Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model.

The mechanistic neuropharmacokinetic (neuroPK) model was established to predict unbound brain-to-plasma partitioning (Kp,uu,brain) by considering in vitro efflux activities of multiple drug resistance 1 (MDR1) and breast cancer resistance protein (BCRP). Herein, we directly compare this model to a computational machine learning approach utilizing physicochemical descriptors and efflux ratios of MDR1 and BCRP-expressing cells for predicting Kp,uu,brain in rats. Two different types of machine learning techniques, Gaussian processes (GP) and random forest regression (RF), were assessed by the time and cluster-split validation methods using 640 internal compounds. The predictivity of machine learning models based on only molecular descriptors in the time-split dataset performed worse than the cluster-split dataset, whereas the models incorporating MDR1 and BCRP efflux ratios showed similar predictivity between time and cluster-split datasets. The GP incorporating MDR1 and BCRP in the time-split dataset achieved the highest correlation (R2 = 0.602). These results suggested that incorporation of MDR1 and BCRP in machine learning is beneficial for robust and accurate prediction. Kp,uu,brain prediction utilizing the neuroPK model was significantly worse compared to machine learning approaches for the same dataset. We also investigated the predictivity of Kp,uu,brain using an external independent test set of 34 marketed drugs. Compared to machine learning models, the neuroPK model showed better predictive performance with R2 of 0.577. This work demonstrates that the machine learning model for Kp,uu,brain achieves maximum predictive performance within the chemical applicability domain, whereas the neuroPK model is applicable more widely beyond the chemical space covered in the training dataset.

Development of CVN424: A Selective and Novel GPR6 Inverse Agonist Effective in Models of Parkinson Disease.

GPR6 is an orphan G-protein-coupled receptor that has enriched expression in the striatopallidal, indirect pathway and medium spiny neurons of the striatum. This pathway is greatly impacted by the loss of the nigro-striatal dopaminergic neurons in Parkinson disease, and modulating this neurocircuitry can be therapeutically beneficial. In this study, we describe the in vitro and in vivo pharmacological characterization of (R)-1-(2-(4-(2,4-difluorophenoxy)piperidin-1-yl)-3-((tetrahydrofuran-3-yl)amino)-7,8-dihydropyrido[3,4-b]pyrazin-6(5H)-yl)ethan-1-one (CVN424), a highly potent and selective small-molecule inverse agonist for GPR6 that is currently undergoing clinical evaluation. CVN424 is brain-penetrant and shows dose-dependent receptor occupancy that attained brain 50% of receptor occupancy at plasma concentrations of 6.0 and 7.4 ng/ml in mice and rats, respectively. Oral administration of CVN424 dose-dependently increases locomotor activity and reverses haloperidol-induced catalepsy. Furthermore, CVN424 restored mobility in bilateral 6-hydroxydopamine lesion model of Parkinson disease. The presence and localization of GPR6 in medium spiny neurons of striatum postmortem samples from both nondemented control and patients with Parkinson disease were confirmed at the level of both RNA (using Nuclear Enriched Transcript Sort sequencing) and protein. This body of work demonstrates that CVN424 is a potent, orally active, and brain-penetrant GPR6 inverse agonist that is effective in preclinical models and is a potential therapeutic for improving motor function in patients with Parkinson disease. SIGNIFICANCE STATEMENT: CVN424 represents a nondopaminergic novel drug for potential use in patients with Parkinson disease.

Prediction of Oral Pharmacokinetics Using a Combination of In Silico Descriptors and In Vitro ADME Properties.

