Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures.

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results.

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

Precision Mapping of Thalamic Deep Brain Stimulation Lead Positions Associated With the Microlesion Effect in Tourette Syndrome.

BACKGROUND: The microlesion effect refers to the improvement of clinical symptoms after deep brain stimulation (DBS) lead placement and is suggested to indicate optimal lead placement. Very few studies have reported its implications in neuropsychiatric disorders. OBJECTIVE: To evaluate the magnitude of the microlesion effect in Tourette syndrome and the relationship between the microlesion effect and the anatomic location of implanted DBS leads. METHODS: Six male patients were included. Their median age at surgery and follow-up period were 25 years (range, 18-47) and 12 months (range, 6-24), respectively. All patients were videotaped pre- and postoperatively, and tic frequencies were counted. We also analyzed the precision of lead placement and evaluated the normative connectome associated with the microlesion area. RESULTS: The microlesion effect was observed as an improvement in tic symptoms in all patients, and the long-term clinical outcomes were favorable. The median motor tic frequency was 20.2 tics/min (range, 9.7-60) at baseline and decreased to 3.2 tics/min (1.2-11.3) in patients on postoperative day 1 ( P = .043) and to 5.7 tics/min (range, 1.9-16.6) in patients on postoperative day 7 ( P = .028). Phonic tic tended to improve immediately after surgery although the changes were not significant. Image analyses revealed that the precise position of the electrode was directed toward the anteromedial centromedian nucleus. Normative connectome analysis demonstrated connections between improvement-related areas and wide areas of the prefrontal cortex. CONCLUSION: This study shows that the microlesion effect may seem as an immediate improvement after optimal DBS lead placement in patients with Tourette syndrome.

Prediction of Compound Plasma Concentration-Time Profiles in Mice Using Random Forest.

Pharmacokinetic (PK) parameters such as clearance (CL) and volume of distribution (Vd) have been the subject of previous in silico predictive models. However, having information of the concentration over time profile explicitly can provide additional value like time above MIC or AUC, etc., to understand both the efficacy and safety-related aspects of a compound. In this work, we developed machine learning models for plasma concentration-time profiles after both i.v. and p.o. dosing for a series of 17 in-house projects. For explanatory variables, MACCS Keys chemical descriptors as well as in silico and experimental in vitro PK parameters were used. The predictive accuracy of random forest (RF), message passing neural network, 2-compartment models using estimated CL and Vdss, and an average model (as a control experiment) was investigated using 5-fold cross-validation (5-fold CV) and leave-one-project-out validation (LOPO-V). The predictive accuracy of RF in 5-fold CV for i.v. and p.o. plasma concentration-time profiles was the best among the models studied, with an RMSE for i.v. dosing at 0.08, 1, and 8 h of 0.245, 0.474, and 0.462, respectively, and an RMSE for p.o. dosing at 0.25, 1, and 8 h of 0.500, 0.612, and 0.509, respectively. Furthermore, by investigating the importance of the in vitro PK parameters using the Gini index, we observed that the general prior knowledge in ADME research was reflected well in the respective feature importance of in vitro parameters such as predicted human Vd (hVd) for the initial distribution, mouse intrinsic CL and unbound fraction of mouse plasma for the elimination process, and Caco2 permeability for the absorption process. Also, this model is the first model that can predict twin peaks in the concentration-time profile much better than a baseline compartment model. Because of its combination of sufficient accuracy and speed of prediction, we found the model to be fit-for-purpose for practical lead optimization.

Neuroanatomical considerations for optimizing thalamic deep brain stimulation in Tourette syndrome.

