Unified Markov thermodynamics based on stochastic forms to classify drugs considering molecular structure, partition system, and biological species: distribution of the antimicrobial G1 on rat tissues.

To date, molecular descriptors do not commonly account for important information beyond chemical structure. The present work, attempts to extend, in this sense, the stochastic molecular descriptors, incorporating information about the specific biphasic partition system, the biological species, and chemical structure inside the molecular descriptors. Consequently, MARCH-INSIDE molecular descriptors may be identified with time-dependent thermodynamic parameters (entropy and mean free energy) of partition process. A classification function was developed to classify data of 423 drugs and up to 14 different partition systems at the same time. The model has shown a high overall accuracy of 92.1% (293 out of 318 cases) in training series and 90% (36 out of 40 cases) in predicting ones. Finally, we illustrate the use of the model by predicting a high probability (%) for G1 (a novel antibacterial drug) to undergo partition on different biotic systems (rat organs): liver (97.7), spleen (97.5), lung (97.4), and adipose tissue (97.6). These theoretical results coincide with herein reported steady state plasma concentrations (c) and partition coefficients (P) in liver (c=42.25+/-7.86/P=4.75), spleen (11.47+/-4.43/P=1.29), lung (17.04+/-3.58/P=1.91), and adipose tissue (28.19+/-11.82/P=3.17). All values were relative to (14)C-labeled-radioactive-G1 in plasma (c=8.9+/-3.05) after 3h of oral administration. In closing, the present stochastic forms derive average thermodynamic parameters fitting on a more clearly physicochemical framework with respect to classic vector-matrix-vector forms, which include, as particular cases, quadratic forms such as Wiener index, Randic invariants, Zagreb descriptors, Harary index, Balaban index, and Marrero-Ponce quadratic molecular indices.