Accurate prediction of oral pharmacokinetics remains challenging. This study investigated quantitative approaches for the prediction of the area under the plasma concentration-time curve after oral administration (AUCp,oral) to rats using the in vitro-in vivo extrapolation (IVIVE), in silico model using machine learning approaches and the combination of the in silico model and in vitro data. A set of 595 structurally diverse compounds with determined AUCp,oral at 1 mg/kg, in vitro intrinsic clearance (CLint), an unbound fraction in plasma (fu,p) in rats, and kinetic solubility at pH 6.8 was used for this assessment. Prediction models developed by two different types of machine learning techniques (i.e., random forest regression and Gaussian processes) were evaluated using three validation methods implementing the time and cluster-split training and test set and fivefold cross-validation. The developed machine learning models have a square of correlation coefficient (R2) in the range of 0.381-0.685 with 33-45% of the compounds being predicted within 2-fold of the observed AUCp,oral value. The predictivity was improved by incorporating CLint, fu,p, and solubility as explanatory variables with R2 = 0.554-0.743. In cases where extraction by the liver is the main elimination pathway and intestinal extraction is negligible, AUCp,oral can be expressed by dose, CLint, and fu,p based on a well-stirred model. By using this conventional IVIVE approach, only 1.7-5.0% of compounds were predicted within the 2-fold error with R2 = 0.354-0.487. Two empirical scaling factors (ESFs) determined by linear regression analysis and machine learning approaches improved the predictivity of AUCp,oral with 33-44% predicted within twofold variability. The IVIVE using ESF predicted by random forest regression showed better predictivity of AUCp,oral with R2 = 0.471-0.618, while it still showed lower predictivity than machine learning approaches applied directly to AUCp,oral prediction. This study demonstrated that the combination of in silico and in vitro parameters is useful to improve the predictivity of the machine learning model for rat AUCp,oral and supports consideration for predicting AUCp,oral for human and other non-clinical species in a similar manner.

In Vitro Metabolism of Slowly Cleared G Protein-Coupled Receptor 139 Agonist TAK-041 Using Rat, Dog, Monkey, and Human Hepatocyte Models (HepatoPac): Correlation with In Vivo Metabolism.

Hepatic metabolism of low-clearance compound TAK-041 was studied in two different in vitro model systems using rat, dog, monkey, and human suspended cryopreserved hepatocytes and HepatoPac micropatterned coculture model primary hepatocytes. The aim of this work was to investigate the most appropriate system to assess the biotransformation of TAK-041, determine any notable species difference in the rate and in the extent of its metabolic pathways, and establish correlation with in vivo metabolism. TAK-041 exhibited very low turnover in suspended cryopreserved hepatocyte suspensions for all species, with no metabolites observed in human hepatocytes. However, incubations conducted for up to 14 days in the HepatoPac model resulted in more robust metabolic turnover. The major biotransformation pathways of TAK-041 proceed via hydroxylation on the benzene ring fused to the oxotriazine moiety and subsequent sulfate, glucuronide, and glutathione conjugation reactions. The glutathione conjugate of TAK-041 undergoes further downstream metabolism to produce the cysteine S-conjugate, which then undergoes N-acetylation to mercapturic acid and/or conversion to ß-lyase-derived thiol metabolites. The minor biotransformation pathways include novel ring closure and hydrolysis, hydroxylation, oxidative N-dealkylation, and subsequent reduction. The HepatoPac model shows a notable species difference in the rate and in the extent of metabolic pathways of TAK-041, with dogs having the fastest metabolic clearance and humans the slowest. Furthermore, the model shows its suitability for establishing correlation with in vivo metabolism of low-turnover and extensively metabolized compounds such as TAK-041, displaying an extensive and unusual downstream sequential ß-lyase-derived thiol metabolism in preclinical species and human. SIGNIFICANCE STATEMENT: This study investigated the most appropriate in vitro system to assess the biotransformation of the low-turnover and extensively metabolized compound TAK-041, determine any notable species difference in the rate and in the extent of its metabolic pathways, and establish correlation with in vivo metabolism. The HepatoPac model was identified and showed its suitability for species comparison and establishing correlation, with in vivo metabolism displaying an extensive and unusual downstream sequential ß-lyase-derived thiol metabolism in preclinical species and human.

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.

