Asina Gijasi1, Coen, J.G. van Hasselt1, Eric L. Nuermberger2, Rada M. Savic3, Rob C. van Wijk1
1Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, 2Center for Tuberculosis Research, Department of Medicine, Johns Hopkins University School of Medicine, 3Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco
Introduction Effective treatment of tuberculosis requires the development of shorter and improved treatment regimens that increase efficacy and safety and reduce the risk of relapse. In order to identify promising combination regimens, determine clinical dosing, and accelerate clinical development, translational pharmacokinetic-pharmacodynamic (PKPD) models are essential. Translational PKPD models have already been validated and applied in monotherapy Phase 2a clinical trials, demonstrating their utility in predicting early bactericidal activity and optimizing dosing strategies and trial design [1, 2]. However, predicting regimen performance in later Phase 2b-c and Phase 3 clinical trials remains challenging, as shown by a number of unsuccessful clinical trials [3-6]. Therefore, we aim to develop a robust model to accurately predict later-phase clinical efficacy. To that aim, we apply back-translation from clinical efficacy to preclinical experiments of rifapentine-containing regimens which were tested in Phase 2b/c (Study 29) and Phase 3 (Study 31) [7, 8]. Methods Bacterial colony-forming units (CFU) over time in a sub-acute infection model (14 days between infection and treatment) of Mycobacterium tuberculosis-infected BALB/c mice treated with a combination of rifapentine (P; 5, 7.5, and 20 mg/kg), isoniazid (H; 25 mg/kg), and pyrazinamide (Z; 150 mg/kg) [9] were modelled with NONMEM (v 7.5.1, ICON Development Solutions, Maryland) using a previously developed translational PKPD framework [10, 11]. PK was based on previously published results [10]. Various PD models were explored and developed, including a monotherapy model for rifapentine, a combination of (interacting) rifapentine, isoniazid and pyrazinamide monotherapy models, and re-estimation of Emax or EC50 relative to monotherapy with rifapentine-exposure driving combination efficacy. Additionally, distinctive PD models were tested for the first month of treatment and thereafter to account for differences between fast- and slow replicating bacteria [12, 13]. The established mouse PKPD relationship was linked with a published human population PK model for rifapentine [14] and with clinical variability in baseline bacterial burden to predict clinical trial outcome, incorporating translational factors such as differences in the fraction unbound to account for inter-species differences in drug binding. Evaluation of the predictive model performance was performed by comparing the simulated proportion of patients achieving solid culture conversion (CFU < 1) with results from Study 29, as published by Dorman et al [7]. Additionally, the predicted time to liquid culture positivity (TTP) was calculated using a published equation [15] for further comparison and validation with observed TTP from Study 29. Results Dose-ranging HPZ combination therapy CFU data in Mycobacterium tuberculosis-infected BALB/c mice were best described by a direct sigmoidal Emax model driven by rifapentine concentrations as parsimonious approach, because combination of rifapentine with isoniazid and pyrazinamide PKPD showed no improvement. PKPD relationships were incorporated for fast- and slow-replicating bacteria, with distinct Emax values for first month of treatment and after. Comparison with rifapentine monotherapy parameters showed a two-fold Emax increase (from 0.299 to 0.620 and 0.503 1/day for fast- and slow-replicating bacteria for mono- and combination therapy, respectively) with an EC50 at 6.02 mg/L. Clinical predictions showed that the simulated proportion of solid culture conversion was similar compared with Study 29 results. Small discrepancies were visible before 28 days, where clinical results showed early conversion in some trial participants. Conclusions Back-translation of clinical trials to preclinical combination therapy PKPD data is essential for improving translational modeling and predicting later-phase clinical outcomes. Our findings contribute to addressing this challenge. To further strengthen our modeling approach, we continue development to predict Phase 3 clinical efficacy, including time to stable sputum culture conversion and relapse. Acknowledgements This research was supported by the PReDiCTR-TB consortium. Additionally, we thank TB-PACTS for providing the clinical trial data.