OBJECTIVE: Deep brain stimulation (DBS) of the centromedian thalamic nucleus has been reportedly used to treat severe Tourette syndrome, yielding promising outcomes. However, it remains unclear how DBS electrode position and stimulation parameters modulate the specific area and related networks. The authors aimed to evaluate the relationships between the anatomical location of stimulation fields and clinical responses, including therapeutic and side effects. METHODS: The authors collected data from 8 patients with Tourette syndrome who were treated with DBS. The authors selected the active contact following threshold tests of acute side effects and gradually increased the stimulation intensity within the therapeutic window such that acute and chronic side effects could be avoided at each programming session. The patients were carefully interviewed, and stimulation-induced side effects were recorded. Clinical outcomes were evaluated using the Yale Global Tic Severity Scale, the Yale-Brown Obsessive-Compulsive Scale, and the Hamilton Depression Rating Scale. The DBS lead location was evaluated in the normalized brain space by using a 3D atlas. The volume of tissue activated was determined, and the associated normative connective analyses were performed to link the stimulation field with the therapeutic and side effects. RESULTS: The mean follow-up period was 10.9 ± 3.9 months. All clinical scales showed significant improvement. Whereas the volume of tissue activated associated with therapeutic effects covers the centromedian and ventrolateral nuclei and showed an association with motor networks, those associated with paresthesia and dizziness were associated with stimulation of the ventralis caudalis and red nucleus, respectively. Depressed mood was associated with the spread of stimulation current to the mediodorsal nucleus and showed an association with limbic networks. CONCLUSIONS: This study addresses the importance of accurate implantation of DBS electrodes for obtaining standardized clinical outcomes and suggests that meticulous programming with careful monitoring of clinical symptoms may improve outcomes.

Oral Salt Loading Test is Associated With 24-Hour Blood Pressure and Organ Damage in Primary Aldosteronism Patients.

OBJECTIVE: In the present study, we investigated the most useful confirmatory test for reflecting the severity of primary aldosteronism (PA), by evaluating 24-hour blood pressure (BP), urine albumin, left ventricular mass (LVM), and intima media thickness (IMT). METHODS: This study included 113 patients (80 PA and 33 non-PA hypertensive patients) who were admitted to Oita University Hospital and evaluated using ambulatory blood pressure monitoring (ABPM). First, casual blood pressure (BP) and ABPM parameters were compared between PA and non-PA patients. Second, patients were divided into PA-positive and PA-negative groups based on confirmatory tests, including the saline infusion test (SIT), captopril challenge test (CCT), and oral salt loading test (OSLT), and casual BP and ABPM parameters were compared between the 2 groups. In addition, urine albumin excretion, LVM, and maximum IMT as markers of organ damage were compared between the 2 groups. RESULTS: The ABPM parameters but not casual BP, were higher in PA patients than in non-PA patients. Nocturnal and 24-hour systolic BP (SBP) in OSLT-positive patients were significantly higher than in OSLT-negative patients. ABPM parameters in other confirmatory tests were not different between the PA-positive and PA-negative groups. Urine albumin excretion in OSLT-positive patients was significantly higher than in the OSLT-negative patients. However, in other confirmatory tests, organ damage markers were not different between the 2 groups. CONCLUSION: The OSLT is potentially useful not only for the diagnosis of PA but also for assessment of 24-hour SBP and organ damage, as indicated by urine albumin excretion.

Anxiety-like behaviors and hippocampal nNOS in response to diet-induced obesity combined with exercise.

A high-fat diet (HFD) and overweight status can induce hippocampal dysfunction, leading to depression and anxiety. Exercise has beneficial effects on emotional behaviors. We previously reported that exercise training rescues HFD-induced excess hippocampal neuronal nitric oxide synthase (nNOS) expression, which is a key regulator of anxiety. Here, we investigated anxiety-like behaviors and hippocampal nNOS expression in response to HFD combined with exercise. Mice were assigned to standard diet, HFD, or HFD with exercise groups for 12 weeks. We found that exercise during the final 6 weeks of the HFD regime improved 12 weeks of HFD-induced defecation, accompanied by rescue of excess nNOS expression. However, anxiety indicators in the elevated plus maze were unchanged. These effects were not apparent after only 1 week of exercise. In conclusion, 6 weeks of exercise training reduced HFD-related anxiety according to one of our measures (defecation), and reversed changes in the hippocampal nNOS/NO pathway.

Short- and long-term effects of high-fat diet feeding and voluntary exercise on hepatic lipid metabolism in mice.