The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CLtot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CLint) prediction. Herein, we directly compare these two approaches for predicting CLtot in rats. A structurally diverse set of 1114 compounds with known in vivo CLtot, in vitro CLint, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CLtot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (fu,p), and CLint. CLtot prediction utilizing in vitro CLint and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CLtot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cfu,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.

In vitro and in vivo effects of 2,4 diaminoquinazoline inhibitors of the decapping scavenger enzyme DcpS: Context-specific modulation of SMN transcript levels.

C5-substituted 2,4-diaminoquinazoline inhibitors of the decapping scavenger enzyme DcpS (DAQ-DcpSi) have been developed for the treatment of spinal muscular atrophy (SMA), which is caused by genetic deficiency in the Survival Motor Neuron (SMN) protein. These compounds are claimed to act as SMN2 transcriptional activators but data underlying that claim are equivocal. In addition it is unclear whether the claimed effects on SMN2 are a direct consequence of DcpS inhibitor or might be a consequence of lysosomotropism, which is known to be neuroprotective. DAQ-DcpSi effects were characterized in cells in vitro utilizing DcpS knockdown and 7-methyl analogues as probes for DcpS vs non-DcpS-mediated effects. We also performed analysis of Smn transcript levels, RNA-Seq analysis of the transcriptome and SMN protein in order to identify affected pathways underlying the therapeutic effect, and studied lysosomotropic and non-lysosomotropic DAQ-DCpSi effects in 2B/- SMA mice. Treatment of cells caused modest and transient SMN2 mRNA increases with either no change or a decrease in SMNΔ7 and no change in SMN1 transcripts or SMN protein. RNA-Seq analysis of DAQ-DcpSi-treated N2a cells revealed significant changes in expression (both up and down) of approximately 2,000 genes across a broad range of pathways. Treatment of 2B/- SMA mice with both lysomotropic and non-lysosomotropic DAQ-DcpSi compounds had similar effects on disease phenotype indicating that the therapeutic mechanism of action is not a consequence of lysosomotropism. In striking contrast to the findings in vitro, Smn transcripts were robustly changed in tissues but there was no increase in SMN protein levels in spinal cord. We conclude that DAQ-DcpSi have reproducible benefit in SMA mice and a broad spectrum of biological effects in vitro and in vivo, but these are complex, context specific, and not the result of simple SMN2 transcriptional activation.

Predicting pharmacokinetic profiles using in silico derived parameters.

Human pharmacokinetic (PK) predictions play a critical role in assessing the quality of potential clinical candidates where the accurate estimation of clearance, volume of distribution, bioavailability, and the plasma-concentration-time profiles are the desired end points. While many methods for conducting predictions utilize in vivo data, predictions can be conducted successfully from in vitro or in silico data, applying modeling and simulation techniques. This approach can be facilitated using commercially available prediction software such as GastroPlus which has been reported to accurately predict the oral PK profile of small drug-like molecules. Herein, case studies are described where GastroPlus modeling and simulation was employed using in silico or in vitro data to predict PK profiles in early discovery. The results obtained demonstrate the feasibility of adequately predicting plasma-concentration-time profiles with in silico derived as well as in vitro measured parameters and hence predicting PK profiles with minimal data. The applicability of this approach can provide key information enabling decisions on either dose selection, chemistry strategy to improve compounds, or clinical protocol design, thus demonstrating the value of modeling and simulation in both early discovery and exploratory development for predicting absorption and disposition profiles.

Potent and cellularly active 4-aminoimidazole inhibitors of cyclin-dependent kinase 5/p25 for the treatment of Alzheimer's disease.

Utilizing structure-based drug design, a 4-aminoimidazole heterocyclic core was synthesized as a replacement for a 2-aminothiazole due to potential metabolically mediated toxicity. The synthetic route utilized allowed for ready synthesis of 1-substituted-4-aminoimidazoles. SAR exploration resulted in the identification of a novel cis-substituted cyclobutyl group that gave improved enzyme and cellular potency against cdk5/p25 with up to 30-fold selectivity over cdk2/cyclin E.