[1] IH Bartelink, Zhang, N., Keizer, R., N Strydom, Converse, P., Dooley, K., EL Nuermberger and Savic, R. (2017). New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response. Clinical and Translational Science, 10(5), pp.366–379. [2] Susanto, B.O., Wicha, S.G., Hu, Y., Coates, A.R.M. and Ulrika S.H. Simonsson (2020). Translational Model-Informed Approach for Selection of Tuberculosis Drug Combination Regimens in Early Clinical Development. Clinical Pharmacology & Therapeutics, 108(2), pp.274–286. [3] Jindani, A., Harrison, T.S., Nunn, A.J., Phillips, P.P.J., Churchyard, G.J., Charalambous, S., Hatherill, M., Hennie Geldenhuys, McIlleron, H.M., Zvada, S.P., Mungofa, S., Shah, N.A., Simukai Zizhou, Magweta, L., Shepherd, J., Sambayawo Nyirenda, Dijk, van, Clouting, H.E., Coleman, D. and Bateson, A.L.E. (2014). High-Dose Rifapentine with Moxifloxacin for Pulmonary Tuberculosis. New England Journal of Medicine, 371(17), pp.1599–1608. [4] Gillespie, S.H., Crook, A.M., McHugh, T.D., Mendel, C.M., Meredith, S.K., Murray, S.R., Pappas, F., Phillips, P.P.J. and Nunn, A.J. (2014). Four-Month Moxifloxacin-Based Regimens for Drug-Sensitive Tuberculosis. New England Journal of Medicine, 371(17), pp.1577–1587. [5] Merle, C.S., Fielding, K., Sow, O.B., Gninafon, M., Lo, M.B., Thuli Mthiyane, Odhiambo, J., Evans Amukoye, Bah, B., Kassa, F., Alimatou N’Diaye, Rustomjee, R., Jong, Horton, J., Perronne, C., Charalambos Sismanidis, Olivier Lapujade, Olliaro, P.L. and Lienhardt, C. (2014). A Four-Month Gatifloxacin-Containing Regimen for Treating Tuberculosis. New England Journal of Medicine, 371(17), pp.1588–1598. [6] Cevik, M., Thompson, L.C., Upton, C., Rolla, V.C., Malahleha, M., Mmbaga, B., Ngubane, N., Abu Bakar, Z., Rassool, M., Variava, E., Dawson, R., Staples, S., Lalloo, U., Louw, C., Conradie, F., Eristavi, M., Samoilova, A., Skornyakov, S.N., Ntinginya, N.E. and Haraka, F. (2024). Bedaquiline-pretomanid-moxifloxacin-pyrazinamide for drug-sensitive and drug-resistant pulmonary tuberculosis treatment: a phase 2c, open-label, multicentre, partially randomised controlled trial. The Lancet Infectious Diseases, 24(9), pp.1003–1014. [7] Dorman, S.E., Goldberg, S., Stout, J.E., G. Muzanyi, Johnson, J.L., Weiner, M., Bozeman, L., Heilig, C.M., Feng, P.-J., Moro, R., Narita, M., P. Nahid, Ray, S., Bates, E., Haile, B., Nuermberger, E.L., Vernon, A. and Schluger, N.W. (2012). Substitution of Rifapentine for Rifampin During Intensive Phase Treatment of Pulmonary Tuberculosis: Study 29 of the Tuberculosis Trials Consortium. The Journal of Infectious Diseases, 206(7), pp.1030–1040. [8] Dorman, S.E., Payam Nahid, Kurbatova, E.V., Phillips, P.P.J., Bryant, K., Dooley, K.E., Engle, M., Goldberg, S.V., Phan, H.T.T., Hakim, J., Johnson, J.L., Lourens, M., Martinson, N.A., Muzanyi, G., Narunsky, K., Nerette, S., Nguyen, N.V., Pham, T.H., Pierre, S. and Purfield, A.E. (2021). Four-Month Rifapentine Regimens with or without Moxifloxacin for Tuberculosis. New England Journal of Medicine, 384(18), pp.1705–1718. [9] Rosenthal IM, Tasneen R, Peloquin CA, Zhang M, Almeida D, Mdluli KE, Karakousis PC, Grosset JH, Nuermberger EL. Dose-ranging comparison of rifampin and rifapentine in two pathologically distinct murine models of tuberculosis. Antimicrob Agents Chemother. 2012 Aug;56(8):4331-40. [10] Ernest JP, Goh JJN, Strydom N, Wang Q, van Wijk RC, Zhang N, Deitchman A, Nuermberger E, Savic RM. Translational predictions of phase 2a first-in-patient efficacy studies for antituberculosis drugs. Eur Respir J. 2023 Aug 31;62(2):2300165. [11] Strydom, N., Ernest, J.P., Imperial, M., Solans, B.P., Wang, Q., Rokeya Tasneen, Tyagi, S., Soni, H., Garcia, A., Bigelow, K., Gengenbacher, M., Zimmerman, M., Xie, M., Sarathy, J.P., Yang, T.J., Véronique Dartois, Nuermberger, E.L. and Savic, R.M. (2024). Dose optimization of TBI-223 for enhanced therapeutic benefit compared to linezolid in antituberculosis regimen. Nature Communications, 15(1). [12] Wang, Q., Goh, J.J., Zhang, N., Nuermberger, E. and Savic, R. (2023). Prospectively predicting BPaMZ Phase IIb outcomes using a translational preclinical mouse to human platform. bioRxiv, p.2023.02.16.528876. [13] Zhang, N., Strydom, N., Tyagi, S., Soni, H., Rokeya Tasneen, Nuermberger, E.L. and Savic, R.M. (2020). Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies. Antimicrobial Agents and Chemotherapy, 64(3). [14] Hibma, J.E., Radtke, K.K., Dorman, S.E., Jindani, A., Dooley, K.E., Weiner, M., McIlleron, H.M. and Savic, R.M. (2020). Rifapentine Population Pharmacokinetics and Dosing Recommendations for Latent Tuberculosis Infection. American Journal of Respiratory and Critical Care Medicine, 202(6), pp.866–877. [15] Gesham Magombedze, Pasipanodya, J.G. and Gumbo, T. (2021). Bacterial load slopes represent biomarkers of tuberculosis therapy success, failure, and relapse. Communications Biology, 4(1).
Reference: PAGE 33 (2025) Abstr 11444 [www.page-meeting.org/?abstract=11444]
Poster: Drug/Disease Modelling - Infection