Jessica Lee 1, Nichada Seriniyom 1, Alessandro Di Deo 1,2, Peter Velickovic 1,2, Oscar Della Pasqua 1,2
1 Clinical Pharmacology and Therapeutics Group, University College London. (London, UK), 2 IAC, National Research Council (Rome, Italy)
Introduction: The development of novel tuberculosis (TB) combination regimens is constrained by long, resource-intensive clinical trials and limited quantitative evaluation of companion drugs, dose and dosing regimen selection before phase II studies are undertaken [1]. Although adaptive platform trials accelerate evaluation, regimen selection frequently relies on empirical evidence that often lack strong scientific rationale and overlooks the pharmacokinetic-pharmacodynamic (PKPD) relationships, contributing to uncertainty in outcome and high attrition rates [2,3]. Translational PKPD modelling offers a quantitative framework to link drug exposure with the overall antibacterial effects across experimental systems and clinical settings, enabling the prediction of treatment and regimen performance before undertaking confirmatory trials. Here we apply a model-informed translational framework to explore the probability of pharmacological success of selected regimens within the PARADIGM4TB study (ClinicalTrials.gov ID: NCT06114628).
Objectives: This study aimed to evaluate the feasibility of an integrated bacterial growth dynamics modelling approach to (i) characterise the antibacterial effects of standard and novel TB regimens currently under investigation in the PARADIGM4TB trial, (ii) predict regimen performance (iii) define criteria to rank and prioritise further evaluation of selected regimens in future Phase III studies.
Methods: Estimates of the antibacterial activity of the selected drugs in humans were based on a published murine PKPD model describing the growth dynamics of Mycobacterium tuberculosis and drug-mediated bacterial clearance [3,4]. The disease model represents fast- and slow-growing bacterial subpopulations linked by phenotypic transition, with antibacterial activity described using the Emax model. Interspecies translation accounted for differences in protein binding, systemic exposure, baseline bacterial burden and treatment delay prior to diagnosis. Site-of-infection exposure was incorporated through lung penetration adjustments for rifampicin.
Published population pharmacokinetic (popPK) models were used within a unified simulation framework [5–13]. Model implementation was evaluated using simulation-based diagnostics comparing predicted concentration–time profiles and exposure metrics with published clinical data. Predicted human exposure was combined with PKPD parameters to simulate antibacterial effects under monotherapy and combination scenarios. The efficacy of drug combinations was parameterised according to a backbone approach in which adjuvant drugs induce shifts in backbone drug potency (EC50) [3]. For backbone agents (rifampicin and bedaquiline), EC50 values for two- and three-drug combinations excluding new chemical entities were obtained from the published murine model [3]. For ganfeborole and BTZ-043, EC50 values were explored through sensitivity analyses under assumed synergistic interaction scenarios and/or informed by published literature [12].
Clinical trial simulations described systemic exposure and bacterial time–kill trajectories in patients receiving Arm A (HRZE; isoniazid, rifampicin, pyrazinamide, ethambutol), Arm D (BPaMG; bedaquiline, pretomanid, moxifloxacin, ganfeborole), Arm F (BPaZG; bedaquiline, pretomanid, pyrazinamide, ganfeborole), Arm J (BPaMT; bedaquiline, pretomanid, moxifloxacin, BTZ-043), and Arm K (BZMT; bedaquiline, pyrazinamide, moxifloxacin, BTZ-043) under PARADIGM4TB phase IIb-like conditions. Simulations were conducted in a virtual adult TB population representative of phase IIb settings. Antibacterial activity was summarised as predicted changes in log10 CFU over time, and regimens were ranked based on bacterial killing dynamics and inter-individual variability.
Results: Murine PKPD parameter translation enabled the prediction of antibacterial activity patterns consistent with reported human monotherapy behaviour and highlighted sensitivity to exposure scaling assumptions. Incorporating rifampicin lung penetration improved alignment between predicted rifampicin-induced CFU decline and reported antibacterial activity in humans. Simulated PK profiles were consistent with published clinical data.
Under combination scenarios, the framework differentiated backbone and adjuvant contributions to overall activity. Predicted time–kill trajectories distinguished regimen performance under trial-like conditions. Novel regimens appear to produce accelerated bacterial decline relative to standard of care, with model-based predictions suggesting bacterial clearance within approximately 5–6 weeks compared with approximately 18 weeks for HRZE. Regimens incorporating new chemical entities (ganfeborole and BTZ043) showed enhanced activity relative to backbone monotherapy, primarily driven by shifts in backbone potency under synergistic assumptions.
Conclusions: Our study illustrates the value of a translational PKPD framework integrating preclinical antibacterial activity with clinical pharmacokinetics to predict and prioritise TB combination regimens. By assessing predicted treatment performance under realistic trial conditions, the approach provides quantitative decision support for adaptive trial design and regimen selection prior to confirmatory studies while reducing reliance on empirical testing of less performant regimens.
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Reference: PAGE 34 (2026) Abstr 11988 [www.page-meeting.org/?abstract=11988]
Poster: Drug/Disease Modelling - Infection