title: Predicting multiple drugs side effects with a general drug-target interaction thermodynamic Markov model.
abstract: Most of present molecular descriptors just consider the molecular structure. In the present article we pretend extending the use of Markov chain models to define novel molecular descriptors, which consider in addition to molecular structure other parameters like target site or toxic effect. Specifically, this molecular descriptor takes into consideration not only the molecular structure but the specific system the drug affects too. Herein, it is developed a general Markov model that describes 39 different drugs side effects grouped in 11 affected systems for 301 drugs, being 686 cases finally. The data was processed by linear discriminant analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/100% for systemic phenomena (47 out of 47)/(12 out of 12) and metabolic (18 out of 18)/(5 out of 5), muscular-skeletal (23 out of 23)/(6 out of 6) and neurological manifestations (33 out of 33)/(8 out of 8); 97.6/96.7% for cardiovascular manifestation (122 out of 125)/(30 out of 31); 97.1/97.5% for breathing manifestations (34 out of 35)/(8 out of 9); 97/99.4% for gastrointestinal manifestations (159 out of 164)/(40 out of 41); 97/95% for endocrine manifestations (32 out of 33)/(7 out of 8); 96.4/94.6% for psychiatric manifestations (53 out of 55)/(13 out of 14); 95.1/99.1% for hematological manifestations (98 out of 103)/(25 out of 26) and 88/92.3% for dermal manifestations (44 out of 50)/(12 out of 13). In addition, we report preliminary experimental reversible decrease of lymphocytes differential count after administration of the antibacterial drug G-1 in mice, which coincide with a posterior probability (P%=74.91) predicted by the model. This article develops a model that encompasses a large number of side effects grouped in specific organ systems in a single stochastic framework for the first time.
authors.affiliation: Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela 15782, Spain.
authors.forename: Humberto
authors.initials: H
authors.lastname: González-Díaz
authors.forename: Maykel
authors.initials: M
authors.lastname: Cruz-Monteagudo
authors.forename: Reinaldo
authors.initials: R
authors.lastname: Molina
authors.forename: Esvieta
authors.initials: E
authors.lastname: Tenorio
authors.forename: Eugenio
authors.initials: E
authors.lastname: Uriarte
mesh_terms: D016002:Discriminant Analysis; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D008390:Markov Chains; D013816:Thermodynamics