Self-Reported Medication Use and Urinary Drug Metabolites in the German Chronic Kidney Disease (GCKD) Study

Background Polypharamacy is common among patients with chronic kidney disease (CKD), but little is known about urinary excretion of many drugs and their metabolites among CKD patients. Methods To evaluate self-reported medication use in relation to urine drug metabolite levels in a large cohort of CKD patients, the Germany Chronic Kidney Disease study, we ascertained self-reported use of 158 substances and 41 medication groups and coded active ingredients according to the Anatomical Therapeutic Chemical classification system. We used a nontargeted mass spectrometry-based approach to quantify metabolites in urine; calculated specificity, sensitivity, and accuracy of medication use and corresponding metabolite measurements; and used multivariable regression models to evaluate associations and prescription patterns. Results Among 4885 participants, there were 108 medication-drug metabolite pairs based on reported medication use and 78 drug metabolites. Accuracy was excellent for measurements of 36 individual substances in which the unchanged drug was measured in urine (median, 98.5%; range 61.1%-100%). For 66 pairs of substances and their related drug metabolites, median measurement-based specificity and sensitivity were 99.2% (range 84.0%-100%) and 71.7% (range 1.2%-100%), respectively. Commonly prescribed medications for hypertension and cardiovascular risk reduction—including angiotensin-II receptor blockers, calcium channel blockers, and metoprolol—showed high sensitivity and specificity. Although self-reported use of prescribed analgesics (acetaminophen, ibuprofen) was <3% each, drug metabolite levels indicated higher usage (acetaminophen, 10%-26%; ibuprofen, 10%-18%). Conclusions This comprehensive screen of associations between urine drug metabolite levels and self-reported medication use supports the use of pharmacometabolomics to assess medication adherence and prescription patterns in persons with CKD, and indicates underreported use of medications available over the counter, such as analgesics.

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites

Drug metabolite identification is a bottleneck of drug metabolism studies. Ion mobility-mass spectrometry (IM-MS) enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used to increase the confidence in the identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we present the production of a large-scale database of drug and drug metabolite CCS values, assembled using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors. This novel prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers for the first time.

MO492SELF-REPORTED MEDICATION USE AND URINARY DRUG METABOLITES IN THE GERMAN CHRONIC KIDNEY DISEASE (GCKD) STUDY: A PHARMACOMETABOLOMIC APPROACH

Background and Aims Chronic kidney disease (CKD) patients are prone to prescription of multiple medications. Medication adherence is a well-recognized problem in the management of patients with chronic diseases requiring polypharmacy. This study aimed to evaluate the connection between self-reported medication use and urine drug metabolite levels in a large cohort of CKD patients, the GCKD study, as a basis for future pharmacometabolomics studies. Method Self-reported medication use of 160 substances and 41 medication groups was ascertained at study baseline and coded according to the Anatomical Therapeutic Chemical classification system. A non-targeted mass spectrometry-based approach (Metabolon HD4™) was used for concomitant metabolite quantification in urine. Specificity, sensitivity and accuracy of medication use and the corresponding urine metabolite measurements were calculated. Multivariable regression models (adjusted to age, sex, eGFR, log(UACR), systolic blood pressure, LDL, log(triglycerides), log(HBA1c) were used to establish associations in prescription patterns. Results Among 4,885 participants, 78 drug metabolites were detected in urine (frequency range: 0.4-58%) and assigned into 110 medication – drug metabolite pairs (MMPs) based on reported individual substances and medication groups. For all 68 MMPs of individual substances, accuracy of medication use and the corresponding drug metabolite measurement was excellent (median 97.0%, range 43%-100%), as was measurement-based specificity (median 99.3%, range 73.3%-100%; Fig. 1). Median measurement-based sensitivity was 72.1% (range 1.1%-100%, Fig. 1). Sensitivity and specificity were especially high for angiotensin-II receptor blockers (92%-96%; 99-100%), calcium channel blockers (85-100%; 91-100%), and metoprolol (90%; 98% respectively) commonly prescribed and important medications for blood pressure control and cardiovascular risk reduction in CKD patients. MMPs showing sensitivity &lt;80% included several substances found in over-the-counter (OTC) analgesic medications, suggesting that their use is not always reported. While self-reported use of the OTC analgesics acetaminophen and ibuprofen was &lt;3% each, their corresponding drug metabolites indicated higher usage (acetaminophen: 10-26%; ibuprofen: 10-18%, depending on the number of evaluated drug metabolites). Typical examples of medication co-prescriptions (e.g., trimethoprim and sulfamethoxazole) were detected as the combined presence of their drug metabolites in urine. This result validates the abstraction of single substances from combination medications and this urine-based metabolomic approach. Conclusion This study provides a comprehensive screen of the associations between urine drug metabolite levels and self-reported medication use. It supports the usefulness of pharmacometabolomics to assess medication use, frequency of OTC analgesics use, and prescription patterns in persons with CKD.

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.