Optimization Efforts for Identification of Novel Highly Potent Keap1-Nrf2 Protein-Protein Interaction Inhibitors.

In research focused on protein-protein interaction (PPI) inhibitors, the optimization process to achieve both high inhibitory activity and favorable physicochemical properties remains challenging. Our previous study reported the discovery of novel and bioavailable Keap1-Nrf2 PPI inhibitor 8 which exhibited moderate in vivo activity in rats. In this work, we present our subsequent efforts to optimize this compound. Two distinct approaches were employed, targeting high energy water molecules and Ser602 as "hot spots" from the anchor with good aqueous solubility, metabolic stability, and membrane permeability. Through ligand efficiency (LE)-guided exploration, we identified two novel inhibitors 22 and 33 with good pharmacokinetics (PK) profiles and more potent in vivo activities, which appear to be promising chemical probes among the existing inhibitors.

Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening.

Various toxicity and pharmacokinetic evaluations as screening experiments are needed at the drug discovery stage. Currently, to reduce the use of animal experiments and developmental expenses, the development of high-performance predictive models based on quantitative structure-activity relationship analysis is desired. From these evaluation targets, we selected 50% lethal dose (LD50), blood-brain barrier penetration (BBBP), and the clearance (CL) pathway for this investigation and constructed predictive models for each target using 636-11,886 compounds. First, we constructed predictive models using the DeepSnap-deep learning (DL) method and images of compounds as features. The calculated area under the curve (AUC) and balanced accuracy (BAC) were, respectively, 0.887 and 0.818 for LD50, 0.893 and 0.824 for BBBP, and 0.883 and 0.763 for the CL pathway. Next, molecular descriptors (MDs) of compounds were calculated using Molecular Operating Environment, alvaDesc, and ADMET Predictor to construct predictive models using the MD-based method. Using these MDs, we constructed predictive models using DataRobot. The calculated AUC and BAC were, respectively, 0.931 and 0.805 for LD50, 0.919 and 0.849 for BBBP, and 0.900 and 0.807 for the CL pathway. In this investigation, we constructed predictive models combining the DeepSnap-DL and MD-based methods. In ensemble models using the mean predictive probability of the DeepSnap-DL and MD-based methods, the calculated AUC and BAC were, respectively, 0.942 and 0.842 for LD50, 0.936 and 0.853 for BBBP, and 0.908 and 0.832 for the CL pathway, with improved predictive performance observed for all variables compared with either single method alone. Moreover, in consensus models that adopted only compounds for which the results of the two methods agreed, the calculated BAC for LD50, BBBP, and the CL pathway were 0.916, 0.918, and 0.847, respectively, indicating higher predictive performance than the ensemble models for all three variables. The predictive models combining the DeepSnap-DL and MD-based methods displayed high predictive performance for LD50, BBBP, and the CL pathway. Therefore, the application of this approach to prediction targets in various drug discovery screenings is expected to accelerate drug discovery.

Methyl and Fluorine Effects in Novel Orally Bioavailable Keap1-Nrf2 PPI Inhibitor.

Oxidative stress is one of the causes of progression of chronic kidney disease (CKD). Activation of the antioxidant protein regulator Nrf2 by inhibition of the Keap1-Nrf2 protein-protein interaction (PPI) is of interest as a potential treatment for CKD. We report the identification of the novel and weak PPI inhibitor 7 with good physical properties by a high throughput screening (HTS) campaign, followed by structural and computational analysis. The installation of only methyl and fluorine groups successfully provided the lead compound 25, which showed more than 400-fold stronger activity. Furthermore, these dramatic substituent effects can be explained by the analysis of using isothermal titration calorimetry (ITC). Thus, the resulting 25, which exhibited high oral absorption and durability, would be a CKD therapeutic agent because of the dose-dependent manner for up-regulation of the antioxidant protein heme oxigenase-1 (HO-1) in rat kidneys.

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data.

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.

Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning.

