Prediction of Drug Penetration in Tuberculosis Lesions.

Journal:
ACS infectious diseases, Volume: 2, Issue: 8
Published:
August 12, 2016
PMID:
27626295
Authors:
Jansy P Sarathy JP, Fabio Zuccotto F, Ho Hsinpin H, Lars Sandberg L, Laura E Via LE, Gwendolyn A Marriner GA, Thierry Masquelin T, Paul Wyatt P, Peter Ray P, VĂ©ronique Dartois V
Abstract:

The penetration of antibiotics in necrotic tuberculosis lesions is heterogeneous and drug-specific, but the factors underlying such differential partitioning are unknown. We hypothesized that drug binding to macromolecules in necrotic foci (or caseum) prevents passive drug diffusion through avascular caseum, a critical site of infection. Using a caseum binding assay and MALDI mass spectrometry imaging of tuberculosis drugs, we showed that binding to caseum inversely correlates with passive diffusion into the necrotic core. We developed a high-throughput assay relying on rapid equilibrium dialysis and a caseum surrogate designed to mimic the composition of native caseum. A set of 279 compounds was profiled in this assay to generate a large data set and explore the physicochemical drivers of free diffusion into caseum. Principle component analysis and modeling of the data set delivered an in silico signature predictive of caseum binding, combining 69 molecular descriptors. Among the major positive drivers of binding were high lipophilicity and poor solubility. Determinants of molecular shape such as the number of rings, particularly aromatic rings, number of sp(2) carbon counts, and volume-to-surface ratio negatively correlated with the free fraction, indicating that low-molecular-weight nonflat compounds are more likely to exhibit low caseum binding properties and diffuse effectively through caseum. To provide simple guidance in the property-based design of new compounds, a rule of thumb was derived whereby the sum of the hydrophobicity (clogP) and aromatic ring count is proportional to caseum binding. These tools can be used to ensure desirable lesion partitioning and guide the selection of optimal regimens against tuberculosis.