Effects of pharmacological inhibition of the sodium-dependent phosphate cotransporter 2b (NPT2b) on intestinal phosphate absorption in mouse and rat models.

An excess phosphate burden in renal disease has pathological consequences for bone, kidney, and heart. Therapies to decrease intestinal phosphate absorption have been used to address the problem, but with limited success. Here, we describe the in vivo effects of a novel potent inhibitor of the intestinal sodium-dependent phosphate cotransporter NPT2b, LY3358966. Following treatment with LY3358966, phosphate uptake into plasma 15 min following an oral dose of radiolabeled phosphate was decreased 74% and 22% in mice and rats, respectively, indicating NPT2b plays a much more dominant role in mice than rats. Following the treatment with LY3358966 and radiolabeled phosphate, mouse feces were collected for 48 h to determine the ability of LY3358966 to inhibit phosphate absorption. Compared to vehicle-treated animals, there was a significant increase in radiolabeled phosphate recovered in feces (8.6% of the dose, p < .0001). Similar studies performed in rats also increased phosphate recovered in feces (5.3% of the dose, p < .05). When used in combination with the phosphate binder sevelamer in rats, there was a further small, but not significant, increase in fecal phosphate. In conclusion, LY3358966 revealed a more prominent role for NPT2b on acute intestinal phosphate uptake into plasma in mice than rats. However, the modest effects on total intestinal phosphate absorption observed in mice and rats with LY3359866 when used alone or in combination with sevelamer highlights the challenge to identify new more effective therapeutic targets and/or drug combinations to treat the phosphate burden in patients with renal disease.

Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles.

High-throughput screening (HTS) has enabled millions of compounds to be assessed for biological activity, but challenges remain in the prioritization of hit series. While biological, absorption, distribution, metabolism, excretion, and toxicity (ADMET), purity, and structural data are routinely used to select chemical matter for further follow-up, the scarcity of historical ADMET data for screening hits limits our understanding of early hit compounds. Herein, we describe a process that utilizes a battery of in-house quantitative structure-activity relationship (QSAR) models to generate in silico ADMET profiles for hit series to enable more complete characterizations of HTS chemical matter. These profiles allow teams to quickly assess hit series for desirable ADMET properties or suspected liabilities that may require significant optimization. Accordingly, these in silico data can direct ADMET experimentation and profoundly impact the progression of hit series. Several prospective examples are presented to substantiate the value of this approach.

Prediction of dihydrofolate reductase inhibition and selectivity using computational neural networks and linear discriminant analysis.

A data set of 345 dihydrofolate reductase inhibitors was used to build QSAR models that correlate chemical structure and inhibition potency for three types of dihydrofolate reductase (DHFR): rat liver (rl), Pneumocystis carinii (pc), and Toxoplasma gondii (tg). Quantitative models were built using subsets of molecular structure descriptors being analyzed by computational neural networks. Neural network models were able to accurately predict log IC(50) values for the three types of DHFR to within +/-0.65 log units (data sets ranged approximately 5.5 log units) of the experimentally determined values. Classification models were also constructed using linear discriminant analysis to identify compounds as selective or nonselective inhibitors of bacterial DHFR (pcDHFR and tgDHFR) relative to mammalian DHFR (rlDHFR). A leave-N-out training procedure was used to add robustness to the models and to prove that consistent results could be obtained using different training and prediction set splits. The best linear discriminant analysis (LDA) models were able to correctly predict DHFR selectivity for approximately 70% of the external prediction set compounds. A set of new nitrogen and oxygen-specific descriptors were developed especially for this data set to better encode structural features, which are believed to directly influence DHFR inhibition and selectivity.

Strategies for conducting ADME studies during lead generation in the drug discovery process.

Although the benefit of early ADME screening is widely recognized in the pharmaceutical industry, the implementation of this paradigm is often performed with insufficient resources and poor collaboration between functions. Dedicated and consistent integration efforts of ADME knowledge during the lead generation (LG) phase of drug discovery enables informed resourcing decisions and also increases the quality of initial starting points for discovery projects. To facilitate the efficient and consistent application of ADME resources to early projects at Eli Lilly and Co, a team of scientists was formed to provide dedicated ADME support to the LG phase of the discovery portfolio. This feature review discusses the working construct of the team, the general philosophy that was employed, and the outcomes of this endeavor.

Prediction of glass transition temperatures from monomer and repeat unit structure using computational neural networks.

