and dosing scheme. All approaches were compared with single-model PopPK dosing, standard dosing, and a nomogram, and externally validated using clinical data. Most MIPD methods outperformed standard dosing. On simulated data, ensembling (30-42 % correct predictions) and ML (36-39 %) exceeded single-model approaches (14-32 %). RT-informed and FAMD ensembling improved performance by 6-10 % over uninformed ensembling on clinical data. In clinical patients receiving continuous infusion, ensembling further improved performance, with FAMD achieving 49 % correct predictions. ML-based ensembling eliminates the need for model selection and increase target attainment.

pharmacology and toxicology↗