Biology subjects

Sordello, S.

Publications and source records attributed to Sordello, S..

5 recordsLinked to original sources

bioRxiv2026

Predicting tuberculosis relapse based on 28-day CFU, RS ratio, and/or drug contribution for novel regimens in the relapsing mouse model

Treatment shortening in tuberculosis therapy is needed, but testing all novel antibiotic combinations is unfeasible. Especially the tuberculosis relapsing mouse model is time- and resource demanding. Therefore, our objective is to develop a computational model predictive of long-term relapse prevention in mice based on short-term biomarkers, increasing the number of regimens that can be tested and prioritize regimens for further development. The innovative ribosomal RNA synthesis (RS) ratio is utilized to characterize drug effect on Mycobacterium tuberculosis health and activity, together with colony forming units (CFU) in murine lungs. Nine datasets of 58 unique regimens with 843 short-term biomarker and 2,239 long-term relapse observations were leveraged for model development in 3 iterations with external validations. The final model included therapeutic predictors, such as CFU and RS ratio change from baseline, and corrected for experimental conditions, to enable unbiased ranking of regimens between experiments. Model performance was optimal without model structure change despite fully separate model development at each iteration. Final external validation had an area under the receiver operator curve of 0.90. Challenging the model by assessing removal of either biomarker showed that performance of CFU only was similar to CFU and RS ratio once the sterilizing contribution of individual drugs to the regimens was accounted for. New drugs without this contribution quantified could benefit from RS ratio determination to predict relapse. Our predictive model can successfully differentiate between 2-, 3-, and 4-month regimens in the relapsing mouse model based on 4-week data only, supporting acceleration of treatment-shortening regimen development. One Sentence SummaryOur predictive model ranks new drug regimens by tuberculosis relapse prevention based on 28-day CFU and RS ratio, or on CFU only for known drugs.

pharmacology and toxicology↗

bioRxiv2026

Next Generation TB Drug Combinations from the Pan-TB Consortium: Combination Efficacy and Contributions of Individual Agents, Evaluated in a BALB/c Mouse TB Model

The Project to Accelerate New Treatments for Tuberculosis (PAN-TB) aims to accelerate development of shorter, simpler and safer pan-TB combinations. We previously identified 3 out of 25 first-generation novel PAN-TB 4-drug combinations, that cured 90% of mice in less than 3 months, at clinically relevant doses in the relapsing mouse model of TB. These regimens include BPa830Sut, BPa286Sut and BQSut286 (B: bedaquiline; Pa: pretomanid; 830: GSK3211830; 286: GSK2556286; Sut: sutezolid; Q: quabodepistat). Here, we assess the efficacy of these combinations where the original candidates are substituted next-generation or more advanced compounds (ganfeborole (656) for 830, sorfequiline, S for B, TBD09 for Sut and TBD11 for 286) and the individual contributions of specific agents. Six novel regimens demonstrated bactericidal activity more rapid than comparators PHMZ (Rifapentine P, Isoniazid H, Moxifloxacin M, Pyrazinamide Z) and BPaMZ. Modelled cure/relapse data showed that SPa286Sut, SPaSut and SPa656Sut cured 90% of mice in about 1 month, while SPa286, SPaQTBD11 and SPaTBD09 in less than 2 months, faster than PHMZ. Consistent with our previous findings, the fastest-curing regimens centered on a diarylquinoline (S), a nitroimidazole (Pa) and an oxazolidinone (TBD09 or Sut) together with an Rv1625c agonist (TBD11 or 286), DprE1 inhibitor (Q) or a LeuRS inhibitor (656). Notably, significant contributions to sterilizing efficacy were demonstrated for S in all combinations and for Pa, Sut, TBD09, Q and TBD11 or 286 in specific S-containing combinations. These findings suggest potential for these novel agents and combinations to improve treatment of both DS-and DR-TB.

pharmacology and toxicology↗

bioRxiv2026

Nonclinical pharmacokinetics and relative efficacy of the first 25 novel tuberculosis drug combinations from the PAN-TB consortium: Use of the BALB/c relapsing mouse model and combination pharmacokinetics within a modeling-based framework

