Multiscale modelling of drug-host-pathogen interaction: quantifying drug and immune contributions to treatment response
- 1Istituto per le Applicazioni del Calcolo ``Mauro Picone'' (IAC), Consiglio Nazionale delle Ricerche (CNR)
Abstract
Background and Objective: Predicting treatment outcomes in infectious diseases requires accounting for the interplay between drug effects, pathogen dynamics, and host immunity. Integrating pharmacological and immunological approaches into a single simulation environment remains a fundamental challenge in both theory and practice. We aimed to develop and validate a multiscale in silico framework coupling these processes, and to quantify their respective contributions to bacterial clearance. Methods: We present the Drug-Host-Pathogen Interaction (DHPI) framework, combining three independent mechanistic components: a physiologically based pharmacokinetic model of drug disposition, a pharmacokinetic-pharmacodynamic model of drug-induced bacterial killing, and a stochastic agent-based model of the immune response. Continuous concentration profiles are time-averaged onto the agent-based time grid, assigned to bacterial phenotypic states, and converted into per-agent killing probabilities, so that drug-mediated and immune-mediated death events are recorded separately at each step. The framework was applied to simulate symptomatic pulmonary tuberculosis. Phenotype-specific drug-efficacy parameters were inferred using Approximate Bayesian Computation from historical clinical data on eight weeks of 600 mg rifampicin monotherapy, and validated against independent early bactericidal activity data over a disjoint time window. Results: The calibrated framework reproduced the observed decline in bacterial load, and matched reported early bactericidal activity over the first week. In a virtual cohort of symptomatic patients, drug-mediated killing accounted for 81-88% and immune-mediated killing for 12-19% of total bacterial elimination over the 60-day treatment course, while the dormant, granuloma-contained fraction rose from 0.20-0.29 in the first week to 0.85-0.89 at treatment completion. Over a follow-up of up to 50 years, patients reaching clinical cure had accumulated more memory lymphocytes during treatment than those progressing to clinical failure or death; moreover, the final outcome depended on the immune changes occurring during therapy rather than on the initial disease stage. Conclusions: The results show that the DHPI framework can reproduce treatment dynamics observed in patients and enable the analysis of how therapy reshapes host immune responses and subsequent disease trajectories. By explicitly representing drug-host-pathogen interactions, it provides a mechanistic basis for in silico treatment simulations and for the study of long-term immune consequences of antimicrobial therapy.
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BibTeX
@misc{ravoni2026multiscale,
title = {{Multiscale modelling of drug-host-pathogen interaction: quantifying drug and immune contributions to treatment response}},
author = {A. Ravoni and E. Mastrostefano and D. Moretti and E. Onofri and F. Pelusi and A. Dokoumetzidis and E. Karakitsios and S. D'Agate and A. Di Deo and U. Villani and P. Tieri and F. Castiglione and O. Della Pasqua},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.30.744418},
url = {https://www.biorxiv.org/content/10.64898/2026.08.30.744418v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Multiscale modelling of drug-host-pathogen interaction: quantifying drug and immune contributions to treatment response
AU - A. Ravoni
AU - E. Mastrostefano
AU - D. Moretti
AU - E. Onofri
AU - F. Pelusi
AU - A. Dokoumetzidis
AU - E. Karakitsios
AU - S. D'Agate
AU - A. Di Deo
AU - U. Villani
AU - P. Tieri
AU - F. Castiglione
AU - O. Della Pasqua
PY - 2026
DA - 2026/08/30
PB - bioRxiv
DO - 10.64898/2026.08.30.744418
UR - https://www.biorxiv.org/content/10.64898/2026.08.30.744418v1
AB - Background and Objective: Predicting treatment outcomes in infectious diseases requires accounting for the interplay between drug effects, pathogen dynamics, and host immunity. Integrating pharmacological and immunological approaches into a single simulation environment remains a fundamental challenge in both theory and practice. We aimed to develop and validate a multiscale in silico framework coupling these processes, and to quantify their respective contributions to bacterial clearance. Methods: We present the Drug-Host-Pathogen Interaction (DHPI) framework, combining three independent mechanistic components: a physiologically based pharmacokinetic model of drug disposition, a pharmacokinetic-pharmacodynamic model of drug-induced bacterial killing, and a stochastic agent-based model of the immune response. Continuous concentration profiles are time-averaged onto the agent-based time grid, assigned to bacterial phenotypic states, and converted into per-agent killing probabilities, so that drug-mediated and immune-mediated death events are recorded separately at each step. The framework was applied to simulate symptomatic pulmonary tuberculosis. Phenotype-specific drug-efficacy parameters were inferred using Approximate Bayesian Computation from historical clinical data on eight weeks of 600 mg rifampicin monotherapy, and validated against independent early bactericidal activity data over a disjoint time window. Results: The calibrated framework reproduced the observed decline in bacterial load, and matched reported early bactericidal activity over the first week. In a virtual cohort of symptomatic patients, drug-mediated killing accounted for 81-88% and immune-mediated killing for 12-19% of total bacterial elimination over the 60-day treatment course, while the dormant, granuloma-contained fraction rose from 0.20-0.29 in the first week to 0.85-0.89 at treatment completion. Over a follow-up of up to 50 years, patients reaching clinical cure had accumulated more memory lymphocytes during treatment than those progressing to clinical failure or death; moreover, the final outcome depended on the immune changes occurring during therapy rather than on the initial disease stage. Conclusions: The results show that the DHPI framework can reproduce treatment dynamics observed in patients and enable the analysis of how therapy reshapes host immune responses and subsequent disease trajectories. By explicitly representing drug-host-pathogen interactions, it provides a mechanistic basis for in silico treatment simulations and for the study of long-term immune consequences of antimicrobial therapy.
ER -