Mathematical modeling of bedaquiline susceptibility transitions in Mycobacterium tuberculosis: From nonlinear curve fitting to interval-censored threshold models
Manaf Al Matar, Dhamia Ahmed Atiyah, Saida S. Ncibi, Khaoula Balgouthi, Walid Wannes, Eihab BashierObjective:
To compare the structural adequacy and predictive performance of logarithmic and bounded nonlinear models for bedaquiline (BDQ) susceptibility and to develop an interval-censored threshold model using isolate-level response data.
Methods:
Using the original isolate-level dataset of 100 clinical
Results:
The logarithmic and lognormal-survival models were numerically indistinguishable in-sample (R
2
=0.902 2; RMSE=5.719; AICc=43.864), while the log-logistic/Hill model yielded the smallest cross-validated RMSE (7.227). Unlike the logarithmic model, the bounded survival models remain within 0-100 and converge to zero resistant isolates as concentration increases. The isolate-level response sequences showed no reversals from a less resistant to a more resistant state as concentration increased, allowing a second analysis using interval-censored latent thresholds. Among lognormal, Weibull, gamma, and log-logistic threshold distributions, the lognormal model had the lowest AICc for both transitions. The estimated median resistance-loss threshold was 0.370 mg/L (bootstrap 95%
Conclusions:
These results show that the apparent success of logarithmic regression is best interpreted as a local empirical approximation to a bounded threshold-survival process rather than evidence of temporal resistance dynamics. The interval-censored formulation uses the full isolate-level information and provides a mathematically admissible and reproducible framework for BDQ susceptibility transitions.