The toxicity, absorption, distribution, metabolism, and excretion properties of some targets are difficult to predict by quantitative structure-activity relationship analysis. Therefore, there is a need for a new prediction method that performs well for these targets. The aim of this study was to develop a new regression model of rat clearance (CL). We constructed a regression model using 1545 in-house compounds for which we had rat CL data. Molecular descriptors were calculated using molecular operating environment, alvaDesc, and ADMET Predictor software. The classification model of DeepSnap and Deep Learning (DeepSnap-DL) with images of the three-dimensional chemical structures of compounds as features was constructed, and the prediction probabilities for each compound were calculated. For molecular descriptor-based methods that use molecular descriptors and conventional machine learning algorithms selected by DataRobot, the correlation coefficient (R 2) and root mean square error (RMSE) were 0.625-0.669 and 0.295-0.318, respectively. We combined molecular descriptors and prediction probability of DeepSnap-DL as features and developed a novel regression method we called the combination model. In the combination model with these two types of features and conventional algorithms selected by DataRobot, R 2 and RMSE were 0.710-0.769 and 0.247-0.278, respectively. This finding shows that the combination model performed better than molecular descriptor-based methods. Our combination model will contribute to the design of more rational compounds for drug discovery. This method may be applicable not only to rat CL but also to other pharmacokinetic and pharmacological activity and toxicity parameters; therefore, applying it to other parameters may help to accelerate drug discovery.

Prediction Model of Clearance by a Novel Quantitative Structure-Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning.

Some targets predicted by machine learning (ML) in drug discovery remain a challenge because of poor prediction. In this study, a new prediction model was developed and rat clearance (CL) was selected as a target because it is difficult to predict. A classification model was constructed using 1545 in-house compounds with rat CL data. The molecular descriptors calculated by Molecular Operating Environment (MOE), alvaDesc, and ADMET Predictor software were used to construct the prediction model. In conventional ML using 100 descriptors and random forest selected by DataRobot, the area under the curve (AUC) and accuracy (ACC) were 0.883 and 0.825, respectively. Conversely, the prediction model using DeepSnap and Deep Learning (DeepSnap-DL) with compound features as images had AUC and ACC of 0.905 and 0.832, respectively. We combined the two models (conventional ML and DeepSnap-DL) to develop a novel prediction model. Using the ensemble model with the mean of the predicted probabilities from each model improved the evaluation metrics (AUC = 0.943 and ACC = 0.874). In addition, a consensus model using the results of the agreement between classifications had an increased ACC (0.959). These combination models with a high level of predictive performance can be applied to rat CL as well as other pharmacokinetic parameters, pharmacological activity, and toxicity prediction. Therefore, these models will aid in the design of more rational compounds for the development of drugs.

Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans.

Despite their importance in determining the dosing regimen of drugs in the clinic, only a few studies have investigated methods for predicting blood-to-plasma concentration ratios (Rb). This study established an Rb prediction model incorporating typical human pharmacokinetics (PK) parameters. Experimental Rb values were compiled for 289 compounds, offering reliable predictions by expanding the applicability domain. Notably, it is the largest list of Rb values reported so far. Subsequently, human PK parameters calculated from plasma drug concentrations, including the volume of distribution (Vd), clearance, mean residence time, and plasma protein binding rate, as well as 2702 kinds of molecular descriptors, were used to construct quantitative structure-PK relationship models for Rb. Among the evaluated PK parameters, logVd correlated best with Rb (correlation coefficient of 0.47). Thus, in addition to molecular descriptors selected by XGBoost, logVd was employed to construct the prediction models. Among the analyzed algorithms, artificial neural networks gave the best results. Following optimization using six molecular descriptors and logVd, the model exhibited a correlation coefficient of 0.64 and a root-mean-square error of 0.205, which were superior to those previously reported for other Rb prediction methods. Since Vd values and chemical structures are known for most medications, the Rb prediction model described herein is expected to be valuable in clinical settings.

Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists.

Identification and functional characterization of uric acid transporter Urat1 (Slc22a12) in rats.

Uric acid transporter URAT1 contributes significantly to reabsorption of uric acid in humans to maintain a constant serum uric acid (SUA) level. Since alteration of SUA level is associated with various diseases, it is important to clarify the mechanism of change in SUA. However, although expression of mRNA of an ortholog of URAT1 (rUrat1) in rats has been reported, functional analysis and localization have not been done. Therefore, rat rUrat1 was functionally analyzed using gene expression systems and isolated brush-border membrane vesicles (BBMVs) prepared from rat kidney, and its localization in kidney was examined immunohistochemically. Uric acid transport by rUrat1 was chloride (Cl-) susceptible with a Km of 1773µM. It was inhibited by benzbromarone and trans-stimulated by lactate and pyrazinecarboxylic acid (PZA). Cl- gradient-susceptible uric acid transport by BBMVs showed similar characteristics to those of uric acid transport by rUrat1. Moreover, rUrat1 was localized at the apical membrane in proximal tubular epithelial cells in rat kidney. Accordingly, rUrat1 is considered to be involved in uric acid reabsorption in rats in the same manner as URAT1 in humans. Therefore, rUrat1 may be a useful model to study issues related to the role of human URAT1.