Exercise is an effective tool for improving high-fat diet induced fat accumulation in the liver. However, the process of fat accumulation in the liver and the efficacy of early intervention with exercise remain unclear. The aim of this study was to investigate the short- and long-term effects of high-fat diet feeding and voluntary exercise on hepatic lipid metabolism in mice. Male C57BL/6J mice aged 6 weeks were randomly divided into two groups, the control group and high-fat diet feeding group, and fed a normal or high-fat diet for 12 weeks. After 6 weeks, mice in the high-fat diet feeding group were further divided into no exercise group and voluntary exercise training group, with mice in the exercise group provided a running wheel for 6 weeks. Body weight, food intake, and wheel rotation counts were measured every second day for 12 weeks. We found that voluntary exercise for 1 week (short-term exercise) significantly reduced fat accumulation in the liver by downregulating the expression of hepatic lipogenesis-associated proteins and upregulating the expression of hepatic lipolysis-associated proteins, as determined through western blotting and histology. Further, voluntary exercise for 6 weeks (long-term exercise) downregulated the expression of hepatic lipogenesis-associated proteins. These results suggest that hepatic lipogenesis and/or hepatic lipolysis mediate the beneficial effects of voluntary exercise on hepatic fat accumulation.

Association between exercise intensity and renal blood flow evaluated using ultrasound echo.

BACKGROUND: High-intensity exercise reduces renal blood flow (RBF) and may transiently exacerbate renal dysfunction. RBF has previously been measured invasively by administration of an indicator material; however, non-invasive measurement is now possible with technological innovations. This study examined variations in RBF at different exercise intensities using ultrasound echo. METHODS: Eight healthy men with normal renal function (eGFRcys 114 ± 19 mL/min/1.73 m2) participated in this study. Using a bicycle ergometer, participants underwent an incremental exercise test using a ramp protocol (20 W/min) until exhaustion in Study 1 and the lactate acid breaking point (LaBP) was calculated. Participants underwent a multi-stage test at exercise intensities of 60, 80, 100, 120, and 140% LaBP in Study 2. RBF was measured by ultrasound echo at rest and 5 min after exercise in Study 1 and at rest and immediately after each exercise in Study 2. To determine the mechanisms behind RBF decline, a catheter was placed into the antecubital vein to study vasoconstriction dynamics. RESULTS: RBF after maximum exercise decreased by 51% in Study 1. In Study 2, RBF showed no significant decrease until 80% LaBP, and showed a significant decrease (31%) at 100% LaBP compared with at rest (p < 0.01). The sympathetic nervous system may be involved in this reduction in RBF. CONCLUSIONS: RBF showed no significant decrease until 80% LaBP, and decreased with an increase in blood lactate. Reduction in RBF with exercise above the intensity at LaBP was due to decreased cross-sectional area rather than time-averaged flow velocity.

Exercise training rescues high fat diet-induced neuronal nitric oxide synthase expression in the hippocampus and cerebral cortex of mice.

Consumption of a high fat diet (HFD) and being overweight both induce functional deterioration and atrophy of the hippocampus. These alterations are associated with mental disorders such as depression and anxiety. Exercise combats obesity and enhances brain health. There is substantial evidence that neuronal nitric oxide synthase (nNOS) is a key regulator of affective behavior, and that increased brain nNOS leads to anxiety while environmental enrichment (EE), which reduces brain nNOS, has anxiolytic effects. In this study we investigated the effects of HFD with and without exercise on nNOS protein and gene expression levels in the brains of mice. Twelve weeks of HFD consumption increased body and mesenteric fat weight, as well as nNOS protein levels in the hippocampus and cerebral cortex. Six weeks of exercise training reduced body fat and rescued hippocampal and cortical nNOS expression levels in HFD-fed mice. Cerebellar nNOS expression was unaffected by HFD and exercise. Our results suggest that HFD-induced brain dysfunction may be regulated by hippocampal and/or cortical nNOS, and that exercise may have therapeutic potential for the treatment of HFD-induced depression and anxiety via the nNOS/NO pathway. In conclusion, exercise reverses HFD-induced changes in hippocampal and cortical nNOS protein levels in mice.