Biology subjects

Jost, F.

Publications and source records attributed to Jost, F..

3 recordsLinked to original sources

bioRxiv2025

Towards pharmacokinetic profile predictions for monoclonal antibodies using sequence based machine learning derived parameters and compartmental modeling

This study presents a novel approach for predicting complete pharmacokinetic (PK) profiles of monoclonal antibodies (mAbs) based solely on their amino acid sequences, addressing a critical gap in early-stage therapeutic antibody development. While current methodologies rely on in vivo testing in specialized models like Tg32 mice or cynomolgus monkeys, our approach enables PK parameter prediction prior to molecule synthesis. Using a dataset of 118 diverse mAbs, we developed a physics-informed neural network (PINN) incorporating a two-compartment PK model. The four parameters of the PK model are predicted by a NN in which the numerical representation of the light and heavy chain sequences derived through sequence-attention and protein language model features serve as input. The model successfully predicted clearance and volume of distribution at steady state with remarkable accuracy, achieving predictions within 2-fold error for 10/13 and 13/13 mAbs in the test set, respectively. Concentration-time profiles were well-characterized with a median geometric mean fold error (GMFE) close to 2, with nearly 70% of predictions having GMFE less than 2. This proof-of-concept study demonstrates the feasibility of sequence-based PK prediction for mAbs, potentially reducing animal testing requirements while accelerating candidate selection in therapeutic antibody development. The approach aligns with regulatory initiatives promoting new approach methodologies and complements existing sequence-based predictions of protein structure and target binding during the design phase.

systems biology↗

bioRxiv2025

Machine Learning Meets Pharmacokinetics: A Comparative Analysis of Predictive Models for Plasma Concentration-Time Profiles

Predicting pharmacokinetic (PK) profiles from molecular structures constitutes a significant advancement in pharmaceutical research with substantial implications for expediting the entire drug discovery process. Our investigation presents a comprehensive comparative analysis of five distinct methodological frameworks for predicting rat plasma concentration-time profiles: four approaches that integrate mechanistic modeling with computational techniques, and one approach employing pure machine learning (ML) architecture without physiological constraints. As reference (1), we used a method that predicts non-compartmental analysis (NCA) parameters to predict plasma PK profiles with a one-compartmental PK model (NCA-ML). Alternative approaches include: (2) a physiologically based PK (PBPK) model utilizing ML-predicted in vitro characteristics; (3) CMT-ML, where neural networks predict compartmental PK model parameters; (4) CMT-PINN, employing physics-informed neural networks; and (5) PURE-ML, using decision trees to predict concentration values at specific timepoints based on predicted volume of distribution and clearance. Assessment using multiple performance metrics demonstrated that the CMT-PINN approach achieved superior predictivity for PK profiles. Models trained directly on concentration-time, rather than derived PK parameters, delivered markedly improved predictive performance, particularly when working with limited training datasets. Our findings confirm the viability of predicting PK behavior from molecular structures before synthesis, representing a significant advancement for subsequent design. Implementation of these computational approaches enables informed compound selection early in the discovery pipeline, concentrating valuable resources for advanced preclinical investigations on only the most promising candidates, with anticipated beneficial effects on overall development timelines.

systems biology↗

bioRxiv2018

Mathematical Models for the Influence of Cytarabine on White Blood Cell Dynamics in Acute Myeloid Leukemia

We investigate the personalisation and prediction accuracy of mathematical models for white blood cell (WBC) count dynamics during consolidation treatment using intermediate or high-dose cytarabine (Ara-C) in acute myeloid leukemia (AML). Ara-C is the clinically most relevant cytotoxic agent for AML treatment.\n\nWe extend the gold-standard model of myelosuppression and a pharmacokinetic model of Ara-C with different hypotheses of Ara-Cs pharmacodynamic effects. We cross-validate 12 mathematical models using dense WBC count measurements from 23 AML patients. Surprisingly, the prediction accuracies are similarly good despite different modelling hypotheses. Therefore, we compare average clinical and calculated WBC recovery times for different Ara-C schedules as a successful methodology for model discrimination. As a result, a new hypothesis of a secondary pharmacodynamic effect on the proliferation rate seems plausible. Furthermore, we demonstrate how personalized predictions of the impact of treatment timing on subsequent nadir values could be used for clinical decision support.\n\nAuthor summaryThe major obstacle in accurately predicting the outcome of a medical therapy is the vast variation in individual response patterns. It concerns both the subjective experience of the patient and the objectively measurable achievement of a clinical remission with restoration of normal blood counts. Here, we address acute myeloid leukemia (AML)-chemotherapy using cytarabine (Ara-C) as this drug is this most important component of AML-treatment. In addition to the wide spectrum of genetic aberrations involved in pathogenesis leading to variations in patient response patterns, another facet of personalised medicine awaits exploration of its full potential: a systematic, mathematical approach to understand and manipulate the dynamics of relevant biomarkers. We use personalised mathematical models to describe and predict white blood cell (WBC) counts during AML consolidation treatment. We analyse why and to what extent low WBC counts, a serious adverse event during therapy, occur. In a comprehensive approach we investigate published models, compare them with our extended models and outline the impact of modelling assumptions and varying chemotherapy schedules on prediction accuracy and model discrimination. Our numerical results confirm the clinical finding that a newly proposed schedule is superior with respect to WBC recovery and shed new light on the reasons why.

systems biology↗