Renal secretion of uric acid by organic anion transporter 2 (OAT2/SLC22A7) in human.

The physiological function of organic anion transporter OAT2 (SLC22A7) remains unclear, but since OAT2 transports purine derivatives, it may be involved in renal handling of uric acid, the final metabolite of purine derivatives. In the present study, we studied uric acid transport in stably OAT2-expressing HEK293 cells (HEK293/OAT2). OAT2 mediated uptake, but not efflux, of [(14)C]uric acid. Uric acid transport was saturable with K(m) of 1168+/-335 muM (mean and S.E.M.) and V(max) of 2.57+/-0.350 nmol/min/mg protein. The [(14)C]uric acid uptake was sensitive to Cl(-) and was enhanced at acidic pH. In cis-inhibition assay, [(14)C]uric acid uptake was inhibited by several mono- or dicarboxylic acids, but it was not trans-stimulated by any of the compounds tested. The pattern of inhibition of OAT2-mediated uric acid transport by various drugs was different from that of OAT1- or OAT3-mediated transport. Furthermore, OAT2-mediated transport of uric acid was inhibited by an antiuricosuric drug, pyrazinecarboxylic acid. These results revealed distinct characteristics of uric acid transport via OAT2 compared with other uric acid transporters, suggesting that OAT2 plays a role in renal uric acid uptake from blood as a first step of tubular secretion. OAT2 may therefore be a potential target for regulating serum uric acid level.

Effects of angiotensin II receptor blockers on renal handling of uric acid in rats.

Elevated serum uric acid level has been associated with increased cardiovascular risk in hypertensive patients. Several angiotensin II receptor blockers exhibit differential effects on regulation of serum uric acid level in humans. We have demonstrated that some angiotensin II receptor blockers trans-stimulate the uptake of uric acid by human URAT1 and others inhibit the transport of uric acid mediated by human URAT1, OAT1, OAT3 and MRP4 in vitro. This study investigated the effects of candesartan, pratosartan and telmisartan on renal handling of uric acid in rats in vivo and in vitro. Candesartan (0.1 mg/kg) significantly decreased the urinary excretion of uric acid and increased the plasma uric acid concentration. The kidney candesartan level after low-dose treatment is close to that required to trans-stimulate uric acid uptake in vitro. Pratosartan exhibited dose-dependent hypouricemic and uricosuric effects, while telmisartan showed no effects on plasma uric acid level. Furthermore, we confirmed the effects of the tested drugs on uric acid transport by rat renal brush border membrane transporter(s) and basolateral Oat1 and Oat3. Effects of angiotensin II receptor blockers in rats may be mainly determined by their intrinsic effects (cis-inhibition and trans-stimulation) on uric acid reabsorption transporter(s) and their pharmacokinetic properties in rats.

Involvement of uric acid transporters in alteration of serum uric acid level by angiotensin II receptor blockers.

PURPOSE: To examine the mechanisms of the alteration of serum uric acid level by angiotensin II receptor blockers (ARBs), the effects of ARBs on renal uric acid transporters, including OAT1, OAT3, OAT4, and MRP4, were evaluated. MATERIALS AND METHODS: Uptakes of uric acid by OAT1-expressing Flp293 cells, by Xenopus oocytes expressing OAT3 or OAT4, and by membrane vesicles from Sf9 cells expressing MRP4 were evaluated in the presence or absence of ARBs. RESULTS: All ARBs inhibited uptake of uric acid or estrone-3-sulfate by OAT1, OAT3 and OAT4 in concentration dependent manners. Among them, the IC50 values of valsartan, olmesartan and pratosartan for OAT3 were comparable to clinically observed unbound maximum plasma concentration of ARBs. Candesartan, losartan, and telmisartan inhibited ATP-dependent uptake of uric acid by MRP4 at 10 microM. The IC50 value of losartan for MRP4 was comparable to the estimated kidney tissue concentration of losartan. No ARBs showed trans-stimulatory effects on the uptake of estrone-3-sulfate by OAT4. CONCLUSION: Valsartan, olmesartan, and pratosartan could inhibit the OAT3-mediated uric acid secretion in clinical situations. Furthermore losartan could inhibit ATP-dependent uric acid secretion by MRP4. These effects may explain partially the alteration of serum uric acid level by ARBs.