An Inductive Logistic Matrix Factorization Model for Predicting Drug-Metabolite Association With Vicus Regularization

Metabolites are closely related to human disease. The interaction between metabolites and drugs has drawn increasing attention in the field of pharmacomicrobiomics. However, only a small portion of the drug-metabolite interactions were experimentally observed due to the fact that experimental validation is labor-intensive, costly, and time-consuming. Although a few computational approaches have been proposed to predict latent associations for various bipartite networks, such as miRNA-disease, drug-target interaction networks, and so on, to our best knowledge the associations between drugs and metabolites have not been reported on a large scale. In this study, we propose a novel algorithm, namely inductive logistic matrix factorization (ILMF) to predict the latent associations between drugs and metabolites. Specifically, the proposed ILMF integrates drug–drug interaction, metabolite–metabolite interaction, and drug-metabolite interaction into this framework, to model the probability that a drug would interact with a metabolite. Moreover, we exploit inductive matrix completion to guide the learning of projection matrices U and V that depend on the low-dimensional feature representation matrices of drugs and metabolites: Fm and Fd. These two matrices can be obtained by fusing multiple data sources. Thus, FdU and FmV can be viewed as drug-specific and metabolite-specific latent representations, different from classical LMF. Furthermore, we utilize the Vicus spectral matrix that reveals the refined local geometrical structure inherent in the original data to encode the relationships between drugs and metabolites. Extensive experiments are conducted on a manually curated “DrugMetaboliteAtlas” dataset. The experimental results show that ILMF can achieve competitive performance compared with other state-of-the-art approaches, which demonstrates its effectiveness in predicting potential drug-metabolite associations.

Psychotropic Drugs Levels in Seminal Fluid: A New Therapeutic Drug Monitoring Analysis?

The aim of this observational study was to develop a new quantitative liquid chromatography-mass spectrometry (LC-MS/MS) method for Therapeutic-Drug-Monitoring (TDM) of psychotropic drugs in seminal fluid to investigate potential gonadotoxic effects in patients with reduced fertility. After the validation of the LC-MS/MS method for psychotropics’ levels determination in seminal fluid, we included 20 male partners of infertile couples with idiopathic and/or unexplained male infertility, treated with psychotropic medications for more than 3 months and 10 untreated fertile controls. General and andrological clinical examination, semen analysis and seminal drugs, and metabolites levels determination were performed for each subject. Of the 20 patients included, 6 were treated with antidepressants; 4 with benzodiazepines and 10 with antipsychotics. Seminal drugs and metabolites levels were detectable in all samples. In particular, alprazolam, olanzapine, and levetiracetam showed seminal and serum similar concentrations, while fluoxetine, quetiapine, and aripiprazole were detectable, but seminal levels were significantly lower than the serum therapeutic range. Sperm progressive motility was significantly reduced in subjects treated with psychotropic drugs compared to the untreated controls (p = 0.03). Sperm concentration and progressive motility were significantly reduced in subjects treated with antipsychotics compared to the untreated controls and to the other classes of psychotropics (p < 0.05). In conclusion, this study reports a validated LC-MS/MS method for the detection of seminal psychotropic levels and preliminary data suggesting a potential correlation of seminal psychotropics with alterations of sperm concentration and motility. Pending larger studies, semen TDM might represent a new pivotal tool in the clinical management of reduced fertility in males treated with psychotropic medications.

TP-MAP - an Integrated Software Package for the Analysis of 1D and 2D Thermal Profiling Data

Thermal profiling (TP) has emerged as a promising experimental methodology for elucidating the molecular targets of drugs and metabolites on a proteome-wide scale. Here, we present the Thermal Profiling Meltome Analysis Program (TP-MAP) software package for the analysis and ranking of 1D and 2D thermal profiling datasets. TP-MAP provides a user-friendly interface to quickly identify hit candidates and further explore targets of interest via intersection and crosslinking to public databases.