The Project to Accelerate New Treatments for Tuberculosis (PAN-TB) aims to accelerate development of shorter, simpler and safer pan-TB combinations, effective for use in both Drug Susceptible (DS)- and Drug Resistant (DR)- TB patients. Towards this aim, bactericidal and sterilizing activity of 25 priority 4-drug combinations was evaluated at doses targeting clinically relevant exposures, in the BALB/c relapsing mouse model of TB. The combinations comprised 8 PAN-TB drugs and candidates: bedaquiline (B), pretomanid (Pa), delamanid (Del), quabodepistat (Q), sutezolid (Sut), GSK2556286 (286), GSK3211830 (830) and ganfeborole (GSK3036656, (656)). Combination PK studies in infected mice enabled dose selection and a population-PK approach guided dosing so that compounds should achieve mean AUC0-24 within 2-fold of their clinical target exposures during the efficacy studies. All test combinations showed time-dependent bactericidal activity, with six regimens reducing lung bacterial burdens below the limit of detection with 8 weeks treatment, similar to the comparator BPaMZ (M is moxifloxacin and Z as pyrazinamide). Cure/Relapse data were modelled to derive population time to cure 90% mice (T90) values. Fifteen PAN-TB combinations had T90s of less than 5 months, sterilizing mice faster than the standard of care for drug susceptible TB, RHZE/RH. The best-performing PAN-TB combinations, BPa830Sut, BPa286Sut and BQSut286, cured 90% of mice in less than 3 months. These 3 top-ranked 4-drug combinations are all centered on a diarylquinoline (B)/oxazolidinone (Sut) core, together with the nitroimidazole (Pa) or a DprE1 inhibitor (Q) plus a novel agent such as the LeuRS inhibitor (830) or the Rv1625c agonist (286).

microbiology↗

bioRxiv2025

A modeling-based framework to evaluate forgiveness of TB drug combinations in a BALB/c relapsing mouse model.

Tuberculosis (TB) remains a leading cause of death due to an infectious agent. Adherence to long and complex TB treatments is supported by methods including directly observed therapy. The negative impact of missed drug doses on clinical outcomes is well-established, highlighting both the importance of adherence support and methods to quantify the ability of a regimen to continue exerting a biologic effect, during gaps in dosing known as "forgiveness" property. To explore the value of the BALB/c Relapsing Mouse Model of TB in evaluating treatment forgiveness, we assessed the impact of weekend dose holidays on the bactericidal, including RS ratio(R), and sterilizing efficacy of RHZE/RH and BPaMZ in perspective of each drug exposure. The cure/relapse data from this study plus multiple historical studies were used to identify a nonlinear mixed-effects Emax model that was used to estimate time to cure 50% and derive time to cure 90% mice (T90). Expected time-dependent bactericidal activity and reductions in RS ratio were observed for both treatments, with more rapid decreases for the BPaMZ groups. The weekend dosing holiday significantly decreased reductions in lung CFU and RS ratio earlier in RHZE/RH treatment, but no such effect was observed for BPaMZ. Similarly, the predicted T90 was significantly greater for RHZE/RH (but not BPaMZ), with weekend doses omitted. No major drug exposure difference was observed between the 2 dosing schedules. Our results suggest BPaMZ is more forgiving of missed doses than RHZE/RH and suggests utility of this methodology to support evaluation of TB treatment forgiveness.

pharmacology and toxicology↗

bioRxiv2025

A Stochastic Simulation-Based Approach to Inform Relapsing Mouse Model (RMM) Study Design for Non-Clinical Assessment of Tuberculosis

The development of new regimens to treat tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb), is critical to improving patient outcomes and decreasing global infectious disease mortality. Early evaluation of candidate regimens in non-clinical models of TB, such as the relapsing mouse model (RMM), remains an important step in prioritizing the most efficacious regimens for further clinical evaluation. Although RMM studies may be informative, they are also animal-, labor-, and time-intensive to complete and represent significant investment in time and resources during non-clinical development. Given the strong pipeline of regimens in development, identification of "leaner" RMM studies may have a significant impact on resource utilization, and hence we compared alternative study designs with the goal of identifying study attributes that can be modified to improve resource use, particularly animal use. By simulating relapse outcomes from "virtual" studies (i.e., groups mice treated for selected durations with control and hypothetical anti-TB regimens) followed by model-based analysis of the simulated data, we were able to compare the "true" (input) values with model estimates of time to 95% cure probability (T95) and assess bias and precision of competing designs. Using this approach, we demonstrated that 28% fewer mice could be used in RMM studies while maintaining low bias and a precision for T95 estimation within +/- 1-2 weeks for most regimens. Therefore, it is expected that RMM studies based upon the alternative designs evaluated herein may be employed to promote improved animal stewardship while generating informative data for decision making.

pharmacology and toxicology↗