Quantitative structure-property relationships (QSPR) are developed to correlate glass transition temperatures and chemical structure. Both monomer and repeat unit structures are used to build several QSPR models for Parts 1 and 2 of this study, respectively. Models are developed using numerical descriptors, which encode important information about chemical structure (topological, electronic, and geometric). Multiple linear regression analysis (MLRA) and computational neural networks (CNNs) are used to generate the models after descriptor generation. Optimization routines (simulated annealing and genetic algorithm) are utilized to find information-rich subsets of descriptors for prediction. A 10-descriptor CNN model was found to be optimal in predicting T(g) values using the monomer structure (Part 1) for 165 polymers. A committee of 10 CNNs produced a training set rms error of 10.1K (r2 = 0.98) and a prediction set rms error of 21.7 K (r2 = 0.92). An 11-descriptor CNN model was developed for 251 polymers using the repeat unit structure (Part 2). A committee of CNNs produced a training set rms error of 21.1K (r2 = 0.96) and a prediction set rms error of 21.9 K (r2 = 0.96).

Development of quantitative structure-activity relationship and classification models for a set of carbonic anhydrase inhibitors.

Mathematical models are developed to find quantitative structure-activity relationships that correlate chemical structure and inhibition toward three carbonic anhydrase (CA) isozymes: CA I, II, and IV. Numerical descriptors are generated to encode important topological, geometric, and electronic features of molecular structure. After descriptor generation, multiple linear regression, and computational neural network (CNN) analyses are performed on various descriptor subsets to find superior models for prediction. Committees of five CNNs were utilized to average final predicted values for the 142-compound data set. For inhibitors of CA I, an 8-5-1 CNN committee produced a training set rms error of 0.105 log K(i) (r(2) = 0.994) and prediction set rms error of 0.208 log K(i) (r(2) = 0.980). Training and prediction set rms errors of 0.140 log K(i) (r(2) = 0.992) and 0.231 log K(i) (r(2) = 0.971), respectively, were produced by a 9-5-1 CNN committee for inhibitors of CA II. For prediction of CA IV inhibitors, an 8-5-1 CNN committee produced training and prediction set rms errors of 0.147 log K(i) (r(2) = 0.992) and 0.211 log K(i) (r(2) = 0.991), respectively. In addition, classification models were built using k-nearest neighbor (kNN) analysis to solve two- and three-class problems for inhibitors of CA IV. A three-descriptor classification model proved superior in labeling compounds as active or inactive inhibitors for the two-class problem. Training and prediction set percent classification rates of 100% and 87.1%, respectively, were obtained. For the three-class (active/moderate/inactive) problem, a five-descriptor model was deemed optimal producing a training set percent classification rate of 98.8% and prediction set rate of 79.0%.

Development and use of hydrophobic surface area (HSA) descriptors for computer-assisted quantitative structure-activity and structure-property relationship studies.

A new series of 25 whole-molecule molecular structure descriptors are proposed. The new descriptors are termed Hydrophobic Surface Area, or HSA descriptors, and are designed to capture information regarding the structural features responsible for hydrophobic and hydrophilic intermolecular interactions. The utility of the HSAs in capturing this type of information is demonstrated using two properties that have a known hydrophobic component. The first study involves the modeling of the inhibition of Gram-positive bacteria cell growth of a series of biarylamides. The second application involves the study of the blood-brain barrier penetration of a diverse series of drug molecules. In both cases, the HSAs are shown to effectively capture information related to the hydrophobic components of these two properties. Additional evaluation of the new class of descriptors shows them to be unique in their ability to measure hydrophobic features among a diverse set of conventional structural descriptors. The HSAs are evaluated regarding their sensitivity to conformational changes and are found to be similar in that regard to other widely used molecular descriptors.

Predicting the genotoxicity of secondary and aromatic amines using data subsetting to generate a model ensemble.

Binary quantitative structure-activity relationship (QSAR) models are developed to classify a data set of 334 aromatic and secondary amine compounds as genotoxic or nongenotoxic based on information calculated solely from chemical structure. Genotoxic endpoints for each compound were determined using the SOS Chromotest in both the presence and absence of an S9 rat liver homogenate. Compounds were considered genotoxic if assay results indicated a positive genotoxicity hit for either the S9 inactivated or S9 activated assay. Each compound in the data set was encoded through the calculation of numerical descriptors that describe various aspects of chemical structure (e.g. topological, geometric, electronic, polar surface area). Furthermore, five additional descriptors that focused on the secondary and aromatic nitrogen atoms in each molecule were calculated specifically for this study. Descriptor subsets were examined using a genetic algorithm search engine interfaced with a k-Nearest Neighbor fitness evaluator to find the most information-rich subsets, which ultimately served as the final predictive models. Models were chosen for their ability to minimize the total number of misclassifications, with special attention given to those models that possessed fewer occurrences of positive toxicity hits being misclassified as nontoxic (false negatives). In addition, a subsetting procedure was used to form an ensemble of models using different combinations of compounds in the training and prediction sets. This was done to ensure that consistent results could be obtained regardless of training set composition. The procedure also allowed for each compound to be externally validated three times by different training set data with the resultant predictions being used in a "majority rules" voting scheme to produce a consensus prediction for each member of the data set. The individual models produced an average training set classification rate of 71.6% and an average prediction set classification rate of 67.7%. However, the model ensemble was able to correctly classify the genotoxicity of 72.2% of all prediction set compounds.