Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning

Adverse events are a serious issue in drug development and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care should be taken in adverse event prediction because this approach tends to be overoptimistic compared with the real-world situation. The time split, which uses the time axis, is considered suitable for real-world prediction. However, the differences in model performance obtained using the time and random splits are not fully understood. To understand the differences, we compared the model performance between the time and random splits using eight types of compound information as input, eight adverse events as targets, and six machine learning algorithms. The random split showed higher area under the curve values than did the time split for six of eight targets. The chemical spaces of the training and test datasets of the time split were similar, suggesting that the concept of applicability domain is insufficient to explain the differences derived from the splitting. The area under the curve differences were smaller for the protein interaction than for the other datasets. Subsequent detailed analyses suggested the danger of confounding in the use of knowledge-based information in the time split. These findings indicate the importance of understanding the differences between the time and random splits in adverse event prediction and suggest that appropriate use of the splitting strategies and interpretation of results are necessary for the real-world prediction of adverse events.Comment: 20 pages, 4 figure

Difficulty in learning chirality for Transformer fed with SMILES

Recent years have seen development of descriptor generation based on representation learning of extremely diverse molecules, especially those that apply natural language processing (NLP) models to SMILES, a literal representation of molecular structure. However, little research has been done on how these models understand chemical structure. To address this, we investigated the relationship between the learning progress of SMILES and chemical structure using a representative NLP model, the Transformer. The results suggest that while the Transformer learns partial structures of molecules quickly, it requires extended training to understand overall structures. Consistently, the accuracy of molecular property predictions using descriptors generated from models at different learning steps was similar from the beginning to the end of training. Furthermore, we found that the Transformer requires particularly long training to learn chirality and sometimes stagnates with low translation accuracy due to misunderstanding of enantiomers. These findings are expected to deepen understanding of NLP models in chemistry.Comment: 20 pages, 6 figure

Evaluating object and region of concentric electrode in bio-electrical impedance measurement

Concentric electrode is easy to use and used widely for measuring bio-electrical impedance. But, its evaluating region was not investigated in detail. Then, the characteristics of concentric electrode were studied from various points of view. In case of use without electrode paste, impedance is determined with the contacting condition between electrode and skin surface over all frequency range. In case of use with electrode past, impedance is composed of stratum corneum in the frequency range of 20 Hz-1 kHz and is mainly composed of subcutaneous tissue in the range of 200 kHz-1 MHz. In the high frequency range, evaluating region of concentric electrode is the area less than the radius or the gap of center electrode

CARRIER-MEDIATED UPTAKE OF H 2 -RECEPTOR ANTAGONISTS BY THE RAT CHOROID PLEXUS: INVOLVEMENT OF RAT ORGANIC ANION TRANSPORTER 3

ABSTRACT: The choroid plexus (CP) acts as a site for the elimination of xenobiotic organic compounds from the cerebrospinal fluid (CSF). The purpose of the present study is to investigate the role of rat organic anion transporter 3 (rOat3; Slc22a8) in the uptake of H 2 -receptor antagonists (cimetidine, ranitidine, and famotidine) by the isolated rat CP. Saturable uptake of cimetidine and ranitidine was observed in rOat3-LLC with K m values of 80 and 120 M, respectively, whereas famotidine was found to be a poor substrate. The steady-state concentration of the H 2 -receptor antagonists in the CSF was significantly increased by simultaneously administered probenecid, although it did not affect their brain and plasma concentrations. Saturable uptake of cimetidine and ranitidine was observed in the isolated rat CP with K m values of 93 and 170 M, respectively, whereas 50% of the uptake of famotidine remained at the highest concentration examined (1 mM). The K i value of ranitidine for the uptake of cimetidine by the isolated CP (50 M) was similar to its own K m value, suggesting that they share the same transporter for their uptake. The inhibition potency of organic anions such as benzylpenicillin, estradiol 17␤-glucuronide, p-aminohippurate, and estrone sulfate for the uptake of cimetidine by the isolated rat CP was similar to that for benzylpenicillin, the uptake of which has been hypothesized to be mediated by rOat3, whereas a minimal effect by tetraethylammonium excludes involvement of organic cation transporter(s). These results suggest that rOat3 is the most likely candidate transporter involved in regulating the CSF concentration of H 2 -receptor antagonists at the CP

A rapid and simple electrochemical detection of the free drug concentration in human serum using boron-doped diamond electrodes

Monitoring drug concentration in blood and reflecting this in the dosage are crucial for safe and effective drug treatment. Most drug assays are based on total concentrations of bound and unbound proteins in the serum, although only the unbound concentration causes beneficial and adverse events. Monitoring the unbound concentration alone is expected to provide a means for further optimisation of drug treatment. However, unbound concentration monitoring has not been routinely used for drug treatment due to the long analysis time and the high cost of conventional methods. Here, we have developed a rapid electrochemical method to determine the unbound concentration in ultrafiltered human serum using boron-doped diamond (BDD) electrodes. When the anticancer drug doxorubicin was used as the test drug, the catalytic doxorubicin-mediated reduction of dissolved oxygen provided a sensitive electrochemical signal, with a detection limit of 0.14 nM. In contrast, the sensitivity of glassy carbon (GC) was inferior under the same conditions due to interference from the dissolved oxygen reduction current. The signal background ratio (S/B) of BDD and GC was 11.5 (10 nM doxorubicin) and 1.1 (50 nM), respectively. The results show that a fast measurement time within ten seconds is possible in the clinical concentration range. Additionally, in the ultrafiltered human serum, the obtained values of unbound doxorubicin concentration showed good agreement with those quantified by conventional liquid chromatography-mass spectrometry. This approach has the potential for application in clinical settings where rapid and simple analysis methods would be beneficial.Reproduced from Analyst., 2022, 147, 4442-4449 with permission from the Royal Society of Chemistry.https://doi.org/10.1039